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+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "DS_Unit_1_Sprint_Challenge_2.ipynb",
+      "version": "0.3.2",
+      "provenance": [],
+      "collapsed_sections": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    }
+  },
+  "cells": [
+    {
+      "metadata": {
+        "id": "i-n_5en3ER1o",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "# Data Science Unit 1 Sprint Challenge 2\n",
+        "\n",
+        "# Storytelling with Data\n",
+        "\n",
+        "In this sprint challenge you'll work with a dataset from **FiveThirtyEight's article, [Every Guest Jon Stewart Ever Had On ‘The Daily Show’](https://fivethirtyeight.com/features/every-guest-jon-stewart-ever-had-on-the-daily-show/)**!"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "Thm2n5FF2Fnp",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "# Part 0 — Run this starter code\n",
+        "\n",
+        "You don't need to add or change anything here. Just run this cell and it loads the data for you, into a dataframe named `df`.\n",
+        "\n",
+        "(You can explore the data if you want, but it's not required to pass the Sprint Challenge.)"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "0rTHgzJIuRS7",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "%matplotlib inline\n",
+        "import matplotlib\n",
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "\n",
+        "df = pd.read_csv('https://raw.githubusercontent.com/fivethirtyeight/data/master/daily-show-guests/daily_show_guests.csv')\n",
+        "df.rename(columns={'YEAR': 'Year', 'Raw_Guest_List': 'Guest'}, inplace=True)\n",
+        "\n",
+        "def get_occupation(group):\n",
+        "    if group in ['Acting', 'Comedy', 'Musician']:\n",
+        "        return 'Acting, Comedy & Music'\n",
+        "    elif group in ['Media', 'media']:\n",
+        "        return 'Media'\n",
+        "    elif group in ['Government', 'Politician', 'Political Aide']:\n",
+        "        return 'Government and Politics'\n",
+        "    else:\n",
+        "        return 'Other'\n",
+        "      \n",
+        "df['Occupation'] = df['Group'].apply(get_occupation)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "OS0nW1vz1itX",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "# Part 1 — What's the breakdown of guests’ occupations per year?\n",
+        "\n",
+        "For example, in 1999, what percentage of guests were actors, comedians, or musicians? What percentage were in the media? What percentage were in politics? What percentage were from another occupation?\n",
+        "\n",
+        "Then, what about in 2000? In 2001? And so on, up through 2015.\n",
+        "\n",
+        "So, **for each year of _The Daily Show_, calculate the percentage of guests from each occupation:**\n",
+        "- Acting, Comedy & Music\n",
+        "- Government and Politics\n",
+        "- Media\n",
+        "- Other\n",
+        "\n",
+        "#### Hints:\n",
+        "1. Use pandas to make a **crosstab** of **`Year`** & **`Occupation`**. ([This documentation](http://pandas.pydata.org/pandas-docs/stable/reshaping.html#cross-tabulations) has examples and explanation.)\n",
+        "2. To get percentages instead of counts, use crosstab's **`normalize`** parameter to normalize over each _row._ ([This documentation](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.crosstab.html) describes the parameter and its options.)\n",
+        "3. You'll know you've calculated the crosstab correctly when the percentage of \"Acting, Comedy & Music\" guests is 90.36% in 1999, and 45% in 2015."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "xjo9v9vBRqCs",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 195
+        },
+        "outputId": "ad987a55-a909-41a0-f8c4-abc1f552632e"
+      },
+      "cell_type": "code",
+      "source": [
+        "df.head()"
+      ],
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Year</th>\n",
+              "      <th>GoogleKnowlege_Occupation</th>\n",
+              "      <th>Show</th>\n",
+              "      <th>Group</th>\n",
+              "      <th>Guest</th>\n",
+              "      <th>Occupation</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1999</td>\n",
+              "      <td>actor</td>\n",
+              "      <td>1/11/99</td>\n",
+              "      <td>Acting</td>\n",
+              "      <td>Michael J. Fox</td>\n",
+              "      <td>Acting, Comedy &amp; Music</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1999</td>\n",
+              "      <td>Comedian</td>\n",
+              "      <td>1/12/99</td>\n",
+              "      <td>Comedy</td>\n",
+              "      <td>Sandra Bernhard</td>\n",
+              "      <td>Acting, Comedy &amp; Music</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>1999</td>\n",
+              "      <td>television actress</td>\n",
+              "      <td>1/13/99</td>\n",
+              "      <td>Acting</td>\n",
+              "      <td>Tracey Ullman</td>\n",
+              "      <td>Acting, Comedy &amp; Music</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>1999</td>\n",
+              "      <td>film actress</td>\n",
+              "      <td>1/14/99</td>\n",
+              "      <td>Acting</td>\n",
+              "      <td>Gillian Anderson</td>\n",
+              "      <td>Acting, Comedy &amp; Music</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>1999</td>\n",
+              "      <td>actor</td>\n",
+              "      <td>1/18/99</td>\n",
+              "      <td>Acting</td>\n",
+              "      <td>David Alan Grier</td>\n",
+              "      <td>Acting, Comedy &amp; Music</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   Year GoogleKnowlege_Occupation     Show   Group             Guest  \\\n",
+              "0  1999                     actor  1/11/99  Acting    Michael J. Fox   \n",
+              "1  1999                  Comedian  1/12/99  Comedy   Sandra Bernhard   \n",
+              "2  1999        television actress  1/13/99  Acting     Tracey Ullman   \n",
+              "3  1999              film actress  1/14/99  Acting  Gillian Anderson   \n",
+              "4  1999                     actor  1/18/99  Acting  David Alan Grier   \n",
+              "\n",
+              "               Occupation  \n",
+              "0  Acting, Comedy & Music  \n",
+              "1  Acting, Comedy & Music  \n",
+              "2  Acting, Comedy & Music  \n",
+              "3  Acting, Comedy & Music  \n",
+              "4  Acting, Comedy & Music  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "sRMc0H_5z6ff",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "year = df.Year\n",
+        "occupation = df.Occupation\n",
+        "year_x_job = pd.crosstab(year, occupation, normalize='index')\n",
+        "\n",
+        "pull = [\"Acting, Comedy & Music\", \"Government and Politics\",\n",
+        "                     \"Media\"]\n",
+        "year_x_job_mod = year_x_job[pull]\n"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "bkRCOQ--a0us",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 225
+        },
+        "outputId": "09226569-8271-4713-92b7-bfe45eb1f192"
+      },
+      "cell_type": "code",
+      "source": [
+        "year_x_job_mod.head()"
+      ],
+      "execution_count": 20,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th>Occupation</th>\n",
+              "      <th>Acting, Comedy &amp; Music</th>\n",
+              "      <th>Government and Politics</th>\n",
+              "      <th>Media</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Year</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>1999</th>\n",
+              "      <td>0.903614</td>\n",
+              "      <td>0.012048</td>\n",
+              "      <td>0.066265</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2000</th>\n",
+              "      <td>0.739645</td>\n",
+              "      <td>0.082840</td>\n",
+              "      <td>0.124260</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2001</th>\n",
+              "      <td>0.726115</td>\n",
+              "      <td>0.038217</td>\n",
+              "      <td>0.197452</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2002</th>\n",
+              "      <td>0.622642</td>\n",
+              "      <td>0.069182</td>\n",
+              "      <td>0.264151</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2003</th>\n",
+              "      <td>0.560241</td>\n",
+              "      <td>0.102410</td>\n",
+              "      <td>0.246988</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "Occupation  Acting, Comedy & Music  Government and Politics     Media\n",
+              "Year                                                                 \n",
+              "1999                      0.903614                 0.012048  0.066265\n",
+              "2000                      0.739645                 0.082840  0.124260\n",
+              "2001                      0.726115                 0.038217  0.197452\n",
+              "2002                      0.622642                 0.069182  0.264151\n",
+              "2003                      0.560241                 0.102410  0.246988"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 20
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "Nqf9oJJDDu-d",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "# Part 2 — Recreate this explanatory visualization:"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "scozkHQc0_eD",
+        "colab_type": "code",
+        "outputId": "5d0410f6-ff1b-402b-a4e7-16d74f83a3c2",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 406
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "from IPython.display import display, Image\n",
+        "url = 'https://fivethirtyeight.com/wp-content/uploads/2015/08/hickey-datalab-dailyshow.png'\n",
+        "example = Image(url, width=500)\n",
+        "display(example)\n",
+        "\n"
+      ],
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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ZBtnkfptMLmk+J6HR1RolfR2cZ4kCMURmERt2i1wRFHvq1F3TaMmmGBsU6Ssc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\n4JhflzBydcIRb30AAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<IPython.core.display.Image object>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "image/png": {
+              "width": 500
+            }
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "W7lw3JzAE6BJ",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**Hint:** use the crosstab you calculated in part 1!\n",
+        "\n",
+        "**Expectations:** Your plot should include:\n",
+        "- 3 lines visualizing \"occupation of guests, by year.\" The shapes of the lines should look roughly identical to 538's example. Each line should be a different color. (But you don't need to use the _same_ colors as 538.)\n",
+        "- Legend or labels for the lines. (But you don't need each label positioned next to its line or colored like 538.)\n",
+        "- Title in the upper left: _\"Who Got To Be On 'The Daily Show'?\"_ with more visual emphasis than the subtitle. (Bolder and/or larger font.)\n",
+        "- Subtitle underneath the title: _\"Occupation of guests, by year\"_\n",
+        "\n",
+        "Any visual element not specifically mentioned in the expectations is an optional bonus, but it's _not_ required to pass the Sprint Challenge.\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "E8XBAr8rz_Na",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "!pip install seaborn --upgrade\n"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "6vygAHBYY6qq",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "import seaborn as sns\n"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "HvqY9jPoZiyT",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "occupation_plotted = [\"Acting, Comedy & Music\", \"Government and Poltics\",\n",
+        "                     \"Media\"]"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "6_Q0vJo1Y9Eb",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 665
+        },
+        "outputId": "f8cad69f-a77f-417e-ff20-7d27ef533cd7"
+      },
+      "cell_type": "code",
+      "source": [
+        "import matplotlib.ticker as ticker\n",
+        "sns.set_style(\"whitegrid\")\n",
+        "sns.despine(offset=10, trim=True)\n",
+        "sns.set_context(\"paper\")\n",
+        "\n",
+        "ax = sns.lineplot(data=year_x_job_mod)\n",
+        "\n",
+        "ax.xaxis.set_major_locator(ticker.MultipleLocator(4))\n",
+        "ax.xaxis.set_major_formatter(ticker.ScalarFormatter())\n",
+        "\n",
+        "# create percentage based tick spacing\n",
+        "# location dependent code, do not move\n",
+        "y_tick_spacing = .25\n",
+        "ax.yaxis.set_major_locator(ticker.MultipleLocator(y_tick_spacing))\n",
+        "ax.yaxis.set_major_locator(plt.MaxNLocator(4)) \n",
+        "\n",
+        "\n",
+        "#format\n",
+        "vals = ax.get_yticks()\n",
+        "ax.set_yticklabels(['{:,.0%}'.format(x) for x in vals])\n",
+        "\n",
+        "plt.show()\n",
+        "\n",
+        "\n",
+        "# bit of an issue with the formatting on the y ticks\n",
+        "# changing it to 5 ticks puts the 100% below the line? I couldn't fix it :P"
+      ],
+      "execution_count": 145,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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nq6CgQNHR0SooKFC3bt305ptvymw2N2y5KOmYz02aTKaTZvjj43/8tdVqVUxMjBITE/Xs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\n9OeCAAAAAElFTkSuQmCC\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7f733ea0a668>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "LuacMjSf2ses",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "# Part 3 — Who were the top 10 guests on _The Daily Show_?\n",
+        "\n",
+        "**Make a plot** that shows their names and number of appearances.\n",
+        "\n",
+        "**Hint:** you can use the pandas `value_counts` method.\n",
+        "\n",
+        "**Expectations:** This can be a simple, quick plot: exploratory, not explanatory. \n",
+        "\n",
+        "If you want, you can add titles and change aesthetics, but it's _not_ required to pass the Sprint Challenge."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "tbwfBN3HsFlh",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "actor_freq =  pd.DataFrame(df.Guest.value_counts().reset_index().head(10))\n",
+        "actor_freq.columns = ['Guest', 'Visits']\n",
+        "#sns.lineplot();"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "bhH2KAvikzh2",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 343
+        },
+        "outputId": "25d28238-4739-4633-a533-02f37cfe5b23"
+      },
+      "cell_type": "code",
+      "source": [
+        "actor_freq"
+      ],
+      "execution_count": 120,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Guest</th>\n",
+              "      <th>Visits</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Fareed Zakaria</td>\n",
+              "      <td>19</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Denis Leary</td>\n",
+              "      <td>17</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Brian Williams</td>\n",
+              "      <td>16</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Ricky Gervais</td>\n",
+              "      <td>13</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Paul Rudd</td>\n",
+              "      <td>13</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>Tom Brokaw</td>\n",
+              "      <td>12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>Reza Aslan</td>\n",
+              "      <td>10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>Bill O'Reilly</td>\n",
+              "      <td>10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>Will Ferrell</td>\n",
+              "      <td>10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>Richard Lewis</td>\n",
+              "      <td>10</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "            Guest  Visits\n",
+              "0  Fareed Zakaria      19\n",
+              "1     Denis Leary      17\n",
+              "2  Brian Williams      16\n",
+              "3   Ricky Gervais      13\n",
+              "4       Paul Rudd      13\n",
+              "5      Tom Brokaw      12\n",
+              "6      Reza Aslan      10\n",
+              "7   Bill O'Reilly      10\n",
+              "8    Will Ferrell      10\n",
+              "9   Richard Lewis      10"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 120
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "AuqtrEmhmJwy",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 665
+        },
+        "outputId": "47c7bf25-1fe4-40c4-b1fe-cf05fdaff5bd"
+      },
+      "cell_type": "code",
+      "source": [
+        "from matplotlib import rcParams\n",
+        "list_actors = actor_freq.Guest.tolist()\n",
+        "\n",
+        "#set figure size\n",
+        "rcParams['figure.figsize']= 11, 11\n",
+        "\n",
+        "sns.lineplot(x='Guest', y='Visits', data=actor_freq, color='sienna')\n",
+        "ax2.set_xticklabels(list_actors);"
+      ],
+      "execution_count": 143,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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JNPP6a2RFmpRIJExHAQCkEEXTwfo729W7bxdFE2nnDQQV6+lSz96dpqMAAFKI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\nQmCC\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7f733e8e1dd8>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "lT1fNMzDmkQS",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/module2-choose-appropriate-visualizations/LS_DS_122_Choose_appropriate_visualizations.ipynb b/module2-choose-appropriate-visualizations/LS_DS_122_Choose_appropriate_visualizations.ipynb
index 964e477..fe02094 100644
--- a/module2-choose-appropriate-visualizations/LS_DS_122_Choose_appropriate_visualizations.ipynb
+++ b/module2-choose-appropriate-visualizations/LS_DS_122_Choose_appropriate_visualizations.ipynb
@@ -1,652 +1,3295 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "_Lambda School Data Science_\n",
-    "# Choose appropriate visualizations"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Upgrade Seaborn\n",
-    "\n",
-    "Make sure you have at least version 0.9.0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install --upgrade seaborn"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import seaborn as sns\n",
-    "sns.__version__"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Fix misleading visualizations"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!wget https://raw.githubusercontent.com/LambdaSchool/DS-Sprint-02-Storytelling-With-Data/master/module2-choose-appropriate-visualizations/misleading.py\n",
-    "    \n",
-    "import misleading"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Fix misleading plot #1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "misleading.plot1()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Fix misleading plot #2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "misleading.plot2()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Fix misleading plot #3"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "misleading.plot3()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Fix misleading plot #4"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "_If you're on Jupyter (not Colab) then uncomment and run this cell below:_"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# import altair as alt\n",
-    "# alt.renderers.enable('notebook')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "misleading.plot4()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Links\n",
-    "- [How to Spot Visualization Lies](https://flowingdata.com/2017/02/09/how-to-spot-visualization-lies/)\n",
-    "- [Where to Start and End Your Y-Axis Scale](http://stephanieevergreen.com/y-axis/)\n",
-    "- [xkcd heatmap](https://xkcd.com/1138/)\n",
-    "- [Surprise Maps: Showing the Unexpected](https://medium.com/@uwdata/surprise-maps-showing-the-unexpected-e92b67398865)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Use Seaborn to visualize distributions and relationships with continuous and discrete variables\n",
-    "\n",
-    "#### Links\n",
-    "- [Seaborn tutorial](https://seaborn.pydata.org/tutorial.html)\n",
-    "- [Seaborn example gallery](https://seaborn.pydata.org/examples/index.html)\n",
-    "- [Chart Chooser](https://extremepresentation.typepad.com/files/choosing-a-good-chart-09.pdf)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 1. Anscombe dataset"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Load dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = sns.load_dataset('anscombe')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### See the data's shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### See the data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### [Group by](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.groupby.html) `'dataset'`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### [Describe](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.describe.html) the groups"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Get the [count](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.count.html), for each column in each group"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Get the [mean](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.mean.html) ..."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Get the [standard deviation](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.std.html) ..."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Get the [correlation](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.corr.html) ..."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Use pandas to [plot](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.plot.html) the groups, as scatter plots"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Use Seaborn to make [relational plots](http://seaborn.pydata.org/generated/seaborn.relplot.html)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Use Seaborn to make [linear model plots](http://seaborn.pydata.org/generated/seaborn.lmplot.html)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Links\n",
-    "- [Seaborn examples: Anscombe's quartet](http://seaborn.pydata.org/examples/anscombes_quartet.html)\n",
-    "- [Wikipedia: Anscombe's quartet](https://en.wikipedia.org/wiki/Anscombe%27s_quartet)\n",
-    "- [The Datasaurus Dozen](https://www.autodeskresearch.com/publications/samestats)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 2. Tips dataset"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Load dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tips = sns.load_dataset('tips')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### See the data's shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### See the first 5 rows"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Describe the data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Make univariate [distribution plots](https://seaborn.pydata.org/generated/seaborn.distplot.html)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Make bivariate [relational plots](https://seaborn.pydata.org/generated/seaborn.relplot.html)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Make univariate [categorical plots](https://seaborn.pydata.org/generated/seaborn.catplot.html)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Make bivariate [categorical plots](https://seaborn.pydata.org/generated/seaborn.catplot.html)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 3. Flights"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Load dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "flights = sns.load_dataset('flights')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### See the data's shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### See the first 5 rows"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Describe the data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Plot year & passengers"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Plot month & passengers"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Create a [pivot table](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.pivot_table.html) of passengers by month and year"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Plot the pivot table as a [heat map](https://seaborn.pydata.org/generated/seaborn.heatmap.html)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "LS_DS_122_Choose_appropriate_visualizations.ipynb",
+      "version": "0.3.2",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    }
+  },
+  "cells": [
+    {
+      "metadata": {
+        "id": "cGJvcj1wCbf9",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "_Lambda School Data Science_\n",
+        "# Choose appropriate visualizations"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "mwahqSwXCbgA",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "# Upgrade Seaborn\n",
+        "\n",
+        "Make sure you have at least version 0.9.0"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "yhmjR501CbgA",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 202
+        },
+        "outputId": "abd8d72e-674c-4159-9b08-e18f9d31a0d7"
+      },
+      "cell_type": "code",
+      "source": [
+        "!pip install --upgrade seaborn"
+      ],
+      "execution_count": 31,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Requirement already up-to-date: seaborn in /usr/local/lib/python3.6/dist-packages (0.9.0)\n",
+            "Requirement already satisfied, skipping upgrade: numpy>=1.9.3 in /usr/local/lib/python3.6/dist-packages (from seaborn) (1.14.6)\n",
+            "Requirement already satisfied, skipping upgrade: pandas>=0.15.2 in /usr/local/lib/python3.6/dist-packages (from seaborn) (0.22.0)\n",
+            "Requirement already satisfied, skipping upgrade: scipy>=0.14.0 in /usr/local/lib/python3.6/dist-packages (from seaborn) (1.1.0)\n",
+            "Requirement already satisfied, skipping upgrade: matplotlib>=1.4.3 in /usr/local/lib/python3.6/dist-packages (from seaborn) (2.1.2)\n",
+            "Requirement already satisfied, skipping upgrade: python-dateutil>=2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.15.2->seaborn) (2.5.3)\n",
+            "Requirement already satisfied, skipping upgrade: pytz>=2011k in /usr/local/lib/python3.6/dist-packages (from pandas>=0.15.2->seaborn) (2018.7)\n",
+            "Requirement already satisfied, skipping upgrade: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=1.4.3->seaborn) (0.10.0)\n",
+            "Requirement already satisfied, skipping upgrade: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=1.4.3->seaborn) (2.3.0)\n",
+            "Requirement already satisfied, skipping upgrade: six>=1.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=1.4.3->seaborn) (1.11.0)\n",
+            "^C\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "C40u00OwCbgE",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "a1dc8818-c6fd-47b1-e374-0d17aeab56ed"
+      },
+      "cell_type": "code",
+      "source": [
+        "import seaborn as sns\n",
+        "sns.__version__"
+      ],
+      "execution_count": 32,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "'0.9.0'"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 32
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "7iKLAmDrCbgH",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "# Fix misleading visualizations"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "JaCa7gvlCbgH",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "%matplotlib inline\n",
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np\n",
+        "import pandas as pd"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "I5UYnCrLCbgL",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 202
+        },
+        "outputId": "e4d40387-9212-4853-f2e9-239a6e611b5f"
+      },
+      "cell_type": "code",
+      "source": [
+        "!wget https://raw.githubusercontent.com/LambdaSchool/DS-Sprint-02-Storytelling-With-Data/master/module2-choose-appropriate-visualizations/misleading.py\n",
+        "    \n",
+        "import misleading"
+      ],
+      "execution_count": 34,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "--2018-11-13 17:37:30--  https://raw.githubusercontent.com/LambdaSchool/DS-Sprint-02-Storytelling-With-Data/master/module2-choose-appropriate-visualizations/misleading.py\n",
+            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
+            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n",
+            "HTTP request sent, awaiting response... 200 OK\n",
+            "Length: 1641 (1.6K) [text/plain]\n",
+            "Saving to: ‘misleading.py.1’\n",
+            "\n",
+            "\rmisleading.py.1       0%[                    ]       0  --.-KB/s               \rmisleading.py.1     100%[===================>]   1.60K  --.-KB/s    in 0s      \n",
+            "\n",
+            "2018-11-13 17:37:30 (181 MB/s) - ‘misleading.py.1’ saved [1641/1641]\n",
+            "\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "p42rv0WXGSyq",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 106
+        },
+        "outputId": "bff719ed-37aa-412a-a53f-7d91d8721080"
+      },
+      "cell_type": "code",
+      "source": [
+        "insurance"
+      ],
+      "execution_count": 35,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Market Share %</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>State Farm</th>\n",
+              "      <td>18.07</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>GEICO</th>\n",
+              "      <td>12.79</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "            Market Share %\n",
+              "State Farm           18.07\n",
+              "GEICO                12.79"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 35
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "TWLBoC4ECbgO",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "#### Fix misleading plot #1"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "QQj7BhlZCbgP",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 314
+        },
+        "outputId": "77499cae-8298-48c4-d2f4-9e1175beec0d"
+      },
+      "cell_type": "code",
+      "source": [
+        "misleading.plot1();"
+      ],
+      "execution_count": 36,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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J4GvACOCRiFgTEf/QzzklSVVUPUPPzAs7Gb67H7JIkvrAO0UlqRAWuiQVwkKXpEJY6JJU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\nAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7f8d36e7e6d8>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "DmjxOeAUEH1F",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 314
+        },
+        "outputId": "cecf74f4-144e-4bd3-91b6-f4d59c49ea6d"
+      },
+      "cell_type": "code",
+      "source": [
+        "insurance = pd.DataFrame(\n",
+        "  index=['State Farm', 'GEICO'],\n",
+        "  data={'Market Share %': [18.07, 12.79]}\n",
+        ")\n",
+        "\n",
+        "insurance.plot.bar();"
+      ],
+      "execution_count": 37,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7f8d36ef60b8>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "fF_vi1jUCbgU",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "#### Fix misleading plot #2"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "mB4TJEfYCbgW",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 310
+        },
+        "outputId": "07610ba1-45e8-458a-85a2-7685e48c2fd5"
+      },
+      "cell_type": "code",
+      "source": [
+        "misleading.plot2(); # does not show 100%"
+      ],
+      "execution_count": 38,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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hWiNVsdWxa5qihhsJYIwxu+U4W1Y4MfdSfHQSQWexVQHzb+KJCtshWivVDLp+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\n2JRSofP/LrNWcuvLhmgAAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7f8d36d63ef0>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "zJjlGbueCbgY",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# make a new row\n",
+        "other = pd.DataFrame(\n",
+        "  index=['Other'],\n",
+        "  data={'Market Share %': [100 - 18.07 - 12.79]})\n",
+        "\n",
+        "insurance_appended = insurance.append(other)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "oA78g53TGjMr",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 364
+        },
+        "outputId": "e9ca5ead-6710-4a13-debc-d27437c63989"
+      },
+      "cell_type": "code",
+      "source": [
+        "insurance_appended.plot.pie('Market Share %', figsize=(6, 6));"
+      ],
+      "execution_count": 40,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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87reh0vL9TIcwScrd8QtAboBIM5uyAgNWBIMZd1PTXdFLdpnOIGgNjDQdwiQp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\nEB4k5S6EEB4k5S6EEB70/wG6aF0DBy/cAAAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7f8d36ed89b0>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "voqZAGUkCbgb",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "#### Fix misleading plot #3"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "aG8uWsoSCbgd",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 336
+        },
+        "outputId": "a1ea7ff0-4825-4d53-f9a0-bbd447c3285c"
+      },
+      "cell_type": "code",
+      "source": [
+        "misleading.plot3()"
+      ],
+      "execution_count": 41,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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SK4GP0p4i/FV0GG0YixWQFdUWtOe3Hb6W1u7TtHOufMEQWVFtC7wD2JdhLuQ5NMuB9wAH\n1WV+dXQYrZ8HROsILFXS+jyZ9lSPJi4rqqcC/wc8F3+HLJXNgX8CzsyK6pHRYbR+kx+xyorq74H/\njs4hDUADPKou8xOig2jpZUW1PfA+YJ/oLOJI4BXuS9hPky5WWVHtCJzJ0u6QLg3ZucA9fUGflqyo\nngu8E7hVdBb9yfnAi+sy/1x0EK1ussUqK6oEfBlXVpfm68N1mT8nOoQWX1ZUGfABfJ3ss08AL63L\n/KLoIGpN+fz4/vhiIS3EvllRPSE6hBZX9//4h/g62XdPA36UFdWDo4OoNckRq6yo7kz7gnHz6CzS\nQF0E3KMu84ujg2i2utH8NwKvB1JwHG245cA/12X+nuggUze5EausqDYBPoylStoY29FeTasRyYpq\na+DztNvRWKqGZXPgsKyoPpQV1U2jw0zZ5IoV8CrggdEhpBHYJyuqZ0WH0GxkRXVX4LvA3tFZtFGe\nA5yUFdUdooNM1aROBWZFdS/gNGCL6CzSSFxJe0rwvOggWrisqPahHcm/ZXQWzczFtIv6fiM6yNRM\nZsSqW139I1iqpFnaGvhQNy9HA5QV1cG0K+tbqsbltsDxWVHtFx1kaiZTrICDgV2jQ0gj9EjgxdEh\nND9ZUaWsqN5NO1HdYjxOmwGHZ0X16uggUzKJU4FZUd0JOIt2cp+k2bsSuFNd5pdGB9GNy4pqU+CD\ntNvSaBrKusxfEx1iCqYyYvU2LFXSYtqa9koy9VxWVJsBx2CpmpoiK6rDokNMwehHrLKi2hM4NTqH\nNAHXAXety/yX0UG0dl2pOhp4anQWhTmsLvOXRYcYsymMWL0jOoA0EVsAh0aH0Np1p/8+hqVq6l7a\nza3TIhl1seq2ZHCZf2npPK0bJVaPdFdtfoR2+xPpZVlR/Wt0iLEabbHq3p2V0TmkiUnA26ND6Abe\nBDwjOoR65ZVZUb0oOsQYjbZYAS8AdokOIU3QXllRPT46hFpZUT2Tdt8/aU2HZUX1yOgQYzPKyetZ\nUW0J/By4XXQWaaLOAu5Zl/mK6CBTlhXVA4ETgZtEZ1FvXQHcvy7zn0UHGYuxjli9EkuVFOmuwPOj\nQ0xZVlR3BP4XS5XWbxvg2Kyoto0OMhajG7HKimp74Bxgy+gs0sRdAOxcl/nV0UGmJiuqWwKnAPeI\nzqLB+AbwqLrMl0cHGboxjli9FkuV1Ac7AK6Xs8S6C3c+jqVK87MX8P7oEGMwqmKVFdVWuJqw1CcH\ndItSaukcDDwuOoQG6flu2rzxRlWsaEuVo1VSf/wFsE90iKnIiuq+tKP20kK9IyuqnaJDDNloilW3\nAN4B0Tkk3cBLowNMQVZUN6NdBNQRQm2MLYEPZ0U1mn6w1Mb0g3sMsHN0CEk38OCsqHaNDjEBb8O1\n+zQbDwJeER1iqMZUrHxXLPWXx+ciyorqYXihgGbrzVlReQHEAoxiuYWsqO4MnE27nYak/rkW2LEu\n88uig4xNd9HOj4A7RmfR6JwB3M8lGOZnLCNWB2CpkvrsZrhg6GL5dyxVWhy7015lqnkY/IhVVlS3\nAH4LbB2dRdJ61cCd6jJfGR1kLLKiegRwfHQOjdoKYLe6zH8cHWQoxjBi9WwsVdIQZICbM89IdyX0\nO6JzaPQ2Bd4eHWJIxlCsXhIdQNIGcxL77Pw9sFt0CE3CY7Oienh0iKEY9KnArKj2Ar4enUPSvPxV\nXeZnR4cYsqyobgr8DPh/0Vk0GacD96nLfLilYYkMfcTqKdEBJM3bk6MDjMDLsFRpae0BPCM6xBAM\nvVg9ITqApHnzuN0IWVFtC7wmOocm6S1ZUd0kOkTfDbZYZUV1b+AO0TkkzdueWVHtEB1iwF4PbBMd\nQpOU4bzmGzXYYoXveqWhSnj8LkhWVBnuiapYr8uKymK/HkMuVk+MDiBpwTx+F+blwBbRITRptwJe\nEB2izwZ5VWBWVDsBv4jOIWnBrgNuW5f5suggQ+FiyOqRXwF3drHftRvqiJXvdqVh2wJ4XHSIgXkW\nlir1w18CeXSIvrJYSYriPKv5cdKw+sTFftdhcKcCs6K6LXAB7TL7koZrGe3pwOuig/Rdt+r1CdE5\npDka4G51mf80OkjfDHHE6vFYqqQx2Ap4WHSIgXB0QH2TcBR1rYZYrDx9II2Hp/VvRFZUd8TNq9VP\n+2ZFtVV0iL4ZVLHqdnN/aHQOSTPjiNWN2w9H6dVPWwLPjg7RN4MqVsBdaE8fSBqHu/iO90Y9NTqA\ntB4+P9cwtGJ13+gAkmYqAfeODtFXWVHdHbhzdA5pPf46K6pbR4fok6EVq/tEB5A0cx7X6+YcNPXd\npjgHcDVDK1aOWEnj43G9bhYrDYHP0zkGU6yyotoU2C06h6SZc8RqLbKi2hFPk2oYHp0V1c2jQ/TF\nYIoVcHfA/3HS+PxlVlS3iQ7RQ0+gnYMm9d3NgEdHh+iLIRUrTxdI4+Wo1Q15ekVD4vO1M6Ri5Quv\nNF4e33N0S1DsFZ1Dmoe9s6IaUqdYNEP6IThiJY2Xx/fq7gdsHh1CmodbA7tEh+iDQRSrrKi2AO4Z\nnUPSonHEanX+PDREPm8ZSLEC7gVsER1C0qK5fVZUO0SH6BFH8DREPm8ZTrHaKTqApEXncf5nvvPX\nEFmsGE6xun10AEmLzuMcyIrqdsAdonNIC7BrVlSTnxtosZLUFx7nLUerNFQ3Be4RHSKaxUpSX3ic\ntzydoiGb/BsDi5WkvvA4b03+F5MGbfJvDCxWkvrC47zlWkAasrtGB4hmsZLUFx7nLX8OGrLJP397\nX6yyotoSuGV0DkmLbvIvyFlR3Yp2Q1tpqCa/Hl3vixW+2EpTsVVWVLeIDhHM1zsN3c26NwiTZbGS\n1CdTP97/IjqANAOTPo4tVpL6ZOrH+9Qfv8Zh0s9ji5WkPpn68T71x69xmPTzeAjFaupzLqQpmfrx\nPulfSBqNST+Ph1CsJr/vkDQhUz/et48OIM3ApJ/HQyhWm0UHkLRkpn683zQ6gDQDk34eD6FYTf0d\nrDQlUz/ep/74NQ6Tfh4PoVhN/R2sNCVTP96n/vg1DpN+HlusJPXJpN/p4uudxmHSx/EQipWk6Wii\nA0jaaJM+jodQrJZHB5C0ZKZ+vE/98WscJv08HkKxuj46gKQlM/XjfeqPX+Mw6efxEIrVpJuvNDFT\nP96n/vg1DpN+Hg+hWE26+UoTM/Xj/eroANIMTPp5PIRiNenmK03M1I/3C6IDSDMw6efxEIrVldEB\nJC2ZqR/v50cHkGZg0s/jIRSrSf8PkiZm6sf71B+/xmHSz2OLlaQ+mfrx/tvoANIMTPo4tlhJ6pOp\nH+9Tf/wah0k/jy1Wkvri0rrMr4sOEczXOw3dVXWZ/z46RKTeF6u6zP8IXBadQ9Kim3yp6H4hXRWd\nQ9oIkz+Oe1+sOpP/HyVNgMd567zoANJGmPw8QYuVpL7wOG/9ODqAtBF+FB0gmsVKUl94nLdOiw4g\nbYTvRQeIZrGS1Bce563J/2LSoE3+jYHFSlJfeJy3vg800SGkBbgSOCc6RDSLlaS+8DgH6jJfBpwd\nnUNagO/XZT75NwVDKVY/iw4gaVE1WCbmmvzpFA2Sp7EZTrH6KTDpBcekkftFXeZXRIfoEX9BaYh8\nQ8BAilVd5iuB06NzSFo0viCvzp+HhsjnLQMpVh3fwUnj5fG9uu/jKL2Gpa7L/NfRIfpgSMXKJiyN\nl8f3HN1WXl+KziHNw+eiA/TFkIqV72ilcfJU/9p9NjqANA8+XzuDKVZ1mf8cuDw6h6SZO6su86uj\nQ/RQBSyPDiFtgEuBk6JD9MVgilXHUStpfDwNuBbdVZLfiM4hbYBj6zJfER2iLyxWkqJ5XK+bp1c0\nBD5P5xhasfKdrTQ+Htfr5oRg9d21wFeiQ/TJ0IqV72ylcVkO/DA6RF/VZX4e7dILUl99tS7za6JD\n9MmgilVd5ucCF0bnkDQzZ3ZLC2jdjo4OIK3HMdEB+mZQxarz1egAkmbGUwg37kOAIwLqowuAT0eH\n6JshFisnyUnj4fF8I+oyvxz4aHQOaS0Or8vcJUHWMMRi9SXayXKShu184LvRIQbisOgA0hquA46I\nDtFHgytW3SS546NzSNpon6/LvIkOMQR1mf8Y+Hp0DmmOT9Zl/rvoEH00uGLV8fSBNHwuJTA/jlqp\nT3w+rsNQi9XnAVd5lYZrGXBidIiB+RxwbnQICTitLvNTo0P01SCLVV3mlwCnROeQtGBfrMv8uugQ\nQ9JtGfL+6BwS8J7oAH02yGLV8TSCNFyezl+Y9wOXRYfQpP0S+Hh0iD4bcrHyhVkapuuA46JDDFG3\nMfNbo3No0l7raPP6DbZY1WX+C+DH0TkkzdvX6jJfFh1iwN4D1NEhNEnfBT4RHaLvBlusOo5aScPj\nafyN0I0WvDY6hybpQJdIuXFDL1b/Ex1A0rwsBz4THWIEPo6b0mtpfaEu829GhxiCQRerbtG8b0Tn\nkLTBPuWighuvGzU4MDqHJmMF8OroEEMx6GLVcZEyaTg8XmekLvOv40UAWhr/VZf5WdEhhmIMxeqz\nuGieNATfr8v829EhRuZA2tOr0mJZBhwSHWJIBl+sukXzDo/OIelGuajgjNVl/n/AG6NzaNReXpf5\nBdEhhmTwxarzQeCP0SEkrdMluKjgYimB70SH0Ch9ri7zo6JDDM0oilVd5hfji7bUZx+sy/wP0SHG\nqBu13xe4JjqLRuVi4B+iQwzRKIpVx0mxUj+5x90iq8v8bKCIzqFReVFd5hdFhxii0RSrusy/Dzgx\nVuqfz9Zl7gUmi+89wAnRITQKH63L3PXmFmg0xarjqJXUPx6XS6Bb2+q5wJXRWTRo5wEviQ4xZGMr\nVp8CvHpB6o8z6zJ3Ed8l0o0M+ktRC7USeG5d5pbzjTCqYlWX+XJ8dyz1yb9FB5iausw/ij93LcyB\ndZkfHx1i6EZVrDrvBs6PDiGJHwMfiQ4xUQcCx0aH0KB8sC5zC/kMjK5Y1WV+DXBwdA5JvKou85XR\nIaao+7k/HfhRdBYNwteAA6JDjMXoilXnKNp3y5JinFCX+RejQ0xZXea/Bx4PuOm11ucc4MndVBrN\nwCiLVbdgnmu6SDEa4FXRIQR1mf8GeCLg4qxam8uBvesyvzw6yJiMslgB1GVe0Q5vSlpax9Rlfnp0\nCLXqMv8O8LzoHOqd64GndIvLaoZGW6w6r6J99yxpafwReF10CK2uLvNjgFdH51BvrASeV5f5idFB\nxmjUxaou8+/hHoLSUnpPXeZ1dAjdUF3mbwdeG51D4VYCz6/L/L+jg4zVqItV57XAddEhpAm4HHhr\ndAitW13mb8MRxSlrgBfUZX5UdJAxG32x6t49vzc6hzQBhzoJtv/qMj8UeE10Di25FbQjVR+KDjJ2\noy9WnbcAV0SHkEbs17jrwWDUZV7Sbn3jHNRpWA483VK1NCZRrOoyvwx4RXQOacT2r8v8j9EhtOHq\nMn8v8BzakQyN17XAE+oy/2R0kKmYRLECqMv8v3CLB2kxfMDFQIepLvOPAPsAV0Vn0aK4CHi0x+fS\nmkyx6rwQuDQ6hDQiv8TR4EGry/wLwP0A1zMal+8B96nL/FvRQaZmUsWqLvMLgf2jc0gjsRLYt9s6\nRQNWl/lZwJ7AF6KzaCaOAh5cl/m50UGmKDXN9OYuZkV1NO0GpZIW7h11mR8YHUKzkxVVAt4AHASk\n2DRagOXAP3Xz5xRkUiNWcxwAnB8dQhqwnwCvjw6h2arLvKnL/BDaeVfLovNoXn4HPMJSFW+Sxapb\na+f50TmkgVoOPNurAMerLvPP0c67Ois6izbIqbTzqU6KDqKJFiuAusy/BBwRnUMaoDe7yfL41WX+\nU2APoKTdsFf9cy3tHpB/XZf5edFh1JrkHKtVsqLaEvghsFN0FmkgTgMeWJe5v2gnJCuq3YH/AnaL\nzqI/+QbwwrrMz4kOotVNdsQKoLuaaV/aq5skrd+1tKcALVUTU5f5GcB9afde9RRwrGW0V7c/zFLV\nT5MesVolK6oDgbdH55B67pl1mR8dHUKxsqL6K+BI4K+js0zQccB+nvbrN4tVJyuqI4HnReeQeuot\ndZkfFB1C/dAty7A/cAiwXXCcKfgN8Brf2AzDpE8FruFFwDejQ0g99Eng4OgQ6o9uWYb30c5PfR1u\ncr9YLgReCuxsqRoOR6zmyIrq1rSXrd4pOovUE98DHlKX+bXRQdRfWVFtAxwIvBy4RXCcMbiMdnrK\nYXWZXxMdRvNjsVpDVlS7AN8KmNeLAAAHE0lEQVQGtonOIgU7D9izLvMLooNoGLKi2g54De1pwpsE\nxxmiq4B3Af9Wl/mV0WG0MBartciK6lG0kwQ3i84iBbmadq+xM6KDaHiyotqRdn2lZwNbBccZgotp\nLwh4Z13ml0SH0caxWK1DVlT7A++LziEFaIAn1WX+2eggGrasqG5Ouy/rfrTLNWh1X6ddqPozdZlf\nF5xFM2KxWo+sqP6DduKgNCVFXeb/Eh1C49ItMrof8Exgy+A4kS4DPgwcUZf5z6LDaPYsVuuRFdWm\nwLHAY6KzSEvkw3WZPyc6hMar2/HiGbTL2+wJpNhES2IFcBLt6b5P1WX+h+A8WkQWqxuRFdVWwInA\nvaOzSIvsq8DenpLQUsmK6nZADuwNPIpxjWRdDnyJ9s35l+oyvyw4j5aIxWoDZEV1K+AEYPfoLNIi\nOR74W5dVUJSsqLYA9qItWXszzD1c/4+2SB0LnFKX+YrgPApgsdpAWVFtS/vLx3KlsTmRdqTKUqXe\n6LbOeQCwR3fblX6NaF0BnAGc3n08pS7zX8VGUh9YrOahK1cn4A7vGo+v0ZYqFyFUr2VFtQmwM38u\nWnsAd6fdUmcx52mtAH4HnElbok4HTq/L/JeL+D01YBareepWZz+B9t2TNGRfB3JLlYYsK6rNaMvV\nDmvctu8+bk27JuHm3cdNaMvS9XNulwMXrHG7sPt4UV3mK5fuEWnoLFYL0M25Og64f3QWaYGOA57i\n6T9Jmi03YV6Auswvp72C5cToLNICfAJ4oqVKkmbPYrVAdZn/Hngc8PnoLNI8HAk8vS7z5dFBJGmM\nLFYboS7zPwJPBo6OziJtgHcBL3S+iCQtHudYzUBWVAk4BDiYaawirGFZDvxjXebufSlJi8xiNUNZ\nUT0e+Cju5q7++B3tJPVvRQeRpCmwWM1Yt6jdZ4FdorNo8k4FnlyX+W+jg0jSVDjHasa63cr3BD4X\nnUWTdiSwl6VKkpaWI1aLpJt39XrgjTjvSktnOfCyuswPjw4iSVNksVpkWVHlwMdoV/+VFtOFtPOp\nTo4OIklTZbFaAllR7Uw77+pu0Vk0Wt+mLVXnRweRpClzjtUSqMv8HOB+wP9EZ9EovR94qKVKkuI5\nYrXEsqJ6MvAe2g1CpY3xS9oFP91aSZJ6whGrJVaX+adpTwkeFRxFw7WSdhX1e1qqJKlfHLEKlBXV\no4EjgCw4iobjJ8Dz6zI/NTqIJOmGHLEKVJf5V4B7AIfRjkJI67IceBOwh6VKkvrLEaueyIrqgbSL\nOrpiu9Z0Gu0o1ZnRQSRJ6+eIVU/UZX4KsBtwKHB9cBz1w7XAK4EHWKokaRgcseqhrKjuBfwL8Jjo\nLArRAJ8EXluX+S+iw0iSNpzFqseyonoQ8FbgIdFZtGSOBQ6qy/wH0UEkSfNnsRqA7urBtwL3ic6i\nRfM14HV1mX87OogkaeEsVgOSFdU+wJuBu0dn0cycSluoTogOIknaeBargcmKahPg6cAbgTsFx9HC\n/Qh4fV3mX4gOIkmaHYvVQGVFtRnwXOAg4A7BcbThzgYOAf6nLnMPPkkaGYvVwGVFtTnwBGA/4BFA\nik2ktVhBOyn9CODLdZm7GKwkjZTFakSyoroz8ELakazbBscRnAf8J3BkXebnRYeRJC0+i9UIZUW1\nBfAk2lGsh8ammZyVwJeAw4Hj6jJfEZxHkrSELFYjlxXVLsA/APsC2wbHGbMLaLck+s+6zH8dHUaS\nFMNiNRFZUd0UeArwd8DDgZvFJhqFq4CvAh8DPl+XuVsRSdLEWawmKCuqm9FOdN8byIEdYxMNyi9o\nJ6IfC3yzLvPrgvNIknrEYiWyotqNtmTtDdwXN+ee63rgZLoyVZf5T4PzSJJ6zGKl1WRFtR3wWNqS\n9Whgq9hEIS4Fvkhbpr5cl/kVwXkkSQNhsdI6dYuQ3g3YY85tV2DLyFwzdgXwA+D0ObefudaUJGkh\nLFaal25LnZ1ZvWztDtwqMtcGugg4gzklqi7zX8ZGkiSNicVKM5EVVUZbsHYCdgC27z6uum2zBDEu\nBS6kXfpg7u0XtCXqt0uQQZI0YRYrLYluuYe1Fa5tgc2Bzda4bUq7Fcz1c27Lu9sl3LBAXegVepKk\naBYrSZKkGfGyekmSpBmxWEmSJM2IxUqSJGlGLFaSJEkzYrGSJEmaEYuVJEnSjFisJEmSZsRiJUmS\nNCMWK0mSpBmxWEmSJM2IxUqSJGlGLFaSJEkzYrGSJEmaEYuVJEnSjFisJEmSZsRiJUmSNCMWK0mS\npBmxWEmSJM2IxUqSJGlGLFaSJEkzYrGSJEmaEYuVJEnSjFisJEmSZsRiJUmSNCMWK0mSpBmxWEmS\nJM2IxUqSJGlGLFaSJEkzYrGSJEmaEYuVJEnSjFisJEmSZsRiJUmSNCMWK0mSpBmxWEmSJM2IxUqS\nJGlGLFaSJEkz8v8B7VusKzwbg08AAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7f8d36d35550>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "tKEjBxYSIink",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "7c763b73-3842-4830-e3c2-1fc2a7074e61"
+      },
+      "cell_type": "code",
+      "source": [
+        "18.07 / 12.79"
+      ],
+      "execution_count": 42,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "1.4128225175918687"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 42
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "r3Ekry-PInza",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "0e8255dc-c4d0-4485-87c6-be6041aadcd4"
+      },
+      "cell_type": "code",
+      "source": [
+        "from math import pi\n",
+        "\n",
+        "(pi*18.07**2) / (pi*12.79**2)"
+      ],
+      "execution_count": 43,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "1.9960674662146263"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 43
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "49NI_e4HI0KJ",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "8cb4f4fd-e092-4a48-acfd-9140eecf724e"
+      },
+      "cell_type": "code",
+      "source": [
+        "from math import sqrt\n",
+        "sqrt(18.07)**2 / sqrt(12.79)**2"
+      ],
+      "execution_count": 44,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "1.4128225175918685"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 44
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "NB5GBRNrCbgf",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 128
+        },
+        "outputId": "99b336be-46e7-4184-96af-bbc139049f4d"
+      },
+      "cell_type": "code",
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "fig, ax = plt.subplots(figsize=(10, 5))\n",
+        "ax.set_xlim((0, 80))\n",
+        "ax.set_ylim((0, 40))\n",
+        "plt.axis('off')\n",
+        "\n",
+        "circle = plt.Circle(xy=(20, 20), radius=sqrt(18.07)\n",
+        "ax.add_artist(circle)\n",
+        "\n",
+        "circle = plt.Circle(xy=(60, 20), radius=sqrt(12.79)\n",
+        "ax.add_artist(circle)\n",
+        "\n",
+        "plt.title('State Farm vs GEICO')\n",
+        "plt.show()"
+      ],
+      "execution_count": 46,
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "SyntaxError",
+          "evalue": "ignored",
+          "traceback": [
+            "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-46-964343b5d0cb>\"\u001b[0;36m, line \u001b[0;32m9\u001b[0m\n\u001b[0;31m    ax.add_artist(circle)\u001b[0m\n\u001b[0m     ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+          ]
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "D76RfkFkCbgi",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "#### Fix misleading plot #4"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "9Qjq-AiyCbgi",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "_If you're on Jupyter (not Colab) then uncomment and run this cell below:_"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "Gt1u-VCECbgk",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# import altair as alt\n",
+        "# alt.renderers.enable('notebook')"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "ox2k1t6TCbgm",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 348
+        },
+        "outputId": "26974673-da74-4649-fce6-357ea07e2fbd"
+      },
+      "cell_type": "code",
+      "source": [
+        "misleading.plot4()"
+      ],
+      "execution_count": 47,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Chart({\n",
+              "  data: UrlData({\n",
+              "    format: TopoDataFormat({\n",
+              "      feature: 'states',\n",
+              "      type: 'topojson'\n",
+              "    }),\n",
+              "    url: 'https://vega.github.io/vega-datasets/data/us-10m.json'\n",
+              "  }),\n",
+              "  encoding: EncodingWithFacet({\n",
+              "    color: Color({\n",
+              "      shorthand: 'State Farm policy premiums',\n",
+              "      type: 'quantitative'\n",
+              "    })\n",
+              "  }),\n",
+              "  height: 300,\n",
+              "  mark: 'geoshape',\n",
+              "  projection: Projection({\n",
+              "    type: 'albersUsa'\n",
+              "  }),\n",
+              "  transform: [LookupTransform({\n",
+              "    from: LookupData({\n",
+              "      data:                    state  id  State Farm policy premiums  \\\n",
+              "      0                Alabama   1                      768213   \n",
+              "      1                 Alaska   2                      128613   \n",
+              "      2                Arizona   4                      760003   \n",
+              "      3               Arkansas   5                      454931   \n",
+              "      4             California   6                     3655463   \n",
+              "      5               Colorado   8                      827143   \n",
+              "      6            Connecticut   9                      169773   \n",
+              "      7               Delaware  10                      191319   \n",
+              "      8   District of Columbia  11                       67231   \n",
+              "      9                Florida  12                     2806322   \n",
+              "      10               Georgia  13                     1761224   \n",
+              "      11                Hawaii  15                      133498   \n",
+              "      12                 Idaho  16                      130613   \n",
+              "      13              Illinois  17                     2256543   \n",
+              "      14               Indiana  18                      861382   \n",
+              "      15                  Iowa  19                      368898   \n",
+              "      16                Kansas  20                      351343   \n",
+              "      17              Kentucky  21                      680433   \n",
+              "      18             Louisiana  22                     1415980   \n",
+              "      19                 Maine  23                      101111   \n",
+              "      20              Maryland  24                      883401   \n",
+              "      21         Massachusetts  25                           0   \n",
+              "      22              Michigan  26                     1580398   \n",
+              "      23             Minnesota  27                      851940   \n",
+              "      24           Mississippi  28                      467887   \n",
+              "      25              Missouri  29                      883774   \n",
+              "      26               Montana  30                      156816   \n",
+              "      27              Nebraska  31                      273727   \n",
+              "      28                Nevada  32                      404688   \n",
+              "      29         New Hampshire  33                      103340   \n",
+              "      30            New Jersey  34                      619623   \n",
+              "      31            New Mexico  35                      261922   \n",
+              "      32              New York  36                     1666653   \n",
+              "      33        North Carolina  37                      842368   \n",
+              "      34          North Dakota  38                       67282   \n",
+              "      35                  Ohio  39                     1240075   \n",
+              "      36              Oklahoma  40                      636063   \n",
+              "      37                Oregon  41                      564416   \n",
+              "      38          Pennsylvania  42                     1654528   \n",
+              "      39          Rhode Island  44                           0   \n",
+              "      40        South Carolina  45                      874436   \n",
+              "      41          South Dakota  46                      101912   \n",
+              "      42             Tennessee  47                      889329   \n",
+              "      43                 Texas  48                     3395165   \n",
+              "      44                  Utah  49                      300403   \n",
+              "      45               Vermont  50                       30003   \n",
+              "      46              Virginia  51                      878312   \n",
+              "      47            Washington  53                      794882   \n",
+              "      48         West Virginia  54                      323608   \n",
+              "      49             Wisconsin  55                      443680   \n",
+              "      50               Wyoming  56                       90412   \n",
+              "      51           Puerto Rico  72                           0   \n",
+              "      \n",
+              "          BERKSHIRE HATHAWAY GRP  STATE TOTAL  \n",
+              "      0                   282947      3025561  \n",
+              "      1                    81366       454623  \n",
+              "      2                   604049      4373055  \n",
+              "      3                    96908      1777917  \n",
+              "      4                  2091720     25255501  \n",
+              "      5                   365797      4057463  \n",
+              "      6                   486498      2795415  \n",
+              "      7                   129033       802005  \n",
+              "      8                   115149       325239  \n",
