DA Week 1( Data Science 2).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "drinks = pd.read_csv('http://bit.ly/drinksbycountry')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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>country</th>\n",
       "      <th>beer_servings</th>\n",
       "      <th>spirit_servings</th>\n",
       "      <th>wine_servings</th>\n",
       "      <th>total_litres_of_pure_alcohol</th>\n",
       "      <th>continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>89</td>\n",
       "      <td>132</td>\n",
       "      <td>54</td>\n",
       "      <td>4.9</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0.7</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>245</td>\n",
       "      <td>138</td>\n",
       "      <td>312</td>\n",
       "      <td>12.4</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>217</td>\n",
       "      <td>57</td>\n",
       "      <td>45</td>\n",
       "      <td>5.9</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  beer_servings  spirit_servings  wine_servings  \\\n",
       "0  Afghanistan              0                0              0   \n",
       "1      Albania             89              132             54   \n",
       "2      Algeria             25                0             14   \n",
       "3      Andorra            245              138            312   \n",
       "4       Angola            217               57             45   \n",
       "\n",
       "   total_litres_of_pure_alcohol continent  \n",
       "0                           0.0      Asia  \n",
       "1                           4.9    Europe  \n",
       "2                           0.7    Africa  \n",
       "3                          12.4    Europe  \n",
       "4                           5.9    Africa  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "106.16062176165804"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.beer_servings.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.groupby('continent').beer_servings.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">beer_servings</th>\n",
       "      <th colspan=\"4\" halign=\"left\">spirit_servings</th>\n",
       "      <th colspan=\"4\" halign=\"left\">wine_servings</th>\n",
       "      <th colspan=\"4\" halign=\"left\">total_litres_of_pure_alcohol</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>count</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>53</td>\n",
       "      <td>61.471698</td>\n",
       "      <td>0</td>\n",
       "      <td>376</td>\n",
       "      <td>53</td>\n",
       "      <td>16.339623</td>\n",
       "      <td>0</td>\n",
       "      <td>152</td>\n",
       "      <td>53</td>\n",
       "      <td>16.264151</td>\n",
       "      <td>0</td>\n",
       "      <td>233</td>\n",
       "      <td>53</td>\n",
       "      <td>3.007547</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>0</td>\n",
       "      <td>247</td>\n",
       "      <td>44</td>\n",
       "      <td>60.840909</td>\n",
       "      <td>0</td>\n",
       "      <td>326</td>\n",
       "      <td>44</td>\n",
       "      <td>9.068182</td>\n",
       "      <td>0</td>\n",
       "      <td>123</td>\n",
       "      <td>44</td>\n",
       "      <td>2.170455</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>0</td>\n",
       "      <td>361</td>\n",
       "      <td>45</td>\n",
       "      <td>132.555556</td>\n",
       "      <td>0</td>\n",
       "      <td>373</td>\n",
       "      <td>45</td>\n",
       "      <td>142.222222</td>\n",
       "      <td>0</td>\n",
       "      <td>370</td>\n",
       "      <td>45</td>\n",
       "      <td>8.617778</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>1</td>\n",
       "      <td>285</td>\n",
       "      <td>23</td>\n",
       "      <td>165.739130</td>\n",
       "      <td>68</td>\n",
       "      <td>438</td>\n",
       "      <td>23</td>\n",
       "      <td>24.521739</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>23</td>\n",
       "      <td>5.995652</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>0</td>\n",
       "      <td>306</td>\n",
       "      <td>16</td>\n",
       "      <td>58.437500</td>\n",
       "      <td>0</td>\n",
       "      <td>254</td>\n",
       "      <td>16</td>\n",
       "      <td>35.625000</td>\n",
       "      <td>0</td>\n",
       "      <td>212</td>\n",
       "      <td>16</td>\n",
       "      <td>3.381250</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>93</td>\n",
       "      <td>333</td>\n",
       "      <td>12</td>\n",
       "      <td>114.750000</td>\n",
       "      <td>25</td>\n",
       "      <td>302</td>\n",
       "      <td>12</td>\n",
       "      <td>62.416667</td>\n",
       "      <td>1</td>\n",
       "      <td>221</td>\n",
       "      <td>12</td>\n",
       "      <td>6.308333</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 min  max           count        mean   \n",
       "continent                                                                      \n",
       "Africa                   53   61.471698   0  376              53   16.339623   \n",
       "Asia                     44   37.045455   0  247              44   60.840909   \n",
       "Europe                   45  193.777778   0  361              45  132.555556   \n",
       "North America            23  145.434783   1  285              23  165.739130   \n",
       "Oceania                  16   89.687500   0  306              16   58.437500   \n",
       "South America            12  175.083333  93  333              12  114.750000   \n",
       "\n",
       "                       wine_servings                       \\\n",
       "              min  max         count        mean min  max   \n",
       "continent                                                   \n",
       "Africa          0  152            53   16.264151   0  233   \n",
       "Asia            0  326            44    9.068182   0  123   \n",
       "Europe          0  373            45  142.222222   0  370   \n",
       "North America  68  438            23   24.521739   1  100   \n",
       "Oceania         0  254            16   35.625000   0  212   \n",
       "South America  25  302            12   62.416667   1  221   \n",
       "\n",
       "              total_litres_of_pure_alcohol                       \n",
       "                                     count      mean  min   max  \n",
       "continent                                                        \n",
       "Africa                                  53  3.007547  0.0   9.1  \n",
       "Asia                                    44  2.170455  0.0  11.5  \n",
       "Europe                                  45  8.617778  0.0  14.4  \n",
       "North America                           23  5.995652  2.2  11.9  \n",
       "Oceania                                 16  3.381250  0.0  10.4  \n",
       "South America                           12  6.308333  3.8   8.3  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.groupby('continent').agg(['count', 'mean', 'min', 'max'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>53</td>\n",
       "      <td>61.471698</td>\n",
       "      <td>0</td>\n",
       "      <td>376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>44</td>\n",
       "      <td>37.045455</td>\n",
       "      <td>0</td>\n",
       "      <td>247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>45</td>\n",
       "      <td>193.777778</td>\n",
       "      <td>0</td>\n",
       "      <td>361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North America</th>\n",
       "      <td>23</td>\n",
       "      <td>145.434783</td>\n",
       "      <td>1</td>\n",
       "      <td>285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>16</td>\n",
       "      <td>89.687500</td>\n",
       "      <td>0</td>\n",
       "      <td>306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South America</th>\n",
       "      <td>12</td>\n",
       "      <td>175.083333</td>\n",
       "      <td>93</td>\n",
       "      <td>333</td>\n",
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      "text/plain": [
       "               count        mean  min  max\n",
       "continent                                 \n",
       "Africa            53   61.471698    0  376\n",
       "Asia              44   37.045455    0  247\n",
       "Europe            45  193.777778    0  361\n",
       "North America     23  145.434783    1  285\n",
       "Oceania           16   89.687500    0  306\n",
       "South America     12  175.083333   93  333"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.groupby('continent').beer_servings.agg(['count', 'mean', 'min', 'max'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1da0a074f28>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\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')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1da0a143198>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "drinks.groupby('continent').sum().plot(kind = 'bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "movies = pd.read_csv('http://bit.ly/imdbratings')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>star_rating</th>\n",
       "      <th>title</th>\n",
       "      <th>content_rating</th>\n",
       "      <th>genre</th>\n",
       "      <th>duration</th>\n",
       "      <th>actors_list</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.3</td>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "      <td>R</td>\n",
       "      <td>Crime</td>\n",
       "      <td>142</td>\n",
       "      <td>[u'Tim Robbins', u'Morgan Freeman', u'Bob Gunt...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.2</td>\n",
       "      <td>The Godfather</td>\n",
       "      <td>R</td>\n",
       "      <td>Crime</td>\n",
       "      <td>175</td>\n",
       "      <td>[u'Marlon Brando', u'Al Pacino', u'James Caan']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.1</td>\n",
       "      <td>The Godfather: Part II</td>\n",
       "      <td>R</td>\n",
       "      <td>Crime</td>\n",
       "      <td>200</td>\n",
       "      <td>[u'Al Pacino', u'Robert De Niro', u'Robert Duv...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9.0</td>\n",
       "      <td>The Dark Knight</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>Action</td>\n",
       "      <td>152</td>\n",
       "      <td>[u'Christian Bale', u'Heath Ledger', u'Aaron E...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.9</td>\n",
       "      <td>Pulp Fiction</td>\n",
       "      <td>R</td>\n",
       "      <td>Crime</td>\n",
       "      <td>154</td>\n",
       "      <td>[u'John Travolta', u'Uma Thurman', u'Samuel L....</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   star_rating                     title content_rating   genre  duration  \\\n",
       "0          9.3  The Shawshank Redemption              R   Crime       142   \n",
       "1          9.2             The Godfather              R   Crime       175   \n",
       "2          9.1    The Godfather: Part II              R   Crime       200   \n",
       "3          9.0           The Dark Knight          PG-13  Action       152   \n",
       "4          8.9              Pulp Fiction              R   Crime       154   \n",
       "\n",
       "                                         actors_list  \n",
       "0  [u'Tim Robbins', u'Morgan Freeman', u'Bob Gunt...  \n",
       "1    [u'Marlon Brando', u'Al Pacino', u'James Caan']  \n",
       "2  [u'Al Pacino', u'Robert De Niro', u'Robert Duv...  \n",
       "3  [u'Christian Bale', u'Heath Ledger', u'Aaron E...  \n",
       "4  [u'John Travolta', u'Uma Thurman', u'Samuel L....  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "star_rating       float64\n",
       "title              object\n",
       "content_rating     object\n",
       "genre              object\n",
       "duration            int64\n",
       "actors_list        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count       979\n",
       "unique       16\n",
       "top       Drama\n",
       "freq        278\n",
       "Name: genre, dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.genre.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">star_rating</th>\n",
       "      <th colspan=\"8\" halign=\"left\">duration</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>genre</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Action</th>\n",
       "      <td>136.0</td>\n",
       "      <td>7.884559</td>\n",
       "      <td>0.333084</td>\n",
       "      <td>7.4</td>\n",
       "      <td>7.600</td>\n",
       "      <td>7.80</td>\n",
       "      <td>8.100</td>\n",
       "      <td>9.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>126.485294</td>\n",
       "      <td>21.982480</td>\n",
       "      <td>80.0</td>\n",
       "      <td>111.00</td>\n",
       "      <td>125.0</td>\n",
       "      <td>137.00</td>\n",
       "      <td>205.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adventure</th>\n",
       "      <td>75.0</td>\n",
       "      <td>7.933333</td>\n",
       "      <td>0.361802</td>\n",
       "      <td>7.4</td>\n",
       "      <td>7.650</td>\n",
       "      <td>7.90</td>\n",
       "      <td>8.100</td>\n",
       "      <td>8.9</td>\n",
       "      <td>75.0</td>\n",
       "      <td>134.840000</td>\n",
       "      <td>33.383942</td>\n",
       "      <td>89.0</td>\n",
       "      <td>109.00</td>\n",
       "      <td>127.0</td>\n",
       "      <td>151.50</td>\n",
       "      <td>224.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Animation</th>\n",
       "      <td>62.0</td>\n",
       "      <td>7.914516</td>\n",
       "      <td>0.292444</td>\n",
       "      <td>7.4</td>\n",
       "      <td>7.700</td>\n",
       "      <td>7.90</td>\n",
       "      <td>8.100</td>\n",
       "      <td>8.6</td>\n",
       "      <td>62.0</td>\n",
       "      <td>96.596774</td>\n",
       "      <td>13.975927</td>\n",
       "      <td>75.0</td>\n",
       "      <td>87.00</td>\n",
       "      <td>94.5</td>\n",
       "      <td>102.00</td>\n",
       "      <td>134.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Biography</th>\n",
       "      <td>77.0</td>\n",
       "      <td>7.862338</td>\n",
       "      <td>0.310364</td>\n",
       "      <td>7.4</td>\n",
       "      <td>7.600</td>\n",
       "      <td>7.80</td>\n",
       "      <td>8.100</td>\n",
       "      <td>8.9</td>\n",
       "      <td>77.0</td>\n",
       "      <td>131.844156</td>\n",
       "      <td>24.283991</td>\n",
       "      <td>85.0</td>\n",
       "      <td>116.00</td>\n",
       "      <td>127.0</td>\n",
       "      <td>146.00</td>\n",
       "      <td>202.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Comedy</th>\n",
       "      <td>156.0</td>\n",
       "      <td>7.822436</td>\n",
       "      <td>0.304922</td>\n",
       "      <td>7.4</td>\n",
       "      <td>7.600</td>\n",
       "      <td>7.80</td>\n",
       "      <td>8.000</td>\n",
       "      <td>8.6</td>\n",
       "      <td>156.0</td>\n",
       "      <td>107.602564</td>\n",
       "      <td>20.101574</td>\n",
       "      <td>68.0</td>\n",
       "      <td>94.00</td>\n",
       "      <td>104.0</td>\n",
       "      <td>117.00</td>\n",
       "      <td>187.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Crime</th>\n",
       "      <td>124.0</td>\n",
       "      <td>7.916935</td>\n",
       "      <td>0.392143</td>\n",
       "      <td>7.4</td>\n",
       "      <td>7.600</td>\n",
       "      <td>7.80</td>\n",
       "      <td>8.100</td>\n",
       "      <td>9.3</td>\n",
       "      <td>124.0</td>\n",
       "      <td>122.298387</td>\n",
       "      <td>25.054406</td>\n",
       "      <td>67.0</td>\n",
       "      <td>104.75</td>\n",
       "      <td>118.0</td>\n",
       "      <td>134.25</td>\n",
       "      <td>229.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drama</th>\n",
       "      <td>278.0</td>\n",
       "      <td>7.902518</td>\n",
       "      <td>0.328041</td>\n",
       "      <td>7.4</td>\n",
       "      <td>7.600</td>\n",
       "      <td>7.90</td>\n",
       "      <td>8.100</td>\n",
       "      <td>8.9</td>\n",
       "      <td>278.0</td>\n",
       "      <td>126.539568</td>\n",
       "      <td>25.637086</td>\n",
       "      <td>64.0</td>\n",
       "      <td>108.00</td>\n",
       "      <td>123.0</td>\n",
       "      <td>138.00</td>\n",
       "      <td>242.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Family</th>\n",
       "      <td>2.0</td>\n",
       "      <td>7.850000</td>\n",
       "      <td>0.070711</td>\n",
       "      <td>7.8</td>\n",
       "      <td>7.825</td>\n",
       "      <td>7.85</td>\n",
       "      <td>7.875</td>\n",
       "      <td>7.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>107.500000</td>\n",
       "      <td>10.606602</td>\n",
       "      <td>100.0</td>\n",
       "      <td>103.75</td>\n",
       "      <td>107.5</td>\n",
       "      <td>111.25</td>\n",
       "      <td>115.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fantasy</th>\n",
       "      <td>1.0</td>\n",
       "      <td>7.700000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7.700</td>\n",
