Exercises_part_B_with_Solutions.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "%matplotlib inline\n",
    "matplotlib.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 id=\"tocheading\">Table of Contents</h1>\n",
    "<div id=\"toc\"></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "$.getScript('misc/kmahelona_ipython_notebook_toc.js')"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%javascript\n",
    "$.getScript('misc/kmahelona_ipython_notebook_toc.js')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting and Knowing your Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Task:** load the following file as a data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fn = r\"data/drinks.csv\" "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Answer:\n",
    "df = pd.read_csv(fn, sep=\",\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Task:** See the first 10 entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "scrolled": 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>AS</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>EU</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>AF</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>EU</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>AF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Antigua &amp; Barbuda</td>\n",
       "      <td>102</td>\n",
       "      <td>128</td>\n",
       "      <td>45</td>\n",
       "      <td>4.9</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>193</td>\n",
       "      <td>25</td>\n",
       "      <td>221</td>\n",
       "      <td>8.3</td>\n",
       "      <td>SA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Armenia</td>\n",
       "      <td>21</td>\n",
       "      <td>179</td>\n",
       "      <td>11</td>\n",
       "      <td>3.8</td>\n",
       "      <td>EU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Australia</td>\n",
       "      <td>261</td>\n",
       "      <td>72</td>\n",
       "      <td>212</td>\n",
       "      <td>10.4</td>\n",
       "      <td>OC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Austria</td>\n",
       "      <td>279</td>\n",
       "      <td>75</td>\n",
       "      <td>191</td>\n",
       "      <td>9.7</td>\n",
       "      <td>EU</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",
       "5  Antigua & Barbuda            102              128             45   \n",
       "6          Argentina            193               25            221   \n",
       "7            Armenia             21              179             11   \n",
       "8          Australia            261               72            212   \n",
       "9            Austria            279               75            191   \n",
       "\n",
       "   total_litres_of_pure_alcohol continent  \n",
       "0                           0.0        AS  \n",
       "1                           4.9        EU  \n",
       "2                           0.7        AF  \n",
       "3                          12.4        EU  \n",
       "4                           5.9        AF  \n",
       "5                           4.9       NaN  \n",
       "6                           8.3        SA  \n",
       "7                           3.8        EU  \n",
       "8                          10.4        OC  \n",
       "9                           9.7        EU  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Answer:\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Task:** Which country has the highest alcohol consumption (total litres of pure alcohol)? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>15</th>\n",
       "      <td>Belarus</td>\n",
       "      <td>142</td>\n",
       "      <td>373</td>\n",
       "      <td>42</td>\n",
       "      <td>14.4</td>\n",
       "      <td>EU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>Lithuania</td>\n",
       "      <td>343</td>\n",
       "      <td>244</td>\n",
       "      <td>56</td>\n",
       "      <td>12.9</td>\n",
       "      <td>EU</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>EU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Grenada</td>\n",
       "      <td>199</td>\n",
       "      <td>438</td>\n",
       "      <td>28</td>\n",
       "      <td>11.9</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>361</td>\n",
       "      <td>170</td>\n",
       "      <td>134</td>\n",
       "      <td>11.8</td>\n",
       "      <td>EU</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           country  beer_servings  spirit_servings  wine_servings  \\\n",
       "15         Belarus            142              373             42   \n",
       "98       Lithuania            343              244             56   \n",
       "3          Andorra            245              138            312   \n",
       "68         Grenada            199              438             28   \n",
       "45  Czech Republic            361              170            134   \n",
       "\n",
       "    total_litres_of_pure_alcohol continent  \n",
       "15                          14.4        EU  \n",
       "98                          12.9        EU  \n",
       "3                           12.4        EU  \n",
       "68                          11.9       NaN  \n",
       "45                          11.8        EU  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Answer\n",
    "df.sort_values(\"total_litres_of_pure_alcohol\", ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Groupby"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Task:** Which continent drinks most beer on average?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "continent\n",
       "AF     61.471698\n",
       "AS     37.045455\n",
       "EU    193.777778\n",
       "OC     89.687500\n",
       "SA    175.083333\n",
       "Name: beer_servings, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Answer\n",
    "# df.groupby(\"continent\").beer_servings.mean()\n",
    "# or \n",
    "df.groupby(\"continent\")[\"beer_servings\"].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Task:** List all unique continents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['AS', 'EU', 'AF', nan, 'SA', 'OC'], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Answer:\n",
    "df[\"continent\"].unique()\n",
    "\n",
    "# or \n",
    "# set(df[\"continent\"].tolist())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Task:** Which countries have missing values in the continent column?"
   ]
  },
  {
   "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>5</th>\n",
       "      <td>Antigua &amp; Barbuda</td>\n",
       "      <td>102</td>\n",
       "      <td>128</td>\n",
       "      <td>45</td>\n",
       "      <td>4.9</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Bahamas</td>\n",
       "      <td>122</td>\n",
       "      <td>176</td>\n",
       "      <td>51</td>\n",
       "      <td>6.3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Barbados</td>\n",
       "      <td>143</td>\n",
       "      <td>173</td>\n",
       "      <td>36</td>\n",
       "      <td>6.3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Belize</td>\n",
       "      <td>263</td>\n",
       "      <td>114</td>\n",
       "      <td>8</td>\n",
       "      <td>6.8</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Canada</td>\n",
       "      <td>240</td>\n",
       "      <td>122</td>\n",
       "      <td>100</td>\n",
       "      <td>8.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Costa Rica</td>\n",
       "      <td>149</td>\n",
       "      <td>87</td>\n",
       "      <td>11</td>\n",
       "      <td>4.4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Cuba</td>\n",
