{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Visualizing a Categorical and a Quantitative Variable\n",
    "> Categorical variables are present in nearly every dataset, but they are especially prominent in survey data. In this chapter, you will learn how to create and customize categorical plots such as box plots, bar plots, count plots, and point plots. Along the way, you will explore survey data from young people about their interests, students about their study habits, and adult men about their feelings about masculinity. 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: images/school_pointplot.png"
   ]
  },
  {
   "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": [
    "# Count plots and bar plots\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Count plots\n",
    "In this exercise, we'll return to exploring our dataset that contains the responses to a survey sent out to young people. We might suspect that young people spend a lot of time on the internet, but how much do they report using the internet each day? Let's use a count plot to break down the number of survey responses in each category and then explore whether it changes based on age."
   ]
  },
  {
   "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>Music</th>\n",
       "      <th>Techno</th>\n",
       "      <th>Movies</th>\n",
       "      <th>History</th>\n",
       "      <th>Mathematics</th>\n",
       "      <th>Pets</th>\n",
       "      <th>Spiders</th>\n",
       "      <th>Loneliness</th>\n",
       "      <th>Parents' advice</th>\n",
       "      <th>Internet usage</th>\n",
       "      <th>Finances</th>\n",
       "      <th>Age</th>\n",
       "      <th>Siblings</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Village - town</th>\n",
       "      <th>Age Category</th>\n",
       "      <th>Interested in Math</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>few hours a day</td>\n",
       "      <td>3.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>female</td>\n",
       "      <td>village</td>\n",
       "      <td>Less than 21</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>few hours a day</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>female</td>\n",
       "      <td>city</td>\n",
       "      <td>Less than 21</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>few hours a day</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>female</td>\n",
       "      <td>city</td>\n",
       "      <td>Less than 21</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>most of the day</td>\n",
       "      <td>2.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>female</td>\n",
       "      <td>city</td>\n",
       "      <td>21+</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>few hours a day</td>\n",
       "      <td>4.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>female</td>\n",
       "      <td>village</td>\n",
       "      <td>Less than 21</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Music  Techno  Movies  History  Mathematics  Pets  Spiders  Loneliness  \\\n",
       "0    5.0     1.0     5.0      1.0          3.0   4.0      1.0         3.0   \n",
       "1    4.0     1.0     5.0      1.0          5.0   5.0      1.0         2.0   \n",
       "2    5.0     1.0     5.0      1.0          5.0   5.0      1.0         5.0   \n",
       "3    5.0     2.0     5.0      4.0          4.0   1.0      5.0         5.0   \n",
       "4    5.0     2.0     5.0      3.0          2.0   1.0      1.0         3.0   \n",
       "\n",
       "   Parents' advice   Internet usage  Finances   Age  Siblings  Gender  \\\n",
       "0              4.0  few hours a day       3.0  20.0       1.0  female   \n",
       "1              2.0  few hours a day       3.0  19.0       2.0  female   \n",
       "2              3.0  few hours a day       2.0  20.0       2.0  female   \n",
       "3              2.0  most of the day       2.0  22.0       1.0  female   \n",
       "4              3.0  few hours a day       4.0  20.0       1.0  female   \n",
       "\n",
       "  Village - town  Age Category  Interested in Math  \n",
       "0        village  Less than 21               False  \n",
       "1           city  Less than 21                True  \n",
       "2           city  Less than 21                True  \n",
       "3           city           21+                True  \n",
       "4        village  Less than 21               False  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survey_data = pd.read_csv('./dataset/young-people-survey-responses.csv', index_col=0)\n",
    "survey_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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 of internet usage\n",
    "sns.catplot(x='Internet usage', data=survey_data, kind='count', aspect=2);\n",
    "plt.tight_layout();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Change the orientation of the plot\n",
    "sns.catplot(y=\"Internet usage\", data=survey_data, kind=\"count\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create column subplots based on age category\n",