       "      <td>7.70</td>\n",
       "      <td>7.700</td>\n",
       "      <td>7.7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>112.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>112.0</td>\n",
       "      <td>112.00</td>\n",
       "      <td>112.0</td>\n",
       "      <td>112.00</td>\n",
       "      <td>112.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Film-Noir</th>\n",
       "      <td>3.0</td>\n",
       "      <td>8.033333</td>\n",
       "      <td>0.305505</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7.900</td>\n",
       "      <td>8.10</td>\n",
       "      <td>8.200</td>\n",
       "      <td>8.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>97.333333</td>\n",
       "      <td>12.096832</td>\n",
       "      <td>88.0</td>\n",
       "      <td>90.50</td>\n",
       "      <td>93.0</td>\n",
       "      <td>102.00</td>\n",
       "      <td>111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>History</th>\n",
       "      <td>1.0</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.000</td>\n",
       "      <td>8.00</td>\n",
       "      <td>8.000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>66.0</td>\n",
       "      <td>66.00</td>\n",
       "      <td>66.0</td>\n",
       "      <td>66.00</td>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Horror</th>\n",
       "      <td>29.0</td>\n",
       "      <td>7.806897</td>\n",
       "      <td>0.337989</td>\n",
       "      <td>7.4</td>\n",
       "      <td>7.500</td>\n",
       "      <td>7.70</td>\n",
       "      <td>8.000</td>\n",
       "      <td>8.6</td>\n",
       "      <td>29.0</td>\n",
       "      <td>102.517241</td>\n",
       "      <td>18.908843</td>\n",
       "      <td>70.0</td>\n",
       "      <td>85.00</td>\n",
       "      <td>104.0</td>\n",
       "      <td>116.00</td>\n",
       "      <td>146.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mystery</th>\n",
       "      <td>16.0</td>\n",
       "      <td>7.975000</td>\n",
       "      <td>0.387298</td>\n",
       "      <td>7.4</td>\n",
       "      <td>7.575</td>\n",
       "      <td>8.10</td>\n",
       "      <td>8.225</td>\n",
       "      <td>8.6</td>\n",
       "      <td>16.0</td>\n",
       "      <td>115.625000</td>\n",
       "      <td>22.585762</td>\n",
       "      <td>69.0</td>\n",
       "      <td>103.75</td>\n",
       "      <td>115.0</td>\n",
       "      <td>129.25</td>\n",
       "      <td>160.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sci-Fi</th>\n",
       "      <td>5.0</td>\n",
       "      <td>7.920000</td>\n",
       "      <td>0.192354</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7.800</td>\n",
       "      <td>7.90</td>\n",
       "      <td>8.000</td>\n",
       "      <td>8.2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>17.478558</td>\n",
       "      <td>91.0</td>\n",
       "      <td>92.00</td>\n",
       "      <td>113.0</td>\n",
       "      <td>117.00</td>\n",
       "      <td>132.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thriller</th>\n",
       "      <td>5.0</td>\n",
       "      <td>7.680000</td>\n",
       "      <td>0.216795</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.500</td>\n",
       "      <td>7.60</td>\n",
       "      <td>7.800</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>114.200000</td>\n",
       "      <td>6.340347</td>\n",
       "      <td>107.0</td>\n",
       "      <td>108.00</td>\n",
       "      <td>116.0</td>\n",
       "      <td>120.00</td>\n",
       "      <td>120.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Western</th>\n",
       "      <td>9.0</td>\n",
       "      <td>8.255556</td>\n",
       "      <td>0.387657</td>\n",
       "      <td>7.6</td>\n",
       "      <td>8.100</td>\n",
       "      <td>8.30</td>\n",
       "      <td>8.500</td>\n",
       "      <td>8.9</td>\n",
       "      <td>9.0</td>\n",
       "      <td>136.666667</td>\n",
       "      <td>28.748913</td>\n",
       "      <td>85.0</td>\n",
       "      <td>131.00</td>\n",
       "      <td>135.0</td>\n",
       "      <td>161.00</td>\n",
       "      <td>175.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          star_rating                                                    \\\n",
       "                count      mean       std  min    25%   50%    75%  max   \n",
       "genre                                                                     \n",
       "Action          136.0  7.884559  0.333084  7.4  7.600  7.80  8.100  9.0   \n",
       "Adventure        75.0  7.933333  0.361802  7.4  7.650  7.90  8.100  8.9   \n",
       "Animation        62.0  7.914516  0.292444  7.4  7.700  7.90  8.100  8.6   \n",
       "Biography        77.0  7.862338  0.310364  7.4  7.600  7.80  8.100  8.9   \n",
       "Comedy          156.0  7.822436  0.304922  7.4  7.600  7.80  8.000  8.6   \n",
       "Crime           124.0  7.916935  0.392143  7.4  7.600  7.80  8.100  9.3   \n",
       "Drama           278.0  7.902518  0.328041  7.4  7.600  7.90  8.100  8.9   \n",
       "Family            2.0  7.850000  0.070711  7.8  7.825  7.85  7.875  7.9   \n",
       "Fantasy           1.0  7.700000       NaN  7.7  7.700  7.70  7.700  7.7   \n",
       "Film-Noir         3.0  8.033333  0.305505  7.7  7.900  8.10  8.200  8.3   \n",
       "History           1.0  8.000000       NaN  8.0  8.000  8.00  8.000  8.0   \n",
       "Horror           29.0  7.806897  0.337989  7.4  7.500  7.70  8.000  8.6   \n",
       "Mystery          16.0  7.975000  0.387298  7.4  7.575  8.10  8.225  8.6   \n",
       "Sci-Fi            5.0  7.920000  0.192354  7.7  7.800  7.90  8.000  8.2   \n",
       "Thriller          5.0  7.680000  0.216795  7.5  7.500  7.60  7.800  8.0   \n",
       "Western           9.0  8.255556  0.387657  7.6  8.100  8.30  8.500  8.9   \n",
       "\n",
       "          duration                                                              \n",
       "             count        mean        std    min     25%    50%     75%    max  \n",
       "genre                                                                           \n",
       "Action       136.0  126.485294  21.982480   80.0  111.00  125.0  137.00  205.0  \n",
       "Adventure     75.0  134.840000  33.383942   89.0  109.00  127.0  151.50  224.0  \n",
       "Animation     62.0   96.596774  13.975927   75.0   87.00   94.5  102.00  134.0  \n",
       "Biography     77.0  131.844156  24.283991   85.0  116.00  127.0  146.00  202.0  \n",
       "Comedy       156.0  107.602564  20.101574   68.0   94.00  104.0  117.00  187.0  \n",
       "Crime        124.0  122.298387  25.054406   67.0  104.75  118.0  134.25  229.0  \n",
       "Drama        278.0  126.539568  25.637086   64.0  108.00  123.0  138.00  242.0  \n",
       "Family         2.0  107.500000  10.606602  100.0  103.75  107.5  111.25  115.0  \n",
       "Fantasy        1.0  112.000000        NaN  112.0  112.00  112.0  112.00  112.0  \n",
       "Film-Noir      3.0   97.333333  12.096832   88.0   90.50   93.0  102.00  111.0  \n",
       "History        1.0   66.000000        NaN   66.0   66.00   66.0   66.00   66.0  \n",
       "Horror        29.0  102.517241  18.908843   70.0   85.00  104.0  116.00  146.0  \n",
       "Mystery       16.0  115.625000  22.585762   69.0  103.75  115.0  129.25  160.0  \n",
       "Sci-Fi         5.0  109.000000  17.478558   91.0   92.00  113.0  117.00  132.0  \n",
       "Thriller       5.0  114.200000   6.340347  107.0  108.00  116.0  120.00  120.0  \n",
       "Western        9.0  136.666667  28.748913   85.0  131.00  135.0  161.00  175.0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.groupby('genre').describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Drama        278\n",
       "Comedy       156\n",
       "Action       136\n",
       "Crime        124\n",
       "Biography     77\n",
       "Adventure     75\n",
       "Animation     62\n",
       "Horror        29\n",
       "Mystery       16\n",
       "Western        9\n",
       "Thriller       5\n",
       "Sci-Fi         5\n",
       "Film-Noir      3\n",
       "Family         2\n",
       "Fantasy        1\n",
       "History        1\n",
       "Name: genre, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.genre.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Drama        0.283963\n",
       "Comedy       0.159346\n",
       "Action       0.138917\n",
       "Crime        0.126660\n",
       "Biography    0.078652\n",
       "Adventure    0.076609\n",
       "Animation    0.063330\n",
       "Horror       0.029622\n",
       "Mystery      0.016343\n",
       "Western      0.009193\n",
       "Thriller     0.005107\n",
       "Sci-Fi       0.005107\n",
       "Film-Noir    0.003064\n",
       "Family       0.002043\n",
       "Fantasy      0.001021\n",
       "History      0.001021\n",
       "Name: genre, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.genre.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(movies.genre.value_counts(normalize=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Drama        0.283963\n",
       "Comedy       0.159346\n",
       "Action       0.138917\n",
       "Crime        0.126660\n",
       "Biography    0.078652\n",
       "Name: genre, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.genre.value_counts(normalize=True).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Crime', 'Action', 'Drama', 'Western', 'Adventure', 'Biography',\n",
       "       'Comedy', 'Animation', 'Mystery', 'Horror', 'Film-Noir', 'Sci-Fi',\n",
       "       'History', 'Thriller', 'Family', 'Fantasy'], dtype=object)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.genre.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.genre.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>star_rating</th>\n",
       "      <th>title</th>\n",
       "      <th>content_rating</th>\n",
       "      <th>genre</th>\n",
       "      <th>duration</th>\n",
       "      <th>actors_list</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.3</td>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "      <td>R</td>\n",
       "      <td>Crime</td>\n",
       "      <td>142</td>\n",
       "      <td>[u'Tim Robbins', u'Morgan Freeman', u'Bob Gunt...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.2</td>\n",
       "      <td>The Godfather</td>\n",
       "      <td>R</td>\n",
       "      <td>Crime</td>\n",
       "      <td>175</td>\n",
       "      <td>[u'Marlon Brando', u'Al Pacino', u'James Caan']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.1</td>\n",
       "      <td>The Godfather: Part II</td>\n",
       "      <td>R</td>\n",
       "      <td>Crime</td>\n",
       "      <td>200</td>\n",
       "      <td>[u'Al Pacino', u'Robert De Niro', u'Robert Duv...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9.0</td>\n",
       "      <td>The Dark Knight</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>Action</td>\n",
       "      <td>152</td>\n",
       "      <td>[u'Christian Bale', u'Heath Ledger', u'Aaron E...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.9</td>\n",
       "      <td>Pulp Fiction</td>\n",
       "      <td>R</td>\n",
       "      <td>Crime</td>\n",
       "      <td>154</td>\n",
       "      <td>[u'John Travolta', u'Uma Thurman', u'Samuel L....</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   star_rating                     title content_rating   genre  duration  \\\n",
       "0          9.3  The Shawshank Redemption              R   Crime       142   \n",
       "1          9.2             The Godfather              R   Crime       175   \n",
       "2          9.1    The Godfather: Part II              R   Crime       200   \n",
       "3          9.0           The Dark Knight          PG-13  Action       152   \n",
       "4          8.9              Pulp Fiction              R   Crime       154   \n",
       "\n",
       "                                         actors_list  \n",
       "0  [u'Tim Robbins', u'Morgan Freeman', u'Bob Gunt...  \n",
       "1    [u'Marlon Brando', u'Al Pacino', u'James Caan']  \n",
       "2  [u'Al Pacino', u'Robert De Niro', u'Robert Duv...  \n",
       "3  [u'Christian Bale', u'Heath Ledger', u'Aaron E...  \n",
       "4  [u'John Travolta', u'Uma Thurman', u'Samuel L....  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.crosstab(movies.genre, movies.content_rating)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['sum'] = df.sum(axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>content_rating</th>\n",
       "      <th>APPROVED</th>\n",
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       "      <td>0</td>\n",
       "      <td>74</td>\n",
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       "      <th>Animation</th>\n",
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      ],
      "text/plain": [
       "content_rating  APPROVED   G  GP  NC-17  NOT RATED  PASSED  PG  PG-13    R  \\\n",
       "genre                                                                        \n",
       "Action                 3   1   1      0          4       1  11     44   67   \n",
       "Adventure              3   2   0      0          5       1  21     23   17   \n",
       "Animation              3  20   0      0          3       0  25      5    5   \n",
       "Biography              1   2   1      0          1       0   6     29   36   \n",
       "Comedy                 9   2   1      1         16       3  23     23   73   \n",
       "Crime                  6   0   0      1          7       1   6      4   87   \n",
       "Drama                 12   3   0      4         24       1  25     55  143   \n",
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       "History                0   0   0      0          0       0   0      0    0   \n",
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       "\n",
       "content_rating  TV-MA  UNRATED  X  sum  \n",
       "genre                                   \n",
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       "Thriller            0        0  0    5  \n",
       "Western             0        0  0    9  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drama</th>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>55</td>\n",
       "      <td>143</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Family</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fantasy</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Film-Noir</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>History</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Horror</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mystery</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sci-Fi</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thriller</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Western</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "content_rating  APPROVED   G  GP  NC-17  NOT RATED  PASSED  PG  PG-13    R  \\\n",
       "genre                                                                        \n",
       "Action                 3   1   1      0          4       1  11     44   67   \n",
       "Adventure              3   2   0      0          5       1  21     23   17   \n",
       "Animation              3  20   0      0          3       0  25      5    5   \n",
       "Biography              1   2   1      0          1       0   6     29   36   \n",
       "Comedy                 9   2   1      1         16       3  23     23   73   \n",
       "Crime                  6   0   0      1          7       1   6      4   87   \n",
       "Drama                 12   3   0      4         24       1  25     55  143   \n",
       "Family                 0   1   0      0          0       0   1      0    0   \n",
       "Fantasy                0   0   0      0          0       0   0      0    1   \n",
       "Film-Noir              1   0   0      0          1       0   0      0    0   \n",
       "History                0   0   0      0          0       0   0      0    0   \n",
       "Horror                 2   0   0      1          1       0   1      2   16   \n",
       "Mystery                4   1   0      0          1       0   1      2    6   \n",
       "Sci-Fi                 1   0   0      0          0       0   0      1    3   \n",
       "Thriller               1   0   0      0          0       0   1      0    3   \n",
       "Western                1   0   0      0          2       0   2      1    3   \n",
       "\n",
       "content_rating  TV-MA  UNRATED  X  \n",
       "genre                              \n",
       "Action              0        3  0  \n",
       "Adventure           0        2  0  \n",
       "Animation           0        1  0  \n",
       "Biography           0        0  0  \n",
       "Comedy              0        4  1  \n",
       "Crime               0       11  1  \n",
       "Drama               1        9  1  \n",
       "Family              0        0  0  \n",
       "Fantasy             0        0  0  \n",
       "Film-Noir           0        1  0  \n",
       "History             0        1  0  \n",
       "Horror              0        5  1  \n",
       "Mystery             0        1  0  \n",