       "      <td>93</td>\n",
       "      <td>137</td>\n",
       "      <td>5</td>\n",
       "      <td>4.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Dominica</td>\n",
       "      <td>52</td>\n",
       "      <td>286</td>\n",
       "      <td>26</td>\n",
       "      <td>6.6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Dominican Republic</td>\n",
       "      <td>193</td>\n",
       "      <td>147</td>\n",
       "      <td>9</td>\n",
       "      <td>6.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>El Salvador</td>\n",
       "      <td>52</td>\n",
       "      <td>69</td>\n",
       "      <td>2</td>\n",
       "      <td>2.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Grenada</td>\n",
       "      <td>199</td>\n",
       "      <td>438</td>\n",
       "      <td>28</td>\n",
       "      <td>11.9</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>Guatemala</td>\n",
       "      <td>53</td>\n",
       "      <td>69</td>\n",
       "      <td>2</td>\n",
       "      <td>2.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>Haiti</td>\n",
       "      <td>1</td>\n",
       "      <td>326</td>\n",
       "      <td>1</td>\n",
       "      <td>5.9</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>Honduras</td>\n",
       "      <td>69</td>\n",
       "      <td>98</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Jamaica</td>\n",
       "      <td>82</td>\n",
       "      <td>97</td>\n",
       "      <td>9</td>\n",
       "      <td>3.4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>Mexico</td>\n",
       "      <td>238</td>\n",
       "      <td>68</td>\n",
       "      <td>5</td>\n",
       "      <td>5.5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>Nicaragua</td>\n",
       "      <td>78</td>\n",
       "      <td>118</td>\n",
       "      <td>1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>Panama</td>\n",
       "      <td>285</td>\n",
       "      <td>104</td>\n",
       "      <td>18</td>\n",
       "      <td>7.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>St. Kitts &amp; Nevis</td>\n",
       "      <td>194</td>\n",
       "      <td>205</td>\n",
       "      <td>32</td>\n",
       "      <td>7.7</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>St. Lucia</td>\n",
       "      <td>171</td>\n",
       "      <td>315</td>\n",
       "      <td>71</td>\n",
       "      <td>10.1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>St. Vincent &amp; the Grenadines</td>\n",
       "      <td>120</td>\n",
       "      <td>221</td>\n",
       "      <td>11</td>\n",
       "      <td>6.3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>Trinidad &amp; Tobago</td>\n",
       "      <td>197</td>\n",
       "      <td>156</td>\n",
       "      <td>7</td>\n",
       "      <td>6.4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>USA</td>\n",
       "      <td>249</td>\n",
       "      <td>158</td>\n",
       "      <td>84</td>\n",
       "      <td>8.7</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          country  beer_servings  spirit_servings  \\\n",
       "5               Antigua & Barbuda            102              128   \n",
       "11                        Bahamas            122              176   \n",
       "14                       Barbados            143              173   \n",
       "17                         Belize            263              114   \n",
       "32                         Canada            240              122   \n",
       "41                     Costa Rica            149               87   \n",
       "43                           Cuba             93              137   \n",
       "50                       Dominica             52              286   \n",
       "51             Dominican Republic            193              147   \n",
       "54                    El Salvador             52               69   \n",
       "68                        Grenada            199              438   \n",
       "69                      Guatemala             53               69   \n",
       "73                          Haiti              1              326   \n",
       "74                       Honduras             69               98   \n",
       "84                        Jamaica             82               97   \n",
       "109                        Mexico            238               68   \n",
       "122                     Nicaragua             78              118   \n",
       "130                        Panama            285              104   \n",
       "143             St. Kitts & Nevis            194              205   \n",
       "144                     St. Lucia            171              315   \n",
       "145  St. Vincent & the Grenadines            120              221   \n",
       "174             Trinidad & Tobago            197              156   \n",
       "184                           USA            249              158   \n",
       "\n",
       "     wine_servings  total_litres_of_pure_alcohol continent  \n",
       "5               45                           4.9       NaN  \n",
       "11              51                           6.3       NaN  \n",
       "14              36                           6.3       NaN  \n",
       "17               8                           6.8       NaN  \n",
       "32             100                           8.2       NaN  \n",
       "41              11                           4.4       NaN  \n",
       "43               5                           4.2       NaN  \n",
       "50              26                           6.6       NaN  \n",
       "51               9                           6.2       NaN  \n",
       "54               2                           2.2       NaN  \n",
       "68              28                          11.9       NaN  \n",
       "69               2                           2.2       NaN  \n",
       "73               1                           5.9       NaN  \n",
       "74               2                           3.0       NaN  \n",
       "84               9                           3.4       NaN  \n",
       "109              5                           5.5       NaN  \n",
       "122              1                           3.5       NaN  \n",
       "130             18                           7.2       NaN  \n",
       "143             32                           7.7       NaN  \n",
       "144             71                          10.1       NaN  \n",
       "145             11                           6.3       NaN  \n",
       "174              7                           6.4       NaN  \n",
       "184             84                           8.7       NaN  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Answer:\n",
    "df[df[\"continent\"].isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Task:** Set \"the\" missing continent with a name of your choice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Answer:\n",
    "df.loc[df[\"continent\"].isnull(), \"continent\"] = \"NAs\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Task:** For each continent print \"the\" statistics (summary stats using \"df.describe()\") for wine consumption."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "continent       \n",
       "AF         count     53.000000\n",
       "           mean      16.264151\n",
       "           std       38.846419\n",
       "           min        0.000000\n",
       "           25%        1.000000\n",
       "           50%        2.000000\n",
       "           75%       13.000000\n",
       "           max      233.000000\n",