+              "      9                  3952147     17333354  \n",
+              "      10                  821326      7571988  \n",
+              "      11                  184743       722550  \n",
+              "      12                   87222       863636  \n",
+              "      13                  412978      7028885  \n",
+              "      14                  201200      3506499  \n",
+              "      15                   58105      1650388  \n",
+              "      16                   76533      1725608  \n",
+              "      17                  186915      2805864  \n",
+              "      18                  369492      4136224  \n",
+              "      19                   68109       699558  \n",
+              "      20                 1059508      4554586  \n",
+              "      21                  473913      4924736  \n",
+              "      22                       0      8462142  \n",
+              "      23                  108942      3412460  \n",
+              "      24                  100827      1760180  \n",
+              "      25                  214191      3606590  \n",
+              "      26                   33538       668513  \n",
+              "      27                   61046      1167135  \n",
+              "      28                  262815      2129828  \n",
+              "      29                  103403       806545  \n",
+              "      30                 1441019      7375823  \n",
+              "      31                  174865      1284430  \n",
+              "      32                 3926684     12633982  \n",
+              "      33                  560700      5543700  \n",
+              "      34                   15516       454376  \n",
+              "      35                  405652      6303148  \n",
+              "      36                  151388      2515842  \n",
+              "      37                  223187      2647434  \n",
+              "      38                  602848      8270326  \n",
+              "      39                   97597       867558  \n",
+              "      40                  426033      3583918  \n",
+              "      41                   15682       508569  \n",
+              "      42                  292357      3769652  \n",
+              "      43                 2127633     19180816  \n",
+              "      44                  139315      1723931  \n",
+              "      45                   52939       359996  \n",
+              "      46                  884489      5105360  \n",
+              "      47                  478922      4780616  \n",
+              "      48                  110806      1209872  \n",
+              "      49                  131845      2950711  \n",
+              "      50                   30187       374169  \n",
+              "      51                       0       444480  ,\n",
+              "      fields: ['State Farm policy premiums'],\n",
+              "      key: 'id'\n",
+              "    }),\n",
+              "    lookup: 'id'\n",
+              "  })],\n",
+              "  width: 500\n",
+              "})"
+            ],
+            "text/html": [
+              "\n",
+              "<!DOCTYPE html>\n",
+              "<html>\n",
+              "<head>\n",
+              "  <style>\n",
+              "    .vega-actions a {\n",
+              "        margin-right: 12px;\n",
+              "        color: #757575;\n",
+              "        font-weight: normal;\n",
+              "        font-size: 13px;\n",
+              "    }\n",
+              "    .error {\n",
+              "        color: red;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "<script src=\"https://cdn.jsdelivr.net/npm//vega@3.3.1\"></script>\n",
+              "<script src=\"https://cdn.jsdelivr.net/npm//vega-lite@2.6.0\"></script>\n",
+              "<script src=\"https://cdn.jsdelivr.net/npm//vega-embed@3.14\"></script>\n",
+              "\n",
+              "</head>\n",
+              "<body>\n",
+              "  <div id=\"vis\"></div>\n",
+              "  <script type=\"text/javascript\">\n",
+              "    var spec = {\"config\": {\"view\": {\"width\": 400, \"height\": 300}}, \"data\": {\"url\": \"https://vega.github.io/vega-datasets/data/us-10m.json\", \"format\": {\"feature\": \"states\", \"type\": \"topojson\"}}, \"mark\": \"geoshape\", \"encoding\": {\"color\": {\"type\": \"quantitative\", \"field\": \"State Farm policy premiums\"}}, \"height\": 300, \"projection\": {\"type\": \"albersUsa\"}, \"transform\": [{\"lookup\": \"id\", \"from\": {\"data\": {\"name\": \"data-b615a17f98b0ca7d11f99dcf36616c5d\"}, \"key\": \"id\", \"fields\": [\"State Farm policy premiums\"]}}], \"width\": 500, \"$schema\": \"https://vega.github.io/schema/vega-lite/v2.6.0.json\", \"datasets\": {\"data-b615a17f98b0ca7d11f99dcf36616c5d\": [{\"state\": \"Alabama\", \"id\": 1, \"State Farm policy premiums\": 768213, \"BERKSHIRE HATHAWAY GRP\": 282947, \"STATE TOTAL\": 3025561}, {\"state\": \"Alaska\", \"id\": 2, \"State Farm policy premiums\": 128613, \"BERKSHIRE HATHAWAY GRP\": 81366, \"STATE TOTAL\": 454623}, {\"state\": \"Arizona\", \"id\": 4, \"State Farm policy premiums\": 760003, \"BERKSHIRE HATHAWAY GRP\": 604049, \"STATE TOTAL\": 4373055}, {\"state\": \"Arkansas\", \"id\": 5, \"State Farm policy premiums\": 454931, \"BERKSHIRE HATHAWAY GRP\": 96908, \"STATE TOTAL\": 1777917}, {\"state\": \"California\", \"id\": 6, \"State Farm policy premiums\": 3655463, \"BERKSHIRE HATHAWAY GRP\": 2091720, \"STATE TOTAL\": 25255501}, {\"state\": \"Colorado\", \"id\": 8, \"State Farm policy premiums\": 827143, \"BERKSHIRE HATHAWAY GRP\": 365797, \"STATE TOTAL\": 4057463}, {\"state\": \"Connecticut\", \"id\": 9, \"State Farm policy premiums\": 169773, \"BERKSHIRE HATHAWAY GRP\": 486498, \"STATE TOTAL\": 2795415}, {\"state\": \"Delaware\", \"id\": 10, \"State Farm policy premiums\": 191319, \"BERKSHIRE HATHAWAY GRP\": 129033, \"STATE TOTAL\": 802005}, {\"state\": \"District of Columbia\", \"id\": 11, \"State Farm policy premiums\": 67231, \"BERKSHIRE HATHAWAY GRP\": 115149, \"STATE TOTAL\": 325239}, {\"state\": \"Florida\", \"id\": 12, \"State Farm policy premiums\": 2806322, \"BERKSHIRE HATHAWAY GRP\": 3952147, \"STATE TOTAL\": 17333354}, {\"state\": \"Georgia\", \"id\": 13, \"State Farm policy premiums\": 1761224, \"BERKSHIRE HATHAWAY GRP\": 821326, \"STATE TOTAL\": 7571988}, {\"state\": \"Hawaii\", \"id\": 15, \"State Farm policy premiums\": 133498, \"BERKSHIRE HATHAWAY GRP\": 184743, \"STATE TOTAL\": 722550}, {\"state\": \"Idaho\", \"id\": 16, \"State Farm policy premiums\": 130613, \"BERKSHIRE HATHAWAY GRP\": 87222, \"STATE TOTAL\": 863636}, {\"state\": \"Illinois\", \"id\": 17, \"State Farm policy premiums\": 2256543, \"BERKSHIRE HATHAWAY GRP\": 412978, \"STATE TOTAL\": 7028885}, {\"state\": \"Indiana\", \"id\": 18, \"State Farm policy premiums\": 861382, \"BERKSHIRE HATHAWAY GRP\": 201200, \"STATE TOTAL\": 3506499}, {\"state\": \"Iowa\", \"id\": 19, \"State Farm policy premiums\": 368898, \"BERKSHIRE HATHAWAY GRP\": 58105, \"STATE TOTAL\": 1650388}, {\"state\": \"Kansas\", \"id\": 20, \"State Farm policy premiums\": 351343, \"BERKSHIRE HATHAWAY GRP\": 76533, \"STATE TOTAL\": 1725608}, {\"state\": \"Kentucky\", \"id\": 21, \"State Farm policy premiums\": 680433, \"BERKSHIRE HATHAWAY GRP\": 186915, \"STATE TOTAL\": 2805864}, {\"state\": \"Louisiana\", \"id\": 22, \"State Farm policy premiums\": 1415980, \"BERKSHIRE HATHAWAY GRP\": 369492, \"STATE TOTAL\": 4136224}, {\"state\": \"Maine\", \"id\": 23, \"State Farm policy premiums\": 101111, \"BERKSHIRE HATHAWAY GRP\": 68109, \"STATE TOTAL\": 699558}, {\"state\": \"Maryland\", \"id\": 24, \"State Farm policy premiums\": 883401, \"BERKSHIRE HATHAWAY GRP\": 1059508, \"STATE TOTAL\": 4554586}, {\"state\": \"Massachusetts\", \"id\": 25, \"State Farm policy premiums\": 0, \"BERKSHIRE HATHAWAY GRP\": 473913, \"STATE TOTAL\": 4924736}, {\"state\": \"Michigan\", \"id\": 26, \"State Farm policy premiums\": 1580398, \"BERKSHIRE HATHAWAY GRP\": 0, \"STATE TOTAL\": 8462142}, {\"state\": \"Minnesota\", \"id\": 27, \"State Farm policy premiums\": 851940, \"BERKSHIRE HATHAWAY GRP\": 108942, \"STATE TOTAL\": 3412460}, {\"state\": \"Mississippi\", \"id\": 28, \"State Farm policy premiums\": 467887, \"BERKSHIRE HATHAWAY GRP\": 100827, \"STATE TOTAL\": 1760180}, {\"state\": \"Missouri\", \"id\": 29, \"State Farm policy premiums\": 883774, \"BERKSHIRE HATHAWAY GRP\": 214191, \"STATE TOTAL\": 3606590}, {\"state\": \"Montana\", \"id\": 30, \"State Farm policy premiums\": 156816, \"BERKSHIRE HATHAWAY GRP\": 33538, \"STATE TOTAL\": 668513}, {\"state\": \"Nebraska\", \"id\": 31, \"State Farm policy premiums\": 273727, \"BERKSHIRE HATHAWAY GRP\": 61046, \"STATE TOTAL\": 1167135}, {\"state\": \"Nevada\", \"id\": 32, \"State Farm policy premiums\": 404688, \"BERKSHIRE HATHAWAY GRP\": 262815, \"STATE TOTAL\": 2129828}, {\"state\": \"New Hampshire\", \"id\": 33, \"State Farm policy premiums\": 103340, \"BERKSHIRE HATHAWAY GRP\": 103403, \"STATE TOTAL\": 806545}, {\"state\": \"New Jersey\", \"id\": 34, \"State Farm policy premiums\": 619623, \"BERKSHIRE HATHAWAY GRP\": 1441019, \"STATE TOTAL\": 7375823}, {\"state\": \"New Mexico\", \"id\": 35, \"State Farm policy premiums\": 261922, \"BERKSHIRE HATHAWAY GRP\": 174865, \"STATE TOTAL\": 1284430}, {\"state\": \"New York\", \"id\": 36, \"State Farm policy premiums\": 1666653, \"BERKSHIRE HATHAWAY GRP\": 3926684, \"STATE TOTAL\": 12633982}, {\"state\": \"North Carolina\", \"id\": 37, \"State Farm policy premiums\": 842368, \"BERKSHIRE HATHAWAY GRP\": 560700, \"STATE TOTAL\": 5543700}, {\"state\": \"North Dakota\", \"id\": 38, \"State Farm policy premiums\": 67282, \"BERKSHIRE HATHAWAY GRP\": 15516, \"STATE TOTAL\": 454376}, {\"state\": \"Ohio\", \"id\": 39, \"State Farm policy premiums\": 1240075, \"BERKSHIRE HATHAWAY GRP\": 405652, \"STATE TOTAL\": 6303148}, {\"state\": \"Oklahoma\", \"id\": 40, \"State Farm policy premiums\": 636063, \"BERKSHIRE HATHAWAY GRP\": 151388, \"STATE TOTAL\": 2515842}, {\"state\": \"Oregon\", \"id\": 41, \"State Farm policy premiums\": 564416, \"BERKSHIRE HATHAWAY GRP\": 223187, \"STATE TOTAL\": 2647434}, {\"state\": \"Pennsylvania\", \"id\": 42, \"State Farm policy premiums\": 1654528, \"BERKSHIRE HATHAWAY GRP\": 602848, \"STATE TOTAL\": 8270326}, {\"state\": \"Rhode Island\", \"id\": 44, \"State Farm policy premiums\": 0, \"BERKSHIRE HATHAWAY GRP\": 97597, \"STATE TOTAL\": 867558}, {\"state\": \"South Carolina\", \"id\": 45, \"State Farm policy premiums\": 874436, \"BERKSHIRE HATHAWAY GRP\": 426033, \"STATE TOTAL\": 3583918}, {\"state\": \"South Dakota\", \"id\": 46, \"State Farm policy premiums\": 101912, \"BERKSHIRE HATHAWAY GRP\": 15682, \"STATE TOTAL\": 508569}, {\"state\": \"Tennessee\", \"id\": 47, \"State Farm policy premiums\": 889329, \"BERKSHIRE HATHAWAY GRP\": 292357, \"STATE TOTAL\": 3769652}, {\"state\": \"Texas\", \"id\": 48, \"State Farm policy premiums\": 3395165, \"BERKSHIRE HATHAWAY GRP\": 2127633, \"STATE TOTAL\": 19180816}, {\"state\": \"Utah\", \"id\": 49, \"State Farm policy premiums\": 300403, \"BERKSHIRE HATHAWAY GRP\": 139315, \"STATE TOTAL\": 1723931}, {\"state\": \"Vermont\", \"id\": 50, \"State Farm policy premiums\": 30003, \"BERKSHIRE HATHAWAY GRP\": 52939, \"STATE TOTAL\": 359996}, {\"state\": \"Virginia\", \"id\": 51, \"State Farm policy premiums\": 878312, \"BERKSHIRE HATHAWAY GRP\": 884489, \"STATE TOTAL\": 5105360}, {\"state\": \"Washington\", \"id\": 53, \"State Farm policy premiums\": 794882, \"BERKSHIRE HATHAWAY GRP\": 478922, \"STATE TOTAL\": 4780616}, {\"state\": \"West Virginia\", \"id\": 54, \"State Farm policy premiums\": 323608, \"BERKSHIRE HATHAWAY GRP\": 110806, \"STATE TOTAL\": 1209872}, {\"state\": \"Wisconsin\", \"id\": 55, \"State Farm policy premiums\": 443680, \"BERKSHIRE HATHAWAY GRP\": 131845, \"STATE TOTAL\": 2950711}, {\"state\": \"Wyoming\", \"id\": 56, \"State Farm policy premiums\": 90412, \"BERKSHIRE HATHAWAY GRP\": 30187, \"STATE TOTAL\": 374169}, {\"state\": \"Puerto Rico\", \"id\": 72, \"State Farm policy premiums\": 0, \"BERKSHIRE HATHAWAY GRP\": 0, \"STATE TOTAL\": 444480}]}};\n",
+              "    var embed_opt = {\"mode\": \"vega-lite\"};\n",
+              "\n",
+              "    function showError(el, error){\n",
+              "        el.innerHTML = ('<div class=\"error\">'\n",
+              "                        + '<p>JavaScript Error: ' + error.message + '</p>'\n",
+              "                        + \"<p>This usually means there's a typo in your chart specification. \"\n",
+              "                        + \"See the javascript console for the full traceback.</p>\"\n",
+              "                        + '</div>');\n",
+              "        throw error;\n",
+              "    }\n",
+              "    const el = document.getElementById('vis');\n",
+              "    vegaEmbed(\"#vis\", spec, embed_opt)\n",
+              "      .catch(error => showError(el, error));\n",
+              "  </script>\n",
+              "</body>\n",
+              "</html>\n"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 47
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "vICGJrAKLn8i",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "url = 'https://raw.githubusercontent.com/LambdaSchool/DS-Sprint-02-Storytelling-With-Data/master/module2-choose-appropriate-visualizations/direct_written_premium_by_state_by_group_private_passenger_auto.csv'\n",
+        "df = pd.read_csv(url)\n",
+        "df.rename(columns={'STATE FARM GRP': 'State Farm policy premiums'}, inplace=True)\n",
+        "variable = 'State Farm policy premiums'"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "_bkJ2qryLt2R",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 195
+        },
+        "outputId": "fe24b046-884b-4f0b-ee54-1774e8806d39"
+      },
+      "cell_type": "code",
+      "source": [
+        "df.head() # want to examine state farm policy premiums / Total"
+      ],
+      "execution_count": 50,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>state</th>\n",
+              "      <th>id</th>\n",
+              "      <th>State Farm policy premiums</th>\n",
+              "      <th>BERKSHIRE HATHAWAY GRP</th>\n",
+              "      <th>STATE TOTAL</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Alabama</td>\n",
+              "      <td>1</td>\n",
+              "      <td>768213</td>\n",
+              "      <td>282947</td>\n",
+              "      <td>3025561</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Alaska</td>\n",
+              "      <td>2</td>\n",
+              "      <td>128613</td>\n",
+              "      <td>81366</td>\n",
+              "      <td>454623</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Arizona</td>\n",
+              "      <td>4</td>\n",
+              "      <td>760003</td>\n",
+              "      <td>604049</td>\n",
+              "      <td>4373055</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Arkansas</td>\n",
+              "      <td>5</td>\n",
+              "      <td>454931</td>\n",
+              "      <td>96908</td>\n",
+              "      <td>1777917</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>California</td>\n",
+              "      <td>6</td>\n",
+              "      <td>3655463</td>\n",
+              "      <td>2091720</td>\n",
+              "      <td>25255501</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "        state  id  State Farm policy premiums  BERKSHIRE HATHAWAY GRP  \\\n",
+              "0     Alabama   1                      768213                  282947   \n",
+              "1      Alaska   2                      128613                   81366   \n",
+              "2     Arizona   4                      760003                  604049   \n",
+              "3    Arkansas   5                      454931                   96908   \n",
+              "4  California   6                     3655463                 2091720   \n",
+              "\n",
+              "   STATE TOTAL  \n",
+              "0      3025561  \n",
+              "1       454623  \n",
+              "2      4373055  \n",
+              "3      1777917  \n",
+              "4     25255501  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 50
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "Px8qFtLLCbgr",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "#### Links\n",
+        "- [How to Spot Visualization Lies](https://flowingdata.com/2017/02/09/how-to-spot-visualization-lies/)\n",
+        "- [Where to Start and End Your Y-Axis Scale](http://stephanieevergreen.com/y-axis/)\n",
+        "- [xkcd heatmap](https://xkcd.com/1138/)\n",
+        "- [Surprise Maps: Showing the Unexpected](https://medium.com/@uwdata/surprise-maps-showing-the-unexpected-e92b67398865)"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "inmy6QpZCbgs",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "# Use Seaborn to visualize distributions and relationships with continuous and discrete variables\n",
+        "\n",
+        "#### Links\n",
+        "- [Seaborn tutorial](https://seaborn.pydata.org/tutorial.html)\n",
+        "- [Seaborn example gallery](https://seaborn.pydata.org/examples/index.html)\n",
+        "- [Chart Chooser](https://extremepresentation.typepad.com/files/choosing-a-good-chart-09.pdf)"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "0MutqrlnCbgu",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## 1. Anscombe dataset"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "KWkpy0vzCbgu",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Load dataset"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "Wm0nWRa0Cbgw",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "df = sns.load_dataset('anscombe')"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "H6RXFuCJCbg2",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### See the data's shape"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "Xo8EwGF_Cbg4",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "f4043abd-5abe-474a-ad41-8e4e82afaa05"
+      },
+      "cell_type": "code",
+      "source": [
+        "df.shape\n"
+      ],
+      "execution_count": 53,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(44, 3)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 53
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "cTJnWQmRCbg6",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### See the data"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "Oi7LPGvGCbg7",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 195
+        },
+        "outputId": "f4218631-0608-46e8-dbfa-e0d204bf4097"
+      },
+      "cell_type": "code",
+      "source": [
+        "df.head()"
+      ],
+      "execution_count": 56,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>dataset</th>\n",
+              "      <th>x</th>\n",
+              "      <th>y</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>I</td>\n",
+              "      <td>10.0</td>\n",
+              "      <td>8.04</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>I</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>6.95</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>I</td>\n",
+              "      <td>13.0</td>\n",
+              "      <td>7.58</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>I</td>\n",
+              "      <td>9.0</td>\n",
+              "      <td>8.81</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>I</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>8.33</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "  dataset     x     y\n",
+              "0       I  10.0  8.04\n",
+              "1       I   8.0  6.95\n",
+              "2       I  13.0  7.58\n",
+              "3       I   9.0  8.81\n",
+              "4       I  11.0  8.33"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 56
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "3Rps4xrkCbg9",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### [Group by](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.groupby.html) `'dataset'`"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "S3ns5XdvCbg-",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "groups = df.groupby('dataset')"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "IVeyv7GVCbhA",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### [Describe](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.describe.html) the groups"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "fZ61fr1CCbhB",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 225
+        },
+        "outputId": "f92aadf1-eb8f-4761-b692-9b9b9fb01acb"
+      },
+      "cell_type": "code",
+      "source": [
+        "groups.describe()"
+      ],
+      "execution_count": 58,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead tr th {\n",
+              "        text-align: left;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead tr:last-of-type th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr>\n",
+              "      <th></th>\n",
+              "      <th colspan=\"8\" halign=\"left\">x</th>\n",
+              "      <th colspan=\"8\" halign=\"left\">y</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th></th>\n",
+              "      <th>count</th>\n",
+              "      <th>mean</th>\n",
+              "      <th>std</th>\n",
+              "      <th>min</th>\n",
+              "      <th>25%</th>\n",
+              "      <th>50%</th>\n",
+              "      <th>75%</th>\n",
+              "      <th>max</th>\n",
+              "      <th>count</th>\n",
+              "      <th>mean</th>\n",
+              "      <th>std</th>\n",
+              "      <th>min</th>\n",
+              "      <th>25%</th>\n",
+              "      <th>50%</th>\n",
+              "      <th>75%</th>\n",
+              "      <th>max</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>dataset</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>I</th>\n",
+              "      <td>11.0</td>\n",
+              "      <td>9.0</td>\n",
+              "      <td>3.316625</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>6.5</td>\n",
+              "      <td>9.0</td>\n",
+              "      <td>11.5</td>\n",
+              "      <td>14.0</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>7.500909</td>\n",
+              "      <td>2.031568</td>\n",
+              "      <td>4.26</td>\n",
+              "      <td>6.315</td>\n",
+              "      <td>7.58</td>\n",
+              "      <td>8.57</td>\n",
+              "      <td>10.84</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>II</th>\n",
+              "      <td>11.0</td>\n",
+              "      <td>9.0</td>\n",
+              "      <td>3.316625</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>6.5</td>\n",
+              "      <td>9.0</td>\n",
+              "      <td>11.5</td>\n",
+              "      <td>14.0</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>7.500909</td>\n",
+              "      <td>2.031657</td>\n",
+              "      <td>3.10</td>\n",
+              "      <td>6.695</td>\n",
+              "      <td>8.14</td>\n",
+              "      <td>8.95</td>\n",
+              "      <td>9.26</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>III</th>\n",
+              "      <td>11.0</td>\n",
+              "      <td>9.0</td>\n",
+              "      <td>3.316625</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>6.5</td>\n",
+              "      <td>9.0</td>\n",
+              "      <td>11.5</td>\n",
+              "      <td>14.0</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>7.500000</td>\n",
+              "      <td>2.030424</td>\n",
+              "      <td>5.39</td>\n",
+              "      <td>6.250</td>\n",
+              "      <td>7.11</td>\n",
+              "      <td>7.98</td>\n",
+              "      <td>12.74</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>IV</th>\n",
+              "      <td>11.0</td>\n",
+              "      <td>9.0</td>\n",
+              "      <td>3.316625</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>19.0</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>7.500909</td>\n",
+              "      <td>2.030579</td>\n",
+              "      <td>5.25</td>\n",
+              "      <td>6.170</td>\n",
+              "      <td>7.04</td>\n",
+              "      <td>8.19</td>\n",
+              "      <td>12.50</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "            x                                               y            \\\n",
+              "        count mean       std  min  25%  50%   75%   max count      mean   \n",
+              "dataset                                                                   \n",
+              "I        11.0  9.0  3.316625  4.0  6.5  9.0  11.5  14.0  11.0  7.500909   \n",
+              "II       11.0  9.0  3.316625  4.0  6.5  9.0  11.5  14.0  11.0  7.500909   \n",
+              "III      11.0  9.0  3.316625  4.0  6.5  9.0  11.5  14.0  11.0  7.500000   \n",
+              "IV       11.0  9.0  3.316625  8.0  8.0  8.0   8.0  19.0  11.0  7.500909   \n",
+              "\n",
+              "                                                   \n",
+              "              std   min    25%   50%   75%    max  \n",
+              "dataset                                            \n",
+              "I        2.031568  4.26  6.315  7.58  8.57  10.84  \n",
+              "II       2.031657  3.10  6.695  8.14  8.95   9.26  \n",
+              "III      2.030424  5.39  6.250  7.11  7.98  12.74  \n",
+              "IV       2.030579  5.25  6.170  7.04  8.19  12.50  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 58
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "HHRzZXgDCbhD",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Get the [count](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.count.html), for each column in each group"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "WjDuhyiBCbhE",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 195
+        },
+        "outputId": "f9302e4d-3c41-4b56-86c8-7fd518b5c985"
+      },
+      "cell_type": "code",
+      "source": [
+        "groups.count()"
+      ],
+      "execution_count": 60,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>x</th>\n",
+              "      <th>y</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>dataset</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>I</th>\n",
+              "      <td>11</td>\n",
+              "      <td>11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>II</th>\n",
+              "      <td>11</td>\n",
+              "      <td>11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>III</th>\n",
+              "      <td>11</td>\n",
+              "      <td>11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>IV</th>\n",
+              "      <td>11</td>\n",
+              "      <td>11</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "          x   y\n",
+              "dataset        \n",
+              "I        11  11\n",
+              "II       11  11\n",
+              "III      11  11\n",
+              "IV       11  11"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 60
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "v2JMcRfgCbhG",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Get the [mean](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.mean.html) ..."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "r3P7Vp0qCbhG",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 195
+        },
+        "outputId": "710d8be0-393e-4f06-dc44-ac1cd721f730"
+      },
+      "cell_type": "code",
+      "source": [
+        "groups.mean()"
+      ],
+      "execution_count": 61,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>x</th>\n",
+              "      <th>y</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>dataset</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>I</th>\n",
+              "      <td>9.0</td>\n",
+              "      <td>7.500909</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>II</th>\n",
+              "      <td>9.0</td>\n",
+              "      <td>7.500909</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>III</th>\n",
+              "      <td>9.0</td>\n",
+              "      <td>7.500000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>IV</th>\n",
+              "      <td>9.0</td>\n",
+              "      <td>7.500909</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "           x         y\n",
+              "dataset               \n",
+              "I        9.0  7.500909\n",
+              "II       9.0  7.500909\n",
+              "III      9.0  7.500000\n",
+              "IV       9.0  7.500909"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 61
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "kdM_HV3iCbhJ",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Get the [standard deviation](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.std.html) ..."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "eGC-IJhgCbhJ",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 195
+        },
+        "outputId": "da80d609-8bb5-442c-db24-164d2362033a"
+      },
+      "cell_type": "code",
+      "source": [
+        "groups.std()"
+      ],
+      "execution_count": 62,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>x</th>\n",
+              "      <th>y</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>dataset</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>I</th>\n",
+              "      <td>3.316625</td>\n",
+              "      <td>2.031568</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>II</th>\n",
+              "      <td>3.316625</td>\n",
+              "      <td>2.031657</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>III</th>\n",
+              "      <td>3.316625</td>\n",
+              "      <td>2.030424</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>IV</th>\n",
+              "      <td>3.316625</td>\n",
+              "      <td>2.030579</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                x         y\n",
+              "dataset                    \n",
+              "I        3.316625  2.031568\n",
+              "II       3.316625  2.031657\n",
+              "III      3.316625  2.030424\n",
+              "IV       3.316625  2.030579"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 62
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "sSly-4EgCbhM",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Get the [correlation](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.corr.html) ..."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "tVrDGvfqCbhM",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 314
+        },
+        "outputId": "40f61de9-b30b-4ff2-eaac-12348c118aa8"
+      },
+      "cell_type": "code",
+      "source": [
+        "groups.corr()"
+      ],
+      "execution_count": 63,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th>x</th>\n",
+              "      <th>y</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>dataset</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th rowspan=\"2\" valign=\"top\">I</th>\n",
+              "      <th>x</th>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.816421</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>y</th>\n",
+              "      <td>0.816421</td>\n",
+              "      <td>1.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th rowspan=\"2\" valign=\"top\">II</th>\n",
+              "      <th>x</th>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.816237</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>y</th>\n",
+              "      <td>0.816237</td>\n",
+              "      <td>1.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th rowspan=\"2\" valign=\"top\">III</th>\n",
+              "      <th>x</th>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.816287</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>y</th>\n",
+              "      <td>0.816287</td>\n",
+              "      <td>1.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th rowspan=\"2\" valign=\"top\">IV</th>\n",
+              "      <th>x</th>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.816521</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>y</th>\n",
+              "      <td>0.816521</td>\n",
+              "      <td>1.000000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                  x         y\n",
+              "dataset                      \n",
+              "I       x  1.000000  0.816421\n",
+              "        y  0.816421  1.000000\n",
+              "II      x  1.000000  0.816237\n",
+              "        y  0.816237  1.000000\n",
+              "III     x  1.000000  0.816287\n",
+              "        y  0.816287  1.000000\n",
+              "IV      x  1.000000  0.816521\n",
+              "        y  0.816521  1.000000"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 63
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ip3WlhacCbhP",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Use pandas to [plot](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.plot.html) the groups, as scatter plots"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "JvQD-G-1CbhQ",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1082
+        },
+        "outputId": "24579881-204d-44aa-b1a7-9d8bff30357e"
+      },
+      "cell_type": "code",
+      "source": [
+        "groups.plot('x', 'y', kind='scatter');"
+      ],
+      "execution_count": 67,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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2ZyNiopN5djr2OQ/sc/WVtb9JiyAiviTpSykzAEDuKrnEBABg83IogqOpAyTAPueBfa6+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\nomF0EMrjugAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7f8d370fb898>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7f8d362b3a58>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7f8d3644dd68>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7f8d361da208>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "fqjHIx3-CbhS",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Use Seaborn to make [relational plots](http://seaborn.pydata.org/generated/seaborn.relplot.html)"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "H-889umCCbhT",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 729
+        },
+        "outputId": "d687629f-b312-4cd3-ebc7-c1a6e5e82bc7"
+      },
+      "cell_type": "code",
+      "source": [
+        "sns.relplot('x', 'y', col='dataset', hue='dataset', col_wrap=2, data=df);"
+      ],
+      "execution_count": 73,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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JSTK8Mpljggf4VwAA3XDnzckXL5ooEknyky8lV7wvGdvZbi6a2bIu+b9vTN5+RvLeX0t+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\nAAAAAElFTkSuQmCC\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7f8d35d68a58>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "OLhQEhEMCbhV",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Use Seaborn to make [linear model plots](http://seaborn.pydata.org/generated/seaborn.lmplot.html)"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ZPF1SfgKCbhX",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 729
+        },
+        "outputId": "520cff25-40c5-4b9e-9cbc-1a8dd868b70a"
+      },
+      "cell_type": "code",
+      "source": [
+        "sns.lmplot('x', 'y', col='dataset', hue='dataset', col_wrap=2, data=df);"
+      ],
+      "execution_count": 74,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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Tk+pVsUK83OEj512OM8Z8FfgqQHd3d9nmJTIunspwerT8B38cOTPGlh09PPdG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DdVXLOYKE2wa+8utu/ezsGxg1PbL118KX3n97EytzL0GDTfWETqyZVKlIeGKA0P44rFho17/I03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\nRURERESqqEAWEREREany/wG6uZn9CVpH5QAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7f8d3704fc50>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "PVjhJh6mCbhZ",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "#### Links\n",
+        "- [Seaborn examples: Anscombe's quartet](http://seaborn.pydata.org/examples/anscombes_quartet.html)\n",
+        "- [Wikipedia: Anscombe's quartet](https://en.wikipedia.org/wiki/Anscombe%27s_quartet)\n",
+        "- [The Datasaurus Dozen](https://www.autodeskresearch.com/publications/samestats)"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "OjxvXAQiCbhZ",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## 2. Tips dataset"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "eflBDsngCbhb",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Load dataset"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "YfH_JF5BCbhb",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "tips = sns.load_dataset('tips')"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "lODejp-tCbhd",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### See the data's shape"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "7PUxT6S_Cbhe",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "1ea739cc-2f37-475f-bc15-71515f936dba"
+      },
+      "cell_type": "code",
+      "source": [
+        "tips.shape"
+      ],
+      "execution_count": 76,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(244, 7)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 76
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "9kvVhgplCbhg",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### See the first 5 rows"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "soVbZvrFCbhg",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 195
+        },
+        "outputId": "f3b2fcc6-5632-4ea7-9d5c-cc3d37bc8f4b"
+      },
+      "cell_type": "code",
+      "source": [
+        "tips.head()"
+      ],
+      "execution_count": 78,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>total_bill</th>\n",
+              "      <th>tip</th>\n",
+              "      <th>sex</th>\n",
+              "      <th>smoker</th>\n",
+              "      <th>day</th>\n",
+              "      <th>time</th>\n",
+              "      <th>size</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>16.99</td>\n",
+              "      <td>1.01</td>\n",
+              "      <td>Female</td>\n",
+              "      <td>No</td>\n",
+              "      <td>Sun</td>\n",
+              "      <td>Dinner</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>10.34</td>\n",
+              "      <td>1.66</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>No</td>\n",
+              "      <td>Sun</td>\n",
+              "      <td>Dinner</td>\n",
+              "      <td>3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>21.01</td>\n",
+              "      <td>3.50</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>No</td>\n",
+              "      <td>Sun</td>\n",
+              "      <td>Dinner</td>\n",
+              "      <td>3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>23.68</td>\n",
+              "      <td>3.31</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>No</td>\n",
+              "      <td>Sun</td>\n",
+              "      <td>Dinner</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>24.59</td>\n",
+              "      <td>3.61</td>\n",
+              "      <td>Female</td>\n",
+              "      <td>No</td>\n",
+              "      <td>Sun</td>\n",
+              "      <td>Dinner</td>\n",
+              "      <td>4</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   total_bill   tip     sex smoker  day    time  size\n",
+              "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
+              "1       10.34  1.66    Male     No  Sun  Dinner     3\n",
+              "2       21.01  3.50    Male     No  Sun  Dinner     3\n",
+              "3       23.68  3.31    Male     No  Sun  Dinner     2\n",
+              "4       24.59  3.61  Female     No  Sun  Dinner     4"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 78
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "1awjAV3GCbhi",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Describe the data"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ic9Z4ONUCbhj",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 284
+        },
+        "outputId": "d779e115-2ac0-4519-ee36-3972ee3378ed"
+      },
+      "cell_type": "code",
+      "source": [
+        "tips.describe()"
+      ],
+      "execution_count": 79,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>total_bill</th>\n",
+              "      <th>tip</th>\n",
+              "      <th>size</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>244.000000</td>\n",
+              "      <td>244.000000</td>\n",
+              "      <td>244.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>19.785943</td>\n",
+              "      <td>2.998279</td>\n",
+              "      <td>2.569672</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>8.902412</td>\n",
+              "      <td>1.383638</td>\n",
+              "      <td>0.951100</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>3.070000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>13.347500</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>2.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>17.795000</td>\n",
+              "      <td>2.900000</td>\n",
+              "      <td>2.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>24.127500</td>\n",
+              "      <td>3.562500</td>\n",
+              "      <td>3.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>50.810000</td>\n",
+              "      <td>10.000000</td>\n",
+              "      <td>6.000000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       total_bill         tip        size\n",
+              "count  244.000000  244.000000  244.000000\n",
+              "mean    19.785943    2.998279    2.569672\n",
+              "std      8.902412    1.383638    0.951100\n",
+              "min      3.070000    1.000000    1.000000\n",
+              "25%     13.347500    2.000000    2.000000\n",
+              "50%     17.795000    2.900000    2.000000\n",
+              "75%     24.127500    3.562500    3.000000\n",
+              "max     50.810000   10.000000    6.000000"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 79
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "z1IjGFhNCbhm",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Make univariate [distribution plots](https://seaborn.pydata.org/generated/seaborn.distplot.html)"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "wBP_jb5XCbhn",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 283
+        },
+        "outputId": "1ecdd126-dd06-4072-c326-3235bcc91220"
+      },
+      "cell_type": "code",
+      "source": [
+        "sns.distplot(tips.tip);"
+      ],
+      "execution_count": 86,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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lbmZ3Al8FgsDXnXNfuODxVOA7wCagHXivc+50fKNeuvGI4z8PtfIPTx/h2Nk+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\nXUTEh1TuIiI+9P8AyBBshPmmH7UAAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7f8d35b71438>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "zx5OdmAAc_q8",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 301
+        },
+        "outputId": "862c4785-cc8e-422f-a87f-be2eec76752a"
+      },
+      "cell_type": "code",
+      "source": [
+        "tips['percent']  = tips.tip / tips.total_bill\n",
+        "sns.distplot(tips['total_bill'])"
+      ],
+      "execution_count": 87,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8d35a6b358>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 87
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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UDPv8vHykmY8um2NTNaPEk9vrp/ycjSuL6ewb5qtP7aV/aMTW6jHTEkxPfx1Q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\nJmpDAAAAAElFTkSuQmCC\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7f8d35a4c4e0>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "fthYISVbCbho",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Make bivariate [relational plots](https://seaborn.pydata.org/generated/seaborn.relplot.html)"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "sHQePtCzc9OO",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 369
+        },
+        "outputId": "aa9d6e13-a11b-41ae-d619-ba67de66c52f"
+      },
+      "cell_type": "code",
+      "source": [
+        "sns.relplot('tip', 'total_bill', data=tips, alpha=.4);"
+      ],
+      "execution_count": 91,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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oVHE8plNhHn/HPNdzZQzFnich93pKlwyQCdGdfpck7wE+CTyvlPpR82P/NY3g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\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7f8d359dcbe0>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "_Ofb17-7dbFe",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 386
+        },
+        "outputId": "603018cf-a7ec-443b-842a-c1562aa4c4c8"
+      },
+      "cell_type": "code",
+      "source": [
+        "sns.relplot('total_bill', 'percent', data=tips, alpha=.3)"
+      ],
+      "execution_count": 94,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<seaborn.axisgrid.FacetGrid at 0x7f8d358d22b0>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 94
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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7I2T3GqjdhNFeO8q6rcH14gPaKFuuVOXYWISljRKVXToThyEYRsEw1OQtgDvY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\nxhjjEwtgY4zxiQWwMcb45P8HlZC8yjXVg44AAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7f8d3599c128>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "Ajwvn7NdCbhu",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Make univariate [categorical plots](https://seaborn.pydata.org/generated/seaborn.catplot.html)"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "LXBcuDaUCbhv",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 67
+        },
+        "outputId": "19f0862e-254d-4fcf-ef7c-d59caf298b92"
+      },
+      "cell_type": "code",
+      "source": [
+        "tips.sex.value_counts()"
+      ],
+      "execution_count": 95,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Male      157\n",
+              "Female     87\n",
+              "Name: sex, dtype: int64"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 95
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "Z6GPm-b1eAhy",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 295
+        },
+        "outputId": "e2652067-3ccd-4cff-da94-c9be4d69064e"
+      },
+      "cell_type": "code",
+      "source": [
+        "tips.sex.value_counts().plot.bar();"
+      ],
+      "execution_count": 96,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7f8d35827f60>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "PguacIo5eFEz",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 369
+        },
+        "outputId": "559e251e-6ce5-4184-f421-c5dd3d46d56d"
+      },
+      "cell_type": "code",
+      "source": [
+        "sns.catplot('sex', data=tips, kind='count');"
+      ],
+      "execution_count": 98,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7f8d359dcba8>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "i2EBfxiOCbhx",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Make bivariate [categorical plots](https://seaborn.pydata.org/generated/seaborn.catplot.html)"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "JgEFCLhICbhy",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 369
+        },
+        "outputId": "16099b8e-3691-49c7-aefe-141a55a59989"
+      },
+      "cell_type": "code",
+      "source": [
+        "sns.catplot('sex', 'tip', data=tips, kind='box');"
+      ],
+      "execution_count": 100,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7f8d35ac4048>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "7oOhLxr1eX52",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 369
+        },
+        "outputId": "dd38d07a-d640-4871-ed62-27d8f5f75d77"
+      },
+      "cell_type": "code",
+      "source": [
+        "sns.catplot('sex', 'tip', data=tips, kind='strip', alpha=.4);"
+      ],
+      "execution_count": 102,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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z5JFS5jnig2ZdsO/hV+3sda6Wo9yqdilH+Y13ppnKVqg2HHwehcey+NipXtKl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\nWRYX+gAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7f8d36c582e8>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "P_bVmUfEeqdw",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 369
+        },
+        "outputId": "1dadee56-fe89-4d53-b0fe-b6a696e0e2e2"
+      },
+      "cell_type": "code",
+      "source": [
+        "sns.catplot('sex', 'tip', data=tips, kind='swarm', alpha=.4);"
+      ],
+      "execution_count": 105,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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gPgG3f9id0kid0b0gZn/Xfb37v4AjX4GHv+kee/SGbur+8Nd6c0/QQewJ9Tb4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\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7f8d3646d8d0>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "YZrRTHoie67v",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 369
+        },
+        "outputId": "dd148ab0-b53b-4922-abc0-dc30e9da498c"
+      },
+      "cell_type": "code",
+      "source": [
+        "sns.catplot('sex', 'tip', data=tips, kind='bar');"
+      ],
+      "execution_count": 106,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAFgCAYAAACFYaNMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7f8d35c97f98>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "mfwv4B3UCbh1",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## 3. Flights"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "reMBw5hkCbh2",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Load dataset"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "1k2SQyJ3Cbh3",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "flights = sns.load_dataset('flights')"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "nsonr-LyCbh4",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### See the data's shape"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "FIRgLwjoCbh5",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "3c34d106-86e9-4bb6-d131-4555f398fcc0"
+      },
+      "cell_type": "code",
+      "source": [
+        "flights.shape"
+      ],
+      "execution_count": 108,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(144, 3)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 108
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "vHfioJA9Cbh7",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### See the first 5 rows"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "yeMaMlUWCbh7",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 195
+        },
+        "outputId": "7a83a33a-19e3-4635-a246-eeafdf1c4fcb"
+      },
+      "cell_type": "code",
+      "source": [
+        "flights.head()"
+      ],
+      "execution_count": 109,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>year</th>\n",
+              "      <th>month</th>\n",
+              "      <th>passengers</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1949</td>\n",
+              "      <td>January</td>\n",
+              "      <td>112</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1949</td>\n",
+              "      <td>February</td>\n",
+              "      <td>118</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>1949</td>\n",
+              "      <td>March</td>\n",
+              "      <td>132</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>1949</td>\n",
+              "      <td>April</td>\n",
+              "      <td>129</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>1949</td>\n",
+              "      <td>May</td>\n",
+              "      <td>121</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   year     month  passengers\n",
