diff --git a/DS_Unit_1_Sprint_Challenge_2.ipynb b/DS_Unit_1_Sprint_Challenge_2.ipynb
new file mode 100644
index 0000000..58fc26d
--- /dev/null
+++ b/DS_Unit_1_Sprint_Challenge_2.ipynb
@@ -0,0 +1,703 @@
+{
+  "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.pyplot as plt\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import seaborn as sns\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": "sRMc0H_5z6ff",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 202
+        },
+        "outputId": "a91641ee-e675-43c8-9db7-fe325d52a0e4"
+      },
+      "cell_type": "code",
+      "source": [
+        "df.head()"
+      ],
+      "execution_count": 2,
+      "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": 2
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "0Dg5Ovh6R_V-",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "XW1YJWkGRqqe",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 601
+        },
+        "outputId": "e1167de9-01dc-4416-d983-47971c23b216"
+      },
+      "cell_type": "code",
+      "source": [
+        "ct = pd.crosstab(df.Year, df.Occupation, normalize='index')\n",
+        "ct = ct*100\n",
+        "ct"
+      ],
+      "execution_count": 3,
+      "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",
+              "      <th>Other</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Year</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>1999</th>\n",
+              "      <td>90.361446</td>\n",
+              "      <td>1.204819</td>\n",
+              "      <td>6.626506</td>\n",
+              "      <td>1.807229</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2000</th>\n",
+              "      <td>73.964497</td>\n",
+              "      <td>8.284024</td>\n",
+              "      <td>12.426036</td>\n",
+              "      <td>5.325444</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2001</th>\n",
+              "      <td>72.611465</td>\n",
+              "      <td>3.821656</td>\n",
+              "      <td>19.745223</td>\n",
+              "      <td>3.821656</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2002</th>\n",
+              "      <td>62.264151</td>\n",
+              "      <td>6.918239</td>\n",
+              "      <td>26.415094</td>\n",
+              "      <td>4.402516</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2003</th>\n",
+              "      <td>56.024096</td>\n",
+              "      <td>10.240964</td>\n",
+              "      <td>24.698795</td>\n",
+              "      <td>9.036145</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2004</th>\n",
+              "      <td>38.414634</td>\n",
+              "      <td>22.560976</td>\n",
+              "      <td>27.439024</td>\n",
+              "      <td>11.585366</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2005</th>\n",
+              "      <td>37.037037</td>\n",
+              "      <td>16.049383</td>\n",
+              "      <td>33.333333</td>\n",
+              "      <td>13.580247</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2006</th>\n",
+              "      <td>36.024845</td>\n",
+              "      <td>19.254658</td>\n",
+              "      <td>29.192547</td>\n",
+              "      <td>15.527950</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2007</th>\n",
+              "      <td>25.531915</td>\n",
+              "      <td>17.021277</td>\n",
+              "      <td>33.333333</td>\n",
+              "      <td>24.113475</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2008</th>\n",
+              "      <td>20.731707</td>\n",
+              "      <td>20.121951</td>\n",
+              "      <td>46.951220</td>\n",
+              "      <td>12.195122</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2009</th>\n",
+              "      <td>20.858896</td>\n",
+              "      <td>20.858896</td>\n",
+              "      <td>36.196319</td>\n",
+              "      <td>22.085890</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2010</th>\n",
+              "      <td>35.151515</td>\n",
+              "      <td>18.181818</td>\n",
+              "      <td>30.303030</td>\n",
+              "      <td>16.363636</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2011</th>\n",
+              "      <td>33.742331</td>\n",
+              "      <td>16.564417</td>\n",
+              "      <td>31.288344</td>\n",
+              "      <td>18.404908</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2012</th>\n",
+              "      <td>26.829268</td>\n",
+              "      <td>20.121951</td>\n",
+              "      <td>31.707317</td>\n",
+              "      <td>21.341463</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2013</th>\n",
+              "      <td>42.771084</td>\n",