       "AS         count     44.000000\n",
       "           mean       9.068182\n",
       "           std       21.667034\n",
       "           min        0.000000\n",
       "           25%        0.000000\n",
       "           50%        1.000000\n",
       "           75%        8.000000\n",
       "           max      123.000000\n",
       "EU         count     45.000000\n",
       "           mean     142.222222\n",
       "           std       97.421738\n",
       "           min        0.000000\n",
       "           25%       59.000000\n",
       "           50%      128.000000\n",
       "           75%      195.000000\n",
       "           max      370.000000\n",
       "NAs        count     23.000000\n",
       "           mean      24.521739\n",
       "           std       28.266378\n",
       "           min        1.000000\n",
       "           25%        5.000000\n",
       "           50%       11.000000\n",
       "           75%       34.000000\n",
       "           max      100.000000\n",
       "OC         count     16.000000\n",
       "           mean      35.625000\n",
       "           std       64.555790\n",
       "           min        0.000000\n",
       "           25%        1.000000\n",
       "           50%        8.500000\n",
       "           75%       23.250000\n",
       "           max      212.000000\n",
       "SA         count     12.000000\n",
       "           mean      62.416667\n",
       "           std       88.620189\n",
       "           min        1.000000\n",
       "           25%        3.000000\n",
       "           50%       12.000000\n",
       "           75%       98.500000\n",
       "           max      221.000000\n",
       "Name: wine_servings, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Answer:\n",
    "# df.groupby('continent').wine_servings.describe()\n",
    "\n",
    "# or\n",
    "\n",
    "df.groupby('continent')[\"wine_servings\"].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Task:** Print the median alcoohol consumption per continent for every column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>AF</th>\n",
       "      <td>32.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AS</th>\n",
       "      <td>17.5</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EU</th>\n",
       "      <td>219.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>10.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NAs</th>\n",
       "      <td>143.0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OC</th>\n",
       "      <td>52.5</td>\n",
       "      <td>37.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>1.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SA</th>\n",
       "      <td>162.5</td>\n",
       "      <td>108.5</td>\n",
       "      <td>12.0</td>\n",
       "      <td>6.85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           beer_servings  spirit_servings  wine_servings  \\\n",
       "continent                                                  \n",
       "AF                  32.0              3.0            2.0   \n",
       "AS                  17.5             16.0            1.0   \n",
       "EU                 219.0            122.0          128.0   \n",
       "NAs                143.0            137.0           11.0   \n",
       "OC                  52.5             37.0            8.5   \n",
       "SA                 162.5            108.5           12.0   \n",
       "\n",
       "           total_litres_of_pure_alcohol  \n",
       "continent                                \n",
       "AF                                 2.30  \n",
       "AS                                 1.20  \n",
       "EU                                10.00  \n",
       "NAs                                6.30  \n",
       "OC                                 1.75  \n",
       "SA                                 6.85  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Answer:\n",
    "df.groupby('continent').median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Task:** Print the mean, min and max values for spirit consumption."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>continent</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AF</th>\n",
       "      <td>16.339623</td>\n",
       "      <td>0</td>\n",
       "      <td>152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AS</th>\n",
       "      <td>60.840909</td>\n",
       "      <td>0</td>\n",
       "      <td>326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EU</th>\n",
       "      <td>132.555556</td>\n",
       "      <td>0</td>\n",
       "      <td>373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NAs</th>\n",
       "      <td>165.739130</td>\n",
       "      <td>68</td>\n",
       "      <td>438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OC</th>\n",
       "      <td>58.437500</td>\n",
       "      <td>0</td>\n",
       "      <td>254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SA</th>\n",
       "      <td>114.750000</td>\n",
       "      <td>25</td>\n",
       "      <td>302</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 mean  min  max\n",
       "continent                      \n",
       "AF          16.339623    0  152\n",
       "AS          60.840909    0  326\n",
       "EU         132.555556    0  373\n",
       "NAs        165.739130   68  438\n",
       "OC          58.437500    0  254\n",
       "SA         114.750000   25  302"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Answer:\n",
    "df.groupby('continent').spirit_servings.agg(['mean', 'min', 'max'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Task:** GroupBy Continent and create a Boxplot. (Hint: using e.g. `figsize=(12, 9), rot=90` might help with legibility.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AF          Axes(0.1,0.679412;0.363636x0.220588)\n",
       "AS     Axes(0.536364,0.679412;0.363636x0.220588)\n",
       "EU          Axes(0.1,0.414706;0.363636x0.220588)\n",
       "NAs    Axes(0.536364,0.414706;0.363636x0.220588)\n",
       "OC              Axes(0.1,0.15;0.363636x0.220588)\n",
       "SA         Axes(0.536364,0.15;0.363636x0.220588)\n",
       "dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/+9+6/fbbVVpaqkGDBikvL0+zZ8+mKHeirq5OVqtV7777rtLS0nTLLbcoLy/P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\nWC8NeO2dDu/HTpyQ8YYa7z59+ninkFRUVKh3796SpPDw8Ca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RH//4R7NLso0+ffqob9++\n6tWrl/eWzLi03r176+DBg96fDxw4oF69eplYEeyEjG8/Mt44Mr79nJbxHPFuwebNm7V//35FRUUp\nISFB48aNU8+ePc0uy/K+/vpr77f5Xr16adKkSZowYQKnI1vxzjvvaO/evaqqqtLEiRM1adIk7g7X\nRsXFxVqzZo3Ky8slSREREfrnf/5nDRw40OTKYGVkvDFkvDFkvHFOy3ga72Z4PB796U9/0o033qje\nvXubXY6tLF68WAkJCZo4cSLzJdvh9ddf16RJkzRkyBCzS7Gturo6SWLeJFpFxhtHxhtDxnecUzKe\ndbyb4XK5tHfvXt15551ml2IrbrdbAwYMUHJystml2Irb7db+/fv1i1/8wuxSbOWtt9665OM333yz\nnyqB3ZDxxpDxxpDxxjg142m8W3DllVfq888/17Bhw8wuxTYCAgJ06tQpNTQ0KCiIj1ZbBQQEKDo6\nWmVlZYqMjDS7HNs4e/as2SXAxsj49iPjjSHjjXFqxjPVpAXz5s1TcXGx+vfvr5CQEO9dzbjL1KWt\nXbtWJ06c0HXXXdfkdJBdv5n6y5IlS/Tll19q2LBhCgkJ8W5PT083sSrAuch4Y8h4Y8h4XMBX1hYs\nXrzY7BJsKSoqSlFRUfJ4PI79ttoZ7rrrLrNLsK3z588rNzdXX3/9tc6fP+/dPmfOHBOrgtWR8caQ\n8caQ8cY5LeM54n0Jn376qb755hvdcMMNqqqqUl1dnW3vlORv586da/KtHq07efKkvvnmG1177bU6\nd+6c3G63evToYXZZlrdq1SpFR0crLy9Pd9xxh9577z3FxMTo/vvvN7s0WBwZbxwZ335kvDFOy3jW\n8W7Bf/7nfyonJ0c5OTmSpIaGBq1Zs8bkqqyvoKBA8+fP17x58yRJx44d07p160yuyvp27NihVatW\n6fe//70kqby83LY3B/C34uJizZw5UyEhIUpMTNSiRYv0+eefm10WLI6MN4aMN4aMN85pGU/j3YL9\n+/crPT3d+40+PDyc02ptsGnTJi1evNi7uP2QIUN05MgRk6uyvnfeeUeZmZneox+XXXYZN1doo8DA\nQElSz5499fe//121tbWMHVpFxhtDxhtDxhvntIxnjncLgoKC5HK55HK5JP1j/Ui07v9etR0QwPe7\n1gQHBzdZJaCxsdH72cOlJSUlqaamRnfddZdWrlypuro65lOiVWS8cWR8+5Hxxjkt42m8W3D99dfr\n5Zdf1pkzZ7Rjxw797//+r6ZNm2Z2WZYXERGhzz77TC6XSw0NDdq+fbtiYmLMLsvyRowYoTfffFPn\nz5/X4cOH9c477+i6664zuyxbuPDvcsSIEVq7du1Fj+/cuVOJiYl+rgpWR8YbQ8YbQ8Yb57SM5+LK\nSzh8+LA++ugjeTwejRo1Stdee63ZJVleVVWVNm3apL/+9a/yeDy69tprdf/993tPS6J5brdbubm5\nOnz4sDwej77//e9r2rRpHBHxgfT0dK1YscLsMmBBZHz7kfHGkPGdx24ZT+Pdgrq6OnXr1k0BAQEq\nKipSUVGRRo0axU0D0Olqamp06tQpDR482OxSHOGxxx7TypUrzS4DFkPGwyxkvG/ZLeOZmNWCJUuW\nqL6+XuXl5Vq2bJl2796tF1980eyyLG/z5s2qra1VQ0ODnnrqKf3yl7/U7t27zS7L8pYuXara2lrV\n1NQoPT1dL730kjZt2mR2WY7AESU0h4w3how3hozvPHbLeBrvSwgJCVF+fr5uvPFGLViwQF999ZXZ\nJVneRx99pNDQUB06dEj9+/fXmjVr9Oc//9nssiyvtrZWoaGhys/P15QpU7R8+XJ9/PHHZpflCJzU\nQ0vI+PYj440h4zuP3TKexrsFHo9HBQUFeu+99zRmzBhJ387RwqVdGKNDhw7p+uuvV2hoqMkV2UNj\nY6MqKiq0d+9e7+cNvnHVVVeZXQIsiIw3how3hozvPHbLeBrvFtx3333aunWrxo0bp0GDBqmkpETx\n8fFml2V5Y8aM0bx583T06FGNHDlSVVVVCg4ONrssy7vzzju1bNkyDRw4UMOGDVNJSYkGDhxodlm2\nUFlZqX/7t3/T8uXLJUlff/21cnNzvY//8pe/NKs0WBgZbwwZbwwZb5zTMp6LKw3asGGDHnjgAbPL\nsKSamhqFhoYqICBAdXV1qqurU9++fSV9u4oAKwe039atW3XbbbeZXYYlLV++XImJidq6daueffZZ\nNTY26rHHHtNzzz1ndmmwMTK+ZWS875HxLXNaxnPE26DPPvvM7BIsKywszHtDhe7du3sDWZK2bNli\nVlm2tm/fPrNLsKzq6mpNmjTJe4FNYGAgN/RAh5HxLSPjfY+Mb5nTMt6+lcOWOMFiDOPWspCQEFVX\nV3tDuaCggHmngEnIKmMYt5Y5LeNZsBR+Zbdlf6yCcWvZPffco5UrV6q4uFgZGRmqqqrSggULzC4L\n6JLIKmMYt5Y5LeNpvA3i2yn8ic9b89xut+rr67V06VIVFRXJ4/EoOjqam6Cgw/g3B3/i89Y8J2Y8\nU02a4Xa79eqrr17yOcnJyX6qxl7q6+svua1///7+LMcxJk6caHYJlhQQEKD169crMDBQgwYN0hVX\nXGHrQIZ/kPHGkfGdg4xvnhMznsa7GQEBAa1eWJOYmOifYmzmN7/5zSW3paWl+bMc2ygqKtJTTz2l\nRx99VJJ0/Phx/elPf/I+fvvtt5tVmuWNHDlS+/bt44gR2oyMN46MN4aMN85pGW/vrw2daMiQIVqx\nYoWuv/56hYSEeLdPmDDBxKqsq7KyUuXl5Tp//ry+/PJL7z+Qs2fP6ty5cyZXZ30vvfSSZs+erZdf\nflmSNHjwYGVnZ+uOO+4wuTLr27Fjh/7yl78oICBA3bp1k8fjkcvl0iuvvGJ2abAwMr59yPiOIeON\nc1rG03i3oL6+Xr169brolq6EcvM+/PBD7dq1S6dOnWpyCrd79+76+c9/bmJl9nD+/HkNGzasyTY7\nL5fkT61NGQCaQ8a3DxnfMWS8cU7LeBrvFsyZM8fsEmwlMTFRiYmJ2rdvH3PVDOjVq5eKi4u9V7bv\n27dP/fr1M7kqe/jb3/7W7PYRI0b4uRLYCRnfPmR8x5Dxxjkt47lzZQuKioq0bt06nT59Ws8995yO\nHz+ugwcPclqoBbt379bkyZP15z//udllkW6++WYTqrKPkpISvfzyy/rss8/Us2dPDRgwQHPnztWA\nAQPMLs3ynnnmGe9/19fX6/PPP1dsbKyWLFliYlWwOjK+fcj4jiHjjXNaxnPEuwXMx2qfC3P86urq\nTK7EnqKiopSRkaG6ujp5PB716NHD7JJsY9GiRU1+Lisr06ZNm8wpBrZBxrcPGd8xZLxxTst4Gu8W\nMB+rfX70ox/J7XarR48eHPkwoL6+Xvn5+SotLZXb7fZuv/POO02syp4iIiJ04sQJs8uAxZHx7UPG\ndwwZ7zt2z3ga7xYwH6v9AgIClJeXRygbsHLlSoWGhio2NlbBwcFml2MrGzZs8P63x+PRsWPHdOWV\nV5pYEeyAjG8/Mt44Mt44p2U8c7xbwHwsYzZt2qTGxkZNmjSpyRJdsbGxJlZlfY8++qiee+45s8uw\npZ07d3r/OzAwUP3799f3vvc98wqCLZDxxpDxxpDxxjkt42m8W8F8rPZ58sknm91u14sg/OWll17S\nT37yE11xxRVml2JLDQ0NOnHihFwul+1vJwz/IuPbh4w3hozvGCdlPI13CyorK/Xv//7vqqio0OOP\nP66vv/5aBQUFmjp1qtmlwYHmz5+v4uJiDRgwQMHBwd4bBGRlZZldmuUdOnRIv//97xUVFSWPx6PS\n0lL96le/0ujRo80uDRZGxsOfyHjjnJbx9v3K0MlefPFFJSYmauvWrZKkyy67TKtXryaUW3Bhqam3\n3nqr2ceZE3hpjz/+uNkl2Narr76qJUuWaODAgZKk4uJiPfPMM7YNZfgHGd8+ZHzHkPHGOS3jabxb\nUF1drUmTJiknJ0fSt/OKuOK9ZReWmjp79qzJldhLbW2tQkNDOc3dAT169PAGsvTtsl2MJ1pDxrcP\nGW8MGd9xTst4Gu8WhISEqLq62nvFe0FBgUJDQ02uyrp+9KMfSZJ++tOfmlyJvWRnZ2vRokVKT0+X\ny+XSd2d+uVwurV271sTq7CE2NlZPP/20rr/+eknfrk4xdOhQ5efnS+IW4GgeGd8+ZLwxZHzHOS3j\nmePdgqNHj2rjxo366quvNGjQIFVVVWnBggUaPHiw2aVZWklJiTZu3KjCwkK5XC4NHz5c9957r6Ki\noswuzdKys7M1YsQIXX311YqJiTG7HFt58cUXL/k4twZHc8h4Y8h4Y8h445yW8TTeLTh//rzefvtt\nffTRR+rRo4eGDx+uH//4x+rWrZvZpVna4sWLNX36dCUkJEiS8vLy9Pbbb2v58uUmV2ZtH3/8sT79\n9FMdOXJEJSUluvLKK3X11VcrOTnZ7NJsb+vWrbrtttvMLgMWQ8YbQ8YbQ8Z3HrtlPBPaWrB27VoV\nFRXptttu049//GMVFRVxSqgNzp07p8mTJyswMFCBgYGaPHmy6uvrzS7L8kaOHKnbb79dd911l6ZN\nm6YvvvhC7777rtllOcK+ffvMLgEWRMYbQ8YbQ8Z3HrtlPHO8W/DVV19p9erV3p9Hjhyp+fPnm1iR\nPYwaNUo5OTmaNGmSXC6X9uzZo9GjR6umpkaSFBYWZnKF1vTUU0/p3LlziouL09VXX62nn35affr0\nMbssR+CkHppDxhtDxhtDxnceu2U8jXcLrrzyShUUFGj48OGSpMLCQg0dOtTkqqxv7969kqT//u//\n9l605PF4lJeXx4Ukl3DFFVfoyy+/1FdffaXQ0FD17NlTPXr04LS3D1z4HALfRcYbQ8YbQ8Z3Hrtl\nPI33//Hoo4/K5XKpsbFRGRkZioyMlCSVlZUpOjra5Oqs7+6779aoUaMUGhqqP/7xj/ryyy91xx13\ncDvhVtx3332Svl2qa+fOnXrxxRdVWVmp119/3dzCHMBuR0PQucj4jiHjjSHjO4/dMp7G+/9YtGiR\n2SXY2ptvvqlJkybp008/1SeffKJbbrlF69at48KbVrz99ts6cuSIjh49qgEDBuiGG27Q1VdfbXZZ\nlrZ582bNmjVLe/fu9S4z1ZyJEyf6sSpYHRnfMWS8MWR8+zk142m8/4/+/fubXYKtXbgBxaFDhzRt\n2jSNGTNGf/jDH0yuyvrOnz+vm2++WbGxsQoMDDS7HFv44IMPdPfddysnJ+eSoXz77bf7sSpYHRnf\nMWS8MWR8+zk142m84VPh4eF6+eWXdfjwYd16662qr6+33WkgM8yYMcPsEmxn1KhRuv/++1VXV6d7\n771XHo/He4MKl8ulV155xewSAcch440h49vPqRnPOt7wqXPnzunDDz/UFVdcocsuu0wVFRX6+9//\nru9///tmlwaHWrlypR577DGzywC6BDIe/ua0jKfxBmB7lZWV+uKLLyRJcXFx6t27t8kVAQB8xUkZ\nT+MNwNb27t2r1157TSNGjJAkHTlyRLNnz7bdBTcAgIs5LeOZ4w3A1t58880mN6OoqqpSZmambUMZ\nAPAPTst4bhkPwNbcbneTO8CFhYXJ7XabWBEAwFeclvEc8QZga6NGjdKyZcuUkJAgSd5bWAMA7M9p\nGc8cbwC2l5+fr08//VSSdPXVV2v8+PEmVwQA8BUnZTyNNwBHW7x4sZYtW2Z2GQCATmC3jGeONwBH\nq6+vN7sEAEAnsVvG03gDcDSXy2V2CQCATmK3jKfxBgAAAPyAxhuAo3EZCwA4l90ynosrAdhaXV2d\nunXrpoCAABUVFamoqEijRo1SUNC3q6X+/e9/1xVXXGFylQAAI5yW8RzxBmBrS5YsUX19vcrLy7Vs\n2TLt3r1bL774ovdxOwUyAKApp2U8jTcA2wsJCVF+fr5uvPFGLViwQF999ZXZJQEAfMRJGU/jDcDW\nPB6PCgoK9N5772nMmDGSZOvbCQMA/sFpGU/jDcDW7rvvPm3dulXjxo3ToEGDVFJSovj4eLPLAgD4\ngNMynosrATjCuXPnFBISYnYZAIBO4JSM54g3AFsrKCjQ/PnzNW/ePEnSsWPHtG7dOpOrAgD4gtMy\nnsYbgK1t2rRJixcvVq9evSRJQ4YM0ZEjR0yuCgDgC07LeBpvALYXGRnZ5OeAAKINAJzCSRkfZHYB\nANARERER+uyzz+RyudTQ0KDt27crJibG7LIAAD7gtIzn4koAtlZVVaVNmzbpr3/9qzwej6699lrd\nf//93tOSAAD7clrG03gDsC23263t27fr5ptvNrsUAICPOTHj7TtJBkCXFxAQoLy8PLPLAAB0Aidm\nPEe8Adjapk2b1NjYqEmTJjVZ4zU2NtbEqgAAvuC0jOfiSgC2dvz4cUnSG2+80WT7kiVLzCgHAOBD\nTst4jngDsLWSkhJFRUW1ug0AYD9Oy3jmeAOwtVWrVrVpGwDAfpyW8Uw1AWBLJ06c0FdffaXa2lrl\n5+d7t589e1b19fUmVgYA6CinZjyNNwBbKioq0qFDh3TmzBm9//773u3du3fXr3/9axMrAwB0lFMz\nnjneAGytoKBAw4cPN7sMAEAncFrG03gDsKVt27bp1ltv1YYNG5p9/IEHHvBzRQAAX3FqxjPVBIAt\nxcTESLLvWq4AgJY5NeM54g0AAAD4AUe8AdjSM888I5fL1eLj6enpfqwGAOBLTs14Gm8AtjRjxgyz\nSwAAdBKnZjxTTQA4WlZWltLS0swuAwDQCeyW8dy5EoCjlZaWml0CAKCT2C3jabwBONql5ggCAOzN\nbhlP4w0AAAD4AY03AEfjMhYAcC67ZTyNNwBHu/vuu80uAQDQSeyW8axqAsCWHn300Wbn9nk8Hrlc\nLmVlZZlQFQDAF5ya8TTeAGzp5MmTl3y8f//+fqoEAOBrTs14Gm8AAADAD7hzJQBbKygo0MaNG/X1\n11+roaFBbrdb3bt31yuvvGJ2aQCADnJaxnNxJQBb27Bhgx555BFddtll2rJlix5++GFNnz7d7LIA\nAD7gtIyn8QZgewMHDpTb7VZAQIBuuOEGffjhh2aXBADwESdlPFNNANhaSEiIGhoaNGTIEG3evFl9\n+/a13bquAIDmOS3jubgSgK2dPHlSffr0UUNDg/7yl7+otrZW06dP18CBA80uDQDQQU7LeBpvALa2\nfft2JScnt7oNAGA/Tst45ngDsLVdu3ZdtG3nzp3+LwQA4HNOy3jmeAOwpffee0/vvfeeSktLtWLF\nCu/2s2fPKiwszMTKAAAd5dSMp/EGYEtXXXWV+vXrp+rqat1yyy3e7d27d9fgwYNNrAwA0FFOzXjm\neAOwvcrKSn3xxReSpGHDhqlPnz4mVwQA8BUnZTyNNwBb27t3r1577TWNGDFCknTkyBHNnj1bEydO\nNLkyAEBHOS3jmWoCwNbefPNNPf30094jIFVVVcrMzLRtKAMA/sFpGc+qJgBsze12NzntGBYWJrfb\nbWJFAABfcVrGc8QbgK2NHj1ay5YtU0JCgiRpz549Gj16tMlVAQB8wWkZzxFvALaXlJSk48eP6/jx\n40pKSjK7HACADzkp4zniDcDW/vrXv2rWrFmaMGGCd9sbb7yhWbNmmVgVAMAXnJbxNN4AbOndd9/V\nO++8o9LSUqWlpXm3nz17VldddZWJlQEAOsqpGc9yggBsqba2VjU1NXr99dd19913e7f36NHD1nc1\nAwA4N+NpvAEAAAA/4OJKAAAAwA9ovAEAAAA/oPEGAAAA/IDGGwAAAPADGm8AAADAD/5/ml4kH/s8\nC/oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x104569710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Answer:\n",
    "df.groupby(\"continent\").boxplot(figsize=(12, 9), rot=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Concatenate, Merge & Join"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Task:** Import the first dataset cars1 and cars2. Assign each to a to a variable called cars1 and cars2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Answer:\n",
    "cars1 = pd.read_csv(\"data/cars1.csv\")\n",
    "cars2 = pd.read_csv(\"data/cars2.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Task:** It seems our first dataset has some unnamed blank columns, fix cars1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Answer:\n",
    "cars1 = cars1.loc[:, \"mpg\":\"car\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Task:** Join cars1 and cars2 into a single DataFrame called cars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Answer:\n",
