Drinks Data Set.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "drinks=pd.read_csv('drinks.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  beer_servings  spirit_servings  wine_servings  \\\n",
       "0  Afghanistan              0                0              0   \n",
       "\n",
       "   total_litres_of_pure_alcohol continent  \n",
       "0                           0.0      Asia  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.head(1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "106.16062176165804"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.beer_servings.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.717098445595855"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.total_litres_of_pure_alcohol.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "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": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.groupby(['continent']).beer_servings.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>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>Oceania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Cook Islands</td>\n",
       "      <td>0</td>\n",
       "      <td>254</td>\n",
       "      <td>74</td>\n",
       "      <td>5.9</td>\n",
       "      <td>Oceania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Fiji</td>\n",
       "      <td>77</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>Oceania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>Kiribati</td>\n",
       "      <td>21</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Oceania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>Marshall Islands</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Oceania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>Micronesia</td>\n",
       "      <td>62</td>\n",
       "      <td>50</td>\n",
       "      <td>18</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Oceania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>Nauru</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Oceania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>203</td>\n",
       "      <td>79</td>\n",
       "      <td>175</td>\n",
       "      <td>9.3</td>\n",
       "      <td>Oceania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>Niue</td>\n",
       "      <td>188</td>\n",
       "      <td>200</td>\n",
       "      <td>7</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Oceania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>Palau</td>\n",
       "      <td>306</td>\n",
       "      <td>63</td>\n",
       "      <td>23</td>\n",
       "      <td>6.9</td>\n",
       "      <td>Oceania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>Papua New Guinea</td>\n",
       "      <td>44</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Oceania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>Samoa</td>\n",
       "      <td>105</td>\n",
       "      <td>18</td>\n",
       "      <td>24</td>\n",
       "      <td>2.6</td>\n",
       "      <td>Oceania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>Solomon Islands</td>\n",
       "      <td>56</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>Oceania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>Tonga</td>\n",
       "      <td>36</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>1.1</td>\n",
       "      <td>Oceania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>Tuvalu</td>\n",
       "      <td>6</td>\n",
       "      <td>41</td>\n",
       "      <td>9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Oceania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>Vanuatu</td>\n",
       "      <td>21</td>\n",
       "      <td>18</td>\n",
       "      <td>11</td>\n",
       "      <td>0.9</td>\n",
       "      <td>Oceania</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              country  beer_servings  spirit_servings  wine_servings  \\\n",
       "8           Australia            261               72            212   \n",
       "40       Cook Islands              0              254             74   \n",
       "59               Fiji             77               35              1   \n",
       "89           Kiribati             21               34              1   \n",
       "106  Marshall Islands              0                0              0   \n",
       "110        Micronesia             62               50             18   \n",
