{
 "cells": [
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   "cell_type": "markdown",
   "id": "1fb91614",
   "metadata": {},
   "source": [
    "# GGR274 Lab 5: Data Transformations, Grouped Data, and Data Visualization\n",
    "\n",
    "## Logistics\n",
    "\n",
    "Like last week, our lab grade will be based on attendance and submission of a few small tasks to MarkUs during the lab session (or by 23:59 on Thursday).\n",
    "\n",
    "Complete the tasks in this Jupyter notebook and submit your completed file to [MarkUs](https://markus-ds.teach.cs.toronto.edu).\n",
    "Here are the instructions for submitting to MarkUs (same as last week):\n",
    "\n",
    "1. Download this file (`Lab_5.ipynb`) from JupyterHub. (See [our JupyterHub Guide](../../../guides/jupyterhub_guide.ipynb) for detailed instructions.)\n",
    "2. Submit this file to MarkUs under the **lab5** assignment. (See [our MarkUs Guide](../../../guides/markus_guide.ipynb) for detailed instructions.)\n",
    "\n",
    "Note: there's no autograding set up for this week's lab, but your TA will be checking that your submitted lab file is complete as part of your \"lab attendance\" grade."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b4c7de0",
   "metadata": {},
   "source": [
    "## Lab 5 Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9e3aec0",
   "metadata": {},
   "source": [
    "In this lab, you will work with  a data set called `time_use_prov`. This is a data set is derived from the Statistics Canada General Social Survey's (GSS) Time Use (TU) Survey Main File, as well as a data set containing information on aggregated provincial data. This week you will plot box plots, bar graphs, and use the logical operators from Week 4 material to develop subset data sets to visualize data on.\n",
    "\n",
    "As usual, these labs are meant to facilitate your understanding of the material from lectures in a low-stakes environment. Please feel free to refer to your lecture content, collaborate with your peers, and seek out help from your TAs."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e6017c0",
   "metadata": {},
   "source": [
    "## Task 1\n",
    "\n",
    "Read CSV file `'time_use_prov.csv'` into a pandas `DataFrame` named `prov_data`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7f393e0c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/0j/ybsv4ncn5w50v40vdh5jjlww0000gn/T/ipykernel_61212/3599942435.py:1: DeprecationWarning: \n",
      "Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),\n",
      "(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)\n",
      "but was not found to be installed on your system.\n",
      "If this would cause problems for you,\n",
      "please provide us feedback at https://github.com/pandas-dev/pandas/issues/54466\n",
      "        \n",
      "  import pandas as pd\n"
     ]
    },
    {
     "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>Unnamed: 0</th>\n",
       "      <th>Participant ID</th>\n",
       "      <th>Urban/Rural</th>\n",
       "      <th>Age Group</th>\n",
       "      <th>Marital Status</th>\n",
       "      <th>sex</th>\n",
       "      <th>Kids under 14</th>\n",
       "      <th>Feeling Rushed</th>\n",
       "      <th>Sleep duration</th>\n",
       "      <th>Work duration</th>\n",
       "      <th>Prov_ab</th>\n",
       "      <th>Employment Rate</th>\n",
       "      <th>Pct house over 30</th>\n",
       "      <th>region</th>\n",
       "      <th>Income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>10000</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>510</td>\n",
       "      <td>0</td>\n",
       "      <td>MB</td>\n",
       "      <td>61.7</td>\n",
       "      <td>11.4</td>\n",
       "      <td>Prairies</td>\n",
       "      <td>68147.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>10009</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>540</td>\n",
       "      <td>0</td>\n",
       "      <td>MB</td>\n",
       "      <td>61.7</td>\n",
       "      <td>11.4</td>\n",
       "      <td>Prairies</td>\n",
       "      <td>68147.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>10016</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>660</td>\n",
       "      <td>0</td>\n",
       "      <td>MB</td>\n",
       "      <td>61.7</td>\n",
       "      <td>11.4</td>\n",
       "      <td>Prairies</td>\n",
       "      <td>68147.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>10023</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>330</td>\n",