+              "0  1949   January         112\n",
+              "1  1949  February         118\n",
+              "2  1949     March         132\n",
+              "3  1949     April         129\n",
+              "4  1949       May         121"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 109
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "3Q1-HfVgCbh8",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Describe the data"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ZiSATbJDCbh_",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 284
+        },
+        "outputId": "a3dae0ec-3e75-4b36-e6bd-da4c7e9869ee"
+      },
+      "cell_type": "code",
+      "source": [
+        "flights.describe()"
+      ],
+      "execution_count": 114,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>year</th>\n",
+              "      <th>passengers</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>144.000000</td>\n",
+              "      <td>144.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>1954.500000</td>\n",
+              "      <td>280.298611</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>3.464102</td>\n",
+              "      <td>119.966317</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>1949.000000</td>\n",
+              "      <td>104.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>1951.750000</td>\n",
+              "      <td>180.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>1954.500000</td>\n",
+              "      <td>265.500000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>1957.250000</td>\n",
+              "      <td>360.500000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>1960.000000</td>\n",
+              "      <td>622.000000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "              year  passengers\n",
+              "count   144.000000  144.000000\n",
+              "mean   1954.500000  280.298611\n",
+              "std       3.464102  119.966317\n",
+              "min    1949.000000  104.000000\n",
+              "25%    1951.750000  180.000000\n",
+              "50%    1954.500000  265.500000\n",
+              "75%    1957.250000  360.500000\n",
+              "max    1960.000000  622.000000"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 114
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "_z2L6XpHCbiC",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Plot year & passengers"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "40qlBdMTCbiD",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 369
+        },
+        "outputId": "6a242a49-a796-4326-cebf-8f89102522e3"
+      },
+      "cell_type": "code",
+      "source": [
+        "sns.catplot('year', 'passengers', data=flights, kind='strip', alpha=.8);"
+      ],
+      "execution_count": 117,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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9qOvxraPd40sQub6hN51la98wWd8nYoV3BjmQNty/PUvncG5O4FuX2VzXEt44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\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7f8d36d28400>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "LXwjfsSUCbiI",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Plot month & passengers"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "TLTyH_SqCbiJ",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1035
+        },
+        "outputId": "ce141ca1-4a6d-4128-e2a3-87d6bd013342"
+      },
+      "cell_type": "code",
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "fig, ax = plt.subplots(figsize=(11,11))\n",
+        "\n",
+        "sns.catplot('month', 'passengers', data=flights, kind='swarm', ax=ax)\n",
+        "\n",
+        "plt.show() # not sure why i'm getting a secondary plot below the swarm?"
+      ],
+      "execution_count": 139,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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r26c6GAC4cvPSq7S8+Dnt6GvTmrI6XT17metIAJA1JnrB0X2SEpJ+lhy/RVK+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\nQmCC\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7f8d36d965f8>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7f8d36c5fb70>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "mcwgPp0vCbiK",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Create a [pivot table](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.pivot_table.html) of passengers by month and year"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "NCq_9kFtCbiK",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 373
+        },
+        "outputId": "41e20f7d-0898-4b38-b0a6-d1afab43817b"
+      },
+      "cell_type": "code",
+      "source": [
+        "flights_pivot = pd.DataFrame.pivot_table(flights, values=\"passengers\", \n",
+        "                                      index=['month', 'year'])\n",
+        "\n",
+        "flights_pivot.head(10) # that's actually an awesome grouping tool! \n"
+      ],
+      "execution_count": 133,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th>passengers</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>month</th>\n",
+              "      <th>year</th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th rowspan=\"10\" valign=\"top\">January</th>\n",
+              "      <th>1949</th>\n",
+              "      <td>112</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1950</th>\n",
+              "      <td>115</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1951</th>\n",
+              "      <td>145</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1952</th>\n",
+              "      <td>171</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1953</th>\n",
+              "      <td>196</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1954</th>\n",
+              "      <td>204</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1955</th>\n",
+              "      <td>242</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1956</th>\n",
+              "      <td>284</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1957</th>\n",
+              "      <td>315</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1958</th>\n",
+              "      <td>340</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "              passengers\n",
+              "month   year            \n",
+              "January 1949         112\n",
+              "        1950         115\n",
+              "        1951         145\n",
+              "        1952         171\n",
+              "        1953         196\n",
+              "        1954         204\n",
+              "        1955         242\n",
+              "        1956         284\n",
+              "        1957         315\n",
+              "        1958         340"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 133
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "giP_CPBzCbiM",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Plot the pivot table as a [heat map](https://seaborn.pydata.org/generated/seaborn.heatmap.html)"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "mpQnVp-7CbiN",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 269
+        },
+        "outputId": "492c3f2f-6b25-454b-9378-1cb507367dca"
+      },
+      "cell_type": "code",
+      "source": [
+        "# redoing the flights problem in seaborn\n",
+        "\n",
+        "flights_sns = flights.pivot('month', 'year', 'passengers')\n",
+        "\n",
+        "ax = sns.heatmap(flights_pivot)"
+      ],
+      "execution_count": 140,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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PvPJgt63TUx/ZsvR3zgq33FD3fJIGAtOAWba3k7QaKTBwWeAO4PO235O0CEl8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\nSUVORK5CYII=\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7f8d36c4efd0>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "3-urzoOYCKa_",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 298
+        },
+        "outputId": "cf6a02ed-c705-48d1-ef32-805cd9329f32"
+      },
+      "cell_type": "code",
+      "source": [
+        "flights_sns = flights.pivot('month', 'year', 'passengers')\n",
+        "ax = sns.heatmap(flights_sns)\n",
+        "\n",
+        "# pixel art flame turned sideways anyone?"
+      ],
+      "execution_count": 142,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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NlrVSZz0kbSrpckn3Sfo/Sd+T1GvxNUknSBpTIu6ixrbUzGxwLCdKba2gJZON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\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7f8d35968eb8>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "pgVeMQz7DCag",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/module3-make-explanatory-visualizations/LS_DS_123_Make_explanatory_visualizations.ipynb b/module3-make-explanatory-visualizations/LS_DS_123_Make_explanatory_visualizations.ipynb
index 94dbed8..9f8a225 100644
--- a/module3-make-explanatory-visualizations/LS_DS_123_Make_explanatory_visualizations.ipynb
+++ b/module3-make-explanatory-visualizations/LS_DS_123_Make_explanatory_visualizations.ipynb
@@ -1,58 +1,1243 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "_Lambda School Data Science_\n",
-    "\n",
-    "# Choose appropriate visualizations\n",
-    "\n",
-    "\n",
-    "Recreate this [example by FiveThirtyEight:](https://fivethirtyeight.com/features/al-gores-new-movie-exposes-the-big-flaw-in-online-movie-ratings/)\n",
-    "\n",
-    "![](https://fivethirtyeight.com/wp-content/uploads/2017/09/mehtahickey-inconvenient-0830-1.png?w=575)\n",
-    "\n",
-    "Using this data:\n",
-    "\n",
-    "https://github.com/fivethirtyeight/data/tree/master/inconvenient-sequel\n",
-    "\n",
-    "### Stretch goals\n",
-    "\n",
-    "Recreate more examples from [FiveThityEight's shared data repository](https://data.fivethirtyeight.com/).\n",
-    "\n",
-    "For example:\n",
-    "- [thanksgiving-2015](https://fivethirtyeight.com/features/heres-what-your-part-of-america-eats-on-thanksgiving/) ([`altair`](https://altair-viz.github.io/gallery/index.html#maps))\n",
-    "- [candy-power-ranking](https://fivethirtyeight.com/features/the-ultimate-halloween-candy-power-ranking/) ([`statsmodels`](https://www.statsmodels.org/stable/index.html))"
-   ]
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "LS_DS_123_Make_explanatory_visualizations.ipynb",
+      "version": "0.3.2",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    }
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
+  "cells": [
+    {
+      "metadata": {
+        "id": "nptHDjRKFfqG",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "_Lambda School Data Science_\n",
+        "\n",
+        "# Choose appropriate visualizations\n",
+        "\n",
+        "\n",
+        "Recreate this [example by FiveThirtyEight:](https://fivethirtyeight.com/features/al-gores-new-movie-exposes-the-big-flaw-in-online-movie-ratings/)\n",
+        "\n",
+        "![](https://fivethirtyeight.com/wp-content/uploads/2017/09/mehtahickey-inconvenient-0830-1.png?w=575)\n",
+        "\n",
+        "Using this data:\n",
+        "\n",
+        "https://github.com/fivethirtyeight/data/tree/master/inconvenient-sequel\n",
+        "\n",
+        "### Stretch goals\n",
+        "\n",
+        "Recreate more examples from [FiveThityEight's shared data repository](https://data.fivethirtyeight.com/).\n",
+        "\n",
+        "For example:\n",
+        "- [thanksgiving-2015](https://fivethirtyeight.com/features/heres-what-your-part-of-america-eats-on-thanksgiving/) ([`altair`](https://altair-viz.github.io/gallery/index.html#maps))\n",
+        "- [candy-power-ranking](https://fivethirtyeight.com/features/the-ultimate-halloween-candy-power-ranking/) ([`statsmodels`](https://www.statsmodels.org/stable/index.html))"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "Ll2pDRKeFfqI",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "import numpy\n",
+        "\n",
+        "chess_df = pd.read_csv('https://raw.githubusercontent.com/fivethirtyeight/data/master/chess-transfers/transfers.csv')"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "FaJ73V1DFmyC",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 195
+        },
+        "outputId": "ae16a0e0-c31d-408d-f820-02bb856570d5"
+      },
+      "cell_type": "code",
+      "source": [
+        "chess_df.head()"
+      ],
+      "execution_count": 101,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>url</th>\n",
+              "      <th>ID</th>\n",
+              "      <th>Federation</th>\n",
+              "      <th>Form.Fed</th>\n",
+              "      <th>Transfer Date</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>https://ratings.fide.com/fedchange.phtml?year=...</td>\n",
+              "      <td>2019221</td>\n",
+              "      <td>USA</td>\n",
+              "      <td>PHI</td>\n",
+              "      <td>12/15/00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>https://ratings.fide.com/fedchange.phtml?year=...</td>\n",
+              "      <td>14401754</td>\n",
+              "      <td>BIH</td>\n",
+              "      <td>CRO</td>\n",
+              "      <td>1/31/00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>https://ratings.fide.com/fedchange.phtml?year=...</td>\n",
+              "      <td>14401762</td>\n",
+              "      <td>BIH</td>\n",
+              "      <td>YUG</td>\n",
+              "      <td>1/31/00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>https://ratings.fide.com/fedchange.phtml?year=...</td>\n",
+              "      <td>2019221</td>\n",
+              "      <td>USA</td>\n",
+              "      <td>PHI</td>\n",
+              "      <td>12/15/00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>https://ratings.fide.com/fedchange.phtml?year=...</td>\n",
+              "      <td>14401754</td>\n",
+              "      <td>BIH</td>\n",
+              "      <td>CRO</td>\n",
+              "      <td>1/31/00</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                                                 url        ID Federation  \\\n",
+              "0  https://ratings.fide.com/fedchange.phtml?year=...   2019221        USA   \n",
+              "1  https://ratings.fide.com/fedchange.phtml?year=...  14401754        BIH   \n",
+              "2  https://ratings.fide.com/fedchange.phtml?year=...  14401762        BIH   \n",
+              "3  https://ratings.fide.com/fedchange.phtml?year=...   2019221        USA   \n",
+              "4  https://ratings.fide.com/fedchange.phtml?year=...  14401754        BIH   \n",
+              "\n",
+              "  Form.Fed Transfer Date  \n",
+              "0      PHI      12/15/00  \n",
+              "1      CRO       1/31/00  \n",
+              "2      YUG       1/31/00  \n",
+              "3      PHI      12/15/00  \n",
+              "4      CRO       1/31/00  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 101
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "J-jJO-ipH5fo",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "de5b5a4d-bcc6-4420-c42c-d7d413e0e8ba"
+      },
+      "cell_type": "code",
+      "source": [
+        "chess_df['Transfer Date'].count() \n",
+        "# many of these are duplicate player ID, even the same transfer date.\n",
+        "\n",
+        "# cross reference from World Chess Federation\n",
+        "# to note the duplicates are not listed on the site\n",
+        "\n",
+        "# https://ratings.fide.com/fedchange.phtml?year=\n",
+        "# examining the years shows a disparity between the created dataframe and the \n",
+        "# World Chess Federation's listed data"
+      ],
+      "execution_count": 102,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "932"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 102
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "HtcbMnERJLzi",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1058
+        },
+        "outputId": "d8306f32-914f-4bf0-8d9e-879a4c2f415b"
+      },
+      "cell_type": "code",
+      "source": [
+        "# transfer by country\n",
+        "chess_df['Federation'].value_counts()"
+      ],
+      "execution_count": 103,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "USA    89\n",
+              "GER    55\n",
+              "CAN    44\n",
+              "ESP    41\n",
+              "RUS    36\n",
+              "FRA    34\n",
+              "CRO    32\n",
+              "BIH    32\n",
+              "TUR    31\n",
+              "CZE    29\n",
+              "AUT    29\n",
+              "SUI    25\n",
+              "MNC    24\n",
+              "AUS    23\n",
+              "ENG    19\n",
+              "SRB    17\n",
+              "UKR    15\n",
+              "ITA    14\n",
+              "BEL    13\n",
+              "SWE    12\n",
+              "AND    11\n",
+              "SCO    10\n",
+              "SIN    10\n",
+              "ROU    10\n",
+              "PLE     9\n",
+              "FIN     9\n",
+              "ISR     9\n",
+              "NOR     9\n",
+              "ARM     9\n",
+              "YUG     8\n",
+              "       ..\n",
+              "JOR     2\n",
+              "BAN     2\n",
+              "EGY     2\n",
+              "JAP     2\n",
+              "BER     2\n",
+              "HON     2\n",
+              "URU     1\n",
+              "LBN     1\n",
+              "LBA     1\n",
+              "MGL     1\n",
+              "MAD     1\n",
+              "ARU     1\n",
+              "MYA     1\n",
+              "PUR     1\n",
+              "MAC     1\n",
+              "VIE     1\n",
+              "TJK     1\n",
+              "HUN     1\n",
+              "CYP     1\n",
+              "TOG     1\n",
+              "KOS     1\n",
+              "QAT     1\n",
+              "ARG     1\n",
+              "GUY     1\n",
+              "DOM     1\n",
+              "ALB     1\n",
+              "ALG     1\n",
+              "TTO     1\n",
+              "KEN     1\n",
+              "MLT     1\n",
+              "Name: Federation, Length: 105, dtype: int64"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 103
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "sZSRRJ3SVsuU",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Split the duplicate entries into a new dataframe to research\n",
+        "\n",
+        "# rename Transfer Date to make it usable below\n",
+        "\n",
+        "chess_df.rename(columns={'Transfer Date':'Transfer_Date',\n",
+        "                        'Form.Fed' : 'Former_Fed'}, inplace=True)\n",
+        "\n",
+        "\n",
+        "#id_dups = chess_df.ID[chess_df.ID.duplicated()].values\n",
+        "transfer_dups = chess_df.Transfer_Date[chess_df.Transfer_Date.duplicated()].values\n",
+        "#former_fed = chess_df.Former_Fed[chess_df.Former_Fed.duplicated()].values\n",
+        "\n",
+        "'''\n",
+        "duplicate_transfers = (chess_df[chess_df.ID.isin(id_dups) & \n",
+        "                                chess_df.Transfer_Date.isin(transfer_dups)\n",
+        "                               & chess_df.Former_Fed.isin(former_fed)])\n",
+        "'''\n",
+        "\n",
+        "duplicate_transfers = chess_df[chess_df.Transfer_Date.isin(transfer_dups)]\n"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "xZ_vQLnbWd06",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 639
+        },
+        "outputId": "69e7061a-162d-4b8c-fa68-a20dd35314fa"
+      },
+      "cell_type": "code",
+      "source": [
+        "duplicate_transfers.head(20) \n",
+        "# note that there are instances of transfer dates being the same, and dup Feds\n",
+        "# for which I have taken into account, but also note the dropped instances are \n",
+        "# cases like rows 0 and 3, where the data has been duplicated exactly\n",
+        "\n",
+        "# simply looking at year 00 you can see every value has a duplicate"
+      ],
+      "execution_count": 109,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>url</th>\n",
+              "      <th>ID</th>\n",
+              "      <th>Federation</th>\n",
+              "      <th>Former_Fed</th>\n",
+              "      <th>Transfer_Date</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>https://ratings.fide.com/fedchange.phtml?year=...</td>\n",
+              "      <td>2019221</td>\n",
+              "      <td>USA</td>\n",
+              "      <td>PHI</td>\n",
+              "      <td>12/15/00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>https://ratings.fide.com/fedchange.phtml?year=...</td>\n",
+              "      <td>14401754</td>\n",
+              "      <td>BIH</td>\n",
+              "      <td>CRO</td>\n",
+              "      <td>1/31/00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>https://ratings.fide.com/fedchange.phtml?year=...</td>\n",
+              "      <td>14401762</td>\n",
+              "      <td>BIH</td>\n",
+              "      <td>YUG</td>\n",
+              "      <td>1/31/00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>https://ratings.fide.com/fedchange.phtml?year=...</td>\n",
+              "      <td>2019221</td>\n",
+              "      <td>USA</td>\n",
+              "      <td>PHI</td>\n",
+              "      <td>12/15/00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>https://ratings.fide.com/fedchange.phtml?year=...</td>\n",
+              "      <td>14401754</td>\n",
+              "      <td>BIH</td>\n",
+              "      <td>CRO</td>\n",
+              "      <td>1/31/00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>https://ratings.fide.com/fedchange.phtml?year=...</td>\n",
+              "      <td>14401762</td>\n",
+              "      <td>BIH</td>\n",
+              "      <td>YUG</td>\n",
+              "      <td>1/31/00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>https://ratings.fide.com/fedchange.phtml?year=...</td>\n",
+              "      <td>6700284</td>\n",
+              "      <td>ESA</td>\n",
+              "      <td>HON</td>\n",
+              "      <td>11/15/01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>https://ratings.fide.com/fedchange.phtml?year=...</td>\n",
+              "      <td>1613782</td>\n",
+              "      <td>AUT</td>\n",
+              "      <td>ISR</td>\n",
+              "      <td>7/9/01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>https://ratings.fide.com/fedchange.phtml?year=...</td>\n",
+              "      <td>2600536</td>\n",
+              "      <td>AUS</td>\n",
+              "      <td>CAN</td>\n",
+              "      <td>11/9/01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>https://ratings.fide.com/fedchange.phtml?year=...</td>\n",
+              "      <td>2603977</td>\n",
+              "      <td>CAN</td>\n",
+              "      <td>YUG</td>\n",
+              "      <td>5/25/01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10</th>\n",
+              "      <td>https://ratings.fide.com/fedchange.phtml?year=...</td>\n",
+              "      <td>2019523</td>\n",
+              "      <td>USA</td>\n",
+              "      <td>AZE</td>\n",
+              "      <td>3/30/01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11</th>\n",
+              "      <td>https://ratings.fide.com/fedchange.phtml?year=...</td>\n",
+              "      <td>2603985</td>\n",
+              "      <td>CAN</td>\n",
+              "      <td>ENG</td>\n",
+              "      <td>5/25/01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>12</th>\n",
+              "      <td>https://ratings.fide.com/fedchange.phtml?year=...</td>\n",
+              "      <td>4682084</td>\n",
+              "      <td>ITA</td>\n",
+              "      <td>GER</td>\n",
+              "      <td>9/12/01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>13</th>\n",
+              "      <td>https://ratings.fide.com/fedchange.phtml?year=...</td>\n",
+              "      <td>2209381</td>\n",
+              "      <td>PAR</td>\n",
+              "      <td>ESP</td>\n",
+              "      <td>12/10/01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>14</th>\n",
+              "      <td>https://ratings.fide.com/fedchange.phtml?year=...</td>\n",
+              "      <td>13601105</td>\n",
+              "      <td>USA</td>\n",
+              "      <td>GEO</td>\n",
+              "      <td>2/13/01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>15</th>\n",
+              "      <td>https://ratings.fide.com/fedchange.phtml?year=...</td>\n",
+              "      <td>4613424</td>\n",
+              "      <td>SUI</td>\n",
+              "      <td>GER</td>\n",
+              "      <td>12/1/01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>16</th>\n",
+              "      <td>https://ratings.fide.com/fedchange.phtml?year=...</td>\n",
+              "      <td>2604019</td>\n",
+              "      <td>CAN</td>\n",
+              "      <td>YUG</td>\n",
+              "      <td>6/9/01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>17</th>\n",
+              "      <td>https://ratings.fide.com/fedchange.phtml?year=...</td>\n",
+              "      <td>933511</td>\n",
+              "      <td>YUG</td>\n",
+              "      <td>BEL</td>\n",
+              "      <td>2/7/01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>18</th>\n",
+              "      <td>https://ratings.fide.com/fedchange.phtml?year=...</td>\n",
+              "      <td>3405400</td>\n",
+              "      <td>CHI</td>\n",
+              "      <td>CRO</td>\n",
+              "      <td>6/13/01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>19</th>\n",
+              "      <td>https://ratings.fide.com/fedchange.phtml?year=...</td>\n",
+              "      <td>4154142</td>\n",
+              "      <td>UKR</td>\n",
+              "      <td>RUS</td>\n",
+              "      <td>1/23/01</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                                                  url        ID Federation  \\\n",
+              "0   https://ratings.fide.com/fedchange.phtml?year=...   2019221        USA   \n",
+              "1   https://ratings.fide.com/fedchange.phtml?year=...  14401754        BIH   \n",
+              "2   https://ratings.fide.com/fedchange.phtml?year=...  14401762        BIH   \n",
+              "3   https://ratings.fide.com/fedchange.phtml?year=...   2019221        USA   \n",
+              "4   https://ratings.fide.com/fedchange.phtml?year=...  14401754        BIH   \n",
+              "5   https://ratings.fide.com/fedchange.phtml?year=...  14401762        BIH   \n",
+              "6   https://ratings.fide.com/fedchange.phtml?year=...   6700284        ESA   \n",
+              "7   https://ratings.fide.com/fedchange.phtml?year=...   1613782        AUT   \n",
+              "8   https://ratings.fide.com/fedchange.phtml?year=...   2600536        AUS   \n",
+              "9   https://ratings.fide.com/fedchange.phtml?year=...   2603977        CAN   \n",
+              "10  https://ratings.fide.com/fedchange.phtml?year=...   2019523        USA   \n",
+              "11  https://ratings.fide.com/fedchange.phtml?year=...   2603985        CAN   \n",
+              "12  https://ratings.fide.com/fedchange.phtml?year=...   4682084        ITA   \n",
+              "13  https://ratings.fide.com/fedchange.phtml?year=...   2209381        PAR   \n",
+              "14  https://ratings.fide.com/fedchange.phtml?year=...  13601105        USA   \n",
+              "15  https://ratings.fide.com/fedchange.phtml?year=...   4613424        SUI   \n",
+              "16  https://ratings.fide.com/fedchange.phtml?year=...   2604019        CAN   \n",
+              "17  https://ratings.fide.com/fedchange.phtml?year=...    933511        YUG   \n",
+              "18  https://ratings.fide.com/fedchange.phtml?year=...   3405400        CHI   \n",
+              "19  https://ratings.fide.com/fedchange.phtml?year=...   4154142        UKR   \n",
+              "\n",
+              "   Former_Fed Transfer_Date  \n",
+              "0         PHI      12/15/00  \n",
+              "1         CRO       1/31/00  \n",
+              "2         YUG       1/31/00  \n",
+              "3         PHI      12/15/00  \n",
+              "4         CRO       1/31/00  \n",
+              "5         YUG       1/31/00  \n",
+              "6         HON      11/15/01  \n",
+              "7         ISR        7/9/01  \n",
+              "8         CAN       11/9/01  \n",
+              "9         YUG       5/25/01  \n",
+              "10        AZE       3/30/01  \n",
+              "11        ENG       5/25/01  \n",
+              "12        GER       9/12/01  \n",
+              "13        ESP      12/10/01  \n",
+              "14        GEO       2/13/01  \n",
+              "15        GER       12/1/01  \n",
+              "16        YUG        6/9/01  \n",
+              "17        BEL        2/7/01  \n",
+              "18        CRO       6/13/01  \n",
+              "19        RUS       1/23/01  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 109
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "7vTBbtCGJsqA",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "chess_df = chess_df.drop_duplicates() \n",
+        "# we want to find unique transfers into countries, so i removed the duplicates\n",
+        "# after confirming them above"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "CkN1RzdcWJip",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 118
+        },
+        "outputId": "24a2a3dd-520c-4d02-f933-7289231f25c5"
+      },
+      "cell_type": "code",
+      "source": [
+        "chess_df.count()"
+      ],
+      "execution_count": 134,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "url              753\n",
+              "ID               753\n",
+              "Federation       753\n",
+              "Former_Fed       749\n",
+              "Transfer_Date    753\n",
+              "dtype: int64"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 134
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "vZ_CIaaFKUnG",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "transfer_country_chess = chess_df[['Federation', 'Former_Fed', 'Transfer_Date']].copy()"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "KakKF3jSODDT",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 195
+        },
+        "outputId": "7a188e6e-ce70-42b9-ba10-a4d95ee8cd75"
+      },
+      "cell_type": "code",
+      "source": [
+        "transfer_country_chess.head()"
+      ],
+      "execution_count": 116,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Federation</th>\n",
+              "      <th>Former_Fed</th>\n",
+              "      <th>Transfer_Date</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>USA</td>\n",
+              "      <td>PHI</td>\n",
+              "      <td>12/15/00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>BIH</td>\n",
+              "      <td>CRO</td>\n",
+              "      <td>1/31/00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>BIH</td>\n",
+              "      <td>YUG</td>\n",
+              "      <td>1/31/00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>ESA</td>\n",
+              "      <td>HON</td>\n",
+              "      <td>11/15/01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>AUT</td>\n",
+              "      <td>ISR</td>\n",
+              "      <td>7/9/01</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "  Federation Former_Fed Transfer_Date\n",
+              "0        USA        PHI      12/15/00\n",
+              "1        BIH        CRO       1/31/00\n",
+              "2        BIH        YUG       1/31/00\n",
+              "6        ESA        HON      11/15/01\n",
+              "7        AUT        ISR        7/9/01"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 116
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "TaWpif1XOFfZ",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "transfered_to_usa = chess_df[chess_df.Federation.isin(['USA'])]\n",
+        "\n"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "g__av0yrSJ6C",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 195
+        },
+        "outputId": "0801de7c-b5a9-4de7-b59d-07b0bb46681a"
+      },
+      "cell_type": "code",
+      "source": [
+        "transfered_to_usa.head()"
+      ],
+      "execution_count": 118,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>url</th>\n",
+              "      <th>ID</th>\n",
+              "      <th>Federation</th>\n",
+              "      <th>Former_Fed</th>\n",
+              "      <th>Transfer_Date</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>https://ratings.fide.com/fedchange.phtml?year=...</td>\n",
+              "      <td>2019221</td>\n",
+              "      <td>USA</td>\n",
+              "      <td>PHI</td>\n",
+              "      <td>12/15/00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10</th>\n",
+              "      <td>https://ratings.fide.com/fedchange.phtml?year=...</td>\n",
+              "      <td>2019523</td>\n",
+              "      <td>USA</td>\n",
+              "      <td>AZE</td>\n",
+              "      <td>3/30/01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>14</th>\n",
+              "      <td>https://ratings.fide.com/fedchange.phtml?year=...</td>\n",
+              "      <td>13601105</td>\n",
+              "      <td>USA</td>\n",
+              "      <td>GEO</td>\n",
+              "      <td>2/13/01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>23</th>\n",
+              "      <td>https://ratings.fide.com/fedchange.phtml?year=...</td>\n",
+              "      <td>2019574</td>\n",
+              "      <td>USA</td>\n",
+              "      <td>RUS</td>\n",
+              "      <td>4/2/01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>44</th>\n",
+              "      <td>https://ratings.fide.com/fedchange.phtml?year=...</td>\n",
+              "      <td>13301918</td>\n",
+              "      <td>USA</td>\n",
+              "      <td>ARM</td>\n",
+              "      <td>3/5/02</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                                                  url        ID Federation  \\\n",
+              "0   https://ratings.fide.com/fedchange.phtml?year=...   2019221        USA   \n",
+              "10  https://ratings.fide.com/fedchange.phtml?year=...   2019523        USA   \n",
+              "14  https://ratings.fide.com/fedchange.phtml?year=...  13601105        USA   \n",
+              "23  https://ratings.fide.com/fedchange.phtml?year=...   2019574        USA   \n",
+              "44  https://ratings.fide.com/fedchange.phtml?year=...  13301918        USA   \n",
+              "\n",
+              "   Former_Fed Transfer_Date  \n",
+              "0         PHI      12/15/00  \n",
+              "10        AZE       3/30/01  \n",
+              "14        GEO       2/13/01  \n",
+              "23        RUS        4/2/01  \n",
+              "44        ARM        3/5/02  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 118
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "aCLgoqHSSY_r",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# we will use this to create new dataframes of the unique transfers\n",
+        "\n",
+        "federation_placeholder = chess_df['Federation'].value_counts(sort=True) \n",
+        "transfer_placeholder = transfered_to_usa['Former_Fed'].value_counts(sort=True)\n",
+        "\n",
+        "# we only want the top 10 in each, transfers into usa only has 10\n",
+        "\n",
+        "federation_placeholder_2 = federation_placeholder.head(10)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "ZA4hhkpCdpTf",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 343
+        },
+        "outputId": "8b43c5fe-9e86-48df-db9a-17b178fc17b4"
+      },
+      "cell_type": "code",
+      "source": [
+        "sorted_usa_transfer = transfer_placeholder.rename_axis('Former_Fed').reset_index(name='Unique_Transfers')\n",
+        "\n",
+        "sorted_usa_transfer.head(10)"
+      ],
+      "execution_count": 144,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Former_Fed</th>\n",
+              "      <th>Unique_Transfers</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>IND</td>\n",
+              "      <td>10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>UKR</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>CUB</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>RUS</td>\n",
+              "      <td>4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>PHI</td>\n",
+              "      <td>4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>ARM</td>\n",
+              "      <td>4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>GEO</td>\n",
+              "      <td>3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>BLR</td>\n",
+              "      <td>3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>AZE</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>PER</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "  Former_Fed  Unique_Transfers\n",
+              "0        IND                10\n",
+              "1        UKR                 5\n",
+              "2        CUB                 5\n",
+              "3        RUS                 4\n",
+              "4        PHI                 4\n",
+              "5        ARM                 4\n",
+              "6        GEO                 3\n",
+              "7        BLR                 3\n",
+              "8        AZE                 2\n",
+              "9        PER                 1"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 144
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "89AmrRGldHjQ",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "sorted_top_ten_transfers = federation_placeholder_2.rename_axis('Federation').reset_index(name='Total_Transfers')"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "V4hzaYoKgNhv",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 343
+        },
+        "outputId": "3b64648b-c592-4ebe-e049-50fdaf67c9b8"
+      },
+      "cell_type": "code",
+      "source": [
+        "sorted_top_ten_transfers.head(10) "
+      ],
+      "execution_count": 146,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Federation</th>\n",
+              "      <th>Total_Transfers</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>USA</td>\n",
+              "      <td>66</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>GER</td>\n",
+              "      <td>41</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>ESP</td>\n",
+              "      <td>32</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>CAN</td>\n",
+              "      <td>30</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>CRO</td>\n",
+              "      <td>28</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>BIH</td>\n",
+              "      <td>28</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>RUS</td>\n",
+              "      <td>27</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>FRA</td>\n",
+              "      <td>27</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>CZE</td>\n",
+              "      <td>26</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>TUR</td>\n",
+              "      <td>26</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "  Federation  Total_Transfers\n",
+              "0        USA               66\n",
+              "1        GER               41\n",
+              "2        ESP               32\n",
+              "3        CAN               30\n",
+              "4        CRO               28\n",
+              "5        BIH               28\n",
+              "6        RUS               27\n",
+              "7        FRA               27\n",
+              "8        CZE               26\n",
+              "9        TUR               26"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 146
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "QsKuehjZgPlW",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "inverse_top_ten = sorted_top_ten_transfers.reindex(index=sorted_top_ten_transfers.index[::-1])\n"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "8uEXiIQBg-aX",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 654
+        },
+        "outputId": "239814a4-00d4-4367-8828-8cd63ca86071"
+      },
+      "cell_type": "code",
+      "source": [
+        "import seaborn as sns\n",
+        "\n",
+        "title= 'Nations that received the highest number of player transfers, 2000-17'\n",
+        "\n",
+        "sns.set(style=\"whitegrid\")\n",
+        "\n",
+        "figure, ax = plt.subplots(figsize=(8, 10))\n",
+        "\n",
+        "sns.set_color_codes(\"pastel\")\n",
+        "\n",
+        "# seaborn removed my inverse sort. Inverse sort shows in pandas plots, but I'm\n",
+        "# going to assume it sorts from least to most built-in \n",
+        "# order allows us to plot as desired\n",
+        "\n",
+        "sns.barplot(x=\"Total_Transfers\", y=\"Federation\", data=inverse_top_ten,\n",
+        "            color=\"b\", order=[\"USA\", \"GER\", \"ESP\", \"CAN\", \"CRO\",\n",
+        "  \"BIH\", \"RUS\", \"FRA\", \"CZE\", \"TUR\"]).set_title(title);\n",
+        "\n",
+        "# TODO : split the incorrect original data and plot alongside to compare"
+      ],
+      "execution_count": 167,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.6/dist-packages/seaborn/categorical.py:1428: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
+            "  stat_data = remove_na(group_data)\n"
+          ],
+          "name": "stderr"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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BALCS2wK+RIkS+uyzz5yPp06dKkmqXbu2jDHpuga/c+dO9e3bV8WKFXNXmQAA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\nQogI9DeRVd/xcegIAQAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7f95a0fc1780>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "p3SAxdJyhkmI",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/module4-sequence-your-narrative/LS_DS_124_Sequence_your_narrative.ipynb b/module4-sequence-your-narrative/LS_DS_124_Sequence_your_narrative.ipynb
index b3c2b22..4a17049 100644
--- a/module4-sequence-your-narrative/LS_DS_124_Sequence_your_narrative.ipynb
+++ b/module4-sequence-your-narrative/LS_DS_124_Sequence_your_narrative.ipynb
@@ -1,55 +1,5240 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "_Lambda School Data Science_\n",
-    "\n",
-    "# Sequence your narrative\n",
-    "\n",
-    "Create a sequence of visualizations inspired by [Hans Rosling's 200 Countries, 200 Years, 4 Minutes](https://www.youtube.com/watch?v=jbkSRLYSojo).\n",
-    "\n",
-    "Using this [data from Gapminder](https://github.com/open-numbers/ddf--gapminder--systema_globalis/):\n",
-    "- https://raw.githubusercontent.com/open-numbers/ddf--gapminder--systema_globalis/master/ddf--datapoints--income_per_person_gdppercapita_ppp_inflation_adjusted--by--geo--time.csv\n",
-    "- https://raw.githubusercontent.com/open-numbers/ddf--gapminder--systema_globalis/master/ddf--datapoints--life_expectancy_years--by--geo--time.csv\n",
-    "- https://raw.githubusercontent.com/open-numbers/ddf--gapminder--systema_globalis/master/ddf--entities--geo--country.csv\n",
-    "- https://raw.githubusercontent.com/open-numbers/ddf--gapminder--systema_globalis/master/ddf--concepts.csv\n",
-    "\n",
-    "### Stretch goals\n",
-    "- [ipywidgets](https://github.com/jupyter-widgets/ipywidgets)\n",
-    "- [Matplotlib animation](https://matplotlib.org/examples/animation/index.html)\n",
-    "- [Connected scatter plots](http://www.thefunctionalart.com/2012/09/in-praise-of-connected-scatter-plots.html)\n",
-    "- [Idyll markup language](https://idyll-lang.org/) for \"scrollytelling\""
-   ]
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "LS_DS_124_Sequence_your_narrative.ipynb",
+      "version": "0.3.2",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    }
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
+  "cells": [
+    {
+      "metadata": {
+        "id": "mRXkzR99XE23",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "_Lambda School Data Science_\n",
+        "\n",
+        "# Sequence your narrative\n",
+        "\n",
+        "Create a sequence of visualizations inspired by [Hans Rosling's 200 Countries, 200 Years, 4 Minutes](https://www.youtube.com/watch?v=jbkSRLYSojo).\n",
+        "\n",
+        "Using this [data from Gapminder](https://github.com/open-numbers/ddf--gapminder--systema_globalis/):\n",
+        "- https://raw.githubusercontent.com/open-numbers/ddf--gapminder--systema_globalis/master/ddf--datapoints--income_per_person_gdppercapita_ppp_inflation_adjusted--by--geo--time.csv\n",
+        "- https://raw.githubusercontent.com/open-numbers/ddf--gapminder--systema_globalis/master/ddf--datapoints--life_expectancy_years--by--geo--time.csv\n",
+        "- https://raw.githubusercontent.com/open-numbers/ddf--gapminder--systema_globalis/master/ddf--entities--geo--country.csv\n",
+        "- https://raw.githubusercontent.com/open-numbers/ddf--gapminder--systema_globalis/master/ddf--concepts.csv\n",
+        "\n",
+        "### Stretch goals\n",
+        "- [ipywidgets](https://github.com/jupyter-widgets/ipywidgets)\n",
+        "- [Matplotlib animation](https://matplotlib.org/examples/animation/index.html)\n",
+        "- [Connected scatter plots](http://www.thefunctionalart.com/2012/09/in-praise-of-connected-scatter-plots.html)\n",
+        "- [Idyll markup language](https://idyll-lang.org/) for \"scrollytelling\""
+      ]
+    },
+    {
+      "metadata": {
+        "id": "jr3-aqnRXP_p",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Variables --> Visual Encodings \n",
+        " \n",
+        "  - Income --> x-axis\n",
+        "  - Lifespan --> y-axis\n",
+        "  - Region --> color\n",
+        "  - Population --> bubble size\n",
+        "  - Year --> animation sequence(alternative: small multiple)\n",
+        "  - Country --> annotations\n",
+        " \n",
+        "Qualitative --> Verbal\n",
+        "  - Editorial / contexual explanation --> audio narration (alternative: text)\n",
+        " "
+      ]
+    },
+    {
+      "metadata": {
+        "id": "xOCwT0RNXE24",
+        "colab_type": "code",
+        "outputId": "d433e7d3-6ba6-4f60-f1f3-875165d5e451",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 302
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "!pip install --upgrade seaborn #restart runtime after running this command"
+      ],
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Collecting seaborn\n",
+            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/a8/76/220ba4420459d9c4c9c9587c6ce607bf56c25b3d3d2de62056efe482dadc/seaborn-0.9.0-py3-none-any.whl (208kB)\n",
+            "\r\u001b[K    4% |█▋                              | 10kB 18.7MB/s eta 0:00:01\r\u001b[K    9% |███▏                            | 20kB 1.8MB/s eta 0:00:01\r\u001b[K    14% |████▊                           | 30kB 2.6MB/s eta 0:00:01\r\u001b[K    19% |██████▎                         | 40kB 1.7MB/s eta 0:00:01\r\u001b[K    24% |███████▉                        | 51kB 2.1MB/s eta 0:00:01\r\u001b[K    29% |█████████▌                      | 61kB 2.5MB/s eta 0:00:01\r\u001b[K    34% |███████████                     | 71kB 2.9MB/s eta 0:00:01\r\u001b[K    39% |████████████▋                   | 81kB 3.2MB/s eta 0:00:01\r\u001b[K    44% |██████████████▏                 | 92kB 3.6MB/s eta 0:00:01\r\u001b[K    49% |███████████████▊                | 102kB 2.8MB/s eta 0:00:01\r\u001b[K    54% |█████████████████▎              | 112kB 2.8MB/s eta 0:00:01\r\u001b[K    59% |███████████████████             | 122kB 4.0MB/s eta 0:00:01\r\u001b[K    63% |████████████████████▌           | 133kB 4.0MB/s eta 0:00:01\r\u001b[K    68% |██████████████████████          | 143kB 7.5MB/s eta 0:00:01\r\u001b[K    73% |███████████████████████▋        | 153kB 7.6MB/s eta 0:00:01\r\u001b[K    78% |█████████████████████████▏      | 163kB 7.6MB/s eta 0:00:01\r\u001b[K    83% |██████████████████████████▊     | 174kB 7.7MB/s eta 0:00:01\r\u001b[K    88% |████████████████████████████▍   | 184kB 7.8MB/s eta 0:00:01\r\u001b[K    93% |██████████████████████████████  | 194kB 7.8MB/s eta 0:00:01\r\u001b[K    98% |███████████████████████████████▌| 204kB 37.3MB/s eta 0:00:01\r\u001b[K    100% |████████████████████████████████| 215kB 23.6MB/s \n",
+            "\u001b[?25hRequirement already satisfied, skipping upgrade: pandas>=0.15.2 in /usr/local/lib/python3.6/dist-packages (from seaborn) (0.22.0)\n",
+            "Requirement already satisfied, skipping upgrade: matplotlib>=1.4.3 in /usr/local/lib/python3.6/dist-packages (from seaborn) (2.1.2)\n",
+            "Requirement already satisfied, skipping upgrade: scipy>=0.14.0 in /usr/local/lib/python3.6/dist-packages (from seaborn) (1.1.0)\n",
+            "Requirement already satisfied, skipping upgrade: numpy>=1.9.3 in /usr/local/lib/python3.6/dist-packages (from seaborn) (1.14.6)\n",
+            "Requirement already satisfied, skipping upgrade: python-dateutil>=2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.15.2->seaborn) (2.5.3)\n",
+            "Requirement already satisfied, skipping upgrade: pytz>=2011k in /usr/local/lib/python3.6/dist-packages (from pandas>=0.15.2->seaborn) (2018.7)\n",
+            "Requirement already satisfied, skipping upgrade: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=1.4.3->seaborn) (0.10.0)\n",
+            "Requirement already satisfied, skipping upgrade: six>=1.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=1.4.3->seaborn) (1.11.0)\n",
+            "Requirement already satisfied, skipping upgrade: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=1.4.3->seaborn) (2.3.0)\n",
+            "Installing collected packages: seaborn\n",
+            "  Found existing installation: seaborn 0.7.1\n",
+            "    Uninstalling seaborn-0.7.1:\n",
+            "      Successfully uninstalled seaborn-0.7.1\n",
+            "Successfully installed seaborn-0.9.0\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "PKzhk5UCYf0w",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "%matplotlib inline\n",
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np\n",
+        "import pandas as pd"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "d-h5yBhAYspx",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "incomes = pd.read_csv('https://raw.githubusercontent.com/open-numbers/ddf--gapminder--systema_globalis/master/ddf--datapoints--income_per_person_gdppercapita_ppp_inflation_adjusted--by--geo--time.csv')\n",
+        "life_exp = pd.read_csv('https://raw.githubusercontent.com/open-numbers/ddf--gapminder--systema_globalis/master/ddf--datapoints--life_expectancy_years--by--geo--time.csv')\n",
+        "population = pd.read_csv('https://raw.githubusercontent.com/open-numbers/ddf--gapminder--systema_globalis/master/ddf--datapoints--population_total--by--geo--time.csv')\n",
+        "entities = pd.read_csv('https://raw.githubusercontent.com/open-numbers/ddf--gapminder--systema_globalis/master/ddf--entities--geo--country.csv')\n",
+        "concepts = pd.read_csv('https://raw.githubusercontent.com/open-numbers/ddf--gapminder--systema_globalis/master/ddf--concepts.csv')"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "RhU2Y1s7Y4vN",
+        "colab_type": "code",
+        "outputId": "c5b3f666-a325-4376-d9b0-85dda94e162f",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "incomes.shape, life_exp.shape, population.shape, entities.shape, concepts.shape"
+      ],
+      "execution_count": 56,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "((44268, 3), (44370, 3), (51939, 3), (273, 33), (590, 16))"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 56
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "Qs-LXzp-ZtNr",
+        "colab_type": "code",
+        "outputId": "9c5eb2ec-ac30-4cc9-f134-0d2ec1f2176a",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 266
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "pd.options.display.max_columns = None\n",
+        "entities.head()"
+      ],
