From 1d4de69df7624f72f43c89b2dfd88dc1cb3a3ae0 Mon Sep 17 00:00:00 2001
From: Daniel Martin-Alarcon
 <37123867+DanielMartinAlarcon@users.noreply.github.com>
Date: Tue, 13 Nov 2018 14:16:06 -0800
Subject: [PATCH 1/9] Created using Colaboratory

---
 ...22_Choose_appropriate_visualizations.ipynb | 4178 ++++++++++++++---
 1 file changed, 3527 insertions(+), 651 deletions(-)

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..4af9df7 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 +1,3528 @@
 {
- "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": [],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/DanielMartinAlarcon/DS-Sprint-02-Storytelling-With-Data/blob/master/module2-choose-appropriate-visualizations/LS_DS_122_Choose_appropriate_visualizations.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "i1Y03IX0CJIu",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "_Lambda School Data Science_\n",
+        "# Choose appropriate visualizations"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "GeIVqWojCJIy",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "# Upgrade Seaborn\n",
+        "\n",
+        "Make sure you have at least version 0.9.0"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "QmmM4WYjCJIz",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 207
+        },
+        "outputId": "bc2e479a-b4a4-46be-e4ee-10ca828ceea8"
+      },
+      "cell_type": "code",
+      "source": [
+        "!pip install --upgrade seaborn"
+      ],
+      "execution_count": 73,
+      "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: 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: pandas>=0.15.2 in /usr/local/lib/python3.6/dist-packages (from seaborn) (0.22.0)\n",
+            "Requirement already satisfied, skipping upgrade: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=1.4.3->seaborn) (2.5.3)\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: pytz in /usr/local/lib/python3.6/dist-packages (from matplotlib>=1.4.3->seaborn) (2018.7)\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: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=1.4.3->seaborn) (0.10.0)\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "K2GmtusLCJI9",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "994f04e6-eb67-49c0-f857-2f5db1db94fc"
+      },
+      "cell_type": "code",
+      "source": [
+        "import seaborn as sns\n",
+        "sns.__version__"
+      ],
+      "execution_count": 74,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "'0.9.0'"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 74
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "zBXRaTyCCJJC",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "# Fix misleading visualizations"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "u9smaR81CJJE",
+        "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": "4QUq72ldCJJI",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 224
+        },
+        "outputId": "5a811a63-89e3-46f1-c148-d3cdedacce88"
+      },
+      "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": 77,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "--2018-11-13 20:44:02--  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.2’\n",
+            "\n",
+            "\rmisleading.py.2       0%[                    ]       0  --.-KB/s               \rmisleading.py.2     100%[===================>]   1.60K  --.-KB/s    in 0s      \n",
+            "\n",
+            "2018-11-13 20:44:02 (176 MB/s) - ‘misleading.py.2’ saved [1641/1641]\n",
+            "\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "WWIGdNjjCJJO",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "#### Fix misleading plot #1"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "9-T_baqaCJJP",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 314
+        },
+        "outputId": "80bca1cc-b81a-456b-97fd-c7902ffc10a1"
+      },
+      "cell_type": "code",
+      "source": [
+        "misleading.plot1();"
+      ],
+      "execution_count": 79,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAEpCAYAAACDc9l6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7fe35649beb8>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "IhzJyGuKCJJT",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 314
+        },
+        "outputId": "83238939-bf77-4da7-c984-9ad525b363d5"
+      },
+      "cell_type": "code",
+      "source": [
+        "insurance = pd.DataFrame(\n",
+        "    index=['State Farm', 'GEICO'], \n",
+        "    data={'Market Share %': [18.07, 12.79]})\n",
+        "\n",
+        "insurance.plot.bar();"
+      ],
+      "execution_count": 84,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7fe35273ecf8>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "jnvSx3c6CJJX",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "#### Fix misleading plot #2"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "Y5Vpil2KCJJZ",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 310
+        },
+        "outputId": "17be7acc-0fd8-4897-8b42-c1a116ffedc0"
+      },
+      "cell_type": "code",
+      "source": [
+        "misleading.plot2();"
+      ],
+      "execution_count": 91,
+      "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 0x7fe3521a4b70>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "GNa4z4IG4knV",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 310
+        },
+        "outputId": "17e7e26e-a77b-49e3-a7f2-0c55c3902d3c"
+      },
+      "cell_type": "code",
+      "source": [
+        "insurance.loc['Others']=[100-18.07-12.79]\n",
+        "insurance.plot.pie('Market Share %', figsize=(5, 5));"
+      ],
+      "execution_count": 92,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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9bcNFsrUYf1ebDpBILR1jPE0p9Vec44l/BO4FfqKUekQpVZigfB0hu9EJNrNb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\nBClGIYSIIMUohBAR/j89JV/sIJi3kwAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7fe352044898>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ltajKxCFCJJm",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "#### Fix misleading plot #3"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "NJRdZBgTCJJo",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 336
+        },
+        "outputId": "d49941ba-e60d-46b3-8506-bf88257462f9"
+      },
+      "cell_type": "code",
+      "source": [
+        "misleading.plot3()"
+      ],
+      "execution_count": 93,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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SK4GP0p4i/FV0GG0YixWQFdUWtOe3Hb6W1u7TtHOufMEQWVFtC7wD2JdhLuQ5NMuB9wAH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\nJGlGLFaSJEkz8v8B7VusKzwbg08AAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7fe3521785c0>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "oe_yziX7CJJv",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 336
+        },
+        "outputId": "2b2a929e-1d45-41c6-a78a-cc189d0fea92"
+      },
+      "cell_type": "code",
+      "source": [
+        "# I made the area proportional to the market share, rather than its square. \n",
+        "# I multiplied both circles by a factor of 3 just to make them easier to see,\n",
+        "# while maintaining the same ratio of areas.\n",
+        "\n",
+        "import matplotlib.pyplot as plt\n",
+        "import math\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=3*math.sqrt(18.07))\n",
+        "ax.add_artist(circle)\n",
+        "\n",
+        "circle = plt.Circle(xy=(60, 20), radius=3*math.sqrt(12.79))\n",
+        "ax.add_artist(circle)\n",
+        "\n",
+        "plt.title('State Farm vs GEICO')\n",
+        "plt.show()"
+      ],
+      "execution_count": 97,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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VvwAW+ulV1GX+odQhdAeLVSLdOwB/BKydOot650Zg+7rMf5s6iIarK1VH4jv/+uDQuszf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\ngg==\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7fe351f435c0>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "VZsaFfcfCJJ2",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "#### Fix misleading plot #4"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "fYGQTXGkCJJ3",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "_If you're on Jupyter (not Colab) then uncomment and run this cell below:_"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "lALRAG7_CJJ6",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# import altair as alt\n",
+        "# alt.renderers.enable('notebook')"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "SGUfxDAmCJJ_",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 348
+        },
+        "outputId": "37f2e977-cd7e-4109-fd48-bef121f087d3"
+      },
+      "cell_type": "code",
+      "source": [
+        "misleading.plot4()"
+      ],
+      "execution_count": 98,
+      "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",
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+              "    }\n",
+              "    .error {\n",
+              "        color: red;\n",
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+              "  </style>\n",
+              "\n",
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{\"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 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+              "    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": 98
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "Yrg_kGxfCJKG",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 348
+        },
+        "outputId": "6ffd2a8c-b8b0-4161-e4fc-14d5f51f2745"
+      },
+      "cell_type": "code",
+      "source": [
+        "df['State Farm market share %'] = df['State Farm policy premiums'] / df['STATE TOTAL']\n",
+        "df.head()\n",
+        "\n",
+        "variable = 'State Farm market share %'\n",
+        "\n",
+        "alt.Chart(states).mark_geoshape().encode(\n",
+        "    alt.Color(variable, type='quantitative')\n",
+        ").transform_lookup(\n",
+        "    lookup='id',\n",
+        "    from_=alt.LookupData(df, 'id', [variable])\n",
+        ").properties(\n",
+        "    width=500,\n",
+        "    height=300\n",
+        ").project(\n",
+        "    type='albersUsa'\n",
+        ")"
+      ],
+      "execution_count": 103,
+      "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 market share %',\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  State Farm market share %  \n",
+              "      0                   282947      3025561                   0.253908  \n",
+              "      1                    81366       454623                   0.282900  \n",
+              "      2                   604049      4373055                   0.173792  \n",
+              "      3                    96908      1777917                   0.255879  \n",
+              "      4                  2091720     25255501                   0.144739  \n",
+              "      5                   365797      4057463                   0.203857  \n",
+              "      6                   486498      2795415                   0.060733  \n",
+              "      7                   129033       802005                   0.238551  \n",
+              "      8                   115149       325239                   0.206713  \n",
+              "      9                  3952147     17333354                   0.161903  \n",
+              "      10                  821326      7571988                   0.232597  \n",
+              "      11                  184743       722550                   0.184760  \n",
+              "      12                   87222       863636                   0.151236  \n",
+              "      13                  412978      7028885                   0.321039  \n",
+              "      14                  201200      3506499                   0.245653  \n",
+              "      15                   58105      1650388                   0.223522  \n",
+              "      16                   76533      1725608                   0.203605  \n",
+              "      17                  186915      2805864                   0.242504  \n",
+              "      18                  369492      4136224                   0.342336  \n",
+              "      19                   68109       699558                   0.144536  \n",
+              "      20                 1059508      4554586                   0.193959  \n",
+              "      21                  473913      4924736                   0.000000  \n",
+              "      22                       0      8462142                   0.186761  \n",
+              "      23                  108942      3412460                   0.249656  \n",
+              "      24                  100827      1760180                   0.265818  \n",
+              "      25                  214191      3606590                   0.245044  \n",
+              "      26                   33538       668513                   0.234574  \n",
+              "      27                   61046      1167135                   0.234529  \n",
+              "      28                  262815      2129828                   0.190010  \n",
+              "      29                  103403       806545                   0.128127  \n",
+              "      30                 1441019      7375823                   0.084007  \n",
+              "      31                  174865      1284430                   0.203921  \n",
+              "      32                 3926684     12633982                   0.131918  \n",
+              "      33                  560700      5543700                   0.151951  \n",
+              "      34                   15516       454376                   0.148076  \n",
+              "      35                  405652      6303148                   0.196739  \n",
+              "      36                  151388      2515842                   0.252823  \n",
+              "      37                  223187      2647434                   0.213194  \n",
+              "      38                  602848      8270326                   0.200056  \n",
+              "      39                   97597       867558                   0.000000  \n",
+              "      40                  426033      3583918                   0.243989  \n",
+              "      41                   15682       508569                   0.200390  \n",
+              "      42                  292357      3769652                   0.235918  \n",
+              "      43                 2127633     19180816                   0.177008  \n",
+              "      44                  139315      1723931                   0.174255  \n",
+              "      45                   52939       359996                   0.083343  \n",
+              "      46                  884489      5105360                   0.172037  \n",
+              "      47                  478922      4780616                   0.166272  \n",
+              "      48                  110806      1209872                   0.267473  \n",
+              "      49                  131845      2950711                   0.150364  \n",
+              "      50                   30187       374169                   0.241634  \n",
+              "      51                       0       444480                   0.000000  ,\n",
+              "      fields: ['State Farm market share %'],\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",
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+              "    }\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 market share %\"}}, \"height\": 300, \"projection\": {\"type\": \"albersUsa\"}, \"transform\": [{\"lookup\": \"id\", \"from\": {\"data\": {\"name\": \"data-d9e3a8993215d131b3b671a2164b42c1\"}, \"key\": \"id\", \"fields\": [\"State Farm market share %\"]}}], \"width\": 500, \"$schema\": \"https://vega.github.io/schema/vega-lite/v2.6.0.json\", \"datasets\": {\"data-d9e3a8993215d131b3b671a2164b42c1\": [{\"state\": \"Alabama\", \"id\": 1, \"State Farm policy premiums\": 768213, \"BERKSHIRE HATHAWAY GRP\": 282947, \"STATE TOTAL\": 3025561, \"State Farm market share %\": 0.2539076224210981}, {\"state\": \"Alaska\", \"id\": 2, \"State Farm policy premiums\": 128613, \"BERKSHIRE HATHAWAY GRP\": 81366, \"STATE TOTAL\": 454623, \"State Farm market share %\": 0.28290033720247326}, {\"state\": \"Arizona\", \"id\": 4, \"State Farm policy premiums\": 760003, \"BERKSHIRE HATHAWAY GRP\": 604049, \"STATE TOTAL\": 4373055, \"State Farm market share %\": 0.1737922344905335}, {\"state\": \"Arkansas\", \"id\": 5, \"State Farm policy premiums\": 454931, \"BERKSHIRE HATHAWAY GRP\": 96908, \"STATE TOTAL\": 1777917, \"State Farm market share %\": 0.25587864900329993}, {\"state\": \"California\", \"id\": 6, \"State Farm policy premiums\": 3655463, \"BERKSHIRE HATHAWAY GRP\": 2091720, \"STATE TOTAL\": 25255501, \"State Farm market share %\": 0.14473927878128412}, {\"state\": \"Colorado\", \"id\": 8, \"State Farm policy premiums\": 827143, \"BERKSHIRE HATHAWAY GRP\": 365797, \"STATE TOTAL\": 4057463, \"State Farm market share %\": 0.20385718859297053}, {\"state\": \"Connecticut\", \"id\": 9, \"State Farm policy premiums\": 169773, \"BERKSHIRE HATHAWAY GRP\": 486498, \"STATE TOTAL\": 2795415, \"State Farm market share %\": 0.06073266402305203}, {\"state\": \"Delaware\", \"id\": 10, \"State Farm policy premiums\": 191319, \"BERKSHIRE HATHAWAY GRP\": 129033, \"STATE TOTAL\": 802005, \"State Farm market share %\": 0.23855088185235754}, {\"state\": \"District of Columbia\", \"id\": 11, \"State Farm policy premiums\": 67231, \"BERKSHIRE HATHAWAY GRP\": 115149, \"STATE TOTAL\": 325239, \"State Farm market share %\": 0.20671260211721226}, {\"state\": \"Florida\", \"id\": 12, \"State Farm policy premiums\": 2806322, \"BERKSHIRE HATHAWAY GRP\": 3952147, \"STATE TOTAL\": 17333354, \"State Farm market share %\": 0.16190299926950086}, {\"state\": \"Georgia\", \"id\": 13, \"State Farm policy premiums\": 1761224, \"BERKSHIRE HATHAWAY GRP\": 821326, \"STATE TOTAL\": 7571988, \"State Farm market share %\": 0.23259730469726048}, {\"state\": \"Hawaii\", \"id\": 15, \"State Farm policy premiums\": 133498, \"BERKSHIRE HATHAWAY GRP\": 184743, \"STATE TOTAL\": 722550, \"State Farm market share %\": 0.18475953221230365}, {\"state\": \"Idaho\", \"id\": 16, \"State Farm policy premiums\": 130613, \"BERKSHIRE HATHAWAY GRP\": 87222, \"STATE TOTAL\": 863636, \"State Farm market share %\": 0.15123616894154482}, {\"state\": \"Illinois\", \"id\": 17, \"State Farm policy premiums\": 2256543, \"BERKSHIRE HATHAWAY GRP\": 412978, \"STATE TOTAL\": 7028885, \"State Farm market share %\": 0.32103854309751834}, {\"state\": \"Indiana\", \"id\": 18, \"State Farm policy premiums\": 861382, \"BERKSHIRE HATHAWAY GRP\": 201200, \"STATE TOTAL\": 3506499, \"State Farm market share %\": 0.245653000328818}, {\"state\": \"Iowa\", \"id\": 19, \"State Farm policy premiums\": 368898, \"BERKSHIRE HATHAWAY GRP\": 58105, \"STATE TOTAL\": 1650388, \"State Farm market share %\": 0.22352198392135667}, {\"state\": \"Kansas\", \"id\": 20, \"State Farm policy premiums\": 351343, \"BERKSHIRE HATHAWAY GRP\": 76533, \"STATE TOTAL\": 1725608, \"State Farm market share %\": 0.20360533794465488}, {\"state\": \"Kentucky\", \"id\": 21, \"State Farm policy premiums\": 680433, \"BERKSHIRE HATHAWAY GRP\": 186915, \"STATE TOTAL\": 2805864, \"State Farm market share %\": 0.24250391323314316}, {\"state\": \"Louisiana\", \"id\": 22, \"State Farm policy premiums\": 1415980, \"BERKSHIRE HATHAWAY GRP\": 369492, \"STATE TOTAL\": 4136224, \"State Farm market share %\": 0.34233639183951353}, {\"state\": \"Maine\", \"id\": 23, \"State Farm policy premiums\": 101111, \"BERKSHIRE HATHAWAY GRP\": 68109, \"STATE TOTAL\": 699558, \"State Farm market share %\": 0.1445355495898839}, {\"state\": \"Maryland\", \"id\": 24, \"State Farm policy premiums\": 883401, \"BERKSHIRE HATHAWAY GRP\": 1059508, \"STATE TOTAL\": 4554586, \"State Farm market share %\": 0.19395857274404305}, {\"state\": \"Massachusetts\", \"id\": 25, \"State Farm policy premiums\": 0, \"BERKSHIRE HATHAWAY GRP\": 473913, \"STATE TOTAL\": 4924736, \"State Farm market share %\": 0.0}, {\"state\": \"Michigan\", \"id\": 26, \"State Farm policy premiums\": 1580398, \"BERKSHIRE HATHAWAY GRP\": 0, \"STATE TOTAL\": 8462142, \"State Farm market share %\": 0.18676098793898754}, {\"state\": \"Minnesota\", \"id\": 27, \"State Farm policy premiums\": 851940, \"BERKSHIRE HATHAWAY GRP\": 108942, \"STATE TOTAL\": 3412460, \"State Farm market share %\": 0.24965567361961752}, {\"state\": \"Mississippi\", \"id\": 28, \"State Farm policy premiums\": 467887, \"BERKSHIRE HATHAWAY GRP\": 100827, \"STATE TOTAL\": 1760180, \"State Farm market share %\": 0.2658177004624527}, {\"state\": \"Missouri\", \"id\": 29, \"State Farm policy premiums\": 883774, \"BERKSHIRE HATHAWAY GRP\": 214191, \"STATE TOTAL\": 3606590, \"State Farm market share %\": 0.24504421073645744}, {\"state\": \"Montana\", \"id\": 30, \"State Farm policy premiums\": 156816, \"BERKSHIRE HATHAWAY GRP\": 33538, \"STATE TOTAL\": 668513, \"State Farm market share %\": 0.23457434634778979}, {\"state\": \"Nebraska\", \"id\": 31, \"State Farm policy premiums\": 273727, \"BERKSHIRE HATHAWAY GRP\": 61046, \"STATE TOTAL\": 1167135, \"State Farm market share %\": 0.2345289962172328}, {\"state\": \"Nevada\", \"id\": 32, \"State Farm policy premiums\": 404688, \"BERKSHIRE HATHAWAY GRP\": 262815, \"STATE TOTAL\": 2129828, \"State Farm market share %\": 0.19000970970425782}, {\"state\": \"New Hampshire\", \"id\": 33, \"State Farm policy premiums\": 103340, \"BERKSHIRE HATHAWAY GRP\": 103403, \"STATE TOTAL\": 806545, \"State Farm market share %\": 0.12812676292085376}, {\"state\": \"New Jersey\", \"id\": 34, \"State Farm policy premiums\": 619623, \"BERKSHIRE HATHAWAY GRP\": 1441019, \"STATE TOTAL\": 7375823, \"State Farm market share %\": 0.08400730332059216}, {\"state\": \"New Mexico\", \"id\": 35, \"State Farm policy premiums\": 261922, \"BERKSHIRE HATHAWAY GRP\": 174865, \"STATE TOTAL\": 1284430, \"State Farm market share %\": 0.2039208053377763}, {\"state\": \"New York\", \"id\": 36, \"State Farm policy premiums\": 1666653, \"BERKSHIRE HATHAWAY GRP\": 3926684, \"STATE TOTAL\": 12633982, \"State Farm market share %\": 0.131918266149184}, {\"state\": \"North Carolina\", \"id\": 37, \"State Farm policy premiums\": 842368, \"BERKSHIRE HATHAWAY GRP\": 560700, \"STATE TOTAL\": 5543700, \"State Farm market share %\": 0.15195050237206198}, {\"state\": \"North Dakota\", \"id\": 38, \"State Farm policy premiums\": 67282, \"BERKSHIRE HATHAWAY GRP\": 15516, \"STATE TOTAL\": 454376, \"State Farm market share %\": 0.14807560258464356}, {\"state\": \"Ohio\", \"id\": 39, \"State Farm policy premiums\": 1240075, \"BERKSHIRE HATHAWAY GRP\": 405652, \"STATE TOTAL\": 6303148, \"State Farm market share %\": 0.19673899454685181}, {\"state\": \"Oklahoma\", \"id\": 40, \"State Farm policy premiums\": 636063, \"BERKSHIRE HATHAWAY GRP\": 151388, \"STATE TOTAL\": 2515842, \"State Farm market share %\": 0.2528231105132993}, {\"state\": \"Oregon\", \"id\": 41, \"State Farm policy premiums\": 564416, \"BERKSHIRE HATHAWAY GRP\": 223187, \"STATE TOTAL\": 2647434, \"State Farm market share %\": 0.21319360558185776}, {\"state\": \"Pennsylvania\", \"id\": 42, \"State Farm policy premiums\": 1654528, \"BERKSHIRE HATHAWAY GRP\": 602848, \"STATE TOTAL\": 8270326, \"State Farm market share %\": 0.20005595910004031}, {\"state\": \"Rhode Island\", \"id\": 44, \"State Farm policy premiums\": 0, \"BERKSHIRE HATHAWAY GRP\": 97597, \"STATE TOTAL\": 867558, \"State Farm market share %\": 0.0}, {\"state\": \"South Carolina\", \"id\": 45, \"State Farm policy premiums\": 874436, \"BERKSHIRE HATHAWAY GRP\": 426033, \"STATE TOTAL\": 3583918, \"State Farm market share %\": 0.24398884126255121}, {\"state\": \"South Dakota\", \"id\": 46, \"State Farm policy premiums\": 101912, \"BERKSHIRE HATHAWAY GRP\": 15682, \"STATE TOTAL\": 508569, \"State Farm market share %\": 0.20038972096215066}, {\"state\": \"Tennessee\", \"id\": 47, \"State Farm policy premiums\": 889329, \"BERKSHIRE HATHAWAY GRP\": 292357, \"STATE TOTAL\": 3769652, \"State Farm market share %\": 0.23591806352416617}, {\"state\": \"Texas\", \"id\": 48, \"State Farm policy premiums\": 3395165, \"BERKSHIRE HATHAWAY GRP\": 2127633, \"STATE TOTAL\": 19180816, \"State Farm market share %\": 0.17700837128097158}, {\"state\": \"Utah\", \"id\": 49, \"State Farm policy premiums\": 300403, \"BERKSHIRE HATHAWAY GRP\": 139315, \"STATE TOTAL\": 1723931, \"State Farm market share %\": 0.17425465404357832}, {\"state\": \"Vermont\", \"id\": 50, \"State Farm policy premiums\": 30003, \"BERKSHIRE HATHAWAY GRP\": 52939, \"STATE TOTAL\": 359996, \"State Farm market share %\": 0.0833425926954744}, {\"state\": \"Virginia\", \"id\": 51, \"State Farm policy premiums\": 878312, \"BERKSHIRE HATHAWAY GRP\": 884489, \"STATE TOTAL\": 5105360, \"State Farm market share %\": 0.17203723145870223}, {\"state\": \"Washington\", \"id\": 53, \"State Farm policy premiums\": 794882, \"BERKSHIRE HATHAWAY GRP\": 478922, \"STATE TOTAL\": 4780616, \"State Farm market share %\": 0.16627187793372236}, {\"state\": \"West Virginia\", \"id\": 54, \"State Farm policy premiums\": 323608, \"BERKSHIRE HATHAWAY GRP\": 110806, \"STATE TOTAL\": 1209872, \"State Farm market share %\": 0.267472922755465}, {\"state\": \"Wisconsin\", \"id\": 55, \"State Farm policy premiums\": 443680, \"BERKSHIRE HATHAWAY GRP\": 131845, \"STATE TOTAL\": 2950711, \"State Farm market share %\": 0.15036375978535343}, {\"state\": \"Wyoming\", \"id\": 56, \"State Farm policy premiums\": 90412, \"BERKSHIRE HATHAWAY GRP\": 30187, \"STATE TOTAL\": 374169, \"State Farm market share %\": 0.24163412789407995}, {\"state\": \"Puerto Rico\", \"id\": 72, \"State Farm policy premiums\": 0, \"BERKSHIRE HATHAWAY GRP\": 0, \"STATE TOTAL\": 444480, \"State Farm market share %\": 0.0}]}};\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": 103
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ctVEiCpnCJKX",
+        "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": "ytkmX4-jCJKY",
+        "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": "pvc1iNRxCJK5",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## 1. Anscombe dataset"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ASdE3keNCJK9",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Load dataset"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "641l56q4CJLD",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "df = sns.load_dataset('anscombe')"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "HahrZu6LCJLM",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### See the data's shape"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "Fp32gMzMCJLP",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "33fc6efd-7738-4f4f-a050-37f584f13477"
+      },
+      "cell_type": "code",
+      "source": [
+        "df.shape"
+      ],
+      "execution_count": 108,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(44, 3)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 108
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "LmMMlobYCJLd",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### See the data"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "B_1uzu_bCJLg",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1413
+        },
+        "outputId": "20fd9938-4e37-4969-e909-c3e6ceaa97e3"
+      },
+      "cell_type": "code",
+      "source": [
+        "df"
+      ],
+      "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>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",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>I</td>\n",
+              "      <td>14.0</td>\n",
+              "      <td>9.96</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>I</td>\n",
+              "      <td>6.0</td>\n",
+              "      <td>7.24</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>I</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>4.26</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>I</td>\n",
+              "      <td>12.0</td>\n",
+              "      <td>10.84</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>I</td>\n",
+              "      <td>7.0</td>\n",
+              "      <td>4.82</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10</th>\n",
+              "      <td>I</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>5.68</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11</th>\n",
+              "      <td>II</td>\n",
+              "      <td>10.0</td>\n",
+              "      <td>9.14</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>12</th>\n",
+              "      <td>II</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>8.14</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>13</th>\n",
+              "      <td>II</td>\n",
+              "      <td>13.0</td>\n",
+              "      <td>8.74</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>14</th>\n",
+              "      <td>II</td>\n",
+              "      <td>9.0</td>\n",
+              "      <td>8.77</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>15</th>\n",
+              "      <td>II</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>9.26</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>16</th>\n",
+              "      <td>II</td>\n",
+              "      <td>14.0</td>\n",
+              "      <td>8.10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>17</th>\n",
+              "      <td>II</td>\n",
+              "      <td>6.0</td>\n",
+              "      <td>6.13</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>18</th>\n",
+              "      <td>II</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>3.10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>19</th>\n",
+              "      <td>II</td>\n",
+              "      <td>12.0</td>\n",
+              "      <td>9.13</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>20</th>\n",
+              "      <td>II</td>\n",
+              "      <td>7.0</td>\n",
+              "      <td>7.26</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>21</th>\n",
+              "      <td>II</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>4.74</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>22</th>\n",
+              "      <td>III</td>\n",
+              "      <td>10.0</td>\n",
+              "      <td>7.46</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>23</th>\n",
+              "      <td>III</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>6.77</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>24</th>\n",
+              "      <td>III</td>\n",
+              "      <td>13.0</td>\n",
+              "      <td>12.74</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25</th>\n",
+              "      <td>III</td>\n",
+              "      <td>9.0</td>\n",
+              "      <td>7.11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>26</th>\n",
+              "      <td>III</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>7.81</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>27</th>\n",
+              "      <td>III</td>\n",
+              "      <td>14.0</td>\n",
+              "      <td>8.84</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>28</th>\n",
+              "      <td>III</td>\n",
+              "      <td>6.0</td>\n",
+              "      <td>6.08</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>29</th>\n",
+              "      <td>III</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>5.39</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>30</th>\n",
+              "      <td>III</td>\n",
+              "      <td>12.0</td>\n",
+              "      <td>8.15</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>31</th>\n",
+              "      <td>III</td>\n",
+              "      <td>7.0</td>\n",
+              "      <td>6.42</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>32</th>\n",
+              "      <td>III</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>5.73</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>33</th>\n",
+              "      <td>IV</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>6.58</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>34</th>\n",
+              "      <td>IV</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>5.76</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>35</th>\n",
+              "      <td>IV</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>7.71</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>36</th>\n",
+              "      <td>IV</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>8.84</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>37</th>\n",
+              "      <td>IV</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>8.47</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>38</th>\n",
+              "      <td>IV</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>7.04</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>39</th>\n",
+              "      <td>IV</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>5.25</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>40</th>\n",
+              "      <td>IV</td>\n",
+              "      <td>19.0</td>\n",
+              "      <td>12.50</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>41</th>\n",
+              "      <td>IV</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>5.56</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>42</th>\n",
+              "      <td>IV</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>7.91</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>43</th>\n",
+              "      <td>IV</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>6.89</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\n",
+              "5        I  14.0   9.96\n",
+              "6        I   6.0   7.24\n",
+              "7        I   4.0   4.26\n",
+              "8        I  12.0  10.84\n",
+              "9        I   7.0   4.82\n",
+              "10       I   5.0   5.68\n",
+              "11      II  10.0   9.14\n",
+              "12      II   8.0   8.14\n",
+              "13      II  13.0   8.74\n",
+              "14      II   9.0   8.77\n",
+              "15      II  11.0   9.26\n",
+              "16      II  14.0   8.10\n",
+              "17      II   6.0   6.13\n",
+              "18      II   4.0   3.10\n",
+              "19      II  12.0   9.13\n",
+              "20      II   7.0   7.26\n",
+              "21      II   5.0   4.74\n",
+              "22     III  10.0   7.46\n",
+              "23     III   8.0   6.77\n",
+              "24     III  13.0  12.74\n",
+              "25     III   9.0   7.11\n",
+              "26     III  11.0   7.81\n",
+              "27     III  14.0   8.84\n",
+              "28     III   6.0   6.08\n",
+              "29     III   4.0   5.39\n",
+              "30     III  12.0   8.15\n",
+              "31     III   7.0   6.42\n",
+              "32     III   5.0   5.73\n",
+              "33      IV   8.0   6.58\n",
+              "34      IV   8.0   5.76\n",
+              "35      IV   8.0   7.71\n",
+              "36      IV   8.0   8.84\n",
+              "37      IV   8.0   8.47\n",
+              "38      IV   8.0   7.04\n",
+              "39      IV   8.0   5.25\n",
+              "40      IV  19.0  12.50\n",
+              "41      IV   8.0   5.56\n",
+              "42      IV   8.0   7.91\n",
+              "43      IV   8.0   6.89"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 109
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "JY2-iy6BCJLk",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### [Group by](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.groupby.html) `'dataset'`"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "EIxxW-MlCJLl",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "groups = df.groupby('dataset')"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "usb12p9JCJLo",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### [Describe](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.describe.html) the groups"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "gaCV0TeaCJLq",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 235
+        },
+        "outputId": "de665b4c-adc4-4188-aac3-d959c882e487"
+      },
+      "cell_type": "code",
+      "source": [
+        "groups.describe()"
+      ],
+      "execution_count": 111,
+      "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": 111
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ZRCwMb3QCJLt",
+        "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": "LaTjDriCCJLu",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 204
+        },
+        "outputId": "ba5dca63-d4d2-4865-e526-e6d970598440"
+      },
+      "cell_type": "code",
+      "source": [
+        "groups.count()"
+      ],
+      "execution_count": 112,
+      "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": 112
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ISiha0GTCJLx",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Get the [mean](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.mean.html) ..."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "fISWsEW5CJLy",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 204
+        },
+        "outputId": "56ea50a6-df15-4779-969c-25f51ed9d10b"
+      },
+      "cell_type": "code",
+      "source": [
+        "groups.mean()"
+      ],
+      "execution_count": 113,
+      "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": 113
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "-STzY_hMCJL1",
+        "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": "KeH12C91CJL3",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 204
+        },
+        "outputId": "a389a938-73f7-4410-9389-d3c4227ec304"
+      },
+      "cell_type": "code",
+      "source": [
+        "groups.std()"
+      ],
+      "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>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": 114
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "i5dznUE7CJL-",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Get the [correlation](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.corr.html) ..."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "hbJZbHh0CJMC",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 328
+        },
+        "outputId": "bf300bb5-7b96-4621-80ee-7d14a1bf607d"
+      },
+      "cell_type": "code",
+      "source": [
+        "groups.corr()"
+      ],
+      "execution_count": 115,
+      "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": 115
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "i9jemTF_CJMF",
+        "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": "6m4k73ZhCJMH",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1081
+        },
+        "outputId": "75232c49-cd8c-4878-c7ce-5ff47cb78144"
+      },
+      "cell_type": "code",
+      "source": [
+        "groups.plot('x','y', kind='scatter');"
+      ],
+      "execution_count": 116,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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2ZyNiopN5djr2OQ/sc/WVtb9JiyAiviTpSykzAEDuKrnEBABg83IogqOpAyTAPueBfa6+\nUvY36cViAEB6ORwRAAAuo/JFYPsq21+1/a+ps5TB9vNsH7P9TduP2X5l6kxFsv0B21+3fcr2PbZ7\nU2cqgu1P2z5r+9QF255v+wHbj7e/DqTM2Enr7O9ft7+v52zfa/t5KTN22qX2+YLX/sh22B4qYuzK\nF4HyW9zuk5K+EBG/KOmlqvC+2x6R9D5JExFxg6SrJL01barC3CXplou2fVDSgxFxnaQH28+r4i79\n7P4+IOmGiBiT9C1JHyo7VMHu0s/us2y/WNIbJD1R1MCVLoLcFrez/VxJN0m6U5Ii4scRcS5tqsJ1\nSeqz3SVpj6TvJc5TiIh4SNIPLtp8m6S724/vlvSmUkMV6FL7GxH3R8T59tP/kjRaerACrfP/WJL+\nRq212Qq7oFvpItAmFrermGsl1SV9pn067A7be1OHKkpEnJH0cbV+U3pS0g8j4v60qUp1dUQ82X78\nlKSrU4Yp2e9I+vfUIYpm+za1PrPlZJHjVLYINru4XcV0SbpR0qci4mWSnlW1Thes0T4nfptaBfgi\nSXttvz1tqjSidftfFrcA2v4zSeclfTZ1liLZ3iPpTyV9uOixKlsEynNxu3lJ8xHxcPv5MbWKoape\nJ+l/I6IeEQ1Jn5f0qsSZyvR92y+UpPbXs4nzFM72OyXdKum3o/r3vr9ErV9yTrZ/jo1KetT2z3V6\noMoWQUR8KCJGI2K/WhcQvxgRlf5tMSKeknTa9vXtTTdL+kbCSEV7QtIrbO+xbbX2t7IXxy/hPkm3\ntx/fLul4wiyFs32LWqd6JyPiR6nzFC0ivhYRL4iI/e2fY/OSbmz/O++oyhZBxt4r6bO25ySNS/po\n4jyFaR/5HJP0qKSvqfX9XMmZp7bvkfRlSdfbnrf9bkkfk/R624+rdXT0sZQZO2md/f07SfskPWD7\nhO2/Txqyw9bZ53LGrv7RFQDgcjgiAIDMUQQAkDmKAAAyRxEAQOYoAgDIHEUAAJmjCAAgcxQBsAW2\nf6W9Ln6v7b3tz0S4IXUuYCuYUAZske2/kNQrqU+tNZ7+MnEkYEsoAmCLbD9H0lckrUh6VUT8JHEk\nYEs4NQRs3aCkfrXWv6nkR2QiDxwRAFtk+z61lji/VtILI+IPEkcCtqQrdQBgN7L9DkmNiPic7ask\n/aft10bEF1NnA64URwQAkDmuEQBA5igCAMgcRQAAmaMIACBzFAEAZI4iAIDMUQQAkDmKAAAy93/Z\nomF0EMrjugAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7fe351b99400>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7fe351b4b278>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7fe351b938d0>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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btzX+7Z63PVhUfWdriv+X/bafsr2zsQ1O2y+ILW0Q2F4q6auSBiJilaRzJN1YbFVnZYuk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\nQ3JPiBbZVgAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7fe351f76080>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "jW-KbvaNCJMK",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Use Seaborn to make [relational plots](http://seaborn.pydata.org/generated/seaborn.relplot.html)"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "kA7vmw3yCJML",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "import seaborn as sns"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "D59XlM5JCJMO",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Use Seaborn to make [linear model plots](http://seaborn.pydata.org/generated/seaborn.lmplot.html)"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "5hJVo1MTCJMP",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 441
+        },
+        "outputId": "1486265a-59cc-4a5b-82b0-54b368160e04"
+      },
+      "cell_type": "code",
+      "source": [
+        "sns.lmplot('x', 'y', col='dataset', data=df, hue='dataset', col_wrap=2, height=3);"
+      ],
+      "execution_count": 118,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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FwvWaxadO1HgVxcFJdhjKyBtoSx3TcAGctwHqVx2fkectdab6fOmvvq+yTwNU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\npZRSypf+H/VPLojvBDTZAAAAAElFTkSuQmCC\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7fe3524f7f98>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "WKW-LKGmCJMT",
+        "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": "YKTnsdRhCJMU",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## 2. Tips dataset"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "KXyu7O_vCJMV",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Load dataset"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "R-9JSkbBCJMY",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "tips = sns.load_dataset('tips')"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "yNhq0ZJPCJMm",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### See the data's shape"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "hfZ12J-2CJMn",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "3d5ec1de-90a3-448e-c273-296c5b7c5b6a"
+      },
+      "cell_type": "code",
+      "source": [
+        "tips.shape"
+      ],
+      "execution_count": 120,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(244, 7)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 120
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ejXdg-1KCJMq",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### See the first 5 rows"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "6Deiy4SrCJMs",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 204
+        },
+        "outputId": "67c2c00c-7de9-46d6-8789-5d1da1b53751"
+      },
+      "cell_type": "code",
+      "source": [
+        "tips.sample(5)"
+      ],
+      "execution_count": 121,
+      "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>167</th>\n",
+              "      <td>31.71</td>\n",
+              "      <td>4.50</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>No</td>\n",
+              "      <td>Sun</td>\n",
+              "      <td>Dinner</td>\n",
+              "      <td>4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>144</th>\n",
+              "      <td>16.43</td>\n",
+              "      <td>2.30</td>\n",
+              "      <td>Female</td>\n",
+              "      <td>No</td>\n",
+              "      <td>Thur</td>\n",
+              "      <td>Lunch</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>64</th>\n",
+              "      <td>17.59</td>\n",
+              "      <td>2.64</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>No</td>\n",
+              "      <td>Sat</td>\n",
+              "      <td>Dinner</td>\n",
+              "      <td>3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>191</th>\n",
+              "      <td>19.81</td>\n",
+              "      <td>4.19</td>\n",
+              "      <td>Female</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>Thur</td>\n",
+              "      <td>Lunch</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>128</th>\n",
+              "      <td>11.38</td>\n",
+              "      <td>2.00</td>\n",
+              "      <td>Female</td>\n",
+              "      <td>No</td>\n",
+              "      <td>Thur</td>\n",
+              "      <td>Lunch</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "     total_bill   tip     sex smoker   day    time  size\n",
+              "167       31.71  4.50    Male     No   Sun  Dinner     4\n",
+              "144       16.43  2.30  Female     No  Thur   Lunch     2\n",
+              "64        17.59  2.64    Male     No   Sat  Dinner     3\n",
+              "191       19.81  4.19  Female    Yes  Thur   Lunch     2\n",
+              "128       11.38  2.00  Female     No  Thur   Lunch     2"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 121
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "l-OZU_OICJMw",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Describe the data"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "KxpFHLigCJMz",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 297
+        },
+        "outputId": "75abaf60-881c-4330-f051-f365533e44fa"
+      },
+      "cell_type": "code",
+      "source": [
+        "tips.describe()"
+      ],
+      "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>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": 66
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "q9x2KUnibby5",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 173
+        },
+        "outputId": "438dc7c0-32d7-4b6b-fd66-69d9122bb357"
+      },
+      "cell_type": "code",
+      "source": [
+        "tips.describe(exclude=[np.number])"
+      ],
+      "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>sex</th>\n",
+              "      <th>smoker</th>\n",
+              "      <th>day</th>\n",
+              "      <th>time</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>244</td>\n",
+              "      <td>244</td>\n",
+              "      <td>244</td>\n",
+              "      <td>244</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>unique</th>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "      <td>4</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>top</th>\n",
+              "      <td>Male</td>\n",
+              "      <td>No</td>\n",
+              "      <td>Sat</td>\n",
+              "      <td>Dinner</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>freq</th>\n",
+              "      <td>157</td>\n",
+              "      <td>151</td>\n",
+              "      <td>87</td>\n",
+              "      <td>176</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "         sex smoker  day    time\n",
+              "count    244    244  244     244\n",
+              "unique     2      2    4       2\n",
+              "top     Male     No  Sat  Dinner\n",
+              "freq     157    151   87     176"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 67
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "tGwjtJB4CJM4",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Make univariate [distribution plots](https://seaborn.pydata.org/generated/seaborn.distplot.html)"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "yElZPu-ICJM5",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 283
+        },
+        "outputId": "65551e81-c204-44e3-da5d-b67f06be702c"
+      },
+      "cell_type": "code",
+      "source": [
+        "sns.distplot(tips.tip);"
+      ],
+      "execution_count": 70,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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lbmZ3Al8FgsDXnXNfuODxVOA7wCagHXivc+50fKNeuvGI4z8PtfIPTx/h2Nk+\n7t5Qxt9tXacjYyQh5mrum2DAuGllETetLMK5ddQ09bDjZDs7T3XwwrFzPL638fy6uekpVBVlUp6b\nRs/gKLnpKeRmhCe+pqeQnRYi8DrlP5fz2MzV72+hzM0zY7mbWRB4CLgDaAB2mdk251ztlNU+CnQ6\n51aa2fuALwLvTUTgmUQijoNN3Tx3pI0f7qqnsWuQyvx0vvHhzdy2usSLSCIJY2asq8hlXUUuv//G\n5QB09I9wtLWXo629HG7ppa59gCOtvdR3DDA6/tqpigMGWakhstNSyEkLkZ0e/Rq9X9PUTUlOGrnp\nKTFdvGY84ugeHKVzYITO/hE6+kfoHBiho3+UroHJ+xO3uwdHGR6LMDIWYXhsnP6RcZxzBMwIBoyg\nGYHAxO2AGQHj/BvR5PuRGRgTdyLOMR5xRNzE7UjEMT71qwMDvvLMUTLCQdJTgqSFg+Slp1CUlUpx\n9pT/ptzPSQsl5aefWLbctwDHnXMnAczsUWArMLXctwKfi97+CfBPZmbOubhPej04Mk5b7/DEH8yU\nP6CGzkGOne2ltqmHzoFRAK5bVsBn7l7DHdUlhHRVJVkgCjLDXL+8kOuXv3aepO/vOMPQaISuwYli\nnfyvd2iM3qFROgdGOdMxwMDI+Pnv+fZLZ87fDgcDZKQGyQyHyAgHiTjHWMQxNu4Yi0QYHovQPTjK\nxf7Vh0MBCjLC5GWkkJ8RZkVxFmkpAcKhAKmhICfb+giYMX6+pB3jkaml7XAOHIBzTD7NxDJ3/s1g\n8s1h8g0hOGVZxDmWFmYyNDrO4Mg4A6PjdA2McKy1l7a+4d9685vMXZyVStEFpV+YGSY7LURWaois\ntBDZqSmkhwOEAgFCQSMlGCAUsPP3gwHDRd94UoITrzuRYin3CqB+yv0G4LqLreOcGzOzbqAQOBeP\nkFN98zen+NLPjvzW8oxwkFWLsrijuoQbV0x8ZC3OTo3304skLTMjPRwkPZxOWW76RdcbG4/QOzxG\n79AYGxfn0tozTO/QKH3D4wyMjNE3PMbgyDiBgP3/8goY4VCA/IwU8jPDFGSGyc+Y+DpZ5hnh4Otu\nAXs9LOPcxKeOtt5h2vqGJ75ecLuhc4B99Z20949c9E0sFv/9vnV84Pqll/8DYjCnO1TN7AHggejd\nPjP77ZaehUNQRALeUDzmx9cE/nxdc/KafjfRT/Bavvv/9Lvz4DV98Ivwwcv/9pjeFWIp90Zg6imc\nldFl063TYGYhIJeJHauv4Zx7GHg4lmCXw8x2O+c2J+rne8GPrwn8+br0mpKDH1/TdGIZ9NkFrDKz\nZWYWBt4HbLtgnW3Ah6K33wVAXs9jAAAELklEQVT8MhHj7SIiEpsZt9yjY+ifBJ5m4lDIbzjnaszs\n88Bu59w24N+A75rZcaCDiTcAERHxSExj7s657cD2C5Z9dsrtIeDd8Y12WRI25OMhP74m8Ofr0mtK\nDn58Tb/FNHoiIuI/OvhbRMSHfFPuZnanmR0xs+Nm9qDXeWbLzBab2bNmVmtmNWb2Ka8zxYuZBc3s\nFTN7wuss8WBmeWb2EzM7bGaHzOwGrzPNlpn9afTv7qCZ/cDMknJWMjP7hpmdNbODU5YVmNkzZnYs\n+jXfy4yJ4otynzJFwl1ANXC/mVV7m2rWxoA/c85VA9cDn/DBa5r0KeCQ1yHi6KvAz5xzq4GrSPLX\nZmYVwB8Dm51z65g4kCJZD5L4FnDnBcseBH7hnFsF/CJ633d8Ue5MmSLBOTcCTE6RkLScc83Oub3R\n271MFEaFt6lmz8wqgbuBr3udJR7MLBe4mYkjxnDOjTjnurxNFRchID163koG0ORxnsvinHueiSP4\nptoKfDt6+9vAfXMaao74pdynmyIh6YtwkplVAVcDO71NEhf/C/gLIOJ1kDhZBrQB34wONX3dzDK9\nDjUbzrlG4B+AOqAZ6HbO/dzbVHFV4pxrjt5uAXw5o6Bfyt23zCwLeAz4E+dcj9d5ZsPM7gHOOuf2\neJ0ljkLANcA/O+euBvpJ8o/50THorUy8cZUDmWb2AW9TJUb0ZEtfHjLol3KPZYqEpGNmKUwU+/ed\nc497nScObgLuNbPTTAyd3WZm3/M20qw1AA3OuclPVT9houyT2e3AKedcm3NuFHgcuNHjTPHUamZl\nANGvZz3OkxB+KfdYpkhIKjYxfd6/AYecc1/xOk88OOf+0jlX6ZyrYuL/0S+dc0m9ReicawHqzezK\n6KI389rpsJNRHXC9mWVE/w7fTJLvJL7A1OlSPgT81MMsCeOLy+xdbIoEj2PN1k1MTBx3wMz2RZf9\nVfRsYZlf/gj4fnTD4iTwex7nmRXn3E4z+wmwl4mjtl4hSc/qNLMfAG8CisysAfhb4AvAj8zso8AZ\n4D3eJUwcnaEqIuJDfhmWERGRKVTuIiI+pHIXEfEhlbuIiA+p3EVEfEjlLgtWdDbHj0dvl0cP/xPx\nBR0KKQtWdM6eJ6IzH4r4irbcZSH7ArDCzPaZ2Y8n5/w2sw+b2U/N7LnonN9/63FOkUvmizNURS7T\ng8A659zGya34KY9tAdYBA8AuM3vSObd77iOKXB5tuYtM7xnnXLtzbpCJibPe4HUgkUuhcheZ3oU7\no7RzSpKKyl0Wsl4g+yKP3RG91mY6E1fqeXHuYonMnsbcZcFyzrWb2YvRHakXTmn7MhNz6VcC39N4\nuyQblbssaM6591/koQbnnC+vrSkLg4ZlRER8SCcxiYj4kLbcRUR8SOUuIuJDKncRER9SuYuI+JDK\nXUTEh1TuIiI+9P8AyBBshPmmH7UAAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7fe352792908>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "F4yfDO_bb4V8",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 284
+        },
+        "outputId": "0cd09305-b510-44de-eaf2-b7d0ad537266"
+      },
+      "cell_type": "code",
+      "source": [
+        "pd.plotting.scatter_matrix(tips);"
+      ],
+      "execution_count": 72,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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x4L9RjuARwF8LIX5bSvnNJeybwyrgkqSzqihccqNrqsK5iTQnRpM0R308urMZ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\nDgzw5dwAAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7fe35216e3c8>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ZV3RzsdkCJM8",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Make bivariate [relational plots](https://seaborn.pydata.org/generated/seaborn.relplot.html)"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "DYn1Vv0CCJNB",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "YpGpfOfACJNH",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Make univariate [categorical plots](https://seaborn.pydata.org/generated/seaborn.catplot.html)"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "BsaeZXX8CJNK",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "XiOOPON5CJNN",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Make bivariate [categorical plots](https://seaborn.pydata.org/generated/seaborn.catplot.html)"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "3RP8cnduCJNP",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "GIzSIMg4CJNU",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## 3. Flights"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "SO4nEAkaCJNV",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Load dataset"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "LjxGhB-sCJNX",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "flights = sns.load_dataset('flights')"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "Czfk1FO-CJNZ",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### See the data's shape"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "w4khqV87CJNa",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "b338f6d3-d929-4001-8dd9-1e7247e3420c"
+      },
+      "cell_type": "code",
+      "source": [
+        "flights.shape"
+      ],
+      "execution_count": 125,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(144, 3)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 125
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "zpwqxs2qCJNc",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### See the first 5 rows"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "uG7dN_EwCJNe",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 204
+        },
+        "outputId": "af8d058c-790c-41c7-97ba-70b690519451"
+      },
+      "cell_type": "code",
+      "source": [
+        "flights.head()"
+      ],
+      "execution_count": 126,
+      "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": 126
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "Yq4waXa7CJNh",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Describe the data"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "rCVQaIGMCJNi",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 297
+        },
+        "outputId": "6293f48a-9b89-40c0-ee93-1373fb7e1d76"
+      },
+      "cell_type": "code",
+      "source": [
+        "flights.describe()"
+      ],
+      "execution_count": 127,
+      "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": 127
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "VePEZjHeCJNn",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Plot year & passengers"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "JTiQ9NQfCJNo",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 283
+        },
+        "outputId": "2a094cbf-9718-49a3-db02-de8915db4656"
+      },
+      "cell_type": "code",
+      "source": [
+        "sns.stripplot(x='year', y='passengers', data=flights);"
+      ],
+      "execution_count": 137,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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B++yiMcCTSe7rFZEVIvI3e3m8iLwrIltE5C8iErDLM+zlLfb6cQN9M0qpw3uuuqFfcqjI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\nVEKaIJRSSiWkCUIppVRC/x/uGrpp+lUATQAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7fe351acccf8>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "KWW0zuX7CJNt",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Plot month & passengers"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "B2LQN5-mCJNu",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 319
+        },
+        "outputId": "30eaf7c8-f3c5-446f-8cea-39b3d76d024e"
+      },
+      "cell_type": "code",
+      "source": [
+        "g = sns.stripplot(x='month', y='passengers', data=flights);\n",
+        "g.set_xticklabels(g.get_xticklabels(), rotation=45);"
+      ],
+      "execution_count": 141,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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YxQ/3pn8j8CJwqZSyE8gCvqasVhEUWPMageffwHpnG/7fr8HaezjWVVLC29vIsb3/N3Tt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\nQmCC\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7fe3516a3cc0>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "0EucmQZJCJNw",
+        "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": "PzhxeU3_CJNx",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 2000
+        },
+        "outputId": "6890c633-ce3e-47c3-9e6c-408e89bdeea5"
+      },
+      "cell_type": "code",
+      "source": [
+        "pivot1 = flights.pivot_table(index=['month','year']);\n",
+        "pivot1"
+      ],
+      "execution_count": 173,
+      "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=\"12\" 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",
+              "    <tr>\n",
+              "      <th>1959</th>\n",
+              "      <td>360</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1960</th>\n",
+              "      <td>417</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th rowspan=\"12\" valign=\"top\">February</th>\n",
+              "      <th>1949</th>\n",
+              "      <td>118</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1950</th>\n",
+              "      <td>126</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1951</th>\n",
+              "      <td>150</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1952</th>\n",
+              "      <td>180</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1953</th>\n",
+              "      <td>196</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1954</th>\n",
+              "      <td>188</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1955</th>\n",
+              "      <td>233</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1956</th>\n",
+              "      <td>277</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1957</th>\n",
+              "      <td>301</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1958</th>\n",
+              "      <td>318</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1959</th>\n",
+              "      <td>342</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1960</th>\n",
+              "      <td>391</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th rowspan=\"6\" valign=\"top\">March</th>\n",
+              "      <th>1949</th>\n",
+              "      <td>132</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1950</th>\n",
+              "      <td>141</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1951</th>\n",
+              "      <td>178</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1952</th>\n",
+              "      <td>193</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1953</th>\n",
+              "      <td>236</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1954</th>\n",
+              "      <td>235</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th rowspan=\"6\" valign=\"top\">October</th>\n",
+              "      <th>1955</th>\n",
+              "      <td>274</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1956</th>\n",
+              "      <td>306</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1957</th>\n",
+              "      <td>347</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1958</th>\n",
+              "      <td>359</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1959</th>\n",
+              "      <td>407</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1960</th>\n",
+              "      <td>461</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th rowspan=\"12\" valign=\"top\">November</th>\n",
+              "      <th>1949</th>\n",
+              "      <td>104</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1950</th>\n",
+              "      <td>114</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1951</th>\n",
+              "      <td>146</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1952</th>\n",
+              "      <td>172</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1953</th>\n",
+              "      <td>180</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1954</th>\n",
+              "      <td>203</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1955</th>\n",
+              "      <td>237</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1956</th>\n",
+              "      <td>271</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1957</th>\n",
+              "      <td>305</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1958</th>\n",
+              "      <td>310</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1959</th>\n",
+              "      <td>362</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1960</th>\n",
+              "      <td>390</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th rowspan=\"12\" valign=\"top\">December</th>\n",
+              "      <th>1949</th>\n",
+              "      <td>118</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1950</th>\n",
+              "      <td>140</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1951</th>\n",
+              "      <td>166</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1952</th>\n",
+              "      <td>194</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1953</th>\n",
+              "      <td>201</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1954</th>\n",
+              "      <td>229</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1955</th>\n",
+              "      <td>278</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1956</th>\n",
+              "      <td>306</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1957</th>\n",
+              "      <td>336</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1958</th>\n",
+              "      <td>337</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1959</th>\n",
+              "      <td>405</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1960</th>\n",
+              "      <td>432</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>144 rows × 1 columns</p>\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\n",
+              "         1959         360\n",
+              "         1960         417\n",
+              "February 1949         118\n",
+              "         1950         126\n",
+              "         1951         150\n",
+              "         1952         180\n",
+              "         1953         196\n",
+              "         1954         188\n",
+              "         1955         233\n",
+              "         1956         277\n",
+              "         1957         301\n",
+              "         1958         318\n",
+              "         1959         342\n",
+              "         1960         391\n",
+              "March    1949         132\n",
+              "         1950         141\n",
+              "         1951         178\n",
+              "         1952         193\n",
+              "         1953         236\n",
+              "         1954         235\n",
+              "...                   ...\n",
+              "October  1955         274\n",
+              "         1956         306\n",
+              "         1957         347\n",
+              "         1958         359\n",
+              "         1959         407\n",
+              "         1960         461\n",
+              "November 1949         104\n",
+              "         1950         114\n",
+              "         1951         146\n",
+              "         1952         172\n",
+              "         1953         180\n",
+              "         1954         203\n",
+              "         1955         237\n",
+              "         1956         271\n",
+              "         1957         305\n",
+              "         1958         310\n",
+              "         1959         362\n",
+              "         1960         390\n",
+              "December 1949         118\n",
+              "         1950         140\n",
+              "         1951         166\n",
+              "         1952         194\n",
+              "         1953         201\n",
+              "         1954         229\n",
+              "         1955         278\n",
+              "         1956         306\n",
+              "         1957         336\n",
+              "         1958         337\n",
+              "         1959         405\n",
+              "         1960         432\n",
+              "\n",
+              "[144 rows x 1 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 173
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "-SYg5RkRCJNz",
+        "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": "GJpST5XgCJNz",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 319
+        },
+        "outputId": "0878b9b4-361d-4640-e206-1e867acae5aa"
+      },
+      "cell_type": "code",
+      "source": [
+        "ax = sns.heatmap(flights.pivot(index='year', columns='month', values='passengers'))\n",
+        "ax.set_xticklabels(g.get_xticklabels(), rotation=45);"
+      ],
+      "execution_count": 176,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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AtBdxgaRkTq7z3IaVkqRlJG1mZvcABwAXSPpYo3K7uc56wBk5yqsork0l7SVp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\nW/chY3G8RXhyqvPHU/n0VM8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+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7fe351bddb38>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "tWMFrrqTFErR",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file