+              "      <td>12.650602</td>\n",
+              "      <td>30.722892</td>\n",
+              "      <td>13.855422</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2014</th>\n",
+              "      <td>39.263804</td>\n",
+              "      <td>12.883436</td>\n",
+              "      <td>32.515337</td>\n",
+              "      <td>15.337423</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2015</th>\n",
+              "      <td>45.000000</td>\n",
+              "      <td>17.000000</td>\n",
+              "      <td>24.000000</td>\n",
+              "      <td>14.000000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "Occupation  Acting, Comedy & Music  Government and Politics      Media  \\\n",
+              "Year                                                                     \n",
+              "1999                     90.361446                 1.204819   6.626506   \n",
+              "2000                     73.964497                 8.284024  12.426036   \n",
+              "2001                     72.611465                 3.821656  19.745223   \n",
+              "2002                     62.264151                 6.918239  26.415094   \n",
+              "2003                     56.024096                10.240964  24.698795   \n",
+              "2004                     38.414634                22.560976  27.439024   \n",
+              "2005                     37.037037                16.049383  33.333333   \n",
+              "2006                     36.024845                19.254658  29.192547   \n",
+              "2007                     25.531915                17.021277  33.333333   \n",
+              "2008                     20.731707                20.121951  46.951220   \n",
+              "2009                     20.858896                20.858896  36.196319   \n",
+              "2010                     35.151515                18.181818  30.303030   \n",
+              "2011                     33.742331                16.564417  31.288344   \n",
+              "2012                     26.829268                20.121951  31.707317   \n",
+              "2013                     42.771084                12.650602  30.722892   \n",
+              "2014                     39.263804                12.883436  32.515337   \n",
+              "2015                     45.000000                17.000000  24.000000   \n",
+              "\n",
+              "Occupation      Other  \n",
+              "Year                   \n",
+              "1999         1.807229  \n",
+              "2000         5.325444  \n",
+              "2001         3.821656  \n",
+              "2002         4.402516  \n",
+              "2003         9.036145  \n",
+              "2004        11.585366  \n",
+              "2005        13.580247  \n",
+              "2006        15.527950  \n",
+              "2007        24.113475  \n",
+              "2008        12.195122  \n",
+              "2009        22.085890  \n",
+              "2010        16.363636  \n",
+              "2011        18.404908  \n",
+              "2012        21.341463  \n",
+              "2013        13.855422  \n",
+              "2014        15.337423  \n",
+              "2015        14.000000  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 3
+        }
+      ]
+    },
+    {
+      "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": "fa15b131-f123-420f-bdb1-795ac4067cec",
+        "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)"
+      ],
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAABMQAAAO2CAMAAAAwo7uMAAAAGXRFWHRTb2Z0d2FyZQBBZG9iZSBJ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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": "YhGhnLj6TPpg",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "5927ed61-4f0e-47da-b4e2-cf4a313d1673"
+      },
+      "cell_type": "code",
+      "source": [
+        "ct.columns"
+      ],
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Index(['Acting, Comedy & Music', 'Government and Politics', 'Media', 'Other'], dtype='object', name='Occupation')"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "TA7RolJJV9jI",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "year = ct.index.tolist()"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "E8XBAr8rz_Na",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 320
+        },
+        "outputId": "e2179daf-3c19-4ea2-8976-aa2678f8ef3b"
+      },
+      "cell_type": "code",
+      "source": [
+        "plt.style.use('fivethirtyeight')\n",
+        "   \n",
+        "\n",
+        "ax = sns.lineplot(x=year, y=ct['Acting, Comedy & Music'].tolist(),lw=3.2)\n",
+        "ax1 = sns.lineplot(x=year, y=ct['Government and Politics'].tolist(), lw=3.2)\n",