       "      <td>0</td>\n",
       "      <td>MB</td>\n",
       "      <td>61.7</td>\n",
       "      <td>11.4</td>\n",
       "      <td>Prairies</td>\n",
       "      <td>68147.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>10047</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>510</td>\n",
       "      <td>0</td>\n",
       "      <td>MB</td>\n",
       "      <td>61.7</td>\n",
       "      <td>11.4</td>\n",
       "      <td>Prairies</td>\n",
       "      <td>68147.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Participant ID  Urban/Rural  Age Group  Marital Status  sex  \\\n",
       "0           0           10000            1          5               5    1   \n",
       "1           1           10009            1          6               3    1   \n",
       "2           2           10016            2          7               1    1   \n",
       "3           3           10023            1          6               1    2   \n",
       "4           4           10047            2          7               1    1   \n",
       "\n",
       "   Kids under 14  Feeling Rushed  Sleep duration  Work duration Prov_ab  \\\n",
       "0              0               1             510              0      MB   \n",
       "1              0               6             540              0      MB   \n",
       "2              0               6             660              0      MB   \n",
       "3              0               3             330              0      MB   \n",
       "4              0               3             510              0      MB   \n",
       "\n",
       "   Employment Rate  Pct house over 30    region   Income  \n",
       "0             61.7               11.4  Prairies  68147.0  \n",
       "1             61.7               11.4  Prairies  68147.0  \n",
       "2             61.7               11.4  Prairies  68147.0  \n",
       "3             61.7               11.4  Prairies  68147.0  \n",
       "4             61.7               11.4  Prairies  68147.0  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "prov_data = pd.read_csv('time_use_prov.csv')\n",
    "\n",
    "prov_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36659d65",
   "metadata": {},
   "source": [
    "## Task 2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8039c2ba",
   "metadata": {},
   "source": [
    "a) Create a new column in `prov_data` named `'age_bin'`.  The values of `'age_bin'` should be obtained from the `'age'` column in `prov_data` which has the values:\n",
    "\n",
    "```\n",
    "            Age group of respondent (groups of 10)\n",
    "\n",
    "           VALUE  LABEL\n",
    "               1  15 to 24 years\n",
    "               2  25 to 34 years\n",
    "               3  35 to 44 years\n",
    "               4  45 to 54 years\n",
    "               5  55 to 64 years\n",
    "               6  65 to 74 years\n",
    "               7  75 years and over\n",
    "              96  Valid skip\n",
    "              97  Don't know\n",
    "              98  Refusal\n",
    "              99  Not stated\n",
    "```\n",
    "\n",
    "`'age_bin'` should have the values `'youth'`, `'young'`, `'middle'`, `'senior'` defined as :\n",
    "\n",
    "- `'youth'`  : ages 15-24\n",
    "- `'young'`  : ages 25-44\n",
    "- `'middle'` : ages 45-64\n",
    "- `'senior'` : ages 65+"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9070d9ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Solution\n",
    "\n",
    "age = prov_data['Age Group']\n",
    "\n",
    "youth = (age == 1)\n",
    "young = (age == 2) | (age == 3)\n",
    "middle = (age == 4) | (age == 5)\n",
    "senior = (age == 6) | (age == 7)\n",
    "\n",
    "prov_data.loc[youth, 'age_bin'] = 'youth'\n",
    "prov_data.loc[young, 'age_bin'] = 'young'\n",
    "prov_data.loc[middle, 'age_bin'] = 'middle'\n",
    "prov_data.loc[senior, 'age_bin'] = 'senior'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56a0188d",
   "metadata": {},
   "source": [
    "b) Compute the distribution of `age_bin` as a count, and store this in `age_bin_count_dist`. Then compute `age_bin` as a proportion of the total population, and store this in `age_bin_prop_dist`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4000d653",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age_bin\n",
       "middle    6530\n",
       "senior    4833\n",
       "young     4724\n",