From 76cb7329cdec49a6d86b6d795d1a33a47185e5ae Mon Sep 17 00:00:00 2001
From: Daniel Martin-Alarcon
 <37123867+DanielMartinAlarcon@users.noreply.github.com>
Date: Tue, 13 Nov 2018 14:42:36 -0800
Subject: [PATCH 2/9] Created using Colaboratory

---
 ...22_Choose_appropriate_visualizations.ipynb | 79 +++++++++++++++----
 1 file changed, 65 insertions(+), 14 deletions(-)

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 4af9df7..e9aa059 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
@@ -145,7 +145,7 @@
         "colab_type": "code",
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 224
+          "height": 204
         },
         "outputId": "5a811a63-89e3-46f1-c148-d3cdedacce88"
       },
@@ -2651,14 +2651,31 @@
       "metadata": {
         "id": "DYn1Vv0CCJNB",
         "colab_type": "code",
-        "colab": {}
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 369
+        },
+        "outputId": "fd8b7922-0b07-4a8a-a995-c70767354b8c"
       },
       "cell_type": "code",
       "source": [
-        ""
+        "sns.relplot(data=tips, x='tip', y='total_bill');"
       ],
-      "execution_count": 0,
-      "outputs": []
+      "execution_count": 181,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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hEOBBK3qYA64p1duIISLblFILy36iAtwQgO38BbMj8MdiGl453YeVj+1Lf7+blnTgkklN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\nAElFTkSuQmCC\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7fe351a384e0>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
     },
     {
       "metadata": {
@@ -2674,14 +2691,31 @@
       "metadata": {
         "id": "BsaeZXX8CJNK",
         "colab_type": "code",
-        "colab": {}
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 369
+        },
+        "outputId": "ff4e60c2-80a8-47d5-c644-ecad04ee67ed"
       },
       "cell_type": "code",
       "source": [
-        ""
+        "sns.catplot('sex', data=tips, kind='count');"
       ],
-      "execution_count": 0,
-      "outputs": []
+      "execution_count": 182,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7fe351a1c588>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
     },
     {
       "metadata": {
@@ -2697,14 +2731,31 @@
       "metadata": {
         "id": "3RP8cnduCJNP",
         "colab_type": "code",
-        "colab": {}
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 369
+        },
+        "outputId": "6a7dc32a-826d-41e7-85ea-e24e57d5552f"
       },
       "cell_type": "code",
       "source": [
-        ""
+        "sns.catplot('sex', 'tip', data=tips, kind='strip', alpha=0.5);"
       ],
-      "execution_count": 0,
-      "outputs": []
+      "execution_count": 183,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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nb4q72xI5S8n/jTJmW4qPHdzAixdi/ODkODVBH+NzOf7mtX6iAZu5TAHH1YzM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\nKHkBGTVXRfK3qa0AAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7fe351a38d30>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
     },
     {
       "metadata": {
@@ -3513,7 +3564,7 @@
     },
     {
       "metadata": {
-        "id": "tWMFrrqTFErR",
+        "id": "AhUO92qiJ6g3",
         "colab_type": "code",
         "colab": {}
       },

From 5ff79e877aaad94551da606903bcf57deb14ee2b Mon Sep 17 00:00:00 2001
From: Daniel Martin-Alarcon
 <37123867+DanielMartinAlarcon@users.noreply.github.com>
Date: Wed, 14 Nov 2018 15:45:24 -0800
Subject: [PATCH 3/9] Created using Colaboratory