+        "ax2 = sns.lineplot(x=year, y=ct['Media'].tolist(), lw=3.2)\n",
+        "\n",
+        "# Title & subtitle text\n",
+        "plt.suptitle(\"Who Got To Be On 'The Daily Show'?\", x=-0.01, y=.95,\n",
+        "             horizontalalignment='left', verticalalignment='top',\n",
+        "             fontsize = 15, fontweight='semibold', color='#333333')\n",
+        "\n",
+        "# Removing spines\n",
+        "for key, spine in ax.spines.items():\n",
+        "    spine.set_visible(False)\n",
+        "\n",
+        "# Y-axis limit\n",
+        "plt.ylim(-5, 105)\n",
+        "# Y-axis ticks & labels\n",
+        "ax.set_yticks([0, 25, 50, 75, 100])\n",
+        "ax.set_yticklabels(labels=['0', '25', '50', '75', '100%'], fontsize=14, color='black')\n",
+        "\n",
+        "\n",
+        "# X-axis ticks & labels\n",
+        "ax.set_xticks([2000, 2004, 2008, 2012])\n",
+        "ax.set_xticklabels(['2000', '2004', '20008', '2012'], fontsize=14, color='black')\n",
+        "\n",
+        "# Horizontal line at y=0\n",
+        "ax.axhline(y=0, color='gray', linewidth=2.5, alpha=0.8) \n"
+      ],
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.lines.Line2D at 0x7f0ad2cc4588>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 11
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAbEAAAEdCAYAAACCDlkkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7f0ad6d83da0>"
+            ]
+          },
+          "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": "mS8vr952sU2j",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 282
+        },
+        "outputId": "ed5957e5-c559-4fb6-d942-07fc2ed69491"
+      },
+      "cell_type": "code",
+      "source": [
+        "df['Guest'].value_counts().head(10).plot(kind='barh');"
+      ],
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfQAAAEJCAYAAABi2tVNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAIABJREFUeJzs3XlYjfn/x/FnWSLbQZxWTKKvSPaS\nUhgjQvYwdqYRNc3Y920sw9BM9m0MU74UstMYoU5DdvIbI5EsYx0zWVLazu8Pl/OdMxVJVMf7cV3n\nutz353N/Pq+77/Wd97k/933O0UtMTFQjhBBCiCJNv6ADCCGEEOLtSUEXQgghdIAUdCGEEEIHSEEX\nQgghdIAUdCGEEEIHSEEXQgghdIAUdCGEEEIHSEEXQgghdIAUdFGoxMXFFXSEPJPsBaMoZ4einV+y\nFy5S0IUQQggdIAVdCCGE0AFS0IUQQggdULygA+SFQqFgw4YNeHh45Gq7MFKpVHTq1ImrV69SuXLl\n127nRdIht3xO/e6ZAkk3CzpF3kj2AmKxpKATCFEoFKordG9vbxQKheZlaWmJp6cnly9f1uoXGxuL\nm9vbFavExESmTJmCnZ0dVapUwcrKiiFDhmSZa968eXh7e2ebsXLlytSrV49Ro0aRmJj4RvPb29sT\nGxtLpUqV3uo8hBBCCChkBR3A1dWV2NhYYmNjCQ0NJTk5mX79+mn1USqVGBgY5HmOxMREPvnkEw4c\nOMA333zDmTNn2LRpE0lJSbRu3ZozZ87kKmNMTAyLFy8mLCyM0aNHv1GGkiVLolQq0dPTy/N5CCGE\nEC8VuoJuYGCAUqlEqVTSoEEDRowYweXLl0lOTtb0USgU7Ny5M89zfP3119y6dYudO3fSvn17LCws\naNq0KZs2bcLS0pKRI0eiVuf8M/EvM5qZmdG6dWu6du3KoUOHtPo8evQIPz8/rKysMDc3p0OHDpw9\ne1bTrlKpUCgUPHz48LV5k5KSsLCwyHLOhw8fxsjIiPv377/hX0AIIYSuKXQF/Z+ePHlCaGgoNjY2\nlC5dOl/GzMzMZNu2bfTs2RMTExOtNn19fXx8fPj999/5v//7v1yNl5CQQHh4OCVKlNDsU6vVeHp6\ncufOHYKDg4mMjMTR0ZHOnTtz9+7dN85cpkwZunfvTlBQkNb+oKAg2rVrR9WqVd94TCGEELql0D0U\nd/DgQczMzIAXV6bm5uaEhITk2/h//vkniYmJ1K5dO9t2a2trAK5cuYKtrS0TJ07MMWNGRgYpKSkA\nzJkzR9MeGRnJhQsXuHLliuaNyJQpUwgLCyM4OBg/P783zj1w4EA+/vhjbt++jampKYmJiezdu5f1\n69e/8VhCCCF0T6Er6I6OjgQEBAAv7nWvXbuWbt26cfDgQczNzQs43QsvMyYnJ7NhwwYSEhIYPny4\npv38+fM8e/YMKysrreNSUlK4du1anuZs2LAhNjY2bNq0idGjR7NlyxYqVqxI27Zt3+pchNAFRf1b\nv4pyfsn+/tSqVeuV7YWuoBsaGmJpaanZXrJkCdWqVWP9+vVMmTLlrcc3MjKiQoUKxMbGZtv+cn/N\nmjVzlXHBggV07NiRBQsWaK7mMzMzqVq1Kvv3789ybLly5fKcfcCAAaxcuZLRo0cTFBREnz59KFas\nWJ7HE0JXvO4/dIVZXFxckc0v2QuXQn0PHUBPTw99fX2th+Lehr6+Pt27d2fr1q3cuXNHqy0zM5Ol\nS5dSp04dbG1tcz3m+PHjCQgI0IxnZ2fH/fv30dfXx9LSUutVpUqVPGfv2bMnt2/fZvXq1Zw/f55P\nP/00z2MJIYTQLYWuoD9//px79+5x7949YmNjGTduHE+fPn3rz53/09SpUzExMaFLly7s37+fW7du\ncfLkSfr27Ut8fDzLli17o4+TOTs7Y21tzcKFC4EXH2tzcHCgb9++/PLLLyQkJHDixAnmzp3L0aNH\n85xboVDg4eHBlClTcHR0fOUqghBCiA9LoSvoR44cwdraGmtraz7++GPOnDnD+vXrcXZ2zrc5Klas\nyMGDB2nTpg3jx4+nYcOG9O7dm9KlSxMeHk6jRo3eeEwfHx8CAwO5ceMGenp6hISE4OzsjJ+fH02b\nNmXw4MFcuXIly5P1b6p///6kpqbSv3//txpHCCGEbtFLTEzM+QPXotAJDQ3lyy+/5NKlSxgaGr6y\nb1H86lch3tRtiyVF+l5oUb6XK9kLl0L3UJzI3rNnz7h//z6LFi1i4MCBry3mAGVah72HZPmrKP+f\nTLIXkCL2pLIQ70qhW3IX2QsICKBJkyZUrFiRsWPHFnQcIYQQhYwU9CJi4sSJ/Pnnn+zZs4fy5csX\ndBwhhBCFjBR0IYQQQgdIQRdCCCF0gBR0IYQQQgdIQRdCCCF0gBR0IYQQQgdIQRdCCCF0gBR0IYQQ\nQgdIQRdCCCF0gHz1qw4rO9C1oCO8sYYFHeAtSPYCMmVNQScQolB4r1fotra2LFmyJFd9N27ciJmZ\n2TtO9D8PHz5EoVCgUqne25yv4+7uLl/zKoQQIlfy5Qrd29ubTZs2AVCsWDFMTEz45JNPmDZtGgqF\nQtPv8OHDufpRkcLK29ubv/76i+Dg4PcyX1BQEMWLyyKKEEKI18u3auHq6sqqVatIT08nNjYWHx8f\nHj16xA8//KDpY2RklF/T5VlqaiolS5Ys6Bi5UrFixYKOIIQQoojItyV3AwMDlEolZmZmtG7dmq5d\nu3Lo0CGtPv9ecn/06BGjRo3C2toapVJJs2bNCA0N1TomIiKC5s2bY2pqSseOHUlISNC0Xbt2jT59\n+lC7dm1MTU1p2bIlYWHaPxlqa2vLvHnzGDlyJNWqVeOzzz4D4MyZM7i4uKBUKnF2dubUqVNv/Td4\n9OgRfn5+WFlZYW5uTocOHTh79qym3dramm3btmm23dzcMDc3Jz09HYD4+HgUCgV//PEHkHXJfdeu\nXTg6OmJsbEyNGjXo0KED9+/ff+vcQgghir