    "cars = cars1.append(cars2)\n",
    "\n",
    "# or\n",
    "cars_ = pd.concat([cars1, cars2], axis=0)\n",
    "\n",
    "# check for equality\n",
    "cars_.equals(cars)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Apply (interspersed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Task:** Create function that returns the first word of the string in the \"car\" column, the manufacturer name. Use the \"apply\" method to create a new column in the DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Answer\n",
    "def grep_firt_word(string_):\n",
    "    return string_.split()[0]\n",
    "    \n",
    "cars[\"vendor\"] = cars[\"car\"].apply(grep_firt_word)\n",
    "\n",
    "# or\n",
    "\n",
    "cars[\"vendor\"] = cars[\"car\"].apply(lambda s: s.split()[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Consider the following DataFrames for the next exercises"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df1 = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'],\n",
    "                     'B': ['B0', 'B1', 'B2', 'B3'],\n",
    "                     'C': ['C0', 'C1', 'C2', 'C3'],\n",
    "                     'D': ['D0', 'D1', 'D2', 'D3']},\n",
    "                     index=[0, 1, 2, 3])\n",
    "\n",
    "df2 = pd.DataFrame({'A': ['A4', 'A5', 'A6', 'A7'],\n",
    "                     'B': ['B4', 'B5', 'B6', 'B7'],\n",
    "                     'C': ['C4', 'C5', 'C6', 'C7'],\n",
    "                     'D': ['D4', 'D5', 'D6', 'D7']},\n",
    "                      index=[4, 5, 6, 7])\n",
    "\n",
    "df3 = pd.DataFrame({'A': ['A8', 'A9', 'A10', 'A11'],\n",
    "                     'B': ['B8', 'B9', 'B10', 'B11'],\n",
    "                     'C': ['C8', 'C9', 'C10', 'C11'],\n",
    "                     'D': ['D8', 'D9', 'D10', 'D11']},\n",
    "                     index=[8, 9, 10, 11])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Task:*** Concatenate the three DataFrames along the rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A0</td>\n",
       "      <td>B0</td>\n",
       "      <td>C0</td>\n",
       "      <td>D0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A1</td>\n",
       "      <td>B1</td>\n",
       "      <td>C1</td>\n",
       "      <td>D1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A2</td>\n",
       "      <td>B2</td>\n",
       "      <td>C2</td>\n",
       "      <td>D2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A3</td>\n",
       "      <td>B3</td>\n",
       "      <td>C3</td>\n",
       "      <td>D3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A4</td>\n",
       "      <td>B4</td>\n",
       "      <td>C4</td>\n",
       "      <td>D4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A5</td>\n",
       "      <td>B5</td>\n",
       "      <td>C5</td>\n",
       "      <td>D5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>A6</td>\n",
       "      <td>B6</td>\n",
       "      <td>C6</td>\n",
       "      <td>D6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>A7</td>\n",
       "      <td>B7</td>\n",
       "      <td>C7</td>\n",
       "      <td>D7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>A8</td>\n",
       "      <td>B8</td>\n",
       "      <td>C8</td>\n",
       "      <td>D8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>A9</td>\n",
       "      <td>B9</td>\n",
       "      <td>C9</td>\n",
       "      <td>D9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>A10</td>\n",
       "      <td>B10</td>\n",
       "      <td>C10</td>\n",
       "      <td>D10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>A11</td>\n",
       "      <td>B11</td>\n",
       "      <td>C11</td>\n",
       "      <td>D11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      A    B    C    D\n",
       "0    A0   B0   C0   D0\n",
       "1    A1   B1   C1   D1\n",
       "2    A2   B2   C2   D2\n",
       "3    A3   B3   C3   D3\n",
       "4    A4   B4   C4   D4\n",
       "5    A5   B5   C5   D5\n",
       "6    A6   B6   C6   D6\n",
       "7    A7   B7   C7   D7\n",
       "8    A8   B8   C8   D8\n",
       "9    A9   B9   C9   D9\n",
       "10  A10  B10  C10  D10\n",
       "11  A11  B11  C11  D11"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Answer:\n",
    "pd.concat([df1, df2, df3], axis=0)\n",
    "\n",
    "# or\n",
    "\n",
    "# df1.append(df2).append(df3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Task:*** How many missing values (NaNs) are produced if you concatenate along the other axis (appending the columns)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "96\n",
      "96\n"
     ]
    }
   ],
   "source": [
    "# Answer\n",
    "print sum(pd.concat([df1, df2, df3], axis=1).isnull().sum())\n",
    "\n",
    "# or\n",
    "\n",
    "shape1 = df1.shape\n",
    "shape2 = df2.shape\n",
    "shape3 = df3.shape\n",
    "num_true_values = shape1[0]*shape1[1] + shape2[0]*shape2[1] + shape3[0]*shape3[1]\n",
    "total_num_values = (shape1[0] + shape2[0] + shape3[0]) * (shape1[1] + shape2[1] + shape3[1])\n",
    "print total_num_values - num_true_values\n",
    "\n",
    "assert sum(pd.concat([df1, df2, df3], axis=1).isnull().sum()) == total_num_values - num_true_values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's consider another data set to do some more Merge, Join & Concatenate exerciseses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "raw_data_1 = {\n",
    "        'subject_id': ['1', '2', '3', '4', '5'],\n",
    "        'first_name': ['Alex', 'Amy', 'Allen', 'Alice', 'Ayoung'], \n",
    "        'last_name': ['Anderson', 'Ackerman', 'Ali', 'Aoni', 'Atiches']}\n",
    "\n",
    "raw_data_2 = {\n",
    "        'subject_id': ['4', '5', '6', '7', '8', '9', '10'],\n",
    "        'first_name': ['Alice', 'Ayoung', 'Bran', 'Bryce', 'Betty', 'Jane', np.nan], \n",
    "        'last_name': ['Aoni', 'Atiches', 'Balwner', 'Brice', 'Btisan', np.nan, 'Doe']}\n",
    "\n",
    "raw_data_3 = {\n",
    "        'subject_id': ['1', '2', '3', '4', '5', '7', '8', '9', '10', '11'],\n",
    "        'test_id': [51, 15, 15, 61, 16, 14, 15, 1, 61, 16]}\n",
    "data1 = pd.DataFrame(raw_data_1, columns = ['subject_id', 'first_name', 'last_name'])\n",
    "data2 = pd.DataFrame(raw_data_2, columns = ['subject_id', 'first_name', 'last_name'])\n",
    "data3 = pd.DataFrame(raw_data_3, columns = ['subject_id','test_id'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Task:** Join the two dataframes, `data1` and `data2`, along rows and assign `all_data`. Make sure that the row index is unique."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>subject_id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Alex</td>\n",
       "      <td>Anderson</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>Alice</td>\n",
       "      <td>Aoni</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  subject_id first_name last_name\n",
       "0          1       Alex  Anderson\n",
       "0          4      Alice      Aoni"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Answer:\n",
    "all_data = pd.concat([data1, data2], ignore_index=True)\n",
    "assert len(set(all_data.index)) == all_data.shape[0]\n",
    "\n",
    "# otherwise you might get unexpected behaviour:\n",
    "all_data = pd.concat([data1, data2], ignore_index=False)\n",
    "all_data.loc[0, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Task:** Join the two dataframes, `data1` and `data2`, along columns and assing to `all_data_col`."
   ]
  },
  {
   "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>subject_id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>subject_id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Alex</td>\n",
