{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a418aa9e",
   "metadata": {},
   "source": [
    "# Grouping data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0643307",
   "metadata": {},
   "source": [
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/jillxoreilly/StatsCourse/blob//main/notebooks/descriptives_groupby.ipynb#)\n",
    "\n",
    "In many datasets, data can be categorized and we would wish to give descriptive statistics separately for each category."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab5d768b",
   "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": "b37a7c8e",
   "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": "254c65ff",
   "metadata": {},
   "source": [
    "### Load and view the data\n",
    "\n",
    "<img src=\"images/carsBanner.png\" width=100% alt=\"Picture of some cars\" >\n",
    "\n",
    "Let's load the datafile \"vehicles.csv\" which contains size data on vehicles parked at a vehicle-ferry terminal at 1pm on Sunday 24th April 2022, which they regard as a representative sample."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "60152969",
   "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>length</th>\n",
       "      <th>height</th>\n",
       "      <th>width</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.9187</td>\n",
       "      <td>1.5320</td>\n",
       "      <td>1.8030</td>\n",
       "      <td>car</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.6486</td>\n",
       "      <td>1.5936</td>\n",
       "      <td>1.6463</td>\n",
       "      <td>car</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.5785</td>\n",
       "      <td>1.5447</td>\n",
       "      <td>1.7140</td>\n",
       "      <td>car</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.5563</td>\n",
       "      <td>1.5549</td>\n",
       "      <td>1.7331</td>\n",
       "      <td>car</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.0321</td>\n",
       "      <td>1.5069</td>\n",
       "      <td>1.7320</td>\n",
       "      <td>car</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1359</th>\n",
       "      <td>15.5000</td>\n",
       "      <td>4.2065</td>\n",
       "      <td>2.5112</td>\n",
       "      <td>truck</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1360</th>\n",
       "      <td>14.4960</td>\n",
       "      <td>4.1965</td>\n",
       "      <td>2.5166</td>\n",
       "      <td>truck</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1361</th>\n",
       "      <td>15.9890</td>\n",
       "      <td>4.1964</td>\n",
       "      <td>2.4757</td>\n",
       "      <td>truck</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1362</th>\n",
       "      <td>14.3700</td>\n",
       "      <td>4.2009</td>\n",
       "      <td>2.5047</td>\n",
       "      <td>truck</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1363</th>\n",
       "      <td>14.2350</td>\n",
       "      <td>4.2016</td>\n",
       "      <td>2.5212</td>\n",
       "      <td>truck</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1364 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       length  height   width   type\n",
       "0      3.9187  1.5320  1.8030    car\n",
       "1      4.6486  1.5936  1.6463    car\n",
       "2      3.5785  1.5447  1.7140    car\n",
       "3      3.5563  1.5549  1.7331    car\n",
       "4      4.0321  1.5069  1.7320    car\n",
       "...       ...     ...     ...    ...\n",
       "1359  15.5000  4.2065  2.5112  truck\n",
       "1360  14.4960  4.1965  2.5166  truck\n",
       "1361  15.9890  4.1964  2.4757  truck\n",
       "1362  14.3700  4.2009  2.5047  truck\n",
       "1363  14.2350  4.2016  2.5212  truck\n",
       "\n",
       "[1364 rows x 4 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vehicles = pandas.read_csv('https://raw.githubusercontent.com/jillxoreilly/StatsCourseBook/main/data/vehicles.csv')\n",
    "display(vehicles)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11272619",
   "metadata": {},
   "source": [
    "That was a long list of vehicles!\n",
    "\n",
    "* What information do we have about each vehicle?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "982fd768",
   "metadata": {},
   "source": [
    "### Obtain descriptive statistics\n",
    "\n",
    "We can use the built in functions in <tt>pandas.describe()</tt> to return descriptives for our data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "48ac89ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1364.000000\n",
       "mean        6.722972\n",
       "std         4.232075\n",
       "min         3.110900\n",
       "25%         3.929450\n",
       "50%         4.419300\n",
       "75%         9.260325\n",
       "max        16.231000\n",