53cg89ISGB8PBwSpQokWMftVpNr169+PXXX1m2bBnH\njx9nzpw5Wsc8f/4cf39/li5dyoEDBzRvAF56+vQpbdu2Zfv27URFRdG5c2f69+/P5cuXteZavnw5\ntWvX5siRI0ybNo2nT5/Sq1cvatSoweHDh5kxYwZTp059q3NWq9V4enpy584dgoODiYyMxNHRkc6d\nO3P37l0AWrRoQVRUFPDi983PnDlDyZIlNUU/KiqKjz76KNtnB+7du8fQoUPp06cPx48fZ9++ffTu\n3futMgshhNAd+bbkfvDgQczMzMjIyCAlJQWAOXPm5Nj/yJEjnDhxgujoaKytrQGoUaOGVp/09HQW\nLlxIrVq1APD19cXHxwe1Wo2enh62trbY2tpq+o8ZM4awsDB27typdWXr6OiIn5+fZnv9+vWkpqay\nbNkyypYti42NDaNHj+bzzz/P8/lHRkZy4cIFrly5QunSpQGYMmUKYWFhBAcH4+fnh5OTE8uXLwfg\nxIkT1KhRg8aNG6NSqWjatClRUVE4OTllO/6dO3dIS0vDw8ODatWqAWBjY5PnvELokri4uIKO8FaK\ncn7J/v68rIU5ybeC7ujoSEBAAMnJyWzYsIGEhASGDx+eY/+YmBiMjY01xTw7BgYGWidgbGxMamoq\niYmJVKxYkaSkJObPn8/PP//M3bt3SU9PJyUlhbp162qN07Ch9odyYmNjqVu3LmXLltXsa9as2Zue\nspbz58/z7NkzrKystPanpKRw7do1AJycnBg1ahR3794lKioKZ2dnGjVqxLZt2xg1ahS//vor06ZN\ny3Z8W1tbXF1dcXR0pFWrVri6uuLh4VEonksQoqC97j90hVlcXFyRzS/ZC5d8W3I3NDTE0tKSunXr\nsmDBAp49e8aCBQveasx/P+Gtp6cHQGZmJgBTp05lx44dTJo0ib1796JSqWjcuDGpqalax5UpU+at\ncuRGZmYmVatWRaVSab1OnjzJ5MmTAahduzZKpRKVSqUp6E5OThw/fpzY2Fj++OOPHK/QixUrxvbt\n2wkNDaVu3boEBgbSqFEjLly48M7PTQghROH3zj6HPn78eAICArhz50627fXr1+fu3bvExsbmeY7o\n6Gh69+6Nh4cH9erVw9TUVHM1/CrW1tZcvHiRpKQkzb6TJ0/mOQeAnZ0d9+/fR19fH0tLS61XlSpV\nNP1atGjBgQMHOHv2LE5OTlSvXp1KlSoREBCQ4/3zl/T09GjWrBkTJkzg8OHDmJiYsH379rfKLYQQ\nQje8s4Lu7OyMtbU1CxcuzLbdxcWFJk2aMGDAAMLDw0lISODw4cPs2bMn13PUrFmTPXv2cO7cOX77\n7Te8vLx4/vz5a4/r0aMHxYsXx8fHh99//53Dhw+zaNGiXM35+PFjYmJitF7Xr1/H1dUVBwcH+vbt\nyy+//EJCQgInTpxg7ty5HD16VHO8k5MT27dvx9LSUrNc7uTkREhISI5X5/DiDce3337LmTNnuHnz\nJvv27eOPP/545S0LIYQQH453+k1xPj4+BAYGcuPGjawT6+uzZcsW7O3t8fLywt7engkTJpCWlpbr\n8efMmUOVKlXo0KEDPXv2pGnTpjRv3vy1x5UtW5bg4GCuXr2Ki4sLU6ZMYcaMGbma89ixY7Rs2VLr\nNXXqVPT09AgJCcHZ2Rk/Pz+aNm3K4MGDuXLlCiYmJprjnZycSE9P1yre2e37t/Lly3P8+HE8PT1p\n3LgxU6ZMYezYsXh6euYqtxBCCN2ml5iYqC7oEEK8VJQfVJHsBaMoZ4einV+yFy7ya2tCCCGEDpCC\nLoQQQugAKehCCCGEDpCCLoQQQugAKehCCCGEDpCCLoQQQugAKehCCCGEDpCCLoQQQugAKehCCCGE\nDpCCLoQQQugAKehCCCGEDij++i5Fj7u7OzY2Nnz77be52i4K5s2bx65duzh27Fi229lR/PjH+4qX\njwwhqijmBsleME7m/JtGQnxQCu0V+o8//oipqSmpqamafampqZiYmGT5RbX4+HgUCgUREREABAUF\nMW3atDzPrVKpUCgUWV4TJkzI85hCCCHEu1Ror9CdnZ159uwZp0+f1hTwU6dOUb58ea5evcqff/6p\n+T1xlUqFgYEB9vb2AFSsWDFfMkRHR2uNZWhomOexUlNTKVmyZK73CyGEEG+i0F6hW1lZYWJigkql\n0uxTqVS4uLjQsGFDoqKitPY3bdqUUqVKAS+W1MeOHfvWGapUqYJSqdS8ypUrp2k7fvw4HTp0wMTE\nhDp16jBq1CgeP36saXd3d2fUqFFMmTKFmjVr0q5dOwAUCgVr1qyhX79+mJqaMmvWLAAuXbpEr169\nMDc3x8rKiqFDh3Lv3r23PgchhBAfhkJb0OHFVfq/C7qTkxNOTk5a+6OionB2dn5vuX777Te6detG\n+/btiYqKIjAwkAsXLuDj46PVLyQkBLVazf79+1m5cqVm//z58/nkk084evQow4YN4+7du3To0IE6\ndeoQHh7Ojh07ePr0KX379iUzM/O9nZcQQoiiq9AuuQM4OTkxbtw4nj9/jlqt5uTJkyxevBhzc3PN\n/ezLly9z9+5dWrZsme/z169fX2s7OjoaCwsLFi9eTNeuXfH19dW0LVq0iJYtW/LgwQOqVKkCQLVq\n1ZgzZ06Wcbt27cqAAQM023PmzKFevXrMnDlTs2/VqlXUqFGDs2fP0rhx4/w+NSF0SlxcXEFHeCtF\nOb9kf39q1ar1yvZCXdBbtmxJSkoKJ06cQK1WY2RkhKWlJUqlkmvXrnHv3j1UKhWGhoY0adIk3+ff\nvXs3CoVCs21iYgLA+fPniY+PZ/v27Zo2tVoNwLVr1zQFvUGDBtmO27BhQ63t8+fPc/ToUczMzLL0\nvXbtmhR0IV7jdf+hK8zi4uKKbH7JXrgU6oJeo0YNLCwsiIqKQq1W06JFCwDKlClDgwYNiIqKIioq\nCgcHB0qUKJHv81evXp3KlStn2Z+ZmcmAAQMYMWJElraXRf9lzuz8e39mZiaffPIJs2fPztL35ZsD\nIYQQ4lUKdUGH/91HV6vV9OnTR7PfycmJyMhIoqKiGDly5HvNZGdnx++//46lpWW+jbd9+3YsLCze\nyRsTIYQQuq9QPxQHLwr6qVOnOH36NE5O//sGiRYtWhAaGsqDBw/e6wNxAH5+fpw5c4avvvpKs/we\nFhbGl19+mafxhg0bxuPHjxk8eDCnTp0iISGBI0eO4Ofnx5MnT/I5vRBCCF1UJAp6amoqVapU0boi\ndnBwIDk5mfLly+d4r/pdqVevHvv27ePGjRt07NgRJycnZs2aleflcRMTE37++Wf09fXp3r07Dg4O\njBkzhpIlS2JgYJDP6YUQQugivcTERHVBhxDipaL8oIpkLxhFOTsU7fySvXAp9FfoQgghhHg9KehC\nCCGEDpCCLoQQQugAKehCCCGEDpCCLoQQQugAKehCCCGEDpCCLoQQQugAKehCCCGEDpCCLoQQQugA\nKehCCCGEDpCCLoQQQuiAQv/zqbrI3d0dGxsbvv3223c6j+LHP97p+O+GIUQVxdwg2QvGSafX9xHi\nQ1Aor9C9vb1RKBQoFAoqV65MvXr1GDVqFImJiQUdTWPXrl1UqlSJzz77rKCjCCGEEIWzoAO4uroS\nGxtLTEwMixcvJiwsjNGjRxd0LI3AwED8/PzYu3dvoXqjIYQQ4sNUaAu6gYEBSqUSMzMzWrduTdeu\nXTl06JBWn0ePHuHn54eVlRXm5uZ06NCBs2fPatptbW01V/r/fF2/fh2ApUuX4ujoiKmpKXXq1MHX\n1zdXxfmPP/5ApVLh6+tLkyZNCAkJ0WpPS0tj3Lhx/Oc//6Fq1arUrVuXGTNm5DhecHAwrVq1wtzc\nHCsrKwYOHMjt27c17SqVCoVCQUREBG3atMHExARXV1fOnTuXmz+lEEKID0ChLej/lJCQQHh4OCVK\nlNDsU6vVeHp6cufOHYKDg4mMjMTR0ZHOnTtz9+5dAA4fPkxsbKzm1a5dO2rXrk3VqlUB0NfXZ968\neRw7dow1a9Zw+vRpxo0b99o8GzdupFWrVlSqVAlPT08CAwO12leuXMnevXv54YcfOH36NOvWrcPK\nyirH8VJTU5k4cSJRUVEEBwfz8OFDhg4dmqXfzJkzmT59OhEREVSqVAkvLy/Uavk5eyGEEIX4obiD\nBw9iZmZGRkYGKSkpAMyZM0fTHhkZyYULF7hy5QqlS5cGYMqUKYSFhREcHIyfnx9GRkaa/t9//z0n\nT54kPDxc03/EiBGa9urVqzNr1iz69u3LypUr0dfP/r2OWq1m48aNzJo1CwAPDw/Gjh3L2bNnadiw\nIQA3b96kZs2aODo6oqenh4WFBfb29jmea//+/TX/rlGjBv7+/jRr1ow//vgDMzMzTdvkyZNp2bIl\nAOPGjcPNzY3bt29r9RFCCPFhKrQF3dHRkYCAAJKTk9mwYQMJCQkMHz5c037+/HmePXuW5co3JSWF\na9euae3bv38/8+bNY9u2bXz00Uea/REREXz33XdcvnyZx48fk5GRQWpqKvfu3cPExCTbXBERESQm\nJuLm5gZA2bJlcXd3JzAwUFPQ+/btS9euXWncuDGtW7embdu2tG3bNsc3CefOnWP+/PlcuHCBxMRE\nzVX3rVu3tIp13bp1Nf82NjYG4MGDB1LQxQcvLi6uoCO8laKcX7K/P7Vq1Xple6Et6IaGhlhaWgKw\