From 30f8d20abbe39f74e29fee7b1dcb50fd52676171 Mon Sep 17 00:00:00 2001
From: Matthew <DreamsOfBunnies@users.noreply.github.com>
Date: Tue, 13 Nov 2018 18:29:23 -0500
Subject: [PATCH] Created using Colaboratory

---
 ...22_Choose_appropriate_visualizations.ipynb | 1644 ++++++++++-------
 1 file changed, 993 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..eee8bf7 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 +1,994 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "_Lambda School Data Science_\n",
-    "# Choose appropriate visualizations"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Upgrade Seaborn\n",
-    "\n",
-    "Make sure you have at least version 0.9.0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install --upgrade seaborn"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import seaborn as sns\n",
-    "sns.__version__"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Fix misleading visualizations"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!wget https://raw.githubusercontent.com/LambdaSchool/DS-Sprint-02-Storytelling-With-Data/master/module2-choose-appropriate-visualizations/misleading.py\n",
-    "    \n",
-    "import misleading"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Fix misleading plot #1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "misleading.plot1()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Fix misleading plot #2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "misleading.plot2()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Fix misleading plot #3"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "misleading.plot3()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Fix misleading plot #4"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "_If you're on Jupyter (not Colab) then uncomment and run this cell below:_"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# import altair as alt\n",
-    "# alt.renderers.enable('notebook')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "misleading.plot4()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Links\n",
-    "- [How to Spot Visualization Lies](https://flowingdata.com/2017/02/09/how-to-spot-visualization-lies/)\n",
-    "- [Where to Start and End Your Y-Axis Scale](http://stephanieevergreen.com/y-axis/)\n",
-    "- [xkcd heatmap](https://xkcd.com/1138/)\n",
-    "- [Surprise Maps: Showing the Unexpected](https://medium.com/@uwdata/surprise-maps-showing-the-unexpected-e92b67398865)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Use Seaborn to visualize distributions and relationships with continuous and discrete variables\n",
-    "\n",
-    "#### Links\n",
-    "- [Seaborn tutorial](https://seaborn.pydata.org/tutorial.html)\n",
-    "- [Seaborn example gallery](https://seaborn.pydata.org/examples/index.html)\n",
-    "- [Chart Chooser](https://extremepresentation.typepad.com/files/choosing-a-good-chart-09.pdf)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 1. Anscombe dataset"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Load dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = sns.load_dataset('anscombe')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### See the data's shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### See the data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### [Group by](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.groupby.html) `'dataset'`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### [Describe](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.describe.html) the groups"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Get the [count](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.count.html), for each column in each group"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Get the [mean](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.mean.html) ..."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Get the [standard deviation](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.std.html) ..."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Get the [correlation](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.corr.html) ..."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Use pandas to [plot](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.plot.html) the groups, as scatter plots"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Use Seaborn to make [relational plots](http://seaborn.pydata.org/generated/seaborn.relplot.html)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Use Seaborn to make [linear model plots](http://seaborn.pydata.org/generated/seaborn.lmplot.html)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Links\n",
-    "- [Seaborn examples: Anscombe's quartet](http://seaborn.pydata.org/examples/anscombes_quartet.html)\n",
-    "- [Wikipedia: Anscombe's quartet](https://en.wikipedia.org/wiki/Anscombe%27s_quartet)\n",
-    "- [The Datasaurus Dozen](https://www.autodeskresearch.com/publications/samestats)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 2. Tips dataset"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Load dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tips = sns.load_dataset('tips')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### See the data's shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### See the first 5 rows"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Describe the data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Make univariate [distribution plots](https://seaborn.pydata.org/generated/seaborn.distplot.html)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Make bivariate [relational plots](https://seaborn.pydata.org/generated/seaborn.relplot.html)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Make univariate [categorical plots](https://seaborn.pydata.org/generated/seaborn.catplot.html)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Make bivariate [categorical plots](https://seaborn.pydata.org/generated/seaborn.catplot.html)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 3. Flights"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Load dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "flights = sns.load_dataset('flights')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### See the data's shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### See the first 5 rows"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Describe the data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Plot year & passengers"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Plot month & passengers"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Create a [pivot table](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.pivot_table.html) of passengers by month and year"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Plot the pivot table as a [heat map](https://seaborn.pydata.org/generated/seaborn.heatmap.html)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "LS_DS_122_Choose_appropriate_visualizations.ipynb",
+      "version": "0.3.2",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    }
+  },
+  "cells": [
+    {
+      "metadata": {
+        "id": "UY3UbFj4ZzH_",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "_Lambda School Data Science_\n",
+        "# Choose appropriate visualizations"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "_hMLw4M3ZzIH",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "# Upgrade Seaborn\n",
+        "\n",
+        "Make sure you have at least version 0.9.0"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "QAA7c9DxZzIL",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# This is a comment\n",
+        "!pip install --upgrade seaborn"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "Gwgrl7jcZzIY",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "import seaborn as sns\n",
+        "sns.__version__"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "3rnsqMBLZzIi",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "# Fix misleading visualizations"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "aJ7XlZYAZzIm",