---
 ...ke_explanatory_visualizations_LESSON.ipynb | 839 ++++++++++++++++++
 1 file changed, 839 insertions(+)
 create mode 100644 module3-make-explanatory-visualizations/LS_DS_123_Make_explanatory_visualizations_LESSON.ipynb

diff --git a/module3-make-explanatory-visualizations/LS_DS_123_Make_explanatory_visualizations_LESSON.ipynb b/module3-make-explanatory-visualizations/LS_DS_123_Make_explanatory_visualizations_LESSON.ipynb
new file mode 100644
index 0000000..54522c9
--- /dev/null
+++ b/module3-make-explanatory-visualizations/LS_DS_123_Make_explanatory_visualizations_LESSON.ipynb
@@ -0,0 +1,839 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "LS_DS_123_Make_explanatory_visualizations_LESSON.ipynb",
+      "version": "0.3.2",
+      "provenance": [],
+      "collapsed_sections": [],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/DanielMartinAlarcon/DS-Sprint-02-Storytelling-With-Data/blob/master/module3-make-explanatory-visualizations/LS_DS_123_Make_explanatory_visualizations_LESSON.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "-8-trVo__vRE",
+        "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/)"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "WSHgM3C6fzpg",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## In-class example"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ya_w5WORGs-n",
+        "colab_type": "code",
+        "outputId": "24ba880f-51b6-4f3c-c4ac-df4ed53e5823",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 355
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "from IPython.display import display, Image\n",
+        "\n",
+        "url = 'https://fivethirtyeight.com/wp-content/uploads/2017/09/mehtahickey-inconvenient-0830-1.png'\n",
+        "example = Image(url=url, width=400)\n",
+        "\n",
+        "display(example)"
+      ],
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "<img src=\"https://fivethirtyeight.com/wp-content/uploads/2017/09/mehtahickey-inconvenient-0830-1.png\" width=\"400\"/>"
+            ],
+            "text/plain": [
+              "<IPython.core.display.Image object>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "HioPkYtUG03B",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "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": "c03JCR85frjC",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Make the figure look right"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "N89pO3kX_-7p",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 207
+        },
+        "outputId": "b1f8eaab-34f6-4bf1-a53d-0b1531b84c1d"
+      },
+      "cell_type": "code",
+      "source": [
+        "%matplotlib inline\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np\n",
+        "\n",
+        "!pip install --upgrade seaborn\n",
+        "import seaborn as sns\n",
+        "\n",
+        "fake = pd.Series([38,3,1,2,1,4,6,5,5,33], index=range(1,11))"
+      ],
+      "execution_count": 7,
+      "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: scipy>=0.14.0 in /usr/local/lib/python3.6/dist-packages (from seaborn) (1.1.0)\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: numpy>=1.9.3 in /usr/local/lib/python3.6/dist-packages (from seaborn) (1.14.6)\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"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "lqqPHAyaPDC-",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 391
+        },
+        "outputId": "63fcf7cb-438a-4d47-c9d5-5d01037a2675"
+      },
+      "cell_type": "code",
+      "source": [
+        "plt.style.use('fivethirtyeight');\n",
+        "ax = sns.barplot(x=fake.index, y=fake.values, color='#EC713B');\n",
+        "\n",
+        "ax.set(xlabel='Rating',\n",
+        "      ylabel='Percent of total votes',\n",
+        "      yticks=range(0,50,10));\n",
+        "\n",
+        "ax.tick_params(labelrotation=0);\n",
+        "\n",
+        "ax.text(x=-2, y=50, s=\"'An Inconvenient Sequel: Truth to Power' is divisive'\", \n",
+        "        fontsize=16, fontweight='bold');\n",
+        "\n",
+        "ax.text(x=-2, y=46, s='IMDB ratings for the film as of Aug. 29')"
+      ],
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Text(-2,46,'IMDB ratings for the film as of Aug. 29')"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 8
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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wd+9eLF68WJg/ZcoUxMbGYv369dizZw8uX76s1h2YlpaGL774AgMGDMCZM2ew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\niESGxZmIiEhkWJyJiIhE5v/wC3YX8Yhm7QAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7fe52be687f0>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "Re84N5aIf_ML",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Populate figure with the real data"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "lhdOLcMcPMCp",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "pd.options.display.max_columns = 50\n",
+        "df = pd.read_csv('https://raw.githubusercontent.com/fivethirtyeight/data/master/inconvenient-sequel/ratings.csv')"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "tqJFHj0FPZ5Q",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Turn the timestamps from strings to actual time stamps\n",
+        "df.timestamp = pd.to_datetime(df.timestamp)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "0AoZ5WX9Pm7x",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# set the timestamp to be the index.\n",
+        "df.set_index('timestamp', inplace=True)\n"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "7iNgbtAyimH2",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "D3FB624cex9H",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 111
+        },
+        "outputId": "413b6294-1446-4702-e82e-6ce616a84d85"
+      },
+      "cell_type": "code",
+      "source": [
+        "columns = ['1_pct','2_pct','3_pct','4_pct','5_pct','6_pct','7_pct','8_pct','9_pct','10_pct']\n",
+        "final = df[columns].tail(1)\n",
+        "final.columns = range(1,11)\n",
+        "final "
+      ],
+      "execution_count": 12,
+      "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>1</th>\n",
+              "      <th>2</th>\n",
+              "      <th>3</th>\n",
+              "      <th>4</th>\n",
+              "      <th>5</th>\n",
+              "      <th>6</th>\n",
+              "      <th>7</th>\n",
+              "      <th>8</th>\n",
+              "      <th>9</th>\n",
+              "      <th>10</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>timestamp</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>2017-08-29 23:10:06.218251</th>\n",
+              "      <td>38.4</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>2.1</td>\n",
+              "      <td>4.1</td>\n",
+              "      <td>5.8</td>\n",
+              "      <td>5.5</td>\n",
+              "      <td>5.5</td>\n",
+              "      <td>33.7</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                              1    2    3    4    5    6    7    8    9     10\n",
+              "timestamp                                                                     \n",
+              "2017-08-29 23:10:06.218251  38.4  2.6  1.4  0.9  2.1  4.1  5.8  5.5  5.5  33.7"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 12
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "MnioWS0Qj9gX",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "colors = ['#333333']*10\n",
+        "colors[0] = '#EC713B'\n",
+        "colors[-1] = '#EC713B'"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "4lgMFBEIgWfw",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 372
+        },
+        "outputId": "f45f1815-3968-46e0-b489-c71445085d04"
+      },
+      "cell_type": "code",
+      "source": [
+        "plt.style.use('fivethirtyeight');\n",
+        "ax = sns.barplot(x=final.columns, y=final.values[0], color='#EC713B');\n",
+        "\n",
+        "ax.set(xlabel='Rating',\n",
+        "      ylabel='Percent of total votes',\n",
+        "      yticks=range(0,50,10));\n",
+        "\n",
+        "ax.tick_params(labelrotation=0);\n",
+        "\n",
+        "ax.text(x=-2, y=50, s=\"'An Inconvenient Sequel: Truth to Power' is divisive'\", \n",
+        "        fontsize=16, fontweight='bold');\n",
+        "\n",
+        "ax.text(x=-2, y=46, s='IMDB ratings for the film as of Aug. 29');"
+      ],
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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NGvTt21dI1JcuXRLGCJQm/oLYC/43MTGBmZmZML8sA0EKvvuFVWQZvm1luRSxuM9aFo6O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\nkwAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7fe52c59d0f0>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "AV2mO2XZmmzH",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Next visualization"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "AQTD9GGXhy_f",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 517
+        },
+        "outputId": "23f79f8d-5964-4ccc-a099-4c95c43971aa"
+      },
+      "cell_type": "code",
+      "source": [
+        "# First, let's look at the graph itself\n",
+        "\n",
+        "url = 'https://fivethirtyeight.com/wp-content/uploads/2017/09/methahickey-inconvenient-0830-6.png'\n",
+        "example = Image(url=url, width=600)\n",
+        "\n",
+        "display(example)"
+      ],
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "<img src=\"https://fivethirtyeight.com/wp-content/uploads/2017/09/methahickey-inconvenient-0830-6.png\" width=\"600\"/>"
+            ],
+            "text/plain": [
+              "<IPython.core.display.Image object>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "hs2C2ZEmINHb",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "I don't want to have to generate fake data that fits this pattern, so I'll work instead on extracting the real data first."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "irMf12IqDGll",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 235
+        },
+        "outputId": "719980ef-3a5a-44fa-edbb-5fcf2590d652"
+      },
+      "cell_type": "code",
+      "source": [
+        "# I create new dataframe columns for the number of respondents in several rating ranges\n",
+        "df['1/10'] = df['respondents']*df['1_pct']*0.01\n",
+        "df['10/10'] = df['respondents']*df['10_pct']*0.01\n",
+        "df['2-9/10'] = df['respondents']-df['1/10']-df['10/10']\n",
+        "\n",
+        "# I separate out the rows for Men and Women, and all users.\n",
+        "\n",
+        "males = df[df.category == 'Males']\n",
+        "females = df[df.category == 'Females']\n",
+        "users = df[df.category == 'IMDb users']\n",
+        "\n",
+        "# And combine men, women, and individual scores into a new dataframe\n",
+        "ratings = pd.DataFrame({'males':males.respondents, \n",
+        "                        'females':females.respondents,\n",
+        "                        '1 out of 10 ratings':users['1/10'],\n",
+        "                        '10 out of 10 ratings':users['10/10'],\n",
+        "                        '2-9 out of 10 ratings':users['2-9/10']\n",
+        "                       })\n",
+        "                        \n",
+        "ratings.head(5) "
+      ],
+      "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>1 out of 10 ratings</th>\n",
+              "      <th>10 out of 10 ratings</th>\n",
+              "      <th>2-9 out of 10 ratings</th>\n",
+              "      <th>females</th>\n",
+              "      <th>males</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>timestamp</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>2017-07-17 12:28:32.785639</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>402.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-07-17 12:28:33.025600</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>78.0</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-07-17 12:28:36.697246</th>\n",
+              "      <td>303.744</td>\n",
+              "      <td>256.284</td>\n",
+              "      <td>117.972</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-07-17 12:34:17.770310</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>402.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-07-17 12:34:17.963319</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>78.0</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                            1 out of 10 ratings  10 out of 10 ratings  \\\n",
+              "timestamp                                                               \n",
+              "2017-07-17 12:28:32.785639                  NaN                   NaN   \n",
+              "2017-07-17 12:28:33.025600                  NaN                   NaN   \n",
+              "2017-07-17 12:28:36.697246              303.744               256.284   \n",
+              "2017-07-17 12:34:17.770310                  NaN                   NaN   \n",
+              "2017-07-17 12:34:17.963319                  NaN                   NaN   \n",
+              "\n",
+              "                            2-9 out of 10 ratings  females  males  \n",
+              "timestamp                                                          \n",
+              "2017-07-17 12:28:32.785639                    NaN      NaN  402.0  \n",
+              "2017-07-17 12:28:33.025600                    NaN     78.0    NaN  \n",
+              "2017-07-17 12:28:36.697246                117.972      NaN    NaN  \n",
+              "2017-07-17 12:34:17.770310                    NaN      NaN  402.0  \n",
+              "2017-07-17 12:34:17.963319                    NaN     78.0    NaN  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 118
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "djmuFOWeFLii",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 235
+        },
+        "outputId": "a79e3583-5531-4d2c-af02-ed27d4c8964d"
+      },
+      "cell_type": "code",
+      "source": [
+        "# Because each entry has a different timestamp, there's a NaN in every single row.\n",
+        "# I interpolate to fill the NaNs, and drop the first and last few rows because \n",
+        "# interpolate doesn't fix them\n",
+        "\n",
+        "ratings.interpolate(method='nearest', inplace=True)\n",
+        "ratings.dropna(inplace=True)\n",
+        "ratings.head()"
+      ],
+      "execution_count": 119,
+      "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>1 out of 10 ratings</th>\n",
+              "      <th>10 out of 10 ratings</th>\n",
+              "      <th>2-9 out of 10 ratings</th>\n",
+              "      <th>females</th>\n",
+              "      <th>males</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>timestamp</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>2017-07-17 12:28:36.697246</th>\n",
+              "      <td>303.744</td>\n",
+              "      <td>256.284</td>\n",
+              "      <td>117.972</td>\n",
+              "      <td>78.0</td>\n",
+              "      <td>402.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-07-17 12:34:17.770310</th>\n",
+              "      <td>303.744</td>\n",
+              "      <td>256.284</td>\n",
+              "      <td>117.972</td>\n",
+              "      <td>78.0</td>\n",
+              "      <td>402.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-07-17 12:34:17.963319</th>\n",
+              "      <td>303.744</td>\n",
+              "      <td>256.284</td>\n",
+              "      <td>117.972</td>\n",
+              "      <td>78.0</td>\n",
+              "      <td>402.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-07-17 12:34:21.250099</th>\n",
+              "      <td>303.744</td>\n",
+              "      <td>256.284</td>\n",
+              "      <td>117.972</td>\n",
+              "      <td>78.0</td>\n",
+              "      <td>402.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-07-17 13:00:02.497484</th>\n",
+              "      <td>303.744</td>\n",
+              "      <td>256.284</td>\n",
+              "      <td>117.972</td>\n",
+              "      <td>78.0</td>\n",
+              "      <td>402.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                            1 out of 10 ratings  10 out of 10 ratings  \\\n",
+              "timestamp                                                               \n",
+              "2017-07-17 12:28:36.697246              303.744               256.284   \n",
+              "2017-07-17 12:34:17.770310              303.744               256.284   \n",
+              "2017-07-17 12:34:17.963319              303.744               256.284   \n",
+              "2017-07-17 12:34:21.250099              303.744               256.284   \n",
+              "2017-07-17 13:00:02.497484              303.744               256.284   \n",
+              "\n",
+              "                            2-9 out of 10 ratings  females  males  \n",
+              "timestamp                                                          \n",
+              "2017-07-17 12:28:36.697246                117.972     78.0  402.0  \n",
+              "2017-07-17 12:34:17.770310                117.972     78.0  402.0  \n",
+              "2017-07-17 12:34:17.963319                117.972     78.0  402.0  \n",
+              "2017-07-17 12:34:21.250099                117.972     78.0  402.0  \n",
+              "2017-07-17 13:00:02.497484                117.972     78.0  402.0  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 119
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "H1uijBv9hL6a",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "VQbS2rC3J8D6",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 310
+        },
+        "outputId": "da009877-11e0-438c-d565-9ff5f09ccc4a"
+      },
+      "cell_type": "code",
+      "source": [
+        "# And now I plot.\n",
+        "\n",
+        "sns.set()\n",
+        "sns.set_style(\"whitegrid\")\n",
+        "sns.set_context(\"notebook\", font_scale=1.1, rc={\"lines.linewidth\": 2.5})\n",
+        "\n",
+        "ax = sns.lineplot(data=ratings);\n",
+        "ax.set(yticks=range(0,1500,500));\n"
+      ],
+      "execution_count": 135,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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ixAh0Oh233347x48fx9+/4hDozMxM2rQpP8cnNDSUzMzMa67rleV36XJpVQeF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\nQmCC\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7fe528bba780>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "IxAWnIK0Yd7Z",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "'''\n",
+        "And those are the only parts that actually worked.  I notice that the way\n",
+        "I calculate the lines for ratings is wrong, clearly because 'IMDB users' is a \n",
+        "category that contains more than just males and females (otherwise those lines\n",
+        "would be squished down further).  I ran out of time to fix that.\n",
+        "\n",
+        "Most of my problems were with @#$%^& Seaborn itself, though, as most of the time \n",
+        "trying to change some of the aesthetic parameters totally backfired for some \n",
+        "reason.  I managed to change the line widths, but not the type of line or their \n",
+        "color, and I didn't even try to change the timestamp labels on the axes.\n",
+        "\n",
+        "'''"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file

From 96877c7709e12653e1de4c144722c7086c1c4686 Mon Sep 17 00:00:00 2001
From: Daniel Martin-Alarcon
 <37123867+DanielMartinAlarcon@users.noreply.github.com>
Date: Thu, 15 Nov 2018 15:43:59 -0800
Subject: [PATCH 4/9] Created using Colaboratory

---
 .../LS_DS_124_Sequence_your_narrative.ipynb   | 771 ++++++++++++++++--
 1 file changed, 718 insertions(+), 53 deletions(-)

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..f64b5f7 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 +1,720 @@
 {
- "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": [],
+      "collapsed_sections": [],
+      "include_colab_link": true
+    },
+    "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": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/DanielMartinAlarcon/DS-Sprint-02-Storytelling-With-Data/blob/master/module4-sequence-your-narrative/LS_DS_124_Sequence_your_narrative.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "JbDHnhet8CWy",
+        "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--datapoints--population_total--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"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "SxTJBgRAW3jD",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Variables\n",
+        "- Income\n",
+        "- Lifespan\n",
+        "\n",
+        "- Population\n",
+        "- Year\n",
+        "- Country"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "3ebEjShbWsIy",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Upgrade Seaborn\n",
+        "\n",
+        "Make sure you have at least version 0.9.0.\n",
+        "\n",
+        "In Colab, go to **Restart runtime** after you run the `pip` command."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "4RSxbu7rWr1p",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 326
+        },
+        "outputId": "20e44bf7-339c-4506-94bc-4b366ca28039"
+      },
+      "cell_type": "code",
+      "source": [
+        "!pip install --upgrade seaborn"
+      ],
+      "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",
+            "\u001b[K    100% |████████████████████████████████| 215kB 23.3MB/s \n",
+            "\u001b[?25hRequirement 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: pandas>=0.15.2 in /usr/local/lib/python3.6/dist-packages (from seaborn) (0.22.0)\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: 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: 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",
+            "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",
+            "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": "5sQ0-7JUWyN4",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "044d9ceb-5803-43b5-d80d-af66b3c621c9"
+      },
+      "cell_type": "code",
+      "source": [
+        "import seaborn as sns\n",
+        "sns.__version__"
+      ],
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "'0.9.0'"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 3
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "S2dXWRTFTsgd",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## More imports"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "y-TgL_mA8OkF",
+        "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": "CZGG5prcTxrQ",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Load & look at data"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "-uE25LHD8CW0",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "62a6d471-c872-468c-eecd-6fd72ed57f1c"
+      },
+      "cell_type": "code",
+      "source": [
+        "income = 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",
+        "lifespan = 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",
+        "\n",
+        "income.shape, lifespan.shape, population.shape, entities.shape"
+      ],
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "((44268, 3), (44370, 3), (51939, 3), (273, 33))"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 6
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "6HYUytvLT8Kf",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Merge data"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "dhALZDsh9n9L",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "https://github.com/pandas-dev/pandas/blob/master/doc/cheatsheet/Pandas_Cheat_Sheet.pdf"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "z6KBarvARP4B",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 204
+        },
+        "outputId": "d79e43b4-efc6-4142-e36c-d9f18fb693b8"
+      },
+      "cell_type": "code",
+      "source": [
+        "df=pd.merge(income, lifespan)\n",
+        "df = pd.merge(df, population)\n",
+        "\n",
+        "variables = ['country', 'name', 'world_6region']\n",
+        "\n",
+        "df = pd.merge(df, entities[variables], \n",
+        "              how='inner', left_on='geo', right_on='country')\n",
+        "\n",
+        "df.drop(columns=['geo', 'country'], inplace=True)\n",
+        "\n",
+        "df.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",
+        "df.head()"
+      ],
+      "execution_count": 14,
+      "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": 14
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "C2osGLJReSwG",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 142
+        },
+        "outputId": "6ff54152-edb6-42d6-ef6c-9c094551ba28"
+      },
+      "cell_type": "code",
+      "source": [
+        "usa = df[df.country=='United States']\n",
+        "usa[usa.year.isin([1818,1918,2018])]"
+      ],
+      "execution_count": 17,
+      "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": 17
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "UbVGIjLpgf5v",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 142
+        },
+        "outputId": "65e04c88-fb95-4c9f-9ad6-3887477bdeb1"
+      },
+      "cell_type": "code",
+      "source": [
+        "china = df[df.country=='China']\n",
+        "china[china.year.isin([1818,1918,2018])]"
+      ],
+      "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></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": 20
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "9jXRAttGgT6L",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 365
+        },
+        "outputId": "d0b7eceb-932c-4182-955b-66414f04f5f3"
+      },
+      "cell_type": "code",
+      "source": [
+        "# df[df.year.isin([2018])]\n",
+        "now = df[df.year==2018]\n",
+        "ax = sns.relplot(x=\"income\", y=\"lifespan\",\n",
+        "                     hue=\"region\", size=\"population\",\n",
+        "                     data=now)\n",
+        "\n",
+        "# ax.set(xscale=\"log\")"
+      ],
+      "execution_count": 57,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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W4rcck5vGPk55aTHxiSkEbZPTbcNUFEWpK2oNvwqEEBiGRmW3oWvC4vsvl0au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\npGLbEtPUwLGQQseyGmYVxBIGLW/7f5R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LqVcY0OhZFu77Bh+LDx2rtcZ2M3P83cvNyrWV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\nxHjU0QAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7fc64d268a90>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "q7xOWs4hjjsj",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "centuries = df[df.year.isin([1818, 1918, 2018])]\n",
+        "\n",
+        "df['population'] = df['population']/1000000\n",
+        "now = df[df.year==2018]\n",
+        "\n",
+        "sns.set()\n",
+        "sns.set_style(\"white\")\n",
+        "fig, ax = plt.subplots()\n",
+        "\n",
+        "ax.scatter(x=\"income\", y=\"lifespan\",\n",
+        "                     c=\"region\",\n",
+        "                     data=now, sizes=(5,500));\n",
+        "\n",
+        "qatar = now[now.country=='Qatar']\n",
+        "qatar_income = qatar.income.values[0]\n",
+        "qatar_lifespan = qatar.lifespan.values[0]\n",
+        "\n",
+        "ax.set(xscale=\"log\");\n",
+        "plt.text(x=qatar_income+10000, y=qatar_lifespan, s=\"Quatar\");\n",
+        "plt.ylim((20,90));\n"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "Lp2-p22DjD7S",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "# Assignment work\n",
+        "\n",
+        "I wanted to get a good grip on Matplotlib before going on to anything fancier, so I used this occasion to focus on the basics."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "AXmBtbQaJxv6",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 367
+        },
+        "outputId": "8ce5d042-6286-4a68-d16e-90b9cca85536"
+      },
+      "cell_type": "code",
+      "source": [
+        "\"\"\"\n",
+        "I want the bubble sizes to be proportional to population.  \n",
+        "The first step is to create a new column with percentile population,\n",
+        "which I can then feed into a size parameter when building the bubble chart.\n",
+        "It looks like the parameter \"s\", for size, is already in unites of area,\n",
+        "so I can make that directly proportional to the population.\n",
+        "\"\"\"\n",
+        "\n",
+        "import matplotlib as mpl\n",
+        "\n",
+        "# I added the multiplier in pop_normal to make the bubbles whichever size \n",
+        "# seemed most appropriate.\n",
+        "df['pop_normal'] = 5000*df['population'] / max(df['population'])\n",
+        "now = df[df.year==2018]\n",
+        "\n",
+        "# I created a dictionary of regional colors\n",
+        "colors = {'america':'yellow', 'south_asia':'red', 'sub_saharan_africa':'blue',\n",
+        "       'europe_central_asia':'orange', 'middle_east_north_africa':'green',\n",
+        "       'east_asia_pacific':'red'}\n",
+        "\n",
+        "# Plot\n",
+        "fig, ax = plt.subplots(figsize=(10,5))\n",
+        "ax.scatter(x=\"income\", y=\"lifespan\", data=now, s='pop_normal', alpha=0.6,\n",
+        "          c=now['region'].apply(lambda x: colors[x]),\n",
+        "          linewidth=2, edgecolor='black');\n",
+        "\n",
+        "# Being able to set a custom axis label was NOT trivial.  This is Darwin's\n",
+        "# solution, which still only sorta makes sense to me.\n",
+        "ax.set_xscale('log')\n",
+        "ax.set(ylim=(20,90),xlim=(100,300000))\n",
+        "ax.xaxis.set_major_formatter(mpl.ticker.StrMethodFormatter('${x:,.0f}'))\n",
+        "\n",
+        "ax.set_title('The world in 2018', fontdict={'fontsize':20});\n",
+        "ax.set_xlabel('Per capita income ($PPP)', fontdict={'fontsize':14}, labelpad=10);\n",
+        "ax.set_ylabel('Life expectancy', fontdict={'fontsize':14});\n",
+        "\n",
+        "\n",
+        "# Creating a custom legend for this one requires craeting a list of Patch objects.\n",
+        "\n",
+        "legend_colors = {'America':'yellow', 'South Asia, East Asia, Pacific':'red', \n",
+        "                 'Sub-Saharan Africa':'blue','Europe, Central Asia':'orange', \n",
+        "                 'Middle East, North Africa':'green'}\n",
+        "\n",
+        "patch_list =[]\n",
+        "for region, color in legend_colors.items():\n",
+        "  patch_list.append(mpl.patches.Patch(facecolor=color, label=region, alpha=0.6, \n",
+        "                                      linewidth=2, edgecolor='black'))\n",
+        "\n",
+        "ax.legend(handles=patch_list, loc='lower right', bbox_to_anchor=(1,0));\n",
+        "#Legen... wait for it... DARY."
+      ],
+      "execution_count": 180,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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SSEtLJS3t6Et8WK0GrrgsifLiMBvWu/hge4D2TpXS5Ciapn1eHwPQv5Awuo5J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\n1gAAACxGWAMAALDYCeVIv5PtS6TtAAAAAElFTkSuQmCC\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7fc64c4bdd30>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file

From 55e0f79705b9aa798416f8c039cddb2d5b163deb Mon Sep 17 00:00:00 2001
From: Daniel Martin-Alarcon
 <37123867+DanielMartinAlarcon@users.noreply.github.com>
Date: Thu, 15 Nov 2018 17:11:11 -0800
Subject: [PATCH 5/9] Created using Colaboratory

---
 ..._123_Make_explanatory_visualizations.ipynb | 1083 ++++++++++++++++-
 1 file changed, 1027 insertions(+), 56 deletions(-)