+      "execution_count": 57,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>country</th>\n",
+              "      <th>alt_5</th>\n",
+              "      <th>alternative_1</th>\n",
+              "      <th>alternative_2</th>\n",
+              "      <th>alternative_3</th>\n",
+              "      <th>alternative_4_cdiac</th>\n",
+              "      <th>arb1</th>\n",
+              "      <th>arb2</th>\n",
+              "      <th>arb3</th>\n",
+              "      <th>arb4</th>\n",
+              "      <th>arb5</th>\n",
+              "      <th>arb6</th>\n",
+              "      <th>g77_and_oecd_countries</th>\n",
+              "      <th>gapminder_list</th>\n",
+              "      <th>god_id</th>\n",
+              "      <th>gwid</th>\n",
+              "      <th>income_groups</th>\n",
+              "      <th>is--country</th>\n",
+              "      <th>iso3166_1_alpha2</th>\n",
+              "      <th>iso3166_1_alpha3</th>\n",
+              "      <th>iso3166_1_numeric</th>\n",
+              "      <th>iso3166_2</th>\n",
+              "      <th>landlocked</th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>main_religion_2008</th>\n",
+              "      <th>name</th>\n",
+              "      <th>pandg</th>\n",
+              "      <th>un_state</th>\n",
+              "      <th>unicode_region_subtag</th>\n",
+              "      <th>upper_case_name</th>\n",
+              "      <th>world_4region</th>\n",
+              "      <th>world_6region</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>abkh</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>others</td>\n",
+              "      <td>Abkhazia</td>\n",
+              "      <td>GE-AB</td>\n",
+              "      <td>i0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>True</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Abkhazia</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>False</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>europe</td>\n",
+              "      <td>europe_central_asia</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>abw</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Aruba</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>others</td>\n",
+              "      <td>Aruba</td>\n",
+              "      <td>AW</td>\n",
+              "      <td>i12</td>\n",
+              "      <td>high_income</td>\n",
+              "      <td>True</td>\n",
+              "      <td>AW</td>\n",
+              "      <td>ABW</td>\n",
+              "      <td>533.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>coastline</td>\n",
+              "      <td>12.50000</td>\n",
+              "      <td>-69.96667</td>\n",
+              "      <td>christian</td>\n",
+              "      <td>Aruba</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>False</td>\n",
+              "      <td>AW</td>\n",
+              "      <td>ARUBA</td>\n",
+              "      <td>americas</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>afg</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Islamic Republic of Afghanistan</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Afghanistan</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>g77</td>\n",
+              "      <td>Afghanistan</td>\n",
+              "      <td>AF</td>\n",
+              "      <td>i1</td>\n",
+              "      <td>low_income</td>\n",
+              "      <td>True</td>\n",
+              "      <td>AF</td>\n",
+              "      <td>AFG</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>landlocked</td>\n",
+              "      <td>33.00000</td>\n",
+              "      <td>66.00000</td>\n",
+              "      <td>muslim</td>\n",
+              "      <td>Afghanistan</td>\n",
+              "      <td>AFGHANISTAN</td>\n",
+              "      <td>True</td>\n",
+              "      <td>AF</td>\n",
+              "      <td>AFGHANISTAN</td>\n",
+              "      <td>asia</td>\n",
+              "      <td>south_asia</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>ago</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Angola</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>g77</td>\n",
+              "      <td>Angola</td>\n",
+              "      <td>AO</td>\n",
+              "      <td>i7</td>\n",
+              "      <td>upper_middle_income</td>\n",
+              "      <td>True</td>\n",
+              "      <td>AO</td>\n",
+              "      <td>AGO</td>\n",
+              "      <td>24.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>coastline</td>\n",
+              "      <td>-12.50000</td>\n",
+              "      <td>18.50000</td>\n",
+              "      <td>christian</td>\n",
+              "      <td>Angola</td>\n",
+              "      <td>ANGOLA</td>\n",
+              "      <td>True</td>\n",
+              "      <td>AO</td>\n",
+              "      <td>ANGOLA</td>\n",
+              "      <td>africa</td>\n",
+              "      <td>sub_saharan_africa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>aia</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>others</td>\n",
+              "      <td>Anguilla</td>\n",
+              "      <td>AI</td>\n",
+              "      <td>i8</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>True</td>\n",
+              "      <td>AI</td>\n",
+              "      <td>AIA</td>\n",
+              "      <td>660.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>coastline</td>\n",
+              "      <td>18.21667</td>\n",
+              "      <td>-63.05000</td>\n",
+              "      <td>christian</td>\n",
+              "      <td>Anguilla</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>False</td>\n",
+              "      <td>AI</td>\n",
+              "      <td>ANGUILLA</td>\n",
+              "      <td>americas</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "  country alt_5                    alternative_1 alternative_2 alternative_3  \\\n",
+              "0    abkh   NaN                              NaN           NaN           NaN   \n",
+              "1     abw   NaN                              NaN           NaN           NaN   \n",
+              "2     afg   NaN  Islamic Republic of Afghanistan           NaN           NaN   \n",
+              "3     ago   NaN                              NaN           NaN           NaN   \n",
+              "4     aia   NaN                              NaN           NaN           NaN   \n",
+              "\n",
+              "  alternative_4_cdiac arb1 arb2 arb3 arb4 arb5 arb6 g77_and_oecd_countries  \\\n",
+              "0                 NaN  NaN  NaN  NaN  NaN  NaN  NaN                 others   \n",
+              "1               Aruba  NaN  NaN  NaN  NaN  NaN  NaN                 others   \n",
+              "2         Afghanistan  NaN  NaN  NaN  NaN  NaN  NaN                    g77   \n",
+              "3              Angola  NaN  NaN  NaN  NaN  NaN  NaN                    g77   \n",
+              "4                 NaN  NaN  NaN  NaN  NaN  NaN  NaN                 others   \n",
+              "\n",
+              "  gapminder_list god_id gwid        income_groups  is--country  \\\n",
+              "0       Abkhazia  GE-AB   i0                  NaN         True   \n",
+              "1          Aruba     AW  i12          high_income         True   \n",
+              "2    Afghanistan     AF   i1           low_income         True   \n",
+              "3         Angola     AO   i7  upper_middle_income         True   \n",
+              "4       Anguilla     AI   i8                  NaN         True   \n",
+              "\n",
+              "  iso3166_1_alpha2 iso3166_1_alpha3  iso3166_1_numeric iso3166_2  landlocked  \\\n",
+              "0              NaN              NaN                NaN       NaN         NaN   \n",
+              "1               AW              ABW              533.0       NaN   coastline   \n",
+              "2               AF              AFG                4.0       NaN  landlocked   \n",
+              "3               AO              AGO               24.0       NaN   coastline   \n",
+              "4               AI              AIA              660.0       NaN   coastline   \n",
+              "\n",
+              "   latitude  longitude main_religion_2008         name        pandg  un_state  \\\n",
+              "0       NaN        NaN                NaN     Abkhazia          NaN     False   \n",
+              "1  12.50000  -69.96667          christian        Aruba          NaN     False   \n",
+              "2  33.00000   66.00000             muslim  Afghanistan  AFGHANISTAN      True   \n",
+              "3 -12.50000   18.50000          christian       Angola       ANGOLA      True   \n",
+              "4  18.21667  -63.05000          christian     Anguilla          NaN     False   \n",
+              "\n",
+              "  unicode_region_subtag upper_case_name world_4region        world_6region  \n",
+              "0                   NaN             NaN        europe  europe_central_asia  \n",
+              "1                    AW           ARUBA      americas              america  \n",
+              "2                    AF     AFGHANISTAN          asia           south_asia  \n",
+              "3                    AO          ANGOLA        africa   sub_saharan_africa  \n",
+              "4                    AI        ANGUILLA      americas              america  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 57
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "3G13FulWaqIO",
+        "colab_type": "code",
+        "outputId": "ccd8fea1-2905-4faa-e6a0-6349dc70f9a6",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 195
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "incomes.head()"
+      ],
+      "execution_count": 58,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>geo</th>\n",
+              "      <th>time</th>\n",
+              "      <th>income_per_person_gdppercapita_ppp_inflation_adjusted</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>abw</td>\n",
+              "      <td>1800</td>\n",
+              "      <td>833</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>abw</td>\n",
+              "      <td>1801</td>\n",
+              "      <td>833</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>abw</td>\n",
+              "      <td>1802</td>\n",
+              "      <td>833</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>abw</td>\n",
+              "      <td>1803</td>\n",
+              "      <td>833</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>abw</td>\n",
+              "      <td>1804</td>\n",
+              "      <td>833</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   geo  time  income_per_person_gdppercapita_ppp_inflation_adjusted\n",
+              "0  abw  1800                                                833    \n",
+              "1  abw  1801                                                833    \n",
+              "2  abw  1802                                                833    \n",
+              "3  abw  1803                                                833    \n",
+              "4  abw  1804                                                833    "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 58
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "IaB-22CXaAao",
+        "colab_type": "code",
+        "outputId": "f0a3d449-f944-43ed-f9f9-efa48263a2d0",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 551
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "concepts.head() # data dictionary"
+      ],
+      "execution_count": 59,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>concept</th>\n",
+              "      <th>color</th>\n",
+              "      <th>concept_type</th>\n",
+              "      <th>description</th>\n",
+              "      <th>description_long</th>\n",
+              "      <th>domain</th>\n",
+              "      <th>drill_up</th>\n",
+              "      <th>indicator_url</th>\n",
+              "      <th>name</th>\n",
+              "      <th>name_catalog</th>\n",
+              "      <th>name_short</th>\n",
+              "      <th>scales</th>\n",
+              "      <th>source</th>\n",
+              "      <th>source_long</th>\n",
+              "      <th>source_url</th>\n",
+              "      <th>tags</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>adults_with_hiv_percent_age_15_49</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>measure</td>\n",
+              "      <td>The estimated percentage of adults aged 15 to ...</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Adults with HIV (%, age 15-49)</td>\n",
+              "      <td>Adults with HIV (%, age 15-49)</td>\n",
+              "      <td>Adults with HIV</td>\n",
+              "      <td>[\"log\", \"linear\"]</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>http://www.gapminder.org/gapminder-world/docum...</td>\n",
+              "      <td>hiv</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>age_at_1st_marriage_women</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>measure</td>\n",
+              "      <td>The mean age, in years, of first marriage for ...</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Age at 1st marriage (women)</td>\n",
+              "      <td>Age at 1st marriage (women)</td>\n",
+              "      <td>Age at 1st marriage</td>\n",
+              "      <td>[\"linear\", \"log\"]</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>http://spreadsheets.google.com/pub?key=t4eF8H_...</td>\n",
+              "      <td>population</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>aged_15_24_employment_rate_percent</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>measure</td>\n",
+              "      <td>Percentage of total population, age group 15-2...</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Aged 15-24 employment rate (%)</td>\n",
+              "      <td>Aged 15-24</td>\n",
+              "      <td>Employment rate (%)</td>\n",
+              "      <td>[\"linear\", \"log\"]</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>http://ilo.org/legacy/english/global-reports/k...</td>\n",
+              "      <td>employment_rate</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>aged_15_24_unemployment_rate_percent</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>measure</td>\n",
+              "      <td>Percentage of total population, age group 15-2...</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Aged 15-24 unemployment rate (%)</td>\n",
+              "      <td>Aged 15-24</td>\n",
+              "      <td>Unemployment rate (%)</td>\n",
+              "      <td>[\"linear\", \"log\"]</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>http://ilo.org/legacy/english/global-reports/k...</td>\n",
+              "      <td>unemployment</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>aged_15_64_labour_force_participation_rate_per...</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>measure</td>\n",
+              "      <td>For age group 15-64, percentage of all labour ...</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Aged 15-64 labour force participation rate (%)</td>\n",
+              "      <td>Aged 15-64</td>\n",
+              "      <td>Labour force participation rate (%)</td>\n",
+              "      <td>[\"linear\", \"log\"]</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>http://ilo.org/legacy/english/global-reports/k...</td>\n",
+              "      <td>labour_force_participation</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                                             concept color concept_type  \\\n",
+              "0                  adults_with_hiv_percent_age_15_49   NaN      measure   \n",
+              "1                          age_at_1st_marriage_women   NaN      measure   \n",
+              "2                 aged_15_24_employment_rate_percent   NaN      measure   \n",
+              "3               aged_15_24_unemployment_rate_percent   NaN      measure   \n",
+              "4  aged_15_64_labour_force_participation_rate_per...   NaN      measure   \n",
+              "\n",
+              "                                         description description_long domain  \\\n",
+              "0  The estimated percentage of adults aged 15 to ...              NaN    NaN   \n",
+              "1  The mean age, in years, of first marriage for ...              NaN    NaN   \n",
+              "2  Percentage of total population, age group 15-2...              NaN    NaN   \n",
+              "3  Percentage of total population, age group 15-2...              NaN    NaN   \n",
+              "4  For age group 15-64, percentage of all labour ...              NaN    NaN   \n",
+              "\n",
+              "  drill_up indicator_url                                            name  \\\n",
+              "0      NaN           NaN                  Adults with HIV (%, age 15-49)   \n",
+              "1      NaN           NaN                     Age at 1st marriage (women)   \n",
+              "2      NaN           NaN                  Aged 15-24 employment rate (%)   \n",
+              "3      NaN           NaN                Aged 15-24 unemployment rate (%)   \n",
+              "4      NaN           NaN  Aged 15-64 labour force participation rate (%)   \n",
+              "\n",
+              "                     name_catalog                           name_short  \\\n",
+              "0  Adults with HIV (%, age 15-49)                      Adults with HIV   \n",
+              "1     Age at 1st marriage (women)                  Age at 1st marriage   \n",
+              "2                      Aged 15-24                  Employment rate (%)   \n",
+              "3                      Aged 15-24                Unemployment rate (%)   \n",
+              "4                      Aged 15-64  Labour force participation rate (%)   \n",
+              "\n",
+              "              scales source source_long  \\\n",
+              "0  [\"log\", \"linear\"]    NaN         NaN   \n",
+              "1  [\"linear\", \"log\"]    NaN         NaN   \n",
+              "2  [\"linear\", \"log\"]    NaN         NaN   \n",
+              "3  [\"linear\", \"log\"]    NaN         NaN   \n",
+              "4  [\"linear\", \"log\"]    NaN         NaN   \n",
+              "\n",
+              "                                          source_url  \\\n",
+              "0  http://www.gapminder.org/gapminder-world/docum...   \n",
+              "1  http://spreadsheets.google.com/pub?key=t4eF8H_...   \n",
+              "2  http://ilo.org/legacy/english/global-reports/k...   \n",
+              "3  http://ilo.org/legacy/english/global-reports/k...   \n",
+              "4  http://ilo.org/legacy/english/global-reports/k...   \n",
+              "\n",
+              "                         tags  \n",
+              "0                         hiv  \n",
+              "1                  population  \n",
+              "2             employment_rate  \n",
+              "3                unemployment  \n",
+              "4  labour_force_participation  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 59
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "oGlxPCH1aI92",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "df1 = pd.merge(incomes, life_exp)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "_PUnmOElbc9L",
+        "colab_type": "code",
+        "outputId": "e06eff3d-86a9-4ada-e9d9-71d485012541",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "incomes.shape, life_exp.shape, df1.shape"
+      ],
+      "execution_count": 61,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "((44268, 3), (44370, 3), (41790, 4))"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 61
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "huCO-0cLbjIp",
+        "colab_type": "code",
+        "outputId": "3c3a760b-0ef2-42db-b6f8-d5c4a1468988",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 195
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "df1.head()"
+      ],
+      "execution_count": 62,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>geo</th>\n",
+              "      <th>time</th>\n",
+              "      <th>income_per_person_gdppercapita_ppp_inflation_adjusted</th>\n",
+              "      <th>life_expectancy_years</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>abw</td>\n",
+              "      <td>1800</td>\n",
+              "      <td>833</td>\n",
+              "      <td>34.42</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>abw</td>\n",
+              "      <td>1801</td>\n",
+              "      <td>833</td>\n",
+              "      <td>34.42</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>abw</td>\n",
+              "      <td>1802</td>\n",
+              "      <td>833</td>\n",
+              "      <td>34.42</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>abw</td>\n",
+              "      <td>1803</td>\n",
+              "      <td>833</td>\n",
+              "      <td>34.42</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>abw</td>\n",
+              "      <td>1804</td>\n",
+              "      <td>833</td>\n",
+              "      <td>34.42</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   geo  time  income_per_person_gdppercapita_ppp_inflation_adjusted  \\\n",
+              "0  abw  1800                                                833       \n",
+              "1  abw  1801                                                833       \n",
+              "2  abw  1802                                                833       \n",
+              "3  abw  1803                                                833       \n",
+              "4  abw  1804                                                833       \n",
+              "\n",
+              "   life_expectancy_years  \n",
+              "0                  34.42  \n",
+              "1                  34.42  \n",
+              "2                  34.42  \n",
+              "3                  34.42  \n",
+              "4                  34.42  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 62
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "qeWegGZNboI2",
+        "colab_type": "code",
+        "outputId": "6ac3ffd3-1f63-47a1-c3c4-8edbe37dac1c",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 266
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "entities.head() # want real name and geographic region"
+      ],
+      "execution_count": 63,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>country</th>\n",
+              "      <th>alt_5</th>\n",
+              "      <th>alternative_1</th>\n",
+              "      <th>alternative_2</th>\n",
+              "      <th>alternative_3</th>\n",
+              "      <th>alternative_4_cdiac</th>\n",
+              "      <th>arb1</th>\n",
+              "      <th>arb2</th>\n",
+              "      <th>arb3</th>\n",
+              "      <th>arb4</th>\n",
+              "      <th>arb5</th>\n",
+              "      <th>arb6</th>\n",
+              "      <th>g77_and_oecd_countries</th>\n",
+              "      <th>gapminder_list</th>\n",
+              "      <th>god_id</th>\n",
+              "      <th>gwid</th>\n",
+              "      <th>income_groups</th>\n",
+              "      <th>is--country</th>\n",
+              "      <th>iso3166_1_alpha2</th>\n",
+              "      <th>iso3166_1_alpha3</th>\n",
+              "      <th>iso3166_1_numeric</th>\n",
+              "      <th>iso3166_2</th>\n",
+              "      <th>landlocked</th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>main_religion_2008</th>\n",
+              "      <th>name</th>\n",
+              "      <th>pandg</th>\n",
+              "      <th>un_state</th>\n",
+              "      <th>unicode_region_subtag</th>\n",
+              "      <th>upper_case_name</th>\n",
+              "      <th>world_4region</th>\n",
+              "      <th>world_6region</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>abkh</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>others</td>\n",
+              "      <td>Abkhazia</td>\n",
+              "      <td>GE-AB</td>\n",
+              "      <td>i0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>True</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Abkhazia</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>False</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>europe</td>\n",
+              "      <td>europe_central_asia</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>abw</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Aruba</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>others</td>\n",
+              "      <td>Aruba</td>\n",
+              "      <td>AW</td>\n",
+              "      <td>i12</td>\n",
+              "      <td>high_income</td>\n",
+              "      <td>True</td>\n",
+              "      <td>AW</td>\n",
+              "      <td>ABW</td>\n",
+              "      <td>533.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>coastline</td>\n",
+              "      <td>12.50000</td>\n",
+              "      <td>-69.96667</td>\n",
+              "      <td>christian</td>\n",
+              "      <td>Aruba</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>False</td>\n",
+              "      <td>AW</td>\n",
+              "      <td>ARUBA</td>\n",
+              "      <td>americas</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>afg</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Islamic Republic of Afghanistan</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Afghanistan</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>g77</td>\n",
+              "      <td>Afghanistan</td>\n",
+              "      <td>AF</td>\n",
+              "      <td>i1</td>\n",
+              "      <td>low_income</td>\n",
+              "      <td>True</td>\n",
+              "      <td>AF</td>\n",
+              "      <td>AFG</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>landlocked</td>\n",
+              "      <td>33.00000</td>\n",
+              "      <td>66.00000</td>\n",
+              "      <td>muslim</td>\n",
+              "      <td>Afghanistan</td>\n",
+              "      <td>AFGHANISTAN</td>\n",
+              "      <td>True</td>\n",
+              "      <td>AF</td>\n",
+              "      <td>AFGHANISTAN</td>\n",
+              "      <td>asia</td>\n",
+              "      <td>south_asia</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>ago</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Angola</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>g77</td>\n",
+              "      <td>Angola</td>\n",
+              "      <td>AO</td>\n",
+              "      <td>i7</td>\n",
+              "      <td>upper_middle_income</td>\n",
+              "      <td>True</td>\n",
+              "      <td>AO</td>\n",
+              "      <td>AGO</td>\n",
+              "      <td>24.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>coastline</td>\n",
+              "      <td>-12.50000</td>\n",
+              "      <td>18.50000</td>\n",
+              "      <td>christian</td>\n",
+              "      <td>Angola</td>\n",
+              "      <td>ANGOLA</td>\n",
+              "      <td>True</td>\n",
+              "      <td>AO</td>\n",
+              "      <td>ANGOLA</td>\n",
+              "      <td>africa</td>\n",
+              "      <td>sub_saharan_africa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>aia</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>others</td>\n",
+              "      <td>Anguilla</td>\n",
+              "      <td>AI</td>\n",
+              "      <td>i8</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>True</td>\n",
+              "      <td>AI</td>\n",
+              "      <td>AIA</td>\n",
+              "      <td>660.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>coastline</td>\n",
+              "      <td>18.21667</td>\n",
+              "      <td>-63.05000</td>\n",
+              "      <td>christian</td>\n",
+              "      <td>Anguilla</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>False</td>\n",
+              "      <td>AI</td>\n",
+              "      <td>ANGUILLA</td>\n",
+              "      <td>americas</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "  country alt_5                    alternative_1 alternative_2 alternative_3  \\\n",
+              "0    abkh   NaN                              NaN           NaN           NaN   \n",
+              "1     abw   NaN                              NaN           NaN           NaN   \n",
+              "2     afg   NaN  Islamic Republic of Afghanistan           NaN           NaN   \n",
+              "3     ago   NaN                              NaN           NaN           NaN   \n",
+              "4     aia   NaN                              NaN           NaN           NaN   \n",
+              "\n",
+              "  alternative_4_cdiac arb1 arb2 arb3 arb4 arb5 arb6 g77_and_oecd_countries  \\\n",
+              "0                 NaN  NaN  NaN  NaN  NaN  NaN  NaN                 others   \n",
+              "1               Aruba  NaN  NaN  NaN  NaN  NaN  NaN                 others   \n",
+              "2         Afghanistan  NaN  NaN  NaN  NaN  NaN  NaN                    g77   \n",
+              "3              Angola  NaN  NaN  NaN  NaN  NaN  NaN                    g77   \n",
+              "4                 NaN  NaN  NaN  NaN  NaN  NaN  NaN                 others   \n",
+              "\n",
+              "  gapminder_list god_id gwid        income_groups  is--country  \\\n",
+              "0       Abkhazia  GE-AB   i0                  NaN         True   \n",
+              "1          Aruba     AW  i12          high_income         True   \n",
+              "2    Afghanistan     AF   i1           low_income         True   \n",
+              "3         Angola     AO   i7  upper_middle_income         True   \n",
+              "4       Anguilla     AI   i8                  NaN         True   \n",
+              "\n",
+              "  iso3166_1_alpha2 iso3166_1_alpha3  iso3166_1_numeric iso3166_2  landlocked  \\\n",
+              "0              NaN              NaN                NaN       NaN         NaN   \n",
+              "1               AW              ABW              533.0       NaN   coastline   \n",
+              "2               AF              AFG                4.0       NaN  landlocked   \n",
+              "3               AO              AGO               24.0       NaN   coastline   \n",
+              "4               AI              AIA              660.0       NaN   coastline   \n",
+              "\n",
+              "   latitude  longitude main_religion_2008         name        pandg  un_state  \\\n",
+              "0       NaN        NaN                NaN     Abkhazia          NaN     False   \n",
+              "1  12.50000  -69.96667          christian        Aruba          NaN     False   \n",
+              "2  33.00000   66.00000             muslim  Afghanistan  AFGHANISTAN      True   \n",
+              "3 -12.50000   18.50000          christian       Angola       ANGOLA      True   \n",
+              "4  18.21667  -63.05000          christian     Anguilla          NaN     False   \n",
+              "\n",
+              "  unicode_region_subtag upper_case_name world_4region        world_6region  \n",
+              "0                   NaN             NaN        europe  europe_central_asia  \n",
+              "1                    AW           ARUBA      americas              america  \n",
+              "2                    AF     AFGHANISTAN          asia           south_asia  \n",
+              "3                    AO          ANGOLA        africa   sub_saharan_africa  \n",
+              "4                    AI        ANGUILLA      americas              america  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 63
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "MVbiK6kJcdq0",
+        "colab_type": "code",
+        "outputId": "8412bf33-9709-4875-cdad-cb196ed5ffee",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 101
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "entities.world_4region.value_counts()"
+      ],
+      "execution_count": 64,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "asia        80\n",
+              "europe      73\n",
+              "africa      61\n",
+              "americas    57\n",
+              "Name: world_4region, dtype: int64"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 64
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "PP7Db1NqcWLl",
+        "colab_type": "code",
+        "outputId": "05088821-389c-4284-b0a7-b1b9283097db",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 134
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "entities.world_6region.value_counts()"
+      ],
+      "execution_count": 65,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "europe_central_asia         77\n",
+              "sub_saharan_africa          53\n",
+              "america                     53\n",
+              "east_asia_pacific           46\n",
+              "middle_east_north_africa    23\n",
+              "south_asia                   8\n",
+              "Name: world_6region, dtype: int64"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 65
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "DHH06jFqcvNK",
+        "colab_type": "code",
+        "outputId": "ae03863c-55e0-4dd1-895b-c0b754cf8272",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 195
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "variables = ['country', 'name', 'world_6region']\n",
+        "entities[variables].head() # show variables, very cool filter process"
+      ],
+      "execution_count": 66,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>country</th>\n",
+              "      <th>name</th>\n",
+              "      <th>world_6region</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>abkh</td>\n",
+              "      <td>Abkhazia</td>\n",
+              "      <td>europe_central_asia</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>abw</td>\n",
+              "      <td>Aruba</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>afg</td>\n",
+              "      <td>Afghanistan</td>\n",
+              "      <td>south_asia</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>ago</td>\n",
+              "      <td>Angola</td>\n",
+              "      <td>sub_saharan_africa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>aia</td>\n",
+              "      <td>Anguilla</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "  country         name        world_6region\n",
+              "0    abkh     Abkhazia  europe_central_asia\n",
+              "1     abw        Aruba              america\n",
+              "2     afg  Afghanistan           south_asia\n",
+              "3     ago       Angola   sub_saharan_africa\n",
+              "4     aia     Anguilla              america"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 66
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "EcN86p-ldBKV",
+        "colab_type": "code",
+        "outputId": "81e5ae91-f21c-40fd-b867-fd24dbd99d60",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 195
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "# preview before assigning\n",
+        "pd.merge(df1,  entities[variables], how='inner', left_on='geo', \n",
+        "         right_on='country').head()"
+      ],
+      "execution_count": 67,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>geo</th>\n",
+              "      <th>time</th>\n",
+              "      <th>income_per_person_gdppercapita_ppp_inflation_adjusted</th>\n",
+              "      <th>life_expectancy_years</th>\n",
+              "      <th>country</th>\n",
+              "      <th>name</th>\n",
+              "      <th>world_6region</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>abw</td>\n",
+              "      <td>1800</td>\n",
+              "      <td>833</td>\n",
+              "      <td>34.42</td>\n",
+              "      <td>abw</td>\n",
+              "      <td>Aruba</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>abw</td>\n",
+              "      <td>1801</td>\n",
+              "      <td>833</td>\n",
+              "      <td>34.42</td>\n",
+              "      <td>abw</td>\n",
+              "      <td>Aruba</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>abw</td>\n",
+              "      <td>1802</td>\n",
+              "      <td>833</td>\n",
+              "      <td>34.42</td>\n",
+              "      <td>abw</td>\n",
+              "      <td>Aruba</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>abw</td>\n",
+              "      <td>1803</td>\n",
+              "      <td>833</td>\n",
+              "      <td>34.42</td>\n",
+              "      <td>abw</td>\n",
+              "      <td>Aruba</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>abw</td>\n",
+              "      <td>1804</td>\n",
+              "      <td>833</td>\n",
+              "      <td>34.42</td>\n",
+              "      <td>abw</td>\n",
+              "      <td>Aruba</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   geo  time  income_per_person_gdppercapita_ppp_inflation_adjusted  \\\n",
+              "0  abw  1800                                                833       \n",
+              "1  abw  1801                                                833       \n",
+              "2  abw  1802                                                833       \n",
+              "3  abw  1803                                                833       \n",
+              "4  abw  1804                                                833       \n",
+              "\n",
+              "   life_expectancy_years country   name world_6region  \n",
+              "0                  34.42     abw  Aruba       america  \n",
+              "1                  34.42     abw  Aruba       america  \n",
+              "2                  34.42     abw  Aruba       america  \n",
+              "3                  34.42     abw  Aruba       america  \n",
+              "4                  34.42     abw  Aruba       america  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 67
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "CwpsEDhIeS-a",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "df1 = pd.merge(df1, population)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "IUF3eAIRdkth",
+        "colab_type": "code",
+        "outputId": "661abec1-4c7d-454b-99f7-64614d6f47a3",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "# drop and rename\n",
+        "df1 = pd.merge(df1, entities[variables], how='inner', left_on='geo', \n",
+        "               right_on='country')\n",
+        "\n",
+        "df1.drop(columns=['geo', 'country'], inplace = True)\n",
+        "\n",
+        "df1.rename(columns={\n",
+        "    'time': 'year', \n",
+        "    'income_per_person_gdppercapita_ppp_inflation_adjusted': 'income',\n",
+        "    'life_expectancy_years': 'lifespan',\n",
+        "    'population_total': 'population',\n",
+        "    'name': 'country',\n",
+        "    'world_6region': 'region'\n",
+        "}, inplace=True)\n",
+        "\n",
+        "df1.shape"
+      ],
+      "execution_count": 69,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(41790, 6)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 69
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "4c5yn5agelG9",
+        "colab_type": "code",
+        "outputId": "8fb74e18-02b2-49cd-ee76-7d52b2d32c86",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 195
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "df1.head()"
+      ],
+      "execution_count": 70,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>year</th>\n",
+              "      <th>income</th>\n",
+              "      <th>lifespan</th>\n",
+              "      <th>population</th>\n",
+              "      <th>country</th>\n",
+              "      <th>region</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1800</td>\n",
+              "      <td>833</td>\n",
+              "      <td>34.42</td>\n",
+              "      <td>19286</td>\n",
+              "      <td>Aruba</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1801</td>\n",
+              "      <td>833</td>\n",
+              "      <td>34.42</td>\n",
+              "      <td>19286</td>\n",
+              "      <td>Aruba</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>1802</td>\n",
+              "      <td>833</td>\n",
+              "      <td>34.42</td>\n",
+              "      <td>19286</td>\n",
+              "      <td>Aruba</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>1803</td>\n",
+              "      <td>833</td>\n",
+              "      <td>34.42</td>\n",
+              "      <td>19286</td>\n",
+              "      <td>Aruba</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>1804</td>\n",
+              "      <td>833</td>\n",
+              "      <td>34.42</td>\n",
+              "      <td>19286</td>\n",
+              "      <td>Aruba</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   year  income  lifespan  population country   region\n",
+              "0  1800     833     34.42       19286   Aruba  america\n",
+              "1  1801     833     34.42       19286   Aruba  america\n",
+              "2  1802     833     34.42       19286   Aruba  america\n",
+              "3  1803     833     34.42       19286   Aruba  america\n",
+              "4  1804     833     34.42       19286   Aruba  america"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 70
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "dbLg4S9xeoy3",
+        "colab_type": "code",
+        "outputId": "198ff8cf-5600-4f3c-d092-e92033da0b62",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 166
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "df1.describe(exclude=[np.number])"
+      ],
+      "execution_count": 71,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>country</th>\n",
+              "      <th>region</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>41790</td>\n",
+              "      <td>41790</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>unique</th>\n",
+              "      <td>194</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>top</th>\n",
+              "      <td>Ireland</td>\n",
+              "      <td>europe_central_asia</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>freq</th>\n",
+              "      <td>219</td>\n",
+              "      <td>10991</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "        country               region\n",
+              "count     41790                41790\n",
+              "unique      194                    6\n",
+              "top     Ireland  europe_central_asia\n",
+              "freq        219                10991"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 71
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "qF650OSWe7uc",
+        "colab_type": "code",
+        "outputId": "a5fa709e-67ee-440e-951d-396398875b8f",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 689
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "df1.country.unique()"
+      ],
+      "execution_count": 72,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array(['Aruba', 'Afghanistan', 'Angola', 'Albania', 'Andorra',\n",
+              "       'United Arab Emirates', 'Argentina', 'Armenia',\n",
+              "       'Antigua and Barbuda', 'Australia', 'Austria', 'Azerbaijan',\n",
+              "       'Burundi', 'Belgium', 'Benin', 'Burkina Faso', 'Bangladesh',\n",
+              "       'Bulgaria', 'Bahrain', 'Bahamas', 'Bosnia and Herzegovina',\n",
+              "       'Belarus', 'Belize', 'Bermuda', 'Bolivia', 'Brazil', 'Barbados',\n",
+              "       'Brunei', 'Bhutan', 'Botswana', 'Central African Republic',\n",
+              "       'Canada', 'Switzerland', 'Chile', 'China', \"Cote d'Ivoire\",\n",
+              "       'Cameroon', 'Congo, Dem. Rep.', 'Congo, Rep.', 'Colombia',\n",
+              "       'Comoros', 'Cape Verde', 'Costa Rica', 'Cuba', 'Cyprus',\n",
+              "       'Czech Republic', 'Germany', 'Djibouti', 'Dominica', 'Denmark',\n",
+              "       'Dominican Republic', 'Algeria', 'Ecuador', 'Egypt', 'Eritrea',\n",
+              "       'Spain', 'Estonia', 'Ethiopia', 'Finland', 'Fiji', 'France',\n",
+              "       'Micronesia, Fed. Sts.', 'Gabon', 'United Kingdom', 'Georgia',\n",
+              "       'Ghana', 'Guinea', 'Gambia', 'Guinea-Bissau', 'Equatorial Guinea',\n",
+              "       'Greece', 'Grenada', 'Greenland', 'Guatemala', 'Guyana',\n",
+              "       'Hong Kong, China', 'Honduras', 'Croatia', 'Haiti', 'Hungary',\n",
+              "       'Indonesia', 'India', 'Ireland', 'Iran', 'Iraq', 'Iceland',\n",
+              "       'Israel', 'Italy', 'Jamaica', 'Jordan', 'Japan', 'Kazakhstan',\n",
+              "       'Kenya', 'Kyrgyz Republic', 'Cambodia', 'Kiribati', 'South Korea',\n",
+              "       'Kuwait', 'Lao', 'Lebanon', 'Liberia', 'Libya', 'St. Lucia',\n",
+              "       'Sri Lanka', 'Lesotho', 'Lithuania', 'Luxembourg', 'Latvia',\n",
+              "       'Macao, China', 'Morocco', 'Moldova', 'Madagascar', 'Maldives',\n",
+              "       'Mexico', 'Marshall Islands', 'Macedonia, FYR', 'Mali', 'Malta',\n",
+              "       'Myanmar', 'Montenegro', 'Mongolia', 'Mozambique', 'Mauritania',\n",
+              "       'Mauritius', 'Malawi', 'Malaysia', 'Namibia', 'Niger', 'Nigeria',\n",
+              "       'Nicaragua', 'Netherlands', 'Norway', 'Nepal', 'New Zealand',\n",
+              "       'Oman', 'Pakistan', 'Panama', 'Peru', 'Philippines',\n",
+              "       'Papua New Guinea', 'Poland', 'Puerto Rico', 'North Korea',\n",
+              "       'Portugal', 'Paraguay', 'Palestine', 'Qatar', 'Romania', 'Russia',\n",
+              "       'Rwanda', 'Saudi Arabia', 'Sudan', 'Senegal', 'Singapore',\n",
+              "       'Solomon Islands', 'Sierra Leone', 'El Salvador', 'Somalia',\n",
+              "       'Serbia', 'South Sudan', 'Sao Tome and Principe', 'Suriname',\n",
+              "       'Slovak Republic', 'Slovenia', 'Sweden', 'Swaziland', 'Seychelles',\n",
+              "       'Syria', 'Chad', 'Togo', 'Thailand', 'Tajikistan', 'Turkmenistan',\n",
+              "       'Timor-Leste', 'Tonga', 'Trinidad and Tobago', 'Tunisia', 'Turkey',\n",
+              "       'Taiwan', 'Tanzania', 'Uganda', 'Ukraine', 'Uruguay',\n",
+              "       'United States', 'Uzbekistan', 'St. Vincent and the Grenadines',\n",
+              "       'Venezuela', 'Vietnam', 'Vanuatu', 'Samoa', 'Yemen',\n",
+              "       'South Africa', 'Zambia', 'Zimbabwe'], dtype=object)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 72
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "P9A0mufcfkWd",
+        "colab_type": "code",
+        "outputId": "0ea1fda7-7c78-49b9-d11d-ded76e01cc25",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1058
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "df1.country == 'United States'"
+      ],
+      "execution_count": 73,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0        False\n",
+              "1        False\n",
+              "2        False\n",
+              "3        False\n",
+              "4        False\n",
+              "5        False\n",
+              "6        False\n",
+              "7        False\n",
+              "8        False\n",
+              "9        False\n",
+              "10       False\n",
+              "11       False\n",
+              "12       False\n",
+              "13       False\n",
+              "14       False\n",
+              "15       False\n",
+              "16       False\n",
+              "17       False\n",
+              "18       False\n",
+              "19       False\n",
+              "20       False\n",
+              "21       False\n",
+              "22       False\n",
+              "23       False\n",
+              "24       False\n",
+              "25       False\n",
+              "26       False\n",
+              "27       False\n",
+              "28       False\n",
+              "29       False\n",
+              "         ...  \n",
+              "41760    False\n",
+              "41761    False\n",
+              "41762    False\n",
+              "41763    False\n",
+              "41764    False\n",
+              "41765    False\n",
+              "41766    False\n",
+              "41767    False\n",
+              "41768    False\n",
+              "41769    False\n",
+              "41770    False\n",
+              "41771    False\n",
+              "41772    False\n",
+              "41773    False\n",
+              "41774    False\n",
+              "41775    False\n",
+              "41776    False\n",
+              "41777    False\n",
+              "41778    False\n",
+              "41779    False\n",
+              "41780    False\n",
+              "41781    False\n",
+              "41782    False\n",
+              "41783    False\n",
+              "41784    False\n",
+              "41785    False\n",
+              "41786    False\n",
+              "41787    False\n",
+              "41788    False\n",
+              "41789    False\n",
+              "Name: country, Length: 41790, dtype: bool"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 73
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "7Yuat41ufr0r",
+        "colab_type": "code",
+        "outputId": "529f8c32-6e5d-43a5-b311-8c94cafc2ffd",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1882
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "df1[df1.country == 'United States'] # boolean indexing, aka return true"
+      ],
+      "execution_count": 74,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>year</th>\n",
+              "      <th>income</th>\n",
+              "      <th>lifespan</th>\n",
+              "      <th>population</th>\n",
+              "      <th>country</th>\n",
+              "      <th>region</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>39381</th>\n",
+              "      <td>1800</td>\n",
+              "      <td>2127</td>\n",
+              "      <td>39.41</td>\n",
+              "      <td>6801854</td>\n",
+              "      <td>United States</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>39382</th>\n",
+              "      <td>1801</td>\n",
+              "      <td>2167</td>\n",
+              "      <td>39.41</td>\n",
+              "      <td>6933517</td>\n",
+              "      <td>United States</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>39383</th>\n",
+              "      <td>1802</td>\n",
+              "      <td>2202</td>\n",
+              "      <td>39.41</td>\n",
+              "      <td>7067728</td>\n",
+              "      <td>United States</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>39384</th>\n",
+              "      <td>1803</td>\n",
+              "      <td>2166</td>\n",
+              "      <td>39.41</td>\n",
+              "      <td>7204538</td>\n",
+              "      <td>United States</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>39385</th>\n",
+              "      <td>1804</td>\n",
+              "      <td>2165</td>\n",
+              "      <td>39.41</td>\n",
+              "      <td>7343995</td>\n",
+              "      <td>United States</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>39386</th>\n",
+              "      <td>1805</td>\n",
+              "      <td>2209</td>\n",
+              "      <td>39.41</td>\n",
+              "      <td>7486152</td>\n",
+              "      <td>United States</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>39387</th>\n",
+              "      <td>1806</td>\n",
+              "      <td>2251</td>\n",