    "sns.catplot(y=\"Internet usage\", data=survey_data, kind=\"count\", col='Age Category');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bar plots with percentages\n",
    "Let's continue exploring the responses to a survey sent out to young people. The variable `\"Interested in Math\"` is True if the person reported being interested or very interested in mathematics, and False otherwise. What percentage of young people report being interested in math, and does this vary based on gender? Let's use a bar plot to find out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a bar plot of interest in math, separated by gender\n",
    "sns.catplot(x='Gender', y='Interested in Math', data=survey_data, kind='bar');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When the y-variable is True/False, bar plots will show the percentage of responses reporting True. This plot shows us that males report a much higher interest in math compared to females."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Customizing bar plots\n",
    "In this exercise, we'll explore data from students in secondary school. The \"study_time\" variable records each student's reported weekly study time as one of the following categories: `\"<2 hours\"`, `\"2 to 5 hours\"`, `\"5 to 10 hours\"`, or `\">10 hours\"`. Do students who report higher amounts of studying tend to get better final grades? Let's compare the average final grade among students in each category using a bar plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": 7,
     "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": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create bar plot of average final grade in each study category\n",
    "sns.catplot(x='study_time', y='G3', data=student_data, kind='bar');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Rearrange the categories\n",
    "sns.catplot(x=\"study_time\", y=\"G3\", data=student_data, kind=\"bar\",\n",
    "            order = [\n",
    "                '<2 hours',\n",
    "                '2 to 5 hours',\n",
    "                '5 to 10 hours',\n",
    "                '>10 hours'\n",
    "                ]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Turn off the confidence intervals\n",
    "sns.catplot(x=\"study_time\", y=\"G3\",\n",
    "            data=student_data,\n",
    "            kind=\"bar\",\n",
    "            order=[\"<2 hours\", \n",
    "                   \"2 to 5 hours\", \n",
    "                   \"5 to 10 hours\", \n",
    "                   \">10 hours\"],\n",
    "            ci=None);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Students in our sample who studied more have a slightly higher average grade, but it's not a strong relationship."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Box plots\n",
    "- What is a box plot?\n",
    "    - Shows the distribution of quantitative data\n",
    "    - See median, spread, skewness, and outliers\n",
    "    - Facilitates comparisons between groups"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create and interpret a box plot\n",
    "Let's continue using the student_data dataset. In an earlier exercise, we explored the relationship between studying and final grade by using a bar plot to compare the average final grade (\"G3\") among students in different categories of \"study_time\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Specify the category ordering\n",
    "study_time_order = [\"<2 hours\", \"2 to 5 hours\", \n",
    "                    \"5 to 10 hours\", \">10 hours\"]\n",
    "\n",
    "# Create a box plot and set the order of the categories\n",
    "sns.catplot(x='study_time', y='G3',\n",
    "            data=student_data,\n",
    "            kind='box',\n",
    "            order=study_time_order);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Omitting outliers\n",
    "Now let's use the `student_data` dataset to compare the distribution of final grades (`\"G3\"`) between students who have internet access at home and those who don't. To do this, we'll use the \"internet\" variable, which is a binary (yes/no) indicator of whether the student has internet access at home.\n",
    "\n",
    "Since internet may be less accessible in rural areas, we'll add subgroups based on where the student lives. For this, we can use the \"location\" variable, which is an indicator of whether a student lives in an urban (`\"Urban\"`) or rural (`\"Rural\"`) location."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 426.25x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a box plot with subgroups and omit the outliers\n",
    "sns.catplot(x='internet', y='G3',\n",
    "            data=student_data,\n",
    "            kind='box',\n",
    "            hue='location',\n",
    "            sym='');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The median grades are quite similar between each group, but the spread of the distribution looks larger among students who have internet access."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adjusting the whiskers\n",