       "Sci-Fi              0        0  0  \n",
       "Thriller            0        0  0  \n",
       "Western             0        0  0  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(movies.genre, movies.content_rating)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>star_rating</th>\n",
       "      <th>title</th>\n",
       "      <th>content_rating</th>\n",
       "      <th>genre</th>\n",
       "      <th>duration</th>\n",
       "      <th>actors_list</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.3</td>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "      <td>R</td>\n",
       "      <td>Crime</td>\n",
       "      <td>142</td>\n",
       "      <td>[u'Tim Robbins', u'Morgan Freeman', u'Bob Gunt...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.2</td>\n",
       "      <td>The Godfather</td>\n",
       "      <td>R</td>\n",
       "      <td>Crime</td>\n",
       "      <td>175</td>\n",
       "      <td>[u'Marlon Brando', u'Al Pacino', u'James Caan']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.1</td>\n",
       "      <td>The Godfather: Part II</td>\n",
       "      <td>R</td>\n",
       "      <td>Crime</td>\n",
       "      <td>200</td>\n",
       "      <td>[u'Al Pacino', u'Robert De Niro', u'Robert Duv...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9.0</td>\n",
       "      <td>The Dark Knight</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>Action</td>\n",
       "      <td>152</td>\n",
       "      <td>[u'Christian Bale', u'Heath Ledger', u'Aaron E...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.9</td>\n",
       "      <td>Pulp Fiction</td>\n",
       "      <td>R</td>\n",
       "      <td>Crime</td>\n",
       "      <td>154</td>\n",
       "      <td>[u'John Travolta', u'Uma Thurman', u'Samuel L....</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   star_rating                     title content_rating   genre  duration  \\\n",
       "0          9.3  The Shawshank Redemption              R   Crime       142   \n",
       "1          9.2             The Godfather              R   Crime       175   \n",
       "2          9.1    The Godfather: Part II              R   Crime       200   \n",
       "3          9.0           The Dark Knight          PG-13  Action       152   \n",
       "4          8.9              Pulp Fiction              R   Crime       154   \n",
       "\n",
       "                                         actors_list  \n",
       "0  [u'Tim Robbins', u'Morgan Freeman', u'Bob Gunt...  \n",
       "1    [u'Marlon Brando', u'Al Pacino', u'James Caan']  \n",
       "2  [u'Al Pacino', u'Robert De Niro', u'Robert Duv...  \n",
       "3  [u'Christian Bale', u'Heath Ledger', u'Aaron E...  \n",
       "4  [u'John Travolta', u'Uma Thurman', u'Samuel L....  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    979.000000\n",
       "mean     120.979571\n",
       "std       26.218010\n",
       "min       64.000000\n",
       "25%      102.000000\n",
       "50%      117.000000\n",
       "75%      134.000000\n",
       "max      242.000000\n",
       "Name: duration, dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.duration.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "120.97957099080695"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.duration.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "242"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.duration.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.duration.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "112    23\n",
       "113    22\n",
       "102    20\n",
       "101    20\n",
       "129    19\n",
       "120    18\n",
       "105    18\n",
       "126    18\n",
       "98     18\n",
       "130    18\n",
       "100    17\n",
       "121    17\n",
       "116    17\n",
       "124    16\n",
       "122    16\n",
       "118    16\n",
       "115    16\n",
       "96     16\n",
       "104    16\n",
       "110    16\n",
       "107    16\n",
       "109    16\n",
       "119    15\n",
       "114    15\n",
       "99     15\n",
       "108    15\n",
       "94     14\n",
       "117    14\n",
       "106    14\n",
       "93     14\n",
       "       ..\n",
       "70      1\n",
       "69      1\n",
       "67      1\n",
       "66      1\n",
       "242     1\n",
       "238     1\n",
       "195     1\n",
       "229     1\n",
       "224     1\n",
       "220     1\n",
       "216     1\n",
       "212     1\n",
       "207     1\n",
       "205     1\n",
       "202     1\n",
       "201     1\n",
       "200     1\n",
       "194     1\n",
       "159     1\n",
       "193     1\n",
       "187     1\n",
       "186     1\n",
       "184     1\n",
       "183     1\n",
       "182     1\n",
       "180     1\n",
       "177     1\n",
       "168     1\n",
       "166     1\n",
       "64      1\n",
       "Name: duration, Length: 133, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.duration.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>star_rating</th>\n",
       "      <th>title</th>\n",
       "      <th>content_rating</th>\n",
       "      <th>genre</th>\n",
       "      <th>duration</th>\n",
       "      <th>actors_list</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.3</td>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "      <td>R</td>\n",
       "      <td>Crime</td>\n",
       "      <td>142</td>\n",
       "      <td>[u'Tim Robbins', u'Morgan Freeman', u'Bob Gunt...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.2</td>\n",
       "      <td>The Godfather</td>\n",
       "      <td>R</td>\n",
       "      <td>Crime</td>\n",
       "      <td>175</td>\n",
       "      <td>[u'Marlon Brando', u'Al Pacino', u'James Caan']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.1</td>\n",
       "      <td>The Godfather: Part II</td>\n",
       "      <td>R</td>\n",
       "      <td>Crime</td>\n",
       "      <td>200</td>\n",
       "      <td>[u'Al Pacino', u'Robert De Niro', u'Robert Duv...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9.0</td>\n",
       "      <td>The Dark Knight</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>Action</td>\n",
       "      <td>152</td>\n",
       "      <td>[u'Christian Bale', u'Heath Ledger', u'Aaron E...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.9</td>\n",
       "      <td>Pulp Fiction</td>\n",
       "      <td>R</td>\n",
       "      <td>Crime</td>\n",
       "      <td>154</td>\n",
       "      <td>[u'John Travolta', u'Uma Thurman', u'Samuel L....</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   star_rating                     title content_rating   genre  duration  \\\n",
       "0          9.3  The Shawshank Redemption              R   Crime       142   \n",
       "1          9.2             The Godfather              R   Crime       175   \n",
       "2          9.1    The Godfather: Part II              R   Crime       200   \n",
       "3          9.0           The Dark Knight          PG-13  Action       152   \n",
       "4          8.9              Pulp Fiction              R   Crime       154   \n",
       "\n",
       "                                         actors_list  \n",
       "0  [u'Tim Robbins', u'Morgan Freeman', u'Bob Gunt...  \n",
       "1    [u'Marlon Brando', u'Al Pacino', u'James Caan']  \n",
       "2  [u'Al Pacino', u'Robert De Niro', u'Robert Duv...  \n",
       "3  [u'Christian Bale', u'Heath Ledger', u'Aaron E...  \n",
       "4  [u'John Travolta', u'Uma Thurman', u'Samuel L....  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1da0a7d1a20>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "movies.duration.plot(kind = 'hist')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1da0bfad080>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "movies.genre.value_counts().plot(kind = 'bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "ufo = pd.read_csv('http://bit.ly/uforeports')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>City</th>\n",
       "      <th>Colors Reported</th>\n",
       "      <th>Shape Reported</th>\n",
       "      <th>State</th>\n",
       "      <th>Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ithaca</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TRIANGLE</td>\n",
       "      <td>NY</td>\n",
       "      <td>6/1/1930 22:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Willingboro</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>NJ</td>\n",
       "      <td>6/30/1930 20:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Holyoke</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OVAL</td>\n",
       "      <td>CO</td>\n",
       "      <td>2/15/1931 14:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abilene</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DISK</td>\n",
       "      <td>KS</td>\n",
       "      <td>6/1/1931 13:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>New York Worlds Fair</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LIGHT</td>\n",
       "      <td>NY</td>\n",
       "      <td>4/18/1933 19:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   City Colors Reported Shape Reported State             Time\n",
       "0                Ithaca             NaN       TRIANGLE    NY   6/1/1930 22:00\n",
       "1           Willingboro             NaN          OTHER    NJ  6/30/1930 20:00\n",
       "2               Holyoke             NaN           OVAL    CO  2/15/1931 14:00\n",
       "3               Abilene             NaN           DISK    KS   6/1/1931 13:00\n",
       "4  New York Worlds Fair             NaN          LIGHT    NY  4/18/1933 19:00"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, 'RED', 'GREEN', 'BLUE', 'ORANGE', 'YELLOW', 'ORANGE YELLOW',\n",
       "       'RED GREEN', 'RED BLUE', 'RED ORANGE', 'RED GREEN BLUE',\n",
       "       'RED YELLOW GREEN', 'RED YELLOW', 'GREEN BLUE',\n",
       "       'ORANGE GREEN BLUE', 'ORANGE GREEN', 'YELLOW GREEN',\n",
       "       'RED YELLOW BLUE', 'ORANGE BLUE', 'RED YELLOW GREEN BLUE',\n",
       "       'YELLOW GREEN BLUE', 'RED ORANGE YELLOW', 'RED ORANGE YELLOW BLUE',\n",
       "       'YELLOW BLUE', 'RED ORANGE GREEN', 'RED ORANGE BLUE',\n",
       "       'ORANGE YELLOW GREEN', 'ORANGE YELLOW BLUE'], dtype=object)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo['Colors Reported'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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>City</th>\n",
       "      <th>Colors Reported</th>\n",
       "      <th>Shape Reported</th>\n",
       "      <th>State</th>\n",
       "      <th>Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18236</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18237</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18238</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18239</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18240</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        City  Colors Reported  Shape Reported  State   Time\n",
       "18236  False             True           False  False  False\n",
       "18237  False             True           False  False  False\n",
       "18238  False             True            True  False  False\n",
       "18239  False            False           False  False  False\n",
       "18240  False             True           False  False  False"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo.isnull().tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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>City</th>\n",
       "      <th>Colors Reported</th>\n",
       "      <th>Shape Reported</th>\n",
       "      <th>State</th>\n",
       "      <th>Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18236</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18237</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18238</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18239</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18240</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       City  Colors Reported  Shape Reported  State  Time\n",
       "18236  True            False            True   True  True\n",
       "18237  True            False            True   True  True\n",
       "18238  True            False           False   True  True\n",
       "18239  True             True            True   True  True\n",
       "18240  True            False            True   True  True"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo.notnull().tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "City                  25\n",
       "Colors Reported    15359\n",
       "Shape Reported      2644\n",
       "State                  0\n",
       "Time                   0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18241, 5)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2486, 5)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo.dropna(how ='any').shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18241, 5)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo.dropna(how ='all').shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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>City</th>\n",
       "      <th>Colors Reported</th>\n",
       "      <th>Shape Reported</th>\n",
       "      <th>State</th>\n",
       "      <th>Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ithaca</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TRIANGLE</td>\n",
       "      <td>NY</td>\n",
       "      <td>6/1/1930 22:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Willingboro</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>NJ</td>\n",
       "      <td>6/30/1930 20:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Holyoke</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OVAL</td>\n",
       "      <td>CO</td>\n",
       "      <td>2/15/1931 14:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abilene</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DISK</td>\n",
       "      <td>KS</td>\n",
       "      <td>6/1/1931 13:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>New York Worlds Fair</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LIGHT</td>\n",
       "      <td>NY</td>\n",
       "      <td>4/18/1933 19:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   City Colors Reported Shape Reported State             Time\n",
       "0                Ithaca             NaN       TRIANGLE    NY   6/1/1930 22:00\n",
       "1           Willingboro             NaN          OTHER    NJ  6/30/1930 20:00\n",
       "2               Holyoke             NaN           OVAL    CO  2/15/1931 14:00\n",
       "3               Abilene             NaN           DISK    KS   6/1/1931 13:00\n",
       "4  New York Worlds Fair             NaN          LIGHT    NY  4/18/1933 19:00"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18237, 5)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo.dropna(subset=['City','Shape Reported'], how = 'all').shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15576, 5)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo.dropna(subset=['City','Shape Reported'], how = 'any').shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1da0cda1f28>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ufo['Shape Reported'].value_counts().plot(kind = 'bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LIGHT        2803\n",
       "NaN          2644\n",
       "DISK         2122\n",
       "TRIANGLE     1889\n",
       "OTHER        1402\n",
       "CIRCLE       1365\n",
       "SPHERE       1054\n",
       "FIREBALL     1039\n",
       "OVAL          845\n",
       "CIGAR         617\n",
       "FORMATION     434\n",
       "VARIOUS       333\n",
       "RECTANGLE     303\n",
       "CYLINDER      294\n",
       "CHEVRON       248\n",
       "DIAMOND       234\n",
       "EGG           197\n",
       "FLASH         188\n",
       "TEARDROP      119\n",
       "CONE           60\n",
       "CROSS          36\n",
       "DELTA           7\n",
       "ROUND           2\n",
       "CRESCENT        2\n",
       "HEXAGON         1\n",
       "FLARE           1\n",
       "PYRAMID         1\n",
       "DOME            1\n",
       "Name: Shape Reported, dtype: int64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo['Shape Reported'].value_counts(dropna = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "ufo['Shape Reported'].fillna(value='VARIOUS',inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VARIOUS      2977\n",
       "LIGHT        2803\n",
       "DISK         2122\n",
       "TRIANGLE     1889\n",
       "OTHER        1402\n",
       "CIRCLE       1365\n",
       "SPHERE       1054\n",
       "FIREBALL     1039\n",
       "OVAL          845\n",
       "CIGAR         617\n",
       "FORMATION     434\n",
       "RECTANGLE     303\n",
       "CYLINDER      294\n",
       "CHEVRON       248\n",
       "DIAMOND       234\n",
       "EGG           197\n",
       "FLASH         188\n",
       "TEARDROP      119\n",
       "CONE           60\n",
       "CROSS          36\n",
       "DELTA           7\n",
       "CRESCENT        2\n",
       "ROUND           2\n",
       "FLARE           1\n",
       "HEXAGON         1\n",
       "DOME            1\n",