+              "      <td>39.41</td>\n",
+              "      <td>7631061</td>\n",
+              "      <td>United States</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>39388</th>\n",
+              "      <td>1807</td>\n",
+              "      <td>2273</td>\n",
+              "      <td>39.41</td>\n",
+              "      <td>7778775</td>\n",
+              "      <td>United States</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>39389</th>\n",
+              "      <td>1808</td>\n",
+              "      <td>2113</td>\n",
+              "      <td>39.41</td>\n",
+              "      <td>7929348</td>\n",
+              "      <td>United States</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>39390</th>\n",
+              "      <td>1809</td>\n",
+              "      <td>2217</td>\n",
+              "      <td>39.41</td>\n",
+              "      <td>8082836</td>\n",
+              "      <td>United States</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>39391</th>\n",
+              "      <td>1810</td>\n",
+              "      <td>2282</td>\n",
+              "      <td>39.41</td>\n",
+              "      <td>8294928</td>\n",
+              "      <td>United States</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>39392</th>\n",
+              "      <td>1811</td>\n",
+              "      <td>2326</td>\n",
+              "      <td>39.41</td>\n",
+              "      <td>8461458</td>\n",
+              "      <td>United States</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>39393</th>\n",
+              "      <td>1812</td>\n",
+              "      <td>2287</td>\n",
+              "      <td>39.41</td>\n",
+              "      <td>8637266</td>\n",
+              "      <td>United States</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>39394</th>\n",
+              "      <td>1813</td>\n",
+              "      <td>2312</td>\n",
+              "      <td>39.41</td>\n",
+              "      <td>8822188</td>\n",
+              "      <td>United States</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>39395</th>\n",
+              "      <td>1814</td>\n",
+              "      <td>2357</td>\n",
+              "      <td>39.41</td>\n",
+              "      <td>9016100</td>\n",
+              "      <td>United States</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>39396</th>\n",
+              "      <td>1815</td>\n",
+              "      <td>2348</td>\n",
+              "      <td>39.41</td>\n",
+              "      <td>9218879</td>\n",
+              "      <td>United States</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>39397</th>\n",
+              "      <td>1816</td>\n",
+              "      <td>2253</td>\n",
+              "      <td>39.41</td>\n",
+              "      <td>9430398</td>\n",
+              "      <td>United States</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>39398</th>\n",
+              "      <td>1817</td>\n",
+              "      <td>2251</td>\n",
+              "      <td>39.41</td>\n",
+              "      <td>9650391</td>\n",
+              "      <td>United States</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>39399</th>\n",
+              "      <td>1818</td>\n",
+              "      <td>2258</td>\n",
+              "      <td>39.41</td>\n",
+              "      <td>9879049</td>\n",
+              "      <td>United States</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>39400</th>\n",
+              "      <td>1819</td>\n",
+              "      <td>2254</td>\n",
+              "      <td>39.41</td>\n",
+              "      <td>10116052</td>\n",
+              "      <td>United States</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>39401</th>\n",
+              "      <td>1820</td>\n",
+              "      <td>2241</td>\n",
+              "      <td>39.41</td>\n",
+              "      <td>10361646</td>\n",
+              "      <td>United States</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>39402</th>\n",
+              "      <td>1821</td>\n",
+              "      <td>2276</td>\n",
+              "      <td>39.41</td>\n",
+              "      <td>10619707</td>\n",
+              "      <td>United States</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>39403</th>\n",
+              "      <td>1822</td>\n",
+              "      <td>2339</td>\n",
+              "      <td>39.41</td>\n",
+              "      <td>10890141</td>\n",
+              "      <td>United States</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>39404</th>\n",
+              "      <td>1823</td>\n",
+              "      <td>2314</td>\n",
+              "      <td>39.41</td>\n",
+              "      <td>11172754</td>\n",
+              "      <td>United States</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>39405</th>\n",
+              "      <td>1824</td>\n",
+              "      <td>2377</td>\n",
+              "      <td>39.41</td>\n",
+              "      <td>11467399</td>\n",
+              "      <td>United States</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>39406</th>\n",
+              "      <td>1825</td>\n",
+              "      <td>2430</td>\n",
+              "      <td>39.41</td>\n",
+              "      <td>11773972</td>\n",
+              "      <td>United States</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>39407</th>\n",
+              "      <td>1826</td>\n",
+              "      <td>2447</td>\n",
+              "      <td>39.41</td>\n",
+              "      <td>12092272</td>\n",
+              "      <td>United States</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>39408</th>\n",
+              "      <td>1827</td>\n",
+              "      <td>2457</td>\n",
+              "      <td>39.41</td>\n",
+              "      <td>12422142</td>\n",
+              "      <td>United States</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>39409</th>\n",
+              "      <td>1828</td>\n",
+              "      <td>2458</td>\n",
+              "      <td>39.41</td>\n",
+              "      <td>12763468</td>\n",
+              "      <td>United States</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>39410</th>\n",
+              "      <td>1829</td>\n",
+              "      <td>2377</td>\n",
+              "      <td>39.41</td>\n",
+              "      <td>13116042</td>\n",
+              "      <td>United States</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>39570</th>\n",
+              "      <td>1989</td>\n",
+              "      <td>36830</td>\n",
+              "      <td>75.16</td>\n",
+              "      <td>250113187</td>\n",
+              "      <td>United States</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>39571</th>\n",
+              "      <td>1990</td>\n",
+              "      <td>37062</td>\n",
+              "      <td>75.40</td>\n",
+              "      <td>252529950</td>\n",
+              "      <td>United States</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>39572</th>\n",
+              "      <td>1991</td>\n",
+              "      <td>36543</td>\n",
+              "      <td>75.55</td>\n",
+              "      <td>254974819</td>\n",
+              "      <td>United States</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>39573</th>\n",
+              "      <td>1992</td>\n",
+              "      <td>37321</td>\n",
+              "      <td>75.71</td>\n",
+              "      <td>257454273</td>\n",
+              "      <td>United States</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>39574</th>\n",
+              "      <td>1993</td>\n",
+              "      <td>37844</td>\n",
+              "      <td>75.72</td>\n",
+              "      <td>260020186</td>\n",
+              "      <td>United States</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>39575</th>\n",
+              "      <td>1994</td>\n",
+              "      <td>38892</td>\n",
+              "      <td>75.79</td>\n",
+              "      <td>262741566</td>\n",
+              "      <td>United States</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>39576</th>\n",
+              "      <td>1995</td>\n",
+              "      <td>39476</td>\n",
+              "      <td>75.91</td>\n",
+              "      <td>265658849</td>\n",
+              "      <td>United States</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>39577</th>\n",
+              "      <td>1996</td>\n",
+              "      <td>40501</td>\n",
+              "      <td>76.24</td>\n",
+              "      <td>268803424</td>\n",
+              "      <td>United States</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>39578</th>\n",
+              "      <td>1997</td>\n",
+              "      <td>41812</td>\n",
+              "      <td>76.60</td>\n",
+              "      <td>272136551</td>\n",
+              "      <td>United States</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>39579</th>\n",
+              "      <td>1998</td>\n",
+              "      <td>43166</td>\n",
+              "      <td>76.74</td>\n",
+              "      <td>275542603</td>\n",
+              "      <td>United States</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>39580</th>\n",
+              "      <td>1999</td>\n",
+              "      <td>44673</td>\n",
+              "      <td>76.78</td>\n",
+              "      <td>278862277</td>\n",
+              "      <td>United States</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>39581</th>\n",
+              "      <td>2000</td>\n",
+              "      <td>45986</td>\n",
+              "      <td>76.90</td>\n",
+              "      <td>281982778</td>\n",
+              "      <td>United States</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>39582</th>\n",
+              "      <td>2001</td>\n",
+              "      <td>45978</td>\n",
+              "      <td>76.99</td>\n",
+              "      <td>284852391</td>\n",
+              "      <td>United States</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>39583</th>\n",
+              "      <td>2002</td>\n",
+              "      <td>46367</td>\n",
+              "      <td>77.12</td>\n",
+              "      <td>287506847</td>\n",
+              "      <td>United States</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>39584</th>\n",
+              "      <td>2003</td>\n",
+              "      <td>47260</td>\n",
+              "      <td>77.27</td>\n",
+              "      <td>290027624</td>\n",
+              "      <td>United States</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>39585</th>\n",
+              "      <td>2004</td>\n",
+              "      <td>48597</td>\n",
+              "      <td>77.49</td>\n",
+              "      <td>292539324</td>\n",
+              "      <td>United States</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>39586</th>\n",
+              "      <td>2005</td>\n",
+              "      <td>49762</td>\n",
+              "      <td>77.65</td>\n",
+              "      <td>295129501</td>\n",
+              "      <td>United States</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>39587</th>\n",
+              "      <td>2006</td>\n",
+              "      <td>50599</td>\n",
+              "      <td>77.91</td>\n",
+              "      <td>297827356</td>\n",
+              "      <td>United States</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>39588</th>\n",
+              "      <td>2007</td>\n",
+              "      <td>51011</td>\n",
+              "      <td>78.12</td>\n",
+              "      <td>300595175</td>\n",
+              "      <td>United States</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>39589</th>\n",
+              "      <td>2008</td>\n",
+              "      <td>50384</td>\n",
+              "      <td>78.33</td>\n",
+              "      <td>303374067</td>\n",
+              "      <td>United States</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>39590</th>\n",
+              "      <td>2009</td>\n",
+              "      <td>48558</td>\n",
+              "      <td>78.56</td>\n",
+              "      <td>306076362</td>\n",
+              "      <td>United States</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>39591</th>\n",
+              "      <td>2010</td>\n",
+              "      <td>49373</td>\n",
+              "      <td>78.74</td>\n",
+              "      <td>308641391</td>\n",
+              "      <td>United States</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>39592</th>\n",
+              "      <td>2011</td>\n",
+              "      <td>49791</td>\n",
+              "      <td>78.83</td>\n",
+              "      <td>311051373</td>\n",
+              "      <td>United States</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>39593</th>\n",
+              "      <td>2012</td>\n",
+              "      <td>50520</td>\n",
+              "      <td>78.91</td>\n",
+              "      <td>313335423</td>\n",
+              "      <td>United States</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>39594</th>\n",
+              "      <td>2013</td>\n",
+              "      <td>51008</td>\n",
+              "      <td>78.93</td>\n",
+              "      <td>315536676</td>\n",
+              "      <td>United States</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>39595</th>\n",
+              "      <td>2014</td>\n",
+              "      <td>51831</td>\n",
+              "      <td>78.92</td>\n",
+              "      <td>317718779</td>\n",
+              "      <td>United States</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>39596</th>\n",
+              "      <td>2015</td>\n",
+              "      <td>52790</td>\n",
+              "      <td>78.85</td>\n",
+              "      <td>319929162</td>\n",
+              "      <td>United States</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>39597</th>\n",
+              "      <td>2016</td>\n",
+              "      <td>53273</td>\n",
+              "      <td>78.84</td>\n",
+              "      <td>322179605</td>\n",
+              "      <td>United States</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>39598</th>\n",
+              "      <td>2017</td>\n",
+              "      <td>54163</td>\n",
+              "      <td>78.99</td>\n",
+              "      <td>324459463</td>\n",
+              "      <td>United States</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>39599</th>\n",
+              "      <td>2018</td>\n",
+              "      <td>54898</td>\n",
+              "      <td>79.14</td>\n",
+              "      <td>326766748</td>\n",
+              "      <td>United States</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>219 rows × 6 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       year  income  lifespan  population        country   region\n",
+              "39381  1800    2127     39.41     6801854  United States  america\n",
+              "39382  1801    2167     39.41     6933517  United States  america\n",
+              "39383  1802    2202     39.41     7067728  United States  america\n",
+              "39384  1803    2166     39.41     7204538  United States  america\n",
+              "39385  1804    2165     39.41     7343995  United States  america\n",
+              "39386  1805    2209     39.41     7486152  United States  america\n",
+              "39387  1806    2251     39.41     7631061  United States  america\n",
+              "39388  1807    2273     39.41     7778775  United States  america\n",
+              "39389  1808    2113     39.41     7929348  United States  america\n",
+              "39390  1809    2217     39.41     8082836  United States  america\n",
+              "39391  1810    2282     39.41     8294928  United States  america\n",
+              "39392  1811    2326     39.41     8461458  United States  america\n",
+              "39393  1812    2287     39.41     8637266  United States  america\n",
+              "39394  1813    2312     39.41     8822188  United States  america\n",
+              "39395  1814    2357     39.41     9016100  United States  america\n",
+              "39396  1815    2348     39.41     9218879  United States  america\n",
+              "39397  1816    2253     39.41     9430398  United States  america\n",
+              "39398  1817    2251     39.41     9650391  United States  america\n",
+              "39399  1818    2258     39.41     9879049  United States  america\n",
+              "39400  1819    2254     39.41    10116052  United States  america\n",
+              "39401  1820    2241     39.41    10361646  United States  america\n",
+              "39402  1821    2276     39.41    10619707  United States  america\n",
+              "39403  1822    2339     39.41    10890141  United States  america\n",
+              "39404  1823    2314     39.41    11172754  United States  america\n",
+              "39405  1824    2377     39.41    11467399  United States  america\n",
+              "39406  1825    2430     39.41    11773972  United States  america\n",
+              "39407  1826    2447     39.41    12092272  United States  america\n",
+              "39408  1827    2457     39.41    12422142  United States  america\n",
+              "39409  1828    2458     39.41    12763468  United States  america\n",
+              "39410  1829    2377     39.41    13116042  United States  america\n",
+              "...     ...     ...       ...         ...            ...      ...\n",
+              "39570  1989   36830     75.16   250113187  United States  america\n",
+              "39571  1990   37062     75.40   252529950  United States  america\n",
+              "39572  1991   36543     75.55   254974819  United States  america\n",
+              "39573  1992   37321     75.71   257454273  United States  america\n",
+              "39574  1993   37844     75.72   260020186  United States  america\n",
+              "39575  1994   38892     75.79   262741566  United States  america\n",
+              "39576  1995   39476     75.91   265658849  United States  america\n",
+              "39577  1996   40501     76.24   268803424  United States  america\n",
+              "39578  1997   41812     76.60   272136551  United States  america\n",
+              "39579  1998   43166     76.74   275542603  United States  america\n",
+              "39580  1999   44673     76.78   278862277  United States  america\n",
+              "39581  2000   45986     76.90   281982778  United States  america\n",
+              "39582  2001   45978     76.99   284852391  United States  america\n",
+              "39583  2002   46367     77.12   287506847  United States  america\n",
+              "39584  2003   47260     77.27   290027624  United States  america\n",
+              "39585  2004   48597     77.49   292539324  United States  america\n",
+              "39586  2005   49762     77.65   295129501  United States  america\n",
+              "39587  2006   50599     77.91   297827356  United States  america\n",
+              "39588  2007   51011     78.12   300595175  United States  america\n",
+              "39589  2008   50384     78.33   303374067  United States  america\n",
+              "39590  2009   48558     78.56   306076362  United States  america\n",
+              "39591  2010   49373     78.74   308641391  United States  america\n",
+              "39592  2011   49791     78.83   311051373  United States  america\n",
+              "39593  2012   50520     78.91   313335423  United States  america\n",
+              "39594  2013   51008     78.93   315536676  United States  america\n",
+              "39595  2014   51831     78.92   317718779  United States  america\n",
+              "39596  2015   52790     78.85   319929162  United States  america\n",
+              "39597  2016   53273     78.84   322179605  United States  america\n",
+              "39598  2017   54163     78.99   324459463  United States  america\n",
+              "39599  2018   54898     79.14   326766748  United States  america\n",
+              "\n",
+              "[219 rows x 6 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 74
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "gspJjSV2fx0H",
+        "colab_type": "code",
+        "outputId": "eacb76e4-49ed-489c-9040-9099b5d0964b",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 136
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "usa = df1[df1.country=='United States']\n",
+        "usa[usa.year.isin([1818, 1918, 2018])]"
+      ],
+      "execution_count": 75,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>year</th>\n",
+              "      <th>income</th>\n",
+              "      <th>lifespan</th>\n",
+              "      <th>population</th>\n",
+              "      <th>country</th>\n",
+              "      <th>region</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>39399</th>\n",
+              "      <td>1818</td>\n",
+              "      <td>2258</td>\n",
+              "      <td>39.41</td>\n",
+              "      <td>9879049</td>\n",
+              "      <td>United States</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>39499</th>\n",
+              "      <td>1918</td>\n",
+              "      <td>9371</td>\n",
+              "      <td>47.18</td>\n",
+              "      <td>106721812</td>\n",
+              "      <td>United States</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>39599</th>\n",
+              "      <td>2018</td>\n",
+              "      <td>54898</td>\n",
+              "      <td>79.14</td>\n",
+              "      <td>326766748</td>\n",
+              "      <td>United States</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       year  income  lifespan  population        country   region\n",
+              "39399  1818    2258     39.41     9879049  United States  america\n",
+              "39499  1918    9371     47.18   106721812  United States  america\n",
+              "39599  2018   54898     79.14   326766748  United States  america"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 75
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "SDmbtDpBf77p",
+        "colab_type": "code",
+        "outputId": "9a323e95-b8c2-4181-e421-14d2be4189e7",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 136
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "china = df1[df1.country=='China']\n",
+        "china[china.year.isin([1818,1918,2018])]"
+      ],
+      "execution_count": 76,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>year</th>\n",
+              "      <th>income</th>\n",
+              "      <th>lifespan</th>\n",
+              "      <th>population</th>\n",
+              "      <th>country</th>\n",
+              "      <th>region</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>7120</th>\n",
+              "      <td>1818</td>\n",
+              "      <td>985</td>\n",
+              "      <td>32.00</td>\n",
+              "      <td>374161494</td>\n",
+              "      <td>China</td>\n",
+              "      <td>east_asia_pacific</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7220</th>\n",
+              "      <td>1918</td>\n",
+              "      <td>989</td>\n",
+              "      <td>22.13</td>\n",
+              "      <td>462444535</td>\n",
+              "      <td>China</td>\n",
+              "      <td>east_asia_pacific</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7320</th>\n",
+              "      <td>2018</td>\n",
+              "      <td>16018</td>\n",
+              "      <td>76.92</td>\n",
+              "      <td>1415045928</td>\n",
+              "      <td>China</td>\n",
+              "      <td>east_asia_pacific</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "      year  income  lifespan  population country             region\n",
+              "7120  1818     985     32.00   374161494   China  east_asia_pacific\n",
+              "7220  1918     989     22.13   462444535   China  east_asia_pacific\n",
+              "7320  2018   16018     76.92  1415045928   China  east_asia_pacific"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 76
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "-lEPBJPOgFNp",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "this_year = df1[df1.year==2018]"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "7_pJr5BcgVwU",
+        "colab_type": "code",
+        "outputId": "a620a62e-12c6-4aab-a081-59435c6956e6",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 360
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "print(this_year.shape)\n",
+        "this_year.sample(10)"
+      ],
+      "execution_count": 78,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "(188, 6)\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>year</th>\n",
+              "      <th>income</th>\n",
+              "      <th>lifespan</th>\n",
+              "      <th>population</th>\n",
+              "      <th>country</th>\n",
+              "      <th>region</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>17654</th>\n",
+              "      <td>2018</td>\n",
+              "      <td>65622</td>\n",
+              "      <td>81.49</td>\n",
+              "      <td>4803748</td>\n",
+              "      <td>Ireland</td>\n",
+              "      <td>europe_central_asia</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>15902</th>\n",
+              "      <td>2018</td>\n",
+              "      <td>7739</td>\n",
+              "      <td>68.15</td>\n",
+              "      <td>782225</td>\n",
+              "      <td>Guyana</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5349</th>\n",
+              "      <td>2018</td>\n",
+              "      <td>14341</td>\n",
+              "      <td>75.70</td>\n",
+              "      <td>210867954</td>\n",
+              "      <td>Brazil</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10652</th>\n",
+              "      <td>2018</td>\n",
+              "      <td>15227</td>\n",
+              "      <td>76.11</td>\n",
+              "      <td>10882996</td>\n",
+              "      <td>Dominican Republic</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>39380</th>\n",
+              "      <td>2018</td>\n",
+              "      <td>21254</td>\n",
+              "      <td>77.57</td>\n",
+              "      <td>3469551</td>\n",
+              "      <td>Uruguay</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6006</th>\n",
+              "      <td>2018</td>\n",
+              "      <td>9929</td>\n",
+              "      <td>74.83</td>\n",
+              "      <td>817054</td>\n",
+              "      <td>Bhutan</td>\n",
+              "      <td>south_asia</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>22691</th>\n",
+              "      <td>2018</td>\n",
+              "      <td>30368</td>\n",
+              "      <td>75.31</td>\n",
+              "      <td>2876475</td>\n",
+              "      <td>Lithuania</td>\n",
+              "      <td>europe_central_asia</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>14156</th>\n",
+              "      <td>2018</td>\n",
+              "      <td>1282</td>\n",
+              "      <td>61.90</td>\n",
+              "      <td>13052608</td>\n",
+              "      <td>Guinea</td>\n",
+              "      <td>sub_saharan_africa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>23786</th>\n",
+              "      <td>2018</td>\n",
+              "      <td>5330</td>\n",
+              "      <td>72.41</td>\n",
+              "      <td>4041065</td>\n",
+              "      <td>Moldova</td>\n",
+              "      <td>europe_central_asia</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1798</th>\n",
+              "      <td>2018</td>\n",
+              "      <td>21035</td>\n",
+              "      <td>77.60</td>\n",
+              "      <td>103050</td>\n",
+              "      <td>Antigua and Barbuda</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       year  income  lifespan  population              country  \\\n",
+              "17654  2018   65622     81.49     4803748              Ireland   \n",
+              "15902  2018    7739     68.15      782225               Guyana   \n",
+              "5349   2018   14341     75.70   210867954               Brazil   \n",
+              "10652  2018   15227     76.11    10882996   Dominican Republic   \n",
+              "39380  2018   21254     77.57     3469551              Uruguay   \n",
+              "6006   2018    9929     74.83      817054               Bhutan   \n",
+              "22691  2018   30368     75.31     2876475            Lithuania   \n",
+              "14156  2018    1282     61.90    13052608               Guinea   \n",
+              "23786  2018    5330     72.41     4041065              Moldova   \n",
+              "1798   2018   21035     77.60      103050  Antigua and Barbuda   \n",
+              "\n",
+              "                    region  \n",
+              "17654  europe_central_asia  \n",
+              "15902              america  \n",
+              "5349               america  \n",
+              "10652              america  \n",
+              "39380              america  \n",
+              "6006            south_asia  \n",
+              "22691  europe_central_asia  \n",
+              "14156   sub_saharan_africa  \n",
+              "23786  europe_central_asia  \n",
+              "1798               america  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 78
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "nXTGQNi7gv6L",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "import seaborn as sns"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "dbn1x_95g4zh",
+        "colab_type": "code",
+        "outputId": "a3787070-56cf-45e3-f768-0fb946daddf2",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 369
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "sns.relplot(x='income', y='lifespan', hue='region', size='population', \n",
+        "            sizes=(5, 200), data=this_year);"
+      ],
+      "execution_count": 80,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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7M0o/Q1UrRCQbmC0ij+FUg3pNVS8WkSeAK3AGVVOA+4CncUqtVqvqzm6q2HdE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\nKKCLiIgkwP8H/tBCpRdd1xEAAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7fdd0e0287b8>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "XAww6V5DmWsU",
+        "colab_type": "code",
+        "outputId": "0c0892f5-3f73-457b-e2a3-90024d55d5ff",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 166
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "this_year[this_year.income > 80000]"
+      ],
+      "execution_count": 81,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>year</th>\n",
+              "      <th>income</th>\n",
+              "      <th>lifespan</th>\n",
+              "      <th>population</th>\n",
+              "      <th>country</th>\n",
+              "      <th>region</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>22910</th>\n",
+              "      <td>2018</td>\n",
+              "      <td>99034</td>\n",
+              "      <td>82.39</td>\n",
+              "      <td>590321</td>\n",
+              "      <td>Luxembourg</td>\n",
+              "      <td>europe_central_asia</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>23348</th>\n",
+              "      <td>2018</td>\n",
+              "      <td>97057</td>\n",
+              "      <td>83.24</td>\n",
+              "      <td>632418</td>\n",
+              "      <td>Macao, China</td>\n",
+              "      <td>east_asia_pacific</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>31498</th>\n",
+              "      <td>2018</td>\n",
+              "      <td>121033</td>\n",
+              "      <td>80.71</td>\n",
+              "      <td>2694849</td>\n",
+              "      <td>Qatar</td>\n",
+              "      <td>middle_east_north_africa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>33031</th>\n",
+              "      <td>2018</td>\n",
+              "      <td>83888</td>\n",
+              "      <td>84.03</td>\n",
+              "      <td>5791901</td>\n",
+              "      <td>Singapore</td>\n",
+              "      <td>east_asia_pacific</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       year  income  lifespan  population       country  \\\n",
+              "22910  2018   99034     82.39      590321    Luxembourg   \n",
+              "23348  2018   97057     83.24      632418  Macao, China   \n",
+              "31498  2018  121033     80.71     2694849         Qatar   \n",
+              "33031  2018   83888     84.03     5791901     Singapore   \n",
+              "\n",
+              "                         region  \n",
+              "22910       europe_central_asia  \n",
+              "23348         east_asia_pacific  \n",
+              "31498  middle_east_north_africa  \n",
+              "33031         east_asia_pacific  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 81
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "2QTzH_9tm904",
+        "colab_type": "code",
+        "outputId": "3f0857e8-17ca-4a10-893d-9bffccb2a473",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1024
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "entities[entities.name=='Macao, China'].T"
+      ],
+      "execution_count": 82,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>145</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>country</th>\n",
+              "      <td>mac</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>alt_5</th>\n",
+              "      <td>MACAU SPECIAL ADMINISTRATIVE REGION OF CHINA</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>alternative_1</th>\n",
+              "      <td>Macau</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>alternative_2</th>\n",
+              "      <td>Macao</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>alternative_3</th>\n",
+              "      <td>China, Macao SAR</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>alternative_4_cdiac</th>\n",
+              "      <td>Macau</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>arb1</th>\n",
+              "      <td>Macao SAR, China</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>arb2</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>arb3</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>arb4</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>arb5</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>arb6</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>g77_and_oecd_countries</th>\n",
+              "      <td>others</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>gapminder_list</th>\n",
+              "      <td>Macao, China</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>god_id</th>\n",
+              "      <td>MO</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>gwid</th>\n",
+              "      <td>i130</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>income_groups</th>\n",
+              "      <td>high_income</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>is--country</th>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>iso3166_1_alpha2</th>\n",
+              "      <td>MO</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>iso3166_1_alpha3</th>\n",
+              "      <td>MAC</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>iso3166_1_numeric</th>\n",
+              "      <td>446</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>iso3166_2</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>landlocked</th>\n",
+              "      <td>coastline</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>latitude</th>\n",
+              "      <td>22.2006</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>longitude</th>\n",
+              "      <td>113.546</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>main_religion_2008</th>\n",
+              "      <td>eastern_religions</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>name</th>\n",
+              "      <td>Macao, China</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>pandg</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>un_state</th>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>unicode_region_subtag</th>\n",
+              "      <td>MO</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>upper_case_name</th>\n",
+              "      <td>MACAU</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>world_4region</th>\n",
+              "      <td>asia</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>world_6region</th>\n",
+              "      <td>east_asia_pacific</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                                                                 145\n",
+              "country                                                          mac\n",
+              "alt_5                   MACAU SPECIAL ADMINISTRATIVE REGION OF CHINA\n",
+              "alternative_1                                                  Macau\n",
+              "alternative_2                                                  Macao\n",
+              "alternative_3                                       China, Macao SAR\n",
+              "alternative_4_cdiac                                            Macau\n",
+              "arb1                                                Macao SAR, China\n",
+              "arb2                                                             NaN\n",
+              "arb3                                                             NaN\n",
+              "arb4                                                             NaN\n",
+              "arb5                                                             NaN\n",
+              "arb6                                                             NaN\n",
+              "g77_and_oecd_countries                                        others\n",
+              "gapminder_list                                          Macao, China\n",
+              "god_id                                                            MO\n",
+              "gwid                                                            i130\n",
+              "income_groups                                            high_income\n",
+              "is--country                                                     True\n",
+              "iso3166_1_alpha2                                                  MO\n",
+              "iso3166_1_alpha3                                                 MAC\n",
+              "iso3166_1_numeric                                                446\n",
+              "iso3166_2                                                        NaN\n",
+              "landlocked                                                 coastline\n",
+              "latitude                                                     22.2006\n",
+              "longitude                                                    113.546\n",
+              "main_religion_2008                                 eastern_religions\n",
+              "name                                                    Macao, China\n",
+              "pandg                                                            NaN\n",
+              "un_state                                                       False\n",
+              "unicode_region_subtag                                             MO\n",
+              "upper_case_name                                                MACAU\n",
+              "world_4region                                                   asia\n",
+              "world_6region                                      east_asia_pacific"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 82
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "73I-YCgCnpAJ",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "qatar = this_year[this_year.country=='Qatar']"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "adPr6szPn1tY",
+        "colab_type": "code",
+        "outputId": "0d364a04-e764-4956-f392-d63c2dcae3a0",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 50
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "qatar.income"
+      ],
+      "execution_count": 84,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "31498    121033\n",
+              "Name: income, dtype: int64"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 84
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "vyamBGBUn_hd",
+        "colab_type": "code",
+        "outputId": "50e0d0e6-dc46-4f6d-d06c-4f60d6c2c053",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "qatar.income.values"
+      ],
+      "execution_count": 85,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([121033])"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 85
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "rRWGyuqzoBRK",
+        "colab_type": "code",
+        "outputId": "50ddadcb-d90f-4af4-a806-a47d3f598e88",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "type(qatar.income.values)"
+      ],
+      "execution_count": 86,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "numpy.ndarray"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 86
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "yG88fJ-CoIQ8",
+        "colab_type": "code",
+        "outputId": "347be5cb-8570-447d-8257-f18ca23cd264",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "type(qatar.income.values[0])"
+      ],
+      "execution_count": 87,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "numpy.int64"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 87
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "a2X7q4ucoJQV",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "qatar_income = qatar.income.values[0]\n",
+        "qatar_lifespan = qatar.lifespan.values[0]"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "wv39rkyRoQ3c",
+        "colab_type": "code",
+        "outputId": "c6df82e7-9e0b-4550-e050-c68131ce14ee",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 382
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "sns.relplot(x='income', y='lifespan', hue='region', size='population', \n",
+        "          data=this_year)\n",
+        "\n",
+        "plt.text(x=qatar_income-5000, y=qatar_lifespan+1, s='Qatar')\n",
+        "\n",
+        "plt.title('Qatar has the highest incomes in 2018');"
+      ],
+      "execution_count": 89,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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Kq5a/5YeVzLrvTmLRKJOu/B0jjp2MQ9/f7lAqSooJBfw43Yl4Ovmz/o3QMe5HaV2WbqF3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\nxhhjTBywgG6MMcbEgf8HfNGCjdsVDBEAAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7fdd0e20fb00>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "Fh42sjX7pViu",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "#plot multiyears"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "KRkoteYfpSYh",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "framing_years = [1818, 1918, 2018]\n",
+        "\n",
+        "centuries = df1[df1.year.isin(framing_years)]"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "NBfTFlJcpgdh",
+        "colab_type": "code",
+        "outputId": "a7798e73-12dc-418c-d705-180ffa475136",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 212
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "centuries.sample(5)"
+      ],
+      "execution_count": 91,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>year</th>\n",
+              "      <th>income</th>\n",
+              "      <th>lifespan</th>\n",
+              "      <th>population</th>\n",
+              "      <th>country</th>\n",
+              "      <th>region</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>10233</th>\n",
+              "      <td>1818</td>\n",
+              "      <td>2080</td>\n",
+              "      <td>46.38</td>\n",
+              "      <td>1161500</td>\n",
+              "      <td>Denmark</td>\n",
+              "      <td>europe_central_asia</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>40037</th>\n",
+              "      <td>2018</td>\n",
+              "      <td>11362</td>\n",
+              "      <td>72.03</td>\n",
+              "      <td>110200</td>\n",
+              "      <td>St. Vincent and the Grenadines</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6125</th>\n",
+              "      <td>1918</td>\n",
+              "      <td>648</td>\n",
+              "      <td>11.08</td>\n",
+              "      <td>146397</td>\n",
+              "      <td>Botswana</td>\n",
+              "      <td>sub_saharan_africa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3012</th>\n",
+              "      <td>1918</td>\n",
+              "      <td>971</td>\n",
+              "      <td>12.65</td>\n",
+              "      <td>1656525</td>\n",
+              "      <td>Benin</td>\n",
+              "      <td>sub_saharan_africa</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2036</th>\n",
+              "      <td>1818</td>\n",
+              "      <td>1986</td>\n",
+              "      <td>34.40</td>\n",
+              "      <td>3366878</td>\n",
+              "      <td>Austria</td>\n",
+              "      <td>europe_central_asia</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       year  income  lifespan  population                         country  \\\n",
+              "10233  1818    2080     46.38     1161500                         Denmark   \n",
+              "40037  2018   11362     72.03      110200  St. Vincent and the Grenadines   \n",
+              "6125   1918     648     11.08      146397                        Botswana   \n",
+              "3012   1918     971     12.65     1656525                           Benin   \n",
+              "2036   1818    1986     34.40     3366878                         Austria   \n",
+              "\n",
+              "                    region  \n",
+              "10233  europe_central_asia  \n",
+              "40037              america  \n",
+              "6125    sub_saharan_africa  \n",
+              "3012    sub_saharan_africa  \n",
+              "2036   europe_central_asia  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 91
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "4QPRRQjfplIW",
+        "colab_type": "code",
+        "outputId": "0ed09000-6a07-414e-9adf-79c68e3457cc",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 393
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "sns.relplot(x='income', y='lifespan', hue='region', size='population', \n",
+        "            col='year', data=centuries)\n",
+        "\n",
+        "plt.xscale('log');"
+      ],
+      "execution_count": 92,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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KAL12ruDwW98ha+Hf0+bzwpk7UXvA0QTv/Ncag2jb9i3gzXP3JJLMUJbvJeDt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\nERERERERkS4oiSYiIiIiIiIiItKF/w+tFVXXduBccQAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7fdd0fc91358>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "RCAx6_mXrXlJ",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## are country counts consistent?"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "b9-orbM-rWpG",
+        "colab_type": "code",
+        "outputId": "b887d124-3e64-4637-dff6-cd1bfd417d12",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 101
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "centuries.groupby('year').country.count()"
+      ],
+      "execution_count": 93,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "year\n",
+              "1818    190\n",
+              "1918    190\n",
+              "2018    188\n",
+              "Name: country, dtype: int64"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 93
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "NRJlh_TErjpT",
+        "colab_type": "code",
+        "outputId": "39bad013-32c3-4d85-f823-edd2c49ff6c2",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 84
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "years_per_country = centuries.groupby('country').year.count()\n",
+        "years_per_country[years_per_country < 3]"
+      ],
+      "execution_count": 94,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "country\n",
+              "Greenland    2\n",
+              "Taiwan       2\n",
+              "Name: year, dtype: int64"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 94
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "T4YRATMhsEeQ",
+        "colab_type": "code",
+        "outputId": "5de3e5c1-de72-478c-ec8c-8c83328ceea1",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 393
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "years = [1918, 1938, 1958, 1978, 1998, 2018]\n",
+        "\n",
+        "years_subset = df1[df1.year.isin(years)]\n",
+        "\n",
+        "sns.relplot(x='income', y='lifespan', hue='region', size='population', \n",
+        "            col='year', data=years_subset)\n",
+        "\n",
+        "plt.xscale('log')\n",
+        "plt.xlim(150, 1500000);"
+      ],