    "In the lesson we saw that there are multiple ways to define the whiskers in a box plot. In this set of exercises, we'll continue to use the `student_data` dataset to compare the distribution of final grades (`\"G3\"`) between students who are in a romantic relationship and those that are not. We'll use the \"romantic\" variable, which is a yes/no indicator of whether the student is in a romantic relationship.\n",
    "\n",
    "Let's create a box plot to look at this relationship and try different ways to define the whiskers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set the whiskers to 0.5 * IQR\n",
    "sns.catplot(x=\"romantic\", y=\"G3\",\n",
    "            data=student_data,\n",
    "            kind=\"box\",\n",
    "            whis=0.5);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Extend the whiskers to the 5th and 95th percentile\n",
    "sns.catplot(x=\"romantic\", y=\"G3\",\n",
    "            data=student_data,\n",
    "            kind=\"box\",\n",
    "            whis=[5, 95]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set the whiskers at the min and max values\n",
    "sns.catplot(x=\"romantic\", y=\"G3\",\n",
    "            data=student_data,\n",
    "            kind=\"box\",\n",
    "            whis=[0, 100]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The median grade is the same between these two groups, but the max grade is higher among students who are not in a romantic relationship."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Point plots\n",
    "- What are point plots?\n",
    "    - Points show mean of quantitative variable\n",
    "    - Vertical lines show 95% condence intervals\n",
    "- Point plots vs. line plots\n",
    "    - Both show:\n",
    "        - Mean of quantitative variable\n",
    "        - 95% condence intervals for the mean\n",
    "    - Differences:\n",
    "        - Line plot has quantitative variable (usually time) on x-axis\n",
    "        - Point plot has categorical variable on x-axis\n",
    "- Point plots vs. bar plots\n",
    "    - Both show:\n",
    "        - Mean of quantitative variable\n",
    "        - 95% condence intervals for the mean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Customizing point plots\n",
    "Let's continue to look at data from students in secondary school, this time using a point plot to answer the question: does the quality of the student's family relationship influence the number of absences the student has in school? Here, we'll use the \"famrel\" variable, which describes the quality of a student's family relationship from 1 (very bad) to 5 (very good)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a point plot of family relationship vs. absences\n",
    "sns.catplot(x='famrel', y='absences', \n",
    "            data=student_data,\n",
    "            kind='point');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Add caps to the confidence interval\n",
    "sns.catplot(x=\"famrel\", y=\"absences\",\n",
    "\t\t\tdata=student_data,\n",
    "            kind=\"point\",\n",
    "            capsize=0.2);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Remove the lines joining the points\n",
    "sns.catplot(x=\"famrel\", y=\"absences\",\n",
    "            data=student_data,\n",
    "            kind=\"point\",\n",
    "            capsize=0.2,\n",
    "            join=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While the average number of absences is slightly smaller among students with higher-quality family relationships, the large confidence intervals tell us that we can't be sure there is an actual association here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Point plots with subgroups\n",
    "Let's continue exploring the dataset of students in secondary school. This time, we'll ask the question: is being in a romantic relationship associated with higher or lower school attendance? And does this association differ by which school the students attend? Let's find out using a point plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 410.875x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a point plot with subgroups\n",
    "sns.catplot(x='romantic', y='absences',\n",
    "            data=student_data,\n",
    "            kind='point',\n",
    "            hue='school');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 410.875x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Turn off the confidence intervals for this plot\n",
    "sns.catplot(x=\"romantic\", y=\"absences\",\n",
    "\t\t\tdata=student_data,\n",
    "            kind=\"point\",\n",
    "            hue=\"school\",\n",
    "            ci=None);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 410.875x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "# Plot the median number of absences instead of the mean\n",
    "sns.catplot(x=\"romantic\", y=\"absences\",\n",
    "\t\t\tdata=student_data,\n",
    "            kind=\"point\",\n",
    "            hue=\"school\",\n",
    "            ci=None,\n",
    "            estimator=np.median);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like students in romantic relationships have a higher average and median number of absences in the GP school, but this association does not hold for the MS school."
   ]
  }
 ],
 "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
}