       "PYRAMID         1\n",
       "Name: Shape Reported, dtype: int64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo['Shape Reported'].value_counts(dropna = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1da16db1f28>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ufo['Shape Reported'].value_counts(dropna = False).plot(kind = 'bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "drinks = pd.read_csv('http://bit.ly/drinksbycountry')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>country</th>\n",
       "      <th>beer_servings</th>\n",
       "      <th>spirit_servings</th>\n",
       "      <th>wine_servings</th>\n",
       "      <th>total_litres_of_pure_alcohol</th>\n",
       "      <th>continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>89</td>\n",
       "      <td>132</td>\n",
       "      <td>54</td>\n",
       "      <td>4.9</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0.7</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>245</td>\n",
       "      <td>138</td>\n",
       "      <td>312</td>\n",
       "      <td>12.4</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>217</td>\n",
       "      <td>57</td>\n",
       "      <td>45</td>\n",
       "      <td>5.9</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  beer_servings  spirit_servings  wine_servings  \\\n",
       "0  Afghanistan              0                0              0   \n",
       "1      Albania             89              132             54   \n",
       "2      Algeria             25                0             14   \n",
       "3      Andorra            245              138            312   \n",
       "4       Angola            217               57             45   \n",
       "\n",
       "   total_litres_of_pure_alcohol continent  \n",
       "0                           0.0      Asia  \n",
       "1                           4.9    Europe  \n",
       "2                           0.7    Africa  \n",
       "3                          12.4    Europe  \n",
       "4                           5.9    Africa  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = drinks.groupby('continent')['beer_servings'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>continent</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>53.0</td>\n",
       "      <td>61.471698</td>\n",
       "      <td>80.557816</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.00</td>\n",
       "      <td>32.0</td>\n",
       "      <td>76.00</td>\n",
       "      <td>376.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>44.0</td>\n",
       "      <td>37.045455</td>\n",
       "      <td>49.469725</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.25</td>\n",
       "      <td>17.5</td>\n",
       "      <td>60.50</td>\n",
       "      <td>247.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>45.0</td>\n",
       "      <td>193.777778</td>\n",
       "      <td>99.631569</td>\n",
       "      <td>0.0</td>\n",
       "      <td>127.00</td>\n",
       "      <td>219.0</td>\n",
       "      <td>270.00</td>\n",
       "      <td>361.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North America</th>\n",
       "      <td>23.0</td>\n",
       "      <td>145.434783</td>\n",
       "      <td>79.621163</td>\n",
       "      <td>1.0</td>\n",
       "      <td>80.00</td>\n",
       "      <td>143.0</td>\n",
       "      <td>198.00</td>\n",
       "      <td>285.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>16.0</td>\n",
       "      <td>89.687500</td>\n",
       "      <td>96.641412</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.00</td>\n",
       "      <td>52.5</td>\n",
       "      <td>125.75</td>\n",
       "      <td>306.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               count        mean        std  min     25%    50%     75%    max\n",
       "continent                                                                     \n",
       "Africa          53.0   61.471698  80.557816  0.0   15.00   32.0   76.00  376.0\n",
       "Asia            44.0   37.045455  49.469725  0.0    4.25   17.5   60.50  247.0\n",
       "Europe          45.0  193.777778  99.631569  0.0  127.00  219.0  270.00  361.0\n",
       "North America   23.0  145.434783  79.621163  1.0   80.00  143.0  198.00  285.0\n",
       "Oceania         16.0   89.687500  96.641412  0.0   21.00   52.5  125.75  306.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>continent</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Africa</td>\n",
       "      <td>53.0</td>\n",
       "      <td>61.471698</td>\n",
       "      <td>80.557816</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.00</td>\n",
       "      <td>32.0</td>\n",
       "      <td>76.00</td>\n",
       "      <td>376.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Asia</td>\n",
       "      <td>44.0</td>\n",
       "      <td>37.045455</td>\n",
       "      <td>49.469725</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.25</td>\n",
       "      <td>17.5</td>\n",
       "      <td>60.50</td>\n",
       "      <td>247.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Europe</td>\n",
       "      <td>45.0</td>\n",
       "      <td>193.777778</td>\n",
       "      <td>99.631569</td>\n",
       "      <td>0.0</td>\n",
       "      <td>127.00</td>\n",
       "      <td>219.0</td>\n",
       "      <td>270.00</td>\n",
       "      <td>361.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>North America</td>\n",
       "      <td>23.0</td>\n",
       "      <td>145.434783</td>\n",
       "      <td>79.621163</td>\n",
       "      <td>1.0</td>\n",
       "      <td>80.00</td>\n",
       "      <td>143.0</td>\n",
       "      <td>198.00</td>\n",
       "      <td>285.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>16.0</td>\n",
       "      <td>89.687500</td>\n",
       "      <td>96.641412</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.00</td>\n",
       "      <td>52.5</td>\n",
       "      <td>125.75</td>\n",
       "      <td>306.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>South America</td>\n",
       "      <td>12.0</td>\n",
       "      <td>175.083333</td>\n",
       "      <td>65.242845</td>\n",
       "      <td>93.0</td>\n",
       "      <td>129.50</td>\n",
       "      <td>162.5</td>\n",
       "      <td>198.00</td>\n",
       "      <td>333.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       continent  count        mean        std   min     25%    50%     75%  \\\n",
       "0         Africa   53.0   61.471698  80.557816   0.0   15.00   32.0   76.00   \n",
       "1           Asia   44.0   37.045455  49.469725   0.0    4.25   17.5   60.50   \n",
       "2         Europe   45.0  193.777778  99.631569   0.0  127.00  219.0  270.00   \n",
       "3  North America   23.0  145.434783  79.621163   1.0   80.00  143.0  198.00   \n",
       "4        Oceania   16.0   89.687500  96.641412   0.0   21.00   52.5  125.75   \n",
       "5  South America   12.0  175.083333  65.242845  93.0  129.50  162.5  198.00   \n",
       "\n",
       "     max  \n",
       "0  376.0  \n",
       "1  247.0  \n",
       "2  361.0  \n",
       "3  285.0  \n",
       "4  306.0  \n",
       "5  333.0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bcb22a81d0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['country', 'beer_servings', 'spirit_servings', 'wine_servings',\n",
       "       'total_litres_of_pure_alcohol', 'continent'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(drinks.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(193, 6)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country                          object\n",
       "beer_servings                     int64\n",
       "spirit_servings                   int64\n",
       "wine_servings                     int64\n",
       "total_litres_of_pure_alcohol    float64\n",
       "continent                        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>beer_servings</th>\n",
       "      <th>spirit_servings</th>\n",
       "      <th>wine_servings</th>\n",
       "      <th>total_litres_of_pure_alcohol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>106.160622</td>\n",
       "      <td>80.994819</td>\n",
       "      <td>49.450777</td>\n",
       "      <td>4.717098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>101.143103</td>\n",
       "      <td>88.284312</td>\n",
       "      <td>79.697598</td>\n",
       "      <td>3.773298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>20.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>76.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>4.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>188.000000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>7.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>376.000000</td>\n",
       "      <td>438.000000</td>\n",
       "      <td>370.000000</td>\n",
       "      <td>14.400000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       beer_servings  spirit_servings  wine_servings  \\\n",
       "count     193.000000       193.000000     193.000000   \n",
       "mean      106.160622        80.994819      49.450777   \n",
       "std       101.143103        88.284312      79.697598   \n",
       "min         0.000000         0.000000       0.000000   \n",
       "25%        20.000000         4.000000       1.000000   \n",
       "50%        76.000000        56.000000       8.000000   \n",
       "75%       188.000000       128.000000      59.000000   \n",
       "max       376.000000       438.000000     370.000000   \n",
       "\n",
       "       total_litres_of_pure_alcohol  \n",
       "count                    193.000000  \n",
       "mean                       4.717098  \n",
       "std                        3.773298  \n",
       "min                        0.000000  \n",
       "25%                        1.300000  \n",
       "50%                        4.200000  \n",
       "75%                        7.200000  \n",
       "max                       14.400000  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "193"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(drinks.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>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>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>South America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Bolivia</td>\n",
       "      <td>167</td>\n",
       "      <td>41</td>\n",
       "      <td>8</td>\n",
       "      <td>3.8</td>\n",
       "      <td>South America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>245</td>\n",
       "      <td>145</td>\n",
       "      <td>16</td>\n",
       "      <td>7.2</td>\n",
       "      <td>South America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Chile</td>\n",
       "      <td>130</td>\n",
       "      <td>124</td>\n",
       "      <td>172</td>\n",
       "      <td>7.6</td>\n",
       "      <td>South America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Colombia</td>\n",
       "      <td>159</td>\n",
       "      <td>76</td>\n",
       "      <td>3</td>\n",
       "      <td>4.2</td>\n",
       "      <td>South America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Ecuador</td>\n",
       "      <td>162</td>\n",
       "      <td>74</td>\n",
       "      <td>3</td>\n",
       "      <td>4.2</td>\n",
       "      <td>South America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Guyana</td>\n",
       "      <td>93</td>\n",
       "      <td>302</td>\n",
       "      <td>1</td>\n",
       "      <td>7.1</td>\n",
       "      <td>South America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>Paraguay</td>\n",
       "      <td>213</td>\n",
       "      <td>117</td>\n",
       "      <td>74</td>\n",
       "      <td>7.3</td>\n",
       "      <td>South America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>Peru</td>\n",
       "      <td>163</td>\n",
       "      <td>160</td>\n",
       "      <td>21</td>\n",
       "      <td>6.1</td>\n",
       "      <td>South America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>Suriname</td>\n",
       "      <td>128</td>\n",
       "      <td>178</td>\n",
       "      <td>7</td>\n",
       "      <td>5.6</td>\n",
       "      <td>South America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>Uruguay</td>\n",
       "      <td>115</td>\n",
       "      <td>35</td>\n",
       "      <td>220</td>\n",
       "      <td>6.6</td>\n",
       "      <td>South America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>Venezuela</td>\n",
       "      <td>333</td>\n",
       "      <td>100</td>\n",
       "      <td>3</td>\n",
       "      <td>7.7</td>\n",
       "      <td>South America</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  beer_servings  spirit_servings  wine_servings  \\\n",
       "6    Argentina            193               25            221   \n",
       "20     Bolivia            167               41              8   \n",
       "23      Brazil            245              145             16   \n",
       "35       Chile            130              124            172   \n",
       "37    Colombia            159               76              3   \n",
       "52     Ecuador            162               74              3   \n",
       "72      Guyana             93              302              1   \n",
       "132   Paraguay            213              117             74   \n",
       "133       Peru            163              160             21   \n",
       "163   Suriname            128              178              7   \n",
       "185    Uruguay            115               35            220   \n",
       "188  Venezuela            333              100              3   \n",
       "\n",
       "     total_litres_of_pure_alcohol      continent  \n",
       "6                             8.3  South America  \n",
       "20                            3.8  South America  \n",
       "23                            7.2  South America  \n",
       "35                            7.6  South America  \n",
       "37                            4.2  South America  \n",
       "52                            4.2  South America  \n",
       "72                            7.1  South America  \n",
       "132                           7.3  South America  \n",
       "133                           6.1  South America  \n",
       "163                           5.6  South America  \n",
       "185                           6.6  South America  \n",
       "188                           7.7  South America  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks[drinks.continent == 'South America']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>continent</th>\n",
       "      <th>beer_servings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Asia</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Europe</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Africa</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Europe</td>\n",
       "      <td>245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Africa</td>\n",
       "      <td>217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>North America</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>South America</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       continent  beer_servings\n",
       "0           Asia              0\n",
       "1         Europe             89\n",
       "2         Africa             25\n",
       "3         Europe            245\n",
       "4         Africa            217\n",
       "5  North America            102\n",
       "6  South America            193"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.loc[0:6, ['continent','beer_servings']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>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",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>89</td>\n",
       "      <td>132</td>\n",
       "      <td>54</td>\n",
       "      <td>4.9</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0.7</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>245</td>\n",
       "      <td>138</td>\n",
       "      <td>312</td>\n",
       "      <td>12.4</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>217</td>\n",
       "      <td>57</td>\n",
       "      <td>45</td>\n",
       "      <td>5.9</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             beer_servings  spirit_servings  wine_servings  \\\n",
       "country                                                      \n",
       "Afghanistan              0                0              0   \n",
       "Albania                 89              132             54   \n",
       "Algeria                 25                0             14   \n",
       "Andorra                245              138            312   \n",
       "Angola                 217               57             45   \n",
       "\n",
       "             total_litres_of_pure_alcohol continent  \n",
       "country                                              \n",
       "Afghanistan                           0.0      Asia  \n",
       "Albania                               4.9    Europe  \n",
       "Algeria                               0.7    Africa  \n",