       "118             Nauru             49                0              8   \n",
       "121       New Zealand            203               79            175   \n",
       "125              Niue            188              200              7   \n",
       "129             Palau            306               63             23   \n",
       "131  Papua New Guinea             44               39              1   \n",
       "146             Samoa            105               18             24   \n",
       "157   Solomon Islands             56               11              1   \n",
       "173             Tonga             36               21              5   \n",
       "178            Tuvalu              6               41              9   \n",
       "187           Vanuatu             21               18             11   \n",
       "\n",
       "     total_litres_of_pure_alcohol continent  \n",
       "8                            10.4   Oceania  \n",
       "40                            5.9   Oceania  \n",
       "59                            2.0   Oceania  \n",
       "89                            1.0   Oceania  \n",
       "106                           0.0   Oceania  \n",
       "110                           2.3   Oceania  \n",
       "118                           1.0   Oceania  \n",
       "121                           9.3   Oceania  \n",
       "125                           7.0   Oceania  \n",
       "129                           6.9   Oceania  \n",
       "131                           1.5   Oceania  \n",
       "146                           2.6   Oceania  \n",
       "157                           1.2   Oceania  \n",
       "173                           1.1   Oceania  \n",
       "178                           1.0   Oceania  \n",
       "187                           0.9   Oceania  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks[drinks.continent=='Oceania']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "37.04545454545455"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks[drinks.continent=='Asia'].beer_servings.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "247"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks[drinks.continent=='Asia'].beer_servings.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "93"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks[drinks.continent=='South America'].beer_servings.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>beer_servings</th>\n",
       "      <th>spirit_servings</th>\n",
       "      <th>wine_servings</th>\n",
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       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
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      "text/plain": [
       "       country  beer_servings  spirit_servings  wine_servings  \\\n",
       "0  Afghanistan              0                0              0   \n",
       "\n",
       "   total_litres_of_pure_alcohol continent  \n",
       "0                           0.0      Asia  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
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   ],
   "source": [
    "drinks.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "178    6\n",
       "Name: beer_servings, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks[drinks.country=='Tuvalu'].beer_servings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th colspan=\"5\" halign=\"left\">beer_servings</th>\n",
       "      <th colspan=\"5\" halign=\"left\">spirit_servings</th>\n",
       "      <th colspan=\"5\" halign=\"left\">wine_servings</th>\n",
       "      <th colspan=\"5\" halign=\"left\">total_litres_of_pure_alcohol</th>\n",
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       "      <th>continent</th>\n",
       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>53</td>\n",
       "      <td>61.471698</td>\n",
       "      <td>3258</td>\n",
       "      <td>0</td>\n",
       "      <td>376</td>\n",
       "      <td>53</td>\n",
       "      <td>16.339623</td>\n",
       "      <td>866</td>\n",
       "      <td>0</td>\n",
       "      <td>152</td>\n",
       "      <td>53</td>\n",
       "      <td>16.264151</td>\n",
       "      <td>862</td>\n",
       "      <td>0</td>\n",
       "      <td>233</td>\n",
       "      <td>53</td>\n",
       "      <td>3.007547</td>\n",
       "      <td>159.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>44</td>\n",
       "      <td>37.045455</td>\n",
       "      <td>1630</td>\n",