+        "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": "ay--1xa-ZzIv",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "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": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "J6EwJoGQZzI3",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "#### Fix misleading plot #1"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "bhcF5BpRZzI6",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "misleading.plot1()"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "u85ewwDGZzJF",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "eHwjBtxhZzJO",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "#### Fix misleading plot #2"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "LF93FVpDZzJR",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "misleading.plot2()"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "4SU32Cj3ZzJc",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "Jtv5n4anZzJl",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "#### Fix misleading plot #3"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "TnuqUSAgZzJn",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "misleading.plot3()"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "cSBua7KsZzJt",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "e9yhiGC_ZzJz",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "#### Fix misleading plot #4"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "hX7godplZzJ2",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "_If you're on Jupyter (not Colab) then uncomment and run this cell below:_"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "yoLtyt-KZzJ5",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# import altair as alt\n",
+        "# alt.renderers.enable('notebook')"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "RqcYNQzlZzKA",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "misleading.plot4()"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "t3QfswQZZzKH",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "S1SaWh2ZZzKM",
+        "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": "Z6MbJGIRZzKN",
+        "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": "6ORN5aujZzKP",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## 1. Anscombe dataset"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "vwPZGAufZzKU",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Load dataset"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "tdgg0a_3ZzKX",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "df = sns.load_dataset('anscombe')"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "s64pMpgvZzKe",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### See the data's shape"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "0ODHpZnVZzKg",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "f9n2LcpBZzKl",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### See the data"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "E_NUtfCZZzKo",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "IwhIfw97ZzKw",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### [Group by](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.groupby.html) `'dataset'`"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "Edv_8iMoZzKy",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "j411Z1WDZzK9",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### [Describe](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.describe.html) the groups"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "YOsv1VQDZzK_",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "ij06orZuZzLD",
+        "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": "90iA_c07ZzLE",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "DBdXYe4rZzLM",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Get the [mean](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.mean.html) ..."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "SyiTp9N-ZzLN",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "x6C2qnNcZzLX",
+        "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": "_6bYoihnZzLa",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "O3iJPQvLZzLj",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Get the [correlation](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.corr.html) ..."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "hC8Oxk0IZzLk",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "yJ02LP8KZzLp",
+        "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": "UyW1yPkoZzLr",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "6K-m-SewZzLy",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Use Seaborn to make [relational plots](http://seaborn.pydata.org/generated/seaborn.relplot.html)"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "-7vUia46ZzL0",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "drU5Rhz0ZzL3",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Use Seaborn to make [linear model plots](http://seaborn.pydata.org/generated/seaborn.lmplot.html)"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "hVRUYDBUZzL4",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "yAsmUjVXZzL8",
+        "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": "4ub8opInZzL9",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## 2. Tips dataset"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "0G6-emmMZzL_",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Load dataset"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "BmP16jt_ZzMA",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "tips = sns.load_dataset('tips')"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "37CARn6NZzMD",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### See the data's shape"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "dUl2UrLbZzME",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "jebZ2O6sZzMJ",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### See the first 5 rows"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "88RZK7jAZzMJ",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "iBOANvxbZzMO",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Describe the data"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "K63vHYukZzMP",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "56QC_mBqZzMW",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Make univariate [distribution plots](https://seaborn.pydata.org/generated/seaborn.distplot.html)"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "Ti7T0uumZzMX",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "DF2eCvhYZzMb",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Make bivariate [relational plots](https://seaborn.pydata.org/generated/seaborn.relplot.html)"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "rk3nR2LkZzMc",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "vLjksRy2ZzMl",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Make univariate [categorical plots](https://seaborn.pydata.org/generated/seaborn.catplot.html)"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "8mhmG809ZzMn",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "A_9GpQotZzMt",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Make bivariate [categorical plots](https://seaborn.pydata.org/generated/seaborn.catplot.html)"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "DUBKmP4qZzMv",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "yOhzAM6YZzMy",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## 3. Flights"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "iuZ4AYxfZzM0",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Load dataset"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "u1qUjbupZzM0",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "flights = sns.load_dataset('flights')"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "_u1Od3ZaZzM4",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### See the data's shape"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "7K4BrKf4ZzM4",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "pIFlF--1ZzM8",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### See the first 5 rows"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ZfyuLck_ZzM9",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "iy1RZ4rtZzNA",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Describe the data"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "lb3GZQM9ZzNB",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "UVBP9u6dZzND",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Plot year & passengers"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "aIGXUwP1ZzNE",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "KKq0FJppZzNH",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Plot month & passengers"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ol9a51CxZzNI",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "vVi4__xvZzNQ",
+        "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": "Y32FaHb0ZzNR",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "bJCCoqX5ZzNV",
+        "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": "1LBKs6SIZzNV",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file