       "Andorra                              12.4    Europe  \n",
       "Angola                                5.9    Africa  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.set_index('country', inplace=True)\n",
    "drinks.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "beer_servings                             245\n",
       "spirit_servings                           145\n",
       "wine_servings                              16\n",
       "total_litres_of_pure_alcohol              7.2\n",
       "continent                       South America\n",
       "Name: Brazil, dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.loc['Brazil']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Afghanistan', 'Albania', 'Algeria', 'Andorra', 'Angola',\n",
       "       'Antigua & Barbuda', 'Argentina', 'Armenia', 'Australia', 'Austria',\n",
       "       ...\n",
       "       'Tanzania', 'USA', 'Uruguay', 'Uzbekistan', 'Vanuatu', 'Venezuela',\n",
       "       'Vietnam', 'Yemen', 'Zambia', 'Zimbabwe'],\n",
       "      dtype='object', name='country', length=193)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['beer_servings', 'spirit_servings', 'wine_servings',\n",
       "       'total_litres_of_pure_alcohol', 'continent'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(193, 5)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "beer_servings                      9\n",
       "spirit_servings                  114\n",
       "wine_servings                      0\n",
       "total_litres_of_pure_alcohol     2.2\n",
       "continent                       Asia\n",
       "Name: India, dtype: object"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.loc['India']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "        text-align: right;\n",
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       "</style>\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",
       "      <th>continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>89</td>\n",
       "      <td>132</td>\n",
       "      <td>54</td>\n",
       "      <td>4.9</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0.7</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>245</td>\n",
       "      <td>138</td>\n",
       "      <td>312</td>\n",
       "      <td>12.4</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>217</td>\n",
       "      <td>57</td>\n",
       "      <td>45</td>\n",
       "      <td>5.9</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             beer_servings  spirit_servings  wine_servings  \\\n",
       "Afghanistan              0                0              0   \n",
       "Albania                 89              132             54   \n",
       "Algeria                 25                0             14   \n",
       "Andorra                245              138            312   \n",
       "Angola                 217               57             45   \n",
       "\n",
       "             total_litres_of_pure_alcohol continent  \n",
       "Afghanistan                           0.0      Asia  \n",
       "Albania                               4.9    Europe  \n",
       "Algeria                               0.7    Africa  \n",
       "Andorra                              12.4    Europe  \n",
       "Angola                                5.9    Africa  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.index.name = None\n",
    "drinks.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>beer_servings</th>\n",
       "      <th>spirit_servings</th>\n",
       "      <th>wine_servings</th>\n",
       "      <th>total_litres_of_pure_alcohol</th>\n",
       "      <th>continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>89</td>\n",
       "      <td>132</td>\n",
       "      <td>54</td>\n",
       "      <td>4.9</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0.7</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>245</td>\n",
       "      <td>138</td>\n",
       "      <td>312</td>\n",
       "      <td>12.4</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>217</td>\n",
       "      <td>57</td>\n",
       "      <td>45</td>\n",
       "      <td>5.9</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country  beer_servings  spirit_servings  wine_servings  \\\n",
       "0  Afghanistan              0                0              0   \n",
       "1      Albania             89              132             54   \n",
       "2      Algeria             25                0             14   \n",
       "3      Andorra            245              138            312   \n",
       "4       Angola            217               57             45   \n",
       "\n",
       "   total_litres_of_pure_alcohol continent  \n",
       "0                           0.0      Asia  \n",
       "1                           4.9    Europe  \n",
       "2                           0.7    Africa  \n",
       "3                          12.4    Europe  \n",
       "4                           5.9    Africa  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.index.name = 'Country'\n",
    "drinks.reset_index().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "        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>beer_servings</th>\n",
       "      <th>spirit_servings</th>\n",
       "      <th>wine_servings</th>\n",
       "      <th>total_litres_of_pure_alcohol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>106.160622</td>\n",
       "      <td>80.994819</td>\n",
       "      <td>49.450777</td>\n",
       "      <td>4.717098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>101.143103</td>\n",
       "      <td>88.284312</td>\n",
       "      <td>79.697598</td>\n",
       "      <td>3.773298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>20.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>76.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>4.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>188.000000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>7.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>376.000000</td>\n",
       "      <td>438.000000</td>\n",
       "      <td>370.000000</td>\n",
       "      <td>14.400000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       beer_servings  spirit_servings  wine_servings  \\\n",
       "count     193.000000       193.000000     193.000000   \n",
       "mean      106.160622        80.994819      49.450777   \n",
       "std       101.143103        88.284312      79.697598   \n",
       "min         0.000000         0.000000       0.000000   \n",
       "25%        20.000000         4.000000       1.000000   \n",
       "50%        76.000000        56.000000       8.000000   \n",
       "75%       188.000000       128.000000      59.000000   \n",
       "max       376.000000       438.000000     370.000000   \n",
       "\n",
       "       total_litres_of_pure_alcohol  \n",
       "count                    193.000000  \n",
       "mean                       4.717098  \n",
       "std                        3.773298  \n",
       "min                        0.000000  \n",
       "25%                        1.300000  \n",
       "50%                        4.200000  \n",
       "75%                        7.200000  \n",
       "max                       14.400000  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <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",
       "      <th>continent</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>106.160622</td>\n",
       "      <td>80.994819</td>\n",
       "      <td>49.450777</td>\n",
       "      <td>4.717098</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>101.143103</td>\n",
       "      <td>88.284312</td>\n",
       "      <td>79.697598</td>\n",
       "      <td>3.773298</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>20.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>76.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>4.200000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>188.000000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>376.000000</td>\n",
       "      <td>438.000000</td>\n",
       "      <td>370.000000</td>\n",
       "      <td>14.400000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        beer_servings  spirit_servings  wine_servings  \\\n",
       "count      193.000000       193.000000     193.000000   \n",
       "unique            NaN              NaN            NaN   \n",
       "top               NaN              NaN            NaN   \n",
       "freq              NaN              NaN            NaN   \n",
       "mean       106.160622        80.994819      49.450777   \n",
       "std        101.143103        88.284312      79.697598   \n",
       "min          0.000000         0.000000       0.000000   \n",
       "25%         20.000000         4.000000       1.000000   \n",
       "50%         76.000000        56.000000       8.000000   \n",
       "75%        188.000000       128.000000      59.000000   \n",
       "max        376.000000       438.000000     370.000000   \n",
       "\n",
       "        total_litres_of_pure_alcohol continent  \n",
       "count                     193.000000       193  \n",
       "unique                           NaN         6  \n",
       "top                              NaN    Africa  \n",
       "freq                             NaN        53  \n",
       "mean                        4.717098       NaN  \n",
       "std                         3.773298       NaN  \n",
       "min                         0.000000       NaN  \n",
       "25%                         1.300000       NaN  \n",
       "50%                         4.200000       NaN  \n",
       "75%                         7.200000       NaN  \n",
       "max                        14.400000       NaN  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.describe(include = 'all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['count', 'unique', 'top', 'freq', 'mean', 'std', 'min', '25%', '50%',\n",
       "       '75%', 'max'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.describe(include = 'all').index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['beer_servings', 'spirit_servings', 'wine_servings',\n",
       "       'total_litres_of_pure_alcohol', 'continent'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.describe(include = 'all').columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "beer_servings    20\n",
       "wine_servings     1\n",
       "Name: 25%, dtype: object"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.describe(include = 'all').loc['25%', ['beer_servings','wine_servings']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th>spirit_servings</th>\n",
       "      <th>wine_servings</th>\n",
       "      <th>total_litres_of_pure_alcohol</th>\n",
       "      <th>continent</th>\n",
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       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>89</td>\n",
       "      <td>132</td>\n",
       "      <td>54</td>\n",
       "      <td>4.9</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0.7</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>245</td>\n",
       "      <td>138</td>\n",
       "      <td>312</td>\n",
       "      <td>12.4</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>217</td>\n",
       "      <td>57</td>\n",
       "      <td>45</td>\n",
       "      <td>5.9</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             beer_servings  spirit_servings  wine_servings  \\\n",
       "Country                                                      \n",
       "Afghanistan              0                0              0   \n",
       "Albania                 89              132             54   \n",
       "Algeria                 25                0             14   \n",
       "Andorra                245              138            312   \n",
       "Angola                 217               57             45   \n",
       "\n",
       "             total_litres_of_pure_alcohol continent  \n",
       "Country                                              \n",
       "Afghanistan                           0.0      Asia  \n",
       "Albania                               4.9    Europe  \n",
       "Algeria                               0.7    Africa  \n",
       "Andorra                              12.4    Europe  \n",
       "Angola                                5.9    Africa  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "drinks.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=193, step=1)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>continent</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>89</td>\n",
       "      <td>132</td>\n",
       "      <td>54</td>\n",
       "      <td>4.9</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0.7</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>245</td>\n",
       "      <td>138</td>\n",
       "      <td>312</td>\n",
       "      <td>12.4</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>217</td>\n",
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       "      <td>45</td>\n",
       "      <td>5.9</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country  beer_servings  spirit_servings  wine_servings  \\\n",
       "0  Afghanistan              0                0              0   \n",
       "1      Albania             89              132             54   \n",
       "2      Algeria             25                0             14   \n",
       "3      Andorra            245              138            312   \n",
       "4       Angola            217               57             45   \n",
       "\n",
       "   total_litres_of_pure_alcohol continent  \n",
       "0                           0.0      Asia  \n",
       "1                           4.9    Europe  \n",
       "2                           0.7    Africa  \n",
       "3                          12.4    Europe  \n",
       "4                           5.9    Africa  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    <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",
       "      <th>continent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>89</td>\n",
       "      <td>132</td>\n",
       "      <td>54</td>\n",
       "      <td>4.9</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0.7</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>245</td>\n",
       "      <td>138</td>\n",
       "      <td>312</td>\n",
       "      <td>12.4</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>217</td>\n",
       "      <td>57</td>\n",
       "      <td>45</td>\n",
       "      <td>5.9</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             beer_servings  spirit_servings  wine_servings  \\\n",
       "Country                                                      \n",
       "Afghanistan              0                0              0   \n",
       "Albania                 89              132             54   \n",
       "Algeria                 25                0             14   \n",
       "Andorra                245              138            312   \n",
       "Angola                 217               57             45   \n",
       "\n",
       "             total_litres_of_pure_alcohol continent  \n",
       "Country                                              \n",
       "Afghanistan                           0.0      Asia  \n",
       "Albania                               4.9    Europe  \n",
       "Algeria                               0.7    Africa  \n",
       "Andorra                              12.4    Europe  \n",
       "Angola                                5.9    Africa  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.set_index('Country', inplace=True)\n",
    "drinks.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country\n",
       "Afghanistan      Asia\n",
       "Albania        Europe\n",
       "Algeria        Africa\n",
       "Andorra        Europe\n",
       "Angola         Africa\n",
       "Name: continent, dtype: object"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.continent.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Africa           53\n",
       "Europe           45\n",
       "Asia             44\n",
       "North America    23\n",
       "Oceania          16\n",
       "South America    12\n",
       "Name: continent, dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.continent.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([53, 45, 44, 23, 16, 12], dtype=int64)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.continent.value_counts().values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "53"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.continent.value_counts()['Africa']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "South America    12\n",
       "Oceania          16\n",
       "North America    23\n",
       "Asia             44\n",
       "Europe           45\n",
       "Africa           53\n",
       "Name: continent, dtype: int64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.continent.value_counts().sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Africa           53\n",
       "Asia             44\n",
       "Europe           45\n",
       "North America    23\n",