       "      <td>0</td>\n",
       "      <td>247</td>\n",
       "      <td>44</td>\n",
       "      <td>60.840909</td>\n",
       "      <td>2677</td>\n",
       "      <td>0</td>\n",
       "      <td>326</td>\n",
       "      <td>44</td>\n",
       "      <td>9.068182</td>\n",
       "      <td>399</td>\n",
       "      <td>0</td>\n",
       "      <td>123</td>\n",
       "      <td>44</td>\n",
       "      <td>2.170455</td>\n",
       "      <td>95.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>45</td>\n",
       "      <td>193.777778</td>\n",
       "      <td>8720</td>\n",
       "      <td>0</td>\n",
       "      <td>361</td>\n",
       "      <td>45</td>\n",
       "      <td>132.555556</td>\n",
       "      <td>5965</td>\n",
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       "      <td>373</td>\n",
       "      <td>45</td>\n",
       "      <td>142.222222</td>\n",
       "      <td>6400</td>\n",
       "      <td>0</td>\n",
       "      <td>370</td>\n",
       "      <td>45</td>\n",
       "      <td>8.617778</td>\n",
       "      <td>387.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North America</th>\n",
       "      <td>23</td>\n",
       "      <td>145.434783</td>\n",
       "      <td>3345</td>\n",
       "      <td>1</td>\n",
       "      <td>285</td>\n",
       "      <td>23</td>\n",
       "      <td>165.739130</td>\n",
       "      <td>3812</td>\n",
       "      <td>68</td>\n",
       "      <td>438</td>\n",
       "      <td>23</td>\n",
       "      <td>24.521739</td>\n",
       "      <td>564</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>23</td>\n",
       "      <td>5.995652</td>\n",
       "      <td>137.9</td>\n",
       "      <td>2.2</td>\n",
       "      <td>11.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>16</td>\n",
       "      <td>89.687500</td>\n",
       "      <td>1435</td>\n",
       "      <td>0</td>\n",
       "      <td>306</td>\n",
       "      <td>16</td>\n",
       "      <td>58.437500</td>\n",
       "      <td>935</td>\n",
       "      <td>0</td>\n",
       "      <td>254</td>\n",
       "      <td>16</td>\n",
       "      <td>35.625000</td>\n",
       "      <td>570</td>\n",
       "      <td>0</td>\n",
       "      <td>212</td>\n",
       "      <td>16</td>\n",
       "      <td>3.381250</td>\n",
       "      <td>54.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South America</th>\n",
       "      <td>12</td>\n",
       "      <td>175.083333</td>\n",
       "      <td>2101</td>\n",
       "      <td>93</td>\n",
       "      <td>333</td>\n",
       "      <td>12</td>\n",
       "      <td>114.750000</td>\n",
       "      <td>1377</td>\n",
       "      <td>25</td>\n",
       "      <td>302</td>\n",
       "      <td>12</td>\n",
       "      <td>62.416667</td>\n",
       "      <td>749</td>\n",
       "      <td>1</td>\n",
       "      <td>221</td>\n",
       "      <td>12</td>\n",
       "      <td>6.308333</td>\n",
       "      <td>75.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>8.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              beer_servings                            spirit_servings  \\\n",
       "                      count        mean   sum min  max           count   \n",
       "continent                                                                \n",
       "Africa                   53   61.471698  3258   0  376              53   \n",
       "Asia                     44   37.045455  1630   0  247              44   \n",
       "Europe                   45  193.777778  8720   0  361              45   \n",
       "North America            23  145.434783  3345   1  285              23   \n",
       "Oceania                  16   89.687500  1435   0  306              16   \n",
       "South America            12  175.083333  2101  93  333              12   \n",
       "\n",
       "                                         wine_servings                        \\\n",
       "                     mean   sum min  max         count        mean   sum min   \n",
       "continent                                                                      \n",
       "Africa          16.339623   866   0  152            53   16.264151   862   0   \n",
       "Asia            60.840909  2677   0  326            44    9.068182   399   0   \n",
       "Europe         132.555556  5965   0  373            45  142.222222  6400   0   \n",
       "North America  165.739130  3812  68  438            23   24.521739   564   1   \n",