       "      <td>Anderson</td>\n",
       "      <td>4</td>\n",
       "      <td>Alice</td>\n",
       "      <td>Aoni</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Amy</td>\n",
       "      <td>Ackerman</td>\n",
       "      <td>5</td>\n",
       "      <td>Ayoung</td>\n",
       "      <td>Atiches</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Allen</td>\n",
       "      <td>Ali</td>\n",
       "      <td>6</td>\n",
       "      <td>Bran</td>\n",
       "      <td>Balwner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Alice</td>\n",
       "      <td>Aoni</td>\n",
       "      <td>7</td>\n",
       "      <td>Bryce</td>\n",
       "      <td>Brice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Ayoung</td>\n",
       "      <td>Atiches</td>\n",
       "      <td>8</td>\n",
       "      <td>Betty</td>\n",
       "      <td>Btisan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>Jane</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Doe</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  subject_id first_name last_name subject_id first_name last_name\n",
       "0          1       Alex  Anderson          4      Alice      Aoni\n",
       "1          2        Amy  Ackerman          5     Ayoung   Atiches\n",
       "2          3      Allen       Ali          6       Bran   Balwner\n",
       "3          4      Alice      Aoni          7      Bryce     Brice\n",
       "4          5     Ayoung   Atiches          8      Betty    Btisan\n",
       "5        NaN        NaN       NaN          9       Jane       NaN\n",
       "6        NaN        NaN       NaN         10        NaN       Doe"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Answer:\n",
    "all_data_col = pd.concat([data1, data2], axis = 1)\n",
    "all_data_col"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Task:** Merge `all_data` and `data3` along the subject_id value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>subject_id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>test_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Alex</td>\n",
       "      <td>Anderson</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Amy</td>\n",
       "      <td>Ackerman</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Allen</td>\n",
       "      <td>Ali</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Alice</td>\n",
       "      <td>Aoni</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Alice</td>\n",
       "      <td>Aoni</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>Ayoung</td>\n",
       "      <td>Atiches</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5</td>\n",
       "      <td>Ayoung</td>\n",
       "      <td>Atiches</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>Bryce</td>\n",
       "      <td>Brice</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>Betty</td>\n",
       "      <td>Btisan</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>Jane</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Doe</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   subject_id first_name last_name  test_id\n",
       "0           1       Alex  Anderson       51\n",
       "1           2        Amy  Ackerman       15\n",
       "2           3      Allen       Ali       15\n",
       "3           4      Alice      Aoni       61\n",
       "4           4      Alice      Aoni       61\n",
       "5           5     Ayoung   Atiches       16\n",
       "6           5     Ayoung   Atiches       16\n",
       "7           7      Bryce     Brice       14\n",
       "8           8      Betty    Btisan       15\n",
       "9           9       Jane       NaN        1\n",
       "10         10        NaN       Doe       61"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Answer:\n",
    "pd.merge(all_data, data3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Task:** How many test_ids have missing values in the first or last name column?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Answer:\n",
    "dfm = pd.merge(all_data, data3, how=\"outer\")\n",
    "cond = dfm[\"test_id\"].notnull() & (dfm[\"first_name\"].isnull() | dfm[\"last_name\"].isnull())\n",
    "sum(cond)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Task:** Merge only the data that has the same 'subject_id' in both `data1` and `data2`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>subject_id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>Alice</td>\n",
       "      <td>Aoni</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>Ayoung</td>\n",
       "      <td>Atiches</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  subject_id first_name last_name\n",
       "0          4      Alice      Aoni\n",
       "1          5     Ayoung   Atiches"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Answer:\n",
    "pd.merge(data1, data2, how='inner')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Transform"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The transform method returns an object that is indexed the same (same size) as the one being grouped."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Task:** Given a DataFrame with a column of group IDs, 'groups', and a column of corresponding integer values, 'vals', replace any negative values in 'vals' with the group mean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>groups</th>\n",
       "      <th>vals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   groups  vals\n",
       "0       1     1\n",
       "1       1    -1\n",
       "2       2     1\n",
       "3       2     2"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame([[1, 1], [1, -1], [2, 1], [2, 2]], columns=[\"groups\", \"vals\"])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    1\n",
       "2    1\n",
       "3    2\n",
       "Name: vals, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Answer\n",
    "def replace_within_group(group):\n",
    "    mask = group < 0\n",
    "    # Select those values where it is < 0, and replace\n",
    "    # them with the mean of the values which are not < 0.\n",
    "    group[mask] = group[~mask].mean() # \"~\" is the \"invert\" or \"complement\" operation\n",
    "    return group\n",
    "\n",
    "df.groupby(['groups'])['vals'].transform(replace_within_group)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Task:** Use groupby in conjunction with transform across multiple columns: We want to group by one to n columns and apply a function on these groups across two columns.\n",
    "    1. Calculate the sum of `a` and `b` and assign it to a column named `e`.\n",
    "    2. Group by 'c' and `d`, and calculate the sum of `e`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame({'a':[1,2,3,4,5,6],\n",
    "                   'b':[1,2,3,4,5,6],\n",
    "                   'c':['q', 'q', 'q', 'q', 'w', 'w'],  \n",
    "                   'd':['z','z','z','o','o','o']})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Answer:\n",
    "df['e'] = df['a'] + df['b']\n",
    "df['f'] = (df.groupby(['c', 'd'])['e'].transform('sum'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Task:** Normalize (standardize) the data by calculating the z-score. Group the data by `year` and calculate the z-score per group. z = (value - mean) / standard_deviation\n",
    "<div style=\"font-size: 150%;\">  \n",
    "$$z=\\frac{x-\\mu}{\\sigma}$$\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "index = pd.date_range('10/1/1999', periods=1100)\n",
    "ser = pd.Series(np.random.normal(0.5, 2, 1100), index=index)\n",
    "ser = ser.rolling(window=100,min_periods=100).mean().dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Answer:\n",