       "Oceania          16\n",
       "South America    12\n",
       "Name: continent, dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.continent.value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "people = pd.Series([3000000,500000], index = ['Albania', 'Andorra'], name='population')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Albania    3000000\n",
       "Andorra     500000\n",
       "Name: population, dtype: int64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "people"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Afghanistan                     NaN\n",
       "Albania                 267000000.0\n",
       "Algeria                         NaN\n",
       "Andorra                 122500000.0\n",
       "Angola                          NaN\n",
       "Antigua & Barbuda               NaN\n",
       "Argentina                       NaN\n",
       "Armenia                         NaN\n",
       "Australia                       NaN\n",
       "Austria                         NaN\n",
       "Azerbaijan                      NaN\n",
       "Bahamas                         NaN\n",
       "Bahrain                         NaN\n",
       "Bangladesh                      NaN\n",
       "Barbados                        NaN\n",
       "Belarus                         NaN\n",
       "Belgium                         NaN\n",
       "Belize                          NaN\n",
       "Benin                           NaN\n",
       "Bhutan                          NaN\n",
       "Bolivia                         NaN\n",
       "Bosnia-Herzegovina              NaN\n",
       "Botswana                        NaN\n",
       "Brazil                          NaN\n",
       "Brunei                          NaN\n",
       "Bulgaria                        NaN\n",
       "Burkina Faso                    NaN\n",
       "Burundi                         NaN\n",
       "Cabo Verde                      NaN\n",
       "Cambodia                        NaN\n",
       "                           ...     \n",
       "Sudan                           NaN\n",
       "Suriname                        NaN\n",
       "Swaziland                       NaN\n",
       "Sweden                          NaN\n",
       "Switzerland                     NaN\n",
       "Syria                           NaN\n",
       "Tajikistan                      NaN\n",
       "Tanzania                        NaN\n",
       "Thailand                        NaN\n",
       "Timor-Leste                     NaN\n",
       "Togo                            NaN\n",
       "Tonga                           NaN\n",
       "Trinidad & Tobago               NaN\n",
       "Tunisia                         NaN\n",
       "Turkey                          NaN\n",
       "Turkmenistan                    NaN\n",
       "Tuvalu                          NaN\n",
       "USA                             NaN\n",
       "Uganda                          NaN\n",
       "Ukraine                         NaN\n",
       "United Arab Emirates            NaN\n",
       "United Kingdom                  NaN\n",
       "Uruguay                         NaN\n",
       "Uzbekistan                      NaN\n",
       "Vanuatu                         NaN\n",
       "Venezuela                       NaN\n",
       "Vietnam                         NaN\n",
       "Yemen                           NaN\n",
       "Zambia                          NaN\n",
       "Zimbabwe                        NaN\n",
       "Length: 193, dtype: float64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.beer_servings * people"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91961\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <tbody>\n",
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       "      <th>Andorra</th>\n",
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       "      <td>138</td>\n",
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       "      <td>12.4</td>\n",
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       "      <td>Africa</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             beer_servings  spirit_servings  wine_servings  \\\n",
       "Afghanistan              0                0              0   \n",
       "Albania                 89              132             54   \n",
       "Algeria                 25                0             14   \n",
       "Andorra                245              138            312   \n",
       "Angola                 217               57             45   \n",
       "\n",
       "             total_litres_of_pure_alcohol continent  population  \n",
       "Afghanistan                           0.0      Asia         NaN  \n",
       "Albania                               4.9    Europe   3000000.0  \n",
       "Algeria                               0.7    Africa         NaN  \n",
       "Andorra                              12.4    Europe    500000.0  \n",
       "Angola                                5.9    Africa         NaN  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([drinks, people], axis = 1).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "ufo = pd.read_csv('http://bit.ly/uforeports')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Colors Reported</th>\n",
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       "      <th>State</th>\n",
       "      <th>Time</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>Ithaca</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TRIANGLE</td>\n",
       "      <td>NY</td>\n",
       "      <td>6/1/1930 22:00</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Willingboro</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NJ</td>\n",
       "      <td>6/30/1930 20:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Holyoke</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OVAL</td>\n",
       "      <td>CO</td>\n",
       "      <td>2/15/1931 14:00</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abilene</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DISK</td>\n",
       "      <td>KS</td>\n",
       "      <td>6/1/1931 13:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>New York Worlds Fair</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LIGHT</td>\n",
       "      <td>NY</td>\n",
       "      <td>4/18/1933 19:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   City Colors Reported Shape Reported State             Time\n",
       "0                Ithaca             NaN       TRIANGLE    NY   6/1/1930 22:00\n",
       "1           Willingboro             NaN          OTHER    NJ  6/30/1930 20:00\n",
       "2               Holyoke             NaN           OVAL    CO  2/15/1931 14:00\n",
       "3               Abilene             NaN           DISK    KS   6/1/1931 13:00\n",
       "4  New York Worlds Fair             NaN          LIGHT    NY  4/18/1933 19:00"
      ]
     },
     "execution_count": 82,
     "metadata": {},
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    }
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    "ufo.head()"
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   "cell_type": "code",
   "execution_count": 84,
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       "                    City\n",
       "1            Willingboro\n",
       "2                Holyoke\n",
       "3                Abilene\n",
       "4   New York Worlds Fair\n",
       "5            Valley City\n",
       "6            Crater Lake\n",
       "7                   Alma\n",
       "8                Eklutna\n",
       "9                Hubbard\n",
       "10               Fontana"
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       "          City Colors Reported Shape Reported State             Time\n",
       "0       Ithaca             NaN       TRIANGLE    NY   6/1/1930 22:00\n",
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       "          City Colors Reported Shape Reported State             Time\n",
       "0       Ithaca             NaN       TRIANGLE    NY   6/1/1930 22:00\n",
       "1  Willingboro             NaN          OTHER    NJ  6/30/1930 20:00\n",
       "2      Holyoke             NaN           OVAL    CO  2/15/1931 14:00"
      ]
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   "execution_count": 90,
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      "text/plain": [
       "                   City Colors Reported Shape Reported State\n",
       "0                Ithaca             NaN       TRIANGLE    NY\n",
       "1           Willingboro             NaN          OTHER    NJ\n",
       "2               Holyoke             NaN           OVAL    CO\n",
       "3               Abilene             NaN           DISK    KS\n",
       "4  New York Worlds Fair             NaN          LIGHT    NY"
      ]
     },
     "execution_count": 90,
     "metadata": {},
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    }
   ],
   "source": [
    "ufo.loc[:,'City':'State'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
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       "      <th>11</th>\n",
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       "      <th>16</th>\n",
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       "      <th>17</th>\n",
       "      <td>Hapeville</td>\n",
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       "      <td>Oneida</td>\n",
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       "      <th>19</th>\n",
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       "      <td>AK</td>\n",
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       "      <th>20</th>\n",
       "      <td>Nebraska</td>\n",
       "      <td>NE</td>\n",
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       "      <th>21</th>\n",
       "      <td>NaN</td>\n",
       "      <td>LA</td>\n",
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       "      <th>22</th>\n",
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       "      <th>23</th>\n",
       "      <td>Owensboro</td>\n",
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       "      <th>26</th>\n",
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       "      <th>18213</th>\n",
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       "      <th>18214</th>\n",
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       "      <th>18215</th>\n",
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       "      <th>18217</th>\n",
       "      <td>Berthoud Pass</td>\n",
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       "      <th>18218</th>\n",
       "      <td>Sisterdale</td>\n",
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       "      <th>18219</th>\n",
       "      <td>Garden Grove</td>\n",
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       "      <th>18220</th>\n",
       "      <td>Shasta Lake</td>\n",
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       "      <th>18221</th>\n",
       "      <td>Franklin</td>\n",
       "      <td>NH</td>\n",
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       "    <tr>\n",
       "      <th>18222</th>\n",
       "      <td>Albrightsville</td>\n",
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       "    <tr>\n",
       "      <th>18223</th>\n",
       "      <td>Greenville</td>\n",
       "      <td>SC</td>\n",
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       "    <tr>\n",
       "      <th>18224</th>\n",
       "      <td>Eufaula</td>\n",
       "      <td>OK</td>\n",
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       "    <tr>\n",
       "      <th>18225</th>\n",
       "      <td>Simi Valley</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>18226</th>\n",
       "      <td>San Francisco</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>18227</th>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18228</th>\n",
       "      <td>Kingsville</td>\n",
       "      <td>TX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18229</th>\n",
       "      <td>Chicago</td>\n",
       "      <td>IL</td>\n",
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       "    <tr>\n",
       "      <th>18230</th>\n",
       "      <td>Pismo Beach</td>\n",
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       "    <tr>\n",
       "      <th>18231</th>\n",
       "      <td>Pismo Beach</td>\n",
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       "    <tr>\n",
       "      <th>18232</th>\n",
       "      <td>Lodi</td>\n",
       "      <td>WI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18233</th>\n",
       "      <td>Anchorage</td>\n",
       "      <td>AK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18234</th>\n",
       "      <td>Capitola</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18235</th>\n",
       "      <td>Fountain Hills</td>\n",
       "      <td>AZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18236</th>\n",
       "      <td>Grant Park</td>\n",
       "      <td>IL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18237</th>\n",
       "      <td>Spirit Lake</td>\n",
       "      <td>IA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18238</th>\n",
       "      <td>Eagle River</td>\n",
       "      <td>WI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18239</th>\n",
       "      <td>Eagle River</td>\n",
       "      <td>WI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18240</th>\n",
       "      <td>Ybor</td>\n",
       "      <td>FL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18241 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       City State\n",
       "0                    Ithaca    NY\n",
       "1               Willingboro    NJ\n",
       "2                   Holyoke    CO\n",
       "3                   Abilene    KS\n",
       "4      New York Worlds Fair    NY\n",
       "5               Valley City    ND\n",
       "6               Crater Lake    CA\n",
       "7                      Alma    MI\n",
       "8                   Eklutna    AK\n",
       "9                   Hubbard    OR\n",
       "10                  Fontana    CA\n",
       "11                 Waterloo    AL\n",
       "12                   Belton    SC\n",
       "13                   Keokuk    IA\n",
       "14                Ludington    MI\n",
       "15              Forest Home    CA\n",
       "16              Los Angeles    CA\n",
       "17                Hapeville    GA\n",
       "18                   Oneida    TN\n",
       "19               Bering Sea    AK\n",
       "20                 Nebraska    NE\n",
       "21                      NaN    LA\n",
       "22                      NaN    LA\n",
       "23                Owensboro    KY\n",
       "24               Wilderness    WV\n",
       "25                San Diego    CA\n",
       "26               Wilderness    WV\n",
       "27                   Clovis    NM\n",
       "28               Los Alamos    NM\n",
       "29             Ft. Duschene    UT\n",
       "...                     ...   ...\n",
       "18211               Holyoke    MA\n",
       "18212                Carson    CA\n",
       "18213              Pasadena    CA\n",
       "18214                Austin    TX\n",
       "18215              El Campo    TX\n",
       "18216          Garden Grove    CA\n",
       "18217         Berthoud Pass    CO\n",
       "18218            Sisterdale    TX\n",
       "18219          Garden Grove    CA\n",
       "18220           Shasta Lake    CA\n",
       "18221              Franklin    NH\n",
       "18222        Albrightsville    PA\n",
       "18223            Greenville    SC\n",
       "18224               Eufaula    OK\n",
       "18225           Simi Valley    CA\n",
       "18226         San Francisco    CA\n",
       "18227         San Francisco    CA\n",
       "18228            Kingsville    TX\n",
       "18229               Chicago    IL\n",
       "18230           Pismo Beach    CA\n",
       "18231           Pismo Beach    CA\n",
       "18232                  Lodi    WI\n",
       "18233             Anchorage    AK\n",
       "18234              Capitola    CA\n",
       "18235        Fountain Hills    AZ\n",
       "18236            Grant Park    IL\n",
       "18237           Spirit Lake    IA\n",
       "18238           Eagle River    WI\n",
       "18239           Eagle River    WI\n",
       "18240                  Ybor    FL\n",
       "\n",
       "[18241 rows x 2 columns]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
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    }
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   "source": [
    "ufo[['City','State']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
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       "          City Colors Reported Shape Reported State\n",
       "0       Ithaca             NaN       TRIANGLE    NY\n",
       "1  Willingboro             NaN          OTHER    NJ\n",
       "2      Holyoke             NaN           OVAL    CO"
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    "ufo.head(3).drop('Time', axis = 1)"
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       "      <td>Oakland</td>\n",
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       "      <td>TRIANGLE</td>\n",
