14_groupby_pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_This introduction to pandas is derived from Data School's pandas Q&A with my own notes and code on top of what was provided._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using \"groupby\" in pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "url = 'http://bit.ly/drinksbycountry'\n",
    "drinks = pd.read_csv(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>beer_servings</th>\n",
       "      <th>spirit_servings</th>\n",
       "      <th>wine_servings</th>\n",
       "      <th>total_litres_of_pure_alcohol</th>\n",
       "      <th>continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>89</td>\n",
       "      <td>132</td>\n",
       "      <td>54</td>\n",
       "      <td>4.9</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0.7</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>245</td>\n",
       "      <td>138</td>\n",
       "      <td>312</td>\n",
       "      <td>12.4</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>217</td>\n",
       "      <td>57</td>\n",
       "      <td>45</td>\n",
       "      <td>5.9</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  beer_servings  spirit_servings  wine_servings  \\\n",
       "0  Afghanistan              0                0              0   \n",
       "1      Albania             89              132             54   \n",
       "2      Algeria             25                0             14   \n",
       "3      Andorra            245              138            312   \n",
       "4       Angola            217               57             45   \n",
       "\n",
       "   total_litres_of_pure_alcohol continent  \n",
       "0                           0.0      Asia  \n",
       "1                           4.9    Europe  \n",
       "2                           0.7    Africa  \n",
       "3                          12.4    Europe  \n",
       "4                           5.9    Africa  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "106.16062176165804"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get mean of the beer_servings' column\n",
    "drinks.beer_servings.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "continent\n",
       "Africa            61.471698\n",
       "Asia              37.045455\n",
       "Europe           193.777778\n",
       "North America    145.434783\n",
       "Oceania           89.687500\n",
       "South America    175.083333\n",
       "Name: beer_servings, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# using .groupby\n",
    "drinks.groupby('continent').beer_servings.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>beer_servings</th>\n",
       "      <th>spirit_servings</th>\n",
       "      <th>wine_servings</th>\n",
       "      <th>total_litres_of_pure_alcohol</th>\n",
       "      <th>continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0.7</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>217</td>\n",
       "      <td>57</td>\n",
       "      <td>45</td>\n",
       "      <td>5.9</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Benin</td>\n",
       "      <td>34</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>1.1</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Botswana</td>\n",
       "      <td>173</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>5.4</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Burkina Faso</td>\n",
       "      <td>25</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>4.3</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         country  beer_servings  spirit_servings  wine_servings  \\\n",
       "2        Algeria             25                0             14   \n",
       "4         Angola            217               57             45   \n",
       "18         Benin             34                4             13   \n",
       "22      Botswana            173               35             35   \n",
       "26  Burkina Faso             25                7              7   \n",
       "\n",
       "    total_litres_of_pure_alcohol continent  \n",
       "2                            0.7    Africa  \n",
       "4                            5.9    Africa  \n",
       "18                           1.1    Africa  \n",
       "22                           5.4    Africa  \n",
       "26                           4.3    Africa  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# here we are accessing all of Africa in the column \"continent\n",
    "drinks[drinks.continent=='Africa'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "beer_servings                   61.471698\n",
       "spirit_servings                 16.339623\n",
       "wine_servings                   16.264151\n",
       "total_litres_of_pure_alcohol     3.007547\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks[drinks.continent=='Africa'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "61.471698113207545"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks[drinks.continent=='Africa'].beer_servings.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "193.77777777777777"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks[drinks.continent=='Europe'].beer_servings.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**This is the same as the number given when we used .groupby**\n",
    "- This is because we are grouping beer_servings by the continent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**.groupby max and min**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "continent\n",
       "Africa           376\n",
       "Asia             247\n",
       "Europe           361\n",
       "North America    285\n",
       "Oceania          306\n",
       "South America    333\n",
       "Name: beer_servings, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.groupby('continent').beer_servings.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "continent\n",
       "Africa            0\n",
       "Asia              0\n",
       "Europe            0\n",
       "North America     1\n",
       "Oceania           0\n",
       "South America    93\n",
       "Name: beer_servings, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.groupby('continent').beer_servings.min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Aggregate findings**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>continent</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>376</td>\n",
       "      <td>61.471698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>247</td>\n",
       "      <td>37.045455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>361</td>\n",
       "      <td>193.777778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North America</th>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>285</td>\n",
       "      <td>145.434783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>306</td>\n",