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..041c584 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 +1,1029 @@
 {
- "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": [],
+      "collapsed_sections": [],
+      "include_colab_link": true
+    },
+    "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": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/DanielMartinAlarcon/DS-Sprint-02-Storytelling-With-Data/blob/master/module3-make-explanatory-visualizations/LS_DS_123_Make_explanatory_visualizations.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "-8-trVo__vRE",
+        "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/)"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "WSHgM3C6fzpg",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## In-class example"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ya_w5WORGs-n",
+        "colab_type": "code",
+        "outputId": "0305ae61-b94b-4bd1-91f4-20d3a3e93712",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 355
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "from IPython.display import display, Image\n",
+        "\n",
+        "url = 'https://fivethirtyeight.com/wp-content/uploads/2017/09/mehtahickey-inconvenient-0830-1.png'\n",
+        "example = Image(url=url, width=400)\n",
+        "\n",
+        "display(example)"
+      ],
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "<img src=\"https://fivethirtyeight.com/wp-content/uploads/2017/09/mehtahickey-inconvenient-0830-1.png\" width=\"400\"/>"
+            ],
+            "text/plain": [
+              "<IPython.core.display.Image object>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "HioPkYtUG03B",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "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": "c03JCR85frjC",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Make the figure look right"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "N89pO3kX_-7p",
+        "colab_type": "code",
+        "outputId": "b16e7c06-4547-4a39-f932-d6f1d6d5c797",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 207
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "%matplotlib inline\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np\n",
+        "\n",
+        "!pip install --upgrade seaborn\n",
+        "import seaborn as sns\n",
+        "\n",
+        "fake = pd.Series([38,3,1,2,1,4,6,5,5,33], index=range(1,11))"
+      ],
+      "execution_count": 5,
+      "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: 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: pandas>=0.15.2 in /usr/local/lib/python3.6/dist-packages (from seaborn) (0.22.0)\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: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=1.4.3->seaborn) (2.5.3)\n",
+            "Requirement already satisfied, skipping upgrade: pytz in /usr/local/lib/python3.6/dist-packages (from matplotlib>=1.4.3->seaborn) (2018.7)\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: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=1.4.3->seaborn) (0.10.0)\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "lqqPHAyaPDC-",
+        "colab_type": "code",
+        "outputId": "851c5369-41de-4a53-fd0d-75d2cfce88a9",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 391
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "plt.style.use('fivethirtyeight');\n",
+        "ax = sns.barplot(x=fake.index, y=fake.values, color='#EC713B');\n",
+        "\n",
+        "ax.set(xlabel='Rating',\n",
+        "      ylabel='Percent of total votes',\n",
+        "      yticks=range(0,50,10));\n",
+        "\n",
+        "ax.tick_params(labelrotation=0);\n",
+        "\n",
+        "ax.text(x=-2, y=50, s=\"'An Inconvenient Sequel: Truth to Power' is divisive'\", \n",
+        "        fontsize=16, fontweight='bold');\n",
+        "\n",
+        "ax.text(x=-2, y=46, s='IMDB ratings for the film as of Aug. 29')"
+      ],
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Text(-2,46,'IMDB ratings for the film as of Aug. 29')"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 6
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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wd+9eLF68WJg/ZcoUxMbGYv369dizZw8uX76s1h2YlpaGL774AgMGDMCZM2ew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\niESGxZmIiEhkWJyJiIhE5v/wC3YX8Yhm7QAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7ff027d52748>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "Re84N5aIf_ML",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Populate figure with the real data"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "lhdOLcMcPMCp",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "pd.options.display.max_columns = 50\n",
+        "df = pd.read_csv('https://raw.githubusercontent.com/fivethirtyeight/data/master/inconvenient-sequel/ratings.csv')"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "tqJFHj0FPZ5Q",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Turn the timestamps from strings to actual time stamps\n",
+        "df.timestamp = pd.to_datetime(df.timestamp)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "0AoZ5WX9Pm7x",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# set the timestamp to be the index.\n",
+        "df.set_index('timestamp', inplace=True)\n"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "7iNgbtAyimH2",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "D3FB624cex9H",
+        "colab_type": "code",
+        "outputId": "0b7336a7-0bdf-4386-919b-d7536c5e17c8",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 111
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "columns = ['1_pct','2_pct','3_pct','4_pct','5_pct','6_pct','7_pct','8_pct','9_pct','10_pct']\n",
+        "final = df[columns].tail(1)\n",
+        "final.columns = range(1,11)\n",
+        "final "
+      ],
+      "execution_count": 10,
+      "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>1</th>\n",
+              "      <th>2</th>\n",
+              "      <th>3</th>\n",
+              "      <th>4</th>\n",
+              "      <th>5</th>\n",
+              "      <th>6</th>\n",
+              "      <th>7</th>\n",
+              "      <th>8</th>\n",
+              "      <th>9</th>\n",
+              "      <th>10</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>timestamp</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>2017-08-29 23:10:06.218251</th>\n",
+              "      <td>38.4</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>2.1</td>\n",
+              "      <td>4.1</td>\n",
+              "      <td>5.8</td>\n",
+              "      <td>5.5</td>\n",
+              "      <td>5.5</td>\n",
+              "      <td>33.7</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                              1    2    3    4    5    6    7    8    9     10\n",
+              "timestamp                                                                     \n",
+              "2017-08-29 23:10:06.218251  38.4  2.6  1.4  0.9  2.1  4.1  5.8  5.5  5.5  33.7"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 10
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "MnioWS0Qj9gX",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "colors = ['#333333']*10\n",
+        "colors[0] = '#EC713B'\n",
+        "colors[-1] = '#EC713B'"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "4lgMFBEIgWfw",
+        "colab_type": "code",
+        "outputId": "36545a18-3269-44ad-c636-7f235a125263",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 372
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "plt.style.use('fivethirtyeight');\n",
+        "ax = sns.barplot(x=final.columns, y=final.values[0], color='#EC713B');\n",
+        "\n",
+        "ax.set(xlabel='Rating',\n",
+        "      ylabel='Percent of total votes',\n",
+        "      yticks=range(0,50,10));\n",
+        "\n",
+        "ax.tick_params(labelrotation=0);\n",
+        "\n",
+        "ax.text(x=-2, y=50, s=\"'An Inconvenient Sequel: Truth to Power' is divisive'\", \n",
+        "        fontsize=16, fontweight='bold');\n",
+        "\n",
+        "ax.text(x=-2, y=46, s='IMDB ratings for the film as of Aug. 29');"
+      ],
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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NGvTt21dI1JcuXRLGCJQm/oLYC/43MTGBmZmZML8sA0EKvvuFVWQZvm1luRSxuM9aFo6O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\nkwAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7ff024354438>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "AV2mO2XZmmzH",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Next visualization"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "AQTD9GGXhy_f",
+        "colab_type": "code",
+        "outputId": "514a0212-888c-40ad-8937-dc0c47ddeaf3",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 517
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "# First, let's look at the graph itself\n",
+        "\n",
+        "url = 'https://fivethirtyeight.com/wp-content/uploads/2017/09/methahickey-inconvenient-0830-6.png'\n",
+        "example = Image(url=url, width=600)\n",
+        "\n",
+        "display(example)"
+      ],
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "<img src=\"https://fivethirtyeight.com/wp-content/uploads/2017/09/methahickey-inconvenient-0830-6.png\" width=\"600\"/>"
+            ],
+            "text/plain": [
+              "<IPython.core.display.Image object>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "hs2C2ZEmINHb",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "I don't want to have to generate fake data that fits this pattern, so I'll work instead on extracting the real data first."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "irMf12IqDGll",
+        "colab_type": "code",
+        "outputId": "cb48532d-36d2-411d-abea-7d70d2d3090e",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 374
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "# I create new dataframe columns for the number of respondents in several rating ranges\n",
+        "df['1/10'] = df['respondents']*df['1_pct']*0.01\n",
+        "df['10/10'] = df['respondents']*df['10_pct']*0.01\n",
+        "df['2-9/10'] = df['respondents']-df['1/10']-df['10/10']\n",
+        "print(df.shape)\n",
+        "df.head()"
+      ],
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "(80053, 29)\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>respondents</th>\n",
+              "      <th>category</th>\n",
+              "      <th>link</th>\n",
+              "      <th>average</th>\n",
+              "      <th>mean</th>\n",
+              "      <th>median</th>\n",
+              "      <th>1_votes</th>\n",
+              "      <th>2_votes</th>\n",
+              "      <th>3_votes</th>\n",
+              "      <th>4_votes</th>\n",
+              "      <th>5_votes</th>\n",
+              "      <th>6_votes</th>\n",
+              "      <th>7_votes</th>\n",
+              "      <th>8_votes</th>\n",
+              "      <th>9_votes</th>\n",
+              "      <th>10_votes</th>\n",
+              "      <th>1_pct</th>\n",
+              "      <th>2_pct</th>\n",
+              "      <th>3_pct</th>\n",
+              "      <th>4_pct</th>\n",
+              "      <th>5_pct</th>\n",
+              "      <th>6_pct</th>\n",
+              "      <th>7_pct</th>\n",
+              "      <th>8_pct</th>\n",
+              "      <th>9_pct</th>\n",
+              "      <th>10_pct</th>\n",
+              "      <th>1/10</th>\n",
+              "      <th>10/10</th>\n",
+              "      <th>2-9/10</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>timestamp</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",
+              "      <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>2017-07-17 12:28:32.785639</th>\n",
+              "      <td>402</td>\n",
+              "      <td>Males</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings-male</td>\n",
+              "      <td>4.6</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>197</td>\n",
+              "      <td>7</td>\n",
+              "      <td>7</td>\n",
+              "      <td>3</td>\n",
+              "      <td>7</td>\n",
+              "      <td>7</td>\n",
+              "      <td>11</td>\n",
+              "      <td>8</td>\n",
+              "      <td>20</td>\n",
+              "      <td>135</td>\n",
+              "      <td>49.0</td>\n",
+              "      <td>1.7</td>\n",
+              "      <td>1.7</td>\n",
+              "      <td>0.7</td>\n",
+              "      <td>1.7</td>\n",
+              "      <td>1.7</td>\n",
+              "      <td>2.7</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>33.6</td>\n",
+              "      <td>196.98</td>\n",
+              "      <td>135.072</td>\n",
+              "      <td>69.948</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-07-17 12:28:33.025600</th>\n",
+              "      <td>78</td>\n",
+              "      <td>Females</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings-fe...</td>\n",
+              "      <td>6.9</td>\n",
+              "      <td>7.7</td>\n",
+              "      <td>10</td>\n",
+              "      <td>16</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>4</td>\n",
+              "      <td>3</td>\n",
+              "      <td>49</td>\n",
+              "      <td>20.5</td>\n",
+              "      <td>1.3</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.3</td>\n",
+              "      <td>1.3</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>3.8</td>\n",
+              "      <td>5.1</td>\n",
+              "      <td>3.8</td>\n",
+              "      <td>62.8</td>\n",
+              "      <td>15.99</td>\n",
+              "      <td>48.984</td>\n",
+              "      <td>13.026</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-07-17 12:28:33.273919</th>\n",
+              "      <td>4</td>\n",
+              "      <td>Aged under 18</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings-age_1</td>\n",
+              "      <td>4.2</td>\n",
+              "      <td>4.2</td>\n",
+              "      <td>3</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>50.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>25.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>25.0</td>\n",
+              "      <td>2.00</td>\n",
+              "      <td>1.000</td>\n",
+              "      <td>1.000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-07-17 12:28:33.495325</th>\n",
+              "      <td>4</td>\n",
+              "      <td>Males under 18</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings-ma...</td>\n",
+              "      <td>4.2</td>\n",
+              "      <td>4.2</td>\n",
+              "      <td>3</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>50.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>25.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>25.0</td>\n",
+              "      <td>2.00</td>\n",
+              "      <td>1.000</td>\n",
+              "      <td>1.000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2017-07-17 12:28:33.722849</th>\n",
+              "      <td>130</td>\n",
+              "      <td>Aged 18-29</td>\n",
+              "      <td>http://www.imdb.com/title/tt6322922/ratings-age_2</td>\n",
+              "      <td>6.3</td>\n",
+              "      <td>6.5</td>\n",
+              "      <td>9</td>\n",
+              "      <td>41</td>\n",
+              "      <td>0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>3</td>\n",
+              "      <td>6</td>\n",
+              "      <td>4</td>\n",
+              "      <td>6</td>\n",
+              "      <td>64</td>\n",
+              "      <td>31.5</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>2.3</td>\n",
+              "      <td>0.8</td>\n",
+              "      <td>1.5</td>\n",
+              "      <td>2.3</td>\n",
+              "      <td>4.6</td>\n",
+              "      <td>3.1</td>\n",
+              "      <td>4.6</td>\n",
+              "      <td>49.2</td>\n",
+              "      <td>40.95</td>\n",
+              "      <td>63.960</td>\n",
+              "      <td>25.090</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                            respondents        category  \\\n",
+              "timestamp                                                 \n",
+              "2017-07-17 12:28:32.785639          402           Males   \n",
+              "2017-07-17 12:28:33.025600           78         Females   \n",
+              "2017-07-17 12:28:33.273919            4   Aged under 18   \n",
+              "2017-07-17 12:28:33.495325            4  Males under 18   \n",
+              "2017-07-17 12:28:33.722849          130      Aged 18-29   \n",
+              "\n",
+              "                                                                         link  \\\n",
+              "timestamp                                                                       \n",
+              "2017-07-17 12:28:32.785639   http://www.imdb.com/title/tt6322922/ratings-male   \n",
+              "2017-07-17 12:28:33.025600  http://www.imdb.com/title/tt6322922/ratings-fe...   \n",
+              "2017-07-17 12:28:33.273919  http://www.imdb.com/title/tt6322922/ratings-age_1   \n",
+              "2017-07-17 12:28:33.495325  http://www.imdb.com/title/tt6322922/ratings-ma...   \n",
+              "2017-07-17 12:28:33.722849  http://www.imdb.com/title/tt6322922/ratings-age_2   \n",
+              "\n",
+              "                            average  mean  median  1_votes  2_votes  3_votes  \\\n",
+              "timestamp                                                                      \n",
+              "2017-07-17 12:28:32.785639      4.6   5.0       2      197        7        7   \n",
+              "2017-07-17 12:28:33.025600      6.9   7.7      10       16        1        0   \n",
+              "2017-07-17 12:28:33.273919      4.2   4.2       3        2        0        0   \n",
+              "2017-07-17 12:28:33.495325      4.2   4.2       3        2        0        0   \n",
+              "2017-07-17 12:28:33.722849      6.3   6.5       9       41        0        3   \n",
+              "\n",
+              "                            4_votes  5_votes  6_votes  7_votes  8_votes  \\\n",
+              "timestamp                                                                 \n",
+              "2017-07-17 12:28:32.785639        3        7        7       11        8   \n",
+              "2017-07-17 12:28:33.025600        1        1        0        3        4   \n",
+              "2017-07-17 12:28:33.273919        0        1        0        0        0   \n",
+              "2017-07-17 12:28:33.495325        0        1        0        0        0   \n",
+              "2017-07-17 12:28:33.722849        1        2        3        6        4   \n",
+              "\n",
+              "                            9_votes  10_votes  1_pct  2_pct  3_pct  4_pct  \\\n",
+              "timestamp                                                                   \n",
+              "2017-07-17 12:28:32.785639       20       135   49.0    1.7    1.7    0.7   \n",
+              "2017-07-17 12:28:33.025600        3        49   20.5    1.3    0.0    1.3   \n",
+              "2017-07-17 12:28:33.273919        0         1   50.0    0.0    0.0    0.0   \n",
+              "2017-07-17 12:28:33.495325        0         1   50.0    0.0    0.0    0.0   \n",
+              "2017-07-17 12:28:33.722849        6        64   31.5    0.0    2.3    0.8   \n",
+              "\n",
+              "                            5_pct  6_pct  7_pct  8_pct  9_pct  10_pct    1/10  \\\n",
+              "timestamp                                                                       \n",
+              "2017-07-17 12:28:32.785639    1.7    1.7    2.7    2.0    5.0    33.6  196.98   \n",
+              "2017-07-17 12:28:33.025600    1.3    0.0    3.8    5.1    3.8    62.8   15.99   \n",
+              "2017-07-17 12:28:33.273919   25.0    0.0    0.0    0.0    0.0    25.0    2.00   \n",
+              "2017-07-17 12:28:33.495325   25.0    0.0    0.0    0.0    0.0    25.0    2.00   \n",
+              "2017-07-17 12:28:33.722849    1.5    2.3    4.6    3.1    4.6    49.2   40.95   \n",
+              "\n",
+              "                              10/10  2-9/10  \n",
+              "timestamp                                    \n",
+              "2017-07-17 12:28:32.785639  135.072  69.948  \n",
+              "2017-07-17 12:28:33.025600   48.984  13.026  \n",
+              "2017-07-17 12:28:33.273919    1.000   1.000  \n",
+              "2017-07-17 12:28:33.495325    1.000   1.000  \n",
+              "2017-07-17 12:28:33.722849   63.960  25.090  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 14
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "rAxHSCGEsTZF",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# The fact that time is sampled to the nanosecond creates a problem, because\n",
+        "# each row for men and women winds up being separate even if they're from the\n",
+        "# same microsecond.  So let's bin that a bit. \n",
+        "\n",
+        "\n",
+        "\n",
+        "males = df[df.category == 'Males'].resample('5T').max().dropna()\n",
+        "females = df[df.category == 'Females'].resample('5T').max().dropna()"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "UoVRc9XO1SSX",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# I combine men and women into a new dataframe, and create new columns for each\n",
+        "# individual score group. \n",
+        "\n",
+        "ratings = pd.DataFrame({'males':males.respondents, \n",
+        "                        'females':females.respondents,\n",
+        "                        '1 out of 10 ratings':males['1/10'] + females['1/10'],\n",
+        "                        '10 out of 10 ratings':males['10/10'] + females['10/10'],\n",
+        "                        '2-9 out of 10 ratings':males['2-9/10'] + females['2-9/10']\n",
+        "                       })"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "VQbS2rC3J8D6",
+        "colab_type": "code",
+        "outputId": "7f6eace2-6bb7-4c17-e75e-51f2b4606b4d",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 310
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "# And now I plot.\n",
+        "\n",
+        "sns.set()\n",
+        "sns.set_style(\"whitegrid\")\n",
+        "sns.set_context(\"notebook\", font_scale=1.1, rc={\"lines.linewidth\": 2.5})\n",
+        "\n",
+        "ax = sns.lineplot(data=ratings);\n",
+        "ax.set(yticks=range(0,1500,500));\n"
+      ],
+      "execution_count": 17,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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RI9Fqtdx2220cOXKEoKCgSsfNycmhbdu2LsvCw8PJycm54lgvPX7Xrl2drxUK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\nfZK+dHqQCU1qwesT+Hw+qm1O1hdUUFHTSGWdgzq7C5fbS02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j+uaiKAo/Fq7lg+3LqXbU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\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7ff024dd1d30>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "IxAWnIK0Yd7Z",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 54
+        },
+        "outputId": "f52eec06-2865-4bc5-f564-8a1dae37a3b8"
+      },
+      "cell_type": "code",
+      "source": [
+        "'''\n",
+        "Since the \n",
+        "\n",
+        "And those are the only parts that actually worked. Most of my problems were \n",
+        "with @#$%^& Seaborn itself, though, as most of the time \n",
+        "trying to change some of the aesthetic parameters totally backfired for some \n",
+        "reason.  I managed to change the line widths, but not the type of line or their \n",
+        "color, and I didn't even try to change the timestamp labels on the axes.\n",
+        "\n",
+        "'''"
+      ],
+      "execution_count": 18,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "\"\\nSince the \\n\\nAnd those are the only parts that actually worked. Most of my problems were \\nwith @#$%^& Seaborn itself, though, as most of the time \\ntrying to change some of the aesthetic parameters totally backfired for some \\nreason.  I managed to change the line widths, but not the type of line or their \\ncolor, and I didn't even try to change the timestamp labels on the axes.\\n\\n\""
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 18
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "2PtS4dD43j3G",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 466
+        },
+        "outputId": "c08b8f4c-c3cc-4f59-d93b-451b911fd763"
+      },
+      "cell_type": "code",
+      "source": [
+        "# Let's plot again, this time with matplotlib only\n",
+        "\n",
+        "fig, ax = plt.subplots(figsize=(8,6))\n",
+        "plt.plot(ratings['males'], linewidth=3, color='#EC713B');\n",
+        "fig.text(0.65,0.65, \"All ratings \\nfrom men\", size='large', color='#EC713B',\n",
+        "        weight='bold');\n",
+        "\n",
+        "plt.plot(ratings['females'], linewidth=3, color='blue');\n",
+        "fig.text(0.55,0.1, \"All ratings from women\", size='large', color='blue',\n",
+        "        weight='bold');\n",
+        "\n",
+        "plt.plot(ratings['1 out of 10 ratings'], linewidth=2, color='#EC713B');\n",
+        "fig.text(0.7,0.42, \"1 out of 10 ratings\", size='medium', color='#EC713B',\n",
+        "        weight='roman');\n",
+        "\n",
+        "plt.plot(ratings['10 out of 10 ratings'], linewidth=2, color='#EC713B');\n",
+        "fig.text(0.7,0.33, \"10 out of 10\", size='medium', color='#EC713B',\n",
+        "        weight='roman');\n",
+        "\n",
+        "plt.plot(ratings['2-9 out of 10 ratings'], linewidth=2, color='#EC713B');\n",
+        "fig.text(0.7,0.25, \"2-9 out of 10\", size='medium', color='#EC713B',\n",
+        "        weight='roman');\n",
+        "\n",
+        "ax.set(yticks=range(0,1600,500));\n",
+        "plt.xticks(['2017-07-23','2017-07-30','2017-08-06','2017-08-13','2017-08-20',\n",
+        "            '2017-08-27'],['7/23','7/30','8/6','8/13','8/20','8/27']);\n",
+        "\n",
+        "fig.suptitle(\"Men dominated Al Gore's IMBD movie rating\",\n",
+        "             fontsize=20, weight='semibold', horizontalalignment='left', \n",
+        "             x=0.08, y=1.025);\n",
+        "ax.set_title('Cumulative number of IMDB ratings for \"An Inconvenient Sequel\" '\n",
+        "             'by\\ngender and score, July 17 through Aug. 29',\n",
+        "             horizontalalignment='left', x=0, fontsize=14);\n"
+      ],
+      "execution_count": 103,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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SPMUCAFauXIklS5bQcbwPbXJycpPP1RjdunXD5cuX0a9fPwDAp0+fcOvWLaF5rlixAhRF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\nTgZm5k4ZWl7TsHKzMLL2IDwF8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2soS0wb5YsvviAxMZHU1NTKMo/Hw44dOxgzZgzDhg3j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\nmc27AAAAAElFTkSuQmCC\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7ff02299bb00>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "PLUYz6G435IG",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file

From 0ac77facaedc21f1a54494318803d4a33a158b10 Mon Sep 17 00:00:00 2001
From: Daniel Martin-Alarcon
 <37123867+DanielMartinAlarcon@users.noreply.github.com>
Date: Fri, 16 Nov 2018 08:02:41 -0800
Subject: [PATCH 6/9] Created using Colaboratory

---
 DS_Unit_1_Sprint_Challenge_2.ipynb | 238 +++++++++++++++++++++++++++++
 1 file changed, 238 insertions(+)
 create mode 100644 DS_Unit_1_Sprint_Challenge_2.ipynb

diff --git a/DS_Unit_1_Sprint_Challenge_2.ipynb b/DS_Unit_1_Sprint_Challenge_2.ipynb
new file mode 100644
index 0000000..872ed75
--- /dev/null
+++ b/DS_Unit_1_Sprint_Challenge_2.ipynb
@@ -0,0 +1,238 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "DS_Unit_1_Sprint_Challenge_2.ipynb",
+      "version": "0.3.2",
+      "provenance": [],
+      "collapsed_sections": [],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/DanielMartinAlarcon/DS-Sprint-02-Storytelling-With-Data/blob/master/DS_Unit_1_Sprint_Challenge_2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "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",
+        "\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": {}
+      },
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "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": "64a105e6-8fa5-45e5-c78e-d29fcd19b3f2",
+        "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": 0,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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Nx1VBFMvhEasTu+oRpidrskpGQ6zEfj4pHxrY69C3v4xuiKYiEEsrYk12BEeVbwk2xVwi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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": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "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": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file

From 23b71d568c3b84f11904665d6ca5436632637512 Mon Sep 17 00:00:00 2001
From: Daniel Martin-Alarcon
 <37123867+DanielMartinAlarcon@users.noreply.github.com>
Date: Fri, 16 Nov 2018 10:42:54 -0800
Subject: [PATCH 7/9] Created using Colaboratory

---
 DS_Unit_1_Sprint_Challenge_2.ipynb | 441 ++++++++++++++++++++++++++++-
 1 file changed, 429 insertions(+), 12 deletions(-)

diff --git a/DS_Unit_1_Sprint_Challenge_2.ipynb b/DS_Unit_1_Sprint_Challenge_2.ipynb
index 872ed75..ae1e8be 100644
--- a/DS_Unit_1_Sprint_Challenge_2.ipynb
+++ b/DS_Unit_1_Sprint_Challenge_2.ipynb
@@ -111,16 +111,163 @@
     },
     {
       "metadata": {
-        "id": "sRMc0H_5z6ff",
+        "id": "5VFXaicISRg6",
         "colab_type": "code",
-        "colab": {}
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 418
+        },
+        "outputId": "eea73b1d-0737-4a90-f399-7a36f00b2d2c"
       },
       "cell_type": "code",
       "source": [
-        ""
+        "# Number of guests per occupation\n",
+        "df_count = pd.crosstab(df['Year'], df['Occupation'])\n",
+        "\n",
+        "# Normalized to 0-1\n",
+        "df_norm = pd.crosstab(df['Year'], df['Occupation'], normalize='index')\n",
+        "\n",
+        "# Normalized to 1% - 100%\n",
+        "df_pct = df_norm.style.format('{:,.2%}'.format)\n",
+        "df_pct"
       ],
-      "execution_count": 0,
-      "outputs": []
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<style  type=\"text/css\" >\n",
+              "</style>  \n",
+              "<table id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002\" > \n",
+              "<thead>    <tr> \n",
+              "        <th class=\"index_name level0\" >Occupation</th> \n",
+              "        <th class=\"col_heading level0 col0\" >Acting, Comedy & Music</th> \n",
+              "        <th class=\"col_heading level0 col1\" >Government and Politics</th> \n",
+              "        <th class=\"col_heading level0 col2\" >Media</th> \n",
+              "        <th class=\"col_heading level0 col3\" >Other</th> \n",
+              "    </tr>    <tr> \n",
+              "        <th class=\"index_name level0\" >Year</th> \n",
+              "        <th class=\"blank\" ></th> \n",
+              "        <th class=\"blank\" ></th> \n",
+              "        <th class=\"blank\" ></th> \n",
+              "        <th class=\"blank\" ></th> \n",
+              "    </tr></thead> \n",
+              "<tbody>    <tr> \n",
+              "        <th id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >1999</th> \n",
+              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row0_col0\" class=\"data row0 col0\" >90.36%</td> \n",
+              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row0_col1\" class=\"data row0 col1\" >1.20%</td> \n",
+              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row0_col2\" class=\"data row0 col2\" >6.63%</td> \n",
+              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row0_col3\" class=\"data row0 col3\" >1.81%</td> \n",
+              "    </tr>    <tr> \n",
+              "        <th id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >2000</th> \n",
+              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row1_col0\" class=\"data row1 col0\" >73.96%</td> \n",
+              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row1_col1\" class=\"data row1 col1\" >8.28%</td> \n",
+              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row1_col2\" class=\"data row1 col2\" >12.43%</td> \n",
+              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row1_col3\" class=\"data row1 col3\" >5.33%</td> \n",
+              "    </tr>    <tr> \n",
+              "        <th id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >2001</th> \n",
+              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row2_col0\" class=\"data row2 col0\" >72.61%</td> \n",
+              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row2_col1\" class=\"data row2 col1\" >3.82%</td> \n",
+              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row2_col2\" class=\"data row2 col2\" >19.75%</td> \n",
+              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row2_col3\" class=\"data row2 col3\" >3.82%</td> \n",
+              "    </tr>    <tr> \n",
+              "        <th id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >2002</th> \n",
+              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row3_col0\" class=\"data row3 col0\" >62.26%</td> \n",
+              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row3_col1\" class=\"data row3 col1\" >6.92%</td> \n",
+              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row3_col2\" class=\"data row3 col2\" >26.42%</td> \n",
+              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row3_col3\" class=\"data row3 col3\" >4.40%</td> \n",
+              "    </tr>    <tr> \n",
+              "        <th id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >2003</th> \n",
+              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row4_col0\" class=\"data row4 col0\" >56.02%</td> \n",
+              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row4_col1\" class=\"data row4 col1\" >10.24%</td> \n",
+              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row4_col2\" class=\"data row4 col2\" >24.70%</td> \n",
+              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row4_col3\" class=\"data row4 col3\" >9.04%</td> \n",
+              "    </tr>    <tr> \n",
+              "        <th id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >2004</th> \n",
+              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row5_col0\" class=\"data row5 col0\" >38.41%</td> \n",
+              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row5_col1\" class=\"data row5 col1\" >22.56%</td> \n",
+              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row5_col2\" class=\"data row5 col2\" >27.44%</td> \n",
+              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row5_col3\" class=\"data row5 col3\" >11.59%</td> \n",
+              "    </tr>    <tr> \n",
+              "        <th id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >2005</th> \n",
+              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row6_col0\" class=\"data row6 col0\" >37.04%</td> \n",
+              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row6_col1\" class=\"data row6 col1\" >16.05%</td> \n",
+              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row6_col2\" class=\"data row6 col2\" >33.33%</td> \n",
+              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row6_col3\" class=\"data row6 col3\" >13.58%</td> \n",
+              "    </tr>    <tr> \n",
+              "        <th id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >2006</th> \n",
+              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row7_col0\" class=\"data row7 col0\" >36.02%</td> \n",
+              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row7_col1\" class=\"data row7 col1\" >19.25%</td> \n",
+              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row7_col2\" class=\"data row7 col2\" >29.19%</td> \n",
+              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row7_col3\" class=\"data row7 col3\" >15.53%</td> \n",
+              "    </tr>    <tr> \n",
+              "        <th id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002level0_row8\" class=\"row_heading level0 row8\" >2007</th> \n",
+              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row8_col0\" class=\"data row8 col0\" >25.53%</td> \n",
+              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row8_col1\" class=\"data row8 col1\" >17.02%</td> \n",
+              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row8_col2\" class=\"data row8 col2\" >33.33%</td> \n",
+              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row8_col3\" class=\"data row8 col3\" >24.11%</td> \n",
+              "    </tr>    <tr> \n",
+              "        <th id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002level0_row9\" class=\"row_heading level0 row9\" >2008</th> \n",
+              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row9_col0\" class=\"data row9 col0\" >20.73%</td> \n",
+              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row9_col1\" class=\"data row9 col1\" >20.12%</td> \n",
+              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row9_col2\" class=\"data row9 col2\" >46.95%</td> \n",
+              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row9_col3\" class=\"data row9 col3\" >12.20%</td> \n",
+              "    </tr>    <tr> \n",
+              "        <th id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002level0_row10\" class=\"row_heading level0 row10\" >2009</th> \n",
+              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row10_col0\" class=\"data row10 col0\" >20.86%</td> \n",
+              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row10_col1\" class=\"data row10 col1\" >20.86%</td> \n",
+              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row10_col2\" class=\"data row10 col2\" >36.20%</td> \n",
+              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row10_col3\" class=\"data row10 col3\" >22.09%</td> \n",
+              "    </tr>    <tr> \n",
+              "        <th id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002level0_row11\" class=\"row_heading level0 row11\" >2010</th> \n",
+              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row11_col0\" class=\"data row11 col0\" >35.15%</td> \n",
+              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row11_col1\" class=\"data row11 col1\" >18.18%</td> \n",
+              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row11_col2\" class=\"data row11 col2\" >30.30%</td> \n",
+              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row11_col3\" class=\"data row11 col3\" >16.36%</td> \n",
+              "    </tr>    <tr> \n",
+              "        <th id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002level0_row12\" class=\"row_heading level0 row12\" >2011</th> \n",
+              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row12_col0\" class=\"data row12 col0\" >33.74%</td> \n",
+              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row12_col1\" class=\"data row12 col1\" >16.56%</td> \n",
+              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row12_col2\" class=\"data row12 col2\" >31.29%</td> \n",
+              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row12_col3\" class=\"data row12 col3\" >18.40%</td> \n",
+              "    </tr>    <tr> \n",
+              "        <th id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002level0_row13\" class=\"row_heading level0 row13\" >2012</th> \n",
+              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row13_col0\" class=\"data row13 col0\" >26.83%</td> \n",
+              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row13_col1\" class=\"data row13 col1\" >20.12%</td> \n",
+              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row13_col2\" class=\"data row13 col2\" >31.71%</td> \n",
+              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row13_col3\" class=\"data row13 col3\" >21.34%</td> \n",
+              "    </tr>    <tr> \n",
+              "        <th id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002level0_row14\" class=\"row_heading level0 row14\" >2013</th> \n",
+              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row14_col0\" class=\"data row14 col0\" >42.77%</td> \n",
+              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row14_col1\" class=\"data row14 col1\" >12.65%</td> \n",
+              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row14_col2\" class=\"data row14 col2\" >30.72%</td> \n",
+              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row14_col3\" class=\"data row14 col3\" >13.86%</td> \n",
+              "    </tr>    <tr> \n",
+              "        <th id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002level0_row15\" class=\"row_heading level0 row15\" >2014</th> \n",
+              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row15_col0\" class=\"data row15 col0\" >39.26%</td> \n",
+              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row15_col1\" class=\"data row15 col1\" >12.88%</td> \n",
+              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row15_col2\" class=\"data row15 col2\" >32.52%</td> \n",
+              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row15_col3\" class=\"data row15 col3\" >15.34%</td> \n",
+              "    </tr>    <tr> \n",
+              "        <th id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002level0_row16\" class=\"row_heading level0 row16\" >2015</th> \n",
+              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row16_col0\" class=\"data row16 col0\" >45.00%</td> \n",
+              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row16_col1\" class=\"data row16 col1\" >17.00%</td> \n",
+              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row16_col2\" class=\"data row16 col2\" >24.00%</td> \n",
+              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row16_col3\" class=\"data row16 col3\" >14.00%</td> \n",
+              "    </tr></tbody> \n",
+              "</table> "
+            ],
+            "text/plain": [
+              "<pandas.io.formats.style.Styler at 0x7faad9b75160>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 2
+        }
+      ]
     },
     {
       "metadata": {
@@ -132,11 +279,21 @@
         "# Part 2 — Recreate this explanatory visualization:"
       ]
     },
+    {
+      "metadata": {
+        "id": "ixYrwzeGXVlX",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Instructions"
+      ]
+    },
     {
       "metadata": {
         "id": "scozkHQc0_eD",
         "colab_type": "code",
-        "outputId": "64a105e6-8fa5-45e5-c78e-d29fcd19b3f2",
+        "outputId": "4de277d6-afc3-4292-b922-5d30bd820162",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 406
@@ -149,7 +306,7 @@
         "example = Image(url, width=500)\n",
         "display(example)"
       ],
-      "execution_count": 0,
+      "execution_count": 3,
       "outputs": [
         {
           "output_type": "display_data",
@@ -190,18 +347,80 @@
         "\n"
       ]
     },
+    {
+      "metadata": {
+        "id": "52xDh3wzXa4P",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Code"
+      ]
+    },
     {
       "metadata": {
         "id": "E8XBAr8rz_Na",
         "colab_type": "code",
-        "colab": {}
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 413
+        },
+        "outputId": "07dfc7b3-319b-40d4-cf03-f22ae3a7e9dd"
       },
       "cell_type": "code",
       "source": [
-        ""
+        "# Create and adjust figure\n",
+        "plt.style.use('fivethirtyeight');\n",
+        "\n",
+        "fig, ax = plt.subplots(figsize=(8,5))\n",
+        "ax.set_facecolor('#F0EFF0')\n",
+        "plt.ylim(0,1.07)\n",
+        "ax.axhline(color='gray', linewidth=6)\n",
+        "ax.tick_params(axis='x', length=17, pad=2, color='silver')\n",
+        "\n",
+        "# Plot each line and label\n",
+        "plt.plot(df_norm['Acting, Comedy & Music'], linewidth=3, color='#008FD5');\n",
+        "fig.text(0.22,0.68, \"Acting, Comedy & Music\", size='large', color='#008FD5',\n",
+        "        weight='bold');\n",
+        "\n",
+        "plt.plot(df_norm['Media'], linewidth=3, color='#81117C');\n",
+        "fig.text(0.53,0.46, \"Media\", size='large', color='#81117C',\n",
+        "        weight='bold');\n",
+        "\n",
+        "plt.plot(df_norm['Government and Politics'], linewidth=3, color='#FE2501');\n",
+        "fig.text(0.6,0.10, \"Government and Politics\", size='large', color='#FE2501',\n",
+        "        weight='bold');\n",
+        "\n",
+        "\n",
+        "# Mark each axis tick\n",
+        "plt.yticks(np.arange(0,1.1,.25),['0','25','50','75','100%'], fontsize=14,\n",
+        "          fontname='DejaVu Sans');\n",
+        "plt.xticks(np.linspace(2000,2012,4, endpoint=True),['2000',\"'04\",\"'08\",\"'12\"],\n",
+        "           fontsize=14, fontname='DejaVu Sans');\n",
+        "\n",
+        "# Titles\n",
+        "fig.suptitle(\"Who Got To Be On 'The Daily Show'?\",\n",
+        "             fontsize=22, weight='bold', horizontalalignment='left', \n",
+        "             x=0.01, y=1.04);\n",
+        "\n",
+        "ax.set_title('Occupation of guests, by year',\n",
+        "             horizontalalignment='left', x=-0.08, y=1.05, fontsize=16);\n"
       ],
-      "execution_count": 0,
-      "outputs": []
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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44osvlHMVExOjHOdnn31mst7s2bPp37+/yXbmz5/Piy++qJyrbdu2mayzd+9e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\nVccbO2G32ln21FIATm05wTdjv2DAPwfT5qq2Ad286Q/MNgf3rS5ybY9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dbwLg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\nSUVORK5CYII=\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7faacc835a20>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
     },
     {
       "metadata": {
@@ -223,7 +442,205 @@
     },
     {
       "metadata": {
-        "id": "tbwfBN3HsFlh",
+        "id": "Slc0FzXFxwYc",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 390
+        },
+        "outputId": "f679005a-1f90-49e7-cf9c-04740b257592"
+      },
+      "cell_type": "code",
+      "source": [
+        "guest_count = df.groupby('Guest').agg('count')\n",
+        "guest_count.sort_values('Year', ascending=False, inplace=True)\n",
+        "appearances = guest_count[0:10]\n",
+        "appearances"
+      ],
+      "execution_count": 16,
+      "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>Occupation</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Guest</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>Fareed Zakaria</th>\n",
+              "      <td>19</td>\n",
+              "      <td>19</td>\n",
+              "      <td>19</td>\n",
+              "      <td>19</td>\n",
+              "      <td>19</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Denis Leary</th>\n",
+              "      <td>17</td>\n",
+              "      <td>17</td>\n",
+              "      <td>17</td>\n",
+              "      <td>17</td>\n",
+              "      <td>17</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Brian Williams</th>\n",
+              "      <td>16</td>\n",
+              "      <td>16</td>\n",
+              "      <td>16</td>\n",
+              "      <td>16</td>\n",
+              "      <td>16</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Paul Rudd</th>\n",
+              "      <td>13</td>\n",
+              "      <td>13</td>\n",
+              "      <td>13</td>\n",
+              "      <td>13</td>\n",
+              "      <td>13</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Ricky Gervais</th>\n",
+              "      <td>13</td>\n",
+              "      <td>13</td>\n",
+              "      <td>13</td>\n",
+              "      <td>13</td>\n",
+              "      <td>13</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Tom Brokaw</th>\n",
+              "      <td>12</td>\n",
+              "      <td>12</td>\n",
+              "      <td>12</td>\n",
+              "      <td>12</td>\n",
+              "      <td>12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Richard Lewis</th>\n",
+              "      <td>10</td>\n",
+              "      <td>10</td>\n",
+              "      <td>10</td>\n",
+              "      <td>10</td>\n",
+              "      <td>10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Bill O'Reilly</th>\n",
+              "      <td>10</td>\n",
+              "      <td>10</td>\n",
+              "      <td>10</td>\n",
+              "      <td>10</td>\n",
+              "      <td>10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Will Ferrell</th>\n",
+              "      <td>10</td>\n",
+              "      <td>10</td>\n",
+              "      <td>10</td>\n",
+              "      <td>10</td>\n",
+              "      <td>10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Reza Aslan</th>\n",
+              "      <td>10</td>\n",
+              "      <td>10</td>\n",
+              "      <td>10</td>\n",
+              "      <td>10</td>\n",
+              "      <td>10</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                Year  GoogleKnowlege_Occupation  Show  Group  Occupation\n",
+              "Guest                                                                   \n",
+              "Fareed Zakaria    19                         19    19     19          19\n",
+              "Denis Leary       17                         17    17     17          17\n",
+              "Brian Williams    16                         16    16     16          16\n",
+              "Paul Rudd         13                         13    13     13          13\n",
+              "Ricky Gervais     13                         13    13     13          13\n",
+              "Tom Brokaw        12                         12    12     12          12\n",
+              "Richard Lewis     10                         10    10     10          10\n",
+              "Bill O'Reilly     10                         10    10     10          10\n",
+              "Will Ferrell      10                         10    10     10          10\n",
+              "Reza Aslan        10                         10    10     10          10"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 16
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "Q1X8YRHbop9A",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 281
+        },
+        "outputId": "1f77b908-d04e-44ee-a305-c7a17baba114"
+      },
+      "cell_type": "code",
+      "source": [
+        "fig, ax = plt.subplots()\n",
+        "x = list(appearances.index)\n",
+        "y = appearances['Year'].values\n",
+        "ax.barh(x,y);\n",
+        "\n",
+        "# I can't figure out how to order those damn bars in a particular order."
+      ],
+      "execution_count": 40,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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EROyRzXugNU11HDf5PfY8NqHYbUOxi6gHKiIiYhUlUBERESsogYqIiFhBY6CVzD6fA3WD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\nRK5CYII=\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7faabade3518>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "q2ZQNsgS0r5t",
         "colab_type": "code",
         "colab": {}
       },

From 6ced67d2dec4be25a2e1aacf24c4936ccfe5ffbc Mon Sep 17 00:00:00 2001
From: Daniel Martin-Alarcon
 <37123867+DanielMartinAlarcon@users.noreply.github.com>
Date: Fri, 16 Nov 2018 10:58:15 -0800
Subject: [PATCH 8/9] Created using Colaboratory

---
 DS_Unit_1_Sprint_Challenge_2.ipynb | 163 ++++-------------------------
 1 file changed, 22 insertions(+), 141 deletions(-)

diff --git a/DS_Unit_1_Sprint_Challenge_2.ipynb b/DS_Unit_1_Sprint_Challenge_2.ipynb
index ae1e8be..6b42890 100644
--- a/DS_Unit_1_Sprint_Challenge_2.ipynb
+++ b/DS_Unit_1_Sprint_Challenge_2.ipynb
@@ -446,160 +446,41 @@
         "colab_type": "code",
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 390
+          "height": 221
         },
-        "outputId": "f679005a-1f90-49e7-cf9c-04740b257592"
+        "outputId": "8c2530de-e0c3-4545-be47-de18a1541dde"
       },
       "cell_type": "code",
       "source": [
         "guest_count = df.groupby('Guest').agg('count')\n",
         "guest_count.sort_values('Year', ascending=False, inplace=True)\n",
-        "appearances = guest_count[0:10]\n",
+        "appearances = guest_count['Year'][0:10]\n",
         "appearances"
       ],
-      "execution_count": 16,
+      "execution_count": 55,
       "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>Occupation</th>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>Guest</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>Fareed Zakaria</th>\n",
-              "      <td>19</td>\n",
-              "      <td>19</td>\n",
-              "      <td>19</td>\n",
-              "      <td>19</td>\n",
-              "      <td>19</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>Denis Leary</th>\n",
-              "      <td>17</td>\n",
-              "      <td>17</td>\n",
-              "      <td>17</td>\n",
-              "      <td>17</td>\n",
-              "      <td>17</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>Brian Williams</th>\n",
-              "      <td>16</td>\n",
-              "      <td>16</td>\n",
-              "      <td>16</td>\n",
-              "      <td>16</td>\n",
-              "      <td>16</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>Paul Rudd</th>\n",
-              "      <td>13</td>\n",
-              "      <td>13</td>\n",
-              "      <td>13</td>\n",
-              "      <td>13</td>\n",
-              "      <td>13</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>Ricky Gervais</th>\n",
-              "      <td>13</td>\n",
-              "      <td>13</td>\n",
-              "      <td>13</td>\n",
-              "      <td>13</td>\n",
-              "      <td>13</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>Tom Brokaw</th>\n",
-              "      <td>12</td>\n",
-              "      <td>12</td>\n",
-              "      <td>12</td>\n",
-              "      <td>12</td>\n",
-              "      <td>12</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>Richard Lewis</th>\n",
-              "      <td>10</td>\n",
-              "      <td>10</td>\n",
-              "      <td>10</td>\n",
-              "      <td>10</td>\n",
-              "      <td>10</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>Bill O'Reilly</th>\n",
-              "      <td>10</td>\n",
-              "      <td>10</td>\n",
-              "      <td>10</td>\n",
-              "      <td>10</td>\n",
-              "      <td>10</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>Will Ferrell</th>\n",
-              "      <td>10</td>\n",
-              "      <td>10</td>\n",
-              "      <td>10</td>\n",
-              "      <td>10</td>\n",
-              "      <td>10</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>Reza Aslan</th>\n",
-              "      <td>10</td>\n",
-              "      <td>10</td>\n",
-              "      <td>10</td>\n",
-              "      <td>10</td>\n",
-              "      <td>10</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
             "text/plain": [
-              "                Year  GoogleKnowlege_Occupation  Show  Group  Occupation\n",
-              "Guest                                                                   \n",
-              "Fareed Zakaria    19                         19    19     19          19\n",
-              "Denis Leary       17                         17    17     17          17\n",
-              "Brian Williams    16                         16    16     16          16\n",
-              "Paul Rudd         13                         13    13     13          13\n",
-              "Ricky Gervais     13                         13    13     13          13\n",
-              "Tom Brokaw        12                         12    12     12          12\n",
-              "Richard Lewis     10                         10    10     10          10\n",
-              "Bill O'Reilly     10                         10    10     10          10\n",
-              "Will Ferrell      10                         10    10     10          10\n",
-              "Reza Aslan        10                         10    10     10          10"
+              "Guest\n",
+              "Fareed Zakaria    19\n",
+              "Denis Leary       17\n",
+              "Brian Williams    16\n",
+              "Paul Rudd         13\n",
+              "Ricky Gervais     13\n",
+              "Tom Brokaw        12\n",
+              "Richard Lewis     10\n",
+              "Bill O'Reilly     10\n",
+              "Will Ferrell      10\n",
+              "Reza Aslan        10\n",
+              "Name: Year, dtype: int64"
             ]
           },
           "metadata": {
             "tags": []
           },
-          "execution_count": 16
+          "execution_count": 55
         }
       ]
     },
@@ -611,25 +492,25 @@
           "base_uri": "https://localhost:8080/",
           "height": 281
         },
-        "outputId": "1f77b908-d04e-44ee-a305-c7a17baba114"
+        "outputId": "3d04cb03-592d-4c25-8811-e035bdf47f95"
       },
       "cell_type": "code",
       "source": [
         "fig, ax = plt.subplots()\n",
         "x = list(appearances.index)\n",
-        "y = appearances['Year'].values\n",
+        "y = appearances.values\n",
         "ax.barh(x,y);\n",
         "\n",
         "# I can't figure out how to order those damn bars in a particular order."
       ],
-      "execution_count": 40,
+      "execution_count": 72,
       "outputs": [
         {
           "output_type": "display_data",
           "data": {
             "image/png": 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EROyRzXugNU11HDf5PfY8NqHYbUOxi6gHKiIiYhUlUBERESsogYqIiFhBY6CVzD6fA3WD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\nRK5CYII=\n",
             "text/plain": [
-              "<matplotlib.figure.Figure at 0x7faabade3518>"
+              "<matplotlib.figure.Figure at 0x7faabd973d30>"
             ]
           },
           "metadata": {
@@ -640,7 +521,7 @@
     },
     {
       "metadata": {
-        "id": "q2ZQNsgS0r5t",
+        "id": "dKAPhk--4B7w",
         "colab_type": "code",
         "colab": {}
       },

From 2b54faf5a11c9a94bc24cfa3614e02e0855782aa Mon Sep 17 00:00:00 2001
From: Daniel Martin-Alarcon
 <37123867+DanielMartinAlarcon@users.noreply.github.com>
Date: Fri, 16 Nov 2018 11:03:10 -0800
Subject: [PATCH 9/9] Created using Colaboratory

---
 DS_Unit_1_Sprint_Challenge_2.ipynb | 231 +++++++++++++++--------------
 1 file changed, 116 insertions(+), 115 deletions(-)