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       "      <td>CA</td>\n",
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       "          City Colors Reported Shape Reported State              Time\n",
       "1694   Oakland             NaN          CIGAR    CA   7/21/1968 14:00\n",
       "2144   Oakland             NaN           DISK    CA    8/19/1971 0:00\n",
       "4686   Oakland             NaN          LIGHT    MD     6/1/1982 0:00\n",
       "7293   Oakland             NaN          LIGHT    CA   3/28/1994 17:00\n",
       "8488   Oakland             NaN            NaN    CA   8/10/1995 21:45\n",
       "8768   Oakland             NaN            NaN    CA  10/10/1995 22:40\n",
       "10816  Oakland             NaN          LIGHT    OR   10/1/1997 21:30\n",
       "10948  Oakland             NaN           DISK    CA  11/14/1997 19:55\n",
       "11045  Oakland             NaN       TRIANGLE    CA   12/10/1997 1:30\n",
       "12322  Oakland             NaN       FIREBALL    CA   10/9/1998 19:40\n",
       "12941  Oakland             NaN       CYLINDER    CA   1/23/1999 21:30\n",
       "16803  Oakland             NaN       TRIANGLE    MD    7/4/2000 23:00\n",
       "17322  Oakland             NaN       CYLINDER    CA    9/1/2000 21:35"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo[ufo.City == 'Oakland']"
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  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "          City Colors Reported Shape Reported State              Time\n",
       "1694   Oakland             NaN          CIGAR    CA   7/21/1968 14:00\n",
       "2144   Oakland             NaN           DISK    CA    8/19/1971 0:00\n",
       "4686   Oakland             NaN          LIGHT    MD     6/1/1982 0:00\n",
       "7293   Oakland             NaN          LIGHT    CA   3/28/1994 17:00\n",
       "8488   Oakland             NaN            NaN    CA   8/10/1995 21:45\n",
       "8768   Oakland             NaN            NaN    CA  10/10/1995 22:40\n",
       "10816  Oakland             NaN          LIGHT    OR   10/1/1997 21:30\n",
       "10948  Oakland             NaN           DISK    CA  11/14/1997 19:55\n",
       "11045  Oakland             NaN       TRIANGLE    CA   12/10/1997 1:30\n",
       "12322  Oakland             NaN       FIREBALL    CA   10/9/1998 19:40\n",
       "12941  Oakland             NaN       CYLINDER    CA   1/23/1999 21:30\n",
       "16803  Oakland             NaN       TRIANGLE    MD    7/4/2000 23:00\n",
       "17322  Oakland             NaN       CYLINDER    CA    9/1/2000 21:35"
      ]
     },
     "execution_count": 94,
     "metadata": {},
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    }
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   "source": [
    "ufo.loc[ufo.City == 'Oakland' , :]"
   ]
  },
  {
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   "execution_count": 95,
   "metadata": {},
   "outputs": [
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       "      <th>10</th>\n",
       "      <td>Fontana</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Waterloo</td>\n",
       "      <td>AL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Belton</td>\n",
       "      <td>SC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Keokuk</td>\n",
       "      <td>IA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Ludington</td>\n",
       "      <td>MI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Forest Home</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Hapeville</td>\n",
       "      <td>GA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Oneida</td>\n",
       "      <td>TN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Bering Sea</td>\n",
       "      <td>AK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Nebraska</td>\n",
       "      <td>NE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>NaN</td>\n",
       "      <td>LA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>NaN</td>\n",
       "      <td>LA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Owensboro</td>\n",
       "      <td>KY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Wilderness</td>\n",
       "      <td>WV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>San Diego</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Wilderness</td>\n",
       "      <td>WV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Clovis</td>\n",
       "      <td>NM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Los Alamos</td>\n",
       "      <td>NM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Ft. Duschene</td>\n",
       "      <td>UT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18211</th>\n",
       "      <td>Holyoke</td>\n",
       "      <td>MA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18212</th>\n",
       "      <td>Carson</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18213</th>\n",
       "      <td>Pasadena</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18214</th>\n",
       "      <td>Austin</td>\n",
       "      <td>TX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18215</th>\n",
       "      <td>El Campo</td>\n",
       "      <td>TX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18216</th>\n",
       "      <td>Garden Grove</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18217</th>\n",
       "      <td>Berthoud Pass</td>\n",
       "      <td>CO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18218</th>\n",
       "      <td>Sisterdale</td>\n",
       "      <td>TX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18219</th>\n",
       "      <td>Garden Grove</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18220</th>\n",
       "      <td>Shasta Lake</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18221</th>\n",
       "      <td>Franklin</td>\n",
       "      <td>NH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18222</th>\n",
       "      <td>Albrightsville</td>\n",
       "      <td>PA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18223</th>\n",
       "      <td>Greenville</td>\n",
       "      <td>SC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18224</th>\n",
       "      <td>Eufaula</td>\n",
       "      <td>OK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18225</th>\n",
       "      <td>Simi Valley</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18226</th>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18227</th>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18228</th>\n",
       "      <td>Kingsville</td>\n",
       "      <td>TX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18229</th>\n",
       "      <td>Chicago</td>\n",
       "      <td>IL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18230</th>\n",
       "      <td>Pismo Beach</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18231</th>\n",
       "      <td>Pismo Beach</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18232</th>\n",
       "      <td>Lodi</td>\n",
       "      <td>WI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18233</th>\n",
       "      <td>Anchorage</td>\n",
       "      <td>AK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18234</th>\n",
       "      <td>Capitola</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18235</th>\n",
       "      <td>Fountain Hills</td>\n",
       "      <td>AZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18236</th>\n",
       "      <td>Grant Park</td>\n",
       "      <td>IL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18237</th>\n",
       "      <td>Spirit Lake</td>\n",
       "      <td>IA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18238</th>\n",
       "      <td>Eagle River</td>\n",
       "      <td>WI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18239</th>\n",
       "      <td>Eagle River</td>\n",
       "      <td>WI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18240</th>\n",
       "      <td>Ybor</td>\n",
       "      <td>FL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18241 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       City State\n",
       "0                    Ithaca    NY\n",
       "1               Willingboro    NJ\n",
       "2                   Holyoke    CO\n",
       "3                   Abilene    KS\n",
       "4      New York Worlds Fair    NY\n",
       "5               Valley City    ND\n",
       "6               Crater Lake    CA\n",
       "7                      Alma    MI\n",
       "8                   Eklutna    AK\n",
       "9                   Hubbard    OR\n",
       "10                  Fontana    CA\n",
       "11                 Waterloo    AL\n",
       "12                   Belton    SC\n",
       "13                   Keokuk    IA\n",
       "14                Ludington    MI\n",
       "15              Forest Home    CA\n",
       "16              Los Angeles    CA\n",
       "17                Hapeville    GA\n",
       "18                   Oneida    TN\n",
       "19               Bering Sea    AK\n",
       "20                 Nebraska    NE\n",
       "21                      NaN    LA\n",
       "22                      NaN    LA\n",
       "23                Owensboro    KY\n",
       "24               Wilderness    WV\n",
       "25                San Diego    CA\n",
       "26               Wilderness    WV\n",
       "27                   Clovis    NM\n",
       "28               Los Alamos    NM\n",
       "29             Ft. Duschene    UT\n",
       "...                     ...   ...\n",
       "18211               Holyoke    MA\n",
       "18212                Carson    CA\n",
       "18213              Pasadena    CA\n",
       "18214                Austin    TX\n",
       "18215              El Campo    TX\n",
       "18216          Garden Grove    CA\n",
       "18217         Berthoud Pass    CO\n",
       "18218            Sisterdale    TX\n",
       "18219          Garden Grove    CA\n",
       "18220           Shasta Lake    CA\n",
       "18221              Franklin    NH\n",
       "18222        Albrightsville    PA\n",
       "18223            Greenville    SC\n",
       "18224               Eufaula    OK\n",
       "18225           Simi Valley    CA\n",
       "18226         San Francisco    CA\n",
       "18227         San Francisco    CA\n",
       "18228            Kingsville    TX\n",
       "18229               Chicago    IL\n",
       "18230           Pismo Beach    CA\n",
       "18231           Pismo Beach    CA\n",
       "18232                  Lodi    WI\n",
       "18233             Anchorage    AK\n",
       "18234              Capitola    CA\n",
       "18235        Fountain Hills    AZ\n",
       "18236            Grant Park    IL\n",
       "18237           Spirit Lake    IA\n",
       "18238           Eagle River    WI\n",
       "18239           Eagle River    WI\n",
       "18240                  Ybor    FL\n",
       "\n",
       "[18241 rows x 2 columns]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo.iloc[:,[0,3]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Colors Reported</th>\n",
       "      <th>Shape Reported</th>\n",
       "      <th>State</th>\n",
       "      <th>Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ithaca</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TRIANGLE</td>\n",
       "      <td>NY</td>\n",
       "      <td>6/1/1930 22:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Willingboro</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>NJ</td>\n",
       "      <td>6/30/1930 20:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Holyoke</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OVAL</td>\n",
       "      <td>CO</td>\n",
       "      <td>2/15/1931 14:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          City Colors Reported Shape Reported State             Time\n",
       "0       Ithaca             NaN       TRIANGLE    NY   6/1/1930 22:00\n",
       "1  Willingboro             NaN          OTHER    NJ  6/30/1930 20:00\n",
       "2      Holyoke             NaN           OVAL    CO  2/15/1931 14:00"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo.iloc[0:3, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "drinks = pd.read_csv('http://bit.ly/drinksbycountry', index_col = 'country')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "<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",
       "      <th>continent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>89</td>\n",
       "      <td>132</td>\n",
       "      <td>54</td>\n",
       "      <td>4.9</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0.7</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>245</td>\n",
       "      <td>138</td>\n",
       "      <td>312</td>\n",
       "      <td>12.4</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>217</td>\n",
       "      <td>57</td>\n",
       "      <td>45</td>\n",
       "      <td>5.9</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             beer_servings  spirit_servings  wine_servings  \\\n",
       "country                                                      \n",
       "Afghanistan              0                0              0   \n",
       "Albania                 89              132             54   \n",
       "Algeria                 25                0             14   \n",
       "Andorra                245              138            312   \n",
       "Angola                 217               57             45   \n",
       "\n",
       "             total_litres_of_pure_alcohol continent  \n",
       "country                                              \n",
       "Afghanistan                           0.0      Asia  \n",
       "Albania                               4.9    Europe  \n",
       "Algeria                               0.7    Africa  \n",
       "Andorra                              12.4    Europe  \n",
       "Angola                                5.9    Africa  "
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91961\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "89"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.ix['Albania', 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91961\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "89"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.ix[1, 'beer_servings']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>245</td>\n",
       "      <td>138</td>\n",
       "      <td>312</td>\n",
       "      <td>12.4</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>217</td>\n",
       "      <td>57</td>\n",
       "      <td>45</td>\n",
       "      <td>5.9</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             beer_servings  spirit_servings  wine_servings  \\\n",
       "country                                                      \n",
       "Afghanistan              0                0              0   \n",
       "Albania                 89              132             54   \n",
       "Algeria                 25                0             14   \n",
       "Andorra                245              138            312   \n",
       "Angola                 217               57             45   \n",
       "\n",
       "             total_litres_of_pure_alcohol continent  \n",
       "country                                              \n",
       "Afghanistan                           0.0      Asia  \n",
       "Albania                               4.9    Europe  \n",
       "Algeria                               0.7    Africa  \n",
       "Andorra                              12.4    Europe  \n",
       "Angola                                5.9    Africa  "
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91961\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
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    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>89</td>\n",
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       "      <td>4.9</td>\n",
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       "      <td>25</td>\n",
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       "      <td>5.9</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "         beer_servings  spirit_servings  wine_servings  \\\n",
       "country                                                  \n",
       "Albania             89              132             54   \n",
       "Algeria             25                0             14   \n",
       "Andorra            245              138            312   \n",
       "Angola             217               57             45   \n",
       "\n",
       "         total_litres_of_pure_alcohol  \n",
       "country                                \n",
       "Albania                           4.9  \n",
       "Algeria                           0.7  \n",
       "Andorra                          12.4  \n",
       "Angola                            5.9  "
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.ix['Albania' : 'Angola', 0:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91961\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>Afghanistan</th>\n",
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       "             beer_servings  spirit_servings\n",
       "country                                    \n",
