{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to Seaborn\n",
    "> What is Seaborn, and when should you use it? In this chapter, you will find out! Plus, you will learn how to create scatter plots and count plots with both lists of data and pandas DataFrames. You will also be introduced to one of the big advantages of using Seaborn - the ability to easily add a third variable to your plots by using color to represent different subgroups. This is the Summary of lecture \"Introduction to Data Visualization with Seaborn\", via datacamp.\n",
    "\n",
    "- toc: true \n",
    "- badges: true\n",
    "- comments: true\n",
    "- author: Chanseok Kang\n",
    "- categories: [Python, Datacamp, Visualization]\n",
    "- image: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (10, 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction to Seaborn\n",
    "- What is seaborn\n",
    "    - Python data visualization library\n",
    "    - Easily create the most common types of plots\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Making a scatter plot with lists\n",
    "In this exercise, we'll use a dataset that contains information about 227 countries. This dataset has lots of interesting information on each country, such as the country's birth rates, death rates, and its gross domestic product (GDP). GDP is the value of all the goods and services produced in a year, expressed as dollars per person.\n",
    "\n",
    "We've created three lists of data from this dataset to get you started. `gdp` is a list that contains the value of GDP per country, expressed as dollars per person. `phones` is a list of the number of mobile phones per 1,000 people in that country. Finally, `percent_literate` is a list that contains the percent of each country's population that can read and write."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Region</th>\n",
       "      <th>Population</th>\n",
       "      <th>Area (sq. mi.)</th>\n",
       "      <th>Pop. Density (per sq. mi.)</th>\n",
       "      <th>Coastline (coast/area ratio)</th>\n",
       "      <th>Net migration</th>\n",
       "      <th>Infant mortality (per 1000 births)</th>\n",
       "      <th>GDP ($ per capita)</th>\n",
       "      <th>Literacy (%)</th>\n",
       "      <th>Phones (per 1000)</th>\n",
       "      <th>Arable (%)</th>\n",
       "      <th>Crops (%)</th>\n",
       "      <th>Other (%)</th>\n",
       "      <th>Climate</th>\n",
       "      <th>Birthrate</th>\n",
       "      <th>Deathrate</th>\n",
       "      <th>Agriculture</th>\n",
       "      <th>Industry</th>\n",
       "      <th>Service</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>ASIA (EX. NEAR EAST)</td>\n",
       "      <td>31056997</td>\n",
       "      <td>647500</td>\n",
       "      <td>48,0</td>\n",
       "      <td>0,00</td>\n",
       "      <td>23,06</td>\n",
       "      <td>163,07</td>\n",
       "      <td>700.0</td>\n",
       "      <td>36,0</td>\n",
       "      <td>3,2</td>\n",
       "      <td>12,13</td>\n",
       "      <td>0,22</td>\n",
       "      <td>87,65</td>\n",
       "      <td>1</td>\n",
       "      <td>46,6</td>\n",
       "      <td>20,34</td>\n",
       "      <td>0,38</td>\n",
       "      <td>0,24</td>\n",
       "      <td>0,38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>EASTERN EUROPE</td>\n",
       "      <td>3581655</td>\n",
       "      <td>28748</td>\n",
       "      <td>124,6</td>\n",