       "Name: length, dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vehicles['length'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "317be025",
   "metadata": {},
   "source": [
    "### Why group the data?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "089a2a2f",
   "metadata": {},
   "source": [
    "You can see above that the mean length of vehicles in the car park is 6.72m.\n",
    "\n",
    "This is surprising as it is rather longer than even a large family car\n",
    "\n",
    "To get a better sense of the length data, I am going to plot them. \n",
    "\n",
    "Don't worry too much about the plotting code for now, as there are dedicated exercises on plotting later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "2e3b7d7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'vehicle length (m)')"
      ]
     },
     "execution_count": 31,
     "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.histplot(data=vehicles, x=\"length\",  bins = np.arange(0,16,0.5))\n",
    "plt.xlabel('vehicle length (m)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "920de4e2",
   "metadata": {},
   "source": [
    "Interesting. It looks like there are several clusters of vehicle lengths. \n",
    "\n",
    "Have a look back at our dataframe - is there some information there that could explain the different clusters?\n",
    "<ul>\n",
    "    <li> Probably the clusters relate to different vehicle types\n",
    "</ul>\n",
    "\n",
    "I can plot vehicle types in different colours (again no need ot worry about the plotting code at this stage)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "6e139522",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'vehicle length (m)')"
      ]
     },
     "execution_count": 30,
     "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.histplot(data=vehicles, x=\"length\", bins = np.arange(0,16,0.5), hue=\"type\")\n",
    "plt.xlabel('vehicle length (m)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "499ed448",
   "metadata": {},
   "source": [
    "Aha. We might want to describe our data separately for each by vehicle type.\n",
    "\n",
    "### Grouping into separate dataframes\n",
    "\n",
    "One way to do this is to create separate dataframes for each vehicle type:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "0aa7145e",
   "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>length</th>\n",
       "      <th>height</th>\n",
       "      <th>width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>981.000000</td>\n",
       "      <td>981.000000</td>\n",
       "      <td>981.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.197994</td>\n",
       "      <td>1.580810</td>\n",
       "      <td>1.791925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.517761</td>\n",
       "      <td>0.059263</td>\n",
       "      <td>0.046921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.110900</td>\n",
       "      <td>1.430400</td>\n",
       "      <td>1.624100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.815400</td>\n",
       "      <td>1.540000</td>\n",
       "      <td>1.760200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.121600</td>\n",
       "      <td>1.574500</td>\n",
       "      <td>1.790400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.518400</td>\n",
       "      <td>1.611900</td>\n",
       "      <td>1.820900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6.102400</td>\n",
       "      <td>1.899300</td>\n",
       "      <td>1.958000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           length      height       width\n",
       "count  981.000000  981.000000  981.000000\n",
       "mean     4.197994    1.580810    1.791925\n",
       "std      0.517761    0.059263    0.046921\n",
       "min      3.110900    1.430400    1.624100\n",
       "25%      3.815400    1.540000    1.760200\n",
       "50%      4.121600    1.574500    1.790400\n",
       "75%      4.518400    1.611900    1.820900\n",
       "max      6.102400    1.899300    1.958000"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars = vehicles[vehicles['type']=='car']\n",
    "cars.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba7987fd",
   "metadata": {},
   "source": [
    "we can see that 981 of the vehicles were cars, and their mean length was 4.198m, much shorter than the mean over all vehicles.\n",
    "\n",
    "Try modifying the code below to get descriptive statistics for trucks:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "15f86927",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>length</th>\n",
       "      <th>height</th>\n",
       "      <th>width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>981.000000</td>\n",
       "      <td>981.000000</td>\n",
       "      <td>981.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.197994</td>\n",