       "      <td>89.687500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South America</th>\n",
       "      <td>12</td>\n",
       "      <td>93</td>\n",
       "      <td>333</td>\n",
       "      <td>175.083333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               count  min  max        mean\n",
       "continent                                 \n",
       "Africa            53    0  376   61.471698\n",
       "Asia              44    0  247   37.045455\n",
       "Europe            45    0  361  193.777778\n",
       "North America     23    1  285  145.434783\n",
       "Oceania           16    0  306   89.687500\n",
       "South America     12   93  333  175.083333"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.groupby('continent').beer_servings.agg(['count', 'min', 'max', 'mean'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**You can get mean of all numeric columns instead of specifying beer_servings**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>beer_servings</th>\n",
       "      <th>spirit_servings</th>\n",
       "      <th>wine_servings</th>\n",
       "      <th>total_litres_of_pure_alcohol</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>continent</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>61.471698</td>\n",
       "      <td>16.339623</td>\n",
       "      <td>16.264151</td>\n",
       "      <td>3.007547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>37.045455</td>\n",
       "      <td>60.840909</td>\n",
       "      <td>9.068182</td>\n",
       "      <td>2.170455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>193.777778</td>\n",
       "      <td>132.555556</td>\n",
       "      <td>142.222222</td>\n",
       "      <td>8.617778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North America</th>\n",
       "      <td>145.434783</td>\n",
       "      <td>165.739130</td>\n",
       "      <td>24.521739</td>\n",
       "      <td>5.995652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>89.687500</td>\n",
       "      <td>58.437500</td>\n",
       "      <td>35.625000</td>\n",
       "      <td>3.381250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South America</th>\n",
       "      <td>175.083333</td>\n",
       "      <td>114.750000</td>\n",
       "      <td>62.416667</td>\n",
       "      <td>6.308333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               beer_servings  spirit_servings  wine_servings  \\\n",
       "continent                                                      \n",
       "Africa             61.471698        16.339623      16.264151   \n",
       "Asia               37.045455        60.840909       9.068182   \n",
       "Europe            193.777778       132.555556     142.222222   \n",
       "North America     145.434783       165.739130      24.521739   \n",
       "Oceania            89.687500        58.437500      35.625000   \n",
       "South America     175.083333       114.750000      62.416667   \n",
       "\n",
       "               total_litres_of_pure_alcohol  \n",
       "continent                                    \n",
       "Africa                             3.007547  \n",
       "Asia                               2.170455  \n",
       "Europe                             8.617778  \n",
       "North America                      5.995652  \n",
       "Oceania                            3.381250  \n",
       "South America                      6.308333  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.groupby('continent').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Visualization**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# allow plots to appear in notebook using matplotlib\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>beer_servings</th>\n",
       "      <th>spirit_servings</th>\n",
       "      <th>wine_servings</th>\n",
       "      <th>total_litres_of_pure_alcohol</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>continent</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>61.471698</td>\n",
       "      <td>16.339623</td>\n",
       "      <td>16.264151</td>\n",
       "      <td>3.007547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>37.045455</td>\n",
       "      <td>60.840909</td>\n",
       "      <td>9.068182</td>\n",
       "      <td>2.170455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>193.777778</td>\n",
       "      <td>132.555556</td>\n",
       "      <td>142.222222</td>\n",
       "      <td>8.617778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North America</th>\n",
       "      <td>145.434783</td>\n",
       "      <td>165.739130</td>\n",
       "      <td>24.521739</td>\n",
       "      <td>5.995652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>89.687500</td>\n",
       "      <td>58.437500</td>\n",
       "      <td>35.625000</td>\n",
       "      <td>3.381250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South America</th>\n",
       "      <td>175.083333</td>\n",
       "      <td>114.750000</td>\n",
       "      <td>62.416667</td>\n",
       "      <td>6.308333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               beer_servings  spirit_servings  wine_servings  \\\n",
       "continent                                                      \n",
       "Africa             61.471698        16.339623      16.264151   \n",
       "Asia               37.045455        60.840909       9.068182   \n",
       "Europe            193.777778       132.555556     142.222222   \n",
       "North America     145.434783       165.739130      24.521739   \n",
       "Oceania            89.687500        58.437500      35.625000   \n",
       "South America     175.083333       114.750000      62.416667   \n",
       "\n",
       "               total_litres_of_pure_alcohol  \n",
       "continent                                    \n",
       "Africa                             3.007547  \n",
       "Asia                               2.170455  \n",
       "Europe                             8.617778  \n",
       "North America                      5.995652  \n",
       "Oceania                            3.381250  \n",
       "South America                      6.308333  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = drinks.groupby('continent').mean()\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1179ad1d0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ccANLliyhcePGnHDCCUyaNIk//elP9O3blxkzZjB8+HAOPvhgnnvuuViyr0mpe//992fc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\nsmnTJlasWBEbrTv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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11799f6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(kind='bar')"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [py3k]",
   "language": "python",
   "name": "Python [py3k]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}