       "      <td>1,26</td>\n",
       "      <td>-4,93</td>\n",
       "      <td>21,52</td>\n",
       "      <td>4500.0</td>\n",
       "      <td>86,5</td>\n",
       "      <td>71,2</td>\n",
       "      <td>21,09</td>\n",
       "      <td>4,42</td>\n",
       "      <td>74,49</td>\n",
       "      <td>3</td>\n",
       "      <td>15,11</td>\n",
       "      <td>5,22</td>\n",
       "      <td>0,232</td>\n",
       "      <td>0,188</td>\n",
       "      <td>0,579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>NORTHERN AFRICA</td>\n",
       "      <td>32930091</td>\n",
       "      <td>2381740</td>\n",
       "      <td>13,8</td>\n",
       "      <td>0,04</td>\n",
       "      <td>-0,39</td>\n",
       "      <td>31</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>70,0</td>\n",
       "      <td>78,1</td>\n",
       "      <td>3,22</td>\n",
       "      <td>0,25</td>\n",
       "      <td>96,53</td>\n",
       "      <td>1</td>\n",
       "      <td>17,14</td>\n",
       "      <td>4,61</td>\n",
       "      <td>0,101</td>\n",
       "      <td>0,6</td>\n",
       "      <td>0,298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>American Samoa</td>\n",
       "      <td>OCEANIA</td>\n",
       "      <td>57794</td>\n",
       "      <td>199</td>\n",
       "      <td>290,4</td>\n",
       "      <td>58,29</td>\n",
       "      <td>-20,71</td>\n",
       "      <td>9,27</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>97,0</td>\n",
       "      <td>259,5</td>\n",
       "      <td>10</td>\n",
       "      <td>15</td>\n",
       "      <td>75</td>\n",
       "      <td>2</td>\n",
       "      <td>22,46</td>\n",
       "      <td>3,27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>WESTERN EUROPE</td>\n",
       "      <td>71201</td>\n",
       "      <td>468</td>\n",
       "      <td>152,1</td>\n",
       "      <td>0,00</td>\n",
       "      <td>6,6</td>\n",
       "      <td>4,05</td>\n",
       "      <td>19000.0</td>\n",
       "      <td>100,0</td>\n",
       "      <td>497,2</td>\n",
       "      <td>2,22</td>\n",
       "      <td>0</td>\n",
       "      <td>97,78</td>\n",
       "      <td>3</td>\n",
       "      <td>8,71</td>\n",
       "      <td>6,25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Country                               Region  Population  \\\n",
       "0     Afghanistan         ASIA (EX. NEAR EAST)             31056997   \n",
       "1         Albania   EASTERN EUROPE                          3581655   \n",
       "2         Algeria   NORTHERN AFRICA                        32930091   \n",
       "3  American Samoa   OCEANIA                                   57794   \n",
       "4         Andorra   WESTERN EUROPE                            71201   \n",
       "\n",
       "   Area (sq. mi.) Pop. Density (per sq. mi.) Coastline (coast/area ratio)  \\\n",
       "0          647500                       48,0                         0,00   \n",
       "1           28748                      124,6                         1,26   \n",
       "2         2381740                       13,8                         0,04   \n",
       "3             199                      290,4                        58,29   \n",
       "4             468                      152,1                         0,00   \n",
       "\n",
       "  Net migration Infant mortality (per 1000 births)  GDP ($ per capita)  \\\n",
       "0         23,06                             163,07               700.0   \n",
       "1         -4,93                              21,52              4500.0   \n",
       "2         -0,39                                 31              6000.0   \n",
       "3        -20,71                               9,27              8000.0   \n",
       "4           6,6                               4,05             19000.0   \n",
       "\n",
       "  Literacy (%) Phones (per 1000) Arable (%) Crops (%) Other (%) Climate  \\\n",
       "0         36,0               3,2      12,13      0,22     87,65       1   \n",
       "1         86,5              71,2      21,09      4,42     74,49       3   \n",
       "2         70,0              78,1       3,22      0,25     96,53       1   \n",
       "3         97,0             259,5         10        15        75       2   \n",
       "4        100,0             497,2       2,22         0     97,78       3   \n",
       "\n",
       "  Birthrate Deathrate Agriculture Industry Service  \n",
       "0      46,6     20,34        0,38     0,24    0,38  \n",
       "1     15,11      5,22       0,232    0,188   0,579  \n",
       "2     17,14      4,61       0,101      0,6   0,298  \n",
       "3     22,46      3,27         NaN      NaN     NaN  \n",
       "4      8,71      6,25         NaN      NaN     NaN  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./dataset/countries-of-the-world.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "gdp = df['GDP ($ per capita)'].tolist()\n",
    "phones = df['Phones (per 1000)'].tolist()\n",
    "percent_literate = df['Literacy (%)'].tolist()\n",
    "region = df['Region'].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create scatter plot with GDP on the x-axis and number of phones on the y-axis\n",
    "sns.scatterplot(x=gdp, y=phones);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Change this scatter plot to have percent literate on the y-axis\n",
    "sns.scatterplot(x=gdp, y=percent_literate);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Making a count plot with a list\n",
    "In the last exercise, we explored a dataset that contains information about 227 countries. Let's do more exploration of this data - specifically, how many countries are in each region of the world?\n",
    "\n",
    "To do this, we'll need to use a count plot. Count plots take in a categorical list and return bars that represent the number of list entries per category. You can create one here using a list of regions for each country, which is a variable named `region`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create count plot with region on the y-axis\n",
    "sns.countplot(y=region);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sub-Saharan Africa contains the most countries in this list. We'll revisit count plots later in the course."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using pandas with Seaborn\n",
    "- What is pandas?\n",
    "    - Python library for data analysis\n",
    "    - Easily read datasets from csv, txt, and other types of files\n",
    "    - Datasets take the form of DataFrame objects"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### \"Tidy\" vs. \"untidy\" data\n",
    "Here, we have a sample dataset from a survey of children about their favorite animals. But can we use this dataset as-is with Seaborn? Let's use Pandas to import the csv file with the data collected from the survey and determine whether it is tidy, which is essential to having it work well with Seaborn."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Making a count plot with a DataFrame\n",