diff --git a/DS_Unit_1_Sprint_Challenge_2.ipynb b/DS_Unit_1_Sprint_Challenge_2.ipynb
index 6b42890..49031f8 100644
--- a/DS_Unit_1_Sprint_Challenge_2.ipynb
+++ b/DS_Unit_1_Sprint_Challenge_2.ipynb
@@ -117,7 +117,7 @@
           "base_uri": "https://localhost:8080/",
           "height": 418
         },
-        "outputId": "eea73b1d-0737-4a90-f399-7a36f00b2d2c"
+        "outputId": "54f01f4e-7d90-494d-e309-f56830ba58ba"
       },
       "cell_type": "code",
       "source": [
@@ -139,7 +139,7 @@
             "text/html": [
               "<style  type=\"text/css\" >\n",
               "</style>  \n",
-              "<table id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002\" > \n",
+              "<table id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002\" > \n",
               "<thead>    <tr> \n",
               "        <th class=\"index_name level0\" >Occupation</th> \n",
               "        <th class=\"col_heading level0 col0\" >Acting, Comedy & Music</th> \n",
@@ -154,112 +154,112 @@
               "        <th class=\"blank\" ></th> \n",
               "    </tr></thead> \n",
               "<tbody>    <tr> \n",
-              "        <th id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >1999</th> \n",
-              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row0_col0\" class=\"data row0 col0\" >90.36%</td> \n",
-              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row0_col1\" class=\"data row0 col1\" >1.20%</td> \n",
-              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row0_col2\" class=\"data row0 col2\" >6.63%</td> \n",
-              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row0_col3\" class=\"data row0 col3\" >1.81%</td> \n",
+              "        <th id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >1999</th> \n",
+              "        <td id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002row0_col0\" class=\"data row0 col0\" >90.36%</td> \n",
+              "        <td id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002row0_col1\" class=\"data row0 col1\" >1.20%</td> \n",
+              "        <td id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002row0_col2\" class=\"data row0 col2\" >6.63%</td> \n",
+              "        <td id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002row0_col3\" class=\"data row0 col3\" >1.81%</td> \n",
               "    </tr>    <tr> \n",
-              "        <th id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >2000</th> \n",
-              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row1_col0\" class=\"data row1 col0\" >73.96%</td> \n",
-              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row1_col1\" class=\"data row1 col1\" >8.28%</td> \n",
-              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row1_col2\" class=\"data row1 col2\" >12.43%</td> \n",
-              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row1_col3\" class=\"data row1 col3\" >5.33%</td> \n",
+              "        <th id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >2000</th> \n",
+              "        <td id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002row1_col0\" class=\"data row1 col0\" >73.96%</td> \n",
+              "        <td id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002row1_col1\" class=\"data row1 col1\" >8.28%</td> \n",
+              "        <td id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002row1_col2\" class=\"data row1 col2\" >12.43%</td> \n",
+              "        <td id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002row1_col3\" class=\"data row1 col3\" >5.33%</td> \n",
               "    </tr>    <tr> \n",
-              "        <th id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >2001</th> \n",
-              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row2_col0\" class=\"data row2 col0\" >72.61%</td> \n",
-              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row2_col1\" class=\"data row2 col1\" >3.82%</td> \n",
-              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row2_col2\" class=\"data row2 col2\" >19.75%</td> \n",
-              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row2_col3\" class=\"data row2 col3\" >3.82%</td> \n",
+              "        <th id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >2001</th> \n",
+              "        <td id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002row2_col0\" class=\"data row2 col0\" >72.61%</td> \n",
+              "        <td id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002row2_col1\" class=\"data row2 col1\" >3.82%</td> \n",
+              "        <td id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002row2_col2\" class=\"data row2 col2\" >19.75%</td> \n",
+              "        <td id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002row2_col3\" class=\"data row2 col3\" >3.82%</td> \n",
               "    </tr>    <tr> \n",
-              "        <th id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >2002</th> \n",
-              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row3_col0\" class=\"data row3 col0\" >62.26%</td> \n",
-              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row3_col1\" class=\"data row3 col1\" >6.92%</td> \n",
-              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row3_col2\" class=\"data row3 col2\" >26.42%</td> \n",
-              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row3_col3\" class=\"data row3 col3\" >4.40%</td> \n",
+              "        <th id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >2002</th> \n",
+              "        <td id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002row3_col0\" class=\"data row3 col0\" >62.26%</td> \n",
+              "        <td id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002row3_col1\" class=\"data row3 col1\" >6.92%</td> \n",
+              "        <td id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002row3_col2\" class=\"data row3 col2\" >26.42%</td> \n",
+              "        <td id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002row3_col3\" class=\"data row3 col3\" >4.40%</td> \n",
               "    </tr>    <tr> \n",
-              "        <th id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >2003</th> \n",
-              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row4_col0\" class=\"data row4 col0\" >56.02%</td> \n",
-              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row4_col1\" class=\"data row4 col1\" >10.24%</td> \n",
-              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row4_col2\" class=\"data row4 col2\" >24.70%</td> \n",
-              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row4_col3\" class=\"data row4 col3\" >9.04%</td> \n",
+              "        <th id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >2003</th> \n",
+              "        <td id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002row4_col0\" class=\"data row4 col0\" >56.02%</td> \n",
+              "        <td id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002row4_col1\" class=\"data row4 col1\" >10.24%</td> \n",
+              "        <td id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002row4_col2\" class=\"data row4 col2\" >24.70%</td> \n",
+              "        <td id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002row4_col3\" class=\"data row4 col3\" >9.04%</td> \n",
               "    </tr>    <tr> \n",
-              "        <th id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >2004</th> \n",
-              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row5_col0\" class=\"data row5 col0\" >38.41%</td> \n",
-              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row5_col1\" class=\"data row5 col1\" >22.56%</td> \n",
-              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row5_col2\" class=\"data row5 col2\" >27.44%</td> \n",
-              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row5_col3\" class=\"data row5 col3\" >11.59%</td> \n",
+              "        <th id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >2004</th> \n",
+              "        <td id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002row5_col0\" class=\"data row5 col0\" >38.41%</td> \n",
+              "        <td id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002row5_col1\" class=\"data row5 col1\" >22.56%</td> \n",
+              "        <td id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002row5_col2\" class=\"data row5 col2\" >27.44%</td> \n",
+              "        <td id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002row5_col3\" class=\"data row5 col3\" >11.59%</td> \n",
               "    </tr>    <tr> \n",
-              "        <th id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >2005</th> \n",
-              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row6_col0\" class=\"data row6 col0\" >37.04%</td> \n",
-              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row6_col1\" class=\"data row6 col1\" >16.05%</td> \n",
-              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row6_col2\" class=\"data row6 col2\" >33.33%</td> \n",
-              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row6_col3\" class=\"data row6 col3\" >13.58%</td> \n",
+              "        <th id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >2005</th> \n",
+              "        <td id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002row6_col0\" class=\"data row6 col0\" >37.04%</td> \n",
+              "        <td id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002row6_col1\" class=\"data row6 col1\" >16.05%</td> \n",
+              "        <td id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002row6_col2\" class=\"data row6 col2\" >33.33%</td> \n",
+              "        <td id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002row6_col3\" class=\"data row6 col3\" >13.58%</td> \n",
               "    </tr>    <tr> \n",
-              "        <th id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >2006</th> \n",
-              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row7_col0\" class=\"data row7 col0\" >36.02%</td> \n",
-              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row7_col1\" class=\"data row7 col1\" >19.25%</td> \n",
-              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row7_col2\" class=\"data row7 col2\" >29.19%</td> \n",
-              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row7_col3\" class=\"data row7 col3\" >15.53%</td> \n",
+              "        <th id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >2006</th> \n",
+              "        <td id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002row7_col0\" class=\"data row7 col0\" >36.02%</td> \n",
+              "        <td id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002row7_col1\" class=\"data row7 col1\" >19.25%</td> \n",
+              "        <td id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002row7_col2\" class=\"data row7 col2\" >29.19%</td> \n",
+              "        <td id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002row7_col3\" class=\"data row7 col3\" >15.53%</td> \n",
               "    </tr>    <tr> \n",
-              "        <th id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002level0_row8\" class=\"row_heading level0 row8\" >2007</th> \n",
-              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row8_col0\" class=\"data row8 col0\" >25.53%</td> \n",
-              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row8_col1\" class=\"data row8 col1\" >17.02%</td> \n",
-              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row8_col2\" class=\"data row8 col2\" >33.33%</td> \n",
-              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row8_col3\" class=\"data row8 col3\" >24.11%</td> \n",
+              "        <th id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002level0_row8\" class=\"row_heading level0 row8\" >2007</th> \n",
+              "        <td id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002row8_col0\" class=\"data row8 col0\" >25.53%</td> \n",
+              "        <td id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002row8_col1\" class=\"data row8 col1\" >17.02%</td> \n",
+              "        <td id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002row8_col2\" class=\"data row8 col2\" >33.33%</td> \n",
+              "        <td id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002row8_col3\" class=\"data row8 col3\" >24.11%</td> \n",
               "    </tr>    <tr> \n",
-              "        <th id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002level0_row9\" class=\"row_heading level0 row9\" >2008</th> \n",
-              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row9_col0\" class=\"data row9 col0\" >20.73%</td> \n",
-              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row9_col1\" class=\"data row9 col1\" >20.12%</td> \n",
-              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row9_col2\" class=\"data row9 col2\" >46.95%</td> \n",
-              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row9_col3\" class=\"data row9 col3\" >12.20%</td> \n",
+              "        <th id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002level0_row9\" class=\"row_heading level0 row9\" >2008</th> \n",
+              "        <td id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002row9_col0\" class=\"data row9 col0\" >20.73%</td> \n",
+              "        <td id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002row9_col1\" class=\"data row9 col1\" >20.12%</td> \n",
+              "        <td id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002row9_col2\" class=\"data row9 col2\" >46.95%</td> \n",
+              "        <td id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002row9_col3\" class=\"data row9 col3\" >12.20%</td> \n",
               "    </tr>    <tr> \n",
-              "        <th id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002level0_row10\" class=\"row_heading level0 row10\" >2009</th> \n",
-              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row10_col0\" class=\"data row10 col0\" >20.86%</td> \n",
-              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row10_col1\" class=\"data row10 col1\" >20.86%</td> \n",
-              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row10_col2\" class=\"data row10 col2\" >36.20%</td> \n",
-              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row10_col3\" class=\"data row10 col3\" >22.09%</td> \n",
+              "        <th id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002level0_row10\" class=\"row_heading level0 row10\" >2009</th> \n",
+              "        <td id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002row10_col0\" class=\"data row10 col0\" >20.86%</td> \n",
+              "        <td id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002row10_col1\" class=\"data row10 col1\" >20.86%</td> \n",
+              "        <td id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002row10_col2\" class=\"data row10 col2\" >36.20%</td> \n",
+              "        <td id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002row10_col3\" class=\"data row10 col3\" >22.09%</td> \n",
               "    </tr>    <tr> \n",
-              "        <th id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002level0_row11\" class=\"row_heading level0 row11\" >2010</th> \n",
-              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row11_col0\" class=\"data row11 col0\" >35.15%</td> \n",
-              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row11_col1\" class=\"data row11 col1\" >18.18%</td> \n",
-              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row11_col2\" class=\"data row11 col2\" >30.30%</td> \n",
-              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row11_col3\" class=\"data row11 col3\" >16.36%</td> \n",
+              "        <th id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002level0_row11\" class=\"row_heading level0 row11\" >2010</th> \n",
+              "        <td id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002row11_col0\" class=\"data row11 col0\" >35.15%</td> \n",
+              "        <td id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002row11_col1\" class=\"data row11 col1\" >18.18%</td> \n",
+              "        <td id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002row11_col2\" class=\"data row11 col2\" >30.30%</td> \n",
+              "        <td id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002row11_col3\" class=\"data row11 col3\" >16.36%</td> \n",
               "    </tr>    <tr> \n",
-              "        <th id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002level0_row12\" class=\"row_heading level0 row12\" >2011</th> \n",
-              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row12_col0\" class=\"data row12 col0\" >33.74%</td> \n",
-              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row12_col1\" class=\"data row12 col1\" >16.56%</td> \n",
-              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row12_col2\" class=\"data row12 col2\" >31.29%</td> \n",
-              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row12_col3\" class=\"data row12 col3\" >18.40%</td> \n",
+              "        <th id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002level0_row12\" class=\"row_heading level0 row12\" >2011</th> \n",
+              "        <td id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002row12_col0\" class=\"data row12 col0\" >33.74%</td> \n",
+              "        <td id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002row12_col1\" class=\"data row12 col1\" >16.56%</td> \n",
+              "        <td id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002row12_col2\" class=\"data row12 col2\" >31.29%</td> \n",
+              "        <td id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002row12_col3\" class=\"data row12 col3\" >18.40%</td> \n",
               "    </tr>    <tr> \n",
-              "        <th id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002level0_row13\" class=\"row_heading level0 row13\" >2012</th> \n",
-              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row13_col0\" class=\"data row13 col0\" >26.83%</td> \n",
-              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row13_col1\" class=\"data row13 col1\" >20.12%</td> \n",
-              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row13_col2\" class=\"data row13 col2\" >31.71%</td> \n",
-              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row13_col3\" class=\"data row13 col3\" >21.34%</td> \n",
+              "        <th id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002level0_row13\" class=\"row_heading level0 row13\" >2012</th> \n",
+              "        <td id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002row13_col0\" class=\"data row13 col0\" >26.83%</td> \n",
+              "        <td id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002row13_col1\" class=\"data row13 col1\" >20.12%</td> \n",
+              "        <td id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002row13_col2\" class=\"data row13 col2\" >31.71%</td> \n",
+              "        <td id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002row13_col3\" class=\"data row13 col3\" >21.34%</td> \n",
               "    </tr>    <tr> \n",
-              "        <th id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002level0_row14\" class=\"row_heading level0 row14\" >2013</th> \n",
-              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row14_col0\" class=\"data row14 col0\" >42.77%</td> \n",
-              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row14_col1\" class=\"data row14 col1\" >12.65%</td> \n",
-              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row14_col2\" class=\"data row14 col2\" >30.72%</td> \n",
-              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row14_col3\" class=\"data row14 col3\" >13.86%</td> \n",
+              "        <th id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002level0_row14\" class=\"row_heading level0 row14\" >2013</th> \n",
+              "        <td id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002row14_col0\" class=\"data row14 col0\" >42.77%</td> \n",
+              "        <td id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002row14_col1\" class=\"data row14 col1\" >12.65%</td> \n",
+              "        <td id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002row14_col2\" class=\"data row14 col2\" >30.72%</td> \n",
+              "        <td id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002row14_col3\" class=\"data row14 col3\" >13.86%</td> \n",
               "    </tr>    <tr> \n",
-              "        <th id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002level0_row15\" class=\"row_heading level0 row15\" >2014</th> \n",
-              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row15_col0\" class=\"data row15 col0\" >39.26%</td> \n",
-              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row15_col1\" class=\"data row15 col1\" >12.88%</td> \n",
-              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row15_col2\" class=\"data row15 col2\" >32.52%</td> \n",
-              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row15_col3\" class=\"data row15 col3\" >15.34%</td> \n",
+              "        <th id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002level0_row15\" class=\"row_heading level0 row15\" >2014</th> \n",
+              "        <td id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002row15_col0\" class=\"data row15 col0\" >39.26%</td> \n",
+              "        <td id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002row15_col1\" class=\"data row15 col1\" >12.88%</td> \n",
+              "        <td id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002row15_col2\" class=\"data row15 col2\" >32.52%</td> \n",
+              "        <td id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002row15_col3\" class=\"data row15 col3\" >15.34%</td> \n",
               "    </tr>    <tr> \n",
-              "        <th id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002level0_row16\" class=\"row_heading level0 row16\" >2015</th> \n",
-              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row16_col0\" class=\"data row16 col0\" >45.00%</td> \n",
-              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row16_col1\" class=\"data row16 col1\" >17.00%</td> \n",
-              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row16_col2\" class=\"data row16 col2\" >24.00%</td> \n",
-              "        <td id=\"T_fda460d8_e9cd_11e8_87e9_0242ac1c0002row16_col3\" class=\"data row16 col3\" >14.00%</td> \n",
+              "        <th id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002level0_row16\" class=\"row_heading level0 row16\" >2015</th> \n",
+              "        <td id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002row16_col0\" class=\"data row16 col0\" >45.00%</td> \n",
+              "        <td id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002row16_col1\" class=\"data row16 col1\" >17.00%</td> \n",
