{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f4e47f5f",
   "metadata": {},
   "source": [
    "# Boxplot\n",
    "\n",
    "Sometimes less is more!\n",
    "\n",
    "We saw in the lecture that if we want to compare several data distributions, it can be useful to have a plot that highlights key features (the median and quartiles) whilst eliminating unnecessary detail\n",
    "\n",
    "The boxplot can do this job\n",
    "\n",
    "## Oxford Weather example\n",
    "\n",
    "We will work with historical data from the Oxford weather centre\n",
    "\n",
    "<img src= \"https://raw.githubusercontent.com/jillxoreilly/StatsCourseBook/main/images/OxfordSnow.jpg\" width=\"50%\"  />"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c368db3e",
   "metadata": {},
   "source": [
    "### Set up Python libraries\n",
    "\n",
    "As usual, run the code cell below to import the relevant Python libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "159964a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set-up Python libraries - you need to run this but you don't need to change it\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats as stats\n",
    "import pandas \n",
    "import seaborn as sns\n",
    "sns.set_theme()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9ce162e",
   "metadata": {},
   "source": [
    "### Load and inspect the data\n",
    "\n",
    "Let's load some historical data about the weather in Oxford, from the file \"OxfordWeather.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0691586e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/q4/twg1yll54y142rc02m5wwbt40000gr/T/ipykernel_17637/3980356552.py:1: DtypeWarning: Columns (6,7) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  weather = pandas.read_csv(\"https://raw.githubusercontent.com/jillxoreilly/StatsCourseBook/main/data/OxfordWeather.csv\")\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>YYYY</th>\n",
       "      <th>MM</th>\n",
       "      <th>DD</th>\n",
       "      <th>Tmax</th>\n",
       "      <th>Tmin</th>\n",
       "      <th>Tmean</th>\n",
       "      <th>Trange</th>\n",
       "      <th>Rainfall_mm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1827</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8.3</td>\n",
       "      <td>5.6</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1827</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1827</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>-2.2</td>\n",
       "      <td>-8.3</td>\n",
       "      <td>-5.3</td>\n",
       "      <td>6.1</td>\n",
       "      <td>9.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1827</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>-1.7</td>\n",
       "      <td>-7.8</td>\n",
       "      <td>-4.8</td>\n",
       "      <td>6.1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1827</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-10.6</td>\n",
       "      <td>-5.3</td>\n",
       "      <td>10.6</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71338</th>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>26</td>\n",
       "      <td>15.2</td>\n",
       "      <td>4.1</td>\n",
       "      <td>9.7</td>\n",
       "      <td>11.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71339</th>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>27</td>\n",
       "      <td>10.7</td>\n",
       "      <td>2.6</td>\n",
       "      <td>6.7</td>\n",
       "      <td>8.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71340</th>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>28</td>\n",
       "      <td>12.7</td>\n",
       "      <td>3.9</td>\n",
       "      <td>8.3</td>\n",
       "      <td>8.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71341</th>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>29</td>\n",
       "      <td>11.7</td>\n",
       "      <td>6.7</td>\n",
       "      <td>9.2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71342</th>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>30</td>\n",
       "      <td>17.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.3</td>\n",
       "      <td>16.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>71343 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       YYYY  MM  DD  Tmax  Tmin  Tmean Trange Rainfall_mm \n",
       "0      1827   1   1   8.3   5.6    7.0    2.7          0.0\n",
       "1      1827   1   2   2.2   0.0    1.1    2.2          0.0\n",
       "2      1827   1   3  -2.2  -8.3   -5.3    6.1          9.7\n",
       "3      1827   1   4  -1.7  -7.8   -4.8    6.1          0.0\n",
       "4      1827   1   5   0.0 -10.6   -5.3   10.6          0.0\n",