    "In this exercise, we'll look at the responses to a survey sent out to young people. Our primary question here is: how many young people surveyed report being scared of spiders? Survey participants were asked to agree or disagree with the statement \"I am afraid of spiders\". Responses vary from 1 to 5, where 1 is \"Strongly disagree\" and 5 is \"Strongly agree\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.read_csv('./dataset/young-people-survey-responses.csv')\n",
    "\n",
    "# Create a count plot with \"Spiders\" on the x-axis\n",
    "sns.countplot('Spiders', data=df);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding a third variable with hue\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hue and scatter plots\n",
    "In the prior video, we learned how `hue` allows us to easily make subgroups within Seaborn plots. Let's try it out by exploring data from students in secondary school. We have a lot of information about each student like their age, where they live, their study habits and their extracurricular activities.\n",
    "\n",
    "For now, we'll look at the relationship between the number of absences they have in school and their final grade in the course, segmented by where the student lives (rural vs. urban area)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "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>school</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>famsize</th>\n",
       "      <th>Pstatus</th>\n",
       "      <th>Medu</th>\n",
       "      <th>Fedu</th>\n",
       "      <th>traveltime</th>\n",
       "      <th>failures</th>\n",
       "      <th>schoolsup</th>\n",
       "      <th>...</th>\n",
       "      <th>goout</th>\n",
       "      <th>Dalc</th>\n",
       "      <th>Walc</th>\n",
       "      <th>health</th>\n",
       "      <th>absences</th>\n",
       "      <th>G1</th>\n",
       "      <th>G2</th>\n",
       "      <th>G3</th>\n",
       "      <th>location</th>\n",
       "      <th>study_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>18</td>\n",
       "      <td>GT3</td>\n",
       "      <td>A</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>Urban</td>\n",
       "      <td>2 to 5 hours</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>17</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>Urban</td>\n",
       "      <td>2 to 5 hours</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>15</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>Urban</td>\n",
       "      <td>2 to 5 hours</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>15</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>Urban</td>\n",
       "      <td>5 to 10 hours</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>16</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>Urban</td>\n",
       "      <td>2 to 5 hours</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  school sex  age famsize Pstatus  Medu  Fedu  traveltime  failures schoolsup  \\\n",
       "0     GP   F   18     GT3       A     4     4           2         0       yes   \n",
       "1     GP   F   17     GT3       T     1     1           1         0        no   \n",
       "2     GP   F   15     LE3       T     1     1           1         3       yes   \n",
       "3     GP   F   15     GT3       T     4     2           1         0        no   \n",