    "key = lambda x: x.year\n",
    "zscore = lambda x: (x - x.mean()) / x.std()\n",
    "transformed = ser.groupby(key).transform(zscore)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Task:** We would expect the result to now have mean 0 and standard deviation 1 within each group, which we can easily check. Calculate the mean and standard deviation within each group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000    1.0\n",
       "2001    1.0\n",
       "2002    1.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Answer:\n",
    "grouped = transformed.groupby(key)\n",
    "grouped.mean()\n",
    "grouped.std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Task:** Visually compare the original and transformed data sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x105005c90>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kA5gtJuMYFBMRVbhbbqnDUUeNLPZqEOWVmObZYtG+itpi1hWTUUqxV4CIiPLrvvvqir0K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ah4770wVrNuwgQA8k85hbygPYp+40Zs//kPaZMmIYkiFa+/TkBjlRIFQcAzeDCSNbamqEkwGPCd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\nNZWaBx9Ut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IFaS8i0GnlHJmV5GIEsfBYX1eJA5IcNG51Mb6+OuTkVr3jFimKtynqxTp61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\nICISZd4/QEKcnOJGTmidyJlnikHvl78U1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3MsNoKJCSCZnGzq605NLQOgYno6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\nAwAAwHiEYgAAABjv/9AuIaep3GrHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x105022b10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Answer:\n",
    "compare = pd.DataFrame({'Original': ser, 'Transformed': transformed}, columns=[\"Transformed\", \"Original\"])\n",
    "compare.plot(figsize=(12,9), color=[\"blue\", \"red\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pivot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Task:** Let's reshape this small example DataFrame of \"extended\" ICU codes. Each person has different code-associations. Only positive associations are listed. Transform (reshape) the DataFrame to a wide format (one column per code) that lists positive and negative (missing) associations as Booleans."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>Person</th>\n",
       "      <th>Code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>D99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a</td>\n",
       "      <td>E32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a</td>\n",
       "      <td>A41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b</td>\n",
       "      <td>D99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>c</td>\n",
       "      <td>D99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>c</td>\n",
       "      <td>A41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Person Code\n",
       "0      a  D99\n",
       "1      a  E32\n",
       "2      a  A41\n",
       "3      b  D99\n",
       "4      c  D99\n",
       "5      c  A41"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({\"Person\": [\"a\", \"a\", \"a\", \"b\", \"c\", \"c\"], \"Code\": [\"D99\", \"E32\", \"A41\", \"D99\", \"D99\", \"A41\"]}, columns=[\"Person\", \"Code\"])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Code</th>\n",
       "      <th>Person</th>\n",
       "      <th>A41</th>\n",
       "      <th>D99</th>\n",
       "      <th>E32</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>c</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Code Person    A41   D99    E32\n",
       "0         a   True  True   True\n",
       "1         b  False  True  False\n",
       "2         c   True  True  False"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Answer:\n",
    "df[\"Values\"] = pd.Series([True]*df.shape[0])\n",
    "dfp = df.pivot(index=\"Person\", columns=\"Code\", values=\"Values\")\n",
    "dfp = dfp.fillna(value=False)\n",
    "dfp = dfp.reset_index()\n",
    "dfp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Combine DataFrames"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Task:** In the *data/microbiome* subdirectory, there are 9 spreadsheets of microbiome data that was acquired from high-throughput RNA sequencing procedures, along with a 10th file that describes the content of each. Write code that imports each of the data spreadsheets and combines them into a single `DataFrame`, adding the identifying information from the metadata spreadsheet as columns in the combined `DataFrame`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>Taxon</th>\n",
       "      <th>Count</th>\n",
       "      <th>BARCODE</th>\n",
       "      <th>GROUP</th>\n",
       "      <th>SAMPLE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Archaea \"Crenarchaeota\" Thermoprotei Desulfuro...</td>\n",
       "      <td>7</td>\n",
       "      <td>MID1</td>\n",
       "      <td>EXTRACTION CONTROL</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Archaea \"Crenarchaeota\" Thermoprotei Desulfuro...</td>\n",
       "      <td>2</td>\n",
       "      <td>MID1</td>\n",
       "      <td>EXTRACTION CONTROL</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Archaea \"Crenarchaeota\" Thermoprotei Sulfoloba...</td>\n",
       "      <td>3</td>\n",
       "      <td>MID1</td>\n",
       "      <td>EXTRACTION CONTROL</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Archaea \"Crenarchaeota\" Thermoprotei Thermopro...</td>\n",
       "      <td>3</td>\n",
       "      <td>MID1</td>\n",
       "      <td>EXTRACTION CONTROL</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Archaea \"Euryarchaeota\" \"Methanomicrobia\" Meth...</td>\n",
       "      <td>7</td>\n",
       "      <td>MID1</td>\n",
       "      <td>EXTRACTION CONTROL</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               Taxon  Count BARCODE  \\\n",
       "0  Archaea \"Crenarchaeota\" Thermoprotei Desulfuro...      7    MID1   \n",
       "1  Archaea \"Crenarchaeota\" Thermoprotei Desulfuro...      2    MID1   \n",
       "2  Archaea \"Crenarchaeota\" Thermoprotei Sulfoloba...      3    MID1   \n",
       "3  Archaea \"Crenarchaeota\" Thermoprotei Thermopro...      3    MID1   \n",
       "4  Archaea \"Euryarchaeota\" \"Methanomicrobia\" Meth...      7    MID1   \n",
       "\n",
       "                GROUP SAMPLE  \n",
       "0  EXTRACTION CONTROL    NaN  \n",
       "1  EXTRACTION CONTROL    NaN  \n",
       "2  EXTRACTION CONTROL    NaN  \n",
       "3  EXTRACTION CONTROL    NaN  \n",
       "4  EXTRACTION CONTROL    NaN  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Answer\n",
    "metadata = pd.read_excel(r\"data/microbiome/metadata.xls\", sheetname='Sheet1')\n",
    "\n",
    "chunks = []\n",
    "for i in range(9):\n",
    "    df_temp = pd.read_excel(r\"data/microbiome/MID{0}.xls\".format(i+1), 'Sheet 1', header=None, names=['Taxon', 'Count'])\n",
    "    df_temp = df_temp.set_index(\"Taxon\")\n",
    "    df_temp.columns = ['Count']\n",
    "    df_temp.index.name = 'Taxon'\n",
    "    for colname in metadata.columns:\n",
    "        df_temp[colname] = metadata.loc[i, colname]\n",
    "    chunks.append(df_temp)\n",
    "\n",
    "df = pd.concat(chunks)\n",
    "df = df.reset_index()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GroupBy Titanic data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the dataset in `titanic.xls`. It contains data on all the passengers that travelled on the Titanic."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Task:**\n",
    "Women and children first?\n",
    "\n",
    "1. Use the `groupby` method to calculate the proportion of passengers that survived by sex.\n",