       "      <td>1.580810</td>\n",
       "      <td>1.791925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.517761</td>\n",
       "      <td>0.059263</td>\n",
       "      <td>0.046921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.110900</td>\n",
       "      <td>1.430400</td>\n",
       "      <td>1.624100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.815400</td>\n",
       "      <td>1.540000</td>\n",
       "      <td>1.760200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.121600</td>\n",
       "      <td>1.574500</td>\n",
       "      <td>1.790400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.518400</td>\n",
       "      <td>1.611900</td>\n",
       "      <td>1.820900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6.102400</td>\n",
       "      <td>1.899300</td>\n",
       "      <td>1.958000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           length      height       width\n",
       "count  981.000000  981.000000  981.000000\n",
       "mean     4.197994    1.580810    1.791925\n",
       "std      0.517761    0.059263    0.046921\n",
       "min      3.110900    1.430400    1.624100\n",
       "25%      3.815400    1.540000    1.760200\n",
       "50%      4.121600    1.574500    1.790400\n",
       "75%      4.518400    1.611900    1.820900\n",
       "max      6.102400    1.899300    1.958000"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# modify the code to get descritives for trucks\n",
    "cars = vehicles[vehicles['type']=='car']\n",
    "cars.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09ef22e0",
   "metadata": {},
   "source": [
    "### <tt>pandas.groupby</tt>\n",
    "\n",
    "We can also use the pandas function <a href=\"https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.groupby.html\"><tt>groupby</tt></a> to split up our dataframe according to a categorical variable, in this case vehicle type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f8ae074e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">length</th>\n",
       "      <th colspan=\"5\" halign=\"left\">height</th>\n",
       "      <th colspan=\"8\" halign=\"left\">width</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
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       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>...</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>car</th>\n",
       "      <td>981.0</td>\n",
       "      <td>4.197994</td>\n",
       "      <td>0.517761</td>\n",
       "      <td>3.1109</td>\n",
       "      <td>3.8154</td>\n",
       "      <td>4.1216</td>\n",
       "      <td>4.5184</td>\n",
       "      <td>6.1024</td>\n",
       "      <td>981.0</td>\n",
       "      <td>1.580810</td>\n",
       "      <td>...</td>\n",
       "      <td>1.6119</td>\n",
       "      <td>1.8993</td>\n",
       "      <td>981.0</td>\n",
       "      <td>1.791925</td>\n",
       "      <td>0.046921</td>\n",
       "      <td>1.6241</td>\n",
       "      <td>1.7602</td>\n",
       "      <td>1.79040</td>\n",
       "      <td>1.82090</td>\n",
       "      <td>1.9580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>towing</th>\n",
       "      <td>53.0</td>\n",
       "      <td>8.672951</td>\n",
       "      <td>0.713460</td>\n",
       "      <td>7.2561</td>\n",
       "      <td>8.1323</td>\n",
       "      <td>8.6894</td>\n",
       "      <td>9.2191</td>\n",
       "      <td>10.0980</td>\n",
       "      <td>53.0</td>\n",
       "      <td>2.897838</td>\n",
       "      <td>...</td>\n",
       "      <td>2.9064</td>\n",
       "      <td>2.9445</td>\n",
       "      <td>53.0</td>\n",
       "      <td>2.248326</td>\n",
       "      <td>0.008222</td>\n",
       "      <td>2.2292</td>\n",
       "      <td>2.2442</td>\n",
       "      <td>2.24790</td>\n",
       "      <td>2.25400</td>\n",
       "      <td>2.2642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>truck</th>\n",
       "      <td>330.0</td>\n",
       "      <td>13.915864</td>\n",
       "      <td>1.343028</td>\n",
       "      <td>11.1480</td>\n",
       "      <td>12.5640</td>\n",
       "      <td>14.3650</td>\n",
       "      <td>15.0750</td>\n",
       "      <td>16.2310</td>\n",
       "      <td>330.0</td>\n",
       "      <td>4.072725</td>\n",
       "      <td>...</td>\n",
       "      <td>4.2009</td>\n",
       "      <td>4.2137</td>\n",
       "      <td>330.0</td>\n",
       "      <td>2.501304</td>\n",
       "      <td>0.015871</td>\n",
       "      <td>2.4629</td>\n",
       "      <td>2.4898</td>\n",
       "      <td>2.50145</td>\n",
       "      <td>2.51155</td>\n",
       "      <td>2.5467</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       length                                                           \\\n",