       "youth     1303\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_bin_count_dist = prov_data['age_bin'].value_counts()\n",
    "\n",
    "age_bin_count_dist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8d3f2471",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age_bin\n",
       "middle    0.375503\n",
       "senior    0.277918\n",
       "young     0.271650\n",
       "youth     0.074928\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_bin_prop_dist = age_bin_count_dist / age_bin_count_dist.sum()\n",
    "\n",
    "age_bin_prop_dist"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03472721",
   "metadata": {},
   "source": [
    "c) Sort the values of `age_bin_prop_dist` in ascending order (smallest to largest) using the `sort_values` method.  The code is\n",
    "\n",
    "```python\n",
    "age_bin_prop_dist.sort_values(ascending = True, inplace = True)\n",
    "```\n",
    "\n",
    "The `inplace = True` parameter in `sort_values` modifies `age_bin_prop_dist`.  What do you predict would happen to `age_bin_prop_dist` if we used `age_bin_prop_dist.sort_values(ascending=True, inplace = False)` instead?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "61700592",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age_bin\n",
       "youth     0.074928\n",
       "young     0.271650\n",
       "senior    0.277918\n",
       "middle    0.375503\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_bin_prop_dist.sort_values(ascending = True, inplace = True)\n",
    "\n",
    "age_bin_prop_dist"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27f4b05f",
   "metadata": {},
   "source": [
    "c) Create a bar plot of `age_bin_prop_dist`. \n",
    "\n",
    "_Feel free to explore different aesthetic options by changing paramters for the plotting function. (See the documentation [here](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.bar.html).)_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ddceeb48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Distribution of age groups'}>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "age_bin_prop_dist.plot.bar()\n",
    "age_bin_prop_dist.plot.bar(\n",
    "    rot = 0, \n",
    "    title = 'Distribution of age groups', \n",
    "    xlabel = ''\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "520210de",
   "metadata": {},
   "source": [
    "## Task 3\n",
    "\n",
    "a) Create a boxplot of `Sleep duration` by `age_bin`.  Store this plot in `sleep_by_age_boxplots`.  Use `figsize = (8, 8)` in the `pandas.DataFrame.boxplot` function. \n",
    "\n",
    "b) Set the label on the y-axis (vertical axis) to `Sleep duration` by using the `.set_ylabel()` method, as follows:\n",
    "\n",
    "```python\n",
    "sleep_by_age_boxplots.set_ylabel('Sleep duration')\n",
    "```\n",
    "\n",
    "_Feel free to customize the plot further to your liking with the help of the [documention](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.boxplot.html)._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "40696a4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Sleep duration')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sleep_by_age_boxplots = prov_data.boxplot(\n",
    "    column = 'Sleep duration', \n",
    "    by = 'age_bin', \n",
    "    figsize = (8,8)\n",
    ");\n",
    "\n",
    "sleep_by_age_boxplots.set_ylabel('Sleep duration')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a9dbba2",
   "metadata": {},
   "source": [
    "Further customization. See [documentation on `pandas.Categorical`](https://pandas.pydata.org/docs/reference/api/pandas.Categorical.html) for more information on the method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f7fa9bc2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prov_data['age_bin ordered'] = pd.Categorical(\n",
    "    prov_data['age_bin'],\n",
    "    categories = [\n",
    "        'youth',\n",
    "        'young',\n",
    "        'middle',\n",
    "        'senior'\n",
    "    ],\n",
    "    ordered = True\n",
    ")\n",
    "\n",
    "sleep_by_age_boxplots = prov_data.boxplot(\n",
    "    column = 'Sleep duration', \n",
    "    by = 'age_bin ordered', \n",
    "    figsize = (8,8),\n",
    "    vert = False\n",
    ");\n",
    "\n",
    "sleep_by_age_boxplots.set_xlabel('');\n",
    "sleep_by_age_boxplots.set_ylabel('');\n",
    "sleep_by_age_boxplots.set_yticklabels([\n",
    "    'Youth',\n",
    "    'Young adult',\n",
    "    'Middle age',\n",
    "    'Senior'\n",
    "]);"
   ]
  }
 ],
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