       "4     GP   F   16     GT3       T     3     3           1         0        no   \n",
       "\n",
       "   ... goout Dalc Walc health absences  G1  G2  G3  location     study_time  \n",
       "0  ...     4    1    1      3        6   5   6   6     Urban   2 to 5 hours  \n",
       "1  ...     3    1    1      3        4   5   5   6     Urban   2 to 5 hours  \n",
       "2  ...     2    2    3      3       10   7   8  10     Urban   2 to 5 hours  \n",
       "3  ...     2    1    1      5        2  15  14  15     Urban  5 to 10 hours  \n",
       "4  ...     2    1    2      5        4   6  10  10     Urban   2 to 5 hours  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "student_data = pd.read_csv('./dataset/student-alcohol-consumption.csv', index_col=0)\n",
    "student_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a scatter plot of absences vs. final grade\n",
    "sns.scatterplot(x=\"absences\", y=\"G3\", data=student_data, hue='location');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Change the legend order in the scatter plot\n",
    "sns.scatterplot(x=\"absences\", y=\"G3\", \n",
    "                data=student_data,\n",
    "                hue='location',\n",
    "                hue_order=['Rural', 'Urban']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hue and count plots\n",
    "Let's continue exploring our dataset from students in secondary school by looking at a new variable. The `\"school\"` column indicates the initials of which school the student attended - either \"GP\" or \"MS\".\n",
    "\n",
    "In the last exercise, we created a scatter plot where the plot points were colored based on whether the student lived in an urban or rural area. How many students live in urban vs. rural areas, and does this vary based on what school the student attends? Let's make a count plot with subgroups to find out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MkiSpMAOZJElSYQYySZKkwgxkkiRJhRnIJEmSCjOQSZIkFWYgkyRJKsxAJkmSVJiBTJIkqTADmSRJUmEGMkmSpMIMZJIkSYUZyCRJkgozkEmSJBVmIJMkSSrMQCZJklSYgUySJKkwA5kkSVJhBjJJkqTCDGSSJEmFGcgkSZIKM5BJkiQVZiCTJEkqzEAmSZJUmIFMkiSpsG4Fsoho606bJEmSNt82mzoYEU3ADsBuEbELENWh9wF71FybJEnSgLDJQAb8HXABjfD1CP8/kL0OXFVjXZIkSQPGJgNZZl4BXBERX8zM7/VSTZIkSQPKu42QAZCZ34uIfweM6nxOZt5UU12SJEkDRrcCWUTMBvYB5gPrquYEDGSSJEnvUbcCGdAK7J+ZWWcxkiRJA1F31yFbAHygzkIkSZIGqu4Gst2ARRHxq4i4Y/1nUydExPUR8XJELOjUdklEPB8R86vPJzsd+0pELImIpyPib7bs50iSJG19unvL8pItuPYNwJW8c57ZrMyc2bkhIvYHpgAfprHExj0RsW9mrkOSJKmf6+5Tlvdt7oUz8/6IGNXN7icCt2TmG8BzEbEEOBj4l839u5IkSVub7r46aWVEvF59VkfEuoh4fQv/5vkR8UR1S3OXqm1P4I+d+nRUbV3VMj0i5kXEvGXLlm1hCZIkSX1HtwJZZu6Ume+rPk3AqTRuR26uq2ksnzEeeBH4TtUeXfTt8onOzLwmM1szs3XYsGFbUIIkSVLf0t1J/W+RmXOBo7fgvJcyc11mvglcS+O2JDRGxPbq1HUk8MKW1CZJkrS16e7CsKd02h1EY12yzV6TLCJGZOaL1e7JNJbTALgDuDki/pHGpP7RwO829/qSJElbo+4+ZfnvO22vBdppTMTfqIj4MXAksFtEdAAzgCMjYjyNMNdO4+XlZObCiLgNWFRd/zyfsJQkSQNFd5+yPGdzL5yZU7tovm4T/b8BfGNz/44kSdLWrrtPWY6MiJ9XC72+FBE/jYiRdRcnSZI0EHR3Uv+PaMzz2oPGchT/o2qTJEnSe9TdQDYsM3+UmWurzw2Aa05IkiT1gO4Gslci4tMRMbj6fBp4tc7CJEmSBoruBrLPAmcA/4fGgq6nAZs90V+SJEnv1N1lLy4Dzs7MFQARsSswk0ZQkyRJ0nvQ3RGyA9aHMYDMXA5MqKckSZKkgaW7gWxQpxeBrx8h6+7omiRJkjahu6HqO8CDEfETGqvsn4GLuEqSJPWI7q7Uf1NEzKPxQvEATsnMRbVWJkmSNEB0+7ZjFcAMYZIkST2su3PIJEmSVBMDmSRJUmEGMkmSpMIMZJIkSYUZyCRJkgozkEmSJBVmIJMkSSrMQCZJklSYgUySJKkwA5kkSVJhBjJJkqTCDGSSJEmFGcgkSZIKM5BJkiQVZiCTJEkqzEAmSZJUmIFMkiSpMAOZJElSYQYySZKkwgxkkiRJhRnIJEmSCjOQSZIkFWYgkyRJKsxAJkmSVJiBTJIkqbDaAllEXB8RL0fEgk5tu0bE3RHxTPW9S9UeEfHdiFgSEU9ExIF11SVJktTX1DlCdgNw3NvaLgbaMnM00FbtA3wCGF19pgNX11iXJElSn1JbIMvM+4Hlb2s+Ebix2r4ROKlT+03Z8K/AzhExoq7aJEmS+pLenkM2PDNfBKi+d6/a9wT+2KlfR9X2DhExPSLmRcS8ZcuW1VqsJElSb+grk/qji7bsqmNmXpOZrZnZOmzYsJrLkiRJql9vB7KX1t+KrL5frto7gL069RsJvNDLtUmSJBXR24HsDuDsavts4Bed2j9TPW15KPDa+lubkiRJ/d02dV04In4MHAnsFhEdwAzgm8BtEfE54A/A6VX3u4BPAkuAVcA5ddUlSZLU19QWyDJz6kYOfayLvgmcV1ctkiRJfVlfmdQvSZI0YBnIJEmSCjOQSZIkFWYgkyRJKsxAJkmSVJiBTJIkqTADmSRJUmEGMkmSpMIMZJIkSYUZyCRJkgozkEmSJBVmIJMkSSrMQCZJklSYgUySJKkwA5kkSVJhBjJJkqTCDGSSJEmFGcgkSZIKM5BJkiQVZiCTJEkqzEAmSZJUmIFMkiSpMAOZJElSYQYySZKkwgxkkiRJhRnIJEmSCjOQSZIkFWYgkyRJKmyb0gVIvWWXL+1SugRtoRWzVpQuQZJq5QiZJElSYQYySZKkwgxkkiRJhRnIJEmSCjOQSZIkFWYgkyRJKsxAJkmSVFiRdcgioh1YCawD1mZma0TsCtwKjALagTMy08WHJElSv1dyhOyozByfma3V/sVAW2aOBtqqfUmSpH6vL92yPBG4sdq+ETipYC2SJEm9plQgS+DXEfFIREyv2oZn5osA1ffuhWqTJEnqVaXeZXlYZr4QEbsDd0fEU909sQpw0wE++MEP1lWfJElSrykyQpaZL1TfLwM/Bw4GXoqIEQDV98sbOfeazGzNzNZhw4b1VsmSJEm16fVAFhE7RsRO67eBjwMLgDuAs6tuZwO/6O3aJEmSSihxy3I48POIWP/3b87MX0bEw8BtEfE54A/A6QVqkyRJ6nW9Hsgy81lgXBftrwIf6+16JEmSSutLy15IkiQNSAYySZKkwgxkkiRJhRnIJEmSCjOQSZIkFWYgkyRJKsxAJkmSVJiBTJIkqTADmSRJUmEGMkmSpMIMZJIkSYUZyCRJkgozkEmSJBVmIJMkSSrMQCZJklSYgUySJKkwA5kkSVJh25QuQJKkrdkuX9qldAnaQitmrShdwgaOkEmSJBVmIJMkSSrMQCZJklSYc8gkqbDm5vbSJei9OKl0AeoPHCGTJEkqzEAmSZJUmIFMkiSpMAOZJElSYQYySZKkwgxkkiRJhRnIJEmSCjOQSZIkFWYgkyRJKsxAJkmSVJiBTJIkqTADmSRJUmEGMkmSpMIMZJIkSYUZyCRJkgrrc4EsIo6LiKcjYklEXFy6HkmSpLr1qUAWEYOBq4BPAPsDUyNi/7JVSZIk1atPBTLgYGBJZj6bmX8BbgFOLFyTJElSrSIzS9ewQUScBhyXmf+h2j8LOCQzz+/UZzowvdrdD3i61wvV1mo34JXSRUjqd/y/Rd21d2YO6+rANr1dybuILtrekhgz8xrgmt4pR/1JRMzLzNbSdUjqX/y/RT2hr92y7AD26rQ/EnihUC2SJEm9oq8FsoeB0RHRHBF/BUwB7ihckyRJUq361C3LzFwbEecDvwIGA9dn5sLCZan/8Fa3pDr4f4vesz41qV+SJGkg6mu3LCVJkgYcA5kkSVJhBjL1OxExPCJujohnI+KRiPiXiDg5Io6MiNci4rGIWBwRM0rXKmnrEBEZEbM77W8TEcsi4s5qf3hE3BkRj0fEooi4q1y12hoZyNSvREQAc4H7M/NDmXkQjad1R1Zd/jkzJwCtwKcj4qBCpUrauvwb8JGI2L7aPxZ4vtPxS4G7M3NcZu4P+C5mbRYDmfqbo4G/ZOYP1jdk5u8z83udO2XmvwGPAPv0cn2Stl7/C/jbansq8ONOx0bQWEsTgMx8ohfrUj9gIFN/82Hg0XfrFBFDgUMBl1WR1F23AFMiogk4AHio07GrgOsi4jcR8Z8jYo8iFWqrZSBTvxYRV1VzOh6umiZFxGPAr4Fvus6dpO6qRr1G0Rgdu+ttx34FfAi4FhgDPBYRXb6zUOpKn1oYVuoBC4FT1+9k5nkRsRswr2r658w8vkhlkvqDO4CZwJHA0M4HMnM5cDNwczXZ/wjgp71doLZOjpCpv/knoCki/r5T2w6lipHU71wPXJqZT3ZujIijI2KHansnGvNT/1CgPm2lHCFTv5KZGREnAbMi4j8Cy2g8HfWfylYmqT/IzA7gii4OHQRcGRFraQx2/DAzH+6in9QlX50kSZJUmLcsJUmSCjOQSZIkFWYgkyRJKsxAJkmSVJiBTJIkqTADmaQBLSKmRcSVPXSt9mohYknaLAYySZKkwgxkkvqliNgxIv5n9S7TBRFxZkRMjIgHq7bfVSuqA+wREb+MiGci4vJO15gaEU9W53/r3dolaUu5Ur+k/uo44IXM/FuAiHg/8BhwZmY+HBHvA/5c9R0PTADeAJ6OiO8B64Bv0ViBfQXw6+otEL/rqj0z5/beT5PU3zhCJqm/ehI4JiK+FRGTgA8CL65/nU1mvp6Za6u+bZn5WmauBhYBewMTgXszc1nVbw6Nl0VvrF2StpiBTFK/lJn/m8Yo1pPAfwNOBjb2rrg3Om2vo3H3IDbSd2PtkrTFDGSS+qWI2ANYlZn/HZgJHEpjrtjE6vhOEbGpaRsPAZMjYreIGAxMBe7bRLskbTHnkEnqr1qAb0fEm8Aa4O9pjG59LyK2pzF/7JiNnZyZL0bEV4DfVOfdlZm/ANhYuyRtqcjc2Ai+JEmSeoO3LCVJkgozkEmSJBVmIJMkSSrMQCZJklSYgUySJKkwA5kkSVJhBjJJkqTC/h++EMQ+3bCK+wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a dictionary mapping subgroup values to colors\n",
    "palette_colors = {'Rural': \"green\", 'Urban': \"blue\"}\n",
    "\n",
    "# Create a count plot of school with location subgroups\n",
    "sns.countplot('school', data=student_data, hue='location' ,palette=palette_colors);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Students at GP tend to come from an urban location, but students at MS are more evenly split."
   ]
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}