+              "        <td id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002row16_col2\" class=\"data row16 col2\" >24.00%</td> \n",
+              "        <td id=\"T_ebcad0b4_e9d1_11e8_b906_0242ac1c0002row16_col3\" class=\"data row16 col3\" >14.00%</td> \n",
               "    </tr></tbody> \n",
               "</table> "
             ],
             "text/plain": [
-              "<pandas.io.formats.style.Styler at 0x7faad9b75160>"
+              "<pandas.io.formats.style.Styler at 0x7f845ece1f28>"
             ]
           },
           "metadata": {
@@ -293,7 +293,7 @@
       "metadata": {
         "id": "scozkHQc0_eD",
         "colab_type": "code",
-        "outputId": "4de277d6-afc3-4292-b922-5d30bd820162",
+        "outputId": "4bbb8d6b-9895-4619-8776-b2cd32f62c84",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 406
@@ -365,7 +365,7 @@
           "base_uri": "https://localhost:8080/",
           "height": 413
         },
-        "outputId": "07dfc7b3-319b-40d4-cf03-f22ae3a7e9dd"
+        "outputId": "6c5001e0-1a3b-406a-c29f-1fca26761017"
       },
       "cell_type": "code",
       "source": [
@@ -411,9 +411,9 @@
         {
           "output_type": "display_data",
           "data": {
-            "image/png": 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2RaVSKTkbFy5cICcnh5iYGCVHon79+oWWtfv7+5t8Nn4LMVQ2BdPcAOP6CaCv29CxY0fl\ns+HGUJiMjAzlb0NOlLHY2FheeOGFAv8ZcoLu3bvH9evXAf3bY940ubu7KzdFnU6Xr3L5o2BcVNGl\nS5d8lYeNc/Tud85KIjY2luPHj5tUng8PD1da0FlYWJhcZzMzM+VzWFhYgUUWxgy5EECxWojZ2dkV\nuK6Bh4eHSXrMzc1NulLIW8T4KBX1+zCcp7I8tyVhfO4N5/WVV15hzpw5zJkzx6QOjnExtPELRUnV\nrl1bOZ5r164pwW1mZqbyHfb09LxvUTnoi8GWL19O586dTX4bOp2O69evExgYyPvvv89LL71kkjOT\nV3GuUUxMDGFhYYD+muTNja5evbpSd0+r1So574aAJioqioSEBAClOLdFixZKJXzDS6hxPc3CXkAL\nc/36dYYPH86JEycA/b39xx9/LLcK7kKUtVJVErC3t6dRo0YEBweTnJzMrVu3TB5yhnLsZs2acezY\nMbKysrh8+TIxMTHKMkU1qc9bd8DJyUn527isOjo6Wvm7WrVq+bZjXPR0vxu+8cPROEu8JIzT4+jo\naLJN4zQZbqLl8UA1TmNBdTRKcs7yytuaDPR1GY4ePcqPP/7I0aNHOXnyJCtWrODZZ59VbuRg2mld\nQcLCwqhSpUqh840D6+JcP+NljL9fBgV9nww5nYb0lpe8162gdJXluS0utVptEtQYzqtWq2XTpk3s\n3LmTiIgIEhISlLo0Bnk/l1Tfvn2VnJKDBw8ybNgw/vzzTyXn+Pnnny/2tmrXrs2cOXNISkoiKCiI\ns2fPcv78ea5evaoEFXfu3GHs2LFs3LhRyTE0VpxrZPxbdHZ2LvAlrGrVqly+fBn45/doXG/o/Pnz\nNG7cWMn5adGiBY6OjqxevVoJhoxzkErSejc1NZWJEycq6XzzzTcZOXJkkUXQQjxpSv1tzltUZnjA\n29raKm8zzZs3V5a5cuWKSQXron6UD1Kh83430/v9gGvVqqX8bWg6a8zd3V3JVg4KCmLatGklTmPe\ndD6ONxXj9N2vbkRehtZkxv98fHx4/fXXlQrNarX6gZoj36+OjqF+BZCvj5S8UlNTTQJz43UNHudO\n5cq6752yqv909epVpfjIxsZGCQi+/PJLvvrqK0JCQoiPjy914FOQnj17Ki00DUVlxi3DilNEllel\nSpXo0aMH//73v1m1ahU7duzg9ddfV74baWlp7N69u8B1S3qNCjsnBf0e89YbMtQXqlmzJlWrVqVp\n06aYmZkpHUYa7s01atQoMMgvzN69e4mMjASge/fujBo16rG8ZwlRGqW+m7Zu3Zrly5cD+mDI8GbS\nqFEj5UbQoEEDrK2tycrK4urVq1y5cgUAa2trkyahD6patWrcunUL0L9lGVd+BEwqEN6vpYqXlxdV\nqlQhNjYWrVbLnj17GDhwYInTY5CSklJgs9v75WY9bMb7LKgHa+NpZZk+46b6hu+KcVa7vb19kXWU\njN+sC9KmTRv27t0LwO7du3n77bcLfSDt27dPeWjXq1fvie5JuDBleW6Ly7hxQ+vWrbGysuLu3bts\n2bJFmT5q1Cj8/f2xt7cnPDycUaNGlcm+HRwc6NKlC3v27OH8+fPExsYqdVwaN25c7J7PMzIyCAsL\nw8rKKl/dMxcXF8aMGUNkZKQSBBXUKqu4jH9fSUlJJl2QGBT0ezTUG7px4wYXLlxQOhg1dGbq4OCA\nj48PV65c4e+//1aCoZIWkdWvX58pU6YAlMn9WojHUanD++eee055EwsJCVECHeNO4ywtLZUeWi9c\nuKC8sRuvWxq+vr7K33krSKvVapPxge7XpFalUpm0dPrhhx+Unrbz0ul0XLx4Md90Nzc35Qaq0+ny\nVeYMDw9XWpJYWFgU2hNzdnZ2kWktDePz8Mcff+TruHH//v0FLltaxrk1huKTGjVqKE3t09LSSE1N\nNclVysrKUvpwul9vt7169VICgKioKBYtWlTgcjExMSxdulT5PHz48FId1+OqLM9tcQQFBZkEPUOH\nDgX0xUmG3A17e3vefPNNvLy8cHNzU+rXAfm+hw/CUJFap9OxbNkypRi6uLlCGzZsoGvXrowYMYJp\n06YV+js0LgosTUem7u7uSq6kVqvNd7+4deuWco4sLS1N7heGwObixYucPn0awGS+4T68e/du5TyU\ntIPbBg0aEBAQQEBAgFJpW4inTamDISsrK+Vt4fr160rT9bxvEIYfZWhoqFJWXlZDcLz66qtKULVj\nxw4WL17MzZs3CQ4OZuLEicpbW7169fINCVKQIUOGKJUsExISeP3111m1ahVXr14lOjqaS5cusX79\neoYMGWLSJ5FxE3vj/m2++eYbfv/9d8LDwzl27Bjjx49XciQCAgJM+twxfjsPDAws84qtBt26dVOK\nBG/fvs3kyZO5fPkyoaGhzJ07lz179ijH9Oqrr5Zo29nZ2fla2oWFhbFu3TqTptPdu3dX/n7ppZeU\nvz/++GOCgoKIiopi7969DBkyhAEDBjBkyJB8PTfnZW1tzSeffKIUYfz888989NFHyvZu3LjB+vXr\nGTFihFIJ38/Pr0R1SZ40ZXVujeVtVXnp0iUWLlzIBx98oAQ0gwYNUorIjb/j6enp7N27l/DwcDZt\n2sTChQuVvmzu3r3L+fPnH7i+HujvK4ZcPkNgZm5uTo8ePYq1vp+fn/JbDg0N5d133+Xw4cNERkYS\nFRXFuXPn+Prrrzly5Aigf6HJW1G6pIyD8a+++oodO3YQHh7O0aNHmTBhghJIDhw40KTfH0Pgk5KS\nYlJfyMBw/g1phZLnDM2ePRs/Pz/8/PyUUgAhnjZlUumgdevWJi26zM3N8w2AmXd4AcN6ZcHT05Pp\n06fzySefkJ2dzYoVK/INGurh4cGXX35ZrDogFhYWLFiwgKlTp3Ls2DGSk5NZuHBhoU2jq1Spwuuv\nv670lgv6ipqXL19mzZo1pKWl5etdGvQ5LuPGjTOZ1rRpUyXXKDAwUGm1Mnjw4PumuyQsLCz48ssv\nGTduHLGxsRw6dCjfG6mtrS2ff/55iYvJDhw4cN8uDDp37mzSyeHw4cM5ffo0f/31F6GhofkGBLWz\ns2PWrFnFyr3w9fXl22+/Zdq0aSQlJRV4bKDPBRwwYIDSc/HTqizPrcG9e/dM+hMyplKpGDp0KO+8\n844yzcvLi7Zt23Ly5El0Oh0ff/yxMm/EiBFkZWWxbt06NBoNb7zxBqNGjTJpcVYSZmZm9OnTh+XL\nlytBRNu2bYvduWnlypX56quvmDRpEsnJyZw7d67QgXWtrKyYNGlSgT3il0Tfvn25cuUK69evJy0t\njU8++STfMu3bt8937Qz1hgzH6eHhYfJ7NQRDhvm1atUqcSX5jIwMJTjN26mtEE+LMguGjPn4+ORr\nQdWkSRPMzc2Vt0Z7e/syHe3d398fHx8fZTiOe/fuYWFhgaenJ126dOHVV18ttAl/QRwcHPjuu+/4\n888/2bVrF8HBwcTFxZGZmUmlSpVwdXWlcePGtGnThk6dOhVYL2XChAm0b9+ewMBAQkJCSE5Oxs7O\nDh8fH/r06UPv3r3zBWejRo0iISGBkydPkpOTQ/Xq1R/aCOB169ZlzZo1JsNxaLVaqlatSps2bRgy\nZEihHbOVlK2tLW5ubnh7e9O7d2+6d+9uUjHb0tKSBQsWsGHDBnbt2kVYWBjZ2dlUqVKFtm3bMmzY\nsBKNdN+uXTu2bNnCli1bOHbsmMmo9e7u7rRs2ZL+/fs/0HAjT5qyPrcFsbOzw93dnVatWvHiiy8q\nHYsamzlzJosXL+b48eMkJCTg6enJoEGDCAgIIDY2lkuXLnHx4kUcHR1NGjI8iL59+5rkYpS04nSr\nVq3YsGEDW7du5eTJk4SFhZGcnIxWq8Xe3h5PT09atWpFQEBAmf1GJk6cSIcOHdiwYQPnz58nKSkJ\ne3t7nnnmGeV+kbfisnG9ISBfkbuLiwve3t5K0/2S5goJUVGoEhMTy75JhxBClKPU1FSef/55srOz\nsbe3Z+fOnQU2WRdCCCiDOkNCCPG42bBhg1LxuXfv3hIICSGKVLYdlQghRDkxNDa4cuWKMn6WpaUl\nQ4YMKeeUCSEedxIMCSGeCjt37szXcOLtt98uszo9QoinlwRDQoingoODg9LFhpeXF0OGDHmgHqeF\nEBWPVKAWQgghRIUmFaiFEEIIUaFJMCSEEEKICk2CISGEEEJUaKUOhrRaLdu2bWPkyJH4+fnRsWNH\nXnzxRebOnUt8fHxZpPGxsHTpUtq1a1feyQD0g89OmjSJDh06MGHChPJOTrkYPXo07777bnknQwgh\nxFOgVK3JtFotkyZN4s8//+SNN95g0qRJ2Nracu3aNZYtW8bevXtZtGhRqcftKQ+zZ8/G1dWVt99+\nG9CPvm089lh5+uuvvzhw4ACTJ08u1sCz5S3vuRRCCCEeJ6XKGVq/fj1Hjhxh/vz5DBs2jLp16+Lh\n4UHnzp1ZunQplStXZurUqWWV1kfq/PnzJp/t7OxMRt4uT8nJyYB+oNfHJU1FyXsuhRBCiMdJqYKh\ndevW4e/vn2+EegAbGxvGjBnD5cuX+fvvv5XpBw4c4LXXXqNTp068+OKL/PLLLybrFTV/xowZDBw4\n0GT5PXv24OvrS2RkJKAvPpk0aRKBgYH06dOHjh078tZbbykDFQJER0czZcoU/P396dixIy+//DIb\nN25U5vfv35/Q0FCWLVumbDtvMVlmZibffvstffr0oX379vTv35/FixejVquVZXx9fdm0aRMLFizg\n+eefp1u3bnz44YckJCQUeV7379/P0KFD6dixI35+fnz44Yfcvn0b0BfXGUb8DggIYPTo0QVuIzs7\nm1mzZtG9e3f8/Pz44osvlHMVExOjHOdnn31mst7s2bPp37+/yXbmz5/Piy++qJyrbdu2mayzd+9e\nhg4dSufOnenevTvjx49XzndB5zIxMZHp06fTu3dvOnbsyMCBA1m1alWR56QwW7Zs4V//+le+6/x/\n//d/BY5GP3HiREaOHJlv+rFjx/D19eXy5csm0y9fvoyvry9BQUEAnDt3jjFjxuDv74+fnx+TJ08m\nNjZWWV6n07Fy5UoGDBhAu3bt6N27N5988gmJiYnKMjNmzGDkyJGsWLGCrl27snXr1gc6diGEEGXj\ngYOhmJgYoqKiaN68eaHLtG7dGktLS86cOQNAUFAQH3/8MX369GHNmjWMHj2axYsXExgYWKz5xRUS\nEsLZs2eZP38+ixYtIiEhgcmTJ6PT6btUmjp1Krdv32bhwoUEBgYyePBgvvrqK06cOAHAypUrsbKy\nYsiQIezYsQN3d/d8+5g5cyZ79+5l8uTJBAYGMmrUKNavX8/ChQtNlluzZg22trYsXbqUzz//nFOn\nTrFs2bJC0378+HEmT55Mly5d+OWXX1iwYAHx8fG8++67ZGZmMnToUCZNmqSk88svvyxwOz/88AM7\nduxgwoQJrFixAjs7O3744YcSnUeAL774gq1btzJy5EjWrFlDQEAAs2bNYu/evYB+CIRp06bRq1cv\n1q1bx5IlS7C0tGTixIlKYJD3XM6ZM4fQ0FDmzJnDhg0bGD16ND/99BM7d+4sUdpu3LjBsWPH+Oab\nb0yuM+iDsBMnTpgEIampqZw6dYq+ffvm21a7du2oWrUqO3bsMJm+f/9+qlWrRuvWrQkLC2Ps2LE4\nOTnxww8/MHfuXCIiIhg/fjwajQaAbdu2sWTJEt555x22bNnCl19+yfnz5/n6669Ntnv37l0uXrzI\nL7/8Qvfu3Ut03EIIIcrWA9cZunv3LgDVqlUrdBlLS0vc3NyUN+e1a9fSvHlzZawgT09PYmJiSE9P\nL9b84kpOTmbKlCnY29sDMHLkSKZNm8aNGzeoW7cun376qZI2QMmZOHnyJO3atcPFxQUAW1tbZRlj\nMTEx7Nu3j8mTJ9OxY0cAatSoQVhYGIGBgbz77rtYWFgo5+ett94CoFatWrRs2ZILFy4Umva1a9fS\npEkTk9yL6dOn8/LLL3P48GF69uyJg4MDAM7OzlSqVKnA7ezatYvevXvTr18/AMaNG8elS5cIDw8v\n9nmMjY1lx44djB8/nueffx6AIUOGEBISwv/+9z969OhBaGgoGo2Gvn37Kudt+vTphIeHo9PpCjyX\noaGhtGzZkkaNGinnyNvbm8qVKxc7baAPbj755JN81/n69ev07NmTuXPnsmfPHgYNGgTA0aNHMTMz\nw9/fP9+2zMzM6NevH5s3b2aAYKtmAAAgAElEQVT8+PGYm5sD+pzKPn36oFKpWLduHfb29sycORMr\nKyvlWAcPHszx48fp1KkTfn5+NG3aFG9vbwDc3d3p2bMnv/76q8n+oqOjWbp0aYGBthBCiEfrgXOG\nzMz0qxoe+oXJyspSlr106RL169c3mT906FBGjBhRrPnFVbt2beUBCSjbjI6OBvRFPwsWLKB///50\n69aNLl26EB0drdTFuZ8rV66g0+nyFQ82bNiQtLQ0pUjLMM2Ys7MzKSkphW770qVL+bbr5eWFg4ND\nviKcwmRlZREbG0u9evVMppe0NdylS5fQarW0atXKZHqLFi24du0aOp2OJk2a4OTkxJgxY/j1118J\nCwvD0dGRhg0bKtc9rw4dOrB582Zmz57N8ePHycjI4Jlnnikw8CxK3utsONe3b9/G3t4ef39/k9ym\n/fv3061bN5N1jPXr14+EhAROnToFwNWrV7lz5w59+vQB4MKFCzRp0kQJhADq1q1LpUqVuHr1KqAv\nHj506BCvvfYa/v7+dOnShVWrVuX7brm4uEggJIQQj4kHzhky3MgNdXUKkp2dTUJCgrJscnIyNjY2\nhS5/v/nFlfdhZ2trC0BKSgppaWmMHj0aJycnPvzwQzw9PTE3N2fcuHHF3n5aWlqB+7GzszOZDyhj\nJRmoVCqluK6wbRf0sLazszPZblGSkpIKTJ+Tk1Ox1jdOC8Cbb76JSqVSpms0GtRqNUlJSbi7u/PT\nTz+xevVqli1bxpw5c6hTpw4ffvhhviDKYOzYsdSsWZPff/+drVu3YmlpSd++fZkwYUK+81WUwq5z\nZmYmoC8qe+utt7h16xZVqlTh5MmTfPfdd4Vuz8PDA19fX3bs2EH79u05cOAAzZo1o2bNmsr5OHLk\nCF26dDFZLzMzU+lGYu7cuWzatImxY8fi6+uLjY0Nmzdv5ueffzZZx/BdEUIIUf4eOBhyc3PDy8uL\no0ePEhAQUOAyZ86cQafT0bp1a0D/NlzUA/1+8wsKJAoqQjM8DPMu4+TkxJkzZ4iLi+PLL7+kSZMm\nyjKGAKI4DMVUqampJtMNaTfMfxD29vb5tmvYdnG3a8i5yM7ONple0DHmPZ8ZGRnK34b9ffXVVwWO\n/G2Y7+XlxdSpU9FqtZw/f57FixfzwQcf8NtvvxVYjGdmZsbAgQMZOHAgiYmJ7Nq1i4ULF2Jvb8/Y\nsWOLdYxQ+HU2BEVNmjShdu3a7N69m9q1a1O5cmVatmxZ5Db/9a9/MXPmTDIzMzlw4ADDhg0zOd62\nbdvywQcf5FvPEJjt3r2bfv36KUW9ADk5OcU+JiGEEI9eqVqTDR48mCNHjvDnn3/mm5eVlcXixYtp\n1qyZUnzx7LPPEhwcbLLcihUrlBZN95tvb2+fr4ipoPo3165dM1nOULzk5eWlPJiMH9JHjhwhNTW1\nyBwbY88++yxmZmacO3fOZHpISAgODg54enoWazsFadCgQb5zcP36ddLS0vIVuRXG2dkZJyenfMVq\nhw4dMvmc93xqtVouXrxokhYzMzMSExPx9PRU/tnY2FCpUiUsLCy4evUqp0+fBvRBTpMmTZgwYQKZ\nmZkF5hpmZmayZ88eJeBzdnbm1VdfpU2bNty4caNYx2eQ9zpfuXIF0BefGfzrX//i4MGD7N69W6n7\nU5SuXbtiY2PD8uXLiYmJManc3LBhQ8LDw6lZs6bJ+cjJyVHqRqnVapydnZV1srKyOHjwIJA/8BRC\nCPF4KFUwNGDAAHr27MmHH37IypUruX79OpGRkRw5coRRo0aRmJjIJ598oiz/2muvcfHiRX788Uci\nIyPZv38/K1asUOq23G9+/fr1SU5OZv369URERLBp06YC+7Cxt7fn888/5/r16wQHB7Ns2TKaNGlC\njRo1qF+/Pubm5qxdu5aIiAj27dvHihUraNq0KTdu3FCanTs6OhISEkJoaGi+AKxq1ar06tWLH3/8\nkcOHDxMREcHWrVsJDAzk1VdfvW89qqIMGzaMCxcusGjRIm7fvs25c+eYMWMGtWrVolOnTsXeTvfu\n3dm9ezd79+7l1q1bzJ07N18uWoMGDQgKCiIoKIhbt27xzTffmAQLbm5u9OrVi/nz53Po0CEiIyM5\ndeoUo0ePZs6cOYC+D6F///vf7Nq1i8jISG7evMm6detwdnZWghLjc5mZmcmCBQv49NNPuXz5MjEx\nMRw5coSzZ8/SokULQF85/+WXX+bIkSNFHqODg4Nync+dO8fixYtp0KCBSSefffr04fbt25w4cUKp\n+1MUS0tLevfuzf/+9z/8/PxMirMGDRpEdHQ0s2fP5tq1a9y6dYvvv/+eYcOGKYFco0aN2LdvH1ev\nXuXy5cu8//77tGnTBtDnlGZlZd03DUIIIR4tVWJiYqleV3U6Hbt27WLz5s1cu3aN7OxsqlWrRteu\nXRk2bFi+YpJdu3axYsUK7ty5g7u7OwEBAQwbNkx5CBc1X61W8/XXX7N//340Gg2dOnWiR48eTJw4\nkS1btuDh4cHo0aMxNzfHz8+PVatWER8fT8OGDZk2bZpS92PLli389NNPJCUl0aRJEyZPnszFixf5\n/PPP8fLyYtWqVaxfv54lS5YAMG/ePE6ePMmKFSuU5vdZWVksXLiQvXv3kpiYSLVq1QgICGD48OHK\nsfj6+jJq1CjefPNN5fhnzJjBuXPn2LRpU6Hn9MCBAyxbtoxbt25ha2tLmzZtGDdunFL3as+ePfz3\nv/9VjrkgqampfPbZZxw/fhw7Ozv69u1LjRo1mD17Nnv27MHZ2Zm7d+8yc+ZMgoODsbW1ZdCgQajV\narZv3670fZOdnc2SJUvYs2cPcXFxuLi40KtXL0aNGoWNjQ06nY7Vq1ezbds2YmJisLW1pWHDhowe\nPZoGDRoA5DuXjo6OzJ8/n+DgYLKysnB3d+f555/njTfewNzcnMjISAICApg5cya9evUq8PgM9b7a\ntWvH8uXLSUhIoFGjRkybNi1fkd57772HWq1m8eLFhZ5zY8HBwbz11lssWbJECdAMzp49y6JFi7h0\n6RKgDyhHjRqlFL/dunWLmTNncuXKFapWrcrIkSNp27Ytb7/9NtHR0SxZsoTAwMD7fgeEEEI8OqUO\nhh43hmAob38/FU1OTg5paWkmRTbff/89GzduVIptHmfz5s2jbdu2Sq7Kg0pISCAgIIBPPvmEbt26\nFWud+fPnc+rUqXwdggohhHg6yaj1T6nFixczcOBADh06RFRUFAcPHmTz5s1Kv0OPs+zsbE6dOmVS\nwb2kUlNTuXbtGpMmTeKZZ57J1wKsIDExMWzdupW1a9eWqCK3EEKIJ1upBmoVj68xY8YA8M0335CQ\nkECVKlUYMGCA0gHk48zKyoo1a9aUahu//vory5cvp2XLlkydOrXQPo+MBQQE4OrqykcffVTiPpmE\nEEI8uZ66YjIhhBBCiJKQYjIhhBBCVGgSDAkhhBCiQpNgSAghhBAVmgRDQgghhKjQJBgSQgghRIUm\nwZAQQgghKjQJhoQQQghRoUkwJIQQQogKTYIhIYQQQlRoEgwJIYQQokKTYEgIIYQQFZoEQ0IIIYSo\n0CQYEkIIIUSFJsGQEEIIISo0CYaEEEIIUaFJMCSEEEKICk2CISGEEEJUaBIMCSGEEKJCsyjvBDx0\nuvJOQOmFXgvFp55PeSdDlJJcx6eDXMeng1zHJ4jq4e9CcoaEEEIIUaFJMCSEEEKICk2CISGEEEJU\naBIMCSGEEKJCk2BICCGEEBWaBENCCCGEqNAkGBJCCCFEhSbBkBBCCCEqNAmGhBBCCFGhSTAkhBBC\niApNgiEhhBBCVGgSDAkhhBCiQitWMHTmzBkmTpxInz598PX15ffffzeZr9PpWLp0KS+88AKdOnVi\n9OjRXL9+3WSZ5ORkpk+fTrdu3ejWrRvTp08nJSVFmR8ZGcnbb79N586defvtt4mMjDRZ/z//+Q+b\nN29+0OMUQgghhChQsYKhjIwM6tatywcffIC1tXW++atXr2bNmjV8+OGHrFy5EhcXF9577z3S0tKU\nZaZOncrly5eZN28e8+bN4/Lly0yfPl2ZP2/ePKpWrcrPP/+Mm5sb8+fPV+YdOnSIhIQEAgICSnOs\nQgghhBD5FCsY6tChA++88w7du3fHzMx0FZ1Ox7p16xg+fDh+fn7UrVuX6dOnk56ezu7duwG4efMm\nJ06cYMqUKTRp0oQmTZowefJkjh49yq1btwAICwujT58+1KpVi759+3Lz5k0AUlNTmTdvHlOmTEGl\nUpXlsQshhBBClL7OUGRkJHFxcbRt21aZZmNjQ/PmzQkODgYgJCQEOzs7mjRpoizTtGlTbG1tlWV8\nfHwICgpCq9Vy8uRJfHx8AFi4cCF9+/bF29u7tEkVQgghhMjHorQbiIuLA6By5com0ytXrszdu3eV\nZZydnU1ydlQqFS4uLsr648aNY/bs2fTv35969eoxefJkgoODOXv2LIsXL2bq1KmEhITQuHFjJk2a\nhIODQ7HSF3ottLSH+Fh4Wo6jopPr+HSQ6/h0kOv4ZDBkjjxMpQ6GykrVqlX57rvvlM85OTmMHz+e\nyZMns2rVKszNzdmwYQOffvopy5cvZ9y4ccXark+9h38SH7bQa6FPxXFUdHIdnw5yHZ8Och2FsVIX\nk7m6ugIQHx9vMj0+Pl6Z5+rqSmJiIjqdTpmv0+lISEhQlslr1apVNG/enCZNmnD69Gn8/f2xsLCg\nZ8+enD59urTJFkIIIYQAyiAY8vDwwNXVlVOnTinTsrKyOHv2rFJH6LnnniM9PZ2QkBBlmZCQEDIy\nMkzqERmEhYWxfft23nnnHQC0Wi1qtRrQ5xhpNJrSJlsIIYQQAihmMVl6ejp37twB9IFJdHQ0V69e\nxcnJiWrVqvHqq6+ycuVKvL29qVWrFsuXL8fW1pZevXoBULt2bdq1a8fs2bOZPHkyALNnz6Zjx454\neXmZ7Eun0zFr1iwmTJig1Atq1qwZmzZtonbt2mzatIlmzZqV2QkQQgghRMVWrJyhS5cuMXToUIYO\nHUpWVhZLly5l6NCh/PDDDwAMHz6cwYMH89VXXzFixAju3bvHggULsLe3V7Yxc+ZMfHx8GD9+POPH\nj8fHx4cZM2bk29fmzZtxcXGhS5cuyrSRI0eiUqkYMWIEKpWKkSNHlva4hRBCCCEAUCUmJuruv9gT\n7Ck4Oqno93SQ6/h0kOv4dJDr+AR5BF0MythkQgghhKjQJBgSQgghRIUmwZAQQgghKjQJhoQQQghR\noUkwJIQQQogKTYIhIYQQQlRoEgwJIYQQokKTYEgIIYQQFZoEQ0IIIYSo0CQYEkIIIUSFJsGQEEII\nISo0CYaEEEIIUaFJMCSEEEKICk2CISGEEEJUaBIMCSGEEKJCk2BICCGEEBWaBENCCCGEqNAkGBJC\nCCFEhSbB0AOIy9Sg0+nKOxlCCCGEKAMSDJWAVqdj1dU0mm+MYcPNjPJOjhBCCCHKgARDJfDDpTTG\nH08kOUfH5KAk4jM15Z0kIYQQQpSSBEMlMNTHjpr25gDcy9Ty8Z/J5ZwiIYQQQpSWBEMl4Ghpxpy2\nlZTPa6+nczAysxxTJIQQQojSkmCohJ73tGWgt63y+f0TiaSrteWYIiGEEEKUhgRDD+CLNpVwtlIB\nEJai4YuzKeWcIiGEEEI8KAmGHkBVW3Nmtv6nuGzhhVTOxWWXY4qEEEII8aAkGHpAQ+vZ0amaFQAa\nHYw7nohaK30PCSGEEE8aCYYekEqlYm57F6xzz+C5uBwWX0wt30QJIYQQosQkGCqFuk4W/KeZk/J5\n9tkUwlLU5ZgiIYQQQpSUBEOl9F5jBxq5WACQrtbxwYlEGapDCCGEeIJIMFRKlmYq5rd3wUzfuIwD\nkVmsvyFDdQghhBBPCgmGykDLKlaMamCvfJ4SlMQ9GapDCCGEeCJIMFRGPm7uhGfuUB3xWVqmBCWV\nc4qEEEIIURwSDJURB0szvm3nrHz+9UYG+yNkqA4hhBDicSfBUBnqUdOGl2qbDtWRliNDdQghhBCP\nM4vyTsDTZrZvJfZHZpKQpeN2qobZZ1P4zKi36ifVx38msfCCvh+lQXVsWdq5cplu/7nAaMLTNCzs\n4MwQH/v7r/AQZWt0rA5NY8ONDC4l5pCWo8PZ2oy2Va0Y1cCBTtWtyzV9xXUrRU3TjTEA/NbLrUzS\nrdHq+Do4hZVX0kjM1uJTyZKPmzvyvKftfdftszOWYzH6ntr7e9mwqpuryfzVV9MYdzxR+Zz4fzVK\nnd6COK+MAMrunAghnnySM1TGqtia81mrf4KfRRdTOXvvyR6qQ6vTsflmuvJ5x+1MMtQP3n3A+OMJ\nygPJYKiPHaMb2FPf2fKBt1sW0nK09N99jw9PJnEuLodO1awZ4mOHh505v9/OpN/ueyy6UHE711x6\nOY0vzqbgamPGS3XsuJ6sZsTBeG6VsH+tA5FZZGtMv0O7wh9NsfLoBvaMbmBPjdw6fkIIITlDD8Fr\n9ez49UYGf0RlodXBe8cTOdC3CpaG9vdPmGPR2USma6lkpcLJ0ozwNA07wzMYWNuuxNvK0ujYGpa/\n6wHjzivL0yd/JXPibjau1mbs7O3GM0bB2ZKLqUwKSmLq6SR61LTGp1L5Bm7l4VBkFgBbe7nhZmOO\nvYWKHy6lcTYuBy/H4t1OPOzMiEzXciQ6i+41bADIVOs4FJVFdTszotIfbtHyF22c77+QEKJCkWDo\nIVCpVMxt50y7rTFkaiAkPodFF1IZ/5xjeSftgWzIzRXqUcOGKrZmLL6oL0IqKBj6/VYG886ncD5e\njZ2FiuZulkxq5kSrKlb8EprGu8f+KQZxXhlBB3crtveukq+YzLCsl4M5W3u5MeF4IqfuZlPJSsV/\nmjnx+rP/FKWdupvF5KAkLsTnUNXWnPcaOxCRpmHe+VQG17VjcSeXYh1nWo6Wn0P1x/p+EweTQAhg\ndEMH0tQ6mrta4uXwz0/n91sZLLyYSkhcDmqdjjpOFrxa1453GjpgkRsA64uI7JicmoybjRlfn0sh\nNUfHC7VsWNDBhcAb6Xx5NoW7GRq6eFjzQ6fKuOSO9aLT6fjhUhprr6dzPUmNlbmKnjWt+ax1Jdxs\nzJVlvg5OYfnlNJKydfhWteL95xxM0r8/IpMX98ahAi4OqkZ1O/26aq2OeuuiSMzWMadtJd6qb7qe\nMXsL/fHkaCE2Q8PByCwsVNDAufi3kq4eNqy5ls7O8EwlGDocnUW6WkffWjb8mqefroKKUAsrAtwa\nlsGSi6lcSVKTqdHh7WDOaz52vNvQAZVKn/aCisnOx+fw+d/JHI/JQqOFZ5wtGN3AgUF1Sx7wCyGe\nPFJM9pDUdrJgsslQHcncTH7yhurI0erYdkv/cArwtmWAt75uyL6ITBKzTN/g119PZ+jBeE7H5tCz\npjVt3a3YH5FFn52xnLmXTX1nS/7lZaMsP7qBPf29i65rkpClZciBOFxtzGjoYkF0hpb3TyTyV2y2\nMv+lvXGcuZdDFVtzOle3Zm5IipLmkjgbl0NGbtFN1+o2BS4zsYkjfjVssDLXP1iXXkpl6MF4TsZk\n06GaNf/ysuVGspppp5MZfSQh3/q772Ty0+U0WlWxIlWt49cbGYw6ksCXZ1NoX80KlQr23Mli+ul/\numaY/lcyk4KSuJmspr+3Lc86W7DuegaD9sWhyR0cePmVNGb9nUJ0hpYeNa2pZmeWb/9+HtbUdjRH\nhz6AMzh5N5vEbB025vDSfXL72ucOTjzmSAKtNscQlqJmXnvnfIFjUXyrWGFnoTIpFjP83dXjwevw\n7LidwYhD8ZxP0H//XqljS3KOjv/+mcyssymFrhealMPzO2LZGZ6p/45623IlUc3bRxJYIuMNClEh\nSDD0EL3byIHnKusfEpkamPAEDtWxL0JfGdzJUkWPmjb4VrXG096cbC0mAYdOp+PTv5IB/RAlq7q5\n8oufK0N97LAwU7HkYiotq1gx0ijX4Ys2zrzdoPBcCIDkHB0jnrHnpy6V2fVCFWo56HMzduY+PH8O\nTSMlR4eVGex5oQoLO7qwp08VItNK3ulldPo/63jY3f+nkZytVY55SnNH1vu7srRzZX7MrVy+4WYG\n5+JM64tdS1Kz64Uq/OznStfcXInfb2Xw2/Nu/NCpMu/n5h7uy+2WITZDo9RRWtalMgs7urCzdxV8\nq1hx5l4Ou+7ol/vhUhoAL9W2ZXU3V37oVJlX8+RqqFQqJUfN+Nrtzj2X//Kyxdm68ONOytYSEp8D\nwKGoLNpUteKPflUZ4mPPjWR1vjpAhbE0g07VrLiTplHOz+7wTJwsVfhWsSrWNgqyN/dcDKpjx5JO\nlfmuvQube7oyobEDdYoowvvqXAqpah2t3CzZ2duNRR1d+LptJewtVCy+mKoEnEKIp5cEQw+RhZmK\nBe2dlaE6/ojKYu319KJXesxsyC2y6FPLFuvc3JCBud0HbLz5zwP1WrKaiNxg4nnPf3JVFnRwIWKo\nR6lan72Yuz9LMxUt3fQPy+gM/b4MD+eWVazwyK0QW9Pe4oFaCamMqnQV57n+Z2w2qbkVyV+u80/g\n0beWDYaY4mi0aTDUoZo1Tlb6mU1d9YFyAxdLvHMf1i2U49Pnuv11LxtDXfUdtzOYdCqRSacSSVXn\nzo/NJluj42qSPtfR+NwHFJDrNrSeHdZmcDwmm7jcXtJ35wYRRbXiu5uhwf/3WFZdTVeOrV1Vaxq4\n6I9h6ME4GgdGFzsgMrQ+2xmeSXBcNhHpGrrXsClVvbqGuWlZcTWNV/bFMTckhfgsLVNbODG4XuE5\nXoej9PWgenraYJb7JXitnj0RQz0491I1zJ/Qun5CiOKTOkMPWTM3K8Y0dFCapX/8Z1Ju3ZvHvyVL\nWo5WyYH5IyqTjlvvApCc23fS0egsYtI1uNuZE5f5T5FZJauyjbFdbf45Vza5AZkmd3f3cvfrZmO6\nz8pF5HAUpqZR66KINM19r5HxMRvvT6VS4WJtRnSGloQ8RYnORufGcCxOlqp80wyZEcnZ/wQXK6/m\nD6Qj0jXEG+3D0fKf7Rd0HSrbmBNQ25b11zPYfjuTTtWsuZqkxsvBnM7VCs+VmfV3MqHJanrWtObb\nds503hbLl+eS6elpQ7ZGx8UENb1q/lN8eD/Pe9rwwQl9hWxDANTbs+CiyeJ6q7499zK1LLmUyu47\nmUqQV8vBnIUdXAoNkA3Xsay/t0KIJ4cEQ4/AlGaO/HYrg9upGhKydEz5M0kpSnmc7QjPJD03WyIy\nXUtknlY+Gh1sCstgTEMHk+IV4yAhLUdLYrYOCxW425V9AGioZJw36DBOQ3E1dbWikpWKpGwdm8My\naOaWPzgYeiCOuk4WvFXfHlejAOxeplbJ8dFodUqAUsW2dA9Y4/N6/dVqJoGhQZZRboxxYHS3kPHx\n3nrWgfXXM/jtVgapOfp1h/rYKRWMCxJ0V5/D9eazDtS0t+C7ds6MOBTPkANxeORe15H1i98/VHU7\nc5q6WupzvrT670fPmjYkZee/buZmhuP8Z1pcVv7lVCoVk5s78VFTR0Lic/gzNpuNNzM4eTeboQfj\nCH2leoHBmrO1GfcytSbfGbVWR0xu7lw1WzPJHRLiKSevQo+AvaUZ3xkN1RF4I0Op3/A423BDnxPx\ngqcNif9Xw+TfW7kPvo25yzxTyQL33Af/9tv/FJ9NDkqiUWA0Y4/pK/MaP29TyqB3bkPRyOnYbGJy\ni+nupKk5Gp1lslxkmobWm2JovSmGEzFZ+bYDYG2uYlRuHablV9I4m6e+z4LzKfx+O5P551O5l6nF\nt6oVDrmtqzYY9cO09VYG2VpQAd1K2alfSzdLDJk9eyP+SfdPl1NZeCGV8/E5WJurqOekf6/ZGf7P\nuV97reAi2dZVrXiusiV/RGWx8WY6Zip9sVBR3HJzyS4l6osl+3vbMqGxA2EpGo7HZNPKzRL/miXL\n2entaUOOFk7fy6Gtu1Wh9ZXcc/d92qi/rk0381eQX3EljQnHE7iZoqaZmxUjGzjws5/+pSMpW0di\nAYEWQMdq+mu0KzxTqR+06WYGjQKjabclBulDXoinn+QMPSLda9gwqI6t0mz4/ROJnAyoioPl4xmP\nxmdqOJDbp8yLtfPXPRngbcuyy2mcvpdDWIoab0cLPm7uxLjjifxwKY2INA1anb5OiKUZ/LupvmJw\nDaPcoVf2xdHO3YqpLR68h+4h9eyYcy6FDI2OHjti6VLdmoORWbjbmXM79Z+shBytjtDc1nxpOYXX\na/l3U0eC43PYFZ5Jz+2xdK9hg7utGcHxOZy5pw8EZvlWonlurtF/WzgxKSiJ2X+ncD4+ByszFb/l\nVk5+q759iVpZFcTVxpzRDRxYcCGVCccT2B+Ryb1MLQcjs6hsbUa/3NZ5bzxrz5Q/k9h2K5OX9t7D\n0kylBC4FefNZeyacSOSvezn417C+bweEQ+rZcTgqi5lnkjlzLxsbcxV7jcbeOxOXw9ADcXjYm/NV\nMfvxed7Thtm5rbx6F9GDdW9PG07dzWbttXR0OlDrdFxLyt8y80aympVX09lzJ5OeNW0wV6k4lZuj\n1dLNkqqFFHv+u6kju8MzCY7PodeOWJ5xtlT6wvqomeMT2z+YEKL4Hs8n8VNqlm8lpW7JnTQNn/+d\nXM4pKtzWW5nkaMHOQmVSKdegnbsV1XNbXBkqWQ9/xp6fOrvQ3NWS/RFZHIrKonN1a3Y8XwXfqvq3\n79pOFrz/nANOlir+vpfDxYTSdTdQzc6ctd0r09DZguh0Dceis/h3U0faVtUHK1YFPP+Kij8tzVSs\n8avMD51caOduzcm7Wfwcmk54qoY+tWzY1suNMQ3/aQE3uqEDK7tWplUVS/ZFZLH9dibPOlvybVtn\nvmpTNsOwzGjlxMxWTnjaW7AlLIO/YrPpW8uGXb3dqJXb39GoBva828gBZysVJ2L0RU9r/FwL3WZf\noy4OhhVj+JNX6tqxvIv+2u69k8W2Wxl42Jkzo6UT/+tWmTZVrNgXkcmBiIJz3QrS1NVKqaf1QhH1\nhd5p6MDI+vY4WuoDzXHP3skAACAASURBVCyNjnXdXckbo8xo5cS0Fk44Wprx640M1lxLJ12tZVxj\nBwL9Cz8XjVz0rcj8a+jrTwXeSMfL0ZwfOrkwttGT2TeYEKJkVImJiU93u9HH7OjWXU9X+n8xU8G+\nPlWUFkSFCb0Wik89n0eRvCdOYpaWi4k5xGVqecHTBnMzFVqdjnZb7nIlSc2Mlk5KZ5dhKWqabYzh\nVEBVni2HYT8ep+v49blkPv87BU97c8686C65HyXwOF1H8eDkOj5BHsHtSYrJHrFX6tiy/no6ByNz\nh+o4lsChflXlYfSAMjU6XtkXR0qOjpZuljRzteLvuGyuJKlxszHjNaMm1euvp1PT3pxnKlXcr/2M\nv5I4HZutNPmf0cpJvntCiApPiskeMZVKxXftnLHNbdVyIUHN9+ell9sHVc3OnB29q9Cnlg3hqRp+\nDk3jXqaWAd627OrtpjSPV2t1WJurWNjBuchWU0+7K4lqTt3NxtPBnG/aVnqg8eWEEOJpU3FfkcuR\nt6MFU5o7MvW0vs7Ql+eS6e9tSx0nuRwP4rnKlvxSRP0Y0HeAOeEJHRuuLK3pXvR5EkKIikhyhsrJ\nmIYOSg/ET+pQHUIIIcTTQIKhcmJhpmJee2cMfcAdjsril0L6hRFCCCHEwyPBUDlq5mrFO0bNtP/7\nZxJ3M0o+wKgQQgghHpwEQ+VscnNHvB31lXwTs3VMOpVUzikSQgghKhYJhsqZnYXpUB2bwjLYHf74\nD9UhhBBCPC0kGHoMdPOw4dW6/wxHMPFkYpmM2yWEEEKI+5Ng6DHxeetKuBoN1fHZmcd3qA4hhBDi\naVJuHdv079+fqKiofNM7dOjAd999x9KlS1m2bJnJvMqVK7Nr165HlcRHytXGnNm+lXg7d6iOpZfS\neLmOHa2qFD1UhxBCCCFKp9yCoZUrV6LR/NNyKi4ujuHDh9O9e3dlmpeXF4sXL1Y+m5sXPbL2k+7l\n3KE69kdmoQPGHU/gUN+q5Z0sIYQQ4qlWbsGQi4uLyedt27Zhb2+Pv7+/Ms3c3Bw3N7dHnbRyo1Kp\n+LadM+223iVdreNigpr551Ppf/9BxYUQQgjxgB6L8R90Oh3btm2jd+/e2NjYKNMjIiJ44YUXsLS0\npHHjxrzzzjvUqFGjRNsOvRZa1sl96N72tGDuTX3x2Fdnk2jWQgVP4HGI/J7E76PIT67j00Gu45PB\nx8fnoe9DlZiYWO5jQJw8eZJx48bx888/88wzzwBw/Phx0tLS8Pb2JiEhgeXLlxMWFsa6detwdna+\nzxaNlPvRlZxaq6PH9lj+jssBoIWThl+er4G7rRlmFXiQ0Sdd6LVQfOo9/B+1eLjkOj4d5Do+QR7B\nY++xyBnasmULDRs2VAIhgPbt25ss07hxYwYMGMD27dsZMmTIo07iI2VhpmJ+Bxe6/nYXjQ7OJJvT\n4NdorM2gloMFXo7meDlY4O1ornz2drDA2VoaBwohhBAlVe7BUHx8PIcPH+ajjz4qcjk7Ozvq1KlD\neHj4I0pZ+XqusiXvNXJg7vlUZVqWFkKT1YQmq4GsfOs4WarwctQHSV4OFng5mOPlqP+/loMFthaS\nqySEEELkVe7B0O+//46VlRU9e/YscrmsrCzCwsJo2bLlI0pZ+Zvc3IlsrY6Dt5OJyrYgMbvoMr/k\nHB0h8TmExOcUOL+arZlJcGScw1TDzhxzMwmWhBBCVDzlGgzpdDq2bt1Kjx49sLOzM5k3b948OnXq\nhLu7OwkJCfz0009kZmbSp0+fckrto2dtrmKWrzOhlWPxqVeLpGwtt1LU3ErVcCtVza0UDbdT1YSl\naLidqiFDU3SwFJ2hJTojm1N388+zUEFNB31wVMfRnP971p6mrtLHkRBCiKdfuQZDf/31F+Hh4Xz6\n6af55t29e5f//ve/JCYm4uLiQuPGjfnpp5+oXr16OaT08VDJyowmrlY0cc0/T6fTcTdDqw+SUjXc\nStEHTGG5wVNEmub/2bvr8LbOu43jX5FBZogDTmI7joMOMzM3acrZ263tuq4rr12btSm3W9sVVmZa\nmdImaZjaMDODncQOm2Vbkm3Ref+QI1mx45BsWcrvc1257HNEz8mRjm89SG1ZyaZAVqmdrFI7K07B\nt5lmvhkex+jmIed+kBBCCBEAGsRosjoVAEfnjVEPNofCcZO9MijZOGq0VwYlZw1Tbnn1tdCC1PDV\nsFjGtgit4RnFxZLRK4FBzmNgkPPoR66U0WSi7mnVKpIjtCRHaKFpcLXbzTYHR412DpfYmLaxmGyj\nHYsD/rSskC+HxjK+pQQiIYQQgUnGYgsA9Fo17aJ1jG8Zytyx8SRHOJc+sTrg1uWFzM0u83EJhRBC\niLohYUhU0yJcy9yx8aRUCUS3LS9ktgQiIYQQAUjCkKhR8zAtc8c2IjXSGYhsCvx5eSG/ZkkgEkII\nEVgkDIlzSgzTMGdMI1pHOruW2RW4fUUhM4+YfVwyIYQQwnskDIlaNQvTMHdsPGlVAtEdK4v45bAE\nIiGEEIFBwpA4ryZ6ZyBqE+UORH9dVcR0CURCCCECgIQhcUEaVwaidtHOQORQ4G+rivjxkAQiIYQQ\n/k3CkLhgCaEa5oyJp32VQHTXqiK+z5RAJIQQwn9JGBIXpVGohjlj4+kQ4wxECnDP6iK+zTD5tmBC\nCCHEJZIwJC5afIizhqhjlUB03xoDX0sgEkII4YckDIlLElcZiDrF6gBnILp/jYEvD0ogEkII4V8k\nDIlLFhuiYfaYeDpXBiKAv6818L8DEoiEEEL4DwlD4rLEBKuZPSaernHuQPTQOgOf7Tf6sFRCCCHE\nhZMwJC5bdLCaWaPj6R7vDkQPry/mk30SiIQQQjR8EoaEV0QHq5kxKp6eVQLR1A3FfLhXApEQQoiG\nTcKQ8JroYDW/jI6nVyN3IHpsYzHv75FAJIQQouGSMCS8KipIzS+j4umTEOTa9/imYt7dU+rDUgkh\nhBDnJmFIeF1kkJqfR8XRt0ogenJTCe/slkAkhBCi4ZEwJOpEhE7N9FFx9GvsDkRPbS7hzV0SiIQQ\nQjQsEoZEnYnQqZk+Mo4BVQLRs1tKeH2nBCIhhBANh4QhUafCdWp+GhnHoCbuQPT81hJe3VHiw1IJ\nIYQQbhKGRJ0L06n5cWQcg5sGu/a9sK2Ul7dLIBJCCOF7EoZEvdBr1fwwIpahVQLRS9tLeWlbCYqi\n+LBkQgghrnQShkS90WvVfD8ijuHN3IHo5R2lvLi9VAKREEIIn5EwJOpVqFbFd8PjGJnoDkSv7ijl\n31JDJIQQwkckDIl6F6JV8c2wOEY3dwei/+40MnFRPnuLrD4smRBCiCuRhCHhEyFaFV8Pi2NM8xDX\nvtWnLQyancvjGw2UWBw+LJ0QQogriYQh4TPBGhVfD4vlvo7haFTOfXYF3t9rotfMHH48ZJamMyGE\nEHVOwpDwqSCNin/3imL1pAQGVpmLKKfMwd9WFTF+QT67CqXpTAghRN2RMCQahPYxOuaMieezwTE0\n1bvflutyLQyZk8s/NxgwVEjTmRBCCO+TMCQaDJVKxXWt9Gy8pjEPpIejrWw6cyjw8T5n09m3GSYc\n0nQmhBDCiyQMiQYnQqfm+Z5RrL06wWOSxrxyB/euMTB2fj47Ciw+LKEQQohAImFINFhtonXMHB3H\nl0NjSdRrXPs35lkYNjePR9ZL05kQQojLJ2FINGgqlYqrk0PZeE0C/+gUjq7yHetQ4NP9JnrMyOGr\ng9J0JoQQ4tJJGBJ+IUyn5ukeUay7OoERVZbzKKhw8MBaA6Pm5bEtX5rOhBBCXDwJQ8KvtI7S8fOo\nOL4eFkvzMHfT2ZZ8K8Pn5vHg2iIKy+0+LKEQQgh/I2FI+B2VSsXEJGfT2SOdIwiqfBcrwBcHzfSY\nmcP/DpiwO6TpTAghxPlJGBJ+S69V82T3SNZPbuyxzllRhcJD6wyMmJfH5jxpOhNCCFE7CUPC77WK\n1PLjiDi+HxFLUri76Wx7gZWR8/K4f00R+dJ0JoQQ4hwkDImAoFKpGNcilPWTG/NY1whC3JmIrzPM\n9JiRwyf7jNJ0JoQQohoJQyKghGpVPNbV2XQ2rkWIa3+xRWHqhmKGzs1jQ26FD0sohBCioZEwJAJS\ncoSW70fE8dPIOFIi3NVEuwqtjJmfz92rpOlMCCGEk4QhEdBGNw9h3dWNeaJbBKEalWv/94fMTFqY\nj8UuzWZCCHGlkzAkAl6IVsXULpFsuCaBq1q6m872Gmx8sNfow5IJIYRoCLS+LoAQ9aVluJZvhsfx\n1q5SntlSAsArO0q5oZWeZlUmcBSB5/NBH1N6wnnOu/+1J4OmDfW4fd3rq9n47noAIhIjuX3VnZf8\nWiXHi/nf4E8AuO67G2netyXr31zDhrfXkdinOdd/P+WSn1sIUTekZkhcce7tGE6HaOf3AJNN4enN\nxT4ukahPh5ceqr7vt+r7vKlJt2Z0va07aePa1OnrCCEujYQhccXRqlW83Cfatf3zkTJWn5YRZleC\n8CbhGI4UUXS40LWv5EQJ+fvyCGscXmevmzwkhSFPD6fLLd3r7DWEEJdOmsnEFWlQ02CuTQ5lRlYZ\nAP9cb2DlpAS0atV5Hin8WYsBSez7ZQ+Hl2bS487eABz53Vkr1KJfS/bP2uu6r6Io7PhyG/tm7KHo\nSCGaIC0pw1JoNqWlx302vrueXd9up6Kkgqbdm9Hzrj7VXremZrKyQjPr31xL1vLDmPLMhDcOI218\nW/o80A9tiK4u/xuEEGeRmiFxxfpXryj0Wmf42Wuw8el+k49LJOpaywFJgGdTmSsMDWjpcd81L69k\nxfO/Y8g2kDauLbGt49g3Yy+bn1qPw+4AYNe3O1j/xhpMuSaSh6YQ1jicxY/Mv6CyzLlzFju/2Y5a\np6H9tR2xVdjZ/OFGVr+80huHKoS4CBKGxBUrMUzD1M4Rru0Xt5WQWyZzDwWy8CbhxLWJ49S