+      "execution_count": 95,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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SPHfv/xFqaODocy+mqLGJHg//mUw0StODD3DIkONZFslQWV1NfOFCAJyVlaQbG7F367bR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\nc8+DZZF36KGkjY10MoXHmf0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C/7N331FSlfcfx9+3TK87s73DAguCSBUQBRERUYMNGwRL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/h8TXB7RIvwTd43/GvFv+jk6cQdw+/gy91f0rZ7F6jOGJfdo/d+swYD+83GfqhS7am8furrfJP/\nDQMyBpBibd37TUUouC21z4ndFjeqkM8kJEmSjjQyaEiSJEmSpENityhcNrozT32xla4ZLga2r10B\no5WWoodCKA4H1uN/R+CaIfxvcwVHt3HiMttp6jpAU2oqWXfcgRGPo3q9CCFa52IkSZJaWa8sD6f2\nbcOqneUM7JBCdraXK1/7lnhC54lJA2uy/5hSU2n78EMYiQTCYsEIh/FPm4a1SxccAwfWa3fP5toM\nHYU7KjGjcPMpvYhqCZxWEw6L/OonSdL+OVP8TLr7QT77z5PkbdqIL6MNIyf9CZc/lX4nnEyf0b/D\n3IqlX1wpfk654m8Yuo7d7aEkXMIdS+6gJFJCSaSE2Rtmc/UxVzd47N5lb4Ta/EmIVJeVB8/rh5bQ\nsZpUPPbkmGw1qxzbJZWF141BEYI0d90sHYmyMnL/ei2xbdsBKJw5k+z77kOx2/c9RaMsdjlOS5LU\nMFU14c/OwZ+dU/OeEShkyPKXUEu2MGTcP/hIK9nvJKzX6mVCtwmc0eUMVGX/4+Se0B5+2L2WDtYc\ngrYKrCk2fhrNtISOVQ+z7IqeGPEI+TEL4XiiXtAQuo5eFajdrKrCMAzkN3lJklqTbujkBfJ4d/O7\ndPV1ZWjWUHz7lGysiFawunA12yu3c3Knk+tlE9ofu9lOSbikZjsvkEfCSNRsV8Yq+evnf0UzNHZX\n7ebdLe+ysmAl4zKO46Rbb8GhW2jTsStm64EzvrUWRSi0dbflHPc5P8v5PFYPdw6/k38t/xcJI8GN\nQ26s928iSZIkHX7yiYQkSZIkSYfEa7fw5zFdmTKiE6oiSHMlv/hqpaXk/eMWggsXYurUCecrbzLh\noUVEtWTJh0d+b+esAU1fqa46G14xKUmS1FwlgShRTcdqUkh1texDu4RuUBKI1pS5+XBNPsd2TSXb\na2fGxL5ENR23zYTdbOI/fxqEATXj50/qlFjwesm8/rpGz9d3bDs2ryokEtQYfnZXrA4TDpMCyBJk\nkiQ1jclsIaNjZ8664Vb0RAIQOPYK1lZsrb8ieO8sRhbVQkdPR7aUbwGgW0q3Bo9R/X7az3qBokcf\nw9ynL5UduhELxvA3Ui7nQFIcDR+330xtQiD2ysKhOF3QwuUtJUmS9ibWvYP6w/sA+OZcynlXLMdk\nPvB35pqAoXA5VOXD2jeT5cyOPg+cGWBzEwlUoS36kQ8WPIXV6WTyvQ+DPZn9x2TEGW/5HtN/poJh\nkN7rTMh5CKhbRlF1u2lzx+3k3/wPhMVC2jXXoJjlfakkSa2rJFzC5I8mUxopBeCO4XcwsdvEOvus\nLFjJ1QuTgejvb3mfZ094ttEyuABhLYyu6zgtTpxmJ8d3OJ652+eytWIrtwy7pU4WHYHArJrRNA0A\nh+rAJEzcufpe0u3pvHLKK9hdrZdF6EiV6czk7pF3g5EMvJIk6bdBCPG1YRjDD3c/pKaRQUOSJEmS\nJB0yr92M1173AaAeCBBcuDC5EQiwI7+sJmAI4Isfizi9XxaqnFCRJOkwKA5EufLVVXyztZQhnVJ4\ncvIx9YJ2DsXushBnPvEV5aE4l43ugm4YTHzya967YiSpLguZntpsHc0JWApENaLxBG6bCYtJxZNm\n47y/D8YArHYTqkmOrZIkNU9rlkM4GG6Lm38O+yejc0aTZk+rU05tb0JV0Tt1Yc2Uv/LppjLee2ol\nN57Uk+mju/xsfTWlppLz2KMU3HMPittDxl+vRTmMK8glSfoNsO01VlucmJT6j/nDVZWU5u3GZLbg\nycjE7qqe2I5UwqoX4duXIFAIkXJYdB+c9TQcdQbpljQ+/HIRANFgkNwN60nJzK451vTlfWAYACgb\n3iN24sPEK6IIReBw1wZeivR0nDffyPa1q1n85IOcfvUNuFNlOTFJklpPLBGrCRgCWFWwijO7nFkn\nw9qPZT/WvN5RuaNOpqB9FYeLeXDFg1REK7h56M3kuHPIcGTw8NiH0XQNt8WNzZT8bh/VopgUE/85\n8T88tuoxOrk6MNRxNAN79qVvWl+ObXssXmvTyy/+2thNMlhIkpqj400fTgJmAO2BncDN22ee+urh\n7dX+CSFMhmFoMmDol0U+SZYkSZIkqVUImw2lOjuQVlJCp0wPPkcysEgImDiwrQwYkiTpsAlGNb7Z\nmnyYuGxbGVURrc7nRiKBVlpGIhBo6PADWrChkPJQHIDXlu9kYIcUNN2gOBDlk+/3HFLfS4MxZn70\nA5P/bylfbiomHNcQQuDwWnF6rZj2LQ/RDJqu19kOVUQp3lVFsCJ6yG1LkiQ1lUdxMT5jLEdbumON\nNX7fGI3r/Hf5Ht5ZuwfdgC1FARL7jGOtzZKTQ/YDD5B15x2Y0uWkuCRJrazbCTD2ZjjqLPjTB+Co\nO+5osRirPnqX12+9gZf/fg1bViyt+SwYilLS8UwKz32L3Is+JNL3nGQQ0AfXQKQSq8VGt6HJOR6z\n1UbbHkfVNqxawNexZjM28lY2rAry4s1f8/6/V9e5V4wGA7z54D188dYr5G/8gY3ffNU6PwtJkqRq\nDrODkdkjAbCqVib1mlSvJONZXc+ig6cDJsXELcNuwWVuOAu6buj897v/8sHWD/gy90tu+vImyiJl\nAKTYUkh3pNcEDFVGK3lnyztMnz+dUCzEdTnTGbjBx/u3/JPNb33ElO4X0DetLw6zoxWvXpKkX5vq\ngKHngA6AqP77uer3D4kQ4h0hxEohxDohxKXV7wWEEPdXv/epEGKIEOJzIcRWIcQZ1fuo1fssF0Ks\nFUJMr35/jBDiSyHEe8D6n9rb63w3CiG+E0KsEULMrH7vkup21ggh3hZCyEHyMJKZhiRJkiRJahWm\nlBQ6vjGb8rfexj5wADaL4KOrjuPbnWV0zXCT7bMduBFJkqRmCJSVUrB1M6k57XClpGKy1C81Yzer\nZHlt5FdEyHBbcVprHyQaiQSRDRvZc9ttmDt0oM3Nf8eUmnpQfTi2cypmVRBPGIzunk5+eZgJA9oS\njGl0ST+0cosb8it5eelOAP788koW3zgOu7llvtoZhsH2khCPLdhEvxwvZ/Rri0UzmHP/SiqLI7hS\nrJxz0yCcXplBQ5KklhFPxKmKV2FVrTj3Ka1TmreLV/9xHXpCY9BpExh29h+wOuo/R/Tazdx1Zm/+\n/MpKnBYTV47rdliC038qp2sYBoGyKPlbyslo78GV0jIBnZIkSTUcqXDc9aDHwFT/u3U8FmXX+u9r\ntnes/ZZex40lUlXJ2zPvpGjHNrwZmYy84SoSwy+n/ab5EKmA8p3Y2w1h7J8u5dizJ2Gx2bB79sqM\nYffC6f+G+V4IFRHvfzGLb1mBYUDx7gA715XQa3gyK5FqMpHRsTPb16wCILNz19b9mUiS9JtVGi5l\nd2A3GfYM7hp5F8F4EJvJhs/qq7dvG2cbZp00C93QcZqddcplVcWqiCai6LpOwkhwepfTWZS7iB2V\nO/Z7/spYJXcvuRuAyz67jAWnfUKhcz29jhvL8HMnYbE3bR5c1xOEKsqpKi7Gk56B05dCYaiQF75/\ngXRHOmd2PRO/LVlKrSJawZbyLQTiAfqm9SXFltLUH5ckSb8cM4B9BxBH9fuHmm3oIsMwSoUQdmC5\nEOJtwAksMAzjeiHE/4C7gROAo4BZwHvANKDCMIzBQggr8JUQYl51mwOBPoZhbNv7REKIk4EzgaGG\nYYSEED/VhJxjGMZz1fvcXd32Y4d4XVIzyaAhSZIkSZJahTCbsXbpQuaNN9S8lw1k+2Q6WkmSWk+w\nvJw37riJsvw8VJOJqY88izc9o95+GR4b7/5lBLvLw+T47KTvVSJMKy0l96oriefmEfn+e5yDBpHy\nh/MPqh8dUh18cf1YKsJx0lwW4ppOn7ZeErpBzzbuQ7pGr6O2HKTbZkYRh9RcHcWBGJOfW0JeRYQ5\nq3LplOain89JZXEEgEBZlHgkAb/drOqSJLWgiBZh+Z7lPLLqEY5OO5qrBl5VZ8Jjy8pl6IlkJrgf\nl37FoNMnNBg0pCgCa2GUR8f1Qo/rFC4rJGNMNlZb/aDRn0OoMsZbM1cQqoyhqILJdw7DkyrvgSVJ\namGKAkrDi3GsdgfDz53EnHtvQzWbGXzmOaiqSjwapWhHch6norAAEUmwTdtGe2d6MmjIlrzJc3i8\nODyN3PC5M+H0R0DXEFEL7lQ7lcVhAHyZtWO03e3h5L/8ldyNP+BJT8ebkdWCFy9JkpRUFinjhkU3\nsHTPUkyKibdOf4suvv2XqU21118UVBmt5LUNr/H0mqc5KvUorht8Hbd/dTsPj3mYx1c/zo2Db2w0\nMEcVKgKBgcGpnU8lZNY46oxT8Vm8KGrTA8dDFRXMuu4KIoEqfJlZnHv7vfxtyd9YXbQagLge59Kj\nL8UwDOZun8tdS+4CktmTbhh8A27LoT1rkCTpiNP+IN8/GFcJISZUv24HdANiwCfV730HRA3DiAsh\nvgM6Vr8/HjhaCHFO9bZ3r2OX7RswVO13wPOGYYQADMP4qY5kn+pgIR/gAua2wHVJzSSDhiRJkiRJ\nkiRJ+tXQExpl+XkAJDSNyqKCBoOGIBk4lOGpP9EiFAXF7QGS7SgeN1pFBSZv0yNl7BYTdoupTqBk\ndkrLZNnNSXHw7IXH8M2WEi48tgOpzpbL+mMYBuF4omY7FNMw20y06eJlz5YK0tu7sdhltgxJklpG\nZaySqxZehaZr/Fj2I6NyRjG2/diaz3sMG8mK9+YQj0bod8IpmK0NB97oCZ1tq4v4cWkBAFldvXQY\n5m00aEiPRBAmE8LUOo/FEnGdUGWsum8GwfJoqwcNRQJxouE4JrOKzW1GVWUZYEn6pYlHo0QCVSQ0\nDavTid3V/MlfRVXJ7t6Lix//L0BNtiCzzUZm564UbN2Mr002wm6hl9oZKnZBek+wNzFTRXVmDocV\nJvxtAFu+LSItx4U/q27GOIfXR7chxzb7OiRJkg4koSdYtmcZAJqusbZo7QGDhhoS0kI8vvpxANYW\nr2VL+RYMYVAcLubekffut7SYx+Lh0XGPsrV8Kz39PSmLlLGzaif90/vTxtkGIZq20idYVkokUAVA\neUE+WjxGcbi45vO8QB4JPUHCSLA0v7bs5LeF3xJLxA76miVJOuLtJFmSrKH3m00IMYZkIM+x1Zl/\nPgdsQNwwDKN6Nx2IAhiGoQshfvryLIArDcOY20CbwYPsygvAWYZhrBFCTAHGHOy1SC2nVYOGhBA+\n4P+APoABXARsBGaTjEjbDpxnGEZZa/ZDkiRJkqTDRyspQY9EUKxWTGlph7s7kiT9ypmtVgaffjbL\n33+bzC7d8bdtd9BtmFJTaffkExQ/939YOnQAIdh18cW0e+qpOuOYVlZGaPkK0BM4hg3DiEaJ7dqF\npUMHzOnpLXlZdXjtZsb3bsP43m1avO0Up4UXLxrK/XM30CnNxZBOqTicFk6e3hctnsBkVnF4Dk/m\nDkmSfn0EAptqI2SEyHZm47F46nzua5PNRY88k5xAdziw2BsOvFFUhYEntWfPlgoGntsVc4aVsCGI\nJRJY9lrdbRgG8Z07KXzwQSydu+D/44WY/P4G2zwUZptK98GZbFpRwOkXdcZTuZ3wWgVz+w6YfC2f\nqi0airPsg2189/luzDaV824ejC+jZQJVJUn6+ezZsom37r4FPaEx7OzzGXTaBKyO+mVtS8IllERK\nSLGm4Lf5UZWGA7pNFgsuS90xzun1cdYNtxILB1EsJvTILvxv/wXaHgMTngVXw8H2++NKsdFv3MHf\nc0uSJLUEi2rh9z1+z+sbX8dv8zM0a2iz2lGFSqotlZJICQA5rhzC8TDtPe0bDBiKaBGsqhUhBE6L\nk9E5o+nl70VxuJhL519K37S+dPR0pDJWSZo9rcHsRvty+VPxt21Hae4u2vfuh9VqZ8bIGVy/6Hp8\nVh/Tj56OqqioqEztM5VFuxcRTUSZfvR0XGZXs65bkqQj2s3Ac9QtURaqfv9QeIGy6oChnsCwgzh2\nLnCZEGJBdRai7kDuAY6ZD9wqhHjlp/Jk1dmG3EC+EMIMTG5CO1IrErUBY63QuBCzgC8Nw/g/IYSF\n5H/UNwOlhmHMFELcBKQYhnHj/toZNGiQsWLFilbrpyRJ0q9MixQpkWPvL1OiqopEZSVCVVG9XpRG\nJgOsnK8AACAASURBVFZ+LlpJCbnXXkto2XKsPXvS/v+ek4FD0q+VHHuPIJFgAC0WQ1EUHF5fs9vR\nKirIv/EmAl98AYZBzrPP4B41CgBD0yh++mmKH38CgM4ffsCOyReQKC/HlJVFxzdmNzlwSA+FiBcU\nEt+9G9tRvTCl1n+YWB6KsXx7KRv3BDjnmLa08bbO+F4eipFbHkYRggy3lVRXy2UxOlKEKsopyd2F\nOzUdpy8Fs/XXd42/IXLs/SULlaIDO+OVGKEY0bxi0tu0w52ahsXW+BinJ3QiwThCEdhdtUGMVdEq\nyqri3D9/G++v2YPDovLR1cfRMbV2wl0rKmbHhRcS274dgKyZ9+I766xWubxwIAaaRvCNlyl66GEA\nMm/+OymTJyMOokxFUwQrorxy25Jk+Uhg9KTu9BmV06LnkKR9HPL4K8feurRYjI8ee4BNy74GwOp0\nMuXBp3Cl1A36KQmX8JfP/sK6knV4rV7mnDGHDMfBB/oAECyGeAiEAmYHOFo+iFKSpBYlx95GlEfK\nCcaDWFQLqfZUFHHwGRcNwyAvmMf87fPpm94XszCTYkshw5GB1VT7nTGiRVhXso6X1r/EuHbjGNNu\nDB6rp6YfW8q3cNXCq3jy+Ce5ZP4lhLUwx2Qew0NjHsJvO/A4GywvQ4vFMFutOLw+4ok45dFyFKHU\nCTyKJ+KURcvQDR23xY3TXD/IVJKkFtEizx2aq+NNH04CZpAsSbYTuHn7zFNfPZQ2hRBW4B2SCV42\nkiwPdjvwgWEYrup9bgcChmE8UL0dMAzDJYRQgLuB00n+bIqAs4ABwHWGYZy213kCe7V3E/BHkmXM\nPjIM42YhxGXADdVtLAXchmFMOZRrk5qv1TINCSG8wChgCoBhGDEgJoQ4k9r0UrOAz4H9Bg1JkiRJ\nknRgeixG1bx55P/jFjCZaP/f/+AcMqRZbWklpRjxGMJixeRvYnryhvoUDBJathyA6IYNaKWlDQYN\n6aEQiWAQoSgNTpZLkiQdDJvTBQd4XhbTEuRVRPg+t4JjOqTQxmOrnzJc04jt3AmGAaqKtX1tyXAj\nHifyw4bkhqqSqKggUV6ePCw/HyMSaXJ/Y7t2s23CBNB1vI8/QOnR7QkkgvRI6UGKLTkGr8ur5JIX\nVwLw0Xf5vDRtSIsH9MQ0ndeX72Lmx8nrmjmxL+cNaoeiHNbnIy0qXFXJh489wM7vViMUhT/+63HS\n2rVEKXhJkprMMKBkE7x3JYrJTsZpT/Phc8+xc90ahFC4YOYjZHTs3OChiYRO8c4q5j+/HofbwvhL\n+uDyJcdCt9VNIBLkk++TJcpCsQSrdpTVCRpCgJGoLcGIprXaZdpdFhKBGEUrV+IaOwZhsRJaswbv\nOeegOlo2C5BqVug2KIP1i/MxWRTa9mj+/bskSYeHajbTsf/AmqChnJ69UU3mevvF9TjrStYBUBGt\nYHfV7mYFDUW1KBGTGac9G5PSqsUIfjZlkTIMwyDFltLkUkCHer75O+YTjAc5o8sZTcoiIklS6/DZ\nfPhszV8wBCCEoK2rLVP6TNnvfhXRCi6ZdwlxPc5nOz9j9mmzyTKySLGl4LP5yHHnMDx7OOtL1xPW\nwgCsLFhJPBFvUj+cvrr3cWbVTLqj/oIks2om3Z5OSaSEcDyMVbX+asZzSZJqVQcIHVKQ0L4Mw4gC\nJzfwkWuvfW7f5xhX9d86yQQx+2Y7+rz6T71jql/PBGbu8/lTwFMH2X2plbTmb5BOJCPDnhdC9ANW\nAlcDmYZh5FfvswfIbOhgIcSlwKUA7dvLh7iSJEk/Bzn2/rLpoRDlb76V3NA0yt98C8fAgQjTwf26\n10pK2HX5X4isWYPr+ONpc9utAAhVPeiAHmG3Y8rORsvLQ01JweSr/wVeD4ep+uwz8m/5J5aOHWn3\n3LOYM5q5UlKSfoHk2Ns69EgEPRJBdbvrZ3RIaOiBItZtKmHGwgI03eCDK0eS4bHV2c2UmkrOSy8S\nLSzA4vVhSql9eKfY7WRcew2R777DMnwEIqsttgH9iXy7GueoUQhz/UmexkQ3bABdxzFsGKvSA/z9\n40kATO41masHXI3dbCe3PFyzf35FmITe8hljw7EECzcU1mx/tqGQM/tnY7f8eh486ppG7obkRJuh\n6xRu2yyDhn6j5Nh7GAUL4bXzoWQLAHp5LvmbNwJgGDqF27c2GjQUDcSZ95/1VBaHqSgM8+28HRx3\nXveazx1mM+cNascrS3fitZsZ3LHuam7V76fdU09ScO+9WDp1xjVu3AG7G4gFiCaieCwezGrTx3YA\nxeHAce+tvLflPcq1Ki44anKLBwwB2Bxmhp3VhQHjO2CyqNhdB9dPSfq5yLG3cUIIug8dQWpOB8JV\nlWR364Hd7a63n1W1MrbdWBbuWkiOK4f2nqb9HEOVFZTvycPu8aI4bby+9U2+zvuaaX2nMThzMHbz\n4c1SfKjyAnncsOgGookoM4+bSWdv51YNHDIMg9kbZ/PE6mTW0U3lm7hl6C0NljCSpMNNjr0ty8Ag\nYdQGoVdEK1hZsJILj7oQgExnJn8f8neCWhCPxUNlrJLj2h6HRW35Mt87Kncwff50ookoTxz/BL1S\nezUry5IkSZIkteZvDxMwEHjKMIwBQBC4ae8djGRttAafdhuG8axhGIMMwxiU3sS0/pIkSdKhkWPv\nL5vicOA544zqDQXvWWc2GDCUqKoiXliIVlLSYDtaSQmRNWsACHz2GVpxMZtHjWbnxZegFRdjJBLE\ncnOpeP8DYjt3occbXyljTk+n4+zX6fDqK3R69x3UBrIMJQIB9tx5F0Y0SnTjxmQZIEn6DZFjb8vT\nysoofvJJdl92OaHly9Gj0doPExrkfYvtxVM4df11vDm5E1FNJ5bQ67UTDQXZ/P1qPpo9i43r1xDT\nE3U+t3TuTPY777J9ytVMeW8rjhn30+n99/Cccgo7L76EeH5+vTYb4hg2FHP79pjaZLIisL7m/dWF\nq4kkkhmLxvXIYEyPdNr57Tw+aSBee9MnhEOVUYIVUeKxxH73c1lV/jy6C6oisKgKF4/s9KsKGAIw\nWa0ce/YfAPCkZ9Cu99GHuUfS4SLH3sPIMCBem43Nkr+CkedfCELgzWxDh779Gz1UKAK7u3b8+ynL\n0E+8DgvXje/BouvHMO/aUWT77Pscr2Dt2pW2//436ddfh8m//xIRZZEyZi6bySXzLmHZnmVEteh+\n99+XAby6/W0eXv8kz//4EjcvuY2KSMVBtdFUdpcFX4YDl8+KapKTRdKRSY69+2dzuWnboxddBw3F\n4fVhGAbB8jICZaVo8RgAKbYUbh9+O59M/ISXTnmJNHvj5b9LwiUs2LmATWWb2LhmGa/983r+e810\nKnbn8vH2j1lVuIqrFlxFZazy57rEVhGMB7ln6T2sKVrDhtIN3LDoBkojpa16zoSRIDeQW7OdH8gn\nrjcti4gk/dzk2NuyPBYPD45+kAEZA5h+9HTyAnnkVuWSnO5M8tv9ZDuz+d+Z/+PDCR9y98i7a7II\nt5SIFuGRVY+QF8yjJFLCjKUzqIz+ssdzSZIk6fBpzSfAu4HdhmEsrd5+i2TQUIEQIsswjHwhRBZQ\n2GgLkiRJkiQ1mWKx4D3tVFzHjUSYTCgeT719ElUByma/QdEDD2Dp1JH2s2bVy+qj+nwoHg96ZSWm\nrCz0yuQXzugPPxD8ZgmOEcPZNvFs9IoKhMNBl48/QslsMHEgkAwcMu/noYRQTVi7dSO8ahUAtu49\nmnH1kiRJtaKbNlHy7HMA7Lp0Ol0+nY/y01gXLoX/XQqlW6F0K6nrX+Qfp1yEy5r8amRoGno0iuJw\nEAkE+OTJhwHI/WEd7XsfnSx9Vk0oCkGTjUtf/oaoppNfmU75ReejB0MABL5YRMr5vz9gf82ZmXR8\n9RUMIfiTWsGCXQsJxoNcPfBq3JbkCvM0t5VHft+feELHazdjMdXNnqRVVEAsjrCYUb3emverSiL8\n76FVBCuijJ/Whw59UjGZG55MVlWFYZ1TWXzjWAQCn+PXl6nC6nDSb/wpHDVqHIqq1kv9LknSz8CR\nBufOgjf/CCYbpm6j6W7JpNuwkcn/L72Nl5awuy2cdGkfVs7dgSvFRs9js+rtk+K0kOJsfCV3NBQk\nd9MGNi35iv4nnUpaTgfUvbLDlQSibCkK0tZnY1PVGt7d8i4AVy+8mo8nfky6qemTbZqhsatqV832\nntAeNKP1SqJJkvTrUr4nnzfu+DuRUIAJN9xK2169UVUTfpsfbPs/tjRSytULr2ZN0RoEgudHP4sn\nPZPKogJ2r/+ObG82W8q3IPjll6EVCKxKbRCpRbW0enkyk2Li8n6Xs7V8K5FEhH8e+0+8Vu+BD5Qk\n6RfPYXYwOmc0PVJ68P6W9/lk2yfcc9w9teOOrkP5DtQVz5PRdiB0Hg0tHDAEyXGoo6djzXY7T7uD\nzorZFEWhInZW7aSdux2ptlRURT3wQZIkSdIvTqsFDRmGsUcIsUsI0cMwjI3A8cD66j9/Ilm37k/A\nu63VB0mSJEn6JdPKytArK5Mlvvz+JpUZUz0e1AaChX5iRMIUPfIIALFt2wmtWIH3lFPq7GPy++n8\n3rvEduzA0qEDu6+6uuYzc7scjHAYvSK5QtoIhUiUlWHKyGj2QzmTP4WcR/9NcMlSLB07YukgUyVL\nknRoFFvtLIqw7DNpoKjgykwGDQGWtI6c2jcbp9WEVl5OxdtzCC5dStplf0Ztl1OnXT2R4Ks3Xqbr\noGGk5rTDZLEiBPgcZgoqo+yoiNG//wBCX30FioLt6L5N7rOpOhNbByOFOWfOwTAMPBYPJqV27Pc5\nGp4E10pLKbjnHqrmzcc7cSLp11xdU0pt49J8qkqSGT2+emsTWZ09mLzWBtsBsFtU7JZfdnmKA7E5\nXXWCvyRJOjSheIi4Hsdj8TTtflA1QfYA9GkLCFaU894jz1Cal8sf7nqgSeUCVbPGkNPaYjKbMVsP\nvsxDuKqK/828HYANXy9i2r+fxeVPluAtD8W46e3vmP9DATk+G/+eUlua12v1HvT9rkW1cNXAq/ih\n9AeqYlXcOfxOvBY5qSxJ0oHpus7Sd94gUJbMELzghWc575/34NhPYOXeEnqCdSXVJVkx2BTchsuf\nSjwaoeeIMYwuV9F0jal9puKxNv4M4ZfAYXZw09Cb0NGJaBFuGXZLMrCqlWW5snj8+MfRDf1nOZ8k\nSUcOs2omx53D+b3OZ/JRk+sGDQaL4D8nJP8GuGAOdD2+xftgUkz8sfcfaetqS/j/2bvv8Kiq9IHj\n33Onz2Qy6YSWBELvHSwgqCiirqLiqljBsrq6uq7u6q7u2te1/VR27QUXdcVeQOxYUCxYAEV6E0JJ\nL9Nn7vn9MTEhkoQkJIbyfp7Hh5k75557LupJ5p73vG8syLHdj8Vj87TqNQqDhZz55pkU+AvwOXy8\n8ptXyHRLtiohhNgftXWu+cuAZ5RSdmAdcB6JkmjPK6VmABuBU9t4DEIIIcQ+J1ZezvZ/3k7F669j\nJCXR7dVXsHfpsvsTd8diwTVwAMFvv0ssaPfqtUsTZbViy87Glp2NjsXodNttlL74Ip7Ro3B0744Z\nDuOdNInKt97Cc+ihxKuqMAMBLJ6WfzG1ZmTgO+7YPbkzIYSoYc/NpePNN+P/4nPSL7gAy87lZ9zp\nMHUWfPkopORg9D0OT3WWoci69ey4804AAl9+Sfe33+Koi/7ADx++R+9DxrFh6bd8/tJzfPXai8y4\n/zG86Q4ykhw8f+FBPLlwHVa3g9S/X0/SqpUk9eiJ9ReZ3JrCUEajZSbqEy8ppWLemwCUzZlD+ozp\nUB00lJVXuwiUlePFaCDLkBBCNCbkryLsr8KwWHEmebE5EsGHJcES/u+b/+Onyp+4dtS19Ejpscvu\n41AsRHm4vCawKNmRDBYrQe3ihXtuoHRrorzLDx+9x2FnTm90HIHyMhbPf43MQ4byedGXHJIzllxf\nHg5rw8GQvxSL1JYYi0ejaF1bnjISM/lifWKBfnNZCDOcwT2H3cO3O77l9L6nk+5M36W/3enq7cqs\nSbPQWpPiSMFq2b/KPgoh2oZhGGT36MUPH74HQGbXvDpZ0WJmjJgZw2ltOOXQhQMv5IElD9DJ04mx\nOeNIvuIoDMPAnezj5KyTmdx9Mm6re7/IGpHlzuK2Q28jruM1mTp/Da1dbkgI0TSBaICt/q2sLlvN\n8Kzh7RLIopSqP2BQx2sDhgBK1pLIqdD60pxpTO09tU36BgjHwhT4CwAoD5dTEamQoCEhhNhPtemT\nCq31d8CIej5qm5+QQgghxP4iEqHizcQCsFlVRfCbb1slaMialkaXmf8mtHw5tq5dsXVofEFbWa04\neuSTfc1fao9Fo6SecTpp06YRXr+e0JKluIYM2eOxCSFEa7H4fKRMPYXkE0/AsNWTntubDUdcX/O2\nqCrM/GVbmayitW1ME6UU/cYdTo9RBxELh3nkknMBiMdiNYvOSilyMzxcPb4rnzw7i5fXruLEP1+P\no8OuJXPaiuFNQjkc6HAYw+NB7ZRpqUNeMlOvHYG/LEx2dx/O/bDkmBCibUXDYZZ//AELZj2CYbHy\n2xtvp1PPPgB8sOkDXl3zKgBXLLiC2ZNn7xL4uKZsDWfPP5uoGeWqEVdxau9TcVld2Bx2ug0dngga\nUoruQ+t7fFTXys8X0nHMMM7+5EKqolXM/P4B5p80nw7Whkvl/pInJY2DT53G2q+/ZORxJ+HYKfOY\n227lkgk9uH3+CjKS7GR70xiZNpGJeROb3H99mhsMKoQQAL3HHEpyeiahqkryBg/D4U5s1CkNlfLM\nj8+wpmwNlw+7nLzkvF0zoalEMPqsSbMIRAOsKl3FhJwJNR9bMX7V4Jpfg9vmbu8hCCF+Jdv82zjp\n9ZMwtUmON4f/HvNf0l3ND+5uE3YPHPEPWHALZPSGvse394hazG1zM77LeD7c/CGDMwaT4mhatjsh\nhBD7HtneJIQQQuyN7HZ8v/kN5S+/jOH14ho2tNW6tmakkzRu7G7bxUpL0eEwym7HulOWDsNmw9Gz\nJ+Eff8TepQuOvn0wmlA6TQghfm31Bgz9QjAS5863VjJn8U90mtKLfpf9gcjir8j8/SVYUlIwrFZc\nSV6CwKgTp7L8ow/oc+hhOJPqLrJ4UlIYO+1c0LrJZSN2pzwQ5bO1RXy5voSzD84jN82NYexaGseS\nkkK3lxNl1TyHHFxTmgzA4baRlWuD3FYZkhDiABQNBVn2wTsAmPEYP378QU3Q0M4LtC6rC8Wuc9S8\ndfOImomgzFfWvMJx3Y/DZXVhd7kZc9Jp9Bt3BDaXC9VACcadxcJh4sqkKlqVGJsZpSJSQQdP04OG\nXF4vI44/mSETJ2N3uetk7khyWjl9VA4nDO6ExVBkJDU9g5EQQrQ2lzeZ7sNG7nL8480f8/DShwH4\nofgH/jf5f2S46wYnem1e8lPy+dOHf6K7rzt3HHbHrzJmIYT4NawpW4NZnS1yU+UmYmasnUe0E6cP\nRp4PQ84AZYGkfTczT5ozjZsOuYlwPIzdsJPmklKMQogEpdR4IKK1/qz6/Sxgrtb6xTa41mPAPVrr\n5a3dt6glK3xCCCHEXsjq85F19VVk/O4ilNOJNf3X3S0TKymh4G/X4V+wANfIEXS++26IxcAwsPh8\nWFNTsR588K86JiGEaAsx02RbRQiAC15dxa3HHcXJZ07D5k1CGbWlvFxJXkafOJVhk47H6nDicO+6\nk9nTzGChWGkp4RUrwGrF0bMn1pS6568v9nPxM98A8PqSAt66YiyZ3l1LUBgOB4787jjyuzfr+kII\n0RQ2p4v+hx3BR7Mfx7BY6Du2NlPFQZ0O4srhV7K2bC0XD7m43h3ek7tN5rmVzxEzYxzf/fg6gUaG\ny8FStY6/vfc3kh3JzJo0iy7ehrNr9h07gTWrvuXMnmfw6obXmdB1/C5ZfIKVETYsKyYaidNjWBbu\n5F2DkWx2OzZ7/UFKPpcNn2vfzspWEizBxCTdmb5r9hEhxD4vFAvVvI7EI2g0xcFi1pWvo1NSJzKc\nGTitTg7tfCgvHP8CVsMqZbSEEPuVoVlDyUvOY0PFBs7ud3ajpRrbhTM58c9+QH5+CNHObvCdAdwG\n5ACbgL9yQ/mz7TsoAMYDVcBnbX0hrfX5bX0NIUFDQgghxF7LmpoKqe3zxcys8uNfsACA4FeLiRUV\nseGUqWAY5Dz5BJ6Rtbsd46ZJ3NTYrZZ2GasQQuwJr9PGjb/pz6X/+wa7xeCw/p2w+1z1trW73Nhd\ndYOF4vE45dsKWPn5QroNGU56lxxsjt0/sDTDYUpmP03xAw8AkHXNX0g7++w6gUrFVeGa1+XBKKZu\nyR0KIcSesTkcDBg/kR4jD8JitdbJtJbqTOW8AecRM2NYjfofMfVM7cn8k+YTNaMk25NxWWvn2IpI\nBW+tf4tHj3qUmI5RFCxqNGgoKTWNXn2Gk8Mgzh10Hi6bm2RH7YJMPGryzTub+O7dTQBsWVHK4Wf3\nwdFAacZANEB5uJyqaBUZroz9YlGkoKqAKxZcQSge4v/G/x/5KfntPSQhRCubmDeRZUXL2FCxgWtG\nXYPNsHHRuxexvGQ5NsPGqye8Sk5yDk6rE5u2EKysoKx8Kw63B5d3/1jEFkIc2DLdmcyaNIu4juOw\nOPA5fO09JCGEaH2JgKFHgZ8fRuYCj3KDjz0JHFJKeYDngS6ABbgZKALuIhE78hVwsdY6rJTaAIzQ\nWhcppUZUtzkX+B0QV0qdCVxW3fU4pdSVQDbw54ayDimlkoDXgFTABlyntX6tvnFprecopT4ErtJa\nL1ZKPQiMBFzAi1rrf7T070HUJUFDYp9mBoMoh6PO4ooQQog9p1xOrFlZxHbswJKSkig1YZpgmpTN\neR7X0KEYVislVWEeW7ien0oC/HlSH7qm7Zp5Qwgh2ko8FsNfVkrZtgLSOnclKbVlqbLzMjw8dd4o\nlFKkeXZfHmdnwfIynvnblUSCQRa9+D/On/lYk4KGdDhM8JtvavtZ/DX6tNNQTidVoRiBSIz+nZK5\n5LB85v+wjb9M6o3XKV/fhBDtw5mUhDMpqcHPGwoYAnBanWRbs+v9zGFxMGPQDGa8PYOqaBXHdT+O\nbr5ujS78uH0pNPQbZywap2hzZc374oIq4jGzwb7WlK3h7PlnE9dxpvWdxqVDLiXJ3vB97u1iZoz/\nfPsffiz5EYBbv7iVe8ffWyewSgix70tzpvHX0X8lYkZItidTFCxieUmiWkPUjLK6dDU5yTkAlG3f\nytN//SOxcJjhx03hoJNPw+H2tOfw61URriAQC2BRFjJcGZIlTQixW/VluBRCiP3MbbDL11939fE9\nyTY0CSjQWh8LoJTyAd8DR2itVyml/gtcDNxb38la6w1KqYeAKq31XdV9zAA6AocCfYDXgYZKlYWA\nKVrrCqVUBvC5Uur1Bsb1S3/TWpcopSzA+0qpQVrrpS35SxB1SaSF2CeZ0SjBZcvY8qerKJk9m1hZ\nWXsPSQgh9ivWjAzyXnyBnFlP0u3VVwhvWJ843qkTGZddSnTTJqI7CvlsTSEPfLiWN5Zu5YL/LqZo\np6wYQgjR1vxlpcy68mJeuPlv/O/6q/CXlba4r/QkB2keO+FAlEBFmHg03qTzzHicSDAIgDbNmte/\nVBaIsKMyRDAaA8BISiLzistRbjeG10vGJZdgVAcMvfLtZsb8831OfnARp4/O4YXfjeHwPh1w2yVo\nSAixf0l2JLOyZCVV0SoA3lz/JpF4pMX92V1WRh/fHavNQBmKg6bkY3c1PHcu3LKQuE7M9x/+9CGh\neKjBtvsCi7KQ68uted8lqQs2y75dak0IUcsf9bOoYBE3LbqJjRUb8Vg9GMrAYXFweu/TAeic1JlB\nGcPZVh6ixB9h7ddfEgsnvqcv/+h9ouEw0XAYf1kZ0dDeMedVRaqYs3IOE1+cyNQ3plJQVdDeQxJC\nCCGE2BvkNPN4Uy0DJiql/qWUGgvkAeu11quqP38KGNeCfl/VWpta6+VAh0baKeA2pdRS4D2gc3X7\nOuPSWpfXc+6pSqlvgG+B/kC/FoxT1EOeOot9Ury0lI3nnIsOBKj64ANcQ4ZgTUlp72EJIcR+QymF\nLSsLW1YWAEmHHUaPDxeA1vx00e8Ir1qFJTWVQ597HqfNIBQ1CUbjaC21c4QQv57SrVuIhhOLHRWF\nO4iGQ5hmnEB5OeGAH2eSF4+v6b8jBioiLHhmBSVb/Iw7rRede6VgtTdeetHu9nDYWTP4Zv7r5I8Y\ng8dXW9omHI1TVBXB1Jqb5y7nu5/K+MukPkwakI3HYcXZvz/5b72FUmCpLkcZiMS48Y3lmBq2lAV5\n9stN/GVSnxb87QghxL5heIfhuKwugrEgR+Qcgc2oG+QSi8coCZcQNaN4bd5Gs+YopcjMSeLMmw9C\no3G4bFhtDc/jk7pN4r/L/4s/6ufMvmfise592TeaQynF1F5TyXJnEYgGmNRtUp1ycEKIfVtpqJSL\n3r0IjeaNtW8w76R5ZLmz8Dl8/H7o7zlv4Hk4LS5+3BzjnCc/oEuKm2dOGYblhWeIR6P0GnMoSim+\nefM1ln/8Af3HH8mgIyY1mknu1xCMBXny+ycBKA4V88mWTzitz2ntOiYhhBBCiL3AJhIlyeo73mLV\n2YSGAZOBW4APGmkeozYJze5Sq++8o7yxtJHTgExguNY6Wl0CzfnLcSml3tda31TToVLdgKuAkVrr\nUqXUrCaMSTSRBA2JfVc8Xv9rIYQQrcYMBAguW0bZiy/hO/EEbJ07E16VCDiPl5aiVq/gjNE5fLux\njNtOGki6x9HOIxZCHEjSu+TgzciksqiQTr37YbU78JeVMfvPlxGsrKBjzz6ccPV1TQ4c2ryihA1L\nigB4+9HvmXbTmJqgoUAkxoaiAF+sL+bIvh3onOLCMBROj4dBE4+h76GHYbU7cbhrswZvKg1w8exv\n+N347ryzfDsAV7+4hEN7ZuBxWDFsNoyszDpjMJSia5qb9UV+AHp38O7x35MQQuwNtGlixuNYEIZq\nbgAAIABJREFUbHWDgjp6OjJ3ylz8UT8+h48UZ905e2PlRs6YdwaBWIA/DP0DZ/Q9A4+t4eAei9WC\nJ6XxgM+f5XhzeP3E14mbcZLsSbhs+36ATaozlRN7nNjewxBCtIFALIAmsVEnFA8RN2ufh/ocPnwO\nH5WhKA98+A3RuGZ9sZ+X14SZft+jxMJhXElewkE/C5/7LwCfPDuLXmMOafegIbvFzuhOo3lv43tY\nlZVhWcPadTxCCCGEEHuJvwKPUrdEWaD6eIsppToBJVrrp5VSZcClQJ5SqofWeg1wFvBRdfMNwHBg\nPnDyTt1UAi2tg+0DdlQHDE2gOjCqnnGd/4vzkgE/UK6U6gAcA3zYwjGIX5CgIbFPsqSkkPPE4xT9\n5wHco0Zh7969vYckhBD7pXhFBZumz4B4nIp58+jx/ntYs7OJbduGcjpx9enDHzM6EItrfC4bhtFY\nALkQQrSupNQ0pt16D5FggGBlBYtfe4m8ocMJVlYAsHX1CuKRppe58fhqAx/dPjtKJea0aNykqDLM\n8f9eSNzU/PuDNcy/fCxZyYnNLHaHE7tj140tn64pojQQITu5dhE60+ugsakyw+vg2QtG8/zizeRn\nejgkP6PJ4xdCiPYWjUfZHtjOD8U/MChzEB3cHTCUQbCygh8+ep/KokL6TTkeZbPgtrpJsidhs9jI\ncmc12Od7G98jEAsAMGflHKb0mNJo0FBDtNaUBiKYyk9JqAi3zU2qI7XRawshxN4ky5XFjAEz+Gjz\nR5zV7yy89l2Dy502C4f1zmThmkQgvNvlxJ2ShtWS2CAei4YxLBbMeBzDYiVuaMKxMDEdI27GG83m\n1lZ8Dh/Xj7me6f2nk+5KJ9WZuvuThBBCCCH2dzeUP8sNPoDbSJQk2wT8lRvKn93DngcCdyqlTCAK\nXEwikOcFpZQV+Ap4qLrtjcDjSqmbqRug8wbwolLqBOCyZl7/GeANpdQyYDGwopFx1dBaL1FKfVvd\n/ifg02ZeVzRCgobEPsmw23ENHUrn++5FOZ0YO+1U9JeX8d3b87DYrAw6/GjczShJIYQQ+zJ/1I/d\nsGOz2HbfuIl0LFabzc00QWvynp9DeM1a7Hm5WDMysNtb73pCCNFcbl8KX897ja9efxHDYqX/hCNx\n+1IIlJfRpe9ArHZ7k/tK75LEpIsGULipkv5jO+NOTpxbGYyyqSRA3Ezs7C72R4iZuy/HeFD3DG6Z\n+yOfrS3ioTOHsWZHFScM6Uymt/HMuR19Li4/omeTxy2EEHuLknAJJ71+EsFYkFRHKi/95iUy3Zls\nX7eGRS8+yzE33cC5709nS+UWrhl1DSfkn4DHXjcAKGbGKA2VorUmyZ7EuC7jeGjpQ8TMGBNzJ+K0\nNj/7uGlq1uyo4rP1W9jMK8xZ9SwKxWNHP8ao7FGtdftCCNGmUpwpXDjoQs7qdxZJtiQc1l0z/dos\nBlOHd2FMt3SUgi6prpqAIQBnUjIn//0Wflz4IV1Gj2Dmjw9x0bCLuf+b+9kW2MZ1Y66ju687SinC\nsTBV0SocFgdJ9rbNRpTmTCPNmdam1xBCCCGE2OckAoT2NEioDq3128Db9Xw0tJ62nwC96jm+Chi0\n06FPfvF5g788aq2LgIPq+WhDfePSWo/f6fW5DfUr9owEDYl9ljIMLN66O2qi4TCfPPMkP3z0PgDB\nigrGTTsPi1X+UxdC7L/iZpy15Wu59+t76ZPWh7P6nbXHO/NiRUVUvPU2rqFDyPrrtZS/9DLJvzke\nw+PB4vNhy5Id2UKIvYNSClW9EGLGYyx5/y3OvP1eYuEIDre7WQHkTo+N/KFZ5A+tnePKQmV8tu1T\neqYM4fhBHVm4poiLDsvH49j975c5aS4+uGo8G4r89O+UzKQBHZt/g0IIsQ8pDhYTjAUBKA2X1ryO\nRSNkdM3li6Iv2Vy5GYB7v7mXibkTdwka2lS5iaJAEcFYkG6+bnRK6sT8k+YTiAZIc6a1aOG6xB/h\nsv99ywXjM/h0/ccAaDQfbPpAgoaEEPsUt82N2+ZutE2K206Ku/7AeZvDQWFqhAW9trB6/YesKVvD\nmQPPZu76uQBcseAKnpz0JHbDzpvr3+R/K/5H3/S+/HnEn0lzSVCPEEIIIYQQ+yOJpBD7FTMew19W\nWvPeX1qCNs12HJEQQrS90lApF717EUXBIj7Z8gkDMgZweM7hLe4vXlHB1htvourddwHo8tij5Dzx\nOIbHg+Fs/s5uIYRoa8OO+Q3RYIBgZSWjT5iKN631Snq9v+l9blh0A26rmxtG/4u/HNyPpGQvPtfu\ns6y57FZy0qzkpDW+sCOEEO0hWFnB1tUrqSotIX/4KDwpe14OpoO7AwMzBrKsaBnju4yvCfDp1Ksv\n3YaOxJHRHYVCo+mf3p+SUAkWw1Inu0RxsJgHlzzI4u2LsRt2XvjNC3T3tawkudYapRQWQ5HssvLN\nxiBTup/OzCV34rK6mNJjyh7fsxBCtCZTmxQHi9nm30a2J5tMd2arX6O7rzteuxeF4q5xd7HVv7Xm\nM5thQ6EoCZVw6xe3ArCufB35vnwuGHRBq49FCCGEEELsn5RSA4HZvzgc1lqPbo/xiMZJ0JDYrzjc\nHo6YfjHz7r8Tw2Iwbtp5zSpJIYQQ+ypDGfW+bo54RQVmIJB4s1P6cv8HC/AeeugejU8IIdqSx5fC\nYWedD1pjsbVuycRNlZsACMQCPLZ8JnfHT8YzZgKJUt9CCLHvWvPV57zz8P0A/NhvIL+58lpc3uQW\n9xeJRwC4d8K9mNrEYXHUZL90J/sYftwUAlE/r5zwCuvL15PqTOXCdy9kVPYo/n7Q3/HaE5mEs9xZ\nLN6+ONGnGWFp4dJmBw0FogGWFy9n3rp5TMydyKDMQcw8fRj/WbCaAb7DmX/SEdgtNlIdex4oJYQQ\nrak4WMzUN6ZSHCqmk6cTT09+utUDh1Kdqfxx+B8JxUIk2ZOoilZxVt+z2OLfwlXDryLdlc6OwI46\n55SFy1p1DEIIIYQQYv+mtV4GDGnvcYimkaAhsd9Jye7IlGv+AUrh3oMHnkIIsa9Ic6XxyMRHuP/b\n++mX1o/BmYOb3Ue8qorS5+ZQeM89WLOyyJ39XyKrV6MMC2nnz2iDUQshRMsEqyKYcY3dacXmsNQc\nb6tytNP6TuPr7V9TGirlhn5XkvTFBpR778kcFKyMYJoap8eKxWrZ/QlCCAFo06Rg9Yqa90Ub1xOP\nxZp2cuU22PwlBMogfzx4MggrC59v/Zxbv7iVnik9uemQm3Ypl2uz2/HZ7fhI5b2N7/HgkgeJ6zjL\ni5fXBBwBeGweju9+PG+se4MURwojs0c2+/5KQiXMeGcGpjZ5cfWLvHrCq+Sn5HPjbwZgGKrZ/Qkh\nxK+lIlJBcagYgAJ/QU2Zx9a2c5mzNEsaVw6/kqiO4rK6AMj2ZHNEzhG8v+l9Onk6cWbfM9tkHEII\nIYQQQoj2J0FDYr/kTpad30KIA4ehDPJT8rl97O3YDBtWo+Ef77GiIrRpYjgcWHy1c6UZCFL88EOJ\nNjt2UPXZInKffhpME2t6epvfgxDiwFEUKCJqRnHb3PgczfudLVAZ4f2nfmT7unLGnNCdXqOzsTvb\n9itNljuLmYfPJBYOkRy3Yz1uQJ35sz35y8K89cgyqkrDHHluP7LzkyVwSAjRJMowGHHcFNZ8uYiQ\nv4qx087F7vpFQKRpgr8Q0OBOB4sNKgrg0QmJwCEAwwpnvkKw00Cu/vhqgrEgW/1b+XLblxzT7ZgG\nrz+522TmrJxDWbiMq0ZeVZNlCCDDlcHVI6/mkiGX4LA4SHc1/3fRomARpq4tVV5QVUB+Sr4EDAkh\n9nopjhR6pfZiVekqBmcOxmPztOn1KsIVrClbw8aKjYztPLYmaCjVmcoNB93AtaOuxWJYyHC1Xvlf\nIYQQQgghxN5FgoaEEEKI/cTPD/caEt2xg03nnEtk/XrSzjuP9N9dhLV64VvZbbhGjMT/0UdgGLiH\nDsGaKuUahBCta7t/O9PenMb2wHam95/OjEEzSLY3PTNkoMxPdjcHYb+bj55bRd7gzBYHDZWGSikK\nFpFkS8Ln8NXstK5PqjMVnC26TJta+fk2tq2rAOD9p37k5L8Mx+OToCEhRNOkduzEuXc/gGmaOFwu\n7M6dJjqtoXAFPDsVYmE4dTZ0HAyfzawNGAIwY/DGZXjOe5OOno6sK18HQEdPx0av3cXbheePfx6t\nNV67F7ulblnxVGfqLpmKmiPHm8OgjEEsLVpKr9Re9Evv1+K+GhIORrFYDaw2mXeFEK0n3ZXOIxMf\nIRQL4bQ6WxQ4CRAzY2g0NqPx0r0rS1cy/e3pAAzOHMzMw2fWzL8pzpRG+6yMVBKOh/HavDisjhaN\nUwghhBBCCNH+JGhICCGEOED4P/uMyPr1AJQ8+SRp088jVliI1mBJ8dHptluJrN+AtUOWZBcSQrSJ\nb3Z8w/bAdgCeWv4UZ/U/q8nnBsrL+ebNpykp2Mzok85m7bdODKP285A/SkVxiEggRnoXD64ke4N9\nVUYqeeC7B3hu5XNYlIXZk2czMGNgi++rIdFwnEgohmEoXN6Gx9NSvqzaYNGkNCeGRTJoCCGazjAs\neFIaCMwJlMDcyyG5E8Sj8OJ0OP892Prdrm1LN2DD4JGJjzB33Vz6pPWhu697o9dWSrVp1oo0Vxoz\nD59JOB7GbrG3eNG9PqapKdnq57MX15DWycPwSbltMscLIfZ+5eFyysPlWJQFi7LgdXhbJTNQU+as\naDyKP+rHYXXssoGoOFjMo0sfpTJSyeXDLyfLndVgP6tLV9e8Xle2jphZf6nKnfv8w7A/4LQ6+ecX\n/2Rp0VIuG3oZh3U5rNEgfCGEEEIIIcTeS4KGhBBCiAOEIz+/5rV7zGjihYVsPPscdDRKzuOP4Ro6\nFPeI4e04QiHE/q5fej/shp2IGWFU9igsqunZGVYu+phlH7wNwLx7b+LsOx/A7qqNGtqyspS3Hvke\ngAHjO3PwifnYGshCFI6FWfDTAgDiOs4nmz/Zo6CheEUFOhLB8PkwbImd19FwjHXfFfHh0ytIyXZz\n3KWD8fhadwd2516pHPO7AVQUheg5skOjgVJCCNEshgFH3w6bPgOHFzxZYHVC/pGw8bO6bTsOAYuN\nDp4MZgycASQWs4uCRVgNKymOlHou0PbSXGlt0m+wMsK8fy+hqjTMTz+WkJXrpdeo7Da5lhBi7xWI\nBpizcg4zv52J3bBz/+H3Y7fYGZk9co/6LQoWMXv5bDw2D6f0OoU0565zWSAaYFHBIh7//nFGZY/i\n3P7n1mQF0loze/lsnlnxDAAl4RLuGHdHnTKQOzsy90heWv0Smyo2cf1B15NkT9qljWmaPPH9EzV9\nbgts4+qRVzNv/TwArvnkGt495d0WBQ3FzTjFoWJ2BHaQ7cmWMmhCCCGEEHsRpdQNQJXW+q426HsD\nMEJrXdTafbcGpVQmMBewA3/QWn/yi88fA+7RWi9vj/G1NgkaEkIIIfYy8YoKzEAQZbFgzdz1gVms\nqAitNZaUlJrF6aaw5+WR+/Rsgsu+J/m4Y9l+622YVVUAFM6cSZeZM7EkN71MkBBCNFe2O5t5J82j\nMFBIZ2/nZpWe0Xrn15rK4h2s/OxDBkw4CpfXy+aVpTWfb1tbTjRqYmugpJjb5mZa32nc8/U9eGwe\nJuVNauEdQay4mK033kRk9So6XH897hEjMOx2IqE4Hz27kljUpOinKjb9UELfgxsv19NcziQb3Yc0\nvHNcCCFazLDB4sfgu2cT74/+J/Q+BoadBavmw+avEsc9GTDl4cSf1cKxMIu3L+aWz28hJzmHWw+9\ntU0XgasiVfxU+RNry9YyuuNoMt2ZbXatnymjNrObkixvQhyQArEAr655FYCIGeHjzR/TOakzgzMH\n71JysamqIlXc9sVtvLvxXQD8UT+XD70cY+f0mkBFpIIrP7oSU5ssK1rGmI5jGNNpDAAaTSQeqWkb\njUfRO/8i/QtZ7iwemfgIpjbx2Dz1lj03MfFH/TXvg7EgdqP2HpNsSShaNhcWh4qZ8toUKiIVdPd1\n54mjn2g001JpqDSRYcniIMOVgVIyBwshhBBi/zXwqYFnALcBOcAm4K/Lzln2bPuOqv0ppaxa6/pT\nZLaeI4BlWuvz67m+pb7j+zIJGhJCCCH2IvHKSkpmP03RzJlYO3Yk73/PYsuu3bkc2bKFn86/gHhF\nBV1mzsQ1aCDK2rQf5xavF/eIEbhHjEBrjXv0KCrfegsA14gRKEfrZsAQQohfclgdZFuzyfY0PyND\nn4PHUrhpPSVbfmL0lN/y5avPs+6br8gbMhyX18vgw7uy5usdREIxxpyQj8PV8Nzotrk5uefJHJ13\nNFbDSpqj5dko/IsWUfXOOwBsuewPdH9rPkZmJoahyMrzkt4jBZvXRmburru2m0trjen3o5xOjOq5\nvyhQxJqyNXRN7kqGMwOHVeZyIcQeChRDJAADp8LaBVC5FQpXJKI3k7Lg9OcgWJpo482uEzAEUBmt\n5I8f/pFgLMjmqs3MXTuXcwec2yZD1VpTFfDz8JKHef+n9+md2puHJz7cquXIfsmdbOf4y4bw+Wtr\nSe+cRJfeTQ+AFULsP9xWN9eMvAavw4tCYVEW/FF/iwOGIJEBszRUGwhfGCgkThwDo5GzqBM4YyiD\n6QOnUxwqpjJSyXVjriPZ0fjmoN3NmVbDyiVDLmGbfxtV0SpuOeQW0l3p3D72dr7a9hVn9juz3oxI\nTVEYKKQiUgHAuvJ1hOPhBtuWh8u59+t7eXnNy6Q703nuuOda9L1CCLH/CcfCVEYrcVqc9WZME0KI\nfVF1wNCjwM/pHHOBRwc+NZA9CRxSSnmA54EugAW4GfgX1Vl/lFIjgLu01uOrTxmslFoEZAB3aK0f\nbaDfjsAcIJlEDMrFWutPlFIPAiMBF/Ci1vofO512mVLqeMAGTNVar1BKjQLuA5xAEDhPa71SKXUu\ncBKQBFiUUscCrwGp1edfp7V+TSmVB8wHFgIHA1uAE7TWwQbGfQFwIYmMQmuAs4BewB2Aq/rv4yCg\nEHgYOBL4vVLqFuAqrfVipdQkEsFdFqBIa31EQ/dR/7+V9idBQ0IIIcReRIdCFD34IACxrVvxf/op\nKSefnPhMa4ofe4zI+vUAbLvhBnKefBJrevMfzimlSD7mGJz9+kEkgr1HDwwJGhJC7AX8UT+BaACL\nYamz+OD2pTDhnAsIVlYyf+ZdRKNhhp16KlZPYje0L9PFadePAsDhsmKxNr64kuxI3u0CSlNYM2sz\nWlizMjFNKC8MYndaOGRGP26Z9yOr11VxY25/fPE4NkvTS7LtzIxECH3/PUUPPIh7zGhSTzmFMkec\nGe/MYF35OmyGjddOeI2uyV33+J6EEAcOf9RPRbgCjSbZnkxSNARvXgU/vJIoO3bqU/DG5TDuKqI6\nTlmgGGUoUtO6YzEans+S7ckEY4nncc3JKtcc0XCMbWsrWPLBNs4YcAkjB4/mrmV3YGqzTa73M6UU\nqdluJp7XD8OqdskAIoTYf5WHywnFQhjKwB/1Uxgs5Pcf/B67Yeexox9jYGbLy90WBYuIm3GuH3M9\n135yLU6rkz8M+wM2Y9fswj67j3sn3MsTy55gVMdR9E7rXefzDFcG/zjoH5jabLXF8yx3FneMu4O4\njtfM68d2P5Zjuh2DoVo+D2Z7sslPyWdt2VomdJmA09pAqlAgEo/w8pqXgUSGou+LvpegISEE/qif\ndze+yyNLH2FMxzFcNvSyNvv9UwghfmW3URsw9DN39fE9yTY0CSjQWh8LoJTykQgaasggYAzgAb5V\nSs3TWhfU0+4M4G2t9a1KKctOY/+b1rqk+tj7SqlBWuul1Z8Vaa2HKaUuAa4CzgdWAGO11jGl1JHV\n93tydfthwKDq/qzAFK11hVIqA/hcKfV6dbuewOla6wuUUs9Xn/90A/f38s+BUNWBQDO01jOVUn8n\nEUh1afVnHuALrfWfqt9T/WcmieCucVrr9Uqpnx9oN3Yfex0JGhItEispIbxmLbbsDlgzMjDcza9Z\nLYQQoh4WC67Bgwl+/TUYBs4BA2o+Ukrh6F37MNDevTvKXn95Mh2PEy8pAaWwZtRfDsKakoI1JaV1\nxy+EOKBURioJxAJYlKVVSs8EogHmr5/PrV/cSndfdx468qE6ZWbsThdozUGnnUmZN8p/VjzMkg0l\nnOM5h1RnKh7frx/86OjTh8733Udo+XJ8p57Kuy8UsPH7YvocnE3FgGReW5L4Dn3B7MW8c8U4spJb\nFjQULytj0/QZ6FAI/8KFuEeMINKzA+vK1wEQNaM1GYeEEKIp4macRQWLuPLDK9Fo7hx3J0emD8b6\nwyuJBlu/S2QXOvsN4u40fihaxu/e+x1Oi5PHj36c/JT8evtNd6bzxNFP8NCSh+iZ2pPDuhyG1pqi\nYBHBWJAkWxJprtqg0IpwBYYymr2wHQ7EmPvvJZimZuP3MOmaQ7lujB237dd5PmG1t2w+F0Lsm8rD\n5Ty69FGeWv4Uucm53D/hfv634n9AojzZ8yuf5+ZDbm7w/FAsRHm4HFObJDuS8dg8NZ8VBYuY/tZ0\n1les59Tep3Lf4ffhtDhJcdb/fd1lczG281j6p/dHaUU0HsXUZp3gnbaYC+sLuN+TgCFIZDl6/KjH\nCcfDOK3ORjMWWQ0rYzuP5ZMtn+CyuuiT1mePri2E2D9URar4+6d/R6P5qfInTuhxwj4fNBSJR9gR\n2MHasrX0S+/3q5TfFULslXKaebyplgF3K6X+BcytzgbUWPvXqrP0BJVSC4BRwKv1tPsKeEIpZQNe\n1Vp/V338VKXUhSTiUjoC/YCfg4Zerv7zaxJZhAB8wFNKqZ6AJpFF6Gfvaq1Lql8r4Dal1DjABDoD\nHao/W7/T9b8G8hq5vwHVwUIpJLIYvd1AuzjwUj3HxwAfa63XA+w0vsbuY68jQUOi2WKlZRRccw3+\njz8Bw6DbKy/j7N179ycKIYTYLWtaGl3uv4/QipXYO3fCmpVV5/PkSZOwpqcTLy3FM3EiwVgUVVaK\nO9mHqt7lrE2T0I8r2HzJJRgeN10ffRR7ly7tcTtCiP2YP+rn5dUvc9fiu+jg7sDTk5/e452+gViA\n27+8nZgZY1XpKr7Y+gXH5R9Xp43d5cad15Gz3ziFklAJiwoWMThrMBO6Ttija7eU1ecj+eijSD76\nKEq2+dn4/QoAdmyoJHtkbamHFJeNxr6AB6uirF9SSPmOAAPHdyEptZ5d1judrwwLTouTyXmTeXPD\nm3TydKJ/Rv/WuzEhxH4vGAvywqoX0GgAXlj1AiMPGULswvex+otJf+fv4OsKSZlUhcu55+t78Ef9\n+KN+HlryEH8/6O947d5d+lVKkZOcw02H3ITVSDx22hHYwW/n/paiYBGjs0dzx2F3kOZM46fKn7jh\nsxvw2r1cN+a6egNQw/Ewa0rX8MqaVzg672j6p/evWQzXWte089lTmNR5Up2FeCGEaC2hWIinlj8F\nwMaKjWwPbOfgzgezsjRRYWBi7sSaOa8+y4uXc/475xMzY/xz7D+ZmDuxppRZQVUB6ysSGYWfX/k8\n5/U/jxTPrgFD4ViYsnAZcR3HYXEwZ+UcHln6CGnONJ6Z/AxdvPvm9/6mlpRMdaZyy6G3UBoqxWv3\n7lGJYSHE/sNQBkm2JCqjlUAi4+W+riRUwpTXphCKh+ic1JmnJz/dKhu1hBD7nE0kSpLVd7zFtNar\nlFLDgMnALUqp94EY1NTE/eVDSb2b9z/3+3F1AM+xwCyl1D3AJyQyCI3UWpcqpWb9ov+fa9PGqY1b\nuRlYoLWeUl1q7MOd2vt3ej0NyASGa62jSqkNO/W9c83bOInSaA2ZBZyotV5SXQJtfAPtQlrreCP9\n/FJj97HXkRzKotl0NJrIgAFgmgSXLWuVfuOBAGY02ip9CSHEvsyank7SIQdjz8vbJZObNSWF5IkT\n8Z1yMkVF25l15cXMvuZySrfWZoOMV1Sw/dZbiO3YQWT9Booeehht1i3TECsrI1ZYiBkOI4QQLRGI\nBnhwSaKc4vbAdhZuWbjHfRrKoGdqz5r3PVJ7NNjWomozPFjV3rEXwuGy4k1LfDdVSjGwk487TxnE\n2QflMnvGaDK9DWdC2vRDMQtmr+Cbtzfx5oPLCFZG6nxuSU0l96lZJE2YQIe/XostJ4dUZyrXjL6G\nt09+m2eOfYYsd1YDvQshxK5cVhfHdj+25v0x3Y7hs21fceS753Hu9/+m8JxXwZNYnHBYHPRPrw1M\n7JXai/c2vEdZqKzB/ndePN9QvoGiYBEAX2z7gnA8THm4nOs/vZ4vt33J+5ve5/Flj9dbWqwsXMY5\nb53DnJVzOP+d8ykPlyfG5LZxzMUD6dw7lbG/7UmSzykBQ0KINmM1rHT1JjI6KhRJtiT6pvbliaOf\nYO6UuYzKHtXguZF4hGd/fJaoGUWj+e/y/+KP1q53ZHuy8doSQZg53pwGS3StKVvD5Jcnc/RLR1MS\nKmHW97OAxOLyx5s/bqU73bulOdPIT8kny52F1bJ3fAcQQrSvNGcaT09+mnP6ncMjEx8h07XvZ+XZ\n7t9OKB4CYEvVFqJxWTcT4gD1VyDwi2OB6uMtppTqBAS01k8Dd5Io+bUBGF7d5JcltE5QSjmVUukk\nAmq+aqDfXGB7damvx6r7TSYR6FOulOoAHNOEIfqALdWvz91Nux3VAUMTqD/Aqim8wNbqDEnTWnD+\n58A4pVQ3gJ3KkzX1PvYK8pu1aDbD4ybj0kvZ8a87sHbsSNIhh+xRf9o0iazfwI677sSRn0/a9OlY\n02SniBBi/xGNxynxR4nGTbxOKz6XfY/7DJSXg6kZftwUPn/pORa9/ByTLvkjFosFZbfjyO+BTk6B\nE0/G0rVrTRaiaEEBpXPmYO+ag45FsXXtinvUKAxbyzMjhqNxQlGTJKcVi9FoGkshxH7EZtgYmjWU\nhVsWYiiDgRkD97jPNGcaMw+fydfbvibPl0fnpM71tkt3pfPoUY/yn+/+Q//0/q1y7dZlvxFSAAAg\nAElEQVTg8Tk4+S/DiQRjONxW3MkOpo7oytQRiQWm0lApcR0n1ZGKxahb1qayJFTzOlARxjTrbtox\nbDZcgwbR6e67MBwOlCVxfqozlVT2rtTr8VickD8GgNNjw2KVvSpCNFc4HsbUJi5rY5vh9ozFsDCh\n6wTmnzQfrTV2q50jXzgSgA0VG1havJwjco8AwGl1cuGgCxnRYQTheJiYjnHdwusYkT2iwfI5O8vz\n5ZHqSKU0XMqwrGE4DAeGMnBbawPkvTZvvWVu4maccDwR6G5qk2A8CIDNYSG3fzqdeqRgs1swZK4R\nQrShdFc6sybNYvG2xfRM7UmGK4OOSR1RqN1myrFb7ByZeyRvb0xUOhjfZXxifjfj4C8kLRbi1d+8\nREFgO128XRrMJvH62teJmInA8lWlqxiZPZJPCz7FUAZDs4a27g23Aq01W/1bWfDTAoZmDSUvOe9X\nKyEphDhwWAwL3VO6c9XIq9p7KK2mi7cLgzIGsbRoKaf2OrVNvxMIIfZey85Z9uzApwYC3EaiJNkm\n4K/Lzln27B52PRC4UyllAlHgYhKZeB5XSt3MrhlxlgILgAzgZq11AfUbD1ytlIoCVcDZWuv1Sqlv\ngRXAT8CnTRjfHSTKel0HzGuk3TPAG0qpZcDi6mu0xPXAF0Bh9Z+7plRuhNa6sLr82stKKQPYAUyk\n6fexV1A7p3LeW40YMUIvXry4vYchdhKvrMQMBFAWC9aMPUuLGC0sZOPpZxAvK6PDtdfgGjoUS0qK\nBA4J0XKtErUhc2/rWbmtkhP/8ynBaJxrj+nDWWNycTtaHrcbKC/n3Uf/zfpvv2LA4UeR0TUX0zQZ\nNun4mjahsnK+2x7kz6/9SNdUF/eeNpTUcCUbzzyLyIYNAHS6+27KXnmFzrf/s8Vzeak/wlOLNrBo\nbTFXHNmTYbmpOKyW3Z4nxH7ogJx7S0IlrCtbRwd3B9Jd6b/6IkA4FsZm2DCMli0Ua1MTqIwQCcaw\nuyx4fPXv6m4N2/3b+dNHf6I8XM5dh91Fz9SedRbH/eVh3nn8B6pKQhxxTj86dEveJ4NttNZs31DB\na//3LUopTvjjUDrk7fvp4cVea7+ce4uCRdz39X0EYgGuHnn1Hpd+bKqSYAkXvHsBq0pXYVVWXjnh\nFfJ8eXXaFAeLufDdC1lVugqX1cXcKXOblOXM1CbFwWL8UT9eu7dmgX1HYAcPL30Yn93Hmf3OJM25\n63OAykglc9fN5bkVz3FU7lFM6zeNFMfuA5Uau0+NJs2Z1mjZSCFEo/b4f569be5ta5WRSoqDxUTM\nCFnurMQ8Vr4ZHjoUgqWQczD8dnZNhrf6fLP9G6a/PZ24jvOn4X/iuPzj2FixkQ7uDqQ50/a6gJzC\nQCFT35hKcagYQxnMnTK3JltTWwhGg5RHEtnofHYfLpsssov9jsy9B5CSUAkxM4bD4sDn8LX3cIQ4\nkMmXRnFAkExDB4jycDkVkQrshh2fw9dgmtumsni9WLzNCrRrkA4ldlV3vOkmyl9/na1/uw7XiBF0\nue9erOlNq2kthBB7s3nLCghGE6VOn/5iIycN67xHQUP+shLWfLUIgPXffs2hx5yIYbEQKy9HRyIo\noMKexJUvL6agPMSmkgBzlxZwdv9UYoU7avqJFe7A2bs3yt7yzEfriqq4973VAJz75Fd8/OcJdEiW\noCEhDhRpzjTSslsn0DsaiRAJ+LE6HDhcTVvwcFgbLve1O6WhUixBBy/c+jWhqigdunk5dEY3kn3u\nRhdcSkOlfLT5I2JmjMNzDq93gbs+T/3wFEsKl9DB3YE31r3BBQMvqPPgz+NzMOnCAei4xpFkw2LZ\n9wKGAKLhOIvnbSAWSZQYWvzmBo6a0R+bQ342CNEUMTPGg989yKtrXwWgKlrFnYfdSbK97YPv0lxp\nPDzxYX4s/pHc5Fwy3buWdvDavdwx7g4WblnI2C5jSXU0LdOZoQwy3ZlkUrfPLHcWfxv1N5RSdQJ4\ngtE4kZiJ12HFa/cypccUjso9CpfVtUeL4lsqt/DHD/9IzIxx9/i76ebr1uK+hBCiObx2L177L56l\nbluWCBgC2PQZRION9tEnrQ9vnfwW4XiYFEcKPoevwaxEe4O4jlMcKgYSwaOFgcI2CxoytcnX27/m\n0g8uBeDfh/+bgzsfXG8GOyGE2Bc09VmDEEII0Rrkt+YDQDAaZM7KOUx+eTKTXp7EmrI17T2kOioX\nfEinf/4TW5fOVH34IQDBxYuJlZS078CEEKKVHNm3AzZLYhFk8sCOuGx7FrPr9Hqx2uzYnC5OufQq\nCi76HWsnHE7p089Q/trrrJ00CSoryPbV7qrrmupGuVx0uuf/sHXujOewcXgnTCB9+nlYklu+CGXf\naVHbaigJuxdCtEhVaQkfP/Mkz994LW8/cC+l2wowzXibXa8oUMQ/Pv0H6zdsIVQVBWD7+krKAxVU\nRCoaPC9mxpi9fDbXf3o9Ny66kYeWPEQ4Fm7SNXOScxicMYTbxzxOZMfRLNsUoTIUrdPGlWTH7XPs\nswFDAFabQedetRlAOvdKwWKTnw5CNEdc185/pjbhV0wQneHKYGyXseQk5zRYBiHNmcZpvU+ju687\nNkvLS9z+zDCMOgFDJf4Id7y1gotmL+aHggpicROn1bnHGe3CsTD3fH0PP5b8yOqy1dz6+a2NzvlC\nCNGWYmaMko4DqTzqJnD6oPMIsDW+ydNtc5PtySY3ObfNs06UhcrYEdhBSajlz2c9Ng9/Gv4nku3J\nTOg6oU0DNYOxIM+seIa4jhPXcZ5d8SzBWONBWEIIIYQQovmUUgOVUt/94p8v2ntcu6OU+k894z6v\nvce1t5BMQweAQCzA3HVzgcQX0rc2vMWAjAHtPKpajh75bL3xRjrfczfWzExihYUYXi8Wn6RcFELs\nH3pmJfHxnycQjMRJddtJcu7Zj1+XN5mz7/w3ZTu2ob/9jvDqRKafopkzyZ09m0J/gMjd/+L+a6/n\n9R+K6JaZxIi8VAyHHc+Y0eQ+9xzKbkMlefCXllD07WKycruRlNa87G5aa7qmufnnSQNZuLqIi8fn\nk+ppedYiIcSBKVBexqt33MT2dYnA9pKCzWz6YQnn3v0gSanN21mntSZUVYlhseBwexps92nBp3xS\n8AlX9L0al9dGsDJKh3wvOyLb8erces8JB/zElMmGig01xzZWbCRqRnGw+4xHk/ImMTLjCKY++B0l\n/giPfLyJd68ch9e55wvuexPDYtD34E5k5/tQSpHSwd3i8nFCHIishpXfD/k9VdEqAtEA14+5nmTH\n3lHirzJSybsb301sSuo2mRN7nNgmi9afrS3iyU83AHD2E1/w9hXjyEre8/KRFsNCx6SONe87eDpg\nU/vXHCyE2DdE41G+L/qeW764hW7Jefz10i9IwwKeXTO8tYfSUCl3Lb6L19e+zuDMwdw34b6aspLN\n4bV7mdp7Ksd2Pxa7xd6mgU5Oi5NJeZNYuGUhkPjd22lpu9LDQgghhBAHKq31MmBIe4+jubTWv2/v\nMezNJGjoAOC2uTmp50ncvfhu7Iadyd0mt/eQ6nANHESXmfdjhsPkPT+H8OrVOHr2lNJkQoj9hstu\nxWVvvR+5Vpud1I6dSMnuSFWwNkuFJSMDMxJBud2k/Pa3mBVFzBiUiiO9Nl254XBgZCYWt6tKipn1\np98TDQXxpmcy7bZ78KTsvsREZSjKl+tLeHf5ds45OI9Thnfm5GGdsVul9IwQovkioWBNwNDPwn4/\nW1Yup/eYQxNtggGi4TAOtxurvf4AHa01JQWbeeeh+3Anp3LkBZc0OKflJucSM2P87Zu/cOsfb8er\nUiiMb6Ug9hM+e+1iRmFliEAkTooO8P5j/8GTmsblv/0Da8vWEjNj/GXkX0iyJzXpPlOcKYTDIcoC\nkZpjJVURyGrS6fsUZ5KNjkkpu28ohKhXpjuTmw6+CVObTZ5jmiNmxoBEgFJzVEYq+cdn/wBgefFy\nJnSd0CYLwDtnsrRbDVQrJSuzGlamD5hOpiuTSDzy/+zdd5iU1fXA8e+d3md2Z3YX2AXpXUBBREFE\nFI0IhGKLxtiDJdZojA2NGk2MP40aO/YIJlaMErGXICBNRARBellge5s+c39/zLCwbGFgWer5PA8P\nM+/c9o56nZn3vOcwvst47OaGsykJIURLKo+Uc/0X11MaLmV52XKOzx/M+C7j9/eyatXEanhv5XsA\nLCpaxKbqTXsUNASpbENOc+PB/HuL0WDkpLYn8cG4D1AofDYfRoP8RiGEEEIIIUQmJGjoMGA32Rnf\neTwj2o3AZDDhsx5YP+Ab3S6M7u0/hJpbt26itRBCHJ7iiSRlwSga8NrMWM1G1pYEmR9xM/CRxzCv\nWEb22F8SLSqi3ZTXKH36Gao+/BBrjx60e+5ZTIFAvTGDlRXEwql03VUlRcRj0XptGlJcHeHSl+cB\n8J9Fm/jspmHk7YW7v4UQYkc6marHE6ysZM47r7N64XwG/vIsug46Hou9fnmaUGUl0x9/iK2rVwIQ\nOKI9g88+v8GxO/k68eIvXmR56XKcXis+ux1rrDVdTNtL7RRVhZnw1CxO7+Gn98/TWb0wte/ZXW4m\nj52MMij8tt27eOKymXjknH48/PFyBrbPpkve3g8GEEIcGppThqspJaESnvv+OYLxIL876nfkOlKR\ni1rrOiXCGmJURkwGE/FkHIMyYDa0TJaeY9pnc/NpXflhYyW/P7Urfueus7llKtuWzYW9Ltxr4wkh\nxJ4wKANeq7e29FeWddc37+xLVqMVv81PSbgEi8FCjuPAyIC0Kx6r54DJzieEEEIIIcTBRIKGDhPy\npUkIIQ5uyzZX8atnZ5PUmucuHEB7v4Ppiwt5cMZq+hZ46d9hODc5XcRsTizVFVR9+CEAkaVLia5b\n12DQkCsrm/zuvdi4bAm9hp6MxZrZndbBaKL2cSiWIKn13jlJIcRhyWKzk9u+I1vXrNp+zO6goHtP\nAIKV5fz0zdfUlJcx45lHOaJPvwaDhpTRgM25PQjH4Wn8s6/b4mZA3gAG5A2oPWa21r34XRWOs640\nSCzhx2jbvj+Gq6vItvgwmnf/YrnTauK0Xq04vlMAm9lwyJUmE0Ic2BLJBJMXT+a1Za8BqUwX9w+5\nn+pYNS/88AIBe4AJXSY0mk3CZ/Xxwmkv8Nbytzij4xl4rB4qI5VURCswKiNeq3evZJPIclq48sTO\nxJJJrJLJUghxCPLb/Tx9ytO8vORlumV346i8o/b3kuoI2AO8Pup1Fm5dSC9/L7JsB1ZQkxBCCCGE\nEGLvkqAhcUCLl5URXb0aoy8LU24ORpfcjS2EODjoWIx4WRk6HMbgdmPK2vMf2ULROI9+uoKqSKqU\nxLNfrmJQJz/De+Ty4IyfWLShghO65mA2GikqqwRtwpSbQ3xrEcpiaTSDm8PrY8zvbyMZj2M0m7G7\nMwsubeO187uTOvPF8q1cNawzXrnoLYRoBofXx7hb7mLWm1NZu3gh/rZHcOKvL8Xh9VEcKmZBbCnH\n3XotW2YuYPknn6EMhgbHsbvcnH71jXz73lt4/AG6HT804zVURCoIxoOYDWYC9lSQpdtmonOuize/\nK+S8i8/GZrejFAwce1ZtwFBZuIxgLIjFaCFgD+wySweAzWzEZm78Ini8uBi0xuDxYLDuvewaQgih\n0YTj4drn4USY6lg1d868k9mFswEwYOCyPpc12N9qsnJU7lH0DfTFYDBQHanmo7Uf8adZf0KheOSk\nRzi53cl7Za0Gg8IqZWWEEIewNq423HrsrUAq29vG6o38Z+V/ODJwJEcGjtyvN38qpWjlbMXpHU7f\nb2sQQgghhBBC7DsZBQ0ppXKAy4H2O/bRWl/SMssSAhKVlWx98G9UvPMOAO1efgnnscc23SepKamJ\nYEARcDd8kaWkOkI0kcRiNOB3yYUYIUTLiBUWsnrceJI1NfjOPpvc39+I0evdo7EsJgPHtM/i4x+3\nANAr30theQivzcw7Vx2PNRmnfaiY8qlT8Zw+ioUVml4v/hP10494evXEmJ3d6NgOT901VYRilAej\nWIwGvA4zDkv9jwpZTgtXn9SZS4a0x2U1YWniDvDqslJWLZyHP78Af0G7OllAhBBiG1e2n2EXXk40\nFMRksWB1OCkNlXLtZ9eyuHgxAK+c/CLHDB9db9/aeZyTLrw8o+CdbSoiFTy16CleW/oa+a58Xjn9\nFXIdueS4bUy9fBDBaBynxUTH36QuohvSQUtl4TLumXUPn6z7hBx7Dq+NfI3WrszL7EbDccq3BNmy\nupIjjgzgzrYS27CBdRdfQqKsjDZ//zvOYwdisFgyHlMIIZoSjAWZ2HciZZEyQvEQdx13F2iojFbW\nttlWKmdH8WScklAJqytX09HbsbakWVWsiumrpwOpgKT3Vr7H4DaDsZmkbK0QQuyoLFxGTawGq9Ha\nYKB5SbiE3/z3N2wNbgXg5V+8zNF5R++PpQohhBBCCHHQU0r5gPO01k/uQd81wACtdfFeWMc9wFda\n60+aO1ZLyzTT0DTga+ATILGLtuIgprVGJxIYTPs/CZWORKiZPbv2ec3/ZjYZNJRMapZvqeLyV+Zh\nNxt56ZKBeJNBls78kkBBO1p36U41Zi5/ZR4L15XTt62Xyb85hpxGgouEEKI5ar79lmRNDQAV771H\nzjW/2+OxjAYDZw1oS9+2PqrDcQxKke+zYTAo/C4reZEwq0afA7EY6qmnOO6zTwkpM+4Rp2A2Z76f\nh2MJ3pi3nvs+WIrRoHjtsmMZ1LHh8hR2ixG7pem7v4OVFUx76D42/7wcgHPveZD8bj0zP3EhxGHF\nbLVi3iGzTkInWFq6tPb5yuAajuo6AOJRqNgMFRsguz248uqMszsBQwCRRITXlqZK9Wys3siirYsY\n0X4EQPpzYsOfFYPxIJ+sS33fKwoVMXPTTM7sembG81aXRXjjL/NAg2P6Gn416RhKn3qa2IYNAGye\nNIl2U6ZibZ23i5GEEGLXqqJVvPrjq7yx/A3O7X4uYzqNoY2rDRXhCm4/9nYe+PYBvBYvF/a6sF7f\nsnAZ498bT2W0kjxHHlPPmEqOIwejwchp7U9j7ua5GJSBCZ3HE68OErEksDqaX6ZMCCEOBeXhcv4y\n5y9MXzMdv83P66Nep5WzVZ02SZ2sDRgCWF+1XoKGhBBCCCEES7v3OA+4H2gHrANu67Fs6ZT9tR6l\nlElrHd9f8+8GH3AVUC9oaF+eg9Z60r6YZ29oOLd/fQ6t9S1a639rrd/a9qdFVyb2uXhZGSXPPEvh\nbbcTTV+s2J8MLheBK69IPfZ48I4b22T7inCMO979gQ1lIVZsrWZTYRHv/u1evvrnC7z9l7sp3bSB\neWtKWbiuHIBF6yuYs7qkxc9DCHF4ch4zEIPTAYBn9ChUM0vMZDksHNvBz+BOfo5q58PrsDD2iZmM\nePhLyreWQiwGgI5GMVRUku337lbAEEBNJM7bCzYCqcxtb87fQDKp93jNyUSCssKNtc9LN21sorUQ\nQtRlN9m5/ujrAShwFXBC/gmpFyo3wj/6wwunwounQ/WWZs1jUiZ6ZqcCGk0GE12zumbUz2K01Gbb\nAOiR3WO35q0oCkF6iw1WRtFaYenUsfZ1c0EBNdXJ3RpTCCEaE4lHeHXpq5SES3jiuyf4dN2nAHht\nXtq72/PwiQ9z7+B7yXPWD1SsiFbUZiPaEtxCOJEqceY0O+nt783bY97mg9H/Ifz5Up6Z+BtWzv+W\nROJg+A1RCCFaXiwZY0CrAbx42otc0vsSlpYsrdfGYXJwyzG3YDFY6B3ozeD8wfthpUIIIYQQ4kCS\nDhh6DjgCUOm/n0sfbxal1K+VUt8qpb5TSj2jlDIqpap3eP1MpdRL6ccvKaWeVkrNAR5USmUrpd5V\nSn2vlJqtlOqTbne3UupVpdQspdQKpdTlO4x3s1JqbrrPn3axtt+k2y1SSr2aPpajlHorPcZcpdTg\nHeZ8QSn1hVJqlVLq2vQwfwE6pc/vb0qpYUqpr5VS7wE/pvu+q5Sar5RaopT67W68d/X6pd+/l5RS\nPyilFiulbtjhvTsz/XhSeu0/KKWeVbt752sLy/Rq4vtKqZFa6+ktuhqxX1V/9RVFf/87AJGffqLd\nC89j8jecYWJfMNjteEaOxDV0KMpobLK8DoDFaKBdtp35a8sA8NlNVBZtv0unsmgL3lY5dfr47FLu\nQQix94WqKom7HBTM+BBzKIzB5cLo8dRpk4xE0JEIBrd7t7Ji2CwmbBaYtbKYworUBZu1SRutTzmF\n6s8+w33qqRizfACU1kT5dnUJoViCoV1ydlmS0Wk1cfaAttz9nyWYDIqzB7TFYNjzzy1Wh5NTf3st\nHz37GNltCuh4VP89HksIcfhxWVyM7zKe0zucjlEZ8dvTn0s3LoBYKPW4ZCVEg7scqzpaTSQRwW1x\nYzFaiMeSRIIxDEZFtiubJ095kp/Lf6bAXYDfltnn320lyWZtmkW37G60c7fbrfPLa+8hp52bonVV\n9DulLcqg8IwZR9LmJlFUhPUXYwgrKfEjhNg7zEYzg9sM5qO1H2FSJo5pdUztax6bBw+eRvtmWbPo\nm9OXRUWLGFYwDKcplUXIaXbSw9+DcDjIx08+ys/fzgJg8Wcf0fHoYzBKWVohhECj+Xz959w7+17O\n6HgGI44YUa+Ny+JibJex/KLDLzAoA9m2pn8DFUIIIYQQh4X7AcdOxxzp43ucbUgp1QM4BxistY4p\npZ4Ezt9FtwLgeK11Qin1OLBQaz1WKTUceAXol27XBxgEOIGFSqkPgN5AF2AgqeCn95RSQ7XWXzWw\ntl7AHem5ipVS2z4YPwo8orX+n1KqHTAD2HYHZ3fgJMAN/KSUegr4I9Bba90vPe4w4Oj0sdXpfpdo\nrUuVUnZgrlLqLa11JtlG6vUD2gP5Wuve6fl8DfT7h9b6nvTrrwKjgP9kMN8+kWnQ0HXAbUqpCBAj\n9Q9Ua60b/1VJHHR0OLz9cSSCbkZ2ib3F6HJhdGX2Q6PTauL2M3rSp8CHw2IkN5DFyGtu4qNnHsPX\nqg1te/UhV1m4YVgHPlpewsmds+mZa2/hMxBCHG6ClRV8/tKzLJv5Ja27dueXN92B01v380G8tJTi\nZ54lunoV9gce4sPlpdjMRk7ukUe200KipobI8uUE587Fc/rpmAsK6gUWdQg48TnMlAdjPLGgmKfu\nvpvWd01Cmc0YfT601rw5fz33T18GwK8HteP2kT2bLClmMxsZf3Q+J/fIxWRUeO3mZr0XZquVDkcN\n4KL/ewqD0YjD423WeEKIw4/b4sZtcQOgk0mClRXY8/tjsLohUgW5PcFSvwROIpmgLFKGUaX2vP+b\n938sLl7Mjf1vZEDuMWxZWsNXry8nq5WDEZf0JNudTZ9AH2wm224FcrZytmJcl3F7dG4Oj4XR1/Ql\nmdQYzQZsDjM4s3GPGcvG5WVEEyby2u75BfdwMEY8msRgUDg8EigvxOHOa/Vyx6A7uPTIS/FZfWTZ\nsjLu67f7eeykx4gmo1iN1jp9DcqAw+6i97BT+HnubNCa3ieejNlWN+ixOFTMzI0z6eDtQAdPB9xW\n9147NyGEOJBVRCr4euPXALy/6n2u6ntVg+1cZhcuswRbCiGEEEKIWo3dobh7dy7WdzLQn1TAC4Ad\n2NpkD3hDa51IPx4CTADQWn+mlPIrpbbFjEzTWoeAkFLqc1KBQkOAU4GF6TYuUkFE9YKGgOHpuYrT\n45emj58C9Nzhd1uPUmrbh+cPtNYRIKKU2grUT6Gc8u0OAUMA1yqltv2w2za9pkyChhrq9xPQMR1Q\n9QHwUQP9TlJK/YFU4Fc2sISDLWhIay2/5hwG3CNGEF7yI9G1a2l1xx2Y/AffXS0Bl5WLB3eofe7s\n2Zvz7n8Eo9GI3e3BvH49v5zzFmO698Iw7yuc3X8F3o5NjCiEELsnFg6xbOaXABQuX0Z1SXG9oKHg\nt99S9vLLOC65jIc/XcnUBZsAuP6ULlw7vAuJoiLWnnc+aE3py6/Q8d13MOXUzZSW47Yx4/qhVIXj\n+OxmHO66WYTiSc2STZW1z1dsqSYaTzYZNATgsZvxNDNYaEdmqxVzM0uzCSEEQPmWzbx+1x9o1aEj\noyd+gzFagXLlgSu3TrtEMsFPZT9x85c30z27O2M7j2XaymkA3PDFDfx33IfMmLyEZFxTUx5hxbwt\nJHuX8MIPLzC642iOb3M8Lkv9CzbReIKKUAyL0YjXsXf2Sbu7fjCP3W2hc//GvttmJhyMsfCjdSz4\ncC3ZrZ388vp+OLyyFwtxuMuyZdULFgrGgpSES9hcs5lO3k5k2xv+HaCx49sU9DySy//xPDqpsblc\nGI3bf24qC5dx3WfX8X3x9wC8evqr9Mvt19hQQghxSPFavdiMNsKJcOqxSTJJCiGEEEKIjKwjVZKs\noePNoYCXtda31jmo1O93eLrzh9aaDMfeOSOJTs/3gNb6md1aZV0GYJDWOrzjwXQQUWSHQwkaj3+p\nPYd05qFTgOO01kGl1BfUP+d6GuuntS5TSvUFTgOuAM4GLtmhnw14EhigtV6vlLo7k/n2JUOmDZVS\nWUqpgUqpodv+tOTCxL5nys4m79Y/UvD4Y1g6d0IZMv7X44BlMltw+bKwu1MBjspiITztXcJ33U7o\njX9hcOyc1U0IIZrHaLbgzEpdVDFZrTh89e/iVpbURWLt9bGucvvnmdXFNSS0Jl5SAjr12SpRWopO\nJuvPY1DkeWx0znURcNe/EGw2GrhhRFfa+x208tiYNLonHnumCQaFEGL/KQ2XUhwspipaVef4/A/e\nJVhRzqrvFjD1rw8RcrSrFzAEUBGtYNLMSayrWsfCrQvrXJjxWX0oVCqrT5o3144z5uOaTr/HGffU\nmxcgEk8we3UpE56axY3//o7iqki9NgeSRDTJgg/XAlBaWMPWdfXPSQghADZUb2D0O6O5ZMYl3PL1\nLZSFy/ZoHKvdgSeQizc3D6ujbga4eDLO6ortN/OtqljVrDULIcTBJMuaxVtj3lnwrJUAACAASURB\nVOLPQ/7Mv0b9S0qPCSGEEEKITN0GBHc6Fkwfb45PgTOVUrkASqlspdQRwBalVA+llAFoKrX616TL\nmaWDaIq11tvuYP+lUsqmlPIDw4C5pEqJXbItM5BSKn/b3A34DDgr3Z8dypN9BFyzrZFSald3IlWR\nKlfWGC9Qlg786U6qpFomGuynlAoABq31W6TKqx29U79tP1AXp9+HMzOcb5/J6OqhUuoyUiXKCoDv\nSL0Bs0iliBKHEIPdDvZDt2SXMTubDm++QdVnn+E64QSMvoZKCgohxO5JBoMkampQJjNOXxbn//lh\nNq9cQW77jjjc9St52o86ipzf30iitIS7L+jBFVMXYjUZuenUbpiNBlSHDnhGjSI4dy6Bq6/C4Kxf\neicT7f1O3rjieDQan83M1qoIVeE4WQ4zfpdknBBCHHhKQiW12Sgm9pnIBT0vwGNN7aMFPXuz6OPp\nALTq0g2TueGSW2aDmTauNvxU9hNFoSLMBjOPD3+c+Vvmc1bXs7CaLIz9/VEsnLGOnHZu/Pku3nlo\nBVUlYdp089Lzovp7bkUwxu9eW0BlOM660iCf/7SVswa0bbk3opmUQeHPd1GysRqDUZGVJ4HyQoiG\n/VD8A4l0hvGFWxfWPm5Q9VbQSbD7YDcyZbgsLiYdN4m7Z93NEZ4jOCH/hOYuWwghDhpmo5l2nna0\n86SqSCSTSZLJJIZD4GZNIYQQQgjRcnosWzplafceAPeTKkm2Dritx7KlU5ozrtb6R6XUHcBH6QCh\nGHA18EfgfaAImEeqjFhD7gZeUEp9TyqI6cIdXvse+BwIAPdqrTcBm5RSPYBZ6cxA1cCvaaAkmtZ6\niVLqz8CXSqkEqZJmFwHXAk+k5zSRKm12RRPnWKKUmqmU+gH4L6mSYTv6ELhCKbWUVGmx2Y2NlWG/\nfODF9PsJUCeLk9a6XCn1HPADsJlUMNUBRWm9c5aoBhoptRg4Bpitte6Xjpy6X2s9vqUXCDBgwAA9\nb968fTGVEEIcCtSum+ya7L2ZSQSDVM2Ywda/Poi1V0/yH3wQk9+/y346mUTHYiiLhZLqKKhUicXa\ncSsrSUYiGF2uVEBnM20sC/GLv39FVSTOiV1zeOScvnhtZgorw3y/oYK+bX208tgwGvbKvz5CHI5k\n790L5hTO4bKPLqt9/ulZn5LrSN14Eq6upqxwI+GaavI6dsHhqR+UuU1JqIT3V71PwB5gcJvB+Gw+\namI1TPlxCv9Y9A9OPeJUJg26C02SqvVx3n3ou9q+v75vEN5A3SCb4qoI5z43m5+3VgPw2mXHMrhz\nYG+e+l4XrIxQvKEab64Dp8eCaRflKYU4SMne20ybazbz6+m/ZktwC9ccdQ3ndT+vwRKNlK2Bl8dA\nsBjOfhXaDwFT5kHooXiI6mg1RmXcZakzIcRBodn77+G49xaHinn2+2fJd+UzrvO42uD4fak8Uo5R\nGXFbmrrxWghxgJK9Vwgh9j25YJKhdMmtaq31Q/t7LWL3ZVqnJKy1DiulUEpZtdbLlFLdWnRlQggh\nxEEgWV1D4Z2TIB4nOPMbggsX4jnllF32UwYDypq60NJQeTGjx8PevLz7Y2EFVZE4AF8uLyKW0BTX\nRBn56NdUhuN47WY+umEoeZ4DqoyqEOIwk+/Kx2QwEU/Gaeduh1Ft3wltLhetu2T2FcRv93Nhrwvr\nHKuKVvHYd48B8OGaD5nYZyKfrP2EU3NHYnOZCVfHyGnnxmyp/xUp4Lby6iUDmTp3HT1be+jVZt9f\n4NldDo+Vdj0lq5wQoj6dTFJTUY7WSbLtHl4f9TqJZAK7yd5wwBDAN/+A8lTZQ6bfBBd/CO68jOe0\nm+zYTYduVmMhhNiVUDzEQ3Mfop2nHTmOHF5Y8gJndjmTfFc+6TuuW9y6ynXcOfNOnGYnfzr+T+Q4\ncvbJvEIIIYQQQogDW6ZBQxuUUj7gXeBjpVQZsLblliWEEEIcHJTJiGf0KELzFxBbtw5LQUG9NqFo\ngopwDAPgd1kbzeaTDIVIlJYRLynBXJCPKXvv3YXdO99LwGWhuDrK6L5tsJgMlAdjVIZTgUQVoRjh\nWBPlKIQQooUEY0GC8SA2o42APcC7Y95lRfkK+ub0xW/fnrmtLFxGYU0hHouHLGsWTktmpRvDNdUE\nK8pRsShPHf8Y187+PVprHGYHry59la82fM09N/4Ze8JFlteNw9Nw2bPWPjs3jpD7JoQQB7/yrZuZ\nesdNhKqrOHXitXQ/fihm+y6CDNv03f440BVMDe+VQgghGpbUSUwGE30Cfbjy0ysBmPbzNN4Y/QYB\ne8tnsCwPl3PHzDtYuHUhAE9+9yS3D7odkyHTywNCCCGEEEI0Tmt9d6ZtlVJ+4NMGXjpZa12y1xa1\nhw709bWEjL4VaK3HpR/erZT6HPCSqtkmhBBCHLaSyQRV0TAbjz2athecj8diw5xX947raDzB1yuK\nuPK1BbhtJt664ng65abv4K4phuqtYPeBPZvo+g2sHj8e4nFcJ59M6wfuJ5JMEI9GMVutOLy+PV5r\nntvGB9eeQCSWwGkzkeWwkExqRh3Zmg9+KGRM3za4bfJjoRBi36qMVPLWireYumwqJ7U9iSv6XsER\n3iM4wntEnXbV0Wqe/O5JXv/pdRSKl37xEkfnHd3gmOXhcmLJGDaTDTtWlv7vCz574WkA+o4YyZSR\nr2BxOrCZbJyQfwIfrP6Asz4Zx9RRU8l1elv8nIUQYn9b+vUXhKoqAZj91ut0PKo/Zusugoa6jwFn\nK6jaBN1Ggj1rH6xUCCEOHU6zk+uOuo5vCr+pPVYSKiGpk/tkfoMy1Mn45ra4MSjDPplbCCGEEEKI\nHaUDb/rt73U05kBfX0vI+OqgUupoYAiggZla62iLrUoIIYQ4CAQrKphy242Ea6oxGI1c+thkjG53\nnTZV4TgPf7ycRFJTHozx6uy13D2mF8nSQtTHt6CWTgOTFa6eR2jBAoinMv8EZ88iHIvy5gN3UbJ+\nLfk9ejP6hj/i3MPAIYNB1Ss95ndZuXdsbyaN6YnFaMDnkDvGhRDNVxOroTpajUEZyLZlYzQ0Xmyx\nOlbNw/MfBmDKsimc2fVMsmz1L0SHE2FmbpoJgEYzc9PMekFDwYpySsNl3LvoAb7dMpcLe13IBV3O\nZ/GnM2rb/PjVZwyacC4ubyqT2x8G/oGLel+Ey+xixpoZVEWrsBltRBIROvs647NltucWV0cIRhPY\nzUZyGig5KYQQ+0o8EWdLaAtLipfQO9CbXEduvSwS7Xr3YdZbU0Fr2vXui8nS+L4ViUfYHNzMstJl\n9MvvR55zREufghBCHLJynDkc3+Z4huYPZUnJEq47+jqc5syyZzaXx+rh3sH38sR3T+Axe7io10US\nNCSEEEIIIYQAMgwaUkpNAs4C3k4felEp9YbW+r4WW5kQQghxgEsmEoRrqus8NnmyKKqK8P2GcgZ2\n8OO2mhjU0c+yzVUAnNAlQLyqiuSWTVhWpC9kxyOw+kucJwzH6PeTKCkh+/LLCVVVUrI+VQ1049If\niIXD4IVEZSWJigoAjF4vRo9nj88hyymBQkKIvSccD/Px2o+ZNHMSboubf478Jx28HRptbzKYsJvs\nhOIhDMrQ6EUTe8LCA8fex1VfXcPRuUczvvN44sl47YXwRCzG3PffxjKwE98UzgLg+cXPc3aXs2nb\nqw9Fa1cD0KZ7T4ym7V+Bsm3ZJHWSCe9NoJe/F1ajlb/O/SsAF/W6iKv6XoXdbKcpJdURrpmygFmr\nSumU4+T13w4ix21rso8QQrSU0kgp46eNJxgP4jK7mDZ2GrmO3Dptctp35JJHniFUVYGvVRusjsYv\nWJeGSxk3bRyxZIxTjziVm4+5maRO4jQ78VolO5sQQuyuHEcO959wP9FEFKfZicPs2Gdz5zpyuXPQ\nnRgwYDBIwJAQQgghhBAiJdNMQ+cDfbXWYQCl1F+A7wAJGhJCCHHYstgdDP31Jcz/4F069BuA02Il\nseg79IJF9Bs2nFvfWsyDZ/Xh2pO7cEaf1njtZlp5bBCsJLRkGaYjz8Ow8AWw+aD9EMxZbejw7jsQ\nj2NwOgkl4ji8PoIV5fhatcFstaITCao+/ZTCW28DoPWDf8V7xhkoY+OZPIQQYl+pjlbzzPfPoNFU\nRiuZ9vM0ru9/faPts6xZ/HPkP5m+ajontj0Rn9UH0RqIpAItiUcoj5j59MVncHi8TLvwHaoSNVTF\nqli5cSVZtizynHn4DG7KNxfS23kcJoOJeDJOjj0Hi8nCoHHnUNCjF7FwmPZ9j8bu3inQUqf+auVs\nxQ/FP9Qenr9lPpFEZJdBQ8FoglmrSgFYWVTD1qqIBA0JIfabklAJwXgQSGVzKw+X1wsastodWO0O\nslq32eV4m2o2EUvGCNgDnN/jfCa8N4HKaCVX9r2S3/T8DS6Lq0XOQwghDmX7M+hy5+xzQgghhBBC\nCJHpt4RNgA0Ip59bgY0tsiIhdhIvL0cZDM3KpCGEEC3B5nTSd8TpdDtuCOt++J7o+g1s/s2FAJjf\neJ2r/vYUyaQm4LGS7cyu7actPqy9+xPc4MV2wQUY/XkodyuUUphzcmrbOZNJLvjrY9SUleLK9uP0\nZZGorqbyg+m1bao+mI775JMxOnc/pXkyFCJeWkq8qAhLu3aYsrN33UkIIZpgM9kY1HoQb1a9CcBx\nbY5rsr3ZaKZrVle69u+aOhAshZmPwcpPYMClhFwd+O+UD9j001J6jDiV6Ws/5G/z/obVaOWx4Y+h\nlOLKT65k8qmTOfHXl7Lg8+m8NvwlVgRXc2ybQfhtfpRd0WXg8Y2uIcuWxXOnPseby9/kVz1+xZcb\nviScCHN1v6szuhhuMxvonOvi563VBFwWclxSnkwIsf/kOnLpmtWV5WXL6ZbVDb/d36zx2nva0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kSpKtA267+unhOwfmtLTrgClKqVuAaTscfwcYDvyYXlu9smNa66J0Jp2pSqltF7buAOoFDWmt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\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7fdd0fcc19e8>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "utJ2CXsIszAZ",
+        "colab_type": "code",
+        "outputId": "2f455272-0a03-40bb-d5a7-a89289f770cc",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 2230
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "for year in years:\n",
+        "\n",
+        "  sns.relplot(x='income', y='lifespan', hue='region', size='population', \n",
+        "             data=df1[df1.year==year])\n",
+        "\n",
+        "  plt.xscale('log')\n",
+        "  plt.xlim(150, 1500000)\n",
+        "  plt.ylim(20, 90)\n",
+        "  plt.title(year)\n",
+        "  plt.axhline(y=50, color='cyan')\n",
+        "  "
+      ],
+      "execution_count": 96,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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+dvfXzWwk8GGmjYiIdGNx5ENviXOIFmTNIEoVWu8Ron/PZwF3kmYq3d0XEQWW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\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7fdd0e0a02b0>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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XnLXW93cTmzxA20lJhgL7qWpIRO4DFqrqySJyGPAYjfPSm+VOB8bj5CefhPOl\n5GUROcjNzta0buOA691rbZbGRCd/Ae5V1f+JyHCcFKe7uvvGAofiJFn5WkT+jjPNebybhQ0ROQRn\nbZPxDWlOgQvcvOqpwDwReV5Vi2P4CJudh/NIeUhDj4Db5d/U31T1Vnf/48AJwH9iuN52EWtAvxK4\nTkTqgADOf1BV1cy41czEhTczk8G33EzlYYfhy88jZXyz3qy4iPZ83JeTjW/y5O1yfWO6UTzyoR+O\nk1ltnttITKWFNT0iPBuROvQA3Ixsqvq2iOSKSMPf82i50w8AjsJJ0gJOzvExQLOADhzmXmuzW37D\nvNQjgN0iGrWZItKQnGG2qtYBdSKyiZZXEP00IpgDXCEip7ivh7l1iiWgRzvva2CU+2VnNvBmlPMO\nFZHf4Hwh6w8sprcFdFW1lToSiC83l+xTT2n7wC4Qrq2l9quvKH3yKTKPO5a0yZMtwYrpa74nelrQ\nDudDx2lUzVTVa7fZKPLLiLdNn2FVEZtoudMF+IOqPtiuWm7LA0xW1W0Wi3ADfKy5z7feg9tiPwLY\nV1WrReRdYshX3tJ5qloqInsARwOX4EypviDivBTgAZxn82tE5OZYrrc9xbywjIjkiMgkETmo4See\nFTOJIVRWxurzL2DLq6+y9rLLCRYXU/bii9Sv/p5wINDd1TNme+jyfOjAHOA0EckHJ3+3uLnMRWRX\nEfEArX1r/wC3i94NcJtVdYu7L1ru9DeACxpa1CIypOHaUbwNnO6eH5lb/E2ctUlwt7e19GwFThd8\nS7KAUjcoj8V5TBCLqOeJyADA444Pux6nez9SQ/De7H4Op8V4ve0m1mlrF+F0uw8FPsf5AD7G6Vox\npmXhMAQbB6MGN21iww03gs/H6NdfwxNl9TdjEsmuy5Y+uXTsrtCFo9xVdYmIXA+86QbvAHApznPn\nV3ASY83H6RqP5mZguoh8ifPl4ryIfQ250wfQmDt9vfvc/mO3RV0J/IQo3fyqulhEfg+8JyIhnG76\nqcAVwP3uNX043fWXtHKPxSLyoYh8BbyG0w0e6XXgEhFZitNd/klLZcV43hCcZGINDd1tej9UtUxE\nHga+wkksNi/G6203sU5bWwTsDXyiqhPdbzW3q+qp8a4g2LS13ixUWUnF229T+sS/6HfkEXgz+rHh\nFmeA7KjZs0kePYqSqjoWfl9Gss/LuB0yyUlP6uZaGxNVjxrRHA9uN3Klqt7T3XUx7RfroLhaVa0V\nEUQkWVWXSeMa7ca0yJuRQeaxx5Jx0EFOCtTf/x6AzBNPxJc3gJr6IH9682uemLsGgF8euTOXHDwa\nvy+emX2NMSbxxNpCfwFnHfbQ/VqXAAAgAElEQVSrcLrZSwG/qh4X3+o5rIWeOEJlZYTDYbSmhvIX\nXsSbl0fhhH045Ykl1IfC7Dc6l3+csxeZKe1bMc6Y7SDhW+jt4T4jnxNl1+ExTh2Lq55ev3iIdZR7\nw+CKm91pDFk4zyGMaRdvdjbhoiJW/+QcgoWFAOSccw4X7n0cD3+6nv87eBTpSZZgxZiezg2KPTan\nek+vXzzE/JdTRPbEmYuowIeqWh+3WpmEE66vJ1RaSqi8HE9q6tZgDlC38DN+etE0zj1kF7LS/Hg9\n1hAyxpj2iulBpYjcCMwEcnFGPv7THWFpTEwCa9fy3VFHs/LEk6hfs4bUHzR+cc465VQycrIYnJ1K\nmrXOjTGmQ2L963k2sEfDggAicgfO9LXb4lUxk1iqPv3UycQGrP/1bxg563nqV67Em52Db/AgPEk2\nst0YYzoj1qHE69l2RZxkYF3XV8ckqvR9JiOpqQCkjNsN8SeRPnkyKWN3wZeV1c21MyYxiciJInJN\nC/sqW9gemYzkXREpiGcdWyIiE0Uk7gOvReS6iNcj3HnvnS0zT0TmishCETkwyv5HRGS3zl6nqVhb\n6OXAYhF5C+cZ+pHApyLyVwBVvaK1k03vo6psrtlMaV0p/VP6MyB1QMfKCQQIlpXh6ZfB6NdfI1xZ\n6bTK++d0cY2NMU2p6svAy91djw6aCBQAcUkK5mZKE5wV+27v4uIPBxap6kVRruuNtr0rxBrQX3B/\nGrzb9VUxPcnmms2cNfssNlZvZGTWSKYfPb3dQV3DYWqXLeP78y8AEYZPn07K+JbzpRuTqO6/5O1m\n+dAv/cdhncqHLk6+8NdxVjrbD2flsn8CtwD5OI9Kd8NZe/wyERmJk90tA3gpohwB7sNpqK0Bog54\nFpGj3LKTge+A81W1pVb+XsCf3GttBqaqaqE46VgvBpKA5cA57hKspwM34azjXo6z1vqtQKqIHICz\njvzTUa5zM85nOsr9/WdV/au77xc0rsX+iKr+2f3M3gDm4iS3+dS9xuc4iVZ+C3jdFeH2w+mJPslN\nVhPtPpvdD7AzcJdbbgGwL87KfQ+693WpiNwG/EpV54vIMTj/Nrw4S/AeLiKTcLLTpQA17mf9dbQ6\nRIqpy11VZzb84HzbW9hkm0kwW+q3sLF6IwAry1dSG6xt44zmwpWVbLrnHsKVlYQrKra+NqYvcYN5\ns3zo7vbO2gn4I0760bHAFJzZSL+i+VrxfwH+rqq7A4UR208BdsEJ/ufiBLJtuOucXw8coap74iwr\n+4toFRIRP84XhNNUdS9gOvB7d/csVd1bVfcAlgIXuttvBI52t5/ozqK6EXhaVSdGC+YRxuIkVJkE\n3CQifvcLxfnAPjhLlU8TkR+4x48BHlDVcap6PlDjXuPsiP33q+o4oAw3K10Lmt2Pqn7epO41OKlo\n56rqHqr6v4jPKg/n38aP3DJOd3ctAw5U1R+4ZcXUgxDrKPd3RSTTXWT/M+BhEflTLOea3ikrOYsx\n2WMA2Ct/L1J9qe06X8Nh8HpJP6Dx8VHy2LGI3wa/mT6ntXzonbVSVRepahinhTlHndXCFuHk9460\nP/CU+/rxiO0HAU+pashdt/3tKNeZjBPwP3Rbs+cRPYMcOF8OxgNvucdej5MHBGC8iHzgLid+NjDO\n3f4hMMNt8XpjuO9Is1W1zk3X2pB69QDgBVWtcnsRZgENf4xWq2pr676vdIMywAKaf46RWrqfpkLA\n81G2Twbeb0gJG5FqNgt41n2ef28r5W4j1i73LFXd4iZpeUxVb3IX2DcJakDqAB466iFqg7Wk+lLJ\nTc2N+dxgaSllzz1H7aJFDLj0UpJ3HUu4tJT0/ffHk5Icx1ob0yPFIx96g8i0o+GI92Gi/31ve2nQ\n6AR4S1XPivHYxaq6b5R9M4CTVfULEZmKk80NVb1ERPYBjgcWuC3sWMWaerVBW2lkm5bXWmtmBlHu\nJ4raiFz0sfgd8I6qnuI+Jng3lpNiHeXuE5HBOPlhX2lHpUwvNiB1AEP7DW1XMAeo/eJLiv74Jyre\nfIvvz5tK6i5jyfrhD/H179/2ycYknpbynncmH3pHfAj82H19dsT294EzRcTr/p0/NMq5nwD7i8hO\nACKSLiI7t3Cdr4E8EdnXPdYvIg0tzH5Aodstv7UOIjJaVeeq6o04z5uH0Xb61NZ8AJwsImkiko7z\nWOGDFo4NuPXpiKj30w6fAAe54xsiU81m0TiTbGqshcUa0G/FGUjwnarOE5FRwLexXsQkplB1NYGi\nIoKbN2+zPVzf+AVXAwG0w40CYxJCPPKhd8SVOAOyFuGkCm3wAs7f8yXAYzipsbehqkU4geUpt3f2\nY5xn1824z79PA+4UkS9w1ixpeC5/A86AtA9xnhM3uFtEFrldzB8BX+CkcN1NRD4XkTPbc6Oq+hlO\n6/lT93qPqOrCFg5/CPhSRP7Vnmu4WrqfWOtZhDOobpb7WTWMFbgL+IOILKQ9K7rGkpylu1lylp4n\nVFnJlldms/H22/ENGsTw6Y+SNNR5TBYsKWHzPx6kdvFi8n/1K1LGj8Pjt2Qrptfr8PSMeIxyN6ap\nWLOt7Qz8HRioquNFZALOSMTtslKcBfSeJ7BhA8sPPQzcfz+Zxx3H4D/cjifZeUYerq1F6+rwZGQg\n3vaOcTGmR7L5lqZHi7XL/WHgWiAAoKpf0vgsxvRFHg8SsVxr2v77ESotJVBYSKiqCk9KCt6sLAvm\nxiQwEXnB7RKP/Dk6Dtc5P8p17u/q67Ry/fujXP/87XX9WMXaN5+mqp82WRAkGIf6mF7Cm53N8Ecf\nZeOdd5J+4AH4Bw9m+RFHQjjMkD/fS7/DDkN8lmjFmEQWkVo73tf5J86iOd1CVS/trmu3R6wt9M0i\nMhp3yoO7zm9h66eYROZJSiL1BxMZ9tCD9J86lbKn/g3BIITDlD7xBOHqpmOAjDHGxFOsAf1SnGXr\nxorIOuAq4JK41crERbCsjC1vvknJU/8mWFy8zb5AURHVCz4jsHEjGoptuqR4vfhycvD260fmccdu\n3d7vmGO3JmIxxhizfbTaJyoiV6rqX4DBqnqEO5/Po6oV26d6pittefU1Nt56KwCV777DDnfdhS8r\ni0BREavOOJNgYSHe7GxGvvwS/vz8mMsVEdIPOIDRb72JhkL4cnJsVLsxxmxnbbXQGx763wfgLqNn\nwbwX0nCY2iVLtr6vX7ESrXdyMGh1NcFC5wlKqKyMYFFRu8v39utH0rBhJI8YgdfSoRpjzHbXVkBf\nKiLfAruIyJcRP4ts6dfeRTweBky7CN+gQUhKCoNuuH5r4PWkp5Oy++4AJI0ciX/gwO6sqjFmOxCR\nk7syJ7eIFDSk1O4OEpH7vWk+chF5VUSyu6tu20ub89BFZBDOKnEnNt2nqqvjVK9t2Dz0rqGqhIqL\nUVW8mZlb54wDBIuLCVdX40lNxTegeZrUcE0NobJyNBTEm5mJNzNze1bdmJ4goeahi8gM4BVVfa67\n69LVROTHOJnh4pJ3vKeyleLMNlSV6vIyANKysrfmLq+ev4DVU6dCMMjA668n+/TTtvlCYEwf0OGA\n/sczT2i2Utwvn36ls/nQfwJcgZOLey7wM+BvwN44CUWeU9Wb3GPvwGmUBYE3cbKPvYKTe7wcJ33n\nd1GuEVP+clU9SEQOwcnxfUJ78nm7SU1OwVm/fAjwhKre4u57EWdd9xTgL6r6kLs9Wg7xqUAB8AhO\nmu9UnPXQ98VJbVqgqptF5Fyc9LIKfKmq58T6mfd0bQ2Ke0ZVz3DX/o2M/AKoqk6Ia+3Mdle2YT0v\n3vU7VMOc9OsbyB0yDFWl7MUXnGlpQPmsWWQed6wFdGNi4Abzh2lMoboj8PAfzzyBjgZ1EdkVOBPY\nX1UDIvIATnKQ36pqiYh4gTnuqp7rcALmWFVVEclW1TIReZm2W+izVPVh95q34eQvv4/G/OXrWujK\nbsjnHRSRI3CCb2t5xSfhpFytBuaJyGxVnQ9c4N5Pqrv9eZxHxQ8DB6nqyoiEJgCo6uciciNOAL/M\nrXvD5zYOJ53rfm5wT6iMUW2t/HGl+/uEjhQuIqtwMuaEgKCqFrgf4NM4OWZXAWeoamlHyjddK1BX\ny/tP/JOS9WsBeO+xRzn+qt+QnJpG9sknU/7iSxAMknXqKXjS051zNm5C62qpWbqM1F12xj9smK0O\nZ8y2WsuH3tFW+uHAXjhBDpzW6CbgDBG5GOdv+2CcHOZLgFrgURF5hfZlzBzvBvJsIAPn8Ss05i9/\nBqe131QWMFNExuA0Btua9vKWqhYDiMgsnHzm84ErRKRh8ZphwBggj+g5xGNxGPCsmzu9vef2eK0O\nilPVQvf36mg/MV7jUFWdqKoF7vtrgDmqOgaY4743nVBaXc+Hyzfz9rKNlFTVd7icQG0t+552Fmfe\nfCeDRu9M9uAd8LqrvaWMG8ewd98n56N5BE84FUlKon7delafcw4rTjwJra5i86OPNpvfHquSqnqK\nKuoIh3v+IyBj2ike+dAFmOn+bZ2oqrsAM3G6kg93e09nAymqGsRpAT+H0zh7vR3XmQFcpqq7A7fg\ndH2jqpfgtHSH4eQvb5pjuSGf93jghw3ntaLp//jqduEfAeyrqnsAC2Mop09rq8u9guYfNDR2uXdk\nZNRJNCaBn4mTuP3qDpRjcJ55z/psHb97xZmSdsnBo/j5ETuT7G9fK7mmooL3npjOkvffJi0rmzNv\nvoOUjH74/M567UF/Mv/bUMalT35MTpqf5y7Zj/T//IfA905K56J772XgNddCjIvSRNpQXsPP/vUZ\n5TUB/jZlT3YZ2A+PJ6HGH5m+7XucbvZo2ztqDvCSiNyrqpvcns/hQBVQLiIDgWOBd0UkA2f57ldF\n5ENghVtGLPnGm+b7XgeN+cuBuSJyLE5gj9TefN5HuvdQA5wMXIDzPL3UfWY/FpjsHvsJ8ICIjGzo\ncm9HS/tt4AUR+ZOqFrfz3B6v1YCuqh1NLr+1COBNEVHgQXdAw8CGlj+wAWhzjtTXNH4DMNtSYPmY\nARRf7Pxb/1OKn9keob2d3qGUFNYfciQcciQi8Fh2Dslp6Vv3B4AlWanUXLQP64DDBIZMmULdnj8A\nwNOvH/6hQ/GkprZv5JAqqwJhNhzjpFY+IBRmrCr+xBpQbBLAux0/9Tq2fYYOncyHrqpLROR6nL+v\nHpz/RS/FacUuA9bgdIuDE5RfEpEUnMbYL9zt/wYeFpErgNOiDYqjMd93kfu7ISbc7XanC86Xiy+A\ngyPOuwuny/16nJ6CtnwKPA8MxRkUN98du3WJiCzFCQOfuPde5D5WmOXe+ybgyBiugaouFpHfA++J\nSAjn85oay7m9QVxHuYvIEHfQRD7wFnA58LKqZkccU6qqOVHOvRhndCXJEybsNfmLL+JWz96upj7I\nksIKVJVdB2WSnuwFaV9ADAeDlKxfS21lBXlDd8RbU0O4rg5ffj7higrIzGJ1eT1FlXUAjB3Uj6wk\nD+GKCrS+Hm9ODni9HXp+XlhWw+oSZ+33nDQ/o/My8HljXZXYmO3j3R42yj1RNIxObxjAZjpuu01b\nE5GbgUpgGnCIqhaKyGDgXff5T4ts2lrrwmFlc1UdKOSkJ+HvYDCsLi8jVF9PzVNPU/zAAwD48vMZ\n/Ic/sPbKKxnw5tss21xDbr8UhmSnkpnaNcu7llTV89pXhZRU1fPjvYeR188ek5keybqN4sACeteJ\nW37LyHXf3ddHAbfizA88D7jD/f1SvOrQV3g8Qn4XBMG0rGxCVVWULV68dVtw0yY8SUloZSW6aAHp\ntRWMOfRofEnbBvNwKER1eRm1lZWkZmWRnhX7okz905M4e59ojxiNMduDm1t8/yab/+KmLe2qaxwN\n3Nlk80o3BeuMrrpOXxbPhNUDcQYfNFznSVV9XUTmAc+IyIXAauCMONbBtJM3PZ3+F15A5UcfQSBA\nv6OPpm71KjKOOgrPDoNZ+dw7jJm0PxlJ207frCov5bFfXUZtVSWDx4zl5F9fT1obQb2ooo7KuiAZ\nyV5rlRvTjbZHvm9VfYPGaW8mDuIW0FV1BbBHlO3FOHMoTQ+0pSZAxfAxDH/tdbyBejxpKZSXlJC7\ndwHlL/+Hg3adSFIo3Oy8sg2F1FZVAlD47TJCwUCr1ymqqOWcRz9l2YYKdsxN47lL9rWgbowxnRDP\nFrrpBbbUBKipD+H1CgMyklm4pozzpn8KwMkTd+C2o0eSkppG4c8uo/47ZxCs7/e/J+lHp25TTs7g\nIWTm5bOlaBOjC/bB6053a0l1fYhlG5zEfauLq9lSEySvs3MqjDGmD7OA3odV1AZ47ONV3PPmN4zI\nTWPmBZNI9TUOqKuoC1K5pYZQyEv96sZ1hGoXfwVNAnpGTn+m/O4egoF6/CmppGW2nkI1LcnHuB0y\nWbx+C6Pz0rtsgJ0xxvRVNjeoD6sJhPjzf78FYFVxNe987eRBP3CnAfi9wqWH7IQUrmPhxhrSL7sC\nAG///uScd17U8tJz+pOVP6jNYA6Q1y+ZGedP4r1fH8K/L55MXj9bF96YnkRERojIVzEcMyXifbem\nUO3rrIXeS4Vra6n79lvKX3mFrOOOJ3mXnfGktO8ZtFeEPYZms+D7UrweYddB/VhVUsV9U35AfTBM\nlh/EP4SC6nqKDjiC4ccdS3JyEkl5zdOrdoQTxC2QG9OLjQCm4K5J7yZUsTnG3cTSp/ZSgQ0bWH7k\nURAIgN/PTm+9iX/QoHaXU1RRy1frtpCd5uej5Zs5w50HHiwuZvM/HqR+5UryrroS/9ChSHIy3tTU\nmMqtD4YoqQpQURugf3oSuRntD9yhYJCSdWtZ/N5/2WnSvuSPGEVSSmzXNyYOetQ8dBEZgbMu+wJg\nT2AxcC5OutB7cBps84CfqmqdmyzrGZwlYWuAKaq6vGledBGpVNUMt/xXVHW8+/pxoGH5yMtU9SMR\n+QTYFViJs5T3QhpTqPYHpgOjcFbGu1hVv3TXJBnubh8O/FlVrVXfBazLvZfSQMAJ5gCBAFrfsaQs\nef1SOGBMLkNzUpkyeUcyU/xU1AYofeopSh9/nKr//Y/vzzvPWQ0uxmAOsK6slkPueYcj732fa2ct\norQDSWNqtpTz5A2/ZMHsF3nm5muprahodxnGJLhdgAdUdVdgC86yrjOAM92EKj7gpxHHl7vb/wb8\nuR3X2QQcqap74qRtbQjA1wAfuAli7m1yzi3AQjdRzHXAYxH7xgJH4ySNucldK950kgX0XsqTmUne\nL35B0sgRDLjySrxZbT+3bonf68wD94rw9Pw1PPL+dwSKNm/dH66qhnDzqWqt+XRlMbUB55y3lm4k\nEGWqW1vC4TDBOmepWdUwgbradpdhTIJbo6oNa7Y/gTMleKWqfuNumwkcFHH8UxG/923Hdfw4674v\nAp7FScvalgNwWvWo6ttArog0JPSarap1bhrTTcSQ08O0zZ6h91K+rCz6n/MTsk89BU9aGp60pumW\n26+8JsCNLy0mv18yp591Lklz51K/bh0Df/MbPBkZ7Spr8qhcMpJ9VNYFOWHCDvh97f/umJyWxhEX\nXcpnr73MmL33bXOhGmP6oKbPTMuApqlMWzq+4XUQt3HnJjuJNuf058BGnLVFPDj51TujLuJ1CItF\nXcI+xF5IVakuLwNVUnNy8HQgIUo0Pq/g8wibKuq44LU1vDjzMZI8IGlpeNPTKamq46PviqmoDXLU\nbgNbfS4+JDuVOb88mJr6EJmpPnLSWp+XHk1yWjq7HXQYO03aF39ysj0/N6a54SKyr6p+jDM4bT7w\nfyKyk6ouB84B3os4/kycZbfPBD52t60C9sJ5vn4iTmu8qSxgraqGReQ82JrQsbUUrB/gpFz9nZvb\nfLOqbpF2Jo4ysbOA3guVFq5j1h03EwoEOPk3N5C/4yjE47SANRQisG4dW958E+/Rx1GVnoXH4yE7\n1U9aso9gSQnhikokNRXfgNyt5wFkpfp5ctpknpm/htP3GgrZWfiSG/+JvLBw/da86/NXlXLrSeNI\nT47+T8jn9TAws/Mrv/mTk/En20h4Y1rwNXCpiEwHlgBX4KQZfVZEGgbF/SPi+BwR+RKnhXyWu+1h\nnPSqX+AMsquKcp0HgOdF5Nwmx3wJhNxzZ+AMimtwMzDdvV41Tu4OE0c2yr2Xqa+t4dW/3s13C5zV\n3AbttDOnXnMzqf2cR1OBTZtYeeJJJE3ahwWnXcIvX/0Oj8D0qXtzQL6fwutvoHLOHLw5OYx8YVbU\nkfHhsOLxSLNt18z6kmfmrwVgz+HZPHre3uSkt7/lbUwv1aOalpGj0GM8fhVOVrPNbR1reicbFNfL\neL0+svIbg3DmgLxtu9yDQUJlZTBhIs8sLQUgrPDveWsI19dTOWcOAKHSUmqXLot6jabBvGHb5YeN\nYUx+BoOzUrj1pPFk2epuxhjTY1iXey/j9fvZ59QzyeifS7C+nglHHkNyWvrW/Z6MDPKuupLqFcs5\n5dj9mbuyBBE4bc+heJKSSD/wQKo++ABPVhYpY1tNQ9/MsP5pPHXxZMKq5KYlRQ38xpjtQ1VXATG1\nzt3jR8StMqZHsC73BBSqrERraqhKSmVLyIPHI2Sl+MlI8REsLiZUUYEnLQ1fbi7SiQF1VXVBPCKk\nJnXNoDxjejj7Bmt6NGuh9xLB4mIChYX48vLw9u+Px99yd7c3IwMyMsjCGZoayZebiy+3tVktsVlf\nVsNNLy+mX4qPa4/d1dZiN8aYbmYBvRcIlpSw9rLLqFn4OZKayqj/vEzS0KHdVp/y6np+9ewXfPRd\nMQDZqX5+fuTOLFpXzutfbeDHew9np/x0knzWcjfGmO3FBsX1AhoIULPwc+d1TQ11X3/TxhnxJngi\n5pL6vEJZdYCzH5nLYx+v5kd//4iSqkA31s8YY/oeC+i9gCQnk3nSiQD4Bg4kZfy4DpUTqqwkXFfX\n9oFtyErzc/fpEzh+98GcNWkYFx84mppAiIbhGDWBEOFeMDbDmN5MRI4Rka9FZLmIXNPd9THdzwbF\n9RKBigqoqwNV/Hl52+wrrqxjSeEWBmWmMCgrhX4p2z5fV1XqV65i4+234x86lLwrLsfXv3+n61RT\nH8LrgSSfl9KqeqZ/uJI3F2/kwgNHctz4QWSk2LQ2k1B6zKA4EfEC3wBHAmtxFpA5S1WXdGvFTLey\nZ+g9nIbDlBSuY+6LzzJ07G6MmbT/NusyllXXc92sRbyxZCMAz/90P/baMQeqi6Hse/CnEgpns/ay\ny6hfsQKA1N13x3/4oQTr6/Alp5DewTXSI0e356Qn8dNDRjN1vxFkpPhItufnxsTTJGC5qq4AEJF/\nAyfhrBZn+igL6D1c9ZZynr75Gmq2lLP0/bfJHTKcIWMbEx3VB8N8tqZs6/uF35fyg1yQD/+MfOxm\nOJz64TbT0zwFe/L87TdStHolA0ftxClX30R6dk6n65qW5CMtyf5JGRNNQUGBDxgAbJ4/f36wk8UN\nAdZEvF8L7NPJMk0vZ8/QezhV3ZpCFJylX8Nhpbiyji21ATJSfFx9zC54BAZnpXDs+EGESjch3721\n9Rzft88y9G/30e+II+g/bRr1AkWrVwKwccVy6muqt/t9GdOXFBQU7AcUASuBIve9MV3KmlM9XEpG\nP0697hY+eHIGg8fswsBRY/i+pJqH/7eCsqp6bjlxPMeMH8yBY/LwiJDXL5kt/1uGZ/x5eN++BrxJ\n6JijSdpxR3a4527w+aiu2EJaVjbV5WWk5/THn5JKaMsWqufPp3r+AnLOPBP/8GFYViRjOs9tmc8G\nGp5tpQCzCwoKBsyfPz/UwWLXAcMi3g91t5k+zAbF9QLhcIjayipCoSC+ugCbX3gJTUllc8EBfLkF\npu4/cpvj61asoOrt1+l3wN54snOQrIF4UhszHGo4TFV5GeUbN5A1cBDp2TnUfrWYVaefDoA3N5dR\nL72Ib8CA7XqfxvRwHfqGW1BQMAinZR6ZfrAWGDl//vwNHaqIk0ntG+BwnEA+D5iiqos7Up5JDNZC\n7wU8Hi/hYICv57zJwI8+pfK11wHIn3YxE049t9nxSTvuiOfE0yEcgsxMPKlp2+wXj4eMnP5k5DSO\ndA8WNyZgCpWVoeFwu+sZLClBQyG8/frhSel86lRjEsRmnADeNKAXdbRAVQ2KyGXAGzi5yadbMDf2\nDL2XqKuuwu/3E1wb0au2dg27DEhtdqx4vfjz8/APGoQ3La3Z/mhSJ0wg87jj8A8dypB77naWj22H\nwMaNrJk2jRXHHU/l++8Trq1t1/nGJCp3ANzxQBlOIC8Dju9EdzsAqvqqqu6sqqNV9fddUFXTy1mX\ney9RVVbKB0/OYNKekym+5jo8qSkMe/BBkoYP77JrhLZUEK6r3aaFXVxZx3vfFJGW5GXSyFz6t5D/\nvPiRR9l0zz0AeNLTGfXaa/jz86Iea0wv1alBJQUFBV4gDyjqbDA3Jhrrcu8l0rNzOPDs8wkHggx/\n6km8fn+XP+P2ZvbDS+Oz9uq6IH966xv+Nfd7AH57/K5MO3BU1HP9I3ZsfD1kCOK1zh9jIrlBvEPP\nzI2JhQX0XiQtM2u7jjyvD4X5ZmPF1vdLC7cQDIXxRQnWaQUFDPnrX6lbsYLsU07ukoxuxhhjYmcB\nvReorapkzeIvWfHZfPY48lgGDN8Rnz961/f/t3fn4VGVZ+PHv/fsM9k3QJawqyAFl4go1SKKuwX3\nrRWtb7VafbX0p9i+dXnb2lrrq3Wt1aqoVSjuuxRRFEWEICAi+6aBELJPMpPZn98fcyCBJARCNsL9\nua5cZJ55zjkPh5B7zrPdbSnd4+Suc4/gmucW4XM5uPmUoU0GcwBHZibpp01o9zYppZRqmo6hHwBK\nNq7jX7ffAoDd6eS/Hv4nqdkd8wQcjycoD0YQZL9ynsfKy0nUBhCPOzlGv5eT9ZTqQnRjBtWl6RP6\nAaC2omLn9/FolFh031KTBv3VrJ7/KXW1NYw69cxdtnmtq/GTiMfxpKRidzZOpmK32+iRtn9L0GJl\nZXz3s2sIr1kDDgd9/nwSRx8AACAASURBVPYgaePGIQ798VNKqbaiM5cOAL2GHEq/I0Ziszs46sxz\nce/j0+03H8/mo2f/wRcvv8Scp58gHAwAUFtZwdt/+wvT77yVolUriEUj7dF8Agu+TAZzgFiMkt//\ngXhlZbtcS6mDgYj0E5GPReRbEVkhIjdb5dkiMltE1lp/ZlnlIiIPW6lWvxaRoxuca7JVf62ITG5Q\nfoyILLeOeVisCTwdcQ3VOhrQDwApGZmce8tUrn38WU646Aq8ael7faxJJKgpr9+/IlBdQSKeXDGz\n5IO3+f6bZVSXbOPNv/6RUG1tq9tYXhvmsY/X8qd3V7Ldv/sa9N2HdUyjEqXUPokBvzbGDAfGAL8U\nkeHA7cAcY8xQYI71GuBMYKj1dS3wd0gGZ+AukoldRgN37QjQVp2fNzjuDKu8I66hWqHd+zytvL2F\nwBZjzDkiMhCYAeQAi4GfGmPa59GwG/GmZ7TqOLHZOG7SxZQXfUc4GOS06/575wcCT0r95jFOj6fV\nM+gTCcPTn23k8bnrAVhfVssDFx9JhjfZhZ8yZgzuoUMJr10LDgc9f3cHjqz9z+6m1IGkoKDATf06\n9HBL9ffEGFMMFFvf14jISpIZ2CYC46xqzwFzgalW+fMmOWlqgYhkisghVt3ZxpgKABGZDZwhInOB\ndGPMAqv8eWAS8H4HXUO1QkcMYt4MrAR2PFb+BXjQGDNDRJ4ArsH6JKfaR2p2Duf+6reYRHyXDwZH\njDuVQHUlVcVbOfHyq/C18kND3BhKGjyVl9WEiTfYOtaRm0v+tGdJ1NYiHg+21FQdP1cHjYKCAhvw\nB5K/CwUwBQUFDwF3FBYW7vsey7sRkQHAUcCXQE8r2ENyzXtP6/um0q32aaG8qIlyOugaqhXa9beq\niPQlueXhPcAUa3xkPHC5VeU54G4O4oBe56+meP1afGnpZPbqjWcft1xtSVltmEA4htflpEdG2i7v\n+dIz+OGlVxINh3B6vIitdSMwTruNX592GBtKA9SGY/z1olFk+XZdVufIyQFdm64OTjuCeUqDsput\nP/9nf04sIqnAq8Atxhh/w142Y4wRkXYd3eqIa6i9196PSX8DboOd24/lAFXGmJj1+qD+RBYOBvnk\nxWdZMfdDACb++n8YMvr4Njt/eW2Y6/+1mEWbKumb5eX1G04gr8GM9VgkzNY1q/jy9ZnkjxjJyFPP\n3NkdX1YT5q1lW0n1ODh1WA+yU/a8ZK13ppenryognoCcFNceu+/L68pJmAReh5dUV9t+gFGqK7G6\n2XcP5livby4oKPh9a7vfRcRJMpi/aIx5zSouEZFDjDHFVnf3dqu8uXSrW6jvPt9RPtcq79tE/Y66\nhmqFdpsUJyLnANuNMYtbefy1IlIoIoWlpa1OStSlxaMRtq1fu/N10epv2+zcibo6gqEoizYlZ5MX\nVdZR4t/190aotpbX/nwX332zjM9mvEDFlmTvV2UwwpSXl/L7d77ltle+5u9z1xOOtrz1dHaKm7w0\nNzZb88G8OFDM5e9ezikvn8Ira16hNtL6iXhKHQDyaH79uljv7zOrt/NpYKUx5oEGb70F7JhFPhl4\ns0H5ldZM9DFAtdVtPgs4TUSyrIlqpwGzrPf8IjLGutaVu52rva+hWqE9Z7mPBX4sIptIToIbDzwE\nZFq5fGEPn8iMMU8aYwqMMQV5ed0nyUc4Fqa8rpxANIDbl8KPfvIzHE4XqVk5HHnaWW1yjUQwiH/2\nbFizilF9k+PivdI99EhvYWMY69dONJZgVXH9lq/fbPUTju33UB8A7254l62BrRgMD371IHWxujY5\nr1JdVCmNl3nskKD1KVTHAj8FxovIUuvrLOBeYIKIrAVOtV4DvAdsANYBTwE3AFgT1f5AMp/6IuD3\nOyavWXX+aR2znvrJah1xDdUKHbJTnIiMA/6fNcv9ZeDVBpPivjbGPL6n47vLTnGBaIDZm2fz9PKn\n+WGfH3LtyGtJs/kIBQKICL6MzDbZqz1WWsq68adgz8zE838PE+ndj5Q0HzYBt9NOhjc5vh2LRNi6\ndhULX59JvxGjGHnK6XjT0glGYry1dCu/eX05TpuN568ZzbEDsrHv4cl7b80rmscNc24AYED6AKad\nMY0cr46tqwNCq/4DFBQU3EPjbvcA8FBhYeF+jaEr1VBnBPRBJJ/Ys4ElwE+MMXscQ+ouAX1bYBun\nvXLazlXYM8+ZybCcYW1+nVh5OZuvnExkfXIZWf6//81VX9SyaFMlN40fws9PHES6taTMJBJEQnU4\nXG6M2Kiui+Jy2BDAH4phEyHT58TjtLdJ26rD1Xxb/i3rq9Yzof8Eeqb0bPkgpbqG1gb03We5J4CH\naaNZ7krtoHu5d6Dtwe1MfGMitdFaBOHd89+lX1q/lg9shWhJCTVzPsJ75Ci2p+cx7rGFGAM2gQW/\nPaXRdq6RWJxl31dz99srGJyXwp3nHkFuauv3bleqG9rffOhttg5dqaboYuAOlO3J5sWzX+SNdW9w\nYp8TyXK33+Yqzp49ST/jdIKLFpFuvmfGxcO44uVVjOqX2WTXeVUwylXPLiQQibNiq5+j87O5auyA\ndmufUgcbK4gXtVhRqVbSgN6BHDYHgzIGMeWYKe1+rUQ0SsVzz1H+jycBGHD9L/jgxivJTPeR09QS\nNGt8PRBJzmb3udumi10ppVTH0L3cD2CxaIJQbYR4vPEwnAmHCa1ctfN1ZNUqBma6sROitKqcYE2E\nhsMtOSlupv98DKcO68EvTx7MqcN0bFsppQ4kOobeBVSGKvnk+08orSvlvKHnkevNbfGYUCDKt59v\nZePSUo48NZ9+R2Tjcu/a4RJas4bvr/kvEKHvP58i0DebO+ffSV2sjtt+8Bv6p/fHt1uO82AkhtNu\nw2nXz3pK7UYzgakuTX9rdwGzN8/mjvl38PCSh7l7/t34w/4Wjwn6I3zx2nq2bfAz66lviARijeqE\n+/Ug8cxf8T9xJ7NYwd1f3M28LfMoLCnk11/eQlWkcQpTn8uhwVypA4SI2EVkiYi8Y70eKCJfWulI\n/y0iLqvcbb1eZ70/oME5fmOVrxaR0xuUn2GVrROR2xuUt/s1VOvob+4uYEvNFuxiZ2zvsfRP708s\n0Tg4785ur39YsNltyenruwmbCBfO/y+uWnQLJXXbCcXqE6iEYiHsrn3/50+EQkSLi6lbsYJYRcXO\n8lAgSk1liKC/6cm7iYShNhwjnuj6PUJKHUB2JL/aYUfyqyFAJcnkV1h/VlrlD1r1sFKuXgocQTJ1\n6ePWhwQ78BjJlKjDgcusuh11DdUKOimuC7hi+BVM6HUmgVU2zDY77kNTwLvnYzypTs64bgTrvypl\n5Ml98aQ4G9Vx2V2cN+Q8Xl/3Ol8Uf8Fdx9/FzR/dTDAW5L4T7yPDk0kwEsPn2vsfg+iWLWycdB4m\nGiV1/Mkc8qc/EXOmsOi9jXw9p4iMPC/n/fpoUjLru/ID4RgLNpTzwoLN/HhUb04Z1nNnalWlujtr\nHfplwK9I7o5ZRDLgTd+fdej7mPxqovU9wCvAo1b9icAMay+QjSKyjmTOcoB1xpgN1rVmABOtNK3t\neg2g7fbAPshoQO8Cevh68P2nQQrf2ABA+Xe1nHhJf6q2bSa3X398GZmNjnH7nAw+qgcDRuZCApqa\nC5HpzuRXx/yK60Zeh8vuItebyzNnPIMxBgdp/OPTzXxdVMXPxg7kqH6Z+NyNfxzifj+hFd8SXLaM\njLPOJLx5MyYaBSDw2eeYaJQ4Cb6ek1yNU11ax/bNfgZm1m/XW10X5efPF5IwMHd1KZ/cOk4Dujoo\nWMH8VWAC9TvF9QT+AVxYUFBwwX4E9X1JfrUzhakxJiYi1Vb9PsCCBudseMzuKU+P66BrqFbSLvcu\nwCQMNRX13eGB6gjrCwt5+Q//w7sP30/Q3/yYejgQY97MNXw47dtdzrFDlieLPml9yPPlISLkenPJ\n8+Wx6fsKooEgS7+r4spnFlJVF236/GvX8t3VV1P2t7+x6eJL8B5+OHYrDWrWlT/F5vFgswt5+cnf\nKTaHkN171wxqxuy6mbX2uquDyGXsGsx3SLHKL23NSfc3+ZXqnvQJvTPUVUGwHGwO8GYhnnQKzhxA\n2fe1hIMxTrp0EB8982cAtqxeQSLRdKYzkzAsmb2ZWCRBeq6Xwvc2cvx5Q5rsfm8oWlJC3qP3conT\nyYW/uJnzZqyiJhSlqX7+0Nr6bHDxqipMIsHA11/DRKPYUlOxp6XhBc65cRSV2wKk5Xjxpe96/Qyf\ng0cuPYoXFmzm3FG9yU5xodRB4lc0DuY7pFjvv9SK8+5IfnUW4AHSaZD8ynqCbpj8akdq0yIrOVYG\nUE7zKU9ppry8A66hWkkD+l6IxCNAcky6IWPMvidTiYZg6XSYZU3ovPBZGD6J1CwPZ/9yJCZhiMdq\nqd6+DYDRky7G6Wo+AA46sgebvymjrKiWI0/NB6l//A36q9m4pJDqkmJGnHwaqTk5mFCYkj/eQ83s\n2QCkeTz8csJkcprZ5jVt3Dgq+jxFdMtWMiZNwpaSgiOr8Q53vnQXvvSm25nqdnL6iF6cODQPr8uO\ny6EdQ+qg0beF91u197Mx5jfAb2CXXBlXWMmvLiSZL2P31KaTgS+s9z8yxhgReQt4SUQeAHoDQ4GF\nJJfoDRWRgSSD7KXA5dYxH7fnNVpzP1SSBvQWbA9u54HCB/A4PNx01E07M4OVBEr45/J/0ie1DxOH\nTMQudqrCVUBy7Drdnd70CSMB+HpG/etl02HoaeBOxZuaDIjGuLjyvkeIx2K4PF7cvqY/4ItNiNTF\nWPz+ZgCK11Zx+f+OASAaDrP43TdZ9p93CQcDLJn1LpPvfwyv2wPO+n92u8vFJaPzSWtq9zjA2asX\nA2b8GxOLIl4vjszG4/l7w2m3keHTQK4OOkUkx8yb8/0e3muNqcAMEfkjyeRXT1vlTwMvWBPSKrC6\n+o0xK0RkJsmJaDHgl8aYOICI3Egyl7kdeMYYs6IDr6FaQQP6HgSiAe5ZcA8fff8RkNy69fbRt1MT\nqWHK3Cl8XfY1AKmuVHr5enH9nOsBuPv4u5k4ZCIOWxO315UCR14OxUuTr4+8Apy+XaqICCmZuz4F\nV4erqQhV4La7yXBlkOJqHOQbzourjvqhoA8nj72d4vmLWfb6G4QCtaRmZZPzuzux3TgFm8tJTkYK\nzhRPo3M15MhreaMbpVSTHiQ5Aa6pT+UB6/39YoyZC8y1vt9A/QzyhnVCwEXNHH8PyZnyu5e/RzLH\n+e7l7X4N1Toa0FuQoH4CaiKR/D5u4tREanaWV4QqKAmU7Hw9a/MsTh9wOqmuXSeHAeD0wMhLYOgE\nYnYfUePAGYvicDWf2SwUCzFz9UweXvIwgvDkhCcZ0zv5JN5jQBrHnNmfko1+jps4iLAdArVVPLjk\nAd7Z8A4Aj53wN/quGoEnJZV4wvBNdYLJ077B7bDx/M9Gk2+PkOHTcW2l2sF0kt3Pu0+MCwCzSXZb\nK9UmtA90D1KcKfzuuN9xav6pnDvoXG446gYcNgfZnmz++qO/MjxnOCf3O5nzh57PyLyR2MSGTWxc\ndthleB3NLCRPxCFWR1BS+XbxMl697x6W/uc9QrW1jaoGogGqwlWEY2FmbZoFgMHwwaYPdi5T86a6\nKDhrIKdfO4K6VDvnPvo5S4tKKSyp3yp3WfU3nDvlNzhT06kJRbn3g1UEI3Eqg1GemreRjaUB1m6p\n5i8frGK7v/FMeaVU61hL0i4ArgUKgRLrz2uB/VmyplQjupf7XqiL1iEieBz1XdMJk6A6XI3D5iDN\nlUYwGsQfSS4vS3el49utG32nyk1EP/w9laf+karKUmJ1IRZNe4FJt95BNM3Ousp15Kfn47a7ub/w\nfg7PPpxT8k8hnohz5/w7+absG6adOY0f5P5gl9NWBSP8/PlCFm2q5JLRvRh1+AbuXfQHMt2ZvHDm\nC5RXpvPUZxu47keDeGvpVp75fBMAU884jEOzU+kVMjyzoYTimhCPXXE0WdYTe3ldOQmTINWZitfZ\nwm43SnVvupe76tK0y30PdnSlZ3oyG+Uut4mNLE99mc/paz6IN7T2Q6qOv47/FM/hvkV/pWdKTx77\n7weJeOG/Zl3DhuoNuO1uZp4zE4c48Dg8nP362aS70nn2jGdxirPJCXcuh41Lj+nLBUccwrJtfno5\nRvP++e/jtDshlsbFT35MLGGYs3I786aezJhBOcQThhS3g942B1/P2sCoY7NYXlxNPJ78kLctsI2r\nZ13NtsA2/vzDPzOu37hdPtQopZTqOrTLvRmVoUru+vwuLn7nYs569Sy+q/luv84XT8QpDZZSdvjp\nxFNy+NtXD2EwbAtsY17FAmwOBxuqkzvFheNhvq/5nhP6nMC0FdNImARV4Spmrp7JI0seYV3Vukbn\nt0UTHOPx4VpYwfnpGQzKcDLpzUkYY4jGDXGrJyYSTxAIxxg3NJcje6Yha2r4+NGvyS/oQVFtiAcu\nPpIsa534Bxs/oKimiFgixn2L7qO6zt9kqlallFKdTwN6M+ImzmdbPgMgZmIsLF64X+dbX72ei96+\niIs/mEzc4eGwrMN2vjeixw9w2pxcdGhygujgzMEMzRpKjieHUXmj6uvljqA4UNxom9fayhCfvLSG\n9V9t5/hJQ1g26ztiwRgJkyCaiJLhdfLgxUdydH4Wd5w9jNCWIHOeXUm6y8lRY3tzyR0jGXqEm18e\n34thh6RjtxK9jMgdsfMaw7KGsXVFNds3+onHNKgrpVRXo2PozfCH/Tz01UPMXDOTVGcqM86ZQf/0\n/q06VyAa4NZPbmXelnkAnDf4PG46+ia+LP6S/PR8BmYMJM2VRlWoitK6Uopqinjoq4c4f8j5nD34\nbNZUriHNlcaq8lVsCWxh8vDJZHqS68FDwSiznvyGolXJVKjDTjgEu9NGv+NTmVsziwsOvYAMdwbR\neJyaYIzNi0qYPzP5hH/ocb0Ye0FfPn3haVZ8OofsPn25+M4/71wyVxOpoaimiO8rihjkOJy5j2zE\n4bJx0W+OJSWj+Vn5SnVTXWoMXUQygX8CI0jurvwzYDXwb2AAsAm42BhTaSVJeQg4CwgCVxljvrLO\nMxn4nXXaPxpjnrPKjwGmkdxC8j3gZmujmOz2vkYb3qaDio6hNyPdnc6NR93IVSOuwm13k+3JbvW5\nXDYXh2cfzoLiBdx45I2c1Pck7GLnnMHn4A/7qairoCbkx/hDLAssY3jucKaOnorP4aMmUsPy0uWM\n6jGKcfnjSHWm7jqObcwuT8yxaILBx/QgM9vNpX0u3Tmu77TbSXdD2ab65XZOp41YJMyKT+cAULGl\niMriLTsDeporjWE5w5C1mcx+fhWJhCEtO3Xfd8dTSlFQUDCQ5E5pWwsLCze2wSkfAj4wxlxo5ST3\nAb8F5hhj7rXyi99OciOYM0nu0DaUZAKUvwPHWcH5LqCA5IeCxSLyljGm0qrzc+BLksH2DOB965zt\nfQ3VChrQ9yDLk7XLxLfWctqd/HT4Tzl9wOm8tvY1zn/rfI7peQz/96P/4831b/LA4gewiY2nTnqC\n4/OO4/PvPuWz0i9I92ZyaPahPLL0EQDenPgmud7kJi+1kVrqYnW4HC4m/Gw4c55bid1u4/jzBuNN\ndeJw2Ru1w+GyM/aCIThddsQuHHv2QIwJktd/IKWbN+L0eMno0XhTq/4jcjhu0iD82+s45qwBzW7x\nqpRqrKCgoIDk5jLDgAjgKigoWAlcV9jKrkcRyQBOAq4CMMZEgIiITATGWdWeI7nhzFSSaUmft55+\nF4hIpogcYtWdbYypsM47GzhDROYC6caYBVb588AkksG2I66hWkEDegfJ8mQRioV4aVUyD0NhSSHV\n4Wre3fAuADZsHOLtyYpX3ySycT1Xnf1jPnN8S5/UPjvPURWugkgQf6SG6evfYNqKaRzX6zimFExh\n7LX5pNsycLeQltSX4eakyw5NXtNuA1xc8D9/oLpkG2k5ufia2NrVm+ri6NP6k0gYbDZ9Oldqb1nB\nfC71m8rsWPt5NDC3oKBgXCuD+kCgFHhWREYBi4GbgZ7GmGKrzjbqt53dmdrUsiOF6Z7Ki5oop4Ou\noVpBJ8W1gfK6cr7zf0dZXdke6zntTg5JOQQAt91NijOFH+efDcCovFGUr1jD8g8/oGT9WuY+8igX\n5U8ky53FoIxBnDfkPAak9oVP/0qgdiuPLn2U2mgtc76fw9qqtdw271YC1Ozp8jvZ7DYrmCelZGTS\n+9DDScvJxW5v/jPe7sG8rDbM/HVlbCoLEAzHCAejhGoje9UGpQ4SzW37ilX+RCvP6yD5oeDvxpij\nSO48d3vDCtaTcruOR3fENdTe0yf0/VRWV8YNH97AyoqVDMwYyDOnP7OzW3x3ud5cXj73ZdZVrqN3\nam+MSXC0OZRXxr9EijuNbYuW7qxrTAKXcbCxcj0/GfYTYokYGaWr4bMHcBw2gUx3JlXhKmxiI8+b\nx/Ky5cRMbI9tjSViVIYqCcaCpLnS9mteQHltmOv/tZhFmyqxCbx14w8pmbOV6u11nHLl4WT02Is1\n+Up1Y9aY+bAWqg0vKCgY2Iox9SKgyBjzpfX6FZIBvUREDjHGFFvd3dut95tLYbqF+u7zHeVzrfK+\nTdSng66hWkGf0PdTXbSOlRUrAdhYvXGXPd4bqgpV8eqaV5m+ajq5vlyueO8Kznj9TIp8Vax85S1e\nmfIreg0awuCC40jP68GPfnoNwapqDs8dTlldGePzx2OPJwN2zn/u5qWTHuS2Y37NE6c+watrX+WK\nw6/AY9/zpi/bg9uZ9OYkznn9HO76/C78dX7qaiNEQnv+INCUeMJQuDk5sz5hYOG6cvyldRSvq+Kj\nF1YRCkT3+ZxKdTO9SY6Z70nEqrdPjDHbgO9FZMf611NIZjPbkcIUGqc2vVKSxgDVVrf5LOA0EckS\nkSzgNGCW9Z5fRMZYs9evpHGa1Pa8hmoFfULfT16nlyGZQ1hXtY6+aX1Jc6Y1WW/BtgXc/cXdnJp/\nKv6In9K6UgAe+/YJ7jj2etYv+II3/voHRk+8iEOPG0vxujUcPvZH9MvK5ojcI5In6eWAI6/Atm42\n/Va+z0/H3kwFMQZlDMLj8DSfstWyvGz5zu1pneLC/32M+a8sIy8/jTETB+FN2/vJbm6nnatPGMAz\nn28iO8XFuEPz+Pit5Idrt8+hY+1KwVagpf9ULqtea9wEvGjNcN8AXE3yIW2miFwDbAYutuq+R3I5\n2TqSS8quBjDGVIjIH4BFVr3f75i8BtxA/ZKy96mfrHZvB1xDtYKuQ99LsXiMinAF4XiYNGfaznXg\nkOx2r43UkupMJdfXdHf7iytf5N6F9zIydySXHX4Zv/nsNwCcM+gcft7rct778x8JBwJ409I54bzz\nOGLMaBwOF7JuNmDg8LMhJRfqqiEWAqcXPHsO4LvbUruFC966gEA0wCunvMEn9xcRDcUBOOPaEQw+\nusc+na8qGKE2HMNlt5Fmt7Hiky2EAjGOOi1f16mr7mifP6UWFBQsJjnW3ZzFhYWFBa1vklL19Am9\nGTsSr6Q4k/NZttRu4eJ3LiYYC3LFsCs4a+BZ9PD1oDRYSs+UnvRN7YtjDxPKzhhwBguLF1JUW8QR\nuUcw4+wZVIQqGJE7gjRHKlf939+JRyOkOcLIJ39GnrsTMvvD2Jthw8eYkm/wHzmVTavqGHx0D1LT\n9i1g1kXrcNlcvD3pbWoiNeSYnri923YGdICgP7JPS9IyfS4yG6RdLThrIMYYXaeuVL3r2HWWe0MB\n4Bcd2hrVrekTehM2VW/i9wt+T64nl6mjp5LjzWH6yun8aeGfgGQ2tefPfJ7bPr2NNZVrSHGm8MbE\nN+iV0muP5/WH/SRMgjRXGnZb43Xi1JbAP06Cmm31ZSJw+cuw+FlW9/wtH/57O0OO6cExl/bG43Y1\nnXN9N9Xhal5d8yqvrXuNsweezeXDLifdlU7V9jqW/GczOX1ScThspGS6GTCy6R4GpVTrdoqzlq49\nAQzHWodOcrz7F61dh65UU3RS3G4qQhVM/XQqi7Yt4v1N7zN91XQAxvQeszPH+Sn5pwCwpnINkNza\ndZN/U4vn9hoX4eJyvnr3Taq2bSWRqH86JuSHjZ/tGswBjIEFfydx/E0UbYiRm59K/lkupnx2C7/9\n7LctLpWD5BauD371IJv9m3l82eNUh6uT6WB9DrxpLjZ/U868l9eS3bu51TVKqdYqTCoAjgDOBo4o\nLCws0GCu2pp2ue9GENyO+u7sHVun9k3tyzvnvUMwGmRL7RbWVK7hxD4nMm/LPHqn9GZwxuBG54qG\nQ4SDQURspGRmUlfj58Xf/gqTSLDwjZeZfP9jpGZlQ6AMlr4EoaqmG+Uvgox+9PlhnKF5dn79+a9Y\nVbEKgD6pfbjt2Nv22M3ttDlx2VxEEhEcNgdue/Lv501zMWp8X6q315Ga7cGXtudNaZRSrWctTWuL\nLV+VapIG9N1kebK476T7eOSrR+jh68GkIZOA5KYwPXzJSWOZ7kziiTgFPQsIx8O47W7yfHm7nCcW\nCbNxSSHvP/oAabl5XHTHPYSCtZhEct/1UG0Nibj1hO7fCl88AhMfb7pRh55J0O3j3g1TmJCYsDMg\nQ/IDR0tj1pnuTP511r94f9P7nNb/NDLcGfXHp7vxpesENqWUOtBpQG9Cr5Re3D32buzYsdkaj0o0\nnOHenHAgwEfTniQWjVBZvIXV8+cxYvxpDDvxZDYt+4qCc87D5bV2gfRmJp/Sq4vgyMuTT+uQnNWe\n1gvGXM/WujIWb19MijOFh8Y9xOvrX6eiroJLD7u0xba4HW6G5QxjWE5Le1wopZQ6UGlAb4bTtn/d\nzzFJkJs/gEBlcrllj4GD8aSkMP7q64hHIjg93gYBPQd+NgvW/AdOug3G3kJdIp2qaju+zBR8TjeZ\ndhu9Unpxx5g7+Pe300l3pXNZ/wvZVlZERp8MXHZNmKKUUgezdpvlLiIe4FPATfKDwyvGmLtEZCAw\nA8ghmVDgp1am+bLdigAADWxJREFUoGZ1hXXo+8If9nN/4f1ckn8+/nXf0bv3QHr1HYQ7ZbdJZ/EY\nNLHULRSIsvabrfgyHfi3Rug7OJfMPh7KQ+X8ZeFf+PC7DwG44YhfcMGASaRlZu+csKeUaje6HlN1\nae05yz0MjDfGjAKOJJkubwzwF+BBY8wQoBK4ph3b0CkiiQiLSxbzk7lXc3/Nc7wVmIvD5cJfVkrR\nym8IVFVC5WZ485ew8EkIVuxyfB215A6AjQveIVy7GIcnhNPuxGP3UBIs2VlvW7gEnydFg7lSSqn2\n63K3svDUWi+d1pcBxgOXW+XPAXeTTHLfbaS50rj12FuZMncKFaEKJg6ZSKC6kmenXE8sHCY3fwAX\n/uwCUr6eAV/PgOwhMGT8zuOj4To+f+opNi9PJmtxOlwM/+HJ2MJR7j7+bm799FZSnalcN/IXpKa2\nPJ7fWaKhOBXbailaVcmQo3uQluvVLWGVUqqdtOsYuojYSXarDwEeA9YDVcbsTAvWLfPfuu1uxhwy\nhlkXzEJEyPHk8P23y4mFwwCUfbeJhKt+pjnR2l2OtxuhrrY+yUuwuop5059j+ZwPOGL8BJ667B84\nXW6yPFkd8vdprbraCK/+ZTHGwJLZ33HZncfplrBKKdVO2nVjGWNM3BhzJMm0eKOBw/f2WBG5VkQK\nRaSwtLS03drYFrYHt/PehvdYXbGa2kgyOHscHvJ8eeR6c5NBvU8/cvrmAzDy1DNxpGZD/vEw+jrI\nP2GX83nTMphww830GnIog445lpHjT2f1/E8BWPHRbJyBeJcP5gB1tVF2TNEIB2Ik4l1/V0KllDpQ\ndcgsd2NMlYh8DBwPZIqIw3pKbzb/rTHmSeBJSE6K64h2tkZZXRmT359MUW0RAC+d/RI/yP1Bo3op\nmVlcdOefSMRiOFwuvCmpcOl0cLjBtWvucJ/Lh6PHIZxzy1RikQgGQyySnDfoSU3D6TkwxszTczwc\nelwvilZWcNRp+bi8uqhCKaXaS7v9hhWRPCBqBXMvMIHkhLiPgQtJznRvmEv3gBSNR3cGc4CvS79u\nMqADpGTsNt7ta/4p2+Xx4rICdywSZvL9j1GyYS19DhuOLyOj2eO6Em+aixMvGUo8msDpseNya0BX\nSqn20p6/YQ8BnrPG0W3ATGPMOyLyLTBDRP4ILAGebsc2tDuPw8OE/hOYvXk2We4sftT3R21+DYfL\nTXbvPmT3bn66QUWogtUVq+mV0ose3h6kuLrGvuwen24nq5RSHUGzrbWBylAlgWgAt91NjjcHm3Rs\nzpuqUBVT501l/tb5CMKLZ7/YZC9BVTBCNJ4gw+vE5Wgi21szKgMRwrEETruQk6qT2tRBS5doqC5N\ns621gSxPFn3T+pLny+vwYA4QTURZsn0JAAbD0u1LG9Upqwnz3zOWMOmx+Xy+rpxQNN6oTlPKa8Pc\n/trXjPnzHG548SvKasNt2nallFJtQwN6N+B1eLl25LUA5HhyGN9vfKM6H64q4dM1ZWypquOm6Uvw\nh6J7de7acIxZK5Kb2Xy5sYKKwB439VNKKdVJdJZSN5DqSuWSwy7hnEHn4LA5yPHkNKpzSEb9zPie\n6R5se9l76HXayUt1U1obJs3tIMOrY+JKKdUV6Rj6QaIqGGH++nJWFvu5bHQ+vTP3bumbMYZt/hAr\ni/0c1iudnmluHHbt2FEHJR1DV12aBvQOFE/EqQhV4I/4yXRnkuNt/CTdFYQCUaLhODa74EtzIbpd\nq1KgAV11cdrl3oFK60q54K0L8Ef8jModxcPjHybbm71f56yriRCLJrA7bfjS9j+FajgYZemH37H4\n/c14Up1cOPUYMvJ8LR+olFKqU2nfaQfaULUBf8QPwLKyZYTj+zdjPOiPMOupb3j+t/P54MnlBP37\nP2EtFkmw+P3NAIRqo3z7efF+n1MppVT704DegQZnDt45YW10r9G4Hfu3pjsairFlTRUAxWuriYRi\nLRzRMrEJmT3rn8h7Dkzf73MqpZRqf9rl3oHyfHm8fO7LBGNBUp2pZHv2r7vd4baTkukmUBXGl+HC\n6d77zWKa40t3MfFXR7Fx6XYye6aQl5+23+dUSinV/nRS3AEuUB2mpjxEWo4HX7oLEZ23o1Q70f9c\nqkvTJ/QDXEqGW3OMK6WU0jF0pZRSqjvQgK6UUkp1AxrQlVJKqW5AA7pSSinVDWhAV0oppboBDehK\nKaVUN6ABXSmllOoGNKArpZRS3YAG9AOISRjqaqNtsme7Ukqp7kV3ijtAJOIJyrbU8un0NWTkeRl7\n4VB86fufLlUppVT3oE/oB4i62ijvPvY1JRv9rFlYwvqvtnd2k5RSSnUhGtAPECLskk2tLTKrKaWU\n6j60y/0A4Ut3c+5No/jyzY1k9/bRf0ROZzdJKaVUF6IB/QCSkefjlKuGYbMJYtNMjkoppeppQD/A\n2B06SqKUUqoxjQ5KKaVUN6ABXSmllOoGNKArpZRS3YAGdKWUUqob0ICulFJKdQMa0JVSSqluQAO6\nUkop1Q1oQFdKKaW6gXYL6CLST0Q+FpFvRWSFiNxslWeLyGwRWWv9mdVebVBKKaUOFu35hB4Dfm2M\nGQ6MAX4pIsOB24E5xpihwBzrtVJKKaX2Q7sFdGNMsTHmK+v7GmAl0AeYCDxnVXsOmNRebVBKKaUO\nFh0yhi4iA4CjgC+BnsaYYuutbUDPjmiDUkop1Z21e3IWEUkFXgVuMcb4ReqzhBljjIiYZo67FrjW\nelkrIqtbuFQGUL2PzdubY/ZUp7n3di9vql7Dst3fzwXKWmjXvurK96epsj29bo/701y72uKYg/ke\n7W39fb1HnXF/PjDGnLGPxyjVcYwx7fYFOIFZwJQGZauBQ6zvDwFWt9G1nmyPY/ZUp7n3di9vql7D\nsibqF7bDv0WXvT97c892u19tfn/0HrXPPdrb+vt6j7rq/dEv/erMr/ac5S7A08BKY8wDDd56C5hs\nfT8ZeLONLvl2Ox2zpzrNvbd7eVP13m7h/bbWle9PU2V7cw/bmt6jlu3rNfa2/r7eo656f5TqNGJM\nkz3e+39ikR8C84DlQMIq/i3JcfSZQD6wGbjYGFPRLo04QIlIoTGmoLPb0VXp/WmZ3qM90/ujuqN2\nG0M3xnwGSDNvn9Je1+0mnuzsBnRxen9apvdoz/T+qG6n3Z7QlVJKKdVxdOtXpZRSqhvQgK6UUkp1\nAxrQlVJKqW5AA3oXJyLDROQJEXlFRK7v7PZ0VSKSIiKFInJOZ7elKxKRcSIyz/pZGtfZ7elqRMQm\nIveIyCMiMrnlI5TqejSgdwIReUZEtovIN7uVnyEiq0VknYjcDmCMWWmM+QVwMTC2M9rbGfblHlmm\nklwOedDYx3tkgFrAAxR1dFs7wz7en4lAXyDKQXJ/VPejAb1zTAN22UJSROzAY8CZwHDgMis7HSLy\nY+Bd4L2ObWanmsZe3iMRmQB8C2zv6EZ2smns/c/RPGPMmSQ/+PxvB7ezs0xj7+/PYcB8Y8wUQHvC\n1AFJA3onMMZ8Cuy+mc5oYJ0xZoMxJgLMIPnUgDHmLeuX8RUd29LOs4/3aBzJFL2XAz8XkYPi53pf\n7pExZsfmTpWAuwOb2Wn28WeoiOS9AYh3XCuVajvtnpxF7bU+wPcNXhcBx1njneeT/CV8MD2hN6XJ\ne2SMuRFARK4CyhoEr4NRcz9H5wOnA5nAo53RsC6iyfsDPAQ8IiInAp92RsOU2l8a0Ls4Y8xcYG4n\nN+OAYIyZ1tlt6KqMMa8Br3V2O7oqY0wQuKaz26HU/jgouiYPEFuAfg1e97XKVD29Ry3Te7Rnen9U\nt6UBvetYBAwVkYEi4gIuJZmZTtXTe9QyvUd7pvdHdVsa0DuBiEwHvgAOE5EiEbnGGBMDbiSZP34l\nMNMYs6Iz29mZ9B61TO/Rnun9UQcbTc6ilFJKdQP6hK6UUkp1AxrQlVJKqW5AA7pSSinVDWhAV0op\npboBDehKKaVUN6ABXSmllOoGNKCrLk9E5nd2G5RSqqvTdehKKaVUN6BP6KrLE5Fa689xIjJXRF4R\nkVUi8qKIiPXesSIyX0SWichCEUkTEY+IPCsiy0VkiYicbNW9SkTeEJHZIrJJRG4UkSlWnQUikm3V\nGywiH4jIYhGZJyKHd95dUEqpPdNsa+pAcxRwBLAV+BwYKyILgX8DlxhjFolIOlAH3AwYY8wPrGD8\nHxE51DrPCOtcHmAdMNUYc5SIPAhcCfwNeBL4hTFmrYgcBzwOjO+wv6lSSu0DDejqQLPQGFMEICJL\ngQFANVBsjFkEYIzxW+//EHjEKlslIpuBHQH9Y2NMDVAjItXA21b5cmCkiKQCJwAvW50AkMxJr5RS\nXZIGdHWgCTf4Pk7rf4YbnifR4HXCOqcNqDLGHNnK8yulVIfSMXTVHawGDhGRYwGs8XMHMA+4wio7\nFMi36rbIesrfKCIXWceLiIxqj8YrpVRb0ICuDnjGmAhwCfCIiCwDZpMcG38csInIcpJj7FcZY8LN\nn6mRK4BrrHOuACa2bcuVUqrt6LI1pZRSqhvQJ3SllFKqG9CArpRSSnUDGtCVUkqpbkADulJKKdUN\naEBXSimlugEN6EoppVQ3oAFdKaWU6gY0oCullFLdwP8HkOCw8CrGzdAAAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7fdd0fe0c358>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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cMoXwipWI389Oj/+RjNFjqJ83j0hFBbHYxl32rVlZXsuNr85i/MT5lFQ30Ds3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\nUkqpGPD/AVpeh4x0Xu7DAAAAAElFTkSuQmCC\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7fdd0fdd9cf8>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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RuMyjjyZq9xGpiRAXQ8QG7g4WlLHWgD+I+nCMJ74o4dW5awBYVxfm8iOH4Ggx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\nrpUAb8TzAAAAAElFTkSuQmCC\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7fdd0dc7dc88>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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p4R4Xbfbeu3kZ1b5oQO/EbA5rwy9AtLQUY0xDy9U02X3NtNFs7Fgc1lUFE9c0\nsKainr55zf+PcA8ZQry+nuCqZY0fGkMkFCLkjHLBexcQioV4bfFrvH3iFGKrixqKRVavxkQitCRW\nU0Px7++gblqiN9HWvTv26y4lRhy3zY3P4WtWvrwuxB+nLOT9eev5+cEFXHPMPjs27LAZh9tGXi8f\nG1fWkN8vvWFioFIpdrmIXEhiotwsErPeVQelY+iKSHExReecy7JjxlD72eeI00n+H+/Be/Ro/Ced\nSM5VV7bJGLTHaeXW8fvjsls4vE8WA/McEKymKhCmqKyONRX11Fsd2HNz6XPoERQMPACb3cHg407E\nJlZiJkYolmjlxk2cMGE8Z/wc18AB2PLz6XrP3VgzWt/WVayJ8Xux2+H8U7jkw0sZ8+oYXlj4AjXh\nmmZlK+oivP7DWurCMZ76cgW1wV37UpPmdzLhmiGc/8fDOOGKwW2SyU2pbTHGPGSMGWqMGWCMOdcY\nU7+n66RSR1KVXlFE9gNebvJRH+D3wH+SnxcCRcCZxpiKrV1rxIgRZubMmSmpZ2dWX1VJOBhAgiEq\nbv89ge++w5aXS+Hrr2PPzSVWV5cYb05La/H8YG0tsWgEl8eLWG2EA1GsdkuLE982qa2uor60CFv5\nErI+uYnwZZ/z2pI4t/5vHgBPnD+c4wcmuuqryiswsSjRunosX36JbeJY5lcvZWHZUjwOF97oUHKs\nfvqmxagNhsnsnke6d8vld5tENmxgwwMPYO/Rk/mnDuaGz28EwCIWPjr9I3LTGpfprq8KMvovnxKM\nxPG7bUy98Si6+F2tXVp1DjrLW7VrKev3M8b8BAwFEBErsBb4H3AL8LEx5n4RuSX5/uZU1UO1rL6q\nknf+9gCrF8zF5fFy5s23Ern2Bpz9+iVasLDViWP1VZVMfepRSletZMwlV+LN7sO0l5eTU+BjxLhe\nuL0tt0C9Uo/3v6PBJLr0A6EIk2dvaDj+1o/rGL1fLg6blfSsTEwsRtzpRM48g6oILFiRx7fLbVw7\nug9L1pRz1uTEPJ9Th3Xnnt7dt/rM9i5d6HbvvSBCn2Ax1mSrv39mf6yW5l9Csjx2plx/JDNWlHN4\n32xydFa6Uqqd210DeWOAZcaYlSIyERid/HwS8Bka0He7UH0dqxfMBSBYV8ui2T8w/KEHcRQUYEt2\nW8djMQI1VcTjcSwWK26/H0sy8K2aP4elM74G4O2H7uPkX/+VdUsqWbekki69fOx7cNeWb+zww88e\ng4/uhO4j8KRncuHhbr5dUY5VhPMOLcDRJHGJWK0Nk9yWF1dw9zsLAZi+tJSPfjGcXx7Vi42BOL88\nbl/SHM1/nY0xxGtrEacTiyNX/y6qAAAgAElEQVQRkC2uRCu7i7ULk382mVU1q+if1Z8sV1bzatqs\n9M310jdX5wEppfYOuyugn01ilyKALsaY4uTr9YDuPrQH2J0ubA4n0XBiTDqvd1/Shg1rOB6ormbe\n5x8x8+03qK+qxJedwyGnns2+hxyB2+fDm9kYAL2Z2URCTSbRbWUUp9Y4MX0mkHbZaKwOFzZ3BqP6\nRZl+8zGIQEZa67PJ4/HGe8QNxKqquGJ4VyQzE7e9eQvbRKMEf/qJjX/5K+5Bg/BfdhnVFgdWEbK9\nTtw2NwX+Agr8bZf7XCml9qSUjaE33EDEAawDBhpjNohIpTEmo8nxCmPMFhtsi8gVJLcpLCgoGL5y\n5cqU1rOziUUilK9bw+yp79Jt3/3pPWwEbl+iJRysq2Pa888w5+P3tzjv0NPO5qCJpxOLRFi7aAEb\nVyxj0NHHUV9t58tXlpLT08shJ/fB7duyi7qiPswTny/j7TnFnHtIAecc0ot09/YvB6uoDfH81yv4\nbnU1143IpdfiWWSNP6HFMf5ISQkrTjqZWGUlrlFHsfG3f+CGV+fRxe/kiQtG6Hi42hmdbgxdRL4y\nxhy+p+uhts/uCOgTgWuMMccn3/8EjDbGFItIPvCZMWa/rV1DJ8WljjGG5P7PDapLN/LUdZcz/Kwz\nyD9gMOG6OmY8/SwVxeuw2u1c9ven8GY132gmFosTDsSw2QV7K1u1riit5ei/fN7wftpNR9MzK414\nLI7Fun2z6EM1ddRXVOKsrsDRvTu2zJaTrURLSlh+yqnESktx/fXvXLzARlFZYoLvjcftyw1j+m3X\n/ZRqotMEdBGxGWM0e9BeZncsW/s5jd3tAJOBC5OvLwTe2g11UK3YPJgDlK5aydBTT2F+9wpOm3Ye\ntyy9l6N+dQM2h5NYJEJtRdkW51itFtxee6vBHMBlt2K3Ju7ncVjxWyzMmrqKj/+ziLJ1tcSarH1v\njTUUIE3iOPLzt7pEzZqVRa9nn8F3/HG4u+dTkN3Yit8nd/fuEqc6t8JbppxTeMuUosJbpsSTP89p\ni+uKyJsi8r2IzE/2aCIitSLy5+RnH4nIwSLymYgsF5GTk2WsyTLficgcEflF8vPRIjJNRCYDCzZd\nr8n9bhaRuSIyW0TuT352efI6s0XkdRFpeUmM2i1S2kIXEQ+wikQO3qrkZ9nAK0ABsJLEsrXy1q+i\nLfTdbe1PC6i1BDnr60uImcSOZncNvZ3q56ZRuqqIi/76GNk9ejaUD9TWEA4EsNpseNIzWl2zHghH\nWVpSx9QF6zlzRE/Kfyxn2suLAXC4rJxz56F4MlrfDjVaVsbqX/yC4Lz52Lp2pferr2DLbS0jZEI8\nFELsdkrrIrw7t5juGW6GF2bu0iYxqtPa4RZ6Mng/CTQNdPXA5UX3T3hhlyojkmWMKRcRN4ltXY8C\nSoHxxpj3ROR/gAeYQCIb2yRjzNBk8M8zxtyT3Cp2OnAG0AuYAgzalKFNRGqNMV4RGQf8jkSK1vom\n9842xpQly94DbDDG/GNXnkvtvJROijPG1AHZm31WRmLWu2qnMrrks3HxLAZlD2J26WysYqVfZj++\nqJxMel4XXN7EzG9jDHWV5cx6/x1mvPkqbp+fc+99iPS8luc5uh02BndPZ3D3dACWrV/dcCwcjG2z\nhR4PBAjOmw9AdP16IiUl2wzoFmfiC0Kuz8mFhxdu1/Mr1YbupXkwJ/n+XmCXAjpwvYicknzdk0R+\n9DCwafLLXCBkjImIyFwSe38AHA8cICKnJ9+nNzl3RpN0q00dCzyzaWOaJo2wQclAngF4gQ928ZnU\nLtD9J9UWXD4fXTK784fcm1kRWE1Pf08Wv/MhsUiYk2+7m7T0DIwxlK1ZRV1lBT9+8A49BwymcN8B\nVG8objWgb27ImJ4s+2EjgZoIA0d1w7GN7VAtbjfuoUMJ/Pgj9u7dsW8jmCvVDrS2jGKXlleIyGgS\nQfawZIv5M8AFRExjt2ucZPpTY0xcRDb9AxPgOmPMBy1cs44d8yzwM2PM7GSCmNE7+iyq7WhAV1uw\nWm3kFfYmUF1N7dI1rPziU3oU7svRZ16EOz0DEaG+uor3//kQ/Y84ilFnnk93m4u6F1/CubGcaI9e\nrU5Wa8prC3LqRd2IG8HusOCwRIHWZ73bsrPp8cgjxOtqsaSlbbN1rlQ7sIpEV3ZLn++KdKAiGcz7\nA4fuwLkfAFeJyCfJ1vu+JDb+2pqpwO9F5PmmXe6ADygWETuJ1Kvbuo5KIQ3onZiJx1sd77bZHfiy\ncxhy3DjiY8ZiaaGcxWrjq1df4PK7/0rR+AkQjVL/9de49t8f//HHb1G+pCZEcVWArn4XOV4nNR9+\nyPo7E5nSxOWi79QPsbhb37oVwJaTDTk7kMpVqT3rVloeQ791F6/7PnCliCwkkff8mx049ykS3e8/\nJLOwlQA/29oJxpj3RWQoMFNEwsC7JJ7hd8C3yWt8SyLAqz1EA3onFKutJfDDD1S/+y4ZZ5+Nq3//\nhh3UWtJSME/zpzP+2l/z7iN/JVRbA9HGFS7xmpotym+sCXL6Y1+zqryebI+D9244Ek9hYcNxR8+e\nbZIARqn2pOj+CS8U3jIFEmPmBSRa5rfu6oQ4Y0wIGNfCIW+TMndudo43+TNOIhhv/qXis+SfLc5J\nvr4fuH+z448Bj+1g9VWKpHwdelvQWe5tK7x2LcuOPQ6MQex2+k6dir3rzm3YV19dhTUcoXby25Q9\n9RSuQQPpdt992LKbt6JXltVx1J8/a3g/5fqR9PdAYO5cQouX4J8wHnuXnatDrL4eU1cHViu2rKxt\nn6DUzuk069DV3klb6J2QCYUa9mc1kQgmHtvpa6X5EzPWbWefhf+kExG7vWEv+KY8Thsj98nmy6Vl\n9MnxkOdzYvW58I4ciXfkyJ2+f7SigrJ//YuKF17EuW8/evz9H9i75e/09ZRSam+lLfROKFpZSeVL\nL1Pz4YdknnsuvrHHY/WmLglJoKaGQE01oUA9roxs4k4P2d7W15vv0LXnz6fotNMb3vsnTiT/zju2\nORav1E7QFrpq17SF3gnZMjLIuuhCMs48A4vX25CJLBXCwQA/vPsW37zxEgB5hX049da7gLYJ6CYY\nbPY+XlODiW97xzmllOpodBZSJ2VxubBlZbVZMI/G4lTUhwlEmnffhwMBvpv8WsP7jUXLqSktaZN7\nAjgKC0k75BAArBkZ5P3qxq3mcVdKqY5KW+idVMNEMhFsOTm7dK1gJMbMonIenLqEg3tn8otRfcn0\nNH5RcLjTCNRUN7y3b2VG/Y6yZWfT/W8PEa+vR+wObFnbXv++Sby+nkhxMaHly0kbOlTXtSul9mra\nQu8E4sFgs27oeH09NVOnsvSYMRSdex6R4uKtnN2ypnMvqgIRLnl2Jj+squDxz5ezaH1j8Hb7/Ey4\n4SbcPj8Wq42DJ55Bmr/1pCo7w5aZiaN7d+x5uYht+7+jRjZsYPlJJ7P2uutZdellRMu2mlJAKaXa\nNW2hd2DxSITQwoWUPfEkaUccTvq4cVgzMojV1bH+jjsxkQiRlSupfPVVcq+/fruuaWIxwitXUj7p\nP6Qdegieww8HcWK3CuFkb7vDZm0ob7XZ6LH/IC748yOAweFy43DvekKmQE2YQG0Ep9uGy2vD2uSe\n2yu8fDkkv+iEliyBXZjtr9TeJrnVa9gY81Xy/bPAO8aY17Z23k7e6yngQWPMgra+tmqkAb0Di1VU\nsPLCizCBADUffYR78AG4MxLZ0BwFPQktXgKAY599tv+a5eWsPOdcYpWVVL78MoWvvEzWwEG8c/1I\nZqwox++y03ez9KRWmw1vZtutDw/Uhvno2QWsml+OzWHhrNsOJqPLjn9JcA8ZgmvAAIKLFpH3m18j\nOjNepcKd6eew2cYy3Fm1q4lZ2sJooBb4KtU3MsZclup7KO1y7/ia7OBmohEgMe7c84knyL3xRro/\n/HCilb2djDHEmuwEF6uspD4cY9aqSj5ZVEKO14nbseOt5R0Ri8ZZNT/RPR4Nx1nzU8VOXceWk0PP\nJ59gn88/I+OMM1O6dE91Uolg/iSJ/dwl+fPJ5Oc7TUQ8IjIlmYd8noicJSJjRGRWMmf5v5OpURGR\nIhHJSb4ekcyPXghcCdwoIj+KyJHJS48Ska+S+dNPb/Hmiet4ReRjEfkheb+JrdUr+flnIjIi+fox\nEZmZzNn+h135e1DNaQu9A7P6/fR88glK//lP0g49DEevwoZj9q5dyfnFFVu/QDwG5cvh2ydgn6Oh\n4HCsPh/d//YQJQ/9DffQIbgGDWJlTYhfvTIbgM9+2sjnvz2arumpC+pWq4Ue/TNZs6gCm91Cj/22\nfyLc5jbf0U6pNpaq9KknAOuMMRMARCQdmAeMMcYsFpH/AFcBf2vpZGNMkYg8DtQaY/6SvMalQD4w\nEugPTAZa634PAqcYY6qTXxa+EZHJrdRrc7clc6lbgY9F5ABjzJyd+UtQzWlA78AsLhdpBx1Ej0ce\nQZzOhtzg27JpIxi7w4br639h//5J+O4JuOorLF0G4h01irRhwxCXC6vXS2xDkxZ73ACp3azI7XNw\n3KUDCVSHcXpsuD2tZ2hTag9LSfpUErnO/yoiDwDvANXACmPM4uTxScA1tBLQt+LN5F7vC0Rka3sx\nC3CviIwikaa1O9Bl83oZY6a1cO6ZInIFifiTDwwANKC3AQ3oHZxYrVj9/u0uHw4G+P6d//Htm69g\nsVr5+S230HXl51C6GIJVAFg2+3KQ53Nyx0kD+GjhBq4Y1Yf0tNQH2DSfgzRf6jbEUaqNpCR9arIV\nfiAwHrgH+GQrxaM0Dq9ua81oqMnrre2Mdy6QCwxPpmAtAlyb10tEPjbG3NVwQZHewG+Ag4wxFcmJ\neG23jrWT04DeDsWqqgjMm0dkXTG+o0dvfZ14sApKfoLVM6DfcZBRAPadn9wVCQb56evEl+p4LMbS\neYvous8Y6DMacvZr8ZyMNAfnHdqL0w7sgcdpw2rZ9g6Z0XAIi83eYia3TeKBALHaWqQN1sortYek\nJH2qiHQDyo0xz4lIJXAtUCgi+xhjlgLnA58nixcBw4H3gNOaXKYG2P5v+82lAxuTwfxokl9aWqjX\n5pPh/EAdUJXsARjHZhne1M7TSXHtUN2MGay+9DLW/+53rL35ZqKVla0XXjcLnj4OPrwNHh8JNet3\n6d4OdxoHn3IGADank/2OGA2jfgtj7gBP6+PNdqsFv9u+zWAej8coXb2SKf/4CzMnv95sw5lm5YJB\naqd9ybLjjk+slV+3bqefSak9JjGb/XJgJYmxqJXA5W0wy30wMENEfgTuAG4HLgZeFZG5JLrBH0+W\n/QPwsIjMBJquzXwbOGWzSXHb63lgRPJeFwCLWqnXPU1PMsbMBmYly78ATN/B+6qt0OQs7VDJI49S\n+sgjANjy8+n9yssYn49gXS0mHsfp8eBMSy4Nm/LbxPj2Jmc8CwNPaXa9UDRETaQGl9WF19H6TO5A\ndTXrlv6EzW4nM787FosFt8+P1d52Xeh1FRVMuulaAtWJ7vvTbr2LwiEHblEuUlLCip+dQqysDIDs\nX1xB3o03tlk9lNoJmpxFtWvaQm+HMk4/Dcc++2DxeOh65x2I38/axQt58tpLePLaS1j8zXSi4XCi\n8P4TGk+0OiB/aLNr1Ufq+XT1p1zw3gXcP+N+KoItL/GKhEN8+9arvPnAH3jtnttZ/v0MvFnZbRrM\nARDTfNe6WMubuYjNhqNX49Cjc7+Wu/uVUkol6Bh6O2Tv2pVezz6LMXGsPh9RE2fWe5MbAuEP702m\n7/CDsTkc0G0YXDq1cQzd17XZtWojtdwy7RZiJsbqmtWc1PckDsk/ZIt7xsJhNixb0vB+7eKFDD72\nBKzWtl1+5vKlc/rtdzPtxf+Q37cf+f1aDtS2zEx6PPw3qt//AHv37rgPHNam9VBKbZuIDAb+u9nH\nIWPMlv+JqD1OA3o7ZctpHK+2xWP0HXEIy3/4DoDew0Zgc7qoClZRHa7GkdWLjPwhOG1bLkuziIUM\nZwZlwWTXtbvlcXBnmodR517E6/fegc3h4LBTz9qhYB6rqSEeDGKx27FmtL5Xu9VqJa+wLyf98has\ndju2rfQA2HJzyTr/vO2ug1KqbRlj5gJDt1lQtQs6hr6XCNbWUFNWSjQcJqNrPsZlY9L8Sfxz9j+x\nW+w8N/45BmQP2OI8Ywxratfw5tI3OajLQQzMGYjP4WvxHrFolGBtNSCk+dOR5Az00toQL3+3GqtF\nOGN4D7K9zb84xCorKX3qKSr++xyeUaPI/8Od2LLabqtXpdoJHUNX7Zq20PcSLq8Pl7cxEJcGSplf\nPp9hecNYULaAqUVTWwzoIkJPX0+uG3bdVq8fN3HKIxXE7DG8dm9DMA+EY/zp/UW8MnMNABuqg9w6\nbn/stsbpF/G6OsqfehqA2qlTiVx1pQZ0pZTazTSg76Uy4oY/dzmGcOVqqoZcT9C1s8tJE9bVruPW\nL2+lh7cHo3qMYlSPUaTZ04jG41QHokwYnI/VAjXBKNG4oVlHud2RyOJWWQl2O7bMnd+KVSml1M7R\ngL6Xsi18G9s7N+IG/H2PIX7qk7t0vYUlC7it9/Ws+Hw6+Q4noaw60tLT8Lns/OPkbpivH0NiYeTw\na7FtlnzFlpNN4WuvUjf9K9KGH4hVA7pSSu12umxtbxSPw9rvG97KxoVYtzeXd7AaqtZC0XQoWQR1\nJQAM9w/m/XvvY/4nU/nowb9CIJGZjbpS7K9egOObh7F/9xi2/54EtSXNLikWC44ePXBPGEc0J5uI\n5hVXqt0QkTtF5DcpunZDJrf2SERyReTbZBa6LTbPEZGnRGTLscq9lLbQ90YWCxxxIyx+HwIVcMJ9\nsFmXe6CmmnAggNVux5ORiYhAfTl8+RB8/QiY5FrwLoPg3FewYm9c2w6Nr+MxKP6x8cKVq8BixRiT\nuGZSXWUFb9x3ByUrixj58wsYcty4xs1vlOrEBk8avEU+9LkXzm0P+dD3KBGxGWOi2y65S8YAc1vK\nxy4i1o6Wp11b6HurrD5w1Vdw43zod3yz/duDtTV89erzPHXdpfz35uupKStNHFj3I5GMAqpPf5rg\niEtALLBhHrxxBW6HjQnX/Ya83n057LSf481MLm+zOWHgqYnXFiuB8z9j1pc1THt5CbUVwYZ7Fi9Z\nxMai5RgTZ9qLk4iEmuZ4UKpzSgbzLfKhJz/faa3kQ98i73mTU4aIyNciskRELt/KdfNF5IvkdrDz\nNrVqt5HD/LomedH7J8sfnLzfrGR+9f2Sn18kIpNF5BMSqVNby6teKCILReTJ5D0/FJFWk1SIyOUi\n8l3y7+N1EUkTkaHAn4CJyedxi0itiPxVRGYDh22Wp/2EZD1mi8jHW3uO9kpb6HsriwW8LWc3jEYi\nzP7wPQDqqypZt3ghnjQn5dYezF24lK5DulLR/0QGdT+Q9LeuhaIvcZh69jn4cAoGD8XudGHflE3N\nnQFj74UhZ4ErnZ8W+vnqjWUAbCyqZsI1B+D2Ocjs1gMRC8bEye5RsNWkK0p1IrszH/oDWyl/AHAo\n4AFmicgUY0xLCRLOAT4wxvwxma98U923lsO81BhzoIhcTSKT2mUk9mo/0hgTFZFjk8+7KTHMgcAB\nyevZaDmvOkA/4OfGmMtF5JXk+c+18nxvGGOeTP5d3ANcaoz5h4j8HhhhjLk2ecwDfGuM+XXyPcmf\nuSS+eI0yxqwQkU3LdLb2HO2OBvQOyGqz0WvIMIp+/B6bw0nXPv0IBEO8eOdtRIIBeO9tTv3TA2zM\nTSPd7oZIAGJhbHY7Nnv6lhf0ZEPfYwCo+7pxN7n6mjDxeGIfA192Lhf+9VHKVq+i2377k5be+uYy\nSnUiuyUfujFmWtMhsBa8ZYwJAAER+RQ4GHizhXLfAf8WETuJ3Oibxtu2lsP8jeTP74Fkdx7pwCQR\n6UciKU3ThTFTjTHlydet5VWHRH73Tff/HijcyvMNSgbyDMALfNBKuRjwegufHwp8YYxZAdCkflt7\njnZHA3oH5Pb5GXfNjdRWVOD2+XH7/NRXVSSCOYAxBOvrcPvtYAz4u4Fj+8a7hxxbwPoV1dRXhTnu\n4gG4vInfb4fLRXb3nmR375mqx1Jqb7Rb8qEnu4i3lvd88x3EWtxRzBjzRTK4TgCeFZEHgWlsPYf5\npvG1GI0x5W7gU2PMKSJSSPMUqXVNXreYV32z62669tbyQj8L/MwYM1tELgJGt1IuaIzZkVm7W3uO\ndkf7RTuoNH8Geb1648vKxma340zzctwV15HVvQcHjD8Rd7qPrJVfQzwCEx+FtO2bqOrNcDL+qsGc\n9tsDyS30YbXqr5BSW3ErifznTbVVPvR6Y8xzwJ9JdGMXkch7Dlt2C08UEZeIZJMIdt+1ct1ewIZk\n9/VTyeu2lMN8W9KBtcnXF22j3BZ51XeCDyhO9iycuxPnfwOMEpHeAE263Lf3OdqFlLbQRSSDxC/F\nIBLfCC8BfgJeJtF9UgScaYxpOQVYJxSNRKgtK6V42WK67dsfb1ZOmyRIcaalMeDI0fQZOgwTrcS9\n5F1sNcVwzXfgywfL9t/D7XXscn2U6gzmXjj3hcGTBkPbz3IfDPxZROJABLiKRAv2aRG5my1bknOA\nT4Ec4O5Wxs8hEex/KyIRoBa4IDmmvCmH+Wq2L4f5n0h0Vd8OTNlKueeBtyWRV30mjXnVd9TvgG+B\nkuTPlve3boUxpiQ5pPCGiFiAjcBxbP9ztAsp3ctdRCYB04wxT4mIg8QEi1uBcmPM/SJyC5BpjLl5\na9fpTHu5V5eW8Mwvf0E0EsbhdnPxg4/jzWqeUKUiWEFtpBan1UmWKwubZQe/lxmTGDe32hN/lFLb\nQ/dyV+1ayvpLk7MuRwFPAxhjwsaYSmAiMClZbBLws1TVYW9UuaGYaCSxBjwcCFBfXdXseFWoioe+\nf4jxb4znlLdOYUP9hh2/iQg40jSYK6VUB5LKAdDeJLo/nkmu4XsquWSgizGmOFlmPY0zGhWQ3a0H\n/tw8ALK698CT0Xwb1XAszNvL3gagOlzNrA2zdnsdO6q6SB1lgTKC0eC2Cyu1FxORwcm12U3/fLun\n67UtIvJoC/W+eE/Xq71I5Ri6jcSEiuuMMd+KyMPALU0LGGOMiLTY558cz7gCoKBgV1d47D08mVmc\nc/dfCIeCOFzuLQK6w+pgbOFYpqyYgsfuYUjekB26fjAaJBwL47F7sO7AuPmeVhmsJGZipDvSsVnb\n/te2MljJk3Of5Is1X3DRwIsYWzgWr8Pb5vdRqj3YW/OcG2Ou2dN1aM9SNoYuIl2Bb4wxhcn3R5II\n6PsAo40xxSKSD3xmjNnq7judaQx9e5QHy6kOV5NmSyPLmbXVABeLRQlUVRGNhLE4HTyz/DlmbZzF\nNUOv4YCcA3DanK2e215srN/IzV/cTGmglDsPu5MDcg/A3sbDBYvLF3Pa240Tgz88/UPyPflteg+1\n19MxdNWupazL3RizHljdZKu8McACYDJwYfKzC4G3UlWHjirLlUWhv5C8tLxWg3ltuJaS+hLKq0uY\ndNN1PH395Xz35mu4Yw6+3/A9V069kqpwVYvntiexeIzHZz/OzA0zKaou4rpPrqMyVNnm9/HYPUjy\n/2uP3YNV9p7eC6WUgtRvLHMd8Hxyhvty4GISXyJeEZFLgZXAmSmuw14tUFNNRfFabA4n/pxcXN5t\nr8aoDFXy7PxneWPxGxzT82hOueoXfPSnP7Poi0858IirgMYtD9s7QXDbGveTcFgdKal7hjODf4/9\nN5+v+ZyJfSeS5cza9klKKdWOpDSgJ7ftG9HCoTGpvG9HEQ4GmfHmq8x8538AnHDNrxg46phtnlcT\nruHpuU8D8PrSNzj1qJOw2mz0H3kUFrudQ7oewtVDrybD2f63Z7VYLFwy6BLKg+VsqN/ALQffQqaz\n7fOtexweRnQdwYiuLf26KqVU+7ddAT25cf3lJDaDaTjHGHNJaqqlAP6/vTuPj7I89z/+ubKHNYEg\nAtqiFkVxQQ0qohX3pf7cSl2r0npqPUr1p7Uutcft6Kn1/OpSl1qtFvRYLe6ouB0VRa1LqAIiooCg\n4MIW1pCQ5fr98dyBIZkkk2VmkuH7fr3mNTP3s9z3PJlXrnnu537uq6aqkoUzN6Uu/eLDMobufyDZ\nOc1fP87Pzqcgu4DK2kpyLIeSvgM5+493U9CtB9ndC7h1wG50z+tOlnWNWd76Fvbl6v2uptqr6Znb\ns8v0LohI24WJyU5397vbsO0CoqQsyzqgHdcTzfP+v+3dV7Ileob+DNF8vv9LNKeupEBet+7se+LJ\nPH/7f5Odm8vePzqhxWAOkG3Z3H3Y3byx6A1GDhhJbnYuxVv327Tf7K4301thbiGFzU7lLNI5zR66\nc6N86Dt/Ojtt+dAtNXnIO0IRcD7QKKCn8jO4+9WpqKcjJHqK1s3dL3f3ie7+RP0jqS0TcnJz2W7P\nUn5x1wOcc/u99Pv+dhuXra5azfL1y6murW60XUVNBVe/fTULVy/kpvdvYvWG1alstogEIZg3yoce\nytvFzH5qZu+He7H/YmbZZrY2ZvmYkEgFMxtvZveEe81vNrM+Zva0mc0ws3fNbPew3rVm9pDFyZ1u\nZr+xKOf4DGucE71h284K6003s4dCWT+LcpV/EB6jYup8wKLc5PPN7MKwm5uAHcLn+28zG21mUy1K\nr/pJ2PZpM5tmUc70c1tx7BptF47feIvywM80s4tjjt2Y8Prq0PaPzexe62TdhYmeoT9nZse4++Sk\ntkYaySsoJK9g8zPT5euXc8O7NzBv5Tyu3PdK9tpqr81uP+ue253BvQcz5asp7FGyR5e4Vi6SoZKS\nD93MdgZOAUaFxCZ303JSkm2A/d291szuAD509xPM7BDgQTbdl94odzpRPo4hRGlXDZhkZj909zfj\ntG0Y8LtQ17KYRCe3A7e6+1tm9j2iFKc7h2VDgYOJ5mCfY2Z/JrrNeVd3Hx72O5pobpNd69OcAj8P\nedULgQ/M7Al3X57AIfcuMFkAACAASURBVGy0HdEl5UHuvmuoL94/zjvd/fqw/CHgWODZBOpLiUQD\n+kXAb82siigRgBHNC9MraS2TJr3+1ev875fR5ZyLXr+IySdO3iyg9ynow40H3EhVbRX5Wfn0KdSI\nbZE0SVY+9EOJMqt9EE4SC4kSijTnsZjUoQcQMrK5+2tm1tfM6v+fx8udfgBwBFA/NWUPogDfKKAD\nh4S6loX91+cWPwzYJeaktpeZ1c/e9Ly7VwFVZraEpmcQfT8mmANcaGYnhtfbhjYlEtDjbTcH2D78\n2HkeeDnOdgeb2WVEP8r6ALPoagHd3VuVuUaSK3aUd8+8aJBYXV0dddRtTNTSp0BBXKQTSEo+dKKT\nqgnufuVmhWa/jnnbMCf6OhITL3e6Ab9397+0qpWbywL2c/fN5lYOAb5h7vOmYtPGzxDO2A8DRrp7\nhZlNofFnbqSp7UKu9z2AI4HziG6p/nnMdgVE1/NL3f0rM7s2kfpSKeFhzmZWbGb7mNkP6x/JbJg0\nbe/+e3PlPldy/A7H88CRD5BjOdz50Z1c8/Y1fLvu23Q3T0Q2SUo+dOBVYIyZbQVR/m4LuczNbGeL\nUoCe2Mz2Uwld9CHALXP3+sE28XKnvwT8vP6M2swG1dcdx2vAT8L2sbnFXyaam4RQ3tLUs2toPg1q\nb6A8BOWhRJcJEhF3OzMrAbLC+LDfEXXvx6oP3svCcRiTYH0pk+hta/9G1O2+DfAR0QH4J1HXiqRY\nUUERp+98OrV1tWRnZfPAxw9w38z7AFi4ZiF3HHIHxQXFVK5bR3Xleiwri+69i7CsrnGbmkim2PnT\n2X+fPXRn6OBR7u7+iUU5ul8OwbsauIDouvNzRImxyoi6xuO5FnjAzGYQ/cA4O2ZZvNzpX4fr9v8M\nZ9RrgZ8Sp5vf3WeZ2Y3AG2ZWS9RNPxa4ELgr1JlD1F1/XjOfcbmZvW1mHwMv0Dgf+YvAeWY2m6i7\n/N2m9pXgdoOIkonV/6PcrPfD3Vea2X3Ax0SJxT5IsL6USWgud4uSz48gmpt9ePhV81/uflKyGwia\ny70ld354J3+ZEfWE7VS8E/cefi89rJBZr7/Ca+PvpbBnL06/4Y8Uba25yUXaoVONaE6G0I281t3/\nX7rbIq2X6KC4SnevNDPMLN/dP7VNc7RLCq2vXs/a6rXkZuduHL1+6tBTWbBqAcsql3H1yKspLihm\n3cpy3n1qYrTNmtV8+s6b7HfSKelsOsvXL+fh2Q+TZVmcNvQ0+hb2TWt7REQySaIBfVEYwv808IqZ\nlRPNwy4ptK56HS8teInbpt3GsJJh3DjqRvoU9qGksITr9r+OmroasujG4pXrKajLYtthuzHnnalg\nxvd23Z0NVZVUr68gJ6+A/G4N76RJrqraKu748A6e+DyavmBl1UouG3FZl5zkRiRTufu1ia4brpG/\nGmfRoQneOpZUnb19yZDoKPf6wRXXhtsYehNdh5AUWle9juv+eR11Xsdbi99i5rKZHLTtQUA0F3ld\nnfPCx99wwd8/pHteNi+f/3OGH3ks3XsXkdetOx9OnsT0V15gyL77s99Jp1DYM3V3HdbV1VFeVb7x\nfXllOXVel7L6RaRjhaDYaXOqd/b2JUNrRrnvFWbw2R1Y5O4bktcsiSfLsuhXuGkK14b5utdX1/LY\ntEUArNtQy6WT5tHr+ztSPGAQNVVVvPXog6xZvpR/TX6GilWpTZ1amFvI5SMup7R/KSO2HsGlIy6l\nIKdT3fEhItKlJTrK/WrgJ8CToehvZvaYu9+QtJZJIyWFJTx49IO8vOBlduu3GwN6bB7QC3OzOXXE\ntrzx2VLc4eQR21CYF+X1zs7JIScvn5oNVVhWFjkF+fGqSKqBPQZy68G3Yhi983unvH4RkUyW6Cj3\nOcAe9RMChOnyPnL3lAyM0yj3xK2tqmZVRQ2O07swl54FUTKX2upqln39FR9PfZUBe+zG8h5VDB+0\nFzlZOeRn53eZzGsiaZTxo9yla0v0v/jXbD4jTj6wuOObI+3VIz+XQcWFbFPcbWMwB8jOzaWsdjaT\nBn3MlfN+z/lvjqO8spzL3ryMJz9/klVVqe2CF5HkMrPjzOyKJpatbaI8NhHJFDMrTWYbm2Jmw83s\nmBTU89uY14PDPe/t3Wc/M3vPzD40swPjLP+rme3S3nriSXSU+ypglpm9QjQN4OHA+2b2JwB3v7C5\njaVzKC4o5rUvXwOgb0FfFq9dzJSvpjDlqynsVrIbedl5LF+/nIWrFzKkeAglhSU6cxfpotx9EjAp\n3e1oo+FAKZCUhGAhS5oRzdj3Xx28+0OBme7+b3HqzY5X3lESDehPhUe9KR3fFEm2YX2HccvoW5i5\nbCY//sGP+e1bm2afNIy55XM584UzqfVa+hT04bH/8xhbdWtqdkcRScRd573WKB/6Bfcc0q6Z4sxs\nMNGdRu8C+xPNWvY34DpgK6JpXXchmnd8nJltR5TdrQfwTMx+DLiD6CTtKyDuYGczOyLsOx+YB/zM\n3Zs6y98buCXUtQwY6+7fWJSK9VwgD5gLnBmmX/0JcA3RHO6riOZZvx4oNLMDiOaQ/0eceq4lOqbb\nh+fb3P1PYdklbJqH/a/ufls4Zi8B7xEltnk/1PERUZKVq4DsMBvc/kS90MeHRDXxPmejzwPsCNwc\n9lsKjCSate8v4XNdYGY3AJe6e5mZHUX03cgmmn73UDPbhygzXQGwPhzrOfHa0FBCp1/uPqH+QfSL\n78MGZdLBlq1fxrTvpvHtum/j5jxvi975vTn8+4dzyd6X0K9bP84adha7luzKL3f/JYN6DOLpeU9T\nG5IxrahcwfyV8zukXpEtVQjmjfKhh/L2+gHwR6LUo0OB04myol1K47nibwf+7O67Ad/ElJ8I7EQU\n/M8iCmSbCXOc/w44zN33IppS9pJ4DTKzXKIfCGPcfW/gAeDGsPhJdx/h7nsAs4FzQvnVwJGh/Lhw\nB9XVwD/cfXi8YB5jKFEylX2Aa8wsN/yg+BmwL9E05b8wsz3D+kOAu919mLv/DFgf6jgjZvld7j4M\nWEnISNeERp/H3T9q0Pb1RGlo33P3Pdz9rZhj1Y/ou/HjsI+fhEWfAge6+55hXwn3ICQ6yn0KcFxY\nfxqwxMzedve4f1Rpn+Xrl/NvL/8b81bOozCnkKePf5qBPQZ2aB3dcrtxyLaHsM/W+1CYU0hBTgH7\n9N+HiXOi2eVyLIdte27boXWKbIGSkg89+MLdZwKY2SzgVXf3MFX34AbrjmJTcHoI+EN4/UPgkZBW\n9Wszey1OPfsRBfy3wzzueUS5POLZiSh3+ith3Ww2/YDYNZydFhGdvb8Uyt8GxpvZRDbdSZWoeGlX\nDwCecvd1AGb2JHAg0cnoQndvbs73L0JQhijWDW5m3aY+T0O1wBNxyvcD3qxPBxuTZrY3MMHMhhBd\n4s6Ns21ciXa593b31SFJy4Pufk2YYF+SYEPdBuatnAfA+pr1fLn6yw4P6AC52bkUZ29KxbrfgP34\n40F/ZNp30zhuh+OUR12k/ZKVDx02TzlaF/O+jvj/21u+pSk+A15x99MSXHeWu4+Ms2w8cIK7Tzez\nsUSZ3HD388xsX+BHwLRwhp2oRNOu1msphWzD/RU2s+544nyeOCpj8tAn4j+B1939xHCZYEqiGyY6\n4inHzAYQ5Yd9rhUNkzYoyC7gyMFHArBNz23YoWiHlNTbu6A3Rww+giv3vZJhJcMozGnuuywiCWgq\n73l786G31tvAqeH1GTHlbwKnmFl2+B9/cJxt3wVGmdkPAMysu5nt2EQ9c4B+ZjYyrJtrZsPCsp7A\nN6FbfmMbzGwHd3/P3a8mut68LS2nTm3OVOAEM+tmZt2JLitMbWLd6tCetoj7eVrhXeCHYXxDbJrZ\n3my6i2xsa3aYaEC/nqg7YZ67f2Bm2wOft6YiSVxxQTFX7XsVL/34JR46+iH6devX8kattGz9Mp7+\n/GmmfTeN1VWrW95ARNoiWfnQW+siogFZM4nShNZ7iuh/+SfAg8TpSnf3pUSB5ZHQM/tPomvXjYTr\n32OAP5jZdKJ02/XX5f+DaEDa20TXiev9t5nNDLeMvQNMJ0rfuouZfWRmrcoq5e7/Ijp7fj/U91d3\n/7CJ1e8FZpjZw62pI2jq8yTazqVEg+qeDMeqfqzAzcDvzexDEu9FBxKcWCbdNLFMxyqvLOfi1y9m\n2pJpANx7+L2MHBivh0xEYrRpYplkjHIXiSfRQXE7An8G+rv7rma2O9FoRE392snUeR0rKlfg7vTK\n60V+TuMpXmvqapi3at7G95+VfxY3oK+oXEFtXe3GmeRWVq2kuq6aPgV9NHWrSIJC8FYAl6RLtMv9\nPuBKoBrA3Wew6XqMBCsqV/DO4neYtWwW81fOZ82GNUmrq7q2mvLKctbXbH6L5MLVCznpmZM46omj\n+NeSf1FTW9No2555Pblq36vIz85nh6IdOGrwUY3WWb5+Ob969Vcc8tgh3DP9HhatXcTRTx7NcU8f\nx2OfPUZlTWXSPpuIdH5m9lToEo99HJmEen4Wp567OrqeZuq/K079P0tV/a2RaP98N3d/P9yGUK9x\npNiCra5azY3v3sjLC18G4JbRt1BZW8kufVs/w9/KypW8uehNvlr7FT/Z8SeNJndZV72ON756gwmf\nTGDkgJGMHTaWooIi6ryO8bPGb0xTetu/buPPh/2ZPtmbj1YvyCngoG0P4oWTXiDLsuhb2LdRG+aU\nz2HGsuhGhodmP8SPtv/RxmUvLXiJk4ac1KpsaauqVrG2ei25Wbn0KehDTlarLg2JSCcTk1Y72fX8\njWjSnLRw9wvSVXdrJXqGvszMdiDc9hDm+v2m+U22LFW1VUxfOn3j+1nLZvH12q/btK+pi6dy1dtX\ncc/0e7hkyiWUV5Zvtnx11WqumHoFnyz/hPs/vp/PV0bjE7MsixFbj9i43h799qAgO37QLcwppF+3\nfnGDOcCgHoM2Tvvat6AvPfN6bnx/4g9OpHtO94Q/z7rqdTz66aMc9cRRHPf0cSxasyjhbUVEJDGJ\nniZdQDQacKiZLQa+oG3D9DNWj7wejBs+jqvfuZq+hX05eNuD2zw6PfaHwJKKJRtnb2uKxYzVOXDQ\ngTx8zMOsrV7Lzn12pltuwzktEtOvsB8Tj53I9KXTOWDQARTnF/Pij1+ktq6WXvnxr803paK6gkfn\nPApEwf21L1/j57v9vNF6tXW1LFi9gIlzJnLAoAMYvtVweua19c4VEZEtS7Oj3M3sIne/3cxGufvb\n4Z6+LHdP3sXhOLrKKPfl65ezZsMa8rLzyM/Kp7iwuE3JTZZWLOXSNy7lu4rv+P0Bv2fXkl3Jzd50\nq+S66nW8tfgtHvrkIUYOGMkZO59BUUFRR36UDrV2w1punXYrEz+bSE5WDo/86BGG9ml818vSiqWc\n8MwJrN4Q3Ub3zAnPsH3v7VPdXJGmKH2qdGotBfSP3H24mf0rzOGbFp05oG+o3UB1XTUV1RVMnDOR\ne2bcQ47l8Lej/sbwrYa3en/lleUYhuPU1tVSlF9ETnbjjpSa2hrW1qylMKeQ/OzEz5bTpbyynBWV\nK+iR24Pe+b3jXn//bt13HPHEEdR5HQAPH/Mwu/fbPdVNFWmKArp0ai2dPs42s8+BncxsRsxjpqZ+\njUa131J2C7954zes2bCGyV9Emf5qvIZn5z/b6v19teYrxr06jl+99ivW16ynpFtJ3GAOkJOdQ1F+\nUZcI5hBNlrND0Q70796/ycF0PfN6cvMPb2ZI0RB+uvNP+V7PjpgdU0TiMbMTOjIvt5mV1qfUTgeL\nyf/eMCe5mU02s87bjdlBWpxYxsy2Jpol7riGy9x9YZLatZnOeob+0KyHuLnsZgD+fY9/p9ZruXfG\nveRYDvcfeT979W/cqVFbV8vX677mzUVvMqL/CLbttS2FOYWs2bCGS9+4lHe+fgeAQ793KL8/8PdN\nTr/q7ny77lve//Z9duu3GwO7D2zVqPPOqqq2inUb1lGQU9Dm6/8iSZJRZ+hmNh54zt0fT3dbOpqZ\nnUqUHS5pucc7oxYHxbn7t8AeKWhLl1Ndtymt6TNzn+FPh/yJUQNHsVW3rZocPb6icgWnPncqqzes\nJsdyeP6k5ynsUUi2ZVOUv+kHZHFBMdmW3Wj7ZeuXUVlTSU5WDqc9fxrLK5eTk5XD5BMnM6DHgI7/\nkCmWn51PfmHX6HUQScQfTzm20Uxxv/7Hc+2eaMbMfgpcSJT97D3gfOBOYARRUpHH3f2asO5NRCdl\nNcDLRFnNjgMOMrPfEaXwnBenjoRymLv7D81sNFGe72Nbk9M7JDY5kWgO80HA/7j7dWHZ00RzuxcA\nt7v7vaE8Xh7xsUAp8Fca5ySfTZQbfpmZnUWUYtaBGe5+ZuJHvXNrNqCb2UR3PznM/xt7Km+Au/sW\nfYHz+B8cz7xV81i8djFX7nMlxQXF0SO/eLNBbLE21G3YOOirxmtYUbmCgT0G0i23G5eNuIySwhKy\nLZuxw8aSl5232bbL1i/j7BfO5ss1X/L3Y/7O8srl0X7qalhSsSQjArpIJgnB/D42pVD9PnDfH085\nlvYEdTPbGTgFGOXu1WZ2N9GdR1e5+wozywZeDbN6LiYKmENDetUid19pZpNo+Qz9SXe/L9R5A1EO\n8zvYlMN8cRNd2fU5vWvM7DCi4NtcbvF9iNKuVgAfmNnz7l4G/Dx8nsJQ/gTRpeL7gB+6+xcxSU0A\ncPePzOxqogA+LrS9/rgNI8rtvn8I7hmVUrKlM/SLwvOxbdm5mS0gyppTC9S4e2k4gP8gyjO7ADjZ\n3cub2kdn1rewL1ftexXVddX0yuu18UvTnO653Tlrl7N45NNHGDVwFAO7b0qL2rewL5eWXgoQd19L\nK5by5ZooSdOc8jmMGTKGxz9/nL37790pcpevrFpJTV0NxfnFZGc17l0Q2QIlKx/6ocDeREEOojPy\nJcDJZnYu0f/2AUR5zD8BKoH7zew5Wpcxs605zFub0/sVd18OG/OXHwCUAReaWf0ENtsCQ4B+xM8j\nnohDgMfcfVkbtu30mg3o7v5NeG7PtfKD6w9ecAXwqrvfFAYwXAFc3o79p1Vrr/MW5Rfxyz1+ydhh\nY8nNyo1uN6tYAQvfgfIF2G5joOfWcbctKSyhpLCEZeuX8cRnT3D7Ibdz/vDzycnKobigOO42qbK0\nYilXTr2S7yq+478O+C926buLgrpI8vKhGzDB3a/cWBCl4XwFGOHu5eEaeUE4S96H6EfAGGAcUWBL\nxHjalsO8tTm9Gw7m8tCFfxgwMnTzTyHqepcmtNTlvobGBxo2dbn3akOdx7MpEfwEoj90lw3obdEr\nr1d0Rare3FfhyTB245On4LR/QPeSRtuVFJYw8diJrN6wmt75vSkpbLxOujw19yne+/Y9AK5860rG\nHzmekm4d177q2mpWVq0Eoh9FTV3SEOlkviTqZo9X3h6vAs+Y2a3uviT0fH4PWAesMrP+wNHAFDPr\nQTR992QzexuYH/aRSM7xhjm/F8OmHObAe2Z2NNHZc6zW5vQ+PHyG9cAJwM+JrqeXh2A+FNgvrPsu\ncLeZbVff5d6KM+3XgKfM7BZ3X97KbTu9ls7Q2ztNlwMvm5kDfwkDGvrXn/kD3wL9W9rJHDb9AshI\n246AsaEXLCsX8pv4nWQG3fpFj05m+ZCTmL/VngAszevJCQVFrUvk2xx31tZW8Vm43LBTbje6K6BL\nik1p22a/ZfNr6NAB+dDd/ZMwmO1lM8siSpx1AfAh0fXrr4i6xSEKys+YWQHRydglofxR4D4zuxAY\nE29QHJtyfi8Nz/Ux4b9Dd7oR/biYDhwUs93NRF3uvwOeT+AjvQ88AWxDNCiuLIzdOs/MZhOFgXfD\nZ18aLis8GT77EuDwBOrA3WeZ2Y3AG2ZWS3S8xiaybVeQ1HzoZjYoDJrYiqgr6FfAJHcvilmn3N0b\n9ReHP9i5APm77773ftOnN1wlc9RUwrcfQ20VlOwIhX2gi3VX19TVsKpqJVW1VfQr7EdugwF97VFb\nV8u8lXNZFQYTFucXsX3R9mTFuQtAJFmmtPG2tWSNcs8U9aPT6wewSdslNaBvVpHZtcBa4BfAaHf/\nxswGAFPcfafmtu2s96F3hOraalZtWEVObQ1FZENed8hLz/3X9bfEFeYUNnnbXTpU11Uz4eMJ3P7h\n7QBcNuIyTh96uq7RS6pl1H3onYUCesdJWg7L2Hnfw+sjgOuBScDZwE3h+ZlktaGzq66tZvrS6fzH\nO//BoO6DuOmHN1GSxmB+zkvnMH/VfPbotwe3H3x7pwnquVm5jNlxDHv335usrCy+3+v7CuYiHcii\n/OKjGhTfHlKXdlQdRwJ/aFD8RUjDOr6j6tmSJTMpdX+iwQf19fzd3V80sw+AiWZ2DrAQODmJbejU\nVlWt4vKpl7OkYgmL1ixi8vzJnDXsrLS0Zdn6ZcxfFY2Vmb50Ouuq13WagA5QVFDEngV7prsZIhkp\nFTm/3f0lNt32JkmQtIDu7vOJM8NcuNfw0GTV25VkZWXRr7AfSyqWADCwx8AWtkievgV9Kc4vpryq\nnIHdBzY55ayIiHROKbuG3h6ZfA19ScUSnvjsCQb3GszIQSM3m/41lWrrallRuYKv133NoB6DOtUt\ncSKdhK6hS6emgC4ikhgFdOnUWkqfKim2fP1yllYsZV31unQ3RUS2UGY22Mw+TmCd02PepzV9qiig\ndyrfrP2GMyafwWGPH8az855l3QYFdRHptAYDGwO6u5e5+4Xpa44ooKfY0oqlLFqziBWVjWcbfHb+\nsyxeu5g6r+MPH/yBipqKNLRQRDq7cHb8qZk9bGazzexxM+tmZoea2YdmNtPMHjCz/LD+AjO7OZS/\nb2Y/COXjzWxMzH7XNlHXVDP7V3jsHxbdBBxoZh+Z2cVmNjokf8HM+pjZ02Y2w8zeDVnfMLNrQ7um\nmNn8MEuddBAF9BRaWrGUMyafwdFPHs01b19DeeXmSeZ2LN5x4+vtem0XNx+6iEiwE3C3u+8MrCaa\n0nU8cIq770Z0F9O/x6y/KpTfCdzWinqWAIe7+15EKVvru9WvAKa6+3B3v7XBNtcBH4YU278FHoxZ\nNhQ4kihl6jVhnnjpAMm8D10a+GLVF3yzLprGfsqiKVTWVm62fM+t9uSew+7hi1VfcMTgI+hTmFGp\nekWkY33l7vXztf8P0bzrX7j7Z6FsAtH87vXB+5GY54YBuDm5wJ1mNpwoFfaOLawPUfrTHwO4+2tm\n1tfM6pNUPO/uVUCVmS0hmrNkUSvaI01QQE+h7XptxyM/eoQNtRtYUrGEvKzN5zvvnd+bUYNGMWpQ\nwwmbREQaaXiL0kqgudmgPM7rGkJPbUh0Ei8Jw8XAd0TzimQR5VZvj6qY17UoDnUYdbmn0IqqFYx9\ncSxnv3g2n674lPzs/HQ3SUS6ru+Z2cjw+nSgDBhcf30cOBN4I2b9U2Ke/xleLwDqc5kfR3Q23lBv\n4Bt3rwv7rL8W2Fz61alE6VYJec2XufvqhD6VtJkCegq9vPBlqmqjH6eT5k2isqa9P3RFZAs2B7gg\npBctJupG/xnwWEg9WgfcE7N+sZnNAC4iOuuGKLXrQWY2HRhJlE+9obuBs8M6Q2PWmQHUmtl0M7u4\nwTbXAnuH+urzdkiSaWKZFPpk+SecMfkMaupqOHPnMzl/+Pn0yOuR7maJSGI6zcQyZjYYeM7dd01w\n/QVEGc2WJbFZkma6dpFC2/fenhdOeoHKmkqKCooUzEVEpMMooKdQQU4BW+dsne5miEgX5+4LgITO\nzsP6g5PWGOk0dA1dREQkA+gMPU1WV62mqraKHrk9KMxVqlIREWkfnaGnwYrKFdzw7g2cOflMXvvq\nNdZuaDTbYoerrKlkzYY1Sa9HRETSQwE9DRasWsCitYu4+aCbqaiuoLyqnJramqTVt6JyBTe9fxO/\nnvJr5q+aT1e4s0FERFpHAT0N+hb25TcjfsO4V8dx/bvXM2bSGFZUNU7W0lGenfcsT3z+BP/85p9c\n+NqFLK9cnrS6RCQ1zOwoM5tjZnPN7Ip0t0fSTwE9DUoKSuiV14vyqig5S0VNBSurViatvtgkL1mW\nhXWe22lFpA3MLBu4Czga2AU4zcx2SW+rJN00KC4Nuud1p7iumNHbjGbKoinstdVe9C1obgrm9jlm\n+2NYvHYxi9Yu4tLSS+lbmLy6RCQl9gHmuvt8ADN7FDge+CStrZK0UkBPkz4Ffbh+1PVsqN1AbnYu\nfQqSl1mtT0EfLt77YmrqauiW2y1p9YhIfKWlpTlACbCsrKysIwbMDAK+inm/CNi3A/YrXZi63NOo\nuKCY/t37JzWY18vLzlMwF0mD0tLS/YGlwBfA0vBepMMpoIuIJEk4M38eKAIKwvPzpaWl2c1u2LLF\nwLYx77cJZbIFU0AXEUmeEqJAHqsA6NfO/X4ADDGz7cwsDzgVmNTOfUoXp2voIiLJswyoZPOgXknU\nBd9m7l5jZuOAl4jykz/g7rPas0/p+nSGLiKSJGEA3I+AlUSBfCXwo7Kystr27tvdJ7v7ju6+g7vf\n2N79SdengC4ikkRlZWXvEHW9bweUhPciHU5d7iIiSRbOyL9Ndzsks+kMXUREJAMooIuIiGQABXQR\nEZEMoIAuIiKSARTQRUS6IDNbYGYzzewjMysLZX3M7BUz+zw8F4dyM7M/hVSrM8xsr5j9nB3W/9zM\nzo4p3zvsf27Y1lJVh7SNArqISNd1sLsPd/fS8P4K4FV3HwK8Gt5DlGZ1SHicC/wZouAMXEOU2GUf\n4Jr6AB3W+UXMdkelsA5pg6QHdDPLNrMPzey58H47M3sv/CL7R5i2UEQkY5WWlhaUlpZ+v7S0tOE0\nsB3teGBCeD0BOCGm/EGPvAsUmdkA4EjgFXdf4e7lwCvAUWFZL3d/190deLDBvpJdh7RBKs7QLwJm\nx7z/A3Cru/8AKAfOSUEbRERSrrS0NLu0tPQmYDkwC1heWlp6UwckZwFw4GUzm2Zm54ay/u7+TXj9\nLdA/vI6XbnVQ2SfsMAAADapJREFUC+WL4pSnqg5pg6QGdDPbhmjaw7+G9wYcAjweVon9dScikmlu\nBMYB3YDu4XlcKG+vA9x9L6Ku7gvM7IexC8NZr3dAPU1KRR2SuGSfod8GXAbUhfd9gZXuXhPe6xeZ\niGSk0L3+K6JAHqs78Kv2dr+7++LwvAR4iuj69HehK5vwvCSs3lS61ebKt4lTTorqkDZIWkA3s2OB\nJe4+rY3bn2tmZWZWtnRpuxITiYikQ3+aPnt1NnVVt5qZdTeznvWvgSOAj4lSqNaPIj8beCa8ngSc\nFUai7wesCt3mLwFHmFlxGKh2BPBSWLbazPYLPatnNdhXsuuQNkjmXO6jgOPM7Bii1IG9gNuJBkrk\nhLP0Jn+Rufu9wL0ApaWl6tIRka7mO6C527C+a8e++wNPhbu8coC/u/uLZvYBMNHMzgEWAieH9ScD\nxwBzgQrgZwDuvsLM/pMovzrA9e6+Irw+HxgPFAIvhAfATSmoQ9rAoksgSa7EbDRwqbsfa2aPAU+4\n+6Nmdg8ww93vbm770tJSLysrS3o7RUSa0ep7pMOAuHFs3u1eAdxRVlZ2RfytRNomHfehXw5cYmZz\nia6p35+GNoiIpMJVwJ1EQXxdeNwRykU6VErO0NtLZ+gi0gm0eRazMACuP/BdWVlZZcc1SWQT5UMX\nEUmyEMQXprsdktk09auIiEgGUEAXERHJAAroIiIiGUABXUSkCzKzB8xsiZl9HFOWEelTm6pDmqeA\nLiLSNY2ncbrRTEmf2lQd0gyNchcRSZKQVe1M4GKivBWLgVuBh8rKymrbs293f9PMBjcoPh4YHV5P\nAKYQzf2xMbUp8K6Z1ac2HU1IbQpgZvWpTacQUpuG8vrUpi+kuQ5phs7QRUSSIATzSUQTy+xONJHW\n7uH9pA5KodpQpqRPbaoOaYYCuohIcpwJHET8bGsHAT9NZuWZkj5VKVoTp4AuIpIcF9M4mNfrDlyS\nhDozJX1qU3VIMxTQRUSSY1A7l7dFpqRPbaoOaYYGxYmIJMdiouvmzS1vMzN7hGjgWImZLSIaSZ6K\n1KbprEOaoeQsIiKJaVVyltLS0rFEA+DidbuvAy4oKyub0AHtEgHU5S4ikiwPAW8QBe9Y60L5/6S8\nRZLRFNBFRJIg3Gd+HHABMANYHp4vAI5r733oIg2py11EJDFtzocukgo6QxcREckACugiIiIZQAFd\nREQkAyigi4h0QU2kT73WzBab2UfhcUzMsitDmtI5ZnZkTPlRoWyumV0RU76dmb0Xyv9hZnmhPD+8\nnxuWD05lHdI0BXQRkSQrLS3drrS0dFRpael2Hbjb8TROnwpwq7sPD4/JAGa2C3AqMCxsc7eZZZtZ\nNnAXUerTXYDTwroAfwj7+gFQDpwTys8BykP5rWG9lNQhzVNAFxFJktLINGAW8Dwwq7S0dFppaWlp\ne/ft7m8CK1pcMXI88Ki7V7n7F0Szue0THnPdfb67bwAeBY4PU7EeAjwetp9AlNq0fl/1E+I8Dhwa\n1k9FHdIMBXQRkSQIQXsKsBfR1Ka9w/NewJSOCOpNGGdmM0KXfHEoa21q077ASnevaVC+2b7C8lVh\n/VTUIc1QQBcRSY6/0Hy2tXuSUOefgR2A4cA3wB+TUId0UgroIiIdLFwr37mF1Xbp4GvquPt37l7r\n7nXAfUTd3dD61KbLgSIzy2lQvtm+wvLeYf1U1CHNUEAXEel4A4ENLayzIazXYepziAcnAvUj4CcB\np4bR49sBQ4D3iTKgDQmjzfOIBrVN8mgK0deBMWH7hmlS61ObjgFeC+unog5phtKnioh0vK+BvBbW\nyQvrtUkT6VNHm9lwwIEFwC8B3H2WmU0EPgFqgAvcvTbsZxxRzvJs4AF3nxWquBx41MxuAD4E7g/l\n9wMPmdlcokF5p6aqDmme5nIXEUlMa9OnTiMaANeUaWVlZckaGCdbIHW5i4gkxy9pnDq13jrgvBS2\nRbYACugiIklQFnUrjgamAeuJbr1aH96PLlO3o3QwXUMXEUmSELRLw2j2gcDXZWVlX6S5WZKhFNBF\nRJIsBHEFckkqdbmLiIhkAAV0ERGRDKCALiIikgGSFtDNrMDM3jez6WY2y8yuC+Vx89+KiIhI2yXz\nDL0KOMTd9yBKFHCUme1H0/lvRUREpI2SFtA9sja8zQ0Pp+n8tyIiItJGSb2GbmbZZvYRsAR4BZhH\n0/lvRUREpI2SGtBDGr/hRGnx9gGGJrqtmZ1rZmVmVrZ06dKktVFERCQTpGSUu7uvJEqTN5Km8982\n3OZedy9199J+/fqlopkiIiJdVjJHufczs6LwuhA4HJhN0/lvRUREpI2SOfXrAGCCmWUT/XCY6O7P\nmdknxM9/KyIiIm2UtIDu7jOAPeOUzye6ni4iIiIdRDPFiYiIZAAFdBERkQyggC4iIpIBFNBFREQy\ngAK6iIhIBlBAFxERyQAK6CIiIhlAAV1ERCQDKKCLiIhkAAV0ERGRDKCALiIikgEU0EVERDKAArqI\niEgGUEAXERHJAAroIiIiGUABXUREJAMooIuIiGQABXQREZEMoIAuIiKSARTQRUREMoACuoiISAZQ\nQBcREckACugiIiIZQAFdREQkAyigi4iIZAAFdBERkQyggC4iIpIBFNBFREQygAK6iIhIBlBAFxER\nyQAK6CIiIhlAAV1ERCQDKKCLiIhkAAV0ERGRDJC0gG5m25rZ62b2iZnNMrOLQnkfM3vFzD4Pz8XJ\naoOIiMiWIpln6DXAr919F2A/4AIz2wW4AnjV3YcAr4b3IiIi0g5JC+ju/o27/yu8XgPMBgYBxwMT\nwmoTgBOS1QYREZEtRUquoZvZYGBP4D2gv7t/ExZ9C/RPRRtEREQyWU6yKzCzHsATwP9199VmtnGZ\nu7uZeRPbnQucG96uNbM5LVTVG1jVyuYlsk1z6zS1rGF5vPViyxouLwGWtdCu1urMxydeWXPvk3F8\nmmpXR2yTKd+hptrR3vW7ynfoRXc/qpXbiKSOuyftAeQCLwGXxJTNAQaE1wOAOR1U173J2Ka5dZpa\n1rA83nqxZXHWL0vC36LTHp9EjlmD49Xhx6ezH6PO8B1qyzHa0r5DeuiRzkcyR7kbcD8w291viVk0\nCTg7vD4beKaDqnw2Sds0t05TyxqWx1vv2RaWd7TOfHzilSVyDDtaZz5GneE71JZ6trTvkEjamHvc\nHu/279jsAGAqMBOoC8W/JbqOPhH4HrAQONndVySlEV2UmZW5e2m629FZ6fi0TMeoeTo+komSdg3d\n3d8CrInFhyar3gxxb7ob0Mnp+LRMx6h5Oj6ScZJ2hi4iIiKpo6lfRUREMoACuoiISAZQQBcREckA\nCuidnJntbGb3mNnjZvbv6W5PZ2Vm3c2szMyOTXdbOhszG21mU8P3aHS629MZmVmWmd1oZneY2dkt\nbyHS+Sigp4GZPWBmS8zs4wblR5nZHDOba2ZXALj7bHc/DzgZGJWO9qZDa45RcDnR7ZBbhFYeHwfW\nAgXAolS3NV1aeYyOB7YBqtmCjpFkFgX09BgPbDaFpJllA3cBRwO7AKeF7HSY2XHA88Dk1DYzrcaT\n4DEys8OBT4AlqW5kGo0n8e/QVHc/muhHz3Upbmc6jSfxY7QT8I67XwKoJ0y6JAX0NHD3N4GGk+ns\nA8x19/nuvgF4lOisAXefFP4hn5HalqZPK4/RaKIUvacDvzCzjP9et+b4uHv9xE7lQH4Km5lWrfwO\nLSI6PgC1qWulSMdJenIWSdgg4KuY94uAfcM1z5OI/hFvSWfo8cQ9Ru4+DsDMxgLLYgLYlqap79BJ\nwJFAEXBnOhrWicQ9RsDtwB1mdiDwZjoaJtJeCuidnLtPAaakuRldgruPT3cbOiN3fxJ4Mt3t6Mzc\nvQI4J93tEGmPjO+a7EIWA9vGvN8mlMkmOkbN0/FpmY6RZCwF9M7jA2CImW1nZnnAqUSZ6WQTHaPm\n6fi0TMdIMpYCehqY2SPAP4GdzGyRmZ3j7jXAOKL88bOBie4+K53tTCcdo+bp+LRMx0i2NErOIiIi\nkgF0hi4iIpIBFNBFREQygAK6iIhIBlBAFxERyQAK6CIiIhlAAV1ERCQDKKBLp2dm76S7DSIinZ3u\nQxcREckAOkOXTs/M1obn0WY2xcweN7NPzexhM7OwbISZvWNm083sfTPraWYFZvY3M5tpZh+a2cFh\n3bFm9rSZvWJmC8xsnJldEtZ518z6hPV2MLMXzWyamU01s6HpOwoiIs1TtjXpavYEhgFfA28Do8zs\nfeAfwCnu/oGZ9QLWAxcB7u67hWD8spntGPaza9hXATAXuNzd9zSzW4GzgNuAe4Hz3P1zM9sXuBs4\nJGWfVESkFRTQpat5390XAZjZR8BgYBXwjbt/AODuq8PyA4A7QtmnZrYQqA/or7v7GmCNma0Cng3l\nM4HdzawHsD/wWOgEgCgnvYhIp6SALl1NVczrWtr+HY7dT13M+7qwzyxgpbsPb+P+RURSStfQJRPM\nAQaY2QiAcP08B5gKnBHKdgS+F9ZtUTjL/8LMfhK2NzPbIxmNFxHpCAro0uW5+wbgFOAOM5sOvEJ0\nbfxuIMvMZhJdYx/r7lVN76mRM4Bzwj5nAcd3bMtFRDqOblsTERHJADpDFxERyQAK6CIiIhlAAV1E\nRCQDKKCLiIhkAAV0ERGRDKCALiIikgEU0EVERDKAArqIiEgG+P/TN6Scm1i3agAAAABJRU5ErkJg\ngg==\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7fdd0dfb0da0>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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7OabnMVhNrV9bLjosaaGL/ZqMoYt9blnFMs79+lK+Gxlg/TDF6z9u4ZR/f8XG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\nxdZO1eYhAAAAAElFTkSuQmCC\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7fdd0fe5d748>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "DPpz37L4tuWU",
+        "colab_type": "code",
+        "outputId": "0d9c0cbf-0d23-44ba-ea3d-5d6d6d9cafff",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 166
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "df1[(df1.year==1918) & (df1.lifespan >= 50)]"
+      ],
+      "execution_count": 97,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>year</th>\n",
+              "      <th>income</th>\n",
+              "      <th>lifespan</th>\n",
+              "      <th>population</th>\n",
+              "      <th>country</th>\n",
+              "      <th>region</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>1917</th>\n",
+              "      <td>1918</td>\n",
+              "      <td>7610</td>\n",
+              "      <td>54.95</td>\n",
+              "      <td>5066003</td>\n",
+              "      <td>Australia</td>\n",
+              "      <td>east_asia_pacific</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10333</th>\n",
+              "      <td>1918</td>\n",
+              "      <td>6393</td>\n",
+              "      <td>56.24</td>\n",
+              "      <td>3165276</td>\n",
+              "      <td>Denmark</td>\n",
+              "      <td>europe_central_asia</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>18211</th>\n",
+              "      <td>1918</td>\n",
+              "      <td>2576</td>\n",
+              "      <td>51.11</td>\n",
+              "      <td>115504</td>\n",
+              "      <td>Iceland</td>\n",
+              "      <td>europe_central_asia</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>28113</th>\n",
+              "      <td>1918</td>\n",
+              "      <td>4479</td>\n",
+              "      <td>50.28</td>\n",
+              "      <td>2576646</td>\n",
+              "      <td>Norway</td>\n",
+              "      <td>europe_central_asia</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       year  income  lifespan  population    country               region\n",
+              "1917   1918    7610     54.95     5066003  Australia    east_asia_pacific\n",
+              "10333  1918    6393     56.24     3165276    Denmark  europe_central_asia\n",
+              "18211  1918    2576     51.11      115504    Iceland  europe_central_asia\n",
+              "28113  1918    4479     50.28     2576646     Norway  europe_central_asia"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 97
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "Nx8v756-uEuk",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## today, no countries are below 50 years lifespan"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "FWJuO2s6uPA4",
+        "colab_type": "code",