       "Oceania         58.437500   935   0  254            16   35.625000   570   0   \n",
       "South America  114.750000  1377  25  302            12   62.416667   749   1   \n",
       "\n",
       "                   total_litres_of_pure_alcohol                              \n",
       "               max                        count      mean    sum  min   max  \n",
       "continent                                                                    \n",
       "Africa         233                           53  3.007547  159.4  0.0   9.1  \n",
       "Asia           123                           44  2.170455   95.5  0.0  11.5  \n",
       "Europe         370                           45  8.617778  387.8  0.0  14.4  \n",
       "North America  100                           23  5.995652  137.9  2.2  11.9  \n",
       "Oceania        212                           16  3.381250   54.1  0.0  10.4  \n",
       "South America  221                           12  6.308333   75.7  3.8   8.3  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.groupby(['continent']).agg(['count', 'mean', 'sum' ,'min' ,'max']) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>sum</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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>53</td>\n",
       "      <td>3258</td>\n",
       "      <td>0</td>\n",
       "      <td>376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>44</td>\n",
       "      <td>1630</td>\n",
       "      <td>0</td>\n",
       "      <td>247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>45</td>\n",
       "      <td>8720</td>\n",
       "      <td>0</td>\n",
       "      <td>361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North America</th>\n",
       "      <td>23</td>\n",
       "      <td>3345</td>\n",
       "      <td>1</td>\n",
       "      <td>285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>16</td>\n",
       "      <td>1435</td>\n",
       "      <td>0</td>\n",
       "      <td>306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South America</th>\n",
       "      <td>12</td>\n",
       "      <td>2101</td>\n",
       "      <td>93</td>\n",
       "      <td>333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               count   sum  min  max\n",
       "continent                           \n",
       "Africa            53  3258    0  376\n",
       "Asia              44  1630    0  247\n",
       "Europe            45  8720    0  361\n",
       "North America     23  3345    1  285\n",
       "Oceania           16  1435    0  306\n",
       "South America     12  2101   93  333"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.groupby(['continent']).beer_servings.agg(['count', 'sum', 'min' ,'max'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xb287908>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LZ8owM7MN6FS4S+pX8vATQNtMmmnAsZI2kzQIGAw8XF2JZmbWUZu0t4GknwAHA30ltQJfAw6WNJRsyGUh8GmAiJgnaSrwGLAaOCMi1tSndDMz25B2wz0ijivTfPVGtr8YuLiaoszMrDo+Q9XMLEEOdzOzBDnczcwS5HA3M0uQw93MLEEOdzOzBDnczcwS5HA3M0uQw93MLEEOdzOzBDnczcwS5HA3M0uQw93MLEEOdzOzBDnczcwS5HA3M0uQw93MLEEOdzOzBDnczcwS1G64S7pG0lJJc0vatpd0l6Qn85/b5e2SNEnSAklzJO1Xz+LNzKy8SvbcrwM+ul7bOcA9ETEYuCd/DHAYMDi/TQCuqk2ZZmbWEe2Ge0Q8ALy4XvMYYEp+fwpwREn79ZH5I7CtpH61KtbMzCrT2TH3nSJiCUD+c8e8vT+wqGS71rztHSRNkDRD0oxly5Z1sgwzMyun1gdUVaYtym0YEZMjYnhEDG9paalxGWZm3Vtnw/35tuGW/OfSvL0V2LlkuwHA4s6XZ2ZmndHZcJ8GjM/vjwduLWk/KZ81sz+wom34xszMus4m7W0g6SfAwUBfSa3A14BLgamSTgWeBY7KN78D+BiwAHgNOKUONZuZFe+CbSrbbtAu9a1jA9oN94g4bgNPjS6zbQBnVFuUmZlVx2eompklyOFuZpYgh7uZWYIc7mZmCXK4m5klyOFuZpYgh7uZWYIc7mZmCXK4m5klyOFuZpYgh7uZWYIc7mZmCXK4m5klyOFuZpYgh7uZWYIc7mZmCXK4m5klyOFuZpagdi+ztzGSFgIrgTXA6ogYLml74KfAQGAhcHRE/L26Ms3MrCNqsed+SEQMjYjh+eNzgHsiYjBwT/7YzMy6UFV77hswBjg4vz8FuB/4Uh0+h4Hn/KrdbRZe+m/1+Ggzs4ZW7Z57AHdKmilpQt62U0QsAch/7ljlZ5iZWQdVu+c+KiIWS9oRuEvS45W+MP9lMAFgl112qbIMMzMrVdWee0Qszn8uBX4JjACel9QPIP+5dAOvnRwRwyNieEtLSzVlmJnZejod7pK2lNSn7T7wYWAuMA0Yn282Hri12iLNzKxjqhmW2Qn4paS297kxIn4j6U/AVEmnAs8CR1VfZveU+gHj1L+fWZE6He4R8RSwb5n25cDoaooyM7Pq+AxVM7MEOdzNzBLkcDczS5DD3cwsQQ53M7MEOdzNzBLkcDczS5DD3cwsQQ53M7MEOdzNzBJUj4t1mBleO8eK5T13M7MEOdzNzBLkcDczS5DH3M2swyo5ngA+plAk77mbmSXIe+5mZuupaKZT7y4opAreczczS5DD3cwsQXULd0kflfSEpAWSzqnX55iZ2TvVJdwl9QT+L3AYsCdwnKQ96/FZZmb2TvXacx8BLIiIpyLiDeAmYEydPsvMzNZTr9ky/YFFJY9bgQ/U6bPMmtcF27S/zaBd6l+HJUcRUfs3lY4CPhIRp+WPxwEjIuIzJdtMACbkD98LPFHzQjasL/BCF35eV/P3a14pfzfw96u1XSOipdwT9dpzbwV2Lnk8AFhcukFETAYm1+nzN0rSjIgYXsRndwV/v+aV8ncDf7+uVK8x9z8BgyUNkrQpcCwwrU6fZWZm66nLnntErJZ0JvA/QE/gmoiYV4/PMjOzd6rb8gMRcQdwR73ev0qFDAd1IX+/5pXydwN/vy5TlwOqZmZWLC8/YGaWIIe7mVmCvOSvWcEk7UW2TMfaRWQj4vriKrKOaNT+6zZj7pK2Awazbgc8UFxFtdUNvp+AE4DdIuJCSbsA/xQRDxdcWlUkfQ04mCwc7iBbj2l6RIwtsi6rTCP3X7cYlpF0GvAA2dTMr+c/LyiyplpK/fvlvg8cAByXP15JtjhdsxsLjAaei4hTgH2BzYotqbYk7S/pT5JekfSGpDWSXi66rhpp2P7rFuEOnAW8H3gmIg4BhgHLii2pplL/fgAfiIgzgNcBIuLvwKbFllQT/4iIt4DVkrYGlgK7FVxTrX2P7Jfyk8DmwGnAdwutqHYatv+6y5j76xHxuiQkbRYRj0t6b9FF1VDq3w/gzXwp6QCQ1AK8VWxJNTFD0rbAj4CZwCtAUw81lRMRCyT1jIg1wLWSfl90TTXSsP3XXcK9Ne+AW4C7JP2d9da6aXKpfz+AScAvgZ0kXUz25/D5xZZUvYj43/ndH0j6DbB1RMwpsqY6eC1fhmS2pG8CS4AtC66pJhq5/7rNAdU2kg4CtgF+k681n5SUv5+k3cnGNwHujYj5RdZTC5I+QfZdVuSPtwUOjohbiq2sdiTtSjZc0Qv4HNm/z+9HxIJCC6uBRu6/bhHukvYH5kXEyvxxH2DPiHio2MqqI2nriHhZ0vblno+IF7u6pnqStB9wINnQzIMR8UjBJVVN0uyIGLpe26yIGFZUTVa5Ru6/7jIscxWwX8njV8u0NaMbgY+TjfUFoJLnggY5sFMLkr4KHAXcTPY9r5X0s4i4qNjKqlZuUkMS/19KmhoRR0v6M/mxklIRsU8BZdVaw/Zfd9lzL/fbdU4i/7i6BUnzgWER8Xr+eHPgkYjYo9jKqiPpGuAlsmmdAXwG2C4iTi6yrlqQ1C8iluTDMu8QEc90dU211sj9112mQj4laaKkXvntLOCpoouqFUmjJG2Z3z9R0uX5ST4pWUjJCVpkc4n/WkwpNfUZ4A3gp8DPyKZ6nlFoRTUSEUvyn8+UuxVdX400bP91lz33HclmWxxK9tv1HuCzEbG00MJqRNIcspMn9gF+DFwNHBkRBxVaWA1JuoVsLv9dZH34r8B0sgN1RMTE4qqzjZF0JPANYEeyITUBERFbF1pY4rpFuKdO0iMRsV8+Lv23iLi6ra3o2mpF0viNPR8RU7qqllqQdGVEfFbSbZQfjz68gLLqQtIC4H+lMLupTTP0X0MM/NeLpC9GxDclfZfyHZDK3t5KSecC44B/yU/2SapvI2JKPlf6PXnTExHxZpE1VenH+c/LCq2iazyfUrDnGr7/kgqAMtr+Qc0otIr6OwY4HjglIp6T9EESOUmkjaSDgSlkY+8CdpY0vlkXR4uImfkv4dMj4sSi66mzGZJ+SnaS3aq2xoj4RXElVacZ+i/pcI+I2/IO2Csizi66nnrJA/1e4HhJ/w94Griy4LJq7dvAhyPiCQBJ7wF+Aryv0KqqEBFrJLVI2jS1E87WszXwGvDhkrYAmjbcofH7L+lwh7Ud0LQBsDF5wB1LtijTcrIj9soXD0tNr7ZgB4iIv0jqVWRBNbIQeFDSNLLzLwCIiMsLq6jG8tUSU7WQBu2/5MM9Nyv/j/8z1u2Apt5zAB4Hfkd2sGoBgKTPFVtS3cyQdDVvj3WeQHbyVrNbnN96AH0KrqUuJPUGTgWGsO71Bj5VWFG107D91y1my0i6tkxzNPs/rnxdi2OBkcBvgJuA/46IQYUWVgeSNiObP3wg2Zj7A2Trk6za6AubhKQtI+LV9rdsPpJ+RrYjcjxwIdkv5vkRcVahhdVQI/Zf0uEu6RsR8SVJR0XEz4qup17yE5iOIBueOZTswOMvI+LOQgurkfy4yZRGPXBVDUkHkJ2XsFVE7CJpX+DTJasNNr22tVbazgrPh9P+JyIOLbq2ajVy/6V+hurH8n9I5xZdSD1FxKsRcUNEfBwYAMwGzim4rJrJ1wBvyadCpuZK4CNkx0yIiEeBDxZaUe21TVl9Sdn1RrcBBhZXTk01bP+lPub+G+AFYMv8sl6lC2u9FRHbFFNW/eQrQf4wv6VkIQ164KpaEbFIKv2nyZqiaqmTycqu8fsVYBqwFfDVYkuqnUbtv6TDPZ/+eLakWyNiTFu7pAPJxv+seTTsgasqLZI0Eoj8L5OJvH1+RhIi4r/zu78loZVKcw3bf0mPuZeSNJRsTPoYsnngN0fE94qtyro7SX2B7wAfIvvL8k7grIhYXmhhNSRpJ+AS4F0RcZikPYEDIuLqgkurWiP3X9LhvoF54F+IiLJLkFrjknQf5ZeQaPqDcqmT9GvgWuC8iNhX0ibArIjYu+DSkpb0sAzdax546r5Qcr838ElgdUG11IykQWTLxg6k5P/HRlh4qob6RsTUfP0jImK1pIYYl65WI/df6uH+SbI99/vyi9fexLoHVa1JRMT6Jyw9KOm3hRRTW7eQTaW7DXir4Frq5VVJO5D/5ZVf9nJFsSXVTMP2X9LDMm1SnwfeHax3ndgeZGvKTIqI9xZUUk1IeigiPlB0HfWUX/v2u8BewFygBRgbEXMKLawGGrn/ukW4l8pD4ijgGI/XNg9JT/P2dWJXkx0UvzAiphdaWJUkHQ8MJjsQV7piYtNf/LtUPs7+XrL+a/blmtdq5P7rduFu1kgk/R+ydfj/ytt/1kdKOx6SzgBuiIiX8sfbAcdFxPeLrax6jdx/DndraG0XXMnvr7OMhKRLIuLLxVVXPUmPA/s04pKxtbKBC9TPiohhRdVUK43cf6kvP2DN79iS++svI/HRriykTh4Fti26iDrroZJTOPO1glJZSqJh+y/12TLW/LSB++UeN6OdgMcl/Yl1x2wLn0pXQ3cCUyX9gOy4yX+QLQ2SgobtP4e7NbrYwP1yj5vR14ouoAt8BTgd+HfePouz6c9OzTVs/zncrdHtW7Lo2+b5ffLHvTf8suYQEevM1Zc0imzdo6afw5/PkLkEOAVYRH7tW7KZTj1okAW2qtHI/edwt4YWET2LrqHe8nWPjgeOJl/3qNiKauZbZIu87RYRKwEk9SG7Hu5lQBIX62jU/vNsGbMCdId1jyQ9Cbwn1guZ/IDq4xExuJjKqtcM/ec9d7NidId1j2L9YM8b10hq9r3Khu8/T4U0K8YngefI1j36kaTRpDH7p9Rjkk5av1HSiWTh2Mwavv88LGNWoJTXPZLUH/gF8A9gJtnspvcDmwOfiIi/FVheTTRy/znczRpEquseSToUGEK2ZzsvIu4puKS6aLT+c7ibmSXIY+5mZglyuJuZJcjhbmaWIIe7WYEkHSnpSUkrJL0saWXJEgvW4Bq5/3xA1axAkhaQnQgzv+harOMauf+8525WrOcbMRisYg3bf95zNyuApCPzuwcB/wTcwrrrgf+iiLqsMs3Qfw53swJIunYjT0dEfKrLirEOa4b+c7ibFUjSqIh4sL02a0yN3H8Od7MCSXokIvZrr80aUyP3n5f8NSuApAOAkUCLpP8seWprIPkLlDS7Zug/h7tZMTYFtiL7f7BPSfvLwNhCKrKOaPj+87CMWUHyKxL9NCIaIgys4yTtGhHPFF1HOd5zNytIfkWi7Yuuw6pyXbmrSjXCkr8Od7NizZI0DfgZ8GpbYyPMk7aKfKHkfm+yKzStLqiWdXhYxqxAG5gv3RDzpK1zJP02Ig4qug7vuZsVKCJOKboG67z1htV6AO8jO2O1cA53swJJGgB8FxhFdo3R6cBZEdFaaGFWqbZrw4psOOZp4NRCK8p5WMasQJLuAm4Efpw3nQicEBH/WlxVlgKHu1mBJM2OiKHttVljktQL+A/gg3nT/cAPI+LNworKeclfs2K9IOlEST3z24nA8qKLsopdRTbO/v389r68rXDeczcrkKRdgO8BB5CN3f6ebMy9IU+MsXVJejQi9m2vrQg+oGpWoIh4Fji86Dqs09ZIendE/BVA0m7AmoJrAhzuZoWQ9NWNPB0R8V9dVoxV42zgPklPkc2Y2RVoiOmtHpYxK4Ckz5dp3pJsGt0OEbFVF5dknSRpM+C9ZOH+eESsauclXcLhblYwSX2As8iCfSrw7YhYWmxVtjGS3g8siojn8scnkS098AxwQUS8WGR94NkyZoWRtL2ki4A5ZEOk+0XElxzsTeGHwBsAkj4IXApcD6wAJhdY11oeczcrgKRvAUeSBcHeEfFKwSVZx/Qs2Ts/BpgcETcDN0uaXWBda3lYxqwAkt4CVpGdsl76P6HIDqhuXUhhVhFJc4GhEbFa0uPAhIh4oO25iNir2Aq9525WiIjwkGhz+wnwW0kvAP8Afgcg6Z/JhmYK5z13M7NOkLQ/0A+4MyJezdveA2wVEY8UWhwOdzOzJPlPQzOzBDnczcwS5HA3AyQNlHR8yePhkibV4XOOkLRnrd/XbH0Od7PMQGBtuEfEjIiYWIfPOQJwuFvdOdzIbaAAAAHbSURBVNwtCZJOkjRH0qOSfixpV0n35G335EvrIuk6SZMk/V7SU5LG5m9xKfAvkmZL+pykgyXdnr/mAknXSLo/f83Eks89UdLD+et+KKln3v6KpIvzev4oaSdJI8lWgPxWvv27u/a/knUnDndrepKGAOcBh+braJ9Ftkb69RGxD3ADUDrE0g84EPg4WagDnAP8LiKGRsQVZT5md+AjwAjga5J6SdqD7OzEUfmVk9YAJ+Tbbwn8Ma/nAeD0iPg9MA04O/+cv9boP4HZO/gkJkvBocDPI+IFgIh4UdIBZKf3Q3Z90m+WbH9LRLwFPCZppwo/41f5an+rJC0FdgJGk11550+SADYH2taFeQO4Pb8/E/A1Ua1LOdwtBWLdU/jLKX2+dElWVfgZpa9ZQ/b/joApEXFume3fjLdPImnb3qzLeFjGUnAPcLSkHSBbbZHscnXH5s+fAExv5z1WAn068bljJe3Y9rmSdq3D55h1mMPdml5EzAMuJlvr41HgcmAicIqkOcA4snH4jZkDrM4PgH6uws99DDgfuDP/nLvIxvM35ibgbEmzfEDV6snLD5iZJch77mZmCXK4m5klyOFuZpYgh7uZWYIc7mZmCXK4m5klyOFuZpYgh7uZWYL+P/syq+hYwdVoAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "drinks.groupby(['continent']).beer_servings.agg(['count', 'min' ,'max']).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xb2768c8>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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uXSxYsKCk/G+xNWvWcPnll/Phhx/SpEkTXnwxdqPu6NGjeeCBB8jJyWHy5MlcdtllpfZ366238s9//pOlS5cye/ZsIJZ0GzduzHvvvcd7773HY489VnLj0+LFi7n99ttZuXIlw4cPLyloBpRa7jc3N5cZM2awfPlyZsyYweeff8769eu57bbbeOedd3j11Vf3+AWUKCYRSX1K7mXo1q0bOTk5FBQUcPDBB3PiiSeyZMkS3nrrrX2Se3p6OpmZmSXb5efns337dhYtWsSwYcPIzMxkzJgxbNiwodT+srKyGDlyJI899hi7d+8GYO7cuTz11FNkZmbSq1cvtmzZwpo1sfL5PXv2JD09HYAuXbqwadMm1q9fz9KlSzn00EM55phj9unj1FNPpXHjxqSlpdG+fXs+/fRTFi9eTN++fTnssMOoV6/eHr8UEsUkIqlPY+5lqFevHq1ateLJJ5/kpJNOolOnTrz++uvk5eXtUyQqvj563bp1+f777ykqKqJJkyYVLpE7ZcoU3n33XV555RUyMzPJzc3F3XnggQf2KUnwxhtv7FMzPjs7mxdeeIEvv/yyQnXc69atS2FhYal13EuLqWnTphXaHxEJj87cy9GnTx8mT55Mnz596N27N1OmTCEzM7PUh3TEa9SoEenp6cycOROIPQxj6dKlpa6fl5dHr169uPXWW2nWrBmff/45/fv355FHHmHXrl0AfPzxx3z77bcJtx8+fDh/+ctfeOGFF8jOzq7wPvbs2ZP58+fz73//m8LCwpIhpdJiEpHUV2vO3JMxdbEyevfuze23386JJ57IIYccQlpa2j5DMmV55pln+M1vfsMf/vAHdu3axfDhw+ncuXPCdcePH8+aNWtwd0499VQ6d+5Mp06dyM/Pp2vXrrg7zZs3Z9asWQm379ChAwUFBRx11FG0aNGiwjEeddRR3HDDDfTq1YsjjzyS9u3b07hx41JjEpHUV24995pQm+q5R1VxHffCwkIGDx7MqFGjGDx4cLX2eUDXc9cdqiktCvXcNSwjQGzqZGZmJh07diQ9PZ1zzjkn7JBEpApqzbBMlNx+++0l4/DFhg0bxo033hhSRDB58uTQ+haR5FNyD8GNN94YaiIXkegrd1jGzJ4ws01mtiKubZKZfWFmucHXmXHLrjeztWb2kZmF90ghEZEDWEXG3KcBAxK03+PumcHXHAAzaw8MBzoE2zxsZomLnoiISLUpN7m7+5vA1xX8vLOBv7j7Tnf/F7AW6FmF+EREpBKqMlvmCjNbFgzbHBq0HQXE3+WyLmjbh5mNNrMlZrZk8+bNVQhDRET2Vtnk/gjwUyAT2ADcHbQnum0z4UR6d5/q7t3dvXvz5s3L7XBV23ZJ/aqIrVu37lFpMZH8/HyeffbZcj8rvjpjIm+88QYDBw4EYPbs2dxxxx0AzJo1i5UrV1Yo3mQbP348HTp0YPz48aH0X1GTJk2q1Gyf+H/ziurXrx9735MhkooqldzdfaO773b3IuAxfhh6WQccHbdqS2B91UIMTzKT+/4YNGgQEydOBMpO7oWFhUntd2+PPvoo77//PnfddVe1fL4KkYlUn0oldzOLv7d9MFA8k2Y2MNzMDjazdKA1sLhqIYZn4sSJ5OXlkZmZyfjx4xk/fjwdO3YkIyOjpLzuxIkTeeutt8jMzOSee+4hPz+f3r1707VrV7p27cqiRYv2u99p06ZxxRVXsGjRImbPns348ePJzMwkLy+Pfv36ccMNN9C3b1/uu+8+Nm/ezNChQ+nRowc9evRg4cKFAMyfP5/MzEwyMzPp0qULBQUFCfty94T7NWjQIL799lt69eq1RynheCNHjmTs2LH07t2bNm3a8Le//W2P+IsNHDiQN954A4AGDRpw880306tXL5bmLGblslxGZZ/F8DP7MXbEUDZv/LLUf5fHHnuMHj160LlzZ4YOHcp33323zzpr167lF7/4BZ07d6Zr167k5eWVuo8QuzM3Ozubtm3bMmLEiJIiavPmzaNLly5kZGQwatQodu7cWWpcIqmo3HnuZvYc0A9oZmbrgFuAfmaWSWzIJR8YA+DuH5rZ88BKoBC43N1r7enZHXfcwYoVK8jNzeXFF19kypQpLF26lK+++ooePXrQp08f7rjjDiZPnlyS2L777jteffVV0tLSWLNmDeeff36l/4w/6aSTGDRoEAMHDtyjENjWrVuZP38+ABdccAHjxo3j5JNP5rPPPqN///6sWrWKyZMn89BDD5GVlcX27dtJS0tL2MdLL71Ebm7uPvs1e/ZsGjRoUG5Fy/z8fObPn09eXh6nnHIKa9euLXP9b7/9lo4dO3LrrbeS86/N/GrYWdz7+LMc1rQZ/5j9Eg/c+QduvfvBhNsOGTKESy+9FIDf/e53PP7441x55ZV7rDNixAgmTpzI4MGD2bFjB0VFRaXuI8AHH3zAhx9+yJFHHklWVhYLFy6ke/fujBw5knnz5tGmTRsuuugiHnnkEa655poy900klZSb3N39/ATNj5ex/u3A7VUJKhUtWLCA888/n7p163L44YfTt29f3nvvPRo1arTHert27eKKK64gNzeXunXr8vHHHyc9lvPOO6/k9WuvvbbHsM0333xDQUEBWVlZXHvttYwYMYIhQ4bQsmXL/dqvQYMGVSiWc889lzp16tC6dWuOO+64Mp80BbEyw0OHDgUgP28Naz9azdgLYjVsdu/eTbOfHFHqtitWrOB3v/sdW7duZfv27fuUQS4oKOCLL74oqYlT/AutrGPXs2fPkn+bzMxM8vPzadiwIenp6bRp0waAiy++mIceemi/k3uZ9UkS/64VSRrdoVpBFS2wds8993D44YezdOlSioqKSj1jror4Ou5FRUW8/fbb1K9ff491Jk6cyFlnncWcOXM44YQTeO2112jbtu0+n1XVwnF7lz42Mw466CCKiopK2nbs2FHyOi0t7Yfnvbrz0zZtefqvcyvU18iRI5k1axadO3dm2rRpJUM9xUrbl7L2cX/r24vUFiocVoaGDRuWjFX36dOHGTNmsHv3bjZv3sybb75Jz54991gHYNu2bbRo0YI6derw9NNPV/mi4d6fv7fTTz+dBx/8YRijeBglLy+v5Nmq3bt3L/WMurT9qqiZM2dSVFREXl4en3zyCccffzytWrUiNzeXoqIiPv/8cxYvTnzZpdVPW/PvLV+xNCe2fNeuXaz9aFWpfRUUFNCiRQt27drFM888s8/yRo0a0bJly5KSyDt37uS7777b731s27Yt+fn5JUNMTz/9NH379q3wv4lIKqg1Z+7tVpf+n766NG3alKysLDp27MgZZ5xBp06d6Ny5M2bGnXfeyRFHHEHTpk056KCD6Ny5MyNHjuSyyy5j6NChzJw5k1NOOWWfpyXtr+HDh3PppZdy//3388ILL+yz/P777+fyyy+nU6dOFBYW0qdPH6ZMmcK9997L66+/Tt26dWnfvj1nnHFGws8fPHgwb7/99j77VVHHH388ffv2ZePGjUyZMoW0tDSysrJIT08nIyODjh070rVr14Tb1vvRj5j86HT+dPMEthd8Q+Hu3Vz4q7H87PjEU1Vvu+02evXqxbHHHktGRkbCX3pPP/00Y8aM4eabb6ZevXrMnDmz1H0s7RdeWloaTz75JMOGDaOwsJAePXowduzYCv+biKQC1XOXShs5cuQ+F3v3R2XruX/41YelLuvQrEOlYqmqRD+vZY+5X1D6h6mee+hUz11ERFJSrRmWiYp//vOfTJgwYY+29PR0Xn755Wrtd/ny5fzyl7/co+3ggw/m3XffLXfb0urPT5s2LZkhlvjjjdexeumef8ldffXVXHLJJdXSn0gUKbnXsP79++8zha8mZGRklDtnvTQ1XX/+htsn17rH7ImkmpQelkmF6wEi5dHPqaSilE3uaWlpbNmyRf9xJKW5O1u2bKmW+xlEqiJlh2VatmzJunXrUDng6Nr47+9LXbaqoH6py77cXnr9mTqba/58JS0trdQ7gEXCkrLJvV69eqSnp4cdhlSjMyo53ezc6eeWumz5xcurFJNIVKTssIyIiFSekruISASl7LCMHOAmNS59WfoxNReHSC2lM3cRkQhSchcRiSAldxGRCCo3uZvZE2a2ycxWxLXdZWarzWyZmb1sZk2C9lZm9r2Z5QZfU6ozeBERSawiZ+7TgAF7tb0KdHT3TsDHwPVxy/LcPTP4UhFsEZEQlJvc3f1N4Ou92ua6e2Hw9h1At+eJiKSQZIy5jwL+Hvc+3cw+MLP5ZtY7CZ8vIiL7qUrz3M3sRqAQKH6g5QbgGHffYmbdgFlm1sHdv0mw7WhgNMAxx2jesohIMlX6zN3MLgYGAiM8KN3o7jvdfUvwOgfIA9ok2t7dp7p7d3fv3rx588qGISIiCVQquZvZAGACMMjdv4trb25mdYPXxwGtgU+SEaiIiFRcucMyZvYc0A9oZmbrgFuIzY45GHjVzADeCWbG9AFuNbNCYDcw1t2/TvjBIiJSbcpN7u5+foLmx0tZ90XgxaoGJSIiVaM7VEVEIkjJXUQkgpTcRUQiSMldRCSClNxFRCJIyV1EJIL0mD0Rkf1R1iMgJ22ruTjKoTN3EZEIUnIXEYkgJXcRkQhSchcRiSAldxGRCFJyFxGJICV3EZEIUnIXEYkgJXcRkQhSchcRiSAldxGRCKpQcjezJ8xsk5mtiGs7zMxeNbM1wfdDg3Yzs/vNbK2ZLTOzrtUVvIiIJFbRM/dpwIC92iYC89y9NTAveA9wBtA6+BoNPFL1MEVEZH9UKLm7+5vA13s1nw1MD15PB86Ja3/KY94BmphZi2QEKyIiFVOVMffD3X0DQPD9J0H7UcDnceutC9pERKSGVMcFVUvQ5vusZDbazJaY2ZLNmzdXQxgiIgeuqiT3jcXDLcH3TUH7OuDouPVaAuv33tjdp7p7d3fv3rx58yqEISIie6tKcp8NXBy8vhj4a1z7RcGsmROAbcXDNyIiUjMq9Jg9M3sO6Ac0M7N1wC3AHcDzZvYr4DNgWLD6HOBMYC3wHXBJkmMWEZFyVCi5u/v5pSw6NcG6DlxelaBERKRqdIeqiEgEKbmLiESQkruISAQpuYuIRJCSu4hIBCm5i4hEUIWmQoqIxGs18ZVSl+XfcVYNRiKl0Zm7iEgEKbmLiESQkruISAQpuYuIRJCSu4hIBCm5i4hEkJK7iEgEKbmLiESQkruISAQpuYuIRJCSu4hIBFW6toyZHQ/MiGs6DrgZaAJcCmwO2m9w9zmVjlBERPZbpZO7u38EZAKYWV3gC+BlYg/EvsfdJyclQhER2W/JGpY5Fchz90+T9HkiIlIFyUruw4Hn4t5fYWbLzOwJMzs00QZmNtrMlpjZks2bNydaRUREKqnKyd3MfgQMAmYGTY8APyU2ZLMBuDvRdu4+1d27u3v35s2bVzUMERGJk4wz9zOA9919I4C7b3T33e5eBDwG9ExCHyIish+SkdzPJ25IxsxaxC0bDKxIQh8iIrIfqvSYPTP7MXAaMCau+U4zywQcyN9rmYiI1IAqJXd3/w5oulfbL6sUkYiIVJnuUBURiSAldxGRCFJyFxGJICV3EZEIUnIXEYkgJXcRkQhSchcRiSAldxGRCKrSTUwiIvKDjOkZpS5bfvHyGoyklif3VhNfKXVZ/h1n1WAkIiKppVYn9wPapMZlLNtWc3GISErSmLuISATpzF1Ekkt/VaYEnbmLiESQkruISAQpuYuIRJCSu4hIBCm5i4hEUJVny5hZPlAA7AYK3b27mR0GzABaEXuO6rnu/u+q9iUiIhWTrDP3U9w90927B+8nAvPcvTUwL3gvIiI1pLqGZc4GpgevpwPnVFM/IiKSQDKSuwNzzSzHzEYHbYe7+waA4PtP9t7IzEab2RIzW7J58+YkhCEiIsWScYdqlruvN7OfAK+a2eqKbOTuU4GpAN27d/ckxCEiIoEqn7m7+/rg+ybgZaAnsNHMWgAE3zdVtR8REam4KiV3MzvEzBoWvwZOB1YAs4GLg9UuBv5alX5ERGT/VHVY5nDgZTMr/qxn3f0fZvYe8LyZ/Qr4DBhWxX5ERGQ/VCm5u/snQOcE7VuAU6vy2SIiUnm6Q1VEJIKU3EVEIkjJXUQkgpTcRVJOA8AAABIjSURBVEQiSMldRCSClNxFRCJIyV1EJIKU3EVEIigZhcOkmrSa+Eqpy/LTajAQEal1lNxFpMZkTM8oddnyi5fXYCTRp2EZEZEIUnIXEYkgJXcRkQhSchcRiSAldxGRCFJyFxGJICV3EZEIqnRyN7Ojzex1M1tlZh+a2dVB+yQz+8LMcoOvM5MXroiIVERVbmIqBP7L3d8PHpKdY2avBsvucffJVQ9PREQqo9LJ3d03ABuC1wVmtgo4KlmBiYhI5SVlzN3MWgFdgHeDpivMbJmZPWFmhyajDxERqbgq15YxswbAi8A17v6NmT0C3AZ48P1uYFSC7UYDowGOOeaYqoYhEhmqvyLJUKUzdzOrRyyxP+PuLwG4+0Z33+3uRcBjQM9E27r7VHfv7u7dmzdvXpUwRERkL1WZLWPA48Aqd/9zXHuLuNUGAysqH56IiFRGVYZlsoBfAsvNLDdouwE438wyiQ3L5ANjqhShiIjst6rMllkAWIJFcyofjoiIJIPuUBURiSAldxGRCFJyFxGJICV3EZEIiu4Dsic1LnVRRnrpN03pJhERiQKduYuIRJCSu4hIBCm5i4hEkJK7iEgEKbmLiESQkruISAQpuYuIRFB057kfwA7khz2satuu1GXtVq+qwUhEwqUzdxGRCFJyFxGJIA3LiNQiGnaqvWr62OnMXUQkgpTcRUQiqNqGZcxsAHAfUBf4b3e/o7r6Sib92SsSDv3fS65qSe5mVhd4CDgNWAe8Z2az3X1ldfQnFaf/QCIHhuoalukJrHX3T9z9P8BfgLOrqS8REdlLdSX3o4DP496vC9pERKQGVNeYuyVo8z1WMBsNjA7ebjezj6o7gB+saAZ8lWhJ+zI/tOxPrUnaP+3fvh+aGvsX5X2DlNu/Y0tbUF3JfR1wdNz7lsD6+BXcfSowtZr6L5OZLXH37mH0XRO0f7VblPcvyvsGqbV/1TUs8x7Q2szSzexHwHBgdjX1JSIie6mWM3d3LzSzK4B/EpsK+YS7f1gdfYmIyL6qbZ67u88B5lTX51dRKMNBNUj7V7tFef+ivG+QQvtn7l7+WiIiUquo/ICISAQpuYuIRJBK/oqkADPrSGwqdFpxm7s/FV5EUlGpeuwOmDF3MzsUaM2eB+DN8CJKrgNg/wwYARzn7rea2THAEe6+OOTQqszMbgH6EUsQc4AzgAXunh1mXFK+VD52B8SwjJn9GniT2NTM3wffJ4UZUzJFff8CDwMnAucH7wuIFaeLgmzgVOBLd78E6AwcHG5IyWNmJ5jZe2a23cz+Y2a7zeybsONKkpQ9dgdEcgeuBnoAn7r7KUAXYHO4ISVV1PcPoJe7Xw7sAHD3fwM/CjekpPne3YuAQjNrBGwCjgs5pmR6kNgv5TVAfeDXwAOhRpQ8KXvsDpQx9x3uvsPMMLOD3X21mR0fdlBJFPX9A9gVlJJ2ADNrDhSFG1LSLDGzJsBjQA6wHaj1w03x3H2tmdV1993Ak2a2KOyYkiRlj92BktzXBQdgFvCqmf2bvWrd1HJR3z+A+4GXgcPN7HZifw7/LtyQksPdLwteTjGzfwCN3H1ZmDEl2XdBGZJcM7sT2AAcEnJMSZHKx+6AuaBazMz6Ao2BfwS15iMlyvtnZm2JjW8C/D93j8TTRcxsMLH92Ra8bwL0c/dZ4UaWHGZ2LLHhinrAOGI/nw+7+9pQA0uCVD52B0RyN7MTgA/dvSB43xBo7+7vhhtZ1ZhZI3f/xswOS7Tc3b+u6Ziqk5l1BU4mNjSz0N3fDzmkpDCzXHfP3KvtA3fvElZMUjGpfOwOlGGZR4Cuce+/TdBWGz0LDCQ21ufsWWraSZELO8lgZjcDw4AXie3nk2Y2093/EG5kSZFoYkOt/79pZs+7+7lmtpy9nucA4O6dQggr2VL22B0oZ+6Jfrsui8gP1wHBzFYBXdx9R/C+PvC+u5f+UNhawsyeALYSm9rpwJXAoe4+Msy4qsrMWrj7hmBYZh/u/mlNx5RsqXzsDpSpkJ+Y2VVmVi/4uhr4JOygksXMsszskOD1hWb25+AmnyjJJ+4GLWJzifPCCSXprgT+A8wAZhKb7nl5qBElgbtvCL5/mugr7PiSJGWP3YFy5v4TYrMtfk7st+s84Bp33xRqYEliZsuI3TzRCXgaeBwY4u59Qw0sicxsFrG5/K8SO4anAQuIXajD3a8KLzopi5kNAf4E/ITYkJoB7u6NQg0s4g6I5B51Zva+u3cNxqW/cPfHi9vCji1ZzOzispa7+/SaiiVZzOxed7/GzP6XxGPSg0IIK+nMbC3wf6Iyuwlqx7FLiYH/6mJmv3X3O83sARIfgKic7RWY2fXAL4Hewc0+kTq27j49mCvdJmj6yN13hRlTEjwdfJ8cahTVb2OUEnsg5Y9dpBJAAsU/UEtCjaL6nQdcAFzi7l+aWR8icpNIMTPrB0wnNvZuwNFmdnFtLo7m7jnBL+JL3f3CsOOpRkvMbAaxm+x2Fje6+0vhhVQ1teHYRTq5u/v/Bgego7uPDzue6hIk9P8HXGBm/wP8C7g35LCS7W7gdHf/CMDM2gDPAd1CjaqK3H23mTU3sx9F7aazOI2A74DT49ocqLXJHVL/2EU6uUPJAajVCaA0QYIbTqwo0xZiV+wtKB4WNfWKEzuAu39sZvXCDCiJ8oGFZjab2D0YALj7n0OLKImCaolRlU+KHrvIJ/fAB8E//kz2PAC1+swBWA28Rexi1VoAMxsXbkjVZomZPc4PY50jiN28FQXrg686QMOQY0k6M0sDfgV0YM/nDYwKLajkSdljd0DMljGzJxM0e23/4QrqWgwHTgL+AfwF+G93Tw81sGpgZgcTmz98MrEx9zeJ1SfZWeaGtYiZHeLu35a/Zu1iZjOJnYhcANxK7BfzKne/OtTAkigVj12kk7uZ/cndJ5jZMHefGXY81SW4gekcYsMzPyd24fFld58bamBJElw3mZ6qF66qysxOJHZvQgN3P8bMOgNj4ioO1mrFtVaK7woPhtP+6e4/Dzu2qkrlYxf1O1TPDH6Qrg87kOrk7t+6+zPuPhBoCeQCE0MOK2mCGuDNg6mQUXQv0J/YdRPcfSnQJ9SIkqt4yupWiz1vtDHQKrxwkiplj13Ux9z/AXwFHBI81iu+sFaRuzcOJ6zqE1SCfDT4ipJ8UvTCVTK4++dm8T+e7A4rlmow1WLP+L0JmA00AG4ON6TkSdVjF+nkHkx/HG9mf3X3s4vbzexkYuN/Unuk7IWrJPjczE4CPPjr5Cp+uEej1nP3/w5ezidClUoDKXvsIj3mHs/MMomNSZ9HbB74i+7+YLhRiYCZNQPuA35B7K/LucDV7r4l1MCSxMwOB/4IHOnuZ5hZe+BEd3885NCqLJWPXaSTeynzwK9z94QlSCV1mdnrJC4hUesvykWdmf0deBK40d07m9lBwAfunhFyaJEW6WEZDqx54FF3XdzrNGAoUBhSLEllZunESse2Iu7/ZCoUn0qSZu7+fFD/CHcvNLOUGJeuqlQ+dlFP7kOJnbm/Hjy89i/seVFVagl33/uGpYVmNj+UYJJvFrHpdP8LFIUcS3X41syaEvzlFTz2clu4ISVNyh67SA/LFIv6PPADwV7Pia1DrKbM/e5+fEghJY2ZvevuvcKOo7oEz759AOgIrACaA9nuvizUwJIglY/dAZHc4wVJYhhwnsZraw8z+xc/PCe2kNhF8VvdfUGogSWBmV0AtCZ2MS6+amIkHgAOEIyzH0/s+EWhXDOQ2sfugEvuIqnGzP4vsVr8efzwp71H5eTDzC4HnnH3rcH7Q4Hz3f3hcCOrulQ+dkruktKKH7gSvN6jjISZ/dHdbwgvuuQws9VAp1QsG5sMpTyg/gN37xJWTMmSyscu6uUHpPYbHvd67zISA2oykGq0FGgSdhDVqI7F3cIZ1AqKSimJlD12UZ8tI7WflfI60fva6nBgtZm9x57jtqFPp0uSucDzZjaF2HWT3xArDRIFKXvslNwl1XkprxO9r61uCTuAanYTcCkwlh/u4qz1d6cGUvbYKblLquscV/StfvCa4H1a6ZvVHu6+x3x9M8siVvuoVs/jD2bI/BG4BPic4Nm3xGY61SFFCmxVRSofOyV3SWnuXjfsGGpCUPvoAuBcgtpH4UaUFHcRK/J2nLsXAJhZQ2LPw50MROJhHal67DRbRiQkUa99ZGZrgDa+V5IJLqiudvfW4URWdbXh2OnMXSQ8Ua995Hsn9qBxt5nV9rPKlD92mgopEp6hwJfEah89ZmanEp0ZQAArzeyivRvN7EJiybE2S/ljp2EZkZBFtfaRmR0FvAR8D+QQm93UA6gPDHb3L0IMLylS+dgpuYukkCjWPjKznwMdiJ3Zfuju80IOqVqk2rFTchcRiSCNuYuIRJCSu4hIBCm5i4hEkJK7SMjMbIiZrTGzbWb2jZkVxJVZkBSWysdOF1RFQmZma4ndDLMq7Fhk/6TysdOZu0j4NqZicpAKSdljpzN3kZCY2ZDgZV/gCGAWe9YEfymMuKR8teHYKbmLhMTMnixjsbv7qBoLRvZLbTh2Su4iITOzLHdfWF6bpJ5UPnZK7iIhM7P33b1reW2SelL52Knkr0hIzOxE4CSguZldG7eoEXBAPKSktqoNx07JXSQ8PwIaEPt/2DCu/RsgO5SIpKJS/thpWEYkRMFTiWa4e0okBNk/Znasu38adhyJ6MxdJETBU4kOCzsOqbRpiZ4qlQolf5XcRcL3gZnNBmYC3xY3psJcaSnXdXGv04g9oakwpFj2oGEZkZCVMmc6JeZKy/4zs/nu3jfsOHTmLhIyd78k7BikcvYaUqsDdCN2x2rolNxFQmZmLYEHgCxizxldAFzt7utCDUwqovjZsEZsOOZfwK9CjSigYRmRkJnZq8CzwNNB04XACHc/LbyopLZTchcJmZnluntmeW2SesysHvAboE/Q9AbwqLvvCi2ogEr+ioTvKzO70MzqBl8XAlvCDkoq5BFi4+wPB1/dgrbQ6cxdJGRmdgzwIHAisfHbRcTG3FPy5hj5gZktdffO5bWFQRdURULm7p8Bg8KOQyplt5n91N3zAMzsOGB3yDEBSu4ioTGzm8tY7O5+W40FI5U1HnjdzD4hNmPmWCAlprZqWEYkJGb2XwmaDyE2la6puzeo4ZCkEszsYOB4Ysl9tbvvLGeTGqHkLpICzKwhcDWxxP48cLe7bwo3KimNmfUAPnf3L4P3FxErPfApMMndvw4zPtBsGZFQmdlhZvYHYBmxYdKu7j5BiT3lPQr8B8DM+gB3AE8B24CpIcZVQmPuIiExs7uAIcSSQYa7bw85JKm4unFn5+cBU939ReBFM8sNMa4SGpYRCYmZFQE7id22Hv8f0YhdUG0USmBSLjNbAWS6e6GZrQZGu/ubxcvcvWO4EerMXSQ07q5h0drrOWC+mX0FfA+8BWBmPyM2NBM6nbmLiFSCmZ0AtADmuvu3QVsboIG7vx9qcCi5i4hEkv4sFBGJICV3EZEIUnIXCZhZKzO7IO59dzO7vxr6OcfM2if7c0XiKbmL/KAVUJLc3X2Ju19VDf2cAyi5S7VScpfIMLOLzGyZmS01s6fN7Fgzmxe0zQtK62Jm08zsfjNbZGafmFl28BF3AL3NLNfMxplZPzP7W7DNJDN7wszeCLa5Kq7fC81scbDdo2ZWN2jfbma3B/G8Y2aHm9lJxCpA3hWs/9Oa/VeSA4WSu0SCmXUAbgR+HtTSvppYjfSn3L0T8AwQP8TSAjgZGEgsqQNMBN5y90x3vydBN22B/kBP4BYzq2dm7YjdoZgVPDlpNzAiWP8Q4J0gnjeBS919ETAbGB/0k5ekfwKRPegmJomKnwMvuPtXAO7+tZmdSOz2fog9n/TOuPVnuXsRsNLMDq9gH68EFf92mtkm4HDgVGJP33nPzADqA8V1Yf4D/C14nQPomahSY5TcJSqMPW/hTyR+eXxZVqtgH/Hb7Cb2/8eA6e5+fYL1d/kPN5IUry9SIzQsI1ExDzjXzJpCrNoiscfVDQ+WjwAWlPMZBUDDSvSbbWY/Ke7XzI6thn5E9ouSu0SCu38I3E6s3sdS4M/AVcAlZrYM+CWxcfiyLAMKgwug4yrY70rgd8DcoJ9XiY3nl+UvwHgz+0AXVKW6qPyAiEgE6cxdRCSClNxFRCJIyV1EJIKU3EVEIkjJXUQkgpTcRUQiSMldRCSClNxFRCLo/wNOYczBlbs/CgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    " drinks.groupby(['continent']).mean().plot(kind='bar')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}