2k5QV\nmrGaLRxfd4yY1FgiEyNd9zPnm9j2+RYAxr45gVGvjOWGH6fQtHszig8YOPL7YQC2f7kVgDYT2zHh\n/asZ89/xtLum43nLUW4oI65tI9L/0IVJn1zDiBdGMejxoQBkLjjo5aMWQpyPNJOJK9o9HcP5JtPE\noRI7JVaF57aU8N7AGF8XS9ShlOGpFBws4MiywwRHBGO32Gk1ItXjPqd3nMZhc9b+HF6aydFVWQBY\nTM6Ff09vP0XykBSKDjn7HlV9fNr4Nmz5aGOtZQiJDmXIU0PJWHCQfTP3YjVZMJ4uBcCUKzWUQtQ3\nCUPiihasUfFy72iuX1oAwLeZZm5rE0avhCAfl0zUlVYjW7P5w40cXZ2NLlTn2uewuWsFLaXuDvW7\nv99Z7TmMp0spN5S5toPC3e+X4MiQavc/mznfxE/Xf0fxURnJKERDIGFIXPFGNg9hfIsQ5h8rB2Dq\nBgO/TWiERjpTB6QmXZsSGqfn+PpjaIM1hMaF0rR7M05sPOa6T3CUO9DcufkeQmP1ru2MzAzSWqdh\nq7C59pUbyl2/m/PPX7Oz46ttFB8tRhem4/rvpxDfvhFHV2fz659/udzDE0JcAukzJATwYu8ogis/\nDdsLrHyTafZtgUSdUalVpAxrhSnHSPHRYpKHtkJ1VvBt0qUJap3zDZG14ohr/85vtnPkl0Pk7ctD\nG6wlOsXZpHp4aabrPvtm7DlvGc6Ep4hmkSSkN0atUXv0FaoatIQQdU/CkBA4F3Z9sJO7M/VzW0oo\nqnD4sESiLqVU6ePTamTrareHxurpeptzTqDfHl/CwgfnMfOW6Sx7eimZ3x8kONLZLNb55q4AZC7M\nYNaff2bOnTM5tib7vK/fpGtTAAozCph//xx+uflHCg8VoI931kAteGAueftyL+8ghRAXTMKQEJUe\n7BRBy3DnshyFFQ5e2Fbi4xKJupI0KAlNsBZNsJakQUk13mfgo0MYOG0IkYmRZCw4wOkdp0kdnUa/\n1wcSmRgFQJdbu9HtLz0Ijgrh5KYT2K0Orvpo8nlfv93kDnT7Sw9C4/RkrzhCaKyeiR9Npvf9/QgK\nD+LkpuNYjBavHrMQ4txUBoPBJ8t2f/zxx3z66ace+2JjY1m4cCHgnMzsk08+YdasWZSWltKxY0em\nTp1KampqTU93bgGwKPmZPgqi7s3JLuNPy5wjhNQqWHZVI7rEeacztZzHwCDnMTDIefQj9dB906cd\nqJOSkvjggw9c2xqNe7HMr776iu+++46nn36apKQkPv30U+6//36mT59OWFiYL4orrgBXtQxheLNg\nfj9ZgUOBf24oZuG4eFQq6UwthBCByqfNZBqNhvj4eNe/mBhnZ0RFUfjhhx+45ZZbGD58OKmpqTzz\nzDOYzWYWLVrkyyKLAKdSqXi5TxSVfWfZkGvhx8NltT9ICCGEX/NpGDpx4gTjx4/n6quv5oknnuDE\niRMAnDx5koKCAvr27eu6b0hICN26dWPnzupzfgjhTWlROu7p4F608+lNxZRYpDO1EEIEKp81k6Wn\np/P000+TnJxMUVERn3/+OX/5y1/44YcfKChwToAXGxvr8ZjY2Fhycy9uhEVGZobXyuxLgXIc/uKa\ncPguKIQ8i5rccgePLT/KQ62sl/28ch4Dg5zHwCDn0T+kpdV93y6fhaH+/ft7bKenp3PNNdcwb948\nOnXq5LXXCYQOctLRzzdeCjJzx8oiAH46peOB3om0i7701cTlPAYGOY+BQc6jqKrBDK3X6/W0atWK\nY8eOERcXB0BhYaHHfQoLC123CVHXrksJpX9j50gymwKPbihGUQJgeKIQQggPDSYMVVRUkJWVRXx8\nPM2aNSMuLo4NGzZ43L59+3Y6d+7sw1KKK4lKpeKVPtFoKgeSrThVwezs8tofJIQQwu/4rJnsrbfe\nYtCgQTRu3JiioiI+++wzysvLmTBhAiqViilTpvDFF1+QnJxMy5Yt+fzzzwkNDWXMmDG+KrK4AqXH\n6rijXRgf7XOuN/XEpmJGJgYTpmsw3yOEEEJcJp+FodzcXJ588kkMBgMxMTGkp6fz2Wef0bSpc5r6\nW265hYqKCl555RXXpIvvvPOOzDEk6t20rpH8cqSM/HIHx0123thl5Mnukb4ulhBCCC/x2QzU9SYA\njk46+vne1xkm7l9jACBIDesnN6ZV5MV9l5Dz6N/Ki8vZ/MEGzJoyRv5jNGqN1A76M/k8+pF6mPNW\nPs1CXICbW+vpEe8cSWZxwLSNBh+XSNQnh83BnDtnseXjTez7YDe/P7EExREA37SEEICEISEuiFql\n4tW+0a4vKIuOV7DwmMxMfaVY+9oqTm467tre89Mulj/7m4wuFCJASBgS4gJ1jw/iljZ61/ZjG4sp\nt8kfw0B3aEkmWz7eVG3/zm+2s+qF5RKIhAgAEoaEuAhPd48kOshZP5RVaufdPUYfl0jUJUO2gcWP\nLHBtJw9NoemwRNf2ts+3sPbVVRKIhPBzEoaEuAhxIRqPkWT/3VnKMaPNhyUSdcVWbmX+vbOxlFYA\nEJEYyZjXx9Pln91pPdbd8XbzhxvZ8PY6XxVTiAahwu7fXwgkDAlxkf7cJoxOsc7O1GV2hSc3Ffu4\nRKIuLH/ud/L2OtdC1ARpGP/uREKiQ1Fr1Ix98ypSRqS67rvhrbVs+mDDuZ5KiIB1zGjjb6sKGbcg\nD4cf15BKGBLiImnUKl7tE+Xa/jW7nOUnZWbqQLL3593s+XGXa3vwk8No0qWpa/tMOEoanOzat/bV\nVWz9bHN9FlMInzFUOHhmczE9Z+Tw46EytuZb+eWI/w4qkTAkxCXo2ziYG1uFurb/uaEYi59XEwun\nvH15/P7UUtd220nt6XRzl2r30wZruerDq2ner6Vr36oXlrPjq231Uk4hfKHCrvD+HiPdZpzmrd1G\nKhzu2zbkWnxXsMskYUiIS/R8zyjCtc7O1AeLbXy0TzpT+7uKkgrm3fMr9gpnP7DYtDiGvzAKlarm\nWd+0ITomfTKZZj3dnaqXP/sbu3/cWS/lFaK+KIrCjCNmes/M4fFNxRRVuL/8dY/XMXdsPK/1jfZh\nCS+PhCEhLlETvYZHu0a4tl/eXspps92HJRKXQ1EUljy6kOJs54SaOr2OCe9NIigsqNbH6fRBTPrs\nWpp0dTej/fb4YvbN2FOn5RWivqw5XcGIeXncvqKIbKP7GpcUruHzITEsndCIgU2CfVjCyydhSIjL\ncFeHcNpGOZflMNoUnt4snan91bbPt3BoUYZre8RLY4htHXdBjw2OCGbyF9eRkN7YuUOBJf9cyMG5\n++uiqMLPFFU42Ftkxe5ns5YfMFiZ8lsBExbmszXf6tofE6zixV5RbLymMdem6FGfo+bUn0gYEuIy\n6NQqXq7Smfqnw2Wsy6nwYYnEpTi5+QRrXl7p2u5ySzfaTmx3Uc8RHBnC5C+vJ75dIwAUh8LCh+aR\nWSVgiStPVqmNPjNz6P9rLu1/Os0j6w2sPl3RoINRjtnOg2uL6PdrLguPuQeHBKvh7+nhbLu2Cfd0\nDCdY4/8h6AwJQ0JcpqHNQrg6KcS1PXV9cYO+0AlP5nwT8++fg8Pm7AnauEsTBk4bcknPFRoTyjVf\nXU9smrNGSbErLHhgDod/O+S18gr/4VAU7l9TRG65872VW+7g0/0mrlqYT4fpp5m63sDanIoGMyTd\naHXw0rYSus/I4YuDZqpexm5KDWXTtY15rmcU0cGBFx0C74iE8IF/94oitPJb0u4iK/87aPJxicSF\ncNgdLHxwHqYcZ+f3kOgQxr87CW2w9pKfUx8fxrVf30B0cozzNawO5t87m+xVWd4osvAjXx40s+p0\nzSOscsocfLLfxPgF+XScfppHNxhY76NgZHMofHHARI8ZOby8oxRTlWWGhjQNZsXERnw0KJaW4Zf+\nuWjoJAwJ4QUtwrU83NndmfpfW0vIL5fO1A3dhrfWcmztUeeGCsa8MYHIxMjaH3QBwhLCufbbG4ls\n4WxCtVvszLlzFsfXH73s5xb+4bjJ5tGH8IH0cOaOjeeOdmE0CvH803vK7OCjfSbGLsgnffpppm00\nsDG37oORoigsOFbGgF9zeXCdgZwy9zj5DjFafh4Zx6zRcXSJq30QQSCQMCSEl9zXMZzkCA0AxRaF\nf20t8XGJRG2ylh9m47vrXdu97+tH8pAUrz1/RNMIrvv2RiKaOUOyvcLG7DtmcnLzCa+9hmiYFEXh\nobUGSq3OMJMWqeXxrpEMbBLMa32j2X9jE2aPief2tmHEnxWMTpodfLDXxOj5+XT+OYcnNhWzKdfi\n9fXvtuZbmLAwnz/8VsiBYveSQk31at4dEM2qiQmMbB5yzmklAo2EISG8JESr4j+93Z2pvzpoZmu+\n/05CFshKThSz6B/zXdstBybR54F+Xn+dyOZRXPvtTYQ1DgfAarby6+2/cHrHKa+/lmg4fjhUxpIT\nzoEUKuCdAdGEaN2hQqNWMbhpMK/3cwajX0fHcVsbPbFn9cU5brLz3h4jo+bn0ennHJ7cVMyWvMsL\nRlmlNm5fXsjwuXmszXFfnyJ0Kp7qHsmWaxvzx7QwNOorIwSdIWFICC8a2yKUMc2d820owNT1hgbT\nOVI42SpszL93DuUG5yiZ8CbhjHljAmpN3VwOo5Oiue7bG9HH6wGwGC3MuvVncnfn1MnrCd86bbbz\n2EaDa/tv7cPo2/jcc/Bo1SqGNAvhzf4xHLipCTNHx3FLmp6YYM8wctxk5909RkbMy6PLLzk8vbmY\nbfkXHowKy+1M22ig18wcZmS5l83QquCv7cLYdl1jHu4cgV57ZcaCK/OohahDL/WOJqjyk7Ul38q3\nmWbfFkh4WPXicnJ2ngZArVUz7p2J6OP0dfqaMa1iuebrGwiJcS7hUlFSwcxbppO/P69OX1fUL0VR\neHi9gWKLM6AkhWt4qvuF90HTqVUMaxbC2wNiOHhTU34ZFccf0/REB3kGo6NGO2/vNjJsbh5df8nh\n2c3FbC+oORiV2RTe2lVK119y+GCvCWuV5TMmJYWwfnICr/aNJj5Ec2kHHSAkDAnhZa0itTyQ7u5M\n/dyWEgxVF/ARPrP/133s/Hq7a3vgY0No1iOxlkd4T3zbRlz79Q0ERzmnYSg3lDPjTz9RmFlQL68v\n6t6srDLmHXXPy/P2gBjCdJf2Z1anVjEiMYR3K4PR9JFx/F9rPZE6z2CUbbTz5m4jQ+fk0X1GDs9v\nKWZngQWHovDDITO9ZubwzJYSSqzuoNQnIYhF4+P5algcraN0l3awAUZlMBgCuw4/AI4uIzODtNZp\nvi6GuAgmq4M+s3I5bnKOKLuzfRh/jcuT8+hDBRn5/HjNt1jNzpl0W49rw/h3J150B9HL/Tye3nGK\nmX+ajsXo7K8RlhDGdd9PISYl5pKfU1w8b19XC8rt9JmVS37lnEK3tdHzZn/vn1OLXWHZyQpmZpmZ\nf7TcI+RUFalTVbstNVLDMz2imNjSzzpG10NRpWZIiDoQplPzQi93Z+pP95vIMPnRxSfAWEwW5t87\n2xWEolNiGPmfMT75g9CkS1Ou/t916PTOb+SmXBMz/vgTxccM53mkaMge21jsCkKJeg3P9Yw6zyMu\nTZBGxZgWIXw4KJaMKU35YUQsN6WGEnFWjVHVIBQfoubVPlGsn9yYSUmh/hWE6omEISHqyKSkEIY0\ndXacdCjw8qEgtuVbKKpweH2YrDg3RVH4bdpiCjMLAdCGaJnw3iSCI3y3sGSzHolc/fm1aEMq17U7\nVcqMm3+i9KRMx+CP5h8tY/phd6fkN/pHExVU939egzUqxrYI5aNBsWTc1JTvhsdyY6tQwitHroVq\nVDzSOYKt1zbmr+3D0V1hI8QuhjST+QFpJvNf+w1WBv6ai+2s92GkTkVyhJakCA3J4VqSIzTO7XAN\nLcK1AbXmj6/t+Hoby5/5zbU9+rVxtL+24yU/nzc/j0dXZzP7jhnYLc7m1KikaK7/YQrhlUPxRd3x\n1nk0VDjoOyuH05UTFt6U6gwnvlRmU9iab6FNlJZGoQHQMboeLoeBO7e2EA1Au2gdd3UI5909Ro/9\nJVaFnYVWdhZag1GbAQAAIABJREFUqz1GBSSGaUgK15AUURmUKgNTUriWhFC1VHNfoNM7TrHy38tc\n2+lTOl9WEPK2lgOTmPDB1cy9axYOq4PibAMz/vgT1313E2GNwnxdPHEBntpc7ApCCSFq/tM72scl\nglCtigFNfFfz6Y8kDAlRx57uHklUkIpV2UXkO0LIMtoxn11VVIWCc06R4yY7a3KqT9qo16pcQSkp\n3FmjdCYwJUVorth5Qs5WVlTG/Htn46gcS9yoYwJDnhnu41JVlzKsFePfneQsq81B0aFCZv5pOtd9\ndyOhsXU75F9cnmUny/k6wz11xmv9ookJwEVMrwQShoSoY0EaFVO7RDI5LIe01kkoikJeuYPsUjtZ\nRhtZpTaySu1kG50/T5jstbbumm0K+ww29hlsNd6eEKJ2NcE1CdXQWK92/gzV0DhUTWO9hkidKqBr\nlxSHwqJ/zKf0ZCkAwZHBTHjv8hZgrUupo1oz9s0JLHhgLopDoeBgPjNv/Zlrv7mRkMqh+KJhKbU6\neGCNu9P75ORQJiWF+rBE4nI0zCuDEAFMpVKREKohIVRDr4TqCyBa7ArHTHayK0PSmcCUbbRzpMR2\nzqG0Z+SWO8gtt7Cxlvn8QjWqKiFJTUKohiZ65+/OAOX8PT5EjdoPQ9Om99eTveKIa3v0a+OIaun7\n5ovapI1vi91iZ9HD80GBvD25zLrtZ6756gafdvYWNXt+SwnHKqfOiAlW8Uqfuhk9JuqHhCEhGpgg\njYrUSC2pkTV/PA0VDlc4ctYq2cgyOsPTUaO9WmftmpTZFWfQKrXXej+NChJC1ZW1Shqa6CuDU+W+\nMwEqIVTTYDp9H12dzbo31ri2e/ytN61GtvZhiS5cu8kdsFvsLH1sEQA5O07z6+0zmPzFdQSFBf7K\n4f5ibU4Fn+w3ubZf7h1NQiB0VL6CSRgSws9EB6vpGhxE1/jqt9kcCifNzpBz1Ggjt8zB6TI7OWY7\nOa7fHZTZL2yYpV2BU2YHp8wOoHpn76piglUMbRrCK32ifDaCxXi6lIUPzXWNIk3s05z+Dw/0SVku\nVccbO2G32ln21FIATm05wTdjv2DAPwfT5qq2Ad286Q/MNgf3rS5ybY9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dbwLg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\nSUVORK5CYII=\n",
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44osvlHMVExOjHOdnn31mst7s2bPp37+/yXbmz5/Piy++qJyrbdu2mayzd+9e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\nVccbO2G32ln21FIATm05wTdjv2DAPwfT5qq2Ad286Q/MNgf3rS5ybY9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dbwLg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\nSUVORK5CYII=\n",
             "text/plain": [
-              "<matplotlib.figure.Figure at 0x7faacc835a20>"
+              "<Figure size 576x360 with 1 Axes>"
             ]
           },
           "metadata": {
@@ -440,6 +440,22 @@
         "If you want, you can add titles and change aesthetics, but it's _not_ required to pass the Sprint Challenge."
       ]
     },
+    {
+      "metadata": {
+        "id": "fW2AOT1s5rvo",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "import matplotlib as mpl\n",
+        "\n",
+        "# Needed because otherwise barh ordering is broken.\n",
+        "!pip install --upgrade matplotlib"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
     {
       "metadata": {
         "id": "Slc0FzXFxwYc",
@@ -448,7 +464,7 @@
           "base_uri": "https://localhost:8080/",
           "height": 221
         },
-        "outputId": "8c2530de-e0c3-4545-be47-de18a1541dde"
+        "outputId": "957102c6-a780-4e63-8d4b-7a18e975e9ed"
       },
       "cell_type": "code",
       "source": [
@@ -457,7 +473,7 @@
         "appearances = guest_count['Year'][0:10]\n",
         "appearances"
       ],
-      "execution_count": 55,
+      "execution_count": 14,
       "outputs": [
         {
           "output_type": "execute_result",
@@ -480,7 +496,7 @@
           "metadata": {
             "tags": []
           },
-          "execution_count": 55
+          "execution_count": 14
         }
       ]
     },
@@ -492,25 +508,23 @@
           "base_uri": "https://localhost:8080/",
           "height": 281
         },
-        "outputId": "3d04cb03-592d-4c25-8811-e035bdf47f95"
+        "outputId": "e13575b4-ce60-4e6a-d1da-7417a262e8ca"
       },
       "cell_type": "code",
       "source": [
         "fig, ax = plt.subplots()\n",
         "x = list(appearances.index)\n",
         "y = appearances.values\n",
-        "ax.barh(x,y);\n",
-        "\n",
-        "# I can't figure out how to order those damn bars in a particular order."
+        "ax.barh(x,y);"
       ],
-      "execution_count": 72,
+      "execution_count": 15,
       "outputs": [
         {
           "output_type": "display_data",
           "data": {
-            "image/png": 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EROyRzXugNU11HDf5PfY8NqHYbUOxi6gHKiIiYhUlUBERESsogYqIiFhBY6CVzD6fA3WD\nvfYYNyh22zjY29YRiNieeqAiIiJWUAIVERGxghKoiIiIFZRARURErFDhBJqRkYG/vz9GoxGj0ci4\nceN48803KSoq4vz588yfP/+GdY1GIydOnLilgE+cOGFZyPu3AgICbqnda5YsWcLZs/Z5Y4eIiFQd\nq+7CvbaY9jXh4eHExsby6KOPMmPGjEoLzhamTZtm6xBERMQOVMpjLF5eXpw+fZqMjAzCwsJYu3Yt\n3333HRERETg6OhIQEMCwYcMA+Oqrr1i8eDHZ2dksXrwYd3d3wsPDOXfuHLm5uQQGBtKnTx+MRiNt\n2rQBYNSoUcyYMcOyMHZ5/fLLL7zxxhsUFBTg6OjIzJkz+eyzz7j33nsJCAhg/vz5ODk5ERoaSmxs\nLOnp6Rw6dIjQ0FAKCwt56623cHFxwdnZmTfffLPUeqYiInJnu+Ux0MLCQvbs2UP79u0t20pKSnj7\n7bdZtmwZ0dHRHDhwALPZDEDDhg2JiIjgwQcfZNeuXVy8eJH777+f1atXM3/+fKKjoy3ttG7dmtDQ\nUD755BMGDBhAZGQk7u7u5Y4tMjKSESNGEBERwfPPP8/7779Pjx49+P777wHIysoiMzMTgOTkZHx8\nfCx1v/zyS5555hkiIyMZNWoUWVlZt3SeRESkZrGqB5qenm4Zhzx+/DgjR47E39+fjIwMAC5cuICL\niwsNGzYEYOnSpZa63t7eADRp0oTs7Gzq1atHamoqf//73zEYDGRnZ1vKenl5AXDy5En69+8PgI+P\nD/v27StXnN9//z3p6enExMRQVFREw4YN6dq1KzExMVy8eJE6depQVFSE2Wzm2LFjTJkyxVL3T3/6\nE2+99Rbp6ekEBATQqlUra06VSI2VlpZm6xCspthtwx5jv9lVz1seAw0LC8PT07PUfgcHB4qLi69b\n19HR0fJzSUkJ27ZtIzs7m6ioKC5evMjo0aMt+52dnS0/GwwGgBu2ez1OTk7Mnz+/TK/VwcGBQ4cO\n0aVLF8xmMwcPHsTNzQ0XFxdLGV9fX9asWcPevXsJDw9n8uTJ9OzZs9zHFqnp7HVFE3tejUWxVy+3\nfAn3pZdeYtWqVZZLtAANGjSguLiYc+fOUVJSwtSpU7l06dJ162dnZ9O8eXMcHBzYtWsXBQUFZcp4\nenqSmpoKwKFDh8odW+fOnfnmm28AOHjwINu2bbNs37x5M126dKFz585s3LjR0jO+ZuPGjWRnZ/PI\nI48wbNgwfvjhh3IfV0REar5bvomoRYsW9OvXj5iYGJ588knL9ldffZWwsDAA+vfvf8MbcPr27UtI\nSAhHjhzhiSeeoGnTprz33nulyjz//PO89tpr7Nq164a/weTk5JR6vGX48OEEBgYyZ84ctm/fjsFg\n4K9//SsAPXr0YNOmTdx7770UFBSQkJDA+PHjS7V3zz33MGPGDOrWrYuLiwuzZs2q+MkREZEay2Ay\nmUpsHURNYp+TyYtUzMHeV+z2cpw9X0pU7NWLZiISERGxghKoiIiIFZRARURErKAFtSuZaWwLW4dQ\nYfY8NqHYbcMen+cTqWzqgYqIiFhBCVRERMQKSqAiIiJWUAIVERGxgm4iqmT2OZGCG+y1x7hBsdvG\nwd62jkDE9tQDFRERsYISqIiIiBWUQEVERKygBCoiImKF255AMzIy8Pf3x2g0YjQaGTduHG+++SZF\nRUW35XjJycn4+vry73//+6blwsPDiYuLuy0xiIhIzVclPVBPT08iIyOJjIwkJiaGgoICYmNjb8ux\nYmNjadmyJdu3b78t7YuIiICNHmPx8vLi9OnTAGzatInY2FgMBgP+/v6MGDGCqKgoEhISADh+/Dih\noaF4e3vz+uuvA1BYWMjs2bPx8PAo1W5RURG7du3ijTfeIDw8nBdffBGA/fv3ExkZSa1atWjUqBFz\n58611MnJyWHWrFmYzWbMZjMhISF4eXnx9NNP89RTTxEXF0dBQQErV66kTp06VXB2RETEHlR5Ai0s\nLGTPnj08/fTTnD17lp07dxIdHQ3AhAkTePjhhwkKCgLghx9+4O2336Zfv36kpaUxfvx4evbsyRdf\nfMHmzZt5+eWXS7V94MABWrVqRY8ePahfvz7Jycl07dqVTZs2MWXKFLp3786uXbswmUyWOllZWQwe\nPBh/f38OHjzI2rVreeuttygqKqJVq1aMHDmSmTNncvDgQfz9/avsPIlUd/Y8obxitw17jP1mCz5U\nSQJNT0/HaDQCV3uUI0eOxN/fnx07dnD69GkmTZoEwJUrV8jIyODuu+/GbDYzb9485s6di7OzM40b\nN+aDDz4gKiqKS5cu0aFDhzLHiY2NZcCAAQAMHDiQ7du307VrVx5++GEWLFjAI488woABA3B3d7fU\nady4MTExMaxbt478/Hxq165t2eft7Q1A06ZNycnJuW3nR8Qe2fNKMoq96tlz7DdSJQn02hgoQFhY\nGJ6enlcP7uSEn58fM2bMKFNn8eLFPPPMM7Rs2RKA1atX88ADDzBkyBC+/vpr9u7dW6p8Xl4ecXFx\nHDt2jE2bNlFYWMilS5eYNm0ajz76KA888AC7d+/mlVdeYcGCBZZ6GzZsoEmTJoSHh3P06FHeeecd\nyz5HR8dKPxciIlIzVPljLC+99BKrVq3CbDbTsWNH4uPjMZvNlJSUsHjxYsxmMzt37uTy5cs88cQT\nlnomkwkPDw9KSkrYs2cPBQUFpdqNi4ujZ8+efPzxx6xbt45PPvmEli1bEh8fz/vvv4+TkxNPPfUU\nAQEBnDx5sky7AN98802ZdkVERK6nyhNoixYt6NevHzExMdx9990MGzaMoKAgxo0bR+PGjXF1dSUi\nIoIzZ85YHn3ZuHEjTz31FIsWLWLKlCkEBASQmJjI/v37Le3Gxsby+OOPlzrWn//8Z3bs2EGzZs14\n8cUX+X//7/+RlpZGr169LGUee+wx1q9fz0svvUTnzp3Jysriyy+/rLLzISIi9slgMplKbB1ETWKf\nk8mLVMzB3lfsdjzLnsfiFHv1opmIRERErKAEKiIiYgUlUBEREStoQe1KZhrbwtYhVJg9j00odtuw\nxwfiRSqbeqAiIiJWUAIVERGxghKoiIiIFTQGWsns8zlQN9hrj3GDYreVmh+7Pd7PIFVLPVAREREr\nKIGKiIhYQQlURETECkqgIiIiVlACFRERsUKV34WbkZHB8OHD6dChAyUlJRQUFDBy5Ej69u1boXbC\nw8Pp168fffr0sWzbsmULkZGRlvU9c3NzeeKJJxgyZEi52ly+fDlt2rQptSzalStXGDZsGJ9//nmF\n4hMRkZrNJo+xeHp6EhkZCUB2djYjR46kV69euLq63nLbAQEBTJkyBYD8/HxL282bN7/ltkVERK6x\n+XOg9evXx93dnaysLC5fvszChQtxcnLCYDAwf/58Ll++TFhYGGvXrgVg1KhRLFiwoFxtu7i40KZN\nG86ePUtCQgInTpxgypQppXqV//znP1m7di1NmzalVq1atGnThpycHMLCwsjLy8Pb2/t2vn0REbFT\nNk+gGRkZZGdn06xZMw4dOkRISAjt27dn9erVbNu2rdQl2orKysoiJSWFkJAQMjMzy+wvKSkhIiKC\nNWvWUK9ePUaNGgXAtm3baN26NdOmTWPHjh1s377d6hhExD5V1wnzq2tc5WGPsd9swQebJND09HSM\nRiMlJSW4uLgwe/ZsnJycaNSoEStXrsRsNnP+/HkGDhxY4bZ37NhBamoq+fn5ZGVlERISQqNGja5b\nNjs7Gzc3N8v+rl27AnDy5Em6d+8OQI8ePax8lyJiz6rjSjn2voKPvcZ+IzYfA/2tJUuWMGrUKHr1\n6sVHH33ElStXMBgMpcoUFhbetO1rY6Bms5lRo0bRrl27MmWutVFSUoKDw39vRC4pKSmz/do2ERGR\n36pWj7GYTCZatGhBfn4+3377LYWFhdSpU4dff/2VkpISzp8/z9mz5Zt/09XVlfHjx7N06VIA6tSp\nw/nz5wE4fPgwcHX8NScnh0uXLlFYWGjZ3rJlS1JTUwGIj4+v7LcpIiI1QLVKoEOHDiU0NJQZM2Yw\ndOhQtm7dys8//4yvry+jR4/m3XffvW6P8kYGDhzI+fPn2b9/P/fddx+nTp3CaDTy448/YjAYcHBw\nIDAwkIkTJxIWFkabNm0AePTRRzly5AjBwcGkp6ffrrcrIiJ2zGAymXSNshLZ52osIvK/quNqLPY8\njmjPsd9IteqBioiI2AslUBERESsogYqIiFjB5hMp1DTVcdzk99jz2IRitw3FLqIeqIiIiFWUQEVE\nRKygBCoiImIFPQdayfQcqIhI9XC770lRD1RERMQKSqAiIiJWUAIVERGxghKoiIiIFW46kUJGRgbD\nhw+nQ4cOlm3t2rVj2rRptzWogIAAduzYUWpbWFgYJpMJgLy8PI4fP05cXNx162/ZsoUTJ04wZcqU\nCh13zZo1dO/e3bKwtoiIyI387kxEN1r8uqotWLDA8vM777yDv79/pR9j9OjRld6miIjUTFZN5VdY\nWEh4eDjnzp0jNzeXwMBA+vTpg9FotKypGRwczNy5c7l48SJFRUWEhITQtm1bEhMTiYiIwMnJiWbN\nmjFz5kwMBgOzZs0iMzOTTp063fTYx44dIz4+npiYGAC2bdvGxo0bcXBwoHXr1rz22mulyq9atQpX\nV1eee+45Zs2ahdlsxmw2ExISgpeXF0OGDOHBBx+kYcOGnD59mn79+tG9e/frlhUREbnGqjHQixcv\ncv/997N69Wrmz59PdHS0ZV/r1q0JDQ3l448/5oEHHiAiIoLp06ezfPlyABYvXsyiRYt49913adSo\nEV9//TXfffcdhYWFxMTE8Mgjj5CdnX3d4xYVFbFgwQLCwsJwcrqa+3Nzc1m+fDnvvfceP/74I8eP\nH7eU/+qrr8jMzGT8+PFkZWUxePBg3n33XYKDg1m7di1w9ZeBBx98kHHjxlnq3aisiIjINb/bA01P\nT8doNFpe+/r6MmrUKFJTU/n73/+OwWAolfCu9dSSk5O5cOEC27ZtA8BsNpOVlcXp06eZPn06cDX5\nNWjQgPPnz1vGHTt37kytWrWuG8v69evp2rVrqV5qvXr1CAkJAeDHH3+0xPKf//yHXbt28cknnwDQ\nuHFjYmJiWLduHfn5+dSuXdvSxv/2em9WVkRE7ENaWtott3GzhQesGgPdsmUL2dnZREVFcfHixVJj\nh87Ozpa/Q0JCSt2Qc/HiRZo0aVKmvQ8//BAHh/92hktKyk6OdPbsWbZs2cL//d//WbYVFBSwcOFC\nPvroI9zd3Zk6dapl308//UTr1q3ZuXMngwYNYsOGDTRp0oTw8HCOHj3KO++8Uybma25WVkRE7MPt\nXnXHqku42dnZNG/eHAcHB3bt2kVBQUGZMl5eXuzevRu42htct24d9erVs7wG+OSTT0hLS6Nly5ak\npqYCV3uu+fn5ZdpbsGABU6ZMKdUbvHz5Mo6Ojri7u5OZmUlqaqolFj8/P2bNmsX7779PVlYWJpMJ\nDw8PAL755pvrxnxNRcqKiMidyaoE2rdvX/bu3UtwcDC1a9emadOmvPfee6XKDB06lDNnzhAYGMi8\nefPo3r07AH/5y1+YO3cugYGBHD58mJYtW/Lggw9iNpuZOHEi27dvp2nTpqXa+v7770lISGDt2rUY\njUbLn/Pnz+Pr68vo0aOJjo5m5MiRLF26lMLCQgAaNmxIUFAQb731Fo899hjr16/npZdeonPnzmRl\nZfHll19e9/1VpKyIiNyZNJl8JdNk8iIi1YMmkxcREamGlEBFRESsoAQqIiJiBY2BCmlpabf9du/b\nRbHbhmK3DcVevagHKiIiYgUlUBERESsogYqIiFhBCVRERMQKuomokmkiBRG505RnwgLdRCQiIiKA\nEqiIiIhVlEBFRESsoAQqIiJihduSQDMyMvD398doNDJx4kSCg4M5cOCAVW2FhISUq5zRaOTEiRNW\nHUNERKSinG5Xw56enkRGRgJw5swZXnnlFd54440K34W1aNGi2xGeiIjILbltCfS3PDw8GDt2LJs3\nb2bGjBls2rSJ2NhYDAYD/v7+jBgxgqioKHJyckhPT+fMmTNMmzaNBx98kICAAHbs2MHWrVvZtGkT\nzs7OtG3blldfffV3j1tUVMSbb75JRkYGhYWFBAUFcd9993HgwAEiIyNxdnbmrrvuYv78+SQnJ/PR\nRx+Rm5uLr68v+fn5GI1GAF5R0g9GAAATN0lEQVR88UWmTJlS427BFhER61VJAgXo2LEjn376KWfP\nnmXnzp1ER0cDMGHCBB5++GEAzp07x7Jly9i3bx+ffvopDz74oKX+unXrWLp0Kc2aNePLL7/EbDbj\n6up602PGxsbi7u7OrFmzMJlMBAcHs379ei5evMjcuXNp0aIFs2fPZv/+/bi5uXHixAk2b95Mbm4u\nRqMRo9FITk4O2dnZSp4iIjeQlpZWqeWqk5v9319lCfTy5cs4Ojpy9OhRTp8+zaRJkwC4cuUKGRkZ\nAHTr1g2Apk2bcvny5VL1BwwYQGhoKIMGDWLAgAG/mzwBkpOTSUpK4vDhwwDk5eVRUFBAw4YNmTdv\nHkVFRZw9e5b77rsPNzc32rZti4uLCy4uLtxzzz0cO3aMU6dOWRK8iIiUVZ4ORk2cSKHKEmhqairt\n2rXDyckJPz8/ZsyYUWp/fHw8jo6OltclJaUnSBozZgyPPPIIX3/9NcHBwaxevZoGDRrc9JjOzs6M\nHTuWgQMHlto+d+5cli5dyh//+EcWLlxYqvw1jz76KF9//TU//fQTwcHBFX6/IiJSs1XJYyxnzpxh\n/fr1DB8+nI4dOxIfH4/ZbKakpITFixdjNptvWr+4uJiIiAjc3d0ZMWIEXbp04eeff/7d43p5ebFn\nzx4Afv31VyIiIgDIycnh7rvv5tKlS8THx1NQUFCmrp+fH4mJieTk5NC8eXMr3rWIiNRkt60Hmp6e\njtFoJD8/n+LiYl599VXuvvtuAIYNG0ZQUBCOjo489NBDv3s51sHBgTp16jBu3Djq1q1LixYtaNeu\nXZlyc+bMoXbt2gD07NmTMWPGEB8fz/jx4ykuLiYwMBCAZ599lgkTJuDp6cnIkSOJjo4u08t0dnam\nVatWdOjQoTJOh4iI1DCaTP4G8vLyCAoKYtWqVdStW7fc9TSZvIjcaTSZvFh8//33jB07lueff75C\nyVNERO4cVXYTkT3p0qUL69evt3UYIiJSjakHKiIiYgX1QCtZecYCqht7HptQ7Lah2G3DnmOvidQD\nFRERsYISqIiIiBWUQEVERKyg50ArmZ4DFRF7UNX3a9TE8Vv1QEVERKygBCoiImIFJVARERErKIGK\niIhYQQlURETEChVKoBkZGfj7+2M0Gpk0aRLjxo0jKSmpTLl9+/axefNmq4MaNmwYZ86csbx+7rnn\n+Pbbby2vQ0ND2bdvHzNnzsRsNhMeHk5cXBxbtmxh+fLlnD9/nvnz51t9fBERkd9T4an8PD09iYyM\nBCAhIYH333+fFStWlCrTq1evWwrKx8eHxMREPDw8MJlM5ObmkpiYiJ+fHwApKSnMmTPnhsdxd3dn\nxowZtxSDiIjIzdzSXLi//vorTZs2BSA8PBxnZ2eys7Pp06cPJ06cYMqUKSxdupSjR4+Sl5fH008/\nzZNPPkl4eDhNmjQhNTWVzMxM5syZU2rhah8fH+Li4vjzn/9MUlISgwYNsvR0T548SfPmzalduzaD\nBw9mw4YNZeLKyMggLCyMtWvXsm3bNjZu3IiDgwOtW7fmtddeY8uWLSQkJGAymfjPf/7DpEmT2L59\nOydPnrTE8te//pXz589TUFBAUFDQLf9SICIiNUuFE2h6ejpGo5H8/HzOnTvHO++8Y9lXr149S4KC\nq4tSN2/enKlTp2I2my0JFCA/P58VK1bw6aef8o9//KNUAu3RowcrV64EICkpiT59+pCQkIDZbCYx\nMREfH59yx5ubm8vy5cu56667CAoK4vjx4wCcPn2aqKgoPv/8c9asWcOHH37Ili1b2L59O05OTphM\nJqKiorh06VKpy8ciIjVBWlraHXHMW3WzyR9u6RLujz/+yIwZM/jwww8B8PLyKlW2Vq1aZGdnM378\neJydnblw4YJln7e3NwBNmzYlJSWlVL369etTu3Ztzp07R0pKCkajkU6dOnHkyBGSkpJ4/PHHyx1v\nvXr1CAkJscSbnZ0NQMeOHTEYDDRu3Jh7770XR0dHGjVqxOHDh2nVqhVXrlxh9uzZ+Pv7M2DAgAqe\nJRGR6q2qZwXSTET/o1WrVtSqVYvMzEwAnJxK5+OEhATi4+NZvXo1kZGRuLi4WPY5Ojpafi4pKTub\noI+PD/v37wfA1dUVb29vkpOTSUlJoWvXruWKr6CggIULFzJv3jxWr15dKsH/9vj/G4urqysxMTE8\n9dRTfPvtt8ybN69cxxMRkTvHLSXQ7Oxszp8/bxkH/V8mk4lmzZrh5OTEnj17KC4upqCgoFxt9+zZ\nk88++4wuXboA0K1bN/bu3Yu7uzuurq7lauPy5cs4Ojri7u5OZmYmqamp5Tr+sWPHiI2Nxdvbm7Cw\nME6ePFmu44mIyJ3D6jFQuDqOGRoairOz83XL+vr6snbtWiZOnMhDDz2En58fCxYsKNdxunfvzrFj\nxxg7diwAjRo14uLFixW6nNqgQQN8fX0ZPXo0bdu2ZeTIkSxdupRhw4bdtF7z5s2JiIjgb3/7G46O\njrzwwgvlPqaIiNwZtBpLJdNqLCJiD7Qay63TTEQiIiJWUAIVERGxghKoiIiIFW5pJiIpq6rHFSqD\nPY9NKHbbUOy2Yc+x10TqgYqIiFhBCVRERMQKSqAiIiJW0BhoJbPP50DdYK89xg2K3TYO9rZ1BCK2\npx6oiIiIFZRARURErKAEKiIiYgUlUBERESvc9puIMjIyGD58OB06dMBgMJCXl8fkyZPx9vZmzZo1\ndO/enfT0dE6cOMGzzz5LWFgYa9euLdWG2Wxm6dKlpKSk4OTkRKNGjZg+fTrNmjUjKioKHx8ffvrp\nJyIjI/Hw8AAgNzeXJ554giFDhtwwtiVLlvDcc8+xdetWGjRoQJs2bdi0aVO5V4wREZE7V5Xchevp\n6UlkZCRwdZHt999/nxUrVjB69Gjg6hJpN7N06VKaNGnCRx99BMDhw4eZPHky69atK1UuICCAKVOm\nAFeXWhs5ciS9evWiefPm12132rRpt/S+RETkzlXll3B//fVXywLc4eHhxMXF3bT85cuX2bdvH2PG\njLFs69atG507d2b37t0MHToULy+vMvVcXFxo06YNZ8+epaioiLlz5zJp0iQCAwM5ePAgAEajkRMn\nTpSpu3nzZkvCB3jxxRdJS0uz5u2KiEgNVSUJ9Noi3OPGjWPZsmWMGDGi3HXPnj1Ly5YtcXIq3Vlu\n164dp06dokGDBri6upapl5WVRUpKCm3atCE2NhZ3d3feffddFi5cyNKlS296zICAAHbv3g1ATk4O\n2dnZmn9SRERKqfJLuD/++CMzZszgww8/LHf94uLiMttKSkpwdHQstW3Hjh2kpqaSn59PVlYWISEh\nNGrUiOTkZJKSkjh8+DAAeXl5FBQU3PB49evX55577uHYsWOcOnWKhx9+uNyxitwp7PmqjGK3DXuM\n/WadpyqfiahVq1bUqlWLzMzMcpVv0aIFp06doqCgAGdnZ8v2f//73/j7+5cqe20M1Gw2M2rUKNq1\naweAs7MzY8eOZeDAgeWO89FHH+Xrr7/mp59+Ijg4uNz1RO4U9npVxp5XNFHs1UuVj4FmZ2dz/vx5\nyzjo76lTpw59+vQhOjrasi05OZkffvgBPz+/69ZxdXVl/Pjxlku1Xl5e7NmzB7g6BhsREfG7x/Xz\n8yMxMZGcnJwb3oQkIiJ3rirpgV4bA4Wrd8eGhoaW6k3+nmnTprFy5UqGDx+Oi4sLDRs2ZP78+WUu\n4f7WwIED2bRpE/v376d///7Ex8czfvx4iouLCQwM/N1jOjs706pVKzp06FDuOEVE5M5hMJlMJbYO\nojrKy8sjKCiIVatWUbdu3XLXs8/J5EUq5mDvK3Z7Oc6eLyUq9upFMxFdx/fff8/YsWN5/vnnK5Q8\nRUTkzqHlzK6jS5curF+/3tZhiIhINaYeqIiIiBXUA61kprEtbB1Chdnz2IRitw17fJ5PpLKpByoi\nImIFJVARERErKIGKiIhYQQlURETECkqgIiIiVlACFRERsYISqIiIiBWUQEVERKygBCoiImIFrcYi\nIiJiBfVARURErKAEKiIiYgUlUBERESsogYqIiFhBCVRERMQKSqAiIiJWUAIVERGxgpOtA7A3S5Ys\n4ciRIxgMBl555RU6depk2XfgwAEiIiJwcHDAz8+P8ePH2zDSst555x2SkpIoKipizJgx9O3b17Jv\n8ODBNG3aFEdHRwDmzJlD06ZNbRVqKYcOHWLGjBm0bt0agDZt2hAaGmrZX93P++eff84///lPy+vU\n1FR2795ted2rVy+6detmeb1q1SrLv4OtnDhxgpCQEIYNG8bQoUPJzMxk9uzZFBUV4e7uTnh4OC4u\nLqXq3Oy7YevY58yZQ2FhIU5OToSHh+Pu7m4p/3ufL1vGHh4ezrFjx6hfvz4AL7zwAr179y5Vp7qe\n97CwMEwmEwAXL16kc+fOvPbaa5byW7ZsITIyEg8PDwB8fX0ZN26cTWK3lhJoBSQkJHD69GliYmI4\nefIkc+fOJSYmxrJ/8eLFvPPOOzRp0oSJEyfSt29fy5fS1uLj4/nPf/5DTEwMJpOJkSNHlkqgAMuX\nL8fNzc1GEd5cjx49WLBgwXX3VefzDld/ORk8eDBw9TP01Vdfldpft25dIiMjbRHadeXm5rJo0SLu\nu+8+y7bVq1fzzDPP0L9/fyIiIvjiiy945plnLPt/77thy9jfffddnnzySQICAti0aRPr169n8uTJ\nperd7PNVVa4XO0BwcDB9+vS5bp3qfN5/ez7nzp1r+Q78VkBAAFOmTKmSGG8HXcKtgIMHD/LQQw8B\n8Mc//pFLly6Rk5MDwNmzZ6lXrx7NmjWz9IQOHjxoy3BL6d69O/PnzwfgrrvuIjc3l6KiIhtHdeuq\n+3n/X++99161/y3b2dmZpUuXluml/elPfwKgd+/eZc7xzb4bVel6sU+fPp1+/foB0KBBA7Kzs6s8\nrvK4Xuy/pzqf92tOnTrFpUuX8PLyqvK4bjcl0ArIysqiYcOGltcNGjQgKyvLsq9BgwaWfQ0bNrTs\nqw4cHR2pXbs2AF988QV+fn5lLhMuWLCAwMBAVq5cSUlJ9Zrh8eTJk7zyyisEBgby3XffWbZX9/P+\nW0ePHqVZs2Zl/pPJz8/nL3/5CxMmTGDdunU2iu6/nJyccHV1LbXNbDZbLtk2atSI8+fPl9p/s+9G\nVbpe7LVr18bR0ZGioiI2b97MwIEDy9S70eerKl0vdoBNmzYxadIkZs6cabkkek11Pu/XfPzxxwwd\nOvS6+xISEpg8eTLBwcH88MMPtzPE20KXcG+T6paArtm9ezdffPEFK1asKLU9KCiIXr16Ua9ePUJD\nQ9m5cycPP/ywjaIs7Z577mHChAn079+fs2fPMmnSJP72t7/h7Oxcpmx1Pe9wdSz08ccfL7N98uTJ\nDBo0CIPBwMSJE+nevbvNxrHKozqf4xspKipi9uzZ9OzZE19f31L7KvL5qmqDBg2iQYMGtGvXjjVr\n1hAdHW2z8VlrFBQUcPjwYaZPn15mX+fOnWnQoAG9e/cmOTmZ119/nQ0bNtggSuupB1oB7u7upX67\n++WXXyy9iZvtqy727dvHBx98wLJly6hbt26pfY899hiNGjXCyckJPz8/Tpw4YaMoy2ratCkBAQEY\nDAY8PDxo3Lgx586dA+zjvF9z6NAhunbtWmb7kCFDcHNzo3bt2vTs2bNanftrateujdlsBq6e4yZN\nmpTaX93/HebMmYOnpyeBgYFl9t3s82Vrvr6+tGvXDoA+ffpw/PjxUvur+3lPSEi44S+DrVq1stwQ\n1bVrVy5cuGB3w0pKoBXwwAMPsHPnTgCOHTtGkyZNqFOnDgDNmzfn8uXLZGRkUFhYyN69e7n//vtt\nGW4pOTk5rFixgiVLllju6PvtvpdeeomCggLg6oe+Ot2Es23bNj766CMAzp8/z6+//mq5Q7i6n/dr\nfvnlF9zc3Mr0ak6dOsVf/vIXSkpKKCwsJDk5uVqd+2t8fX3ZtWsXADt37uSBBx4otf9m3w1b27Zt\nG87OzgQFBd1w/40+X7Y2ffp0zp49C1z9XrZp06bU/up83uHqsEXbtm2vu2/t2rXExsYCV+/gbdiw\noc3vPq8oLWdWQStXriQxMREHBwdCQ0P597//TZ06dejbty8JCQmsXLkSgH79+vHCCy/YONr/+uyz\nz4iOjsbT09OyrWfPnrRp04a+ffvy8ccfs3XrVmrVqkX79u0JCQnBYDDYMOL/unz5MrNmzeLSpUsU\nFhYyYcIELly4YBfn/ZrU1FQiIyNZvnw5AGvWrKF79+507dqVFStWEB8fj4ODA3369LH5TUapqaks\nX76cn376CScnJ5o0acKcOXOYM2cOeXl5/OEPf+Cvf/0rTk5OzJw5k1mzZuHq6lrmu3Gt52Tr2C9c\nuICLi4slsfzxj39k+vTpltiLiorKfL78/PyqRexDhw5lzZo1uLq64ubmxqxZs2jUqJFdnPe33nqL\nqKgovL29CQgIsJQNCQlh0aJFZGZm8vrrr1NcXExRURFTp061uxuNlEBFRESsoEu4IiIiVlACFRER\nsYISqIiIiBWUQEVERKygBCoiImIFJVARERErKIGKiIhYQQlURETECv8fCV+t5m4BncoAAAAASUVO\nRK5CYII=\n",
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3Dg8PD+rUqUPbtm3JyckhPDycvLw8fHx8bufbFxERO2XzOdCMjAyys7Np3rw5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\nYII=\n",
             "text/plain": [
-              "<matplotlib.figure.Figure at 0x7faabd973d30>"
+              "<Figure size 432x288 with 1 Axes>"
             ]
           },
           "metadata": {
@@ -518,19 +532,6 @@
           }
         }
       ]
-    },
-    {
-      "metadata": {
-        "id": "dKAPhk--4B7w",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "cell_type": "code",
-      "source": [
-        ""
-      ],
-      "execution_count": 0,
-      "outputs": []
     }
   ]
 }
\ No newline at end of file