+        "outputId": "9bb67637-eec1-4d9d-fff0-da914ea9290f",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 47
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "df1[(df1.year==2018) & (df1.lifespan < 50)]"
+      ],
+      "execution_count": 98,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>year</th>\n",
+              "      <th>income</th>\n",
+              "      <th>lifespan</th>\n",
+              "      <th>population</th>\n",
+              "      <th>country</th>\n",
+              "      <th>region</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "Empty DataFrame\n",
+              "Columns: [year, income, lifespan, population, country, region]\n",
+              "Index: []"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 98
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "qplqX7D9uwFq",
+        "colab_type": "code",
+        "outputId": "cb69a902-ae20-4473-ada2-d2b2536d8ae2",
+        "colab": {
+          "resources": {
+            "http://localhost:8080/nbextensions/google.colab/tabbar.css": {
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var Bd=function(a){var b=Oc(a),c=yd(a);b.a(c,"focus",a.Ia).a(c,"blur",a.Qa).a(a.da||(a.da=new K(yd(a))),"key",a.Ra)};g=U.prototype;g.P=function(){Cd(this,-1);this.s&&sd(this.s,!1);this.U=!1;U.i.P.call(this)};g.u=function(){U.i.u.call(this);this.da&&(this.da.N(),this.da=null);this.ea=this.s=this.K=this.Ea=null};g.Wa=function(){return!0};
g.Xa=function(a){var b=Tc(this,a.target);if(-1<b&&b!=this.l){var c=M(this,this.l);c&&Q(c,!1);this.l=b;c=M(this,this.l);this.U&&qd(c,!0);this.s&&c!=this.s&&(c.v&64?sd(c,!0):sd(this.s,!1))}b=this.f();v(b,"The DOM element for the container cannot be null.");null!=a.target.f()&&Cb(b,"activedescendant",a.target.f().id)};g.cb=function(a){a.target==M(this,this.l)&&(this.l=-1);a=this.f();v(a,"The DOM element for the container cannot be null.");a.removeAttribute(Bb("activedescendant"))};
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U.prototype.Fa=function(a){Cd(this,a)};var V=function(){};u(V,N);ka(V);V.prototype.a=function(){return"goog-tab"};V.prototype.h=function(){return"tab"};V.prototype.c=function(a){var b=V.i.c.call(this,a);(a=a.Pa)&&b&&(b.title=a||"");return b};V.prototype.g=function(a,b){b=V.i.g.call(this,a,b);var c=b.title||"";c&&(a.Pa=c);a.o&8&&(c=a.c)&&q(c.Y)&&(R(a,8,!1),c.Y(a));return b};var Jd=function(a,b,c){O.call(this,a,b||V.R(),c);td(this,8,!0);this.M|=9};u(Jd,O);Vc("goog-tab",function(){return new Jd(null)});var W=function(){this.h="tablist"};u(W,Xc);ka(W);W.prototype.b=function(){return"goog-tab-bar"};W.prototype.g=function(a,b,c){this.j||(this.a||Kd(this),this.j=wa(this.a));var d=this.j[b];d?(ad(a,Ld(d)),a.C=d):W.i.g.call(this,a,b,c)};W.prototype.c=function(a){var b=W.i.c.call(this,a);this.a||Kd(this);b.push(this.a[a.C]);return b};var Kd=function(a){var b=a.b();a.a={top:b+"-top",bottom:b+"-bottom",start:b+"-start",end:b+"-end"}};var Y=function(a,b,c){a=a||"top";ad(this,Ld(a));this.C=a;U.call(this,this.J,b||W.R(),c);Md(this)};u(Y,U);g=Y.prototype;g.D=null;g.F=function(){Y.i.F.call(this);Md(this)};g.u=function(){Y.i.u.call(this);this.D=null};g.Fa=function(a){Y.i.Fa.call(this,a);this.qa(a)};g.Y=function(a){a?rd(a,!0):this.D&&rd(this.D,!1)};g.qa=function(a){this.Y(M(this,a))};
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var Md=function(a){Oc(a).a(a,"select",a.ab).a(a,"unselect",a.bb).a(a,"disable",a.Za).a(a,"hide",a.$a)},Ld=function(a){return"start"==a||"end"==a?"vertical":"horizontal"};Vc("goog-tab-bar",function(){return new Y});function Od(a){var b={top:"bottom",bottom:"top",start:"right",end:"left"}[a.location],c=a.elementId,d=document.createElement("style");d.textContent="\n    fieldset {\n      padding: 10px;\n      border: 1px solid #369;\n    }\n\n    #"+c+" .goog-tab-content {\n      min-height: 3em;\n      margin: 0;\n      border: "+a.border+" solid "+a.borderColor+";\n      border-top: 0;\n      height: "+a.contentHeight+";\n      padding: 4px 8px;\n      margin-right: 4px;\n      background: #fff;\n      overflow: auto;\n    }\n\n    #"+
c+" .goog-tab-bar-"+a.location+" .goog-tab-selected {\n      border: 1px solid "+a.borderColor+";\n      border-"+b+": 0px;\n    }\n\n    #"+c+" .goog-tab-bar-"+a.location+" {\n      padding-"+a.location+": 5px !important;\n      border-"+b+": 1px solid "+a.borderColor+" !important;\n      background: white;\n    }\n\n    #"+c+" .goog-tab-bar {\n       margin: 0;\n       border: 0;\n       padding: 0;\n       list-style: none;\n       cursor: default;\n       outline: none;\n       background: white;\n       margin-right: 4px;\n      }\n\n     #"+
c+" .goog-tab {\n       position: relative;\n       padding: 4px 8px;\n       color: black;\n       text-decoration: initial;\n       cursor: default;\n      }";return d}
var Pd=function(a){var b=a.elementId,c=a.tabNames,d=a.selectedIndex;"contentBorder"in a||(a.contentBorder="0px");"contentHeight"in a||(a.contentHeight="initial");"borderColor"in a||(a.borderColor="#a7a7a7");a.location||(a.location="top");var e=document.querySelector("#"+b),f=document.createElement("div");f.classList.add("goog-tab-bar");var h=a.location;f.classList.add("goog-tab-bar-"+h);for(var k=[],p=ia(c),r=p.next();!r.done;r=p.next()){r=r.value;var S=document.createElement("div");S.classList.add("goog-tab");
S.textContent=r;f.appendChild(S);k.push(S)}"bottom"!=h&&e.appendChild(f);p=null;if("top"==h||"bottom"==h)p=document.createElement("div"),p.classList.add("goog-tab-bar-clear");"top"==h&&e.appendChild(p);S=document.createElement("div");S.classList.add("goog-tab-content");var oa=[];c=ia(c);for(r=c.next();!r.done;r=c.next())r=document.createElement("div"),r.id=e.id+"_content_"+oa.length,r.style.display="none",S.appendChild(r),oa.push(r);e.appendChild(S);"bottom"==h&&(e.appendChild(p),e.appendChild(f));
var Ba=new Y(h);Pc(Ba,f);var X=-1;Ba.a("select",function(a){a=k.indexOf(a.target.f());a!=X&&(0<=X&&X<oa.length&&(oa[X].style.display="none",X=-1),0<=a&&a<oa.length&&(X=a,oa[X].style.display="inline",window.dispatchEvent(new Event("resize")),google.colab.output.resizeIframeToContent()),Ba.qa(X))});Ba.qa(d);window[b]={setSelectedTabIndex:function(a){Ba.qa(a)}};document.head.appendChild(Od(a))},Qd=["colab_lib","createTabBar"],Z=l;Qd[0]in Z||!Z.execScript||Z.execScript("var "+Qd[0]);
for(var Rd;Qd.length&&(Rd=Qd.shift());)Qd.length||void 0===Pd?Z[Rd]&&Z[Rd]!==Object.prototype[Rd]?Z=Z[Rd]:Z=Z[Rd]={}:Z[Rd]=Pd;
",
+              "ok": true,
+              "headers": [
+                [
+                  "content-type",
+                  "application/javascript"
+                ]
+              ],
+              "status": 200,
+              "status_text": ""
+            }
+          },
+          "base_uri": "https://localhost:8080/",
+          "height": 414
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "from google.colab import widgets\n",
+        "tb = widgets.TabBar([str(year) for year in years])\n",
+        "for tab, year in zip(tb, years):\n",
+        "  sns.relplot(x='income', y='lifespan', hue='region', size='population',\n",
+        "             sizes=(10, 200), \n",
+        "             data=df1[df1.year==year])\n",
+        "\n",
+        "  plt.xscale('log')\n",
+        "  plt.xlim(150, 1500000)\n",
+        "  plt.ylim(20, 90)"
+      ],
+      "execution_count": 99,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "<link rel=stylesheet type=text/css href='/nbextensions/google.colab/tabbar.css'></link>"
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ]
+          },
+          "metadata": {
+            "tags": [
+              "outputarea_id2"
+            ]
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "<script src='/nbextensions/google.colab/tabbar_main.min.js'></script>"
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ]
+          },
+          "metadata": {
+            "tags": [
+              "outputarea_id2"
+            ]
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "<div id=\"id2\"></div>"
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ]
+          },
+          "metadata": {
+            "tags": [
+              "outputarea_id2"
+            ]
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/javascript": [
+              "window[\"46db07fe-e945-11e8-9ea1-0242ac1c0002\"] = colab_lib.createTabBar({\"location\": \"top\", \"elementId\": \"id2\", \"tabNames\": [\"1918\", \"1938\", \"1958\", \"1978\", \"1998\", \"2018\"], \"initialSelection\": 0, \"contentBorder\": [\"0px\"], \"contentHeight\": [\"initial\"], \"borderColor\": [\"#a7a7a7\"]});\n",
+              "//# sourceURL=js_42243258da"
+            ],
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ]
+          },
+          "metadata": {
+            "tags": [
+              "outputarea_id2"
+            ]
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/javascript": [
+              "window[\"46db62a8-e945-11e8-9ea1-0242ac1c0002\"] = window[\"id2\"].setSelectedTabIndex(0);\n",
+              "//# sourceURL=js_d3d26aea4b"
+            ],
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ]
+          },
+          "metadata": {
+            "tags": [
+              "outputarea_id2"
+            ]
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/javascript": [
+              "window[\"46dcf65e-e945-11e8-9ea1-0242ac1c0002\"] = google.colab.output.getActiveOutputArea();\n",
+              "//# sourceURL=js_3bd5a9056d"
+            ],
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ]
+          },
+          "metadata": {
+            "tags": [
+              "id2_content_0",
+              "outputarea_id2"
+            ]
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/javascript": [
+              "window[\"46dd4e1a-e945-11e8-9ea1-0242ac1c0002\"] = document.querySelector(\"#id2_content_0\");\n",
+              "//# sourceURL=js_6cbf723043"
+            ],
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ]
+          },
+          "metadata": {
+            "tags": [
+              "id2_content_0",
+              "outputarea_id2"
+            ]
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/javascript": [
+              "window[\"46dda31a-e945-11e8-9ea1-0242ac1c0002\"] = google.colab.output.setActiveOutputArea(window[\"46dd4e1a-e945-11e8-9ea1-0242ac1c0002\"]);\n",
+              "//# sourceURL=js_ee0e219238"
+            ],
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ]
+          },
+          "metadata": {
+            "tags": [
+              "id2_content_0",
+              "outputarea_id2"
+            ]
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/javascript": [
+              "window[\"46de2538-e945-11e8-9ea1-0242ac1c0002\"] = window[\"id2\"].setSelectedTabIndex(0);\n",
+              "//# sourceURL=js_7884e60baf"
+            ],
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ]
+          },
+          "metadata": {
+            "tags": [
+              "id2_content_0",
+              "outputarea_id2"
+            ]
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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aFDfG3ReZWasZgTY0SjezD4meJWwCEu5eHp6zu5co+86HwInuvqy982jKPV6J\nZcvw1avJKiwku7S00+dJ1tVBVhZZeXnd2DuRPkPfdqVPa3eE7u6Lws+uTK8fGFLyNbuMKD3hNRaV\n9bsMuLQL55cuyhk6FIYO7dI5ElVVLL7uOnJGjGTYGadH5xQRkR7T7j10M1tpZita+bPSzFZ08prH\nAJPD68nAsZ08j/QhDR9+yPKHprDiscfwhLICi/R1ZnasdWNNbjMrN7M/dNf5OnH9NbXfrUU9cjN7\n3Mw6P/3YT2xwlXuXTm42H1hGdP/9z+5+i5lVu3tp2G7Asub3bdGUe9/mTU0kFi+m6vY7KDnyCFY+\n+xylx3+NvI033vDBIv1HRk25m9kkYKq7P9DbfeluZnYyUWW4b/Z2X3pS2inlOmk/d19oZiOJKu+s\nkxrP3d3MWv1GYWbnEqXEY9NNu6OGQeZJepKq1VU0JBsoySuhOK/N8sSxSixdxicXXsToH1/Bwku+\nR+Mnn1A7cyZjb/wjOV24Jy+SKa476aj1MsV9796pXcoUZ2b/A1wE5BGlHz0fuBHYg6igyAPu/pOw\n7zVEjx4ngKeJqo8dDXwplML+mrv/t5VrnEP0/3AeMA/4hrvXmtkJwE+I1kctd/f9zewA4PvufpSZ\n7UlUrjUfWA2c6e7vtfE5ziDKtV5ClJL27+7+07DtYaK87vlEz33fEtoPJ/p9ZgNL3H1iOE85cCtR\n6tSCUHN8H6LSpuXuvsTMTiMqL+vAbHf/Rvq/9b4t3VzuneLuC8PPxUT5gvcEPre1xe7HEJW7a+3Y\nW9y93N3LR4wYEWc3+62ldUs54R8ncMSDR1C5urLX+pGVl0v+Vlux+s03GTRhGwAKdtgB0+I4keZg\nvl499NDeKWa2LXASsK+770IUWE8FrnD3cmAnomC9k5mVEQXM7cOTSVe7+0vAo8APQs3u9YJ5kFb9\n8laO62g97z2J6o7vBJwQAjHAWaGmejlwkZmVtVNDHIA26pE3/962JyrnelA4tmWymn4tthF6at73\n8PpQ4GdE/4hOB64JPx+Jqw+ZzjCGDBpCTWMNedm9FzyzS0oY+cMf4IkEQ778ZfzSS8kqKiK7sGUJ\naJEBqb166J0dpU8EdgdmhrLWBUSDoxPD7GYOMIaohvnbQB3wVzObCkztwHV2MLOrgVKgmCjJGKyt\nX34f0Wi/pRJgspltRTQS3lA1s2nuXgVgZg8R1TOvIArix4V9NgG2AkbQeg3xdBwE3N+8ULuDx/Z5\ncU65jwKmhH9sOcBd7v6kmc0E7jOzs4GPgBNj7ENGKyso47bDbiPpSUoGlfRqX7SqXaRNcdRDN2Cy\nu/9oTUNUgnMasIe7Lwv3yPO83LhRAAAgAElEQVTdPRGmwCcSVUH7NlFgS8ckOle/vKP1vFveevUw\nhX8wsE+Y5n+eaOpd2hBbQHf3D4CdW2mvIvqHJd1geMHw3u6CiLTvY6Jp9tbaO2s68IiZXe/ui0N+\nj02BVcByMxsFHAE8b2bFQKG7P25mLwIfhHOkU2+8Zb3vhbC2fjkww8yOIBo9p+poPe9DwmdYTfTk\n01lE99OXhWA+Adg77PsKcLOZjXf3+WY2rAMj7WeJBpq/c/eqDh7b58V6D11ERLq/Hrq7v010L/jp\nUDhrGlAPvEF0//ouomlxiILy1LDfv4FLQvs9wA/Co11btHGpjtQvT9XRet6vAg8Cs4EH3b0CeBLI\nMbN3iG7RvhI+e1s1xDfI3d8CfgH8Mxz7u3SP7Q9ifWytu+ixNRHpAzr92Focq9wzRfPqdHf/dm/3\npb+L+7E1EZEBLwRvBXCJlQK6xCa5ejWNCxfSsGABBTvtTM6wzi2cq6qpZ1V9grpEktLCXEoLcsnL\nye7m3ooMXD1R79vMDgOubdE8392PI1p8J12kgC6xaVq+nA+OPQ4SCUZdfjnDTut4/obPV9Txv3e8\nxpufVAMwpCCHm0/ZjfJxw8jPVVAX6Q7ufkEPXOMp1j72JjHQojiJjxmWE31ntIKCdnetXFnP316c\nz5ufVFPbkKChbjXLVtby44fnrAnmACtWJzhrUgXVtQ2xdl1EpL/RCF1ikz10KJs/NpWmpUvJHdvy\nqZa16hub+PWT73L/awvIyTIqvrcPL025m80PPY7p76yfSLChKcmr85dx9C7tf0kQERlINELPYMmG\nBmpnzWLxDX8gUVXV49fPyssjb+ONKdhxR3KGtp3TPcuMsuIo011BbjbJRD2zpj1OY31dT3VVRKTf\n0wg9gzUtX87CCy8isXgxg8ZtRskxx/R2l1qVm5PFuftvzqHbjWZ0ST752Y0cePq5eG0NE7cdybS3\n1x2l52Vnsed4ZaYTiVPI8DbV3XfYwD5fcPe7wvty4DR3v6gHuigtKKBnsKzCQsrO+19WPvEkhXvs\n0dvdadewokEMKxoU3hWw6xFfwd25enwDlSvXXxRXWqjCLyJ9wDjgFMIjeSEhjJKG9BIllslwybo6\nknV1/bqM6ZKlK1mxpJq6hgTDyoYwbESpHluT3tCn6qGH0fGTwGvAbsBbwGlE5UJ/SzRgmwl8y93r\nzexD4D6ilLCrgVPcfV7LuuhmVuPuxakj9PD6DqAoXP7b7v6Smb0CbAvMByYTZaprLqE6DLgN2Jwo\nM9657j7bzK4iSrCzefj5e3f/Qwy/ogFH99AzXFZ+fr8O5gAlidUkTj8J+/oxFM59Q8FcZK1tgJvd\nfVtgBVFa10nASe6+I1FQ/1bK/stD+43A7ztwncXAIe6+G1HZ1uYAfBnwQihTen2LY34KvBFKtl4O\n3J6ybQJwGFHZ1J+EXPHSRZpylz4vp6yM8Q9PIVlTQ05ZWW93R6Qv+cTdm3O2/50o9/p8d/9PaJsM\nXMDa4H13ys+WAbg9ucCNZtZce33rNI7Zj6jGOe7+bKhlPiRse8zd64F6M1tMVJ1zQQf6I61QQJc+\nz7KzyR01CkaN6u2uiPQ1Le+ZVgPtfev1Vl4nCLO1ZpYFtLZA5bvA50QVNLOI6qt3RX3K6yYUi7qF\nptxFRPqvTc1sn/D6FKIFaePMbMvQ9g3gnyn7n5Ty8+Xw+kOguZ750USj8ZZKgEXungznbL7v1V4J\n1heISq4SapsvcfcVaX0q6RR9KxIR6b/eAy4ws9uAt4GLiMqM3m9mzYvi/i9l/6GhjGo98PXQ9hei\n2uqziBbZrWrlOjcDD5rZaS32mQ00hWMnES2Ka3YVcFu4Xi1wetc+qmyIVrmLiKSnL65yb/c58Rb7\nf0hUpnRJjN2SXqQpdxERkQygKXcRkX7I3T8E0hqdh/3HxdYZ6RM0QhcREckACugiIiIZQAFdREQk\nAyigi4iIZAAFdBGRfsjMDjez98xsnpld1tv9kd6ngC4i0s+YWTZwE1HltO2Ar5vZdr3bK+ltCugS\nK29qIllfv+EdRaQj9gTmufsH7t4A3AMc08t9kl6mgC6xaaqtZeXTT7Po8itIVFb2dndEMsnGwCcp\n7xeENhnAlFhGYpNctYrPfvozmqqrKZ54ECVHHtnbXRLpNeXl5UcDhwDTKioqHu3t/kjm0QhdYpNd\nVMTon/+MkmOPoWiPPXu7OyK9JgTzu4FvA3eH912xENgk5f3Y0CYDmEboEpuswkIGH3wwxQccQFZu\naxUZRQaMQ4DC8LowvO/KKH0msJWZjScK5CcTlU+VAUwjdImVmbUazFesbuTV+Ut5bPYilq5q6IWe\nifSoaUQlRAk/p3XlZO6eIBrtPwW8A9zn7m91qYfS76l8qvSK/y6uYeLv/gnAL47bgVP32qyXeySy\nQV0qn6p76BI3TblLr8jJNszAHQpys3u7OyKxC0FcgVxio4AuvaKsOI8nL96fZbUNbDN68Ab3TzY0\n4LW1ZA0ejGXrC4CISEuxB/SQ0agCWOjuR4VFHPcAZcBrwDdCYgQZQIoH5bLN6PQWyiWqqlh6++3U\nzqyg5JijGXzoYeQMLY25hyIi/UtPLIq7mGjRRrNrgevdfUtgGXB2D/RB+qlkXR2Vf/oTVX++hdWv\nv85nP7mK2hkzertbIiJ9TqwB3czGAl8Gbg3vDTgIeCDsMhk4Ns4+SP+WrKmh9sWX1mlb+cw0PJHo\npR6JiPRNcY/Qfw/8EEiG92VAdXjkApSuUDYga/Bgig84YJ22IUceieVo+YeISKrYArqZHQUsdvfX\nOnn8uWZWYWYVlcoDPmBlDRpE2TnfZMT3LqFov/3Y6LrfUrDb7r3dLZFeZWabmNlzZva2mb1lZheH\n9mFmNs3M3g8/h4Z2M7M/hFKrs81st5RznR72f9/MTk9p393M5oRj/hBmWHvkGtI5sT2Hbma/Ar4B\nJIB8YAgwBTgMGO3uCTPbB7jK3Q9r71x6Dl08kSC5uo7swcW93RUZuDoVbMrLy/OArwLfAkYDnwF/\nAh6qqKjo1IJgMxsDjHH3181sMNEC42OBM4Cl7n5NqJE+1N0vNbMjgQuBI4G9gBvcfS8zG0a0aLkc\n8HCe3d19mZm9ClwEzAAeB/7g7k+Y2a/jvkZnficS4wjd3X/k7mPdfRxRWsJn3f1U4Dng+LDb6cAj\ncfVBMofl5CiYS79TXl6+KfAf4BZgf2Dr8PMW4D9he4e5+yJ3fz28Xkm08HhjohKqk8NuqWuUjgFu\n98grQGn4UnAYMM3dl7r7MqIMdoeHbUPc/RWPRn23tzhX3NeQTuiN1K+XApeY2Tyie+p/7YU+SJqS\n9fUkV6/u7W6I9DthZP4vosIpLZMtDA7t/wr7dZqZjQN2JRrljnL3RWHTZ8Co8LqtcqvttS9opZ0e\nuoZ0Qo+sLHL354Hnw+sPAJXe6gcSS5aw+PrrSa5YychLf0je2LG93SWR/uSrRIOWtjIhZYftxwH3\nduYCZlYMPAh8x91XpN6Cdnc3s1hze/fENSR9Ks4irUrW1fH5b69j+YMPsXLaNBZ863wSVVW93S2R\n/uRbwIbuExWH/TrMzHKJgvmd7v5QaP48TGU332dfHNrbKrfaXvvYVtp76hrSCQro0ipvaiK5bNma\n903Lq/Fksp0jRKSF0d283xphNfhfgXfc/Xcpmx4lWpsE665RehQ4LaxE3xtYHqbNnwIONbOhYbX6\nocBTYdsKM9s7XOu0FueK+xrSCXqYV1qVXVTEyB9dRv38+SRXrmSj3/6W7FKlWxXpgM+IFsGls19H\n7Uv0FNEcM3sztF0OXAPcZ2ZnAx8BJ4ZtjxOtPp9HVL71TAB3X2pmPyeqrw7wM3dfGl6fD0wCCoAn\nwh966BrSCSqfKm1yd5qqqnB3sktLW61rLjKAdOixtfLy8pOJVrO3V31oJXBORUVFp+6hi6TSlLu0\nyczIGT6c3BEjFMxFOu4hYCnQ1Mb2prB9So/1SDKaArqISAxC0pj9iR7HqmmxuSa079/Z5DIiLSmg\ni4jEpKKi4mOi++jfBP4JvBd+fhPYOmwX6Ra6hy4ikh7lGZc+TSN0ERGRDKDH1kREYlReXp4FTAQO\nJ8oMVwU8CUyvqKhQcgfpNhqhi4jEpLy8/EyiPOYPAd8lSsTy3fD+k/Ly8jO6cn4zyzazN8xsang/\n3sxmhHKk95pZXmgfFN7PC9vHpZzjR6H9PTM7LKX98NA2L1RVo6euIZ2jgC7dpqmmhsYlS0g2aNGu\nSHl5+a+BG4GNiFK8Nt+Dt/B+I+Cm8vLya7twmYuJKq01uxa43t23BJYBZ4f2s4Flof36sB9mth1R\nNcztiWYQbg5fErKBm4AjgO2Ar4d9e+oa0gkK6NItEsuXU3XLLXx08tdZ/frreCLR210S6TVhZH4B\nULiBXQuBb3dmpG5mY4EvA7eG9wYcBDwQdmlZ2rS55OkDwMSw/zHAPe5e7+7zibK87Rn+zHP3D9y9\nAbgHOKYnrtHR34OspYDexyWqq6l9/XUaPvyIZH19b3enTV5XR9Utf6FxwQIq/3gjyVWrertLIr0i\n3DO/mg0H82aFwC/CcR3xe+CHQPN9+DKg2t2bv02nliNdU8I0bF8e9u9oydOeuIZ0kgJ6H9f48cd8\ndMqpfHDMMTRVL+/t7rTJ8vIoOf54sgYPpuysM7HCdP8vE8k4E2k/3WtrBhONfNNiZkcBi939tQ5e\nRzKYVrn3cdlDh2KFheRutBGWve73r2RdHU3V1Vh2NjkjRvR43xKVlSSqq8kpKyNn2DBG/eD7jLj4\nIrIHD1aqWBnIDmfDZVNbKia6l/xMmvvvCxxtZkcC+cAQ4Aag1Mxywgg5tRxpcwnTBWaWA5QQrbZv\nq7QpbbRX9cA1pJM0Qu/jckaPZosnn2CzSX8jZ/jwdbYlPvuMeQcfwocnn0xiyZIe7Vdi2TKWTZnC\nwu98l8o/3khTbS3ZJSVR3vf8/B7ti0gfU0bHk9AYMCzdnd39R+4+1t3HES04e9bdTwWeA44Pu7Us\nbdpc8vT4sL+H9pPDCvXxwFbAq0SV0bYKK9rzwjUeDcfEeo10fweyPo3Q+7is3FyyRo5sdVuysRES\nCZKraqGdjH+JqiqStbVkFReTM3Rol/uUqKyk6m+TaPjoI0b98IfUvf8f0CI4kWZVgNOxoO5EhVq6\n6lLgHjO7GniDqGY64ecdZjYvXOdkAHd/y8zuA94GEsAF7t4EYGbfJqplng3c5u5v9eA1pBOU+rUf\na6qpiabc8/LIGT4cy1p/wqVp5UoW/eQqVj7+OCO//z2GnXVWq/ulfc0VK/n0ssuoefbZqCE7my2e\nfIK8TTZp/0CR/i+tAF1eXn4I0XPmHZl2Xwl8taKiIt0pd5H1aMq9B9UsreLf995B1cJP8GTXE0Rl\nFxeTN3YsuSNHth2kk0mSK1cCUTDuqmR9HbWpX66ammj4SPUlRFJMB1Z08JiVwLMx9EUGEAX0HjRj\nyn3MeOheHv/jb1ld0/Xgmo7skhI2uuZXjH/4YYadcXqXRucAWYWFDD7k4DXvraCAQVtu0dVuimSM\nkM71CqA2zUNqgSuUBla6SgG9B223/0GUjhrDzgcfQe6gHlw45s7Se+5h+SOPkqiu7tKpsouKGPm9\n7zHmF1cz7Oyz2fyRh8kuK+umjopkhoqKiklEWeI2FNRrgRvD/iJdonvoPaipsZG6VTXk5ueTl1/Q\nY9etfuBBFv34xwBsMe3p2O93N9XUYHl5ZOXlxXodkR7W4fKpIQPcL4ieM29O/+pADdE0+xUK5tJd\ntMq9B2Xn5lJU2vVV5h1VsPvuZJeWkjt2LFkF636RqF9dS1NjIwWDhxBlaey8RHU1tS+/TPWDD5E3\nfhxlZ51NzuhRXT6vSH9VUVExqby8/HaipDFHAiOBxcDjwLOaZpfupBH6AOBNTTQtXQZZRk6YHvdk\nkurFn/Gvv/+NlUuXsOvhR7H5bntSUNzRBFfhGokES++8k8W/umZNW87IEYx/8CFyRgxv50iRfqMz\nI/RBwAlEj3ptDzQCucBbRMVL7q+oqOi7OZ2lX9E99AEgyiQ3fE0wT65ezaqqJdxz5Q+ZN/NlPv/v\n+zx50/Usev/dTl+jqbqaZbffvk5bYnElDR9/1KW+i/RX5eXlewKfAjcDOxB9IcgLP3cI7Z+Wl5fv\n0Znzm1mpmT1gZu+a2Ttmto+ZDTOzaWb2fvg5NOxrZvaHUKZ0tpntlnKe08P+75vZ6Sntu5vZnHDM\nH0KhFXriGtI5CugDUMMnn1C76FNql6+7QO6t558h0dhAU2ceqTPDCtbP3265eTSpUIsMMCFIP0uU\n/a2taa/BYftznQzqNwBPuvsEYGeiMqqXAdPdfSuix+eaa4wfQZShbSvgXOBPEAVn4CfAXkTVz37S\nHKDDPuekHHd4aO+Ja0gnKKAPQE1Ll5KTlU1WdvY67WO325Gq1UmumDKXl/9bRX2iKe1zZg8bxsjv\nXbJOW/7225FYUknjQqVnloEjTLM/CRSleUgR8GQ4Li1mVgLsT8jS5u4N7l7NuiVMW5Y2vd0jrxDl\nYx8DHAZMc/el7r4MmAYcHrYNcfdXQvrW22m9TGpc15BOUEAfgAZNmEDeoHyOOP+75IVFcuN3KWfb\nfQ/g8TmLuGfmJ3z//lmsWN245pim5ctJLFvW5jnNjMI99mDzqf9g+Le/zUa/+Q0jf/ADPvvJVTQt\n7Y6MliL9xglE98k7Io+1+dHTMR6oBP5mZm+Y2a1mVgSMcvdFYZ/PgFHhdUdLmG4cXrdsp4euIZ2g\nVe4DUE5pKcWlpWy52WaM3X5HPOnk5A0iv7iYQ7fL4tl3F/O13cZSNCj655GoqmLRT66iqbqasdf/\nrs3KbtnFxWRvuSUjvr0lTStXUvvqq4y64nIGTZjQkx9PpLddSsfLpxYTTV3fmeb+OcBuwIXuPsPM\nbmDt1DcA7u5mFuuq5564hqRPAX2AqlxZR2OTU1JYQvGgtf8MNh5ayE2n7kZ+TjZ5OdEETnLVKmqe\niVJMN3z8cVqlWrMHD2bwxInxdF6kjyovL88mWs3eGduXl5dnV1RUpHOvawGwwN1nhPcPEAX0z81s\njLsvClPai8P2tkqYLgQOaNH+fGgf28r+9NA1pBM05T4ALamp5/E5n/H5ijqW1Tast31Ifu6aYA6Q\nNXgww88/n9KTTyZv/Pie7KpIf1NM9GhaZyRIs6CLu38GfGJm24SmiUTVzFJLmLYsbXpaWIm+N7A8\nTJs/BRxqZkPDQrVDgafCthVmtndYeX4arZdJjesa0gkaocfEm5poXLSIunffpXDXXdc8MtYXZJsx\npiSf425+ia1HFXPXN/dm+OC21+PkDB1K2fnfgmRS2d9E2ldDx++fN8sJx6frQuDOUEv8A+BMokHa\nfWZ2NvARcGLY93GixDbziNLNngng7kvN7OdEtckBfubuzYtezgcmAQXAE+EPwDU9cA3pBCWWiUmi\nspJP/vc86ufNo+S44xh1xeV9KhjOnL+UE/78MtuNGcIdZ+9JWXHaC2xFBqp0y6fOIXrOvKPmVlRU\n7NiJ40QAjdBj4+4MO/MM8jbdlFUzZ0IXq5x1l6qaKCnVhDGDefHSA8nLyerVYN6UdJbU1JN0p7Qg\nl4I8/ZOUfu9aoqQxHVkYt5Jo5CvSabFFGTPLN7NXzWyWmb1lZj8N7ePNbEbIDHRvmC7KKN7URNWt\nf+XTH/yQxdddR8lXvkJWTucD1eIVdSxYVsvKus7emotUrqzjjL/N5MxJM6lrTLLx0EJGDG676pu7\n07i4ksZFi0gsX96la7elqqaew3//L7547XNUrlQGTMkI99Px++iNRAvbRDotzmFjPXCQu+8M7EKU\nSGBvom+v17v7lsAy4OwY+9ArLDub/G2jR7XyttqarKJ080usr3JlPcfd/BL7Xfsc73/ekdtr61tZ\nl2DOwuXMXrCcmvrEmvb6RBPVrSyOa1y0iKV//zuJykqalizpcunV1jjRKD3pTrLv3/0R2aCQm/1w\nIN0UiauAw5XTXboqtvnNkPmnOQLlhj9OVHXolNA+GbiKkCIwkwyeOJGi55/DBg0iuzithattamiK\nUrE2diYla4qhhXn88rgdycqC0oJo3U5tQ4In5izivooF/PaEndlkWJS+dVltA5815lL6jbP49KLz\naXxrLps/NhVKS7vUh5bKivKYdsmXSDQ5pYWdXUsk0rdUVFTMLC8vP5AoY1wurU+/ryQamR9eUVEx\ns5XtIh0S6w1LM8sGXgO2BG4C/gtUu3vz8DBjMwNlDxlC9pAhXT7P8OI8HrtwP1Y3NlFa2PbdidqG\nBE1JZ3B+20FxaFEep+y16Zr3qxsSVNU0cMP0eXy8tJZn3vmcM/eNHkt77t3FXHLfLEoLc5l62Y9p\nOOmrJKqqur2Wek52FqOGtD3tL9JfhaC+EVEGuMuInk9PEP2/O5dotvIBjcylu8Qa0N29CdjFzEqB\nKUDaKcPM7FyiBP9suummG9g7c9SuaGDRvGpGjR9C8dB8zIyRGwh4S1fVc/20//BpdR2/OG5HhuTn\nUDhow3+1y2ob+ek/3uKnR2/Pv+ct4cs7jVmzrTHMCiSTTs7o0ZRMmUrOxiO79uFEBpgQrO8E7gxJ\nZ4qBmjSTx4h0SI8sKXb3ajN7DtiHKGF/Thilt5kZyN1vAW6B6LG1nuhnb2tqTPLylHm8+/JnjN58\nCEeevxMFxRteM1hT38Qdr3wMwJHzlrDrpqVsPmLD0/xZWcYrHyylLpHkpq/vSknKDMCh241mwugh\njB6Sz/R3P+ev/17AzaeOYJuuTzqIDEghiMezulSEeFe5jwgjc8ysADiEqLzfc6wtQpCaZWjAs2wY\nvkl0q61so2Kystf+9SQbG2lavpzW8gbk58Lp+2zKIduOZMKYwSyqrkvresOL8pj+vS/xuxN2ZkjB\nulP1Q4vy2HmTUnKyjbtf/YT/Vtbw7LuVXfh0IiISpzhH6GOAyeE+ehZwn7tPNbO3gXvM7GrgDUL5\nv4EqsXQpdXPnklVYSN4WW7LNXqPZfJcR5ORmMagg+utJNjZS+9LLVN16K6N/ehWDNt98nXOsTlZS\nNHo65ZsOZXDhpmxUMrS1S60nnfvXw4ry+OMpu/Kv/1Ry5I5j2t1XRER6T5yr3GcDu7bS/gFRkfsB\nL1FdzaeXX8Gq558HYPhFF1F21pnkD1s3yCZXrWLJn//M6tdfZ/nDjzDyku+us31QziAenHc3owpH\n8bUJX2ZoQR64g6WV2KpdZsa4siLG7dP2o3dLVzXw5NxFjBicz57jh1FSoNXqIiI9TWm5epE3NrLq\nhRfWvK955hmGnnwSWfnrBvTsIUMY8/OfUT1lCkNPPXW98wzPH87U46ZiZgwvGA6rlkDFXyErF3Y/\nHQrjzSP/7qIVXD5lLgD/vvRABXQRkV6ggN6LLDeX4gMOoGb6dAAGH3YYWYWFa7avWLKaea8tZpu9\nRlO0xRaM+v73Wz1PTnYOIwpTSpoueR+e+2X0eouDYg/omwwrZEh+DqWFeeRl940UtyIiA40Cei/K\nKS1lzM9/Rv2pp2JFheRtttma0Xl9bSPP3/ken7yzlBVLVvOlU7bB0p1CL90kCuJZOVA8KsZPEBlT\nks8zl3wJM2NEO1XbREQkPgrovSxn2DByvrDP+u152Wy//0bUVNcxYZ8xrQbzpasaeGz2p7y/uIaz\n9h3PpsMKycoyGLwRfOvlqDZUUfc9O95QlyDZ5OQXrTulnpOdtcFn5UVEJF4K6H1Udk4Wm21fxpgt\nShjURkrUKW8s5OdT3wZg6uxFPHnxF6PAmpUFg9cfma+qXkqioZFBRcXkdzC//OqaBmZOnc+yRbUc\ncvZ2FA7RSFxEpC/RDc8+LCcvGyvIpqq2gRWr1y3e1JRM8s6iKEdFXnYWw4ryaGqnuknt8moe+MWV\n3Hrh2Sxd+EmH+9LUmGTO8wtZ8N4ylleu7vDxIiISLwX0Pu6Nj6s56Lp/cv9rC6hrXJstMjsri3P3\n34KNS/OZcvbe/O4LW1OUNBINTaxaXs/qmhbV08zIHZSPWRbZuR1fhZ6bn81h52zPnl8ZT+nIwg0f\nICIiPUpT7n3cKx9UUVqYyzufLqehKUl+bvaabZsPL+KxC/fjjSnzefvfnzJvixIOPG1b7vn5DLYu\nH0X5UePJzcuicMggCoeUcMz3ryDZlGBQYXrV35pqa8Gd7KIiBhXksuXu8S+wExGRzlFA7+P+Z6/N\n2G/L4eRmZ603nZKTnUVJYR6jxg/h7X9/yvDNBlO/qpFkwln80Uo+fb+a2c9+wlcu2oXCwXkUlaaX\nQQ4gsWQJn/3yl+DO6CuuIGf48O79YCIi0q0U0GOUWL4ckklyhm44kK5e2cDyytWUjCigYPDaIikN\nTUlO/PMrALx46YEUtyiPamZstkMRp/1iZ7JyBpGVncexl+xK8dBBPPnnuRSV5uEp99abEgksK4us\nrPbvtiyfOpWVjz8BQMH221P2zW+m/blFRKTn6R56TBJVVSy6/HIWfPtCEkuWtLtvUyJJ7YoG3OGz\n+ctJNKy9V56XncW4skI2H15EbitJW+pX1/LG449w64Vns+Ct1xlUkM2wjYv4YFYlXzx5a3Y5ZDOW\nLqqhsb6JVdXLeOGuycybO5ealava7VP+NtuseT1oQtpVb0VEpJcooMckWV9PzfRnyd11F1Y3JWio\na3tleN2qRqbeOIuHfvMatSsaycpe+8z5yCH5PHDeF7jvvH1afdY7UV/P+6++RLIpwbsvv0BTIkF+\nYS7jdxrBh7OXkJObxbsvfYZ7ktnPPMXYL0zkd7MamDxzIdW1axfONa2soWHhQhoro4pq+TvswOZP\nPMHmTzxOwU47deNvJpKsr6dh4UKW/2Mq9fPn01RT0+3XEBEZSDTlHpPs4mI2vfsu5i9dzAM/uIDD\nv/Udtt57v1YTxLjD6pXRY2mrltWtUzYVYHg72dcKBg/hqO/+//buPDzq+k7g+Psz85uZTCY34Uok\nHBLCJcql4gkKLh4g9Rl5sHEAABDGSURBVMC2VsVjK/q428ruCu52H9enLVrX1rZWa/EodnW3VeqF\nsorrBWqVUwzIjQLhEnJOJslMZua7f8wACeSYQCYzGT6v55mHzPf4zTcffk8+87u+33ls+Og9xkyd\nhsMVaZvTK52xl/fn4K5aJlwzGGeagwFnjaG0OsjbGw7w9oYDXDe26Mh2Gvfu4eurZ2D16cPARS9j\n5edjz8zsjFC0KLh/PzumTccEAiBC/xf+i/SxY+P2eUopleo0oceJPSsL14gR7F7wAUG/n52lX1B8\n9gTEHgl5vbeGmkMH8eTkkpaRw/X3j+Pgbi9FI9qfdz0YCGE5I3e72+x2ehYNYOJNtx/XzuV2cFpJ\n5Pp9nTeAtzKLs4Zk88BVdsYOyMVhHf1yIZYDLAubx0O4jefZO0v14sWRZA5gDBULnydtxIjjFqZR\nSikVG03ocWQ5nVz0g1sZev5F9Bo0GJv9aLh3b/iSxY89TK+Bp3Pt/Q/SozCHHoVtP04WCoY5tNvL\nmqW7GH5+AQXFOThc9jb7HLbls/188tdtjLiwgDMv6M1dL6zhHy4ZzHdGFyIiNPbsQ8GrS2jwhdi5\nK0xx580Y26K0kSObvx91BnICz8crpZSK0GvocebJyWXg6HF4snOalWf36o3NbpF/WlGzRN+WBl8j\nb/x2HTvWHmTJU6VU1Qcoq6yjwhdot2/h0Fwycl0MOCOfpRv2s6eqnlfX7qG6vpFnP97Bg+9spTYr\nl/de30/Povidaj/MfeaZ9LrvX3ANG0bebbeRc+21iD22LydKKaWOJ8bE//TqyRo3bpxZtWpVoofR\noka/H7+vFpvdTvoxSbvdfnU+bHaL9KysmPrU1QR4/bG1VOzzMeLiAkp72/nl0i38eHIx90wajNXG\n0qWhYJgGXyPGGCrqGlm+s5yLSnrhsmycPT+yfOuiOycwPD8Dt8eB2Npe2c2Ew4Sqq7G53Sd8mtwE\ng5FtZGRgc+nc8CrpxbjcoVKJoUfoJ6ly3x6evud23vnDb6n31jSrC/l8+FasoOLFFwlWVDSrc7hc\nZOTmxZzMAdKznEz/0Vlccfcoxl05kPro4211gRDtfS+zWzY82S62fH6At3+xhvz1tfRw2HFYNu6d\nMoQZowsZ0NNDeqaz3WQO4N+6ld2z76Lq5ZcJeb0x/w5NiWVh9eihyVwppTqBXkM/Sb6qSsKhINXf\nHiAcDjerC3u97Jp1K4TDOHr3IXPypSf9eZ4cFwNzIgnwjgsHMXNcPzLdFg4rtu9mxeN7c+DrGkrO\n7YPTZZHutDP74kGEwwa3M7bdob62jvInnqBh3ToavvySrKlTIY53xCullGqfJvST1GfwEG559AnS\nMjKPu04uDgfuMWMIbNuGq7i40z87z+Mkz+Nsv2ETmXlpTL51OHaHLbJ2OuCyOnbtujZswzbz+1jr\nN5A5dSo4OzYGpZRSnU+vocdZsKICQiHseXlJc9NXOBTGXxfE4bIfefytIw7UNPCfb61nxqAMRp3e\nm6z82OeIV6ob02voKqnpEXqcWXl5MbUL1NfT2FCPAcRmIz0zC4nOtx6oDxJsDJHmcRw36UxHmbDh\n4O5aPvrvzQw7ry8l5/TB6e7YbpCf4eK+K0diAHcHzxAopZSKD03oCVZXU42vqpKVry9i91el9B5U\nzITrvoffV4sjzY3l8LB+2T62rDjA1DtH0qOg9WfVQ14vwfJyfMuX4+zfn7ThI7Dym09U0+gP8fkb\nOzi4y0v53loGje55JKHX1DdS6w/itGzkZ7R+o5rdJi1OQ6uUUipxNKEnkK+qkjd//QhlG0uPlE2b\ncz8vPTiPxgY/Mx+YT1pGL0o/LKPe28iOtQdbTejhQADv+++zb+68I2WukhKKnn2m2dKnlsvO6ClF\nVO7zMXRCXyzH0SP+1TsruXXhSiYP682j148iJ12PvpVSqrvQx9YSpK66ilceeoBwOMigMeOxR2dJ\nE7ERDDRiTJhgIMDqJS8z5bahnDGxkOEXFLS6vVBVFQfmP9SszL95M3Vr1jQrs9mEvoNzuG7eOEZP\nKcKVfnR2tqr6yAQ1Fb4A4W5wb4VSSqmj9Ag9AcKhEOs/eo+R553D0EI79upvqJ7+b7z4s5+x5q3X\nuOE/HqbR38DezV+x/v3/5dCu7Vwz70HcbSzSQjBEuLr6uGL/1q1w2WXNyiyHDSv7+G1NHNKL9+Zc\nTJbbQZ4nUn/Q62fBsu1cNaqA4QWZOJLkxj6llFLNaUJPgHpvDavffJWb7/9n3M9fDEDmJRn0KS5h\n06fL2L56BTbLjt8XWbN8/7YtrP9gKWOvnIGthYRa7/UiAq4hQ/Bv2dKsLnPSJce1D4VChIPBIyuz\nHZbrcZJ7zE1u72zYz9PLv+b/Nn7LS3dOoGemJnSllEpGeso9AWoryqmrriKEDTz5IDbC+UOpr4kc\nYTf6G44k88PWvbvkuJnoDtu++nP+/OhP6fmLh3AOGgSApKWRPeefkL59m7X119Wxe0Mp5Xtq+Kb0\nIA2+xjbHOqmkJxcMzufeycVkpGkyV0qpZKVH6Angq64CYPEzC7li1iIsu401Hy6jYk9Z630qK2lt\nzoBwKER52S7Wrl7ByMeeQhoDWNluVpQ3ckFG8xncgv4G7FYGS36/ictuG8SBHRvJ79cPT07Lz5IX\n5qbz5I1jSHPYccY4G51SSqmupwk9AQ7fALd/2xae+8m/x9bH2frSosVnT6Bg6Bk0eA1//f02Bo3p\nxehphYzPt+NyND+qttLScHnSGXtFEZs/e5Mv3l5MyYQLuWz2P+JMc7e4/Sy3LmuqlFLJThN6VwrU\nQShAbq8+IEK7K6o0UTBkGJaz5Zvi3JlZrDngxxY2TJ87lswMJwE7fLr9EJbNxvgBeWRH72Z3udPJ\n7+ciq2cIhzWEL4DepxfHvISrUkqp5KR/xbtKfRWsfAZ2foLzyqfof8ZZ7Pxybczdz732u6R5PK3W\nj+mfi7c+iMdlke6yOFRRx+wXIo+sfTx30pGEDmCz2UlLt3P62LO586k/YTmcWA49CldKqe5ME3pX\nCfph2SMQ9OOq2sr5V36HXaXrMCbcbtf8ogHk9OrTZhu3w8LtOPrfme60M6mkJ5bdRpqj5ZvZXOke\nXOmtf0lQSinVfejiLF0l4IOyVbD7c8yYWTTUG3Zu2cSS3/2yzaSe3bsPNzzwMJk98ltt05qqugAC\nZOuMb0p1Bl2cRSW1uCV0EekH/AnoDRhggTHmNyKSB/wFGAB8A8w0xlS2ta2USOgtCDTUU7m3jI9e\neI7dG0qb1bnSPYyaPJUxV1xNwJlBMBQmy+1o9Wg7FsHycsK+OmwZnpgXjVFKHaEJXSW1eCb0vkBf\nY8waEckEVgMzgFlAhTHmYRGZB+QaY+a2ta3unNDrqqsINNTjSvfgzsxqsU19rZdAnY9Du3fR6G/A\nk5NHbt8C0jIyqW00/OS19SzdcIBX7j6PkYXZJzSOYGUVe++7D9/y5eTedBM97/0x9vT0k/nVlDrV\naEJXSS1u19CNMfuAfdGfvSKyESgErgYmRps9D3wItJnQuyt/nY/3//gHNv9tOZfecTdnTbmixXbu\njEzcGZlkt3CdvLHBz5dl1QRCYbZ9W0txroW/zofNbuHJzjmyxCpAqKGBcGUlpjGIPTsLe/bR5G+C\nQfzbt0fGtXkTJhCADiT0sN+PaWhotk2llFLJo0tuihORAcBo4HOgdzTZA+wncko+ZeX268f0GQ/h\nzswkGA5i2ToW8lyX8OodoznkFwpy09m5biWLfzUfd1Y2Nz/yOBm5R0+dhyoq+Ob6mYTKy+k5Zw55\nN/0AmzvybLmVl0vRM09T8+675EybhpWTc6Rf8OBBQl4v9h49sFpI2CGfj5o336JmyVv0nT8fZ2Hh\nCUZDKaVUvMT9pjgRyQA+An5ujHlFRKqMMTlN6iuNMcdNUyYiPwR+GH1bAmxu56OygeNXJzn5Pm21\naa3u2PKW2jUtO7Y+HzjUzrg6Kpnj01JZW+/jEZ/WxtUZfU7lGMXavqMxSkR8Dhljpnawj1JdxxgT\ntxfgAN4B5jQp20zk2jpAX2BzJ33Wgnj0aatNa3XHlrfUrmlZC+1XxeH/ImnjE0vMjolXp8dHYxSf\nGMXavqMxStb46EtfiXzFbXJuERHgWWCjMeZXTareAG6J/nwL8HonfeTiOPVpq01rdceWt9RucTv1\nnS2Z49NSWSwx7Gwao/Z19DNibd/RGCVrfJRKmHje5X4BsBwoBQ4/aP2vRK6jvwQUATuJPLZWEZdB\ndFMissoYMy7R40hWGp/2aYzapvFRqSied7l/TOuPeVwar89NEQsSPYAkp/Fpn8aobRoflXK6xUxx\nSimllGqbLnCtlFJKpQBN6EoppVQK0ISulFJKpQBN6ElORIaJyFMiskhE7kr0eJKViHhEZJWIXJXo\nsSQjEZkoIsuj+9LERI8n2YiITUR+LiKPi8gt7fdQKvloQk8AEXlORL4VkfXHlE8Vkc0isi26cA3G\nmI3GmNnATOD8RIw3EToSo6i5RB6HPGV0MEYGqAXSgLKuHmsidDA+VwOnAY2cIvFRqUcTemIsBJpN\nISkiduAJ4HJgOPA9ERkerZsOvAUs6dphJtRCYoyRiEwBvgK+7epBJthCYt+PlhtjLifyxefBLh5n\noiwk9viUAJ8aY+YAeiZMdUua0BPAGLMMOHYynbOBbcaYHcaYAPBnIkcNGGPeiP4xvrFrR5o4HYzR\nROBc4PvA34vIKbFfdyRGxpjDkztVAq4uHGbCdHAfKiMSG4BQ141Sqc7TJautqZgUArubvC8Dzole\n77yGyB/hU+kIvSUtxsgYcw+AiMwisoBGuIW+p4rW9qNrgL8DcoDfJWJgSaLF+AC/AR4XkQuBZYkY\nmFInSxN6kjPGfEhkzXjVDmPMwkSPIVkZY14BXkn0OJKVMaYOuD3R41DqZJwSpya7iT1AvybvT4uW\nqaM0Ru3TGLVN46NSlib05LESKBaRgSLiBL5LZGU6dZTGqH0ao7ZpfFTK0oSeACLyP8DfgBIRKROR\n240xQeAeIuvHbwReMsZsSOQ4E0lj1D6NUds0PupUo4uzKKWUUilAj9CVUkqpFKAJXSmllEoBmtCV\nUkqpFKAJXSmllEoBmtCVUkqpFKAJXSmllEoBmtBV0hORTxM9BqWUSnb6HLpSSimVAvQIXSU9EamN\n/jtRRD4UkUUisklEXhQRidaNF5FPRWSdiKwQkUwRSRORP4pIqYisFZFJ0bazROQ1EXlXRL4RkXtE\nZE60zWcikhdtd7qIvC0iq0VkuYgMTVwUlFKqbbramupuRgMjgL3AJ8D5IrIC+AtwgzFmpYhkAfXA\njwBjjDkjmoyXisiQ6HZGRreVBmwD5hpjRovIY8DNwK+BBcBsY8xWETkHeBK4pMt+U6WU6gBN6Kq7\nWWGMKQMQkS+AAUA1sM8YsxLAGFMTrb8AeDxatklEdgKHE/oHxhgv4BWRamBxtLwUGCUiGcB5wMvR\nkwAQWZNeKaWSkiZ01d34m/wc4sT34abbCTd5H45u0wZUGWPOOsHtK6VUl9Jr6CoVbAb6ish4gOj1\ncwtYDtwYLRsCFEXbtit6lP+1iFwf7S8icmY8Bq+UUp1BE7rq9owxAeAG4HERWQe8S+Ta+JOATURK\niVxjn2WM8be+pePcCNwe3eYG4OrOHblSSnUefWxNKaWUSgF6hK6UUkqlAE3oSimlVArQhK6UUkql\nAE3oSimlVArQhK6UUkqlAE3oSimlVArQhK6UUkqlAE3oSimlVAr4f2qkU3LHDg3zAAAAAElFTkSu\nQmCC\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7fdd0e26b080>"
+            ]
+          },
+          "metadata": {
+            "tags": [
+              "id2_content_0",
+              "outputarea_id2",
+              "user_output"
+            ]
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/javascript": [
+              "window[\"47523e32-e945-11e8-9ea1-0242ac1c0002\"] = google.colab.output.setActiveOutputArea(window[\"46dcf65e-e945-11e8-9ea1-0242ac1c0002\"]);\n",
+              "//# sourceURL=js_5179d92b99"
+            ],
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ]
+          },
+          "metadata": {
+            "tags": [
+              "id2_content_0",
+              "outputarea_id2"
+            ]
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/javascript": [
+              "window[\"475527dc-e945-11e8-9ea1-0242ac1c0002\"] = google.colab.output.getActiveOutputArea();\n",
+              "//# sourceURL=js_601e82349e"
+            ],
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ]
+          },
+          "metadata": {
+            "tags": [
+              "id2_content_1",
+              "outputarea_id2"
+            ]
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/javascript": [
+              "window[\"475580ba-e945-11e8-9ea1-0242ac1c0002\"] = document.querySelector(\"#id2_content_1\");\n",
+              "//# sourceURL=js_9dd6bc0333"
+            ],
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ]
+          },
+          "metadata": {
+            "tags": [
+              "id2_content_1",
+              "outputarea_id2"
+            ]
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/javascript": [
+              "window[\"4755e41a-e945-11e8-9ea1-0242ac1c0002\"] = google.colab.output.setActiveOutputArea(window[\"475580ba-e945-11e8-9ea1-0242ac1c0002\"]);\n",
+              "//# sourceURL=js_805eb59406"
+            ],
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ]
+          },
+          "metadata": {
+            "tags": [
+              "id2_content_1",
+              "outputarea_id2"
+            ]
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/javascript": [
+              "window[\"47563500-e945-11e8-9ea1-0242ac1c0002\"] = window[\"id2\"].setSelectedTabIndex(1);\n",
+              "//# sourceURL=js_9fe603bc20"
+            ],
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ]
+          },
+          "metadata": {
+            "tags": [
+              "id2_content_1",
+              "outputarea_id2"
+            ]
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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rwHRgZQeHuAoY5/a9FpjUal1L7fSPcbXTVfVNvPv9H4lXu/x52gm2qjoX+Bvw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\nqSzw/wHvAuyC7mflLAAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7fdd0e0c68d0>"
+            ]
+          },
+          "metadata": {
+            "tags": [
+              "id2_content_1",
+              "outputarea_id2",
+              "user_output"
+            ]
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/javascript": [
+              "window[\"47d79a3c-e945-11e8-9ea1-0242ac1c0002\"] = google.colab.output.setActiveOutputArea(window[\"475527dc-e945-11e8-9ea1-0242ac1c0002\"]);\n",
+              "//# sourceURL=js_0265bf16d9"
+            ],
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ]
+          },
+          "metadata": {
+            "tags": [
+              "id2_content_1",
+              "outputarea_id2"
+            ]
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/javascript": [
+              "window[\"47da0948-e945-11e8-9ea1-0242ac1c0002\"] = google.colab.output.getActiveOutputArea();\n",
+              "//# sourceURL=js_908b2590b4"
+            ],
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ]
+          },
+          "metadata": {
+            "tags": [
+              "id2_content_2",
+              "outputarea_id2"
+            ]
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/javascript": [
+              "window[\"47da4804-e945-11e8-9ea1-0242ac1c0002\"] = document.querySelector(\"#id2_content_2\");\n",
+              "//# sourceURL=js_937e8684ad"
+            ],
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ]
+          },
+          "metadata": {
+            "tags": [
+              "id2_content_2",
+              "outputarea_id2"
+            ]
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/javascript": [
+              "window[\"47da9156-e945-11e8-9ea1-0242ac1c0002\"] = google.colab.output.setActiveOutputArea(window[\"47da4804-e945-11e8-9ea1-0242ac1c0002\"]);\n",
+              "//# sourceURL=js_0539de946c"
+            ],
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ]
+          },
+          "metadata": {
+            "tags": [
+              "id2_content_2",
+              "outputarea_id2"
+            ]
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/javascript": [
+              "window[\"47dae80e-e945-11e8-9ea1-0242ac1c0002\"] = window[\"id2\"].setSelectedTabIndex(2);\n",
+              "//# sourceURL=js_48be3c947c"
+            ],
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ]
+          },
+          "metadata": {
+            "tags": [
+              "id2_content_2",
+              "outputarea_id2"
+            ]
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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XVIR4PEh6OmkDBxApW0vG8OEAeNLT8bhX/Z0VKSuj7IEHyDn4EAJjRuNJT9/4RsakGBHx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\n3lhMqS5rYMXCSoaOLiIz19/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8OIdAzvqPt+UccgiBXXfFV1CAr6CAIuDGfW5EUbL9PdsT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\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7fdd0dbd8e48>"
+            ]
+          },
+          "metadata": {
+            "tags": [
+              "id2_content_2",
+              "outputarea_id2",
+              "user_output"
+            ]
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/javascript": [
+              "window[\"4843c1f8-e945-11e8-9ea1-0242ac1c0002\"] = google.colab.output.setActiveOutputArea(window[\"47da0948-e945-11e8-9ea1-0242ac1c0002\"]);\n",
+              "//# sourceURL=js_211fb40525"
+            ],
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ]
+          },
+          "metadata": {
+            "tags": [
+              "id2_content_2",
+              "outputarea_id2"
+            ]
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/javascript": [
+              "window[\"48455a0e-e945-11e8-9ea1-0242ac1c0002\"] = google.colab.output.getActiveOutputArea();\n",
+              "//# sourceURL=js_adc51871b6"
+            ],
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ]
+          },
+          "metadata": {
+            "tags": [
+              "id2_content_3",
+              "outputarea_id2"
+            ]
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/javascript": [
+              "window[\"48459d16-e945-11e8-9ea1-0242ac1c0002\"] = document.querySelector(\"#id2_content_3\");\n",
+              "//# sourceURL=js_d134bd99bc"
+            ],
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ]
+          },
+          "metadata": {
+            "tags": [
+              "id2_content_3",
+              "outputarea_id2"
+            ]
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/javascript": [
+              "window[\"4845dcd6-e945-11e8-9ea1-0242ac1c0002\"] = google.colab.output.setActiveOutputArea(window[\"48459d16-e945-11e8-9ea1-0242ac1c0002\"]);\n",
+              "//# sourceURL=js_c76e6a429c"
+            ],
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ]
+          },
+          "metadata": {
+            "tags": [
+              "id2_content_3",
+              "outputarea_id2"
+            ]
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/javascript": [
+              "window[\"48465a1c-e945-11e8-9ea1-0242ac1c0002\"] = window[\"id2\"].setSelectedTabIndex(3);\n",
+              "//# sourceURL=js_f7ced7a9f2"
+            ],
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ]
+          },
+          "metadata": {
+            "tags": [
+              "id2_content_3",
+              "outputarea_id2"
+            ]
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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3nTSxftS7x2kjd+bhyC5Z6HIvuABbTtvmrivVDpvaub0tvCQTW9WKyHhgejvO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\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7fdd0db99f60>"
+            ]
+          },
+          "metadata": {
+            "tags": [
+              "id2_content_3",
+              "outputarea_id2",
+              "user_output"
+            ]
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/javascript": [
+              "window[\"48b1f77c-e945-11e8-9ea1-0242ac1c0002\"] = google.colab.output.setActiveOutputArea(window[\"48455a0e-e945-11e8-9ea1-0242ac1c0002\"]);\n",
+              "//# sourceURL=js_1a55834bcc"
+            ],
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ]
+          },
+          "metadata": {
+            "tags": [
+              "id2_content_3",
+              "outputarea_id2"
+            ]
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/javascript": [
+              "window[\"48b38f42-e945-11e8-9ea1-0242ac1c0002\"] = google.colab.output.getActiveOutputArea();\n",
+              "//# sourceURL=js_ffa41b526f"
+            ],
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ]
+          },
+          "metadata": {
+            "tags": [
+              "id2_content_4",
+              "outputarea_id2"
+            ]
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/javascript": [
+              "window[\"48b3dac4-e945-11e8-9ea1-0242ac1c0002\"] = document.querySelector(\"#id2_content_4\");\n",
+              "//# sourceURL=js_0a9688acba"
+            ],
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ]
+          },
+          "metadata": {
+            "tags": [
+              "id2_content_4",
+              "outputarea_id2"
+            ]
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/javascript": [
+              "window[\"48b429de-e945-11e8-9ea1-0242ac1c0002\"] = google.colab.output.setActiveOutputArea(window[\"48b3dac4-e945-11e8-9ea1-0242ac1c0002\"]);\n",
+              "//# sourceURL=js_1aeb84efcc"
+            ],
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ]
+          },
+          "metadata": {
+            "tags": [
+              "id2_content_4",
+              "outputarea_id2"
+            ]
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/javascript": [
+              "window[\"48b46dc2-e945-11e8-9ea1-0242ac1c0002\"] = window[\"id2\"].setSelectedTabIndex(4);\n",
+              "//# sourceURL=js_78a2488d8d"
+            ],
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ]
+          },
+          "metadata": {
+            "tags": [
+              "id2_content_4",
+              "outputarea_id2"
+            ]
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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lUqU1np05EvjOGLPRfr9RRLoD2F8bfHjYGPNvY8xYY8zYgjbKv92Zudweug4Y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\nEvsAAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7fdd0df27438>"
+            ]
+          },
+          "metadata": {
+            "tags": [
+              "id2_content_4",
+              "outputarea_id2",
+              "user_output"
+            ]
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/javascript": [
+              "window[\"49323cd4-e945-11e8-9ea1-0242ac1c0002\"] = google.colab.output.setActiveOutputArea(window[\"48b38f42-e945-11e8-9ea1-0242ac1c0002\"]);\n",
+              "//# sourceURL=js_846769d066"
+            ],
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ]
+          },
+          "metadata": {
+            "tags": [
+              "id2_content_4",
+              "outputarea_id2"
+            ]
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/javascript": [
+              "window[\"4933dd3c-e945-11e8-9ea1-0242ac1c0002\"] = google.colab.output.getActiveOutputArea();\n",
+              "//# sourceURL=js_2acffb7ac8"
+            ],
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ]
+          },
+          "metadata": {
+            "tags": [
+              "id2_content_5",
+              "outputarea_id2"
+            ]
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/javascript": [
+              "window[\"49342bde-e945-11e8-9ea1-0242ac1c0002\"] = document.querySelector(\"#id2_content_5\");\n",
+              "//# sourceURL=js_8878ab7924"
+            ],
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ]
+          },
+          "metadata": {
+            "tags": [
+              "id2_content_5",
+              "outputarea_id2"
+            ]
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/javascript": [
+              "window[\"49346de2-e945-11e8-9ea1-0242ac1c0002\"] = google.colab.output.setActiveOutputArea(window[\"49342bde-e945-11e8-9ea1-0242ac1c0002\"]);\n",
+              "//# sourceURL=js_72454a62a5"
+            ],
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ]
+          },
+          "metadata": {
+            "tags": [
+              "id2_content_5",
+              "outputarea_id2"
+            ]
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/javascript": [
+              "window[\"4934b04a-e945-11e8-9ea1-0242ac1c0002\"] = window[\"id2\"].setSelectedTabIndex(5);\n",
+              "//# sourceURL=js_c00ca3ea6e"
+            ],
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ]
+          },
+          "metadata": {
+            "tags": [
+              "id2_content_5",
+              "outputarea_id2"
+            ]
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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Zyf/zQ1S8/TbZt9yMwbT/PyIGmw3n8cfhGDYUg9WKGTjz1jswmM2Y6wiobruF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\nvMr9vgAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7fdd0dc2eb00>"
+            ]
+          },
+          "metadata": {
+            "tags": [
+              "id2_content_5",
+              "outputarea_id2",
+              "user_output"
+            ]
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/javascript": [
+              "window[\"499ed4b6-e945-11e8-9ea1-0242ac1c0002\"] = google.colab.output.setActiveOutputArea(window[\"4933dd3c-e945-11e8-9ea1-0242ac1c0002\"]);\n",
+              "//# sourceURL=js_c926081db4"
+            ],
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ]
+          },
+          "metadata": {
+            "tags": [
+              "id2_content_5",
+              "outputarea_id2"
+            ]
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "krnqBnTLsN1T",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        ""
+      ]
+    },
+    {
+      "metadata": {
+        "id": "JiucBW8xRTRr",
+        "colab_type": "code",
+        "outputId": "4a171e3a-bfbb-4abb-db62-49e8fdf6bf79",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 195
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "df1.head()"
+      ],
+      "execution_count": 100,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>year</th>\n",
+              "      <th>income</th>\n",
+              "      <th>lifespan</th>\n",
+              "      <th>population</th>\n",
+              "      <th>country</th>\n",
+              "      <th>region</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1800</td>\n",
+              "      <td>833</td>\n",
+              "      <td>34.42</td>\n",
+              "      <td>19286</td>\n",
+              "      <td>Aruba</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1801</td>\n",
+              "      <td>833</td>\n",
+              "      <td>34.42</td>\n",
+              "      <td>19286</td>\n",
+              "      <td>Aruba</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>1802</td>\n",
+              "      <td>833</td>\n",
+              "      <td>34.42</td>\n",
+              "      <td>19286</td>\n",
+              "      <td>Aruba</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>1803</td>\n",
+              "      <td>833</td>\n",
+              "      <td>34.42</td>\n",
+              "      <td>19286</td>\n",
+              "      <td>Aruba</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>1804</td>\n",
+              "      <td>833</td>\n",
+              "      <td>34.42</td>\n",
+              "      <td>19286</td>\n",
+              "      <td>Aruba</td>\n",
+              "      <td>america</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   year  income  lifespan  population country   region\n",
+              "0  1800     833     34.42       19286   Aruba  america\n",
+              "1  1801     833     34.42       19286   Aruba  america\n",
+              "2  1802     833     34.42       19286   Aruba  america\n",
+              "3  1803     833     34.42       19286   Aruba  america\n",
+              "4  1804     833     34.42       19286   Aruba  america"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 100
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "3kXhLRyvRMP2",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "life_more_than_eighty_three = df1[df1.lifespan > 83]"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "4qJvwIHPRXZH",
+        "colab_type": "code",
+        "outputId": "334beb99-05b1-48f5-ed4c-36ae93e950af",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 134
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "life_more_than_eighty_three.count()"
+      ],
+      "execution_count": 102,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "year          29\n",
+              "income        29\n",
+              "lifespan      29\n",
+              "population    29\n",
+              "country       29\n",
+              "region        29\n",
+              "dtype: int64"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 102
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "J5GNKxOha269",
+        "colab_type": "code",
+        "outputId": "52486a5d-3db3-472a-8601-bf7f86d9d2be",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "df1.shape"
+      ],
+      "execution_count": 103,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(41790, 6)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 103
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "I4FJPG6qVe0C",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "sorted_lifespan = df1.sort_values(by=['lifespan'], \n",
+        "                                 ascending=False)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "n3540_IlZCGH",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "d4d4c271-ce29-4878-dc1a-d735ae477ff9"
+      },
+      "cell_type": "code",
+      "source": [
+        "uniques = sorted_lifespan.country.unique().tolist()\n",
+        "\n",
+        "  \n",
+        "top_ten_uniques = uniques[:9]\n",
+        "print(top_ten_uniques)\n",
+        "\n",
+        "countries_subset = df1[df1.country.isin(top_ten_uniques)]\n"
+      ],
+      "execution_count": 160,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "['Japan', 'Singapore', 'Hong Kong, China', 'Switzerland', 'Macao, China', 'Spain', 'Australia', 'Andorra', 'Italy']\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "sV6UDqrgRaId",
+        "colab_type": "code",
+        "outputId": "fcf64e67-c544-4986-86f9-6a5af42279ac",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 708
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "tableau20 = [(31, 119, 180), (174, 199, 232), (255, 127, 14), (255, 187, 120),    \n",
+        "             (44, 160, 44), (152, 223, 138), (214, 39, 40), (255, 152, 150),    \n",
+        "             (148, 103, 189), (197, 176, 213), (140, 86, 75), (196, 156, 148),    \n",
+        "             (227, 119, 194), (247, 182, 210), (127, 127, 127), (199, 199, 199),    \n",
+        "             (188, 189, 34), (219, 219, 141), (23, 190, 207), (158, 218, 229)]\n",
+        "\n",
+        "# will saturate the RGB values into 0/1, aka matplotlib format\n",
+        "for i in range(len(tableau20)):    \n",
+        "    r, g, b = tableau20[i]    \n",
+        "    tableau20[i] = (r / 255., g / 255., b / 255.)\n",
+        "    \n",
+        "plt.figure(figsize=(12, 12))\n",
+        "\n",
+        "\n",
+        "#removes the lines between ticks on the plot\n",
+        "ax = plt.subplot(111)\n",
+        "ax.spines['top'].set_visible(False)\n",
+        "ax.spines['bottom'].set_visible(False)\n",
+        "ax.spines['right'].set_visible(False)\n",
+        "ax.spines['left'].set_visible(False)\n",
+        "\n",
+        "plt.ylim(0, 95) # lifespan limiter\n",
+        "plt.xlim(1800, 2025) # timer period limiter\n",
+        "\n",
+        "# increase axis tick size for readability\n",
+        "plt.yticks(range(0, 91, 10), [str(x) + ' years' for x in range(0, 91, 10)], \n",
+        "          fontsize=14, color=\"sienna\", alpha=0.6)\n",
+        "plt.xticks(fontsize=14, color=\"sienna\", alpha=0.6)\n",
+        "\n",
+        "#create custom tick lines \n",
+        "for tick in range(10, 91, 10):\n",
+        "  plt.plot(range(1800, 2025), [tick] * len(range(1800, 2025)), \"--\",\n",
+        "          lw=0.4, color=\"purple\", alpha=0.6)\n",
+        "  \n",
+        "# remove default ticks\n",
+        "plt.tick_params(axis='both', which='both', bottom='off', top='off', \n",
+        "               labelbottom='on', left='off', right='off', labelleft='on')\n",
+        "\n",
+        "for rank, column in enumerate(unique_top_ten):\n",
+        "  # give a line to each unique, assign color with T20\n",
+        "  plt.plot(countries_subset.year.values,\n",
+        "           countries_subset.lifespan.values,\n",
+        "          lw=2.5, color=tableau20[rank])\n",
+        "\n",
+        "  # hah, what a hot mess.\n"
+      ],
+      "execution_count": 167,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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0Za1y7Yb06xzNG1eOICk6tM2uKW1PQYCIiIiI12fLs7j3s9WUVBoj7v27RPPC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WgS0AUwtb8vcxOju5XaNGt1fLe/kGVpWbIQI18XO+ZMiuKu4cGsO3qWWe9vo9gw8NieGwf588zU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\nKagmIcS9QyL2EZN5gIfGhnMo/7Ds+q5mA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ntQawi8n6PPAkhFfHi7iFHeTHUaWpsWa5dk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\nJEk5YxAgSZIk5cz/Dw+Cn79JE8cPAAAAAElFTkSuQmCC\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7fdd0dd3a518>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "FJ6ANHjIT9Ar",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file