       "...     ...  ..  ..   ...   ...    ...    ...          ...\n",
       "71338  2022   4  26  15.2   4.1    9.7   11.1            0\n",
       "71339  2022   4  27  10.7   2.6    6.7    8.1            0\n",
       "71340  2022   4  28  12.7   3.9    8.3    8.8            0\n",
       "71341  2022   4  29  11.7   6.7    9.2      5            0\n",
       "71342  2022   4  30  17.6   1.0    9.3   16.6            0\n",
       "\n",
       "[71343 rows x 8 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "weather = pandas.read_csv(\"https://raw.githubusercontent.com/jillxoreilly/StatsCourseBook/main/data/OxfordWeather.csv\")\n",
    "display(weather)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a8c3adf",
   "metadata": {},
   "source": [
    "Have a look at the dataframe. \n",
    "\n",
    "What do you think is contained in each column? \n",
    "\n",
    "<ul>\n",
    "<li>Each row is a single day from 1827 to 2022. The columns YYYY,MM,DD give the date.\n",
    "<li>The columns Tmax, Tmin and Tmean give information about the temperature\n",
    "<li>We also have a record of the rainfall each day\n",
    "</ul>\n",
    "\n",
    "### Plot the temperature\n",
    "\n",
    "Say we want to plot the mean temperature in each month of the year. We have almost 200 datapoints for every date (and 30ish dates within each month, so 6000 measurements per month!)\n",
    "\n",
    "We can summarise the distribution of temperatures in each month using a boxplot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a3b7b19e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='MM', ylabel='Tmax'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=weather, x=\"MM\", y=\"Tmax\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b23d10be",
   "metadata": {},
   "source": [
    "Using a simple boxplot for each month, we can easily see the trend across months for warmer weather in the summer and cooler weather in the winter.\n",
    "\n",
    "Within each month we can also see some information about the distribution - for example:\n",
    "\n",
    "<ul>\n",
    "    <li> Temperatures are more variable in winter and summer, than in autumn and spring\n",
    "    <li> In winter, the distribution of temperatures has negative skew (there are some unusually cold years) but in summer the converse is true - this is evident from the position of the median within each box but is clearer in a violinplot (see below)\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98466ee9",
   "metadata": {},
   "source": [
    "### Comparing patterns\n",
    "\n",
    "Has the pattern of temperatures changed over the years?\n",
    "\n",
    "Let's compare temperatures in the 19th, 20th and 21st century. \n",
    "\n",
    "To do so we add a 'century' column to our dataframe. We use the <a href=\"https://pandas.pydata.org/docs/reference/api/pandas.cut.html\"><tt>pandas.cut</tt></a> function to categorise the values of year into 19thC, 20thC and 21stC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c6ce8480",
   "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>YYYY</th>\n",
       "      <th>MM</th>\n",
       "      <th>DD</th>\n",
       "      <th>Tmax</th>\n",
       "      <th>Tmin</th>\n",
       "      <th>Tmean</th>\n",
       "      <th>Trange</th>\n",
       "      <th>Rainfall_mm</th>\n",
       "      <th>CCCC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1827</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8.3</td>\n",
       "      <td>5.6</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18thC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1827</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18thC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1827</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>-2.2</td>\n",
       "      <td>-8.3</td>\n",
       "      <td>-5.3</td>\n",
       "      <td>6.1</td>\n",
       "      <td>9.7</td>\n",
       "      <td>18thC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1827</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>-1.7</td>\n",
       "      <td>-7.8</td>\n",
       "      <td>-4.8</td>\n",
       "      <td>6.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18thC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1827</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-10.6</td>\n",
       "      <td>-5.3</td>\n",
       "      <td>10.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18thC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71338</th>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>26</td>\n",
       "      <td>15.2</td>\n",
       "      <td>4.1</td>\n",