       "        count       mean       std      min      25%      50%      75%   \n",
       "type                                                                     \n",
       "car     981.0   4.197994  0.517761   3.1109   3.8154   4.1216   4.5184   \n",
       "towing   53.0   8.672951  0.713460   7.2561   8.1323   8.6894   9.2191   \n",
       "truck   330.0  13.915864  1.343028  11.1480  12.5640  14.3650  15.0750   \n",
       "\n",
       "                height            ...                  width            \\\n",
       "            max  count      mean  ...     75%     max  count      mean   \n",
       "type                              ...                                    \n",
       "car      6.1024  981.0  1.580810  ...  1.6119  1.8993  981.0  1.791925   \n",
       "towing  10.0980   53.0  2.897838  ...  2.9064  2.9445   53.0  2.248326   \n",
       "truck   16.2310  330.0  4.072725  ...  4.2009  4.2137  330.0  2.501304   \n",
       "\n",
       "                                                            \n",
       "             std     min     25%      50%      75%     max  \n",
       "type                                                        \n",
       "car     0.046921  1.6241  1.7602  1.79040  1.82090  1.9580  \n",
       "towing  0.008222  2.2292  2.2442  2.24790  2.25400  2.2642  \n",
       "truck   0.015871  2.4629  2.4898  2.50145  2.51155  2.5467  \n",
       "\n",
       "[3 rows x 24 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vehicles.groupby(['type']).describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a561698",
   "metadata": {},
   "source": [
    "Yikes, that was an unweildy table!\n",
    "\n",
    "It may be preferable to output descriptives only for one measure (eg length):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "e39d894a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>car</th>\n",
       "      <td>981.0</td>\n",
       "      <td>4.197994</td>\n",
       "      <td>0.517761</td>\n",
       "      <td>3.1109</td>\n",
       "      <td>3.8154</td>\n",
       "      <td>4.1216</td>\n",
       "      <td>4.5184</td>\n",
       "      <td>6.1024</td>\n",
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       "      <th>towing</th>\n",
       "      <td>53.0</td>\n",
       "      <td>8.672951</td>\n",
       "      <td>0.713460</td>\n",
       "      <td>7.2561</td>\n",
       "      <td>8.1323</td>\n",
       "      <td>8.6894</td>\n",
       "      <td>9.2191</td>\n",
       "      <td>10.0980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>truck</th>\n",
       "      <td>330.0</td>\n",
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       "      <td>1.343028</td>\n",
       "      <td>11.1480</td>\n",
       "      <td>12.5640</td>\n",
       "      <td>14.3650</td>\n",
       "      <td>15.0750</td>\n",
       "      <td>16.2310</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        count       mean       std      min      25%      50%      75%  \\\n",
       "type                                                                     \n",
       "car     981.0   4.197994  0.517761   3.1109   3.8154   4.1216   4.5184   \n",
       "towing   53.0   8.672951  0.713460   7.2561   8.1323   8.6894   9.2191   \n",
       "truck   330.0  13.915864  1.343028  11.1480  12.5640  14.3650  15.0750   \n",
       "\n",
       "            max  \n",
       "type             \n",
       "car      6.1024  \n",
       "towing  10.0980  \n",
       "truck   16.2310  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vehicles.groupby(['type'])['length'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "837e21f4",
   "metadata": {},
   "source": [
    "... or to output one descriptive (such as the mean) at a time, rather than the whole table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "cf15c7ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>car</th>\n",
       "      <td>4.197994</td>\n",
       "      <td>1.580810</td>\n",
       "      <td>1.791925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>towing</th>\n",
       "      <td>8.672951</td>\n",
       "      <td>2.897838</td>\n",
       "      <td>2.248326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>truck</th>\n",
       "      <td>13.915864</td>\n",
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      ],
      "text/plain": [
       "           length    height     width\n",
       "type                                 \n",
       "car      4.197994  1.580810  1.791925\n",
       "towing   8.672951  2.897838  2.248326\n",
       "truck   13.915864  4.072725  2.501304"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vehicles.groupby(['type']).mean()"
   ]
  }
 ],
 "metadata": {
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