    "2. Calculate the same proportion, but by class and sex.\n",
    "3. Create age categories: children (under 14 years), adolescents (14-20), adult (21-64), and senior(65+), and calculate survival proportions by age category, class and sex. Additionally, count the number of passengers per group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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></th>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>agecat</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">child</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">1</th>\n",
       "      <th>female</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2</th>\n",
       "      <th>female</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">3</th>\n",
       "      <th>female</th>\n",
       "      <td>0.483871</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.324324</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">adolescent</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">1</th>\n",
       "      <th>female</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.200000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2</th>\n",
       "      <th>female</th>\n",
       "      <td>0.923077</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.117647</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">3</th>\n",
       "      <th>female</th>\n",
       "      <td>0.542857</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.125000</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">adult</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">1</th>\n",
       "      <th>female</th>\n",
       "      <td>0.965517</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.343284</td>\n",
       "      <td>134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2</th>\n",
       "      <th>female</th>\n",
       "      <td>0.868421</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.078125</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">3</th>\n",
       "      <th>female</th>\n",
       "      <td>0.441860</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.159184</td>\n",
       "      <td>245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">senior</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">1</th>\n",
       "      <th>female</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.142857</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <th>male</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <th>male</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              mean  count\n",
       "agecat     pclass sex                    \n",
       "child      1      female  0.000000      1\n",
       "                  male    1.000000      5\n",
       "           2      female  1.000000     14\n",
       "                  male    1.000000     11\n",
       "           3      female  0.483871     31\n",
       "                  male    0.324324     37\n",
       "adolescent 1      female  1.000000     15\n",
       "                  male    0.200000      5\n",
       "           2      female  0.923077     13\n",
       "                  male    0.117647     17\n",
       "           3      female  0.542857     35\n",
       "                  male    0.125000     64\n",
       "adult      1      female  0.965517    116\n",
       "                  male    0.343284    134\n",
       "           2      female  0.868421     76\n",
       "                  male    0.078125    128\n",
       "           3      female  0.441860     86\n",
       "                  male    0.159184    245\n",
       "senior     1      female  1.000000      1\n",
       "                  male    0.142857      7\n",
       "           2      male    0.000000      2\n",
       "           3      male    0.000000      3"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Answers\n",
    "titanic = pd.read_excel(\"data/titanic.xls\", \"titanic\")\n",
    "\n",
    "# 1.\n",
    "# explicit version\n",
    "# titanic.groupby('sex')['survived'].agg(\"mean\")\n",
    "# or short version\n",
    "titanic.groupby('sex')['survived'].mean()\n",
    "\n",
    "# 2.\n",
    "titanic.groupby(['pclass','sex'])['survived'].mean()\n",
    "\n",
    "# 3.\n",
    "titanic['agecat'] = pd.cut(titanic.age, [0, 13, 20, 64, 100], labels=['child', 'adolescent', 'adult', 'senior'])\n",
    "titanic.groupby(['agecat', 'pclass','sex'])['survived'].mean()\n",
    "titanic.groupby(['agecat', 'pclass','sex'])['survived'].agg([\"mean\", \"count\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot the number of survivors grouped by sex and passenger class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10536cd10>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wU26RvtHMS0RETEflJSIipqPyEhER01F5iYiI6ai8RETEdFReIiJiOiovEREx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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1053790d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "titanic.groupby(['sex','pclass'])[\"survived\"].sum().plot(kind='barh')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Task:** Let's also look at the deaths (and not only at the survivors) within the groups and create a stacked Barplot of survivers vs. deaths grouped by sex and passenger-class (as before).\n",
    "1. Convert the \"survived\" column to boolean values\n",
    "2. Compute the cross tabulation (a.k.a. contingency table) of passenger-class and sex vs. survived. Assign the result to the variable name \"death_counts\" --> checkout pd.crosstab()\n",
    "3. Create a stacked barplot of the computed death_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1055cc610>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DAwGIiYnB4XAA4HA4iI2NBSAwMJCwsDCOHz9OZGRkIzVBRETOpk5BHxAQwIIFCzhx4gTP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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1053575d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Answer\n",
    "# 1.\n",
    "titanic.survived = titanic.survived.astype(bool)\n",
    "\n",
    "# 2.\n",
    "death_counts = pd.crosstab([titanic.pclass, titanic.sex], titanic.survived)\n",
    "\n",
    "# 3.\n",
    "death_counts.plot(kind='bar', stacked=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Task:** Another way of comparing the groups is to look at the survival rate, by adjusting for the number of people in each group.\n",
    "Create a stacked, horizontal Barplot of the adjusted death counts\n",
    "1. Sum the death_counts per passenger-class and sex, and convert to data type float (for Python 2.x division purposes).\n",
    "2. Compute the adjusted survival rate by dividing the death_counts by the result from 1. \n",
    "3. Plot a stacked, horizontal Barplot from the result of 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1059cf590>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NGjVi5cqV7NixA5PJRFlZGSdPnqzytS65ubn8+OOPzJw5EwCr1UpJSQmdO3fG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\n22/zpz/9icDAQB9U6Tnu9L9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      "text/plain": [
       "<matplotlib.figure.Figure at 0x105041d50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Answer\n",
    "\n",
    "# 1.\n",
    "sum_death_counts = death_counts.sum(axis=1).astype(float)\n",
    "\n",
    "# 2. \n",
    "adjusted_death_counts = death_counts.div(sum_death_counts, axis=0)\n",
    "\n",
    "# 3.\n",
    "adjusted_death_counts.plot(kind='barh', stacked=True)\n",
    "\n",
    "# or in short:\n",
    "# death_counts.div(death_counts.sum(1).astype(float), axis=0).plot(kind='barh', stacked=True)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}