       "      <td>9.7</td>\n",
       "      <td>11.1</td>\n",
       "      <td>0</td>\n",
       "      <td>20thC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71339</th>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>27</td>\n",
       "      <td>10.7</td>\n",
       "      <td>2.6</td>\n",
       "      <td>6.7</td>\n",
       "      <td>8.1</td>\n",
       "      <td>0</td>\n",
       "      <td>20thC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71340</th>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>28</td>\n",
       "      <td>12.7</td>\n",
       "      <td>3.9</td>\n",
       "      <td>8.3</td>\n",
       "      <td>8.8</td>\n",
       "      <td>0</td>\n",
       "      <td>20thC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71341</th>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>29</td>\n",
       "      <td>11.7</td>\n",
       "      <td>6.7</td>\n",
       "      <td>9.2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>20thC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71342</th>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>30</td>\n",
       "      <td>17.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.3</td>\n",
       "      <td>16.6</td>\n",
       "      <td>0</td>\n",
       "      <td>20thC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>71343 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       YYYY  MM  DD  Tmax  Tmin  Tmean Trange Rainfall_mm    CCCC\n",
       "0      1827   1   1   8.3   5.6    7.0    2.7          0.0  18thC\n",
       "1      1827   1   2   2.2   0.0    1.1    2.2          0.0  18thC\n",
       "2      1827   1   3  -2.2  -8.3   -5.3    6.1          9.7  18thC\n",
       "3      1827   1   4  -1.7  -7.8   -4.8    6.1          0.0  18thC\n",
       "4      1827   1   5   0.0 -10.6   -5.3   10.6          0.0  18thC\n",
       "...     ...  ..  ..   ...   ...    ...    ...          ...    ...\n",
       "71338  2022   4  26  15.2   4.1    9.7   11.1            0  20thC\n",
       "71339  2022   4  27  10.7   2.6    6.7    8.1            0  20thC\n",
       "71340  2022   4  28  12.7   3.9    8.3    8.8            0  20thC\n",
       "71341  2022   4  29  11.7   6.7    9.2      5            0  20thC\n",
       "71342  2022   4  30  17.6   1.0    9.3   16.6            0  20thC\n",
       "\n",
       "[71343 rows x 9 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "weather['CCCC'] = pandas.cut(x=weather['YYYY'], bins=[1800,1900,2000,2100], labels=[\"19thC\",\"20thC\",\"21stC\"])\n",
    "display(weather)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8dca89df",
   "metadata": {},
   "source": [
    "Now we can use the argument <tt>hue</tt> in <a href=\"https://seaborn.pydata.org/generated/seaborn.boxplot.html\"><tt>seaborn.boxplot</tt></a> to produce parallel box plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d0fdeff7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='MM', ylabel='Tmean'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=weather, x=\"MM\", y=\"Tmean\", hue=\"CCCC\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6f206ae",
   "metadata": {},
   "source": [
    "It looks like Oxford has been getting a bit warmer over the centuries."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f32c45f",
   "metadata": {},
   "source": [
    "## Violinplot\n",
    "\n",
    "Using Python, you can make a slighly fancier version of the boxplot called a violinplot. \n",
    "\n",
    "The violinplot shows the full distribution of data rather than the summary captured in a boxplot - the violin body is basically a KDE plot.\n",
    "\n",
    "Let's give it a try using the function <a href=\"https://seaborn.pydata.org/generated/seaborn.violinplot.html\"><tt>sns.violinplot</tt></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "c5843dab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='MM', ylabel='Tmax'>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(data=weather, x=\"MM\", y=\"Tmax\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68544006",
   "metadata": {},
   "source": [
    "This is a nice compromise - still easy to \"eyeball\" the pattern across categories (in this case, across months) but giving plenty of detail within each category also\n",
    "\n",
    "Note for example that the trend for:\n",
    "<ul>\n",
    "    <li>negative skew in temperature in winter (outliers are cold days)\n",
    "    <li>positive skew in summer (outliers are hot days)\n",
    "</ul>\n",
    "...is much more clearly visible in the violin plot than in a box plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fe44802f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}