nYMECOnbsyIIFC5g4cSIAmZmZVK1alf3792c5tly5cpp/X7x4ES8vL7799lucnP73+ZYbN27g6enJ\ngAEDmDRpEpUqVeL8+fMMHTqU1NTUHHP99NNPPHr0CFNTU80+tVpN2bJlmT17NoaGhjRo0ICYmBgO\nHTpEREQE3t7e1KtXjx07dmQp6klJSXTv3h1XV1dWrVpFlSpVePjwIe3bt8+S45+3HPT09DRzC/Gh\ne91/6AqzuLi4Iptfshcuhbag/9v48ePp2bMngwYNwsTEBDs7O+7fv4++vj41atTI9piHDx/Su3dv\nBgwYwIABA7Tazp49S2pqKvPmzaNYsWIAhIWFvTLD33//zd69e1mxYgV2dnZabR4eHuzcuZM+ffoA\nL95UeHh44OHhQd++ffn444+Jj4/PsqIQFxfHw4cPmTp1quY8du3alds/ixBCCAEUkYfiAJydnbG2\ntmbhwoXAi4+1OTg40LdvX3755RcSEhI4ceIEc+fO5ejRo8CLe9Ompqb4+Phw7949zSsjI4OaNWuS\nmZnJ8uXLSUhIYOvWraxcufKVGTZv3ky5cuXo1asXNjY2Wq9OnTrx008/AS+ent+6dSuxsbHEx8ez\nZcsWypcvr3VV/5K5uTkGBgasWbOGhIQEfv75Z+bOnZvPfz0hhBC6rsgUdAAfHx8CAwO5ceMGenp6\nhISE4OzsjJ+fH02bNmXw4MFcuXJFc//76NGjREdHU6dOHaytrTWvW7duUa9ePb755huWL1+Og4MD\nP/30E19//fUr5w8MDMTd3V1zRf9PHh4eHDt2jCtXrlCuXDkWL15MmzZtcHFx4cKFC2zZsgVDQ8Ms\nxxkZGbFixQr27t2Lvb098+fP13r4TwghhMgNvcTERLkJKwqNonxfS7IXjKKcHYp2fsleuBSpK3Qh\nhBBCZE8KuhBCCKEDpKALIYQQOkAKuhBCCKEDpKALIYQQOkAKuhBCCKEDpKALIYQQOkAKuhBCCKED\npKALIYQQOkAKuhBCCKEDpKALIYQQOqDI/HyqeHMB1RcWdAQhPlh+18cUdATxgZEr9HdkyJAhWX6D\nXQghhHhX3ltBVygUr3x5e3u/rygArFu3Tmt+CwsL2rZty6FDh95rDiGEECI/vLcl99jYWM2/f/75\nZ7744gutfaVKlXpfUTQqVqxIdHQ0AI8fP2blypX07duX8+fPo1Qqsz0mNTWVkiVLvs+YQgghxGu9\ntyt0pVKpeVWoUCHHfefPn8fd3R1jY2M++ugjfH19efLkiWacl0vZCxYsoFatWlSrVo05c+aQkZHB\nrFmzsLS0xNramuXLl782k56enmb+WrVqMWnSJFJSUrTeaNSuXZtFixbx+eefY2FhgY+PT65y/tvZ\ns2exsrJi/vz5AFy+fBlPT09q1aqFmZkZrq6uWqsDy5cvx8nJSbMdFhaGQqHQOq8BAwYwduzY156n\nEEII3Veo7qE/fvyY7t27U6VKFQ4dOsT69euJjIxk1KhRWv0OHz7Mn3/+yb59+/jmm2/49ttv6dWr\nF8WLF+eXX37hyy+/ZPLkyVy8eDHXc6elpfHf//6XMmXKYGNjo9UWEBBA/fr1iYyMZMKECbnO+VJE\nRAQeHh5MnDiR8ePHA5CUlESHDh3YuXMnkZGRfPLJJ/Tp04eEhAQAnJyc+O233/j7778BiIqKonLl\nykRFRQGgVqs5evSoVtEXQgjx4SpUT7lv2rSJzMxMVqxYQenSpQFYtGgRPXv2ZPr06ZibmwNQuXJl\nvvnmG/T19alVqxZLlizh77//ZtKkSQB4e3vz/fffExUVlaU4/9Nff/2FmZkZAMnJyZQuXZoVK1Zg\nZGSk1a9Vq1aMHDlSs71q1apc5QTYtWsXI0aMICAggO7du2v2N2zYkIYNG2q2J02axL59+9i9eze+\nvr7Uq1ePChUqEBUVRadOnYiKisLX15fvv/+ezMxMLl26xMOHD2nRokWe/tZCiHcrLi7unfQtbCT7\n+1OrVq1Xtheqgn758mXq16+vKZIADg4OqNVqLl++rCmUderUQV//f4sLVapUoVq1alpjGRkZ8eDB\ng1fOp1AoNMvcSUlJhIeH4+XlRfny5XF1ddX0+2fhfZOcJ06cYO/evQQGBtKhQwetMR4/fsw333zD\nwYMHuXv3LhkZGaSkpHDz5k0A9PX1cXR0JCoqipYtW/Lbb7+xY8cOVqxYwYULFzh+/Dj/+c9/srz5\nEEIUDq/7j+9LcXFxue5b2Ej2wqVQFfRX0dPT0/y7RIkSWdqy26dWq185pr6+PpaWlpptW1tbfvnl\nF77//nutgm5oaJinnJaWligUCoKCgmjbtq1WxgkTJhAdHc3MmTOxtLSkdOnSDBkyhLS0NE0fJycn\ngoKCOHbsGDY2NigUClq0aIFKpeLEiROy3C6EEEKjUN1Dr127NjExMSQnJ2v2RUdHo6en997eSRUr\nVkxr/uzkNqeRkRG7du3i6tWrDBo0SKtYR0dH069fPzp16kTdunVRKpWa++cvOTk58fvvv7Nz505N\n8XZyckKlUsn9cyGEEFoKVUHv06cP+vr6jBgxgosXLxIZGcmYMWPo0aOH1n3p/KJWq7l37x737t0j\nISGBNWvWEBUVlWV5/G1yVq1ald27d3PlyhWtom5lZcWuXbu4cOECFy5cYOjQoWRkZGgd+/I++pYt\nW3B2dgZeFPTw8HC5fy6EEEJLoSro5cuXZ9u2bdy/f5/WrVszcOBAnJ2d8ff3fyfz/f3331hbW2Nt\nbY2DgwNr1qxh+vTp+Pr65mvOqlWrsmvXLuLi4hg8eDBpaWksWLCAMmXKaJ5ud3V1pVGjRlrHvbyP\nrlarad68OfBidaBy5cpy/1wIIYQWvcTExFffaBbiPSrKD6pI9oJRlLND0c4v2QuXQnWFLoQQQoi8\nkYIuhBBC6AAp6EIIIYQOkIIuhBBC6AAp6EIIIYQOkIIuhBBC6AAp6EIIIYQOkIIuhBBC6AAp6EII\nIYQOkIIuhBBC6AD56lcdtmbNmoKOIITIB5999llBR8hWUf761KKcPScFcoVua2vLkiVLctV348aN\nmJmZveNEhdOHfO5CCCHeTL4WdG9vbxQKBQqFgsqVK1OvXj1GjRpFYmKiVr/Dhw8zdOjQ/Jz6jaWl\npbFkyRJcXFwwMzPDwsICR0dHZsyYwa1btwo020vdunXj3LlzBR1DCCFEEVA8vwd0dXVl1apVpKen\nExsbi4+PD48ePeKHH37Q9Cnon/1MTU2lW7duXLhwgfHjx9O8eXOMjIy4desW27dvZ+nSpXzzzTd5\nHj8tLY0SJUq8dc7SpUtTunTptx5HCCGE7sv3JXcDAwOUSiVmZma0bt2arl27cujQIa0+/15yf/To\nEaNGjcLa2hqlUkmzZs0IDQ3NdvzExETatWtHt27dSEpKomHDhlmW769evYpCocjx6nb58uUcPXqU\nXbt2MWLECBo2bIiFhQXNmzeQzZRXAAAgAElEQVRnwYIFzJs3T9NXrVYTEBBAgwYNMDY2xtHRkeDg\nYE379evXUSgUbN26lU6dOmFsbMwPP/yAsbEx+/fv15r30KFDGBkZ8eDBAwBmzJhBkyZNMDY2xtbW\nlmnTppGSkqLp/+8l91u3btGnTx9q1KiBiYkJTZs2Zdu2bdmeoxBCiA9Lvl+h/1NCQgLh4eGvvFpV\nq9X06tWLxMREli1bhpWVFXFxcVqF7aU7d+7QvXt3rK2tWbVqFSVLlqR///5s3LgRX19fTb+goCBs\nbW1p0KBBtnNu2bKFVq1aYWdnl227np6e5t+zZ89m586dLFy4ECsrK06ePImfnx8KhYJ27dpp+s2c\nOZPZs2ezZMkSSpQoQXR0NFu2bKF9+/aaPiEhIbRq1YoqVaoAYGhoyNKlSzExMSE2NpZRo0ZRsmRJ\npkyZkm2u0aNH8/z5c3bv3k25cuW4cuVKjn9XIYQQH5Z8L+gHDx7EzMyMjIwMTVGeM2dOjv2PHDnC\niRMniI6OxtraGoAaNWpk6RcfH0+XLl1o06YNixYtQl//xeLCp59+yty5czl58iRNmzYlIyODzZs3\n89VXX+U459WrV3FyctLaN3ToUMLCwgCwsLAgOjqapKQkli1bRmhoKI6Ojppsp0+fZu3atVoF3cvL\nCw8PD812r169GDp0KE+ePKFcuXIkJyezd+9e/P39NX3GjRun+Xf16tUZNWoUS5YsybGg37x5k86d\nO2Nra5vj30kIoXvi4uIKOkKOCnO21ylq2V/3VH6+F3RHR0cCAgJITk5mw4YNJCQkMHz48Bz7x8TE\nYGxsrCnm2UlNTcXNzY3OnTuzcOFCrTalUkm7du0ICgqiadOmHDx4kL///ptevXq9Ue65c+cyefJk\nAgMD2bp1KwCxsbGkpKTQo0cPrav2tLQ0qlWrpnV8w4YNtbbbtm1L6dKl2bNnD3369GH//v2o1Wrc\n3d01fXbu3MmKFSuIj48nKSmJjIwMMjIycsw4fPhwRo0aRXh4OC4uLnTs2DHHVQghhO4orB+vKsof\n/SrK2XOS7/fQDQ0NsbS0pG7duixYsIBnz56xYMGCtxqzRIkStGrVigMHDnDjxo0s7QMGDGD79u08\ne/aMoKAgOnbsiEKhyHG8mjVrZnlnplQqsbS0pFKlSpp9mZmZAGzatAmVSqV5RUdHZ7nHX6ZMmSyZ\nu3btypYtW4AXy+3u7u4YGhoCcPLkSYYMGULr1q3ZvHkzkZGRTJ48mbS0tBxzDxgwgPPnz/Ppp59y\n5coVPvnkE637/UIIIT5c7/xz6OPHjycgIIA7d+5k216/fn3u3r1LbGxsjmPo6emxYsUKHBwc6NSp\nEzdv3tRq//jjjylXrhzr1q0jLCyMfv36vTJTjx49OHz4MGfPnn1lP2trawwMDLh58yaWlpZar39f\noWenV69eREREcOnSJcLDw/H09NS0RUdHY2Jiwrhx42jUqBE1a9bMcl7ZMTMzY9CgQaxfv55Jkyax\nYcOG1x4jhBBC973zgu7s7Iy1tXWWpfKXXFxcaNKkCQMGDCA8PJyEhAQOHz7Mnj17tIPq67Ny5Urs\n7e3p2LGjVvErVqwYn376KbNmzcLExAQXF5dXZhoxYgQODg54eHiwbNkyzp49q5l33759FCtWDIBy\n5crh6+vL1KlTCQwMJD4+npiYGNatW8f69etfe+729vZYWFgwbNgwKleurJXLysqKO3fuEBISQkJC\nAj/88MNrn1gfP348Bw8eJCEhgZiYGA4ePPjKWxVCCCE+HO/lm+J8fHwIDAzMdrlcX1+fLVu2YG9v\nj5eXF/b29kyYMCHbpWd9fX1WrFiBvb19liv1fv36kZqayqeffqp1vzs7BgYG7Ny5kzFjxrB582Y6\ndOhAs2bNGDt2LLa2tuzYsUPTd/LkyUyYMIGlS5fi4OBA165d2bVrF9WrV8/Vuffs2ZP/+7//o1u3\nbpo3CgDt27fniy++YOLEibRo0YLDhw8zadKkV46VmZnJuHHjsLe3p2vXrlStWpUVK1bkKocQQgjd\npjPf5X7q1CnatWvHuXPnsLCwKOg4Io+K8oMqkr1gFOXsULTzS/bC5Z1+Dv19eP78OX/++Sdz5syh\nY8eOUsyFEEJ8kIr8z6du3boVW1tbHj58+MrPuwshhBC6rMhfoX/66ad8+umnBR1DCCGEKFBF/gpd\nCCGEEFLQhRBCCJ0gBV0IIYTQAVLQhRBCCB0gBV0IIYTQAVLQhRBCCB0gBV0IIYTQAVLQhRBCCB1Q\n5L9Y5l2YN28eu3bt4tixYwUy/86dOxk4cCCJiYk59lmyZAmrV6/mwoULOfaZun7gu4j37v1a0AHe\ngmQvGEU5O7w2/9eD5GeSxesVqSt0b29vFAoFCoUCIyMj7OzsmDJlCklJSe89y8scCoUCMzMzWrRo\nwcaNG997DiGEEAKK4BW6q6srq1atIi0tjWPHjvHFF1/w7Nkz/P3933uWxYsX065dO549e0ZoaCgj\nR47E2NiYNm3avPcsQgghPmxF6godXvyWuVKpxNzcnJ49e9KzZ0/27t0LQEZGBj4+PtSvXx9jY2Ma\nNWpEQEAAmZmZmuO9vb3x9PTUGnPevHk0b978jbNUqFABpVLJRx99xOjRo6lYsSKHDh3StG/cuBEz\nMzOtY1QqFQqFgocPH2r2bdq0iXr16mFiYoKnpyf379/PMldAQAC1a9fGzMyMzz//nKdPn75xXiGE\nELqryF2h/1upUqVIS0sDIDMzExMTE9avX0/lypU5c+YMfn5+VKxYkQEDBryzDBkZGezatYu///6b\nEiVKvNGxp06dYsSIEUyePJkuXbqgUqmYNWuWVp/t27cze/ZsFixYgLOzMzt27CAgIACFQpGfpyGE\nEKIIK9IF/fTp02zduhUXFxcASpQoweTJkzXt1atX5/z582zbtu2dFPTPP/+cESNGkJKSQkZGBpUq\nVXrjeVauXImLiwtjxowBwMrKijNnzhAYGKjps2LFCvr06cPgwYMBGDNmDCqVivj4+Pw7GSGEEEVa\nkSvoBw8exMzMjPT0dNLS0ujQoQMLFizQtK9bt46ffvqJmzdvkpKSQlpaGhYWFu8ky6xZs/j444+5\ndesWkydP5osvvsDS0vKNxoiNjcXNzU1rX9OmTbUKemxsLP3798/SRwq6EB+GuLi4go6Qo8Kc7XWK\nWvZatWq9sr3IFXRHR0cCAgIoXrw4JiYmWkvcoaGhTJw4ka+//ppmzZpRvnx51qxZw549ezR99PX1\nUavVWmOmp6fnKYtSqcTS0hJLS0s2bNiAi4sLdnZ21K5dO9/nEkJ8uF73H/KCEhcXV2izvU5Rzp6T\nIvdQnKGhIZaWllSrVi3L/epjx47RuHFjvLy8aNCgAZaWlly7dk2rj5GREXfv3tXa96rPcueWpaUl\nHTt2ZNq0aVpzPXv2jMePH+c4l7W1NadOndLa9+/t3PQRQgjxYStyBf1VrKysiImJ4ZdffuHq1ass\nWLCAo0ePavVp2bIlMTExBAYGEh8fT0BAANHR0fky/8iRI/n55585ffo0AE2aNKFMmTLMmjWL+Ph4\ndu7cydq1a7WO+fzzzzly5Aj+/v5cvXqVDRs2aK0oAAwfPpxNmzaxYcMGrl69ir+/v2YOIYQQAnSs\noA8ePJguXbowbNgwWrVqxY0bNxg5cqRWnzZt2jB+/Hhmz56Nq6srN27cYNiwYfkyf7169XB1dWX2\n7NkAVKxYkdWrV3P48GEcHR3ZsGGD1kN78OJe+JIlS1i3bh0tWrRg9+7dTJgwQatPt27dmDBhAl9/\n/TUtW7bk4sWLjBgxIl8yCyGE0A16iYmJ6td3E+L9KMr3tSR7wSjK2aFo55fshYtOXaELIYQQHyop\n6EIIIYQOkIIuhBBC6AAp6EIIIYQOkIIuhBBC6AAp6EIIIYQOkIIuhBBC6AAp6EIIIYQOkIIuhBBC\n6AAp6EIIIYQOkIIuhBBC6AD5LncdlnTIraAjCCHEa5VpHfbe55Tvcs8nKpUKhULBw4cPC2L619q4\ncSNmZmZ53hZCCCHetzwXdG9vbxQKheZlaWmJp6cnly9ffu2x9vb2xMbGUqlSpbxO/1pPnz7FyMiI\n4OBgrf2+vr4oFArOnj2rtd/NzQ0vLy/gxc+Vnjt3LtdzvWl/IYQQIr+91RW6q6srsbGxxMbGEhoa\nSnJyMv369XvlMWlpaZQsWRKlUoment7bTP9KZcuWpVGjRkRFRWntV6lUmJuba+1PSkri9OnTtGzZ\nEoDSpUtTpUqVXM/1pv2FEEKI/PZWBd3AwAClUolSqaRBgwaMGDGCy5cvk5ycDMD169dRKBRs3bqV\nTp06YWxszI8//phlyf2vv/5i6NCh2NjYYGxsjIODA0FBQVpzubu7M3r0aGbNmoWlpSVWVlZMmTKF\nzMzMHPM5OzujUqk02zdv3uT27duMHDlSa//x48dJS0vTFPQ3XUL/d/9r167Rp08fateujampKS1b\ntiQsTPseka2tLfPnz8fb2xtzc3Pq1q1LaGgoiYmJDBkyBDMzMxo1asShQ4c0x6SlpTFu3Dj+85//\nULVqVerWrcuMGTNynVMIIYTuyrd76E+ePCE0NBQbGxtKly6t1TZz5kyGDRtGdHQ07u7uWY5NSUnB\nzs6OzZs3Ex0dzfDhw/nqq6+IiIjQ6rdlyxaKFSvGgQMH+Pbbb1mxYgWhoaE5ZnJ2diYhIYGbN28C\nL67OGzVqxCeffMKxY8dIT0/X7K9evTrVqlV72z8D8GK5v23btmzfvp2oqCg6d+5M//79s9yOWLFi\nBY0bNyYiIoIuXbrg7e3NZ599Rtu2bVGpVDg6OuLl5UVKSgoAK1euZO/evfzwww+cPn2adevWYWVl\nlS+ZhRBCFG3F3+bggwcPaq5Mk5KSMDc3JyQkJEs/Ly8vPDw8NNvx8fFa7aampnzxxRea7UGDBhEZ\nGcnWrVtxcXHR7Le2tmby5MkAWFlZsWHDBiIiIujRo0e2+ezt7SlZsiQqlYq+ffuiUqlwcnKiZs2a\nlC1blnPnztGkSRNUKpXm6jw/2NraYmtrq9keM2YMYWFh7Ny5k7Fjx2r2t2nThmHDhgEwceJEli1b\nxkcffUSfPn0AGDt2LEFBQfz+++80bNiQmzdvUrNmTRwdHdHT08PCwgJ7e/t8yy2EEAUhLi7ug5o3\nr173VP5bFXRHR0cCAgIASExMZO3atXTr1o2DBw9ibm6u6dewYcNXjpORkcF3331HaGgod+7cITU1\nldTUVJycnLT61a1bV2vb2NiYBw8e5Dhu6dKlNQW7b9++REVFsWTJEgBatGiBSqXC2tqac+fOaR6I\nyw9JSUnMnz+fn3/+mbt375Kenk5KSkqW/P/cLlu2LIaGhlr7qlatCqA5x759+9K1a1caN25M69at\nadu2LW3btkVfX75OQAhRdBXEx8fkY2v/YmhoiKWlJZaWljRq1IglS5bw5MkT1q9fr9WvTJkyrxxn\nyZIlLF26lC+++IKdO3eiUqlwd3cnNTVVq1+JEiW0tvX09FCrX/0xemdnZ6KiokhISODevXs0a9YM\neFHQo6KiiI6OJj09PV+v0KdOncqOHTuYNGkSe/fuRaVS0bhx41ydT/HixbW2Ac1zAg0aNCAmJobp\n06eTmZmJt7c3Xbp0eeVzBEIIIT4M+Xppp6enh76+vuahuNw6duwYbm5u9O7dm/r16/PRRx9x5cqV\nfMnk7OzMzZs3CQoKolGjRhgaGgLg5OREdHQ0hw4donbt2hgbG+fLfADR0dH07t0bDw8P6tWrh6mp\nKdeuXcuXscuVK4eHhwf+/v6EhIQQGRmZ5RaGEEKID89bLbk/f/6ce/fuAS+W3NesWcPTp09xc3uz\nbyizsrJi+/btHDt2jMqVK7N69Wpu3LihdR86r5o2bUqpUqVYtWoVn3/+uWZ/rVq1KFu2LEFBQfTq\n1eut5/mnmjVrsmfPHjp06ECJEiWYP38+z58/f+txly5dirGxMba2tpQoUYItW7ZQvnx5TE1N8yG1\nEEKIouytCvqRI0ewtrYGXlw51qpVi/Xr1+Ps7PxG44wdO5br16/Ts2dPSpUqRd++fenZsyeXLl16\nm3jAi4/WNWvWjMjIyCz35Fu0aEFoaGi+LrcDzJkzB19fXzp06IBCocDb2ztfCnq5cuVYvHgx8fHx\n6OnpYWtry5YtWzSrDkIIIT5c8l3uolApyg+qSPaCUZSzQ9HOL9kLF3k8WgghhNABUtCFEEIIHSAF\nXQghhNABUtCFEEIIHSAFXQghhNABUtCFEEIIHSAFXQghhNABUtCFEEIIHSAFXQghhNABUtCFEEII\nHSBf/arDyg50LegIQgjxXj3dcCRX/eSrX4s4lUqFQqHg4cOHBR1FCCGEyFcFWtC9vb1RKBQoFAqM\njIywsrKiY8eOrFmzhrS0tHyfz97entjYWCpVqpSn4zdu3IiZmVk+pxJCCCHeXoFfobu6uhIbG0tM\nTAyhoaG4ubkxb9482rdvT1JSUr7OVbJkSZRKJXp6evk67ruUnp6OWi13RYQQQrxagRd0AwMDlEol\npqam1K9fHx8fH/bs2cP58+cJCAjQ9EtNTWX69OnY2NhgYmJCq1atCA8P17S/XE6PiIigTZs2mJiY\n4Orqyrlz57L0ebnk/ujRI7y8vLCyskKpVGJnZ8fy5cvzfC5qtZqAgAAaNGiAsbExjo6OBAcHa/WZ\nMWMGTZo0wdjYGFtbW6ZNm0ZKSoqmfd68eTRv3pyNGzfSoEEDqlatyubNm/noo4+y/Kb6Z599Ru/e\nvfOcVwghhO4o8IKeHRsbG9q0acPu3bs1+0aOHMmvv/7KmjVrOHbsGH369KF3795cuHBB69iZM2cy\nffp0IiIiqFSpEl5eXjle4c6ePZuLFy8SHBzMyZMnWbp0KaampnnOPXv2bAIDA1m4cCHR0dF89dVX\nfPXVV/z888+aPoaGhixdupTjx4+zaNEiQkNDWbhwodY4169fZ+vWraxfv56oqCg6duxIZmYm+/bt\n0/R59OgRe/bsoX///nnOK4QQQncUL+gAOfnPf/5DREQEANeuXWPr1q3ExMRgYWEBgJeXF0eOHGH9\n+vUsWrRIc9zkyZNp2bIlAOPGjcPNzY3bt29ne+/75s2b2NnZ0bhxYwCqVauW57xJSUksW7aM0NBQ\nHB0dAahRowanT59m7dq1tGvXTpPpperVqzNq1CiWLFnClClTNPtTU1NZtWoVVatW1ezr1asXQUFB\ndO3aFYCtW7dSrlw5zbhCCCFePL3+LvoWBq97Kr/QFnS1Wq25133+/HnUajUODg5afZ4/f64p3i/V\nrVtX829jY2MAHjx4kG1BHzp0KAMHDuTcuXO0atUKNzc3nJyc8pQ3NjaWlJQUevTooXWPPi0tTeuN\nws6dO1mxYgXx8fEkJSWRkZFBRkaG1limpqZaxRxgwIABuLi48Mcff2BmZkZQUBB9+vShePFC+z+h\nEEK8d7n9KJoufmyt0FaDS5cuUaNGDQAyMzPR09Pj0KFDlChRQqtfqVKltLb/2f6ysOa05N62bVsu\nXLjAL7/8QkREBJ6ennh4eOTpPnpmZiYAmzZt0qwivPSy6J48eZIhQ4Ywfvx45s6dS4UKFdi3bx9T\np07V6l+mTJks49va2mJnZ8d///tf3N3dOXv2LKtXr37jnEIIIXRToSzoFy9eJDw8nDFjxgBQv359\n1Go19+7dy3JF/rYqV65M79696d27N23btmXo0KF89913GBgYvNE41tbWGBgYcPPmTVxcXLLtEx0d\njYmJiday+82bN3M9x8CBAwkICODhw4c4ODjo3LtLIYQQeVfgBf358+fcu3ePzMxM/vzzTyIiIvD3\n96dBgwb4+voCYGVlRa9evRgxYgRz5szBzs6Ov//+m6ioKKpXr07nzp3zNPfLserUqUN6ejq7d++m\nRo0aryzmmZmZxMTEaO0rXrw4NjY2+Pr6MnXqVNRqNS1atODp06ecOnUKfX19Bg0ahJWVFXfu3CEk\nJIRmzZoRHh7Otm3bcp23e/fuTJ48mXXr1uHv75+ncxZCCKGbCrygHzlyBGtra4oVK0aFChWoU6cO\nEyZMYNCgQZQsWVLTb9myZSxcuJBp06Zx+/ZtKlasSKNGjXB2ds7z3AYGBsyePZvr169jYGBA06ZN\n2bx58yuPSU5OzrJKUKlSJeLj45k8eTJVqlRh6dKljB49mnLlymFra4ufnx8A7du354svvmDixImk\npKTQqlUrJk2axOjRo3OVt1y5cnTp0oVdu3ZpHo4TQgghQL7Lvcjp0aMHpqamLF68uKCjvBNF+UEV\nyV4winJ2KNr5JXvhUuBX6CJ3EhMTOXr0KIcOHSIqKqqg4wghhChkpKAXEc7OziQmJjJt2jRsbGwK\nOo4QQohCRgp6EfHvb8QTQggh/qlQfvWrEEIIId6MFHQhhBBCB0hBF0IIIXSAFHQhhBBCB0hBF0II\nIXSAFHQhhBBCB0hBF0IIIXSAFHQhhBBCB+jcd7nfu3cPa2tr9u/fT/Pmzd/pXLNnzyYsLOydfBWr\njY0NPj4+jBgxIs9jKH78Ix8TCSGEyKvEwWbvfI7XXqF7e3ujUCiyvP79E6JFxZEjR7I9n5evLl26\nFHREACIjIxk0aFBBxxBCCFFE5OqrX11dXVm1apXWvsqVK+d50rS0NEqUKJHn49+Go6MjsbGxWfbv\n2bOHMWPGMHTo0AJI9T+pqamULFkSIyOjAs0hhBCiaMnVPXQDAwOUSqXWq3jxF+8FDhw4gJubG9Wq\nVaNGjRr06NGDuLg4zbHx8fEoFApCQ0Pp2LEjxsbGBAYGAnDs2DHat2+PsbExNjY2jBkzhidPnmiO\nzczMxN/fHzs7O4yNjXF0dGTr1q1a2U6dOoWzszNKpZKWLVty5syZV55LyZIls5zLX3/9xfTp0xk3\nbhydOnXS9J0yZQqNGzfG2NiY+vXrM2PGDJ4/f57j2NevX6dx48b4+PiQkZHBn3/+yZAhQ7CxscHE\nxITmzZuzadMmrWPc3NwYO3YskyZNombNmri7uwMvltyXL1+u6bd48WIcHR0xNTXFxsaGL7/8kkeP\nHr3yXIUQQnw43vqhuGfPnjFy5EgOHz7M7t27KV26NH369CEtLU2r34wZM/Dy8iI6Oho3NzdiYmLo\n3r07nTp14tdff2XDhg2cPXsWPz8/zTEzZ85k8+bN+Pv7Ex0djZ+fH76+voSHhwPw5MkTevXqRa1a\ntThy5AjTpk1j6tSpb5Q/MTGRvn374urqyoQJE7TaypUrx/Llyzl+/DjffvstISEhfPfdd9mOc/Hi\nRdzc3HB3d2fp0qUUK1aM5ORkGjZsyObNmzl27BifffYZvr6+We65b9q0ieLFixMWFsbSpUuzHV9f\nX59vvvmGo0ePsmrVKo4fP87EiRPf6FyFEELortc+FOft7U1ISAilSpXS7GvevHmWK+WXHj9+TPXq\n1Tlw4ABNmzYlPj6eRo0aMW/ePLy9vTX9hg0bRtmyZfn+++81+86ePUurVq24du0axYsXx8rKit27\nd9OsWTNNn7Fjx3Lz5k02b97M2rVrmTNnDr/99huGhoYAbNy4kZEjR+bqobiMjAx69uzJ3bt3OXDg\nAGXLln1l/9WrV7NmzRpOnjwJ/O+hOH9/fzw9PfHz8+PLL7985RgDBgygcuXKmjcGbm5uPHv2jMjI\nSK1+r3soLiwsjMGDB3P79m309PSy7SMPxQkhROHwPh6Ky9U9dEdHRwICAjTb/yzuV69eZe7cuZw6\ndYq//vqLzMxM1Go1t27domnTppp+DRs21Brz3Llz3Lhxgy1btmj2qdUv3ltcu3aN9PR0nj9/Tteu\nXbWOS0tLw9LSEoDY2Fjq1aunKeaAVvF/nenTp3P27FkOHz6cbTEPDQ1l1apVXLt2jaSkJNLT09HX\n117UuHXrFl26dGHy5MmMHDlSqy09PR1/f3927NjBnTt3SE1N5fnz57Rq1UqrX4MGDV6b9ciRI/j7\n+xMXF8eTJ0/IyMggOTmZP//8kypVquT6nIUQQrx//7wVnVe1atV6ZXuuCrqhoaGmiP5br169qF69\nOosXL8bY2Bh9fX3s7e1JTU3NMsY/ZWZmMmjQIIYPH55lTFNTU86dOwdAcHAwpqamWu358UBdSEgI\nK1euZNu2bdSoUSNL+8sl8okTJ9K6dWsqVKjAnj17mDVrllY/IyMjTExM2LZtG/369aNChQqatu+/\n/56VK1cyb9486tSpQ9myZZk+fTqPHz/WGuPff5t/S0hIwNPTkyFDhjB16lQqVqzImTNn8PLyyvJ3\nFkIIUfi8rhjnh1wV9Jzcv3+fq1evsmTJEhwdHQE4ffo0mZmZrz3Wzs6O2NjYHN8o1KlTh5IlS3Lr\n1i2cnJyy7WNtbc3WrVtJTk6mdOnSAJrl8Fc5d+4cfn5+zJo1CxcXl2z7HD9+HAsLC8aMGaPZd+PG\njSz9SpUqRXBwML169aJbt26EhoZqinp0dDQdOnTA09MTeLECceXKFapWrfrajP905swZMjMzmTNn\njmaFYO/evW80hhBCCN32Vg/FVa5cmYoVK7J+/Xri4+NRqVSMGTMmy7J0dr766iuOHz/OmDFjiImJ\nIT4+nv379/PVV18BUKFCBUaMGMGkSZPYuHEj165d4/z586xdu5affvoJeLE6AODr68ulS5cIDw/P\n8aG1lx48eEC/fv1wdXWle/fu3Lt3T+t1//59AGrWrMmtW7fYunUr165dY/Xq1Wzfvj3bMQ0NDTXP\nGXTr1k3z9HnNmjU5cuQIx48fJzY2llGjRvHHH29+X9vS0pK0tDRWrlxJQkICISEhrF69+o3HEUII\nobveqqAXK1aMH3/8kZiYGJo3b8748eOZPn16rpbE69evz969e7l69SodOnTA2dmZr7/+GqVSqekz\nffp0xowZQ0BAAM2aNaNbt27s3buX6tWrA1C+fHmCg4O5dOkSLVu2ZPr06cycOfOV8+7fv59bt26x\nf/9+rK2ts7zq1KkDQJH1P3QAAAwKSURBVKdOnRgxYgTjx4/H2dmZqKioVz5V/s+i3r17dx49esSE\nCROws7Oje/fuuLu7U6FChSzPBORGgwYNmDNnDkuWLMHBwYH//ve/rz1PIYQQHxad++pXUbTFxcW9\nl3tN74JkLxhFOTsU7fySvXCRH2cRQgghdIAUdCGEEEIHSEEXQgghdIAUdCGEEEIHyENxQgghhA6Q\nK3QhhBBCB0hBF0IIIXSAFHQhhBBCB0hBF0IIIXSAFHQhhBBCB0hBL4LWrl1L/fr1USqVuLi4cPTo\n0Vf2j4qKwsXFBaVSiZ2dHevWrXtPSf/H39+fVv/f3rkHVVm8cfxjgEiiHERuFpeSE6DBoAfBmGzw\nEhYmKlFIThFqecGsmVDBmOziSIRgMsJJJBilMAFJxAinCzORwy2H0sFGqQR1VCCYJFQC5Pz+cHh/\nvwOHczwlL8hvPzPMsO/7POt3lt3ncd/dnZ07FycnJ6ZOnUp4eDhnzpzR69PY2IhCoRjw880338ik\n+jYJCQkDNDzyyCN6ferq6ggODsbBwQFPT08SExPRaOQ/UOLl5aWzDfsuNtKFLnu5+syJEydYvnw5\nnp6eKBQKPvvsM633Go2GhIQEPDw8cHBwYNGiRfzyyy8G6y0qKsLf3x87Ozv8/f0pLi6WVXt3dzfb\ntm0jICCAKVOm4O7uzurVq7l48aLeOsvLy3X+Pc6dOyebdoB169YN0LBgwQKD9coRewxp19V+CoVC\n6ybN/oyU2GMs/+r6VIH8FBYWEhsbS3JyMrNnzyYzM5PnnnuOyspKnJycBtg3NDTw/PPPs2LFCjIy\nMqisrOTNN9/ExsaGJUuWyKb7hx9+YNWqVcycORONRsOOHTtYunQpVVVVWFtb6/U9fPgwjz76qFQ2\nZD8UKJVKjh07JpVNTEwGtW1vb2fZsmUEBATw3XffUV9fT3R0NPfffz+vvfaaHHIlysrKuHXrllS+\nevUqgYGBLF26VK9famoqCxculMoTJ04cMo3/y/Xr15k2bRoRERGsXbt2wPvdu3eTlpZGWloaSqWS\nDz/8kGXLllFTU8OECRN01lldXc3KlSuJi4tj8eLFFBcX8/LLL3P8+HF8fX1l0X7jxg1+/vlnYmJi\n8PLyor29nfj4eMLCwjhx4gSmpvpDcWVlpVa/nzx58l3TbUh7H4GBgezdu1cqjx07Vm+dcsUeQ9rP\nnj2rVa6trWX58uUGxwCMjNhjDCKh32OkpaXxwgsvEBkZCUBSUhLffvstWVlZbNu2bYB9dnY2Dg4O\nJCUlAbfvkP/xxx/Zs2ePrAm9sLBQq7x3716cnZ2prKzk6aef1us7adIkrVv4hgNTU9M71pCfn8/N\nmzdRq9VYWFgwbdo0zp07R3p6Ohs2bGDMmDFDrPa/9A/8OTk5TJgwweCtf1ZWVsPS5kFBQQQFBQGw\nfv16rXcajQa1Ws0bb7wh9V21Wo1SqaSgoICoqCiddarVaubMmSPNyNzd3SkvL0etVvPJJ5/Iot3K\nyoojR45oPdu1axezZ8/m7NmzTJ8+XW/dtra22NjY3DWt/dGnvQ9zc3Oj+oRcsceQ9v6aS0pKcHNz\n4/HHHzdY90iIPcYgPrnfQ3R1dfHTTz8xb948refz5s2jqqpKp091dfUA+/nz51NbW0t3d/eQaTVE\nR0cHvb29KBQKg7Yvvvgibm5uLFy4kKKiIhnUDaShoQEPDw+8vb1ZuXIlDQ0Ng9pWV1fz2GOPYWFh\nIT2bP38+V65cobGxUQa1utFoNOTk5BAeHq6lTRexsbE8/PDDzJ07l6ysLHp7e2VSOTiNjY00NTVp\n9WcLCwsCAgIG7f8ANTU1OseAPh85+OuvvwDuaAwEBgbi7u5OSEgI33///VBL00lFRQVubm6oVCo2\nbtxIS0uLXvuRGHs6OjooLCyUJkSGGAmxxxhEQr+HaG1t5datW9ja2mo9t7W1pbm5WadPc3OzTvue\nnh5aW1uHTKshYmNj8fLyws/Pb1AbS0tL3n//fbKzs8nPz+eJJ54gKiqKQ4cOyagUfH19SU9Pp6Cg\ngNTUVJqamggKCqKtrU2n/WBt3vduuCgrK6OxsZGXXnpJr93WrVvJysriyJEjhIaGEh8fT3Jyskwq\nB6epqQnAqP7f52esz1DT1dVFfHw8Tz31FA888MCgdg4ODqSkpJCTk0NOTg5KpZIlS5YY3Ddzt1mw\nYAEff/wxRUVFbN++nZMnTxISEsLff/89qM9IjD0FBQV0dXURERGh126kxB5jEZ/cBbKzdetWKisr\nKS0t1bsWbWNjo7XmPGPGDNra2ti9ezfh4eFySAXgySef1Cr7+vri4+NDbm4uGzZskE3Hv2X//v3M\nnDkTLy8vvXabN2+Wfvf29qa3t5fk5GQ2bdo01BL/L+jp6eHVV1/l2rVrHDx4UK+tUqnUurPbz8+P\nCxcukJqaSkBAwFBLlXj22Wel36dPn46Pjw9eXl4cP36ckJAQ2XT8W/bv309wcLDBPQgjJfYYi5ih\n30PY2NhgYmIy4FNXS0sLdnZ2On3s7Ox02puamg7pmtxgxMXFcfjwYY4ePYqrq6vR/iqVit9///3u\nCzMCS0tLPDw8BtUxWJv3vRsOWlpaKCkpueNPjf+LSqWivb19WGe08N+1UGP6f5+fsT5DRU9PD6tW\nraKuro6ioiImTZpkdB0jYQw4OjoyZcoUvTpGWuw5deoUtbW1/2gMwMhod0OIhH4PMXbsWHx8fCgr\nK9N6XlZWhr+/v04fPz8/nfYzZszAzMxsyLTqYsuWLVIyN3TsazBOnz497JtUOjs7qa+vH1SHn58f\nFRUVdHZ2Ss/KyspwdHTExcVFLpla5ObmYm5urjXTulNOnz7NuHHjsLKyGgJld46Liwv29vZa/bmz\ns5OKiopB+z/ArFmzjBozQ0V3dzdRUVHU1dVRXFz8j/vxSBgDra2tXLlyRa+OkRR74Pbs3MXFhcDA\nwH/kPxLa3RDik/s9RnR0NGvWrEGlUuHv709WVhZXr16VdviuWbMGQDpeEhUVxb59+4iNjSUqKoqq\nqipyc3PJzMyUVXdMTAyHDh3i008/RaFQSOuh48ePx9LSEoB3332XkydPcvToUeB2EjIzM8Pb25v7\n7ruP0tJSMjMzeeedd2TV3rfW+eCDD/LHH3+QlJTEjRs3pHW4/rrDwsJITExk/fr1xMTE8Ouvv/LR\nRx+xefNmWXe496HRaDhw4AChoaFSW/eRkZHBvn37qKmpAeCrr76iubmZWbNmYWFhQXl5OQkJCURG\nRmJubj7kWjs6OqRZUG9vL5cuXeLUqVNYW1vj5OTEunXrSElJQalU4ubmxs6dOxk/fjxhYWFSHSEh\nIahUKunUx9q1awkODmbXrl0sWrSIY8eOUV5eTmlpqWzaHR0diYyMpLa2loMHDzJmzBhpDEycOFHa\npNh//Kanp+Ps7IynpyddXV3k5eXx5ZdfcuDAAdm0W1tb88EHHxASEoK9vT0XLlzgvffew9bWlmee\neUaqY7hij6E+A7ePDebn57Nx40adY3Ckxh5jEQn9HiM0NJS2tjaSkpJoamrC09OTvLw8nJ2dAbh0\n6ZKWvaurK3l5edJGJwcHBxITE2U9sgZIg7j/v7tlyxbi4uKA22ekz58/r/V+586dXLx4ERMTE6ZO\nncqePXtkX8O6fPkyq1evprW1lcmTJ+Pr68vXX38ttXl/3VZWVnzxxRfExMQwd+5cFAoF0dHRw7be\nXl5ezm+//UZGRsaAd62trdTX10tlMzMzMjMzeeutt+jt7cXV1ZW4uDheeeUVWbTW1tayePFiqZyQ\nkEBCQgIRERGo1Wpef/11bt68yaZNm/jzzz9RqVQUFhZqnUE/f/681kazvv/4bt++nR07dvDQQw+R\nlZV1V8+gG9IeGxtLSUkJwIAZYlpaGitWrAAGjt/u7m7efvttLl++zLhx46Tx3ndMSw7tKSkpnDlz\nhs8//5xr165hb2/PnDlzyM7O1mr34Yo9hvoM3D42e/36damd+zNSY4+xiPvQBQKBQCAYBYg1dIFA\nIBAIRgEioQsEAoFAMAoQCV0gEAgEglGASOgCgUAgEIwCREIXCAQCgWAUIBK6QCAQCASjAJHQBQKB\nQCAYBYiELhAIBALBKEAkdIFAIBAIRgH/AQlZU2bewF9YAAAAAElFTkSuQmCC\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7f0ad6e34c18>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "0qA6FrAmr2z-",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file