       "Afghanistan              0                0\n",
       "Albania                 89              132"
      ]
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   "source": [
    "drinks.ix[0:2,0:2]"
   ]
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  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Colors Reported</th>\n",
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       "      <th>State</th>\n",
       "      <th>Time</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>Ithaca</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TRIANGLE</td>\n",
       "      <td>NY</td>\n",
       "      <td>6/1/1930 22:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Willingboro</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>NJ</td>\n",
       "      <td>6/30/1930 20:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Holyoke</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OVAL</td>\n",
       "      <td>CO</td>\n",
       "      <td>2/15/1931 14:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abilene</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DISK</td>\n",
       "      <td>KS</td>\n",
       "      <td>6/1/1931 13:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>New York Worlds Fair</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LIGHT</td>\n",
       "      <td>NY</td>\n",
       "      <td>4/18/1933 19:00</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   City Colors Reported Shape Reported State             Time\n",
       "0                Ithaca             NaN       TRIANGLE    NY   6/1/1930 22:00\n",
       "1           Willingboro             NaN          OTHER    NJ  6/30/1930 20:00\n",
       "2               Holyoke             NaN           OVAL    CO  2/15/1931 14:00\n",
       "3               Abilene             NaN           DISK    KS   6/1/1931 13:00\n",
       "4  New York Worlds Fair             NaN          LIGHT    NY  4/18/1933 19:00"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18241, 5)"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "ufo = pd.read_csv('http://bit.ly/uforeports')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Abilene</td>\n",
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       "      <td>6/1/1931 13:00</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>New York Worlds Fair</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LIGHT</td>\n",
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       "                   City Colors Reported Shape Reported State             Time\n",
       "0                Ithaca             NaN       TRIANGLE    NY   6/1/1930 22:00\n",
       "1           Willingboro             NaN          OTHER    NJ  6/30/1930 20:00\n",
       "2               Holyoke             NaN           OVAL    CO  2/15/1931 14:00\n",
       "3               Abilene             NaN           DISK    KS   6/1/1931 13:00\n",
       "4  New York Worlds Fair             NaN          LIGHT    NY  4/18/1933 19:00"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "ufo.drop('City', axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "  Colors Reported Shape Reported State             Time\n",
       "0             NaN       TRIANGLE    NY   6/1/1930 22:00\n",
       "1             NaN          OTHER    NJ  6/30/1930 20:00\n",
       "2             NaN           OVAL    CO  2/15/1931 14:00\n",
       "3             NaN           DISK    KS   6/1/1931 13:00\n",
       "4             NaN          LIGHT    NY  4/18/1933 19:00"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>RED</td>\n",
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       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>RED</td>\n",
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       "      <td>AK</td>\n",
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       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>RED</td>\n",
       "      <td>FORMATION</td>\n",
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       "      <td>7/10/1945 1:30</td>\n",
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       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>GREEN</td>\n",
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       "      <td>CA</td>\n",
       "      <td>6/30/1946 19:00</td>\n",
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       "      <th>82</th>\n",
       "      <td>BLUE</td>\n",
       "      <td>CHEVRON</td>\n",
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       "      <td>7/15/1947 21:00</td>\n",
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       "      <th>91</th>\n",
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       "      <td>IN</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>ORANGE</td>\n",
       "      <td>CIRCLE</td>\n",
       "      <td>AL</td>\n",
       "      <td>8/15/1949 22:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>GREEN</td>\n",
       "      <td>DISK</td>\n",
       "      <td>CA</td>\n",
       "      <td>6/10/1950 0:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>ORANGE</td>\n",
       "      <td>CIGAR</td>\n",
       "      <td>ID</td>\n",
       "      <td>7/2/1950 13:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>BLUE</td>\n",
       "      <td>DISK</td>\n",
       "      <td>KS</td>\n",
       "      <td>4/15/1951 0:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>GREEN</td>\n",
       "      <td>DISK</td>\n",
       "      <td>MS</td>\n",
       "      <td>6/15/1951 20:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>GREEN</td>\n",
       "      <td>SPHERE</td>\n",
       "      <td>WY</td>\n",
       "      <td>7/3/1951 12:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>BLUE</td>\n",
       "      <td>DISK</td>\n",
       "      <td>UT</td>\n",
       "      <td>7/10/1951 23:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>ORANGE</td>\n",
       "      <td>TRIANGLE</td>\n",
       "      <td>TX</td>\n",
       "      <td>4/15/1952 16:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>RED</td>\n",
       "      <td>FIREBALL</td>\n",
       "      <td>VA</td>\n",
       "      <td>6/1/1952 22:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>GREEN</td>\n",
       "      <td>OVAL</td>\n",
       "      <td>VA</td>\n",
       "      <td>7/13/1952 21:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>RED</td>\n",
       "      <td>SPHERE</td>\n",
       "      <td>MA</td>\n",
       "      <td>4/1/1953 18:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>YELLOW</td>\n",
       "      <td>FIREBALL</td>\n",
       "      <td>OK</td>\n",
       "      <td>4/15/1953 16:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>RED</td>\n",
       "      <td>FIREBALL</td>\n",
       "      <td>OH</td>\n",
       "      <td>6/30/1953 0:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>BLUE</td>\n",
       "      <td>DISK</td>\n",
       "      <td>MT</td>\n",
       "      <td>7/4/1953 14:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>ORANGE</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>NM</td>\n",
       "      <td>8/15/1953 19:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>GREEN</td>\n",
       "      <td>DISK</td>\n",
       "      <td>IN</td>\n",
       "      <td>11/21/1953 22:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>RED</td>\n",
       "      <td>OVAL</td>\n",
       "      <td>MO</td>\n",
       "      <td>7/1/1954 21:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>RED</td>\n",
       "      <td>CIRCLE</td>\n",
       "      <td>CA</td>\n",
       "      <td>7/1/1954 22:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>RED</td>\n",
       "      <td>DISK</td>\n",
       "      <td>TX</td>\n",
       "      <td>9/9/1954 12:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311</th>\n",
       "      <td>ORANGE</td>\n",
       "      <td>CIRCLE</td>\n",
       "      <td>NJ</td>\n",
       "      <td>12/15/1954 23:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314</th>\n",
       "      <td>YELLOW</td>\n",
       "      <td>EGG</td>\n",
       "      <td>MA</td>\n",
       "      <td>5/1/1955 15:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>ORANGE</td>\n",
       "      <td>CIRCLE</td>\n",
       "      <td>IN</td>\n",
       "      <td>6/15/1955 0:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>354</th>\n",
       "      <td>ORANGE</td>\n",
       "      <td>CYLINDER</td>\n",
       "      <td>TN</td>\n",
       "      <td>6/1/1956 20:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18110</th>\n",
       "      <td>RED</td>\n",
       "      <td>LIGHT</td>\n",
       "      <td>AZ</td>\n",
       "      <td>12/11/2000 23:45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18115</th>\n",
       "      <td>RED GREEN</td>\n",
       "      <td>TEARDROP</td>\n",
       "      <td>OH</td>\n",
       "      <td>12/12/2000 18:45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18117</th>\n",
       "      <td>RED GREEN</td>\n",
       "      <td>CIRCLE</td>\n",
       "      <td>NH</td>\n",
       "      <td>12/12/2000 23:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18121</th>\n",
       "      <td>BLUE</td>\n",
       "      <td>LIGHT</td>\n",
       "      <td>CA</td>\n",
       "      <td>12/15/2000 1:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18123</th>\n",
       "      <td>GREEN</td>\n",
       "      <td>LIGHT</td>\n",
       "      <td>MD</td>\n",
       "      <td>12/15/2000 5:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18127</th>\n",
       "      <td>ORANGE</td>\n",
       "      <td>SPHERE</td>\n",
       "      <td>AK</td>\n",
       "      <td>12/15/2000 16:36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18129</th>\n",
       "      <td>RED</td>\n",
       "      <td>DISK</td>\n",
       "      <td>PA</td>\n",
       "      <td>12/15/2000 18:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18134</th>\n",
       "      <td>GREEN</td>\n",
       "      <td>LIGHT</td>\n",
       "      <td>WA</td>\n",
       "      <td>12/16/2000 0:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18141</th>\n",
       "      <td>YELLOW</td>\n",
       "      <td>CIRCLE</td>\n",
       "      <td>CA</td>\n",
       "      <td>12/17/2000 18:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18144</th>\n",
       "      <td>ORANGE</td>\n",
       "      <td>TRIANGLE</td>\n",
       "      <td>OH</td>\n",
       "      <td>12/17/2000 19:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18148</th>\n",
       "      <td>BLUE</td>\n",
       "      <td>VARIOUS</td>\n",
       "      <td>NJ</td>\n",
       "      <td>12/18/2000 2:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18158</th>\n",
       "      <td>ORANGE</td>\n",
       "      <td>LIGHT</td>\n",
       "      <td>CA</td>\n",
       "      <td>12/19/2000 23:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18167</th>\n",
       "      <td>RED</td>\n",
       "      <td>CIRCLE</td>\n",
       "      <td>AZ</td>\n",
       "      <td>12/21/2000 19:05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18170</th>\n",
       "      <td>BLUE</td>\n",
       "      <td>CIRCLE</td>\n",
       "      <td>AZ</td>\n",
       "      <td>12/22/2000 3:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18172</th>\n",
       "      <td>RED</td>\n",
       "      <td>OVAL</td>\n",
       "      <td>MS</td>\n",
       "      <td>12/23/2000 4:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18177</th>\n",
       "      <td>RED</td>\n",
       "      <td>LIGHT</td>\n",
       "      <td>AZ</td>\n",
       "      <td>12/23/2000 22:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18181</th>\n",
       "      <td>ORANGE</td>\n",
       "      <td>CIRCLE</td>\n",
       "      <td>CA</td>\n",
       "      <td>12/24/2000 18:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18184</th>\n",
       "      <td>GREEN</td>\n",
       "      <td>FIREBALL</td>\n",
       "      <td>OH</td>\n",
       "      <td>12/24/2000 22:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18191</th>\n",
       "      <td>RED</td>\n",
       "      <td>VARIOUS</td>\n",
       "      <td>MD</td>\n",
       "      <td>12/25/2000 19:15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18192</th>\n",
       "      <td>ORANGE</td>\n",
       "      <td>LIGHT</td>\n",
       "      <td>GA</td>\n",
       "      <td>12/25/2000 20:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18194</th>\n",
       "      <td>GREEN</td>\n",
       "      <td>FIREBALL</td>\n",
       "      <td>NH</td>\n",
       "      <td>12/26/2000 18:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18195</th>\n",
       "      <td>GREEN</td>\n",
       "      <td>FIREBALL</td>\n",
       "      <td>NH</td>\n",
       "      <td>12/26/2000 18:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18196</th>\n",
       "      <td>GREEN</td>\n",
       "      <td>OVAL</td>\n",
       "      <td>MA</td>\n",
       "      <td>12/26/2000 18:23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18197</th>\n",
       "      <td>GREEN</td>\n",
       "      <td>FIREBALL</td>\n",
       "      <td>MA</td>\n",
       "      <td>12/26/2000 18:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18210</th>\n",
       "      <td>BLUE</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>UT</td>\n",
       "      <td>12/28/2000 17:51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18213</th>\n",
       "      <td>GREEN</td>\n",
       "      <td>FIREBALL</td>\n",
       "      <td>CA</td>\n",
       "      <td>12/28/2000 19:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18216</th>\n",
       "      <td>ORANGE</td>\n",
       "      <td>LIGHT</td>\n",
       "      <td>CA</td>\n",
       "      <td>12/29/2000 16:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18220</th>\n",
       "      <td>BLUE</td>\n",
       "      <td>DISK</td>\n",
       "      <td>CA</td>\n",
       "      <td>12/29/2000 20:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18233</th>\n",
       "      <td>RED</td>\n",
       "      <td>VARIOUS</td>\n",
       "      <td>AK</td>\n",
       "      <td>12/31/2000 21:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18239</th>\n",
       "      <td>RED</td>\n",
       "      <td>LIGHT</td>\n",
       "      <td>WI</td>\n",
       "      <td>12/31/2000 23:45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2490 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Colors Reported Shape Reported State              Time\n",
       "12                RED         SPHERE    SC   6/30/1939 20:00\n",
       "19                RED          OTHER    AK   4/30/1943 23:00\n",
       "36                RED      FORMATION    VA    7/10/1945 1:30\n",
       "44              GREEN         SPHERE    CA   6/30/1946 19:00\n",
       "82               BLUE        CHEVRON    CA   7/15/1947 21:00\n",
       "84               BLUE           DISK    CA    8/8/1947 22:00\n",
       "91                RED         SPHERE    IN   5/10/1948 19:00\n",
       "111            ORANGE         CIRCLE    AL   8/15/1949 22:00\n",
       "129             GREEN           DISK    CA    6/10/1950 0:00\n",
       "138            ORANGE          CIGAR    ID    7/2/1950 13:00\n",
       "152              BLUE           DISK    KS    4/15/1951 0:30\n",
       "157             GREEN           DISK    MS   6/15/1951 20:30\n",
       "163             GREEN         SPHERE    WY    7/3/1951 12:00\n",
       "164              BLUE           DISK    UT   7/10/1951 23:30\n",
       "174            ORANGE       TRIANGLE    TX   4/15/1952 16:00\n",
       "178               RED       FIREBALL    VA    6/1/1952 22:00\n",
       "202             GREEN           OVAL    VA   7/13/1952 21:00\n",
       "226               RED         SPHERE    MA    4/1/1953 18:00\n",
       "229            YELLOW       FIREBALL    OK   4/15/1953 16:00\n",
       "238               RED       FIREBALL    OH    6/30/1953 0:00\n",
       "241              BLUE           DISK    MT    7/4/1953 14:00\n",
       "249            ORANGE          OTHER    NM   8/15/1953 19:00\n",
       "256             GREEN           DISK    IN  11/21/1953 22:30\n",
       "288               RED           OVAL    MO    7/1/1954 21:00\n",
       "289               RED         CIRCLE    CA    7/1/1954 22:00\n",
       "304               RED           DISK    TX    9/9/1954 12:30\n",
       "311            ORANGE         CIRCLE    NJ  12/15/1954 23:10\n",
       "314            YELLOW            EGG    MA    5/1/1955 15:00\n",
       "323            ORANGE         CIRCLE    IN    6/15/1955 0:00\n",
       "354            ORANGE       CYLINDER    TN    6/1/1956 20:00\n",
       "...               ...            ...   ...               ...\n",
       "18110             RED          LIGHT    AZ  12/11/2000 23:45\n",
       "18115       RED GREEN       TEARDROP    OH  12/12/2000 18:45\n",
       "18117       RED GREEN         CIRCLE    NH  12/12/2000 23:10\n",
       "18121            BLUE          LIGHT    CA   12/15/2000 1:09\n",
       "18123           GREEN          LIGHT    MD   12/15/2000 5:30\n",
       "18127          ORANGE         SPHERE    AK  12/15/2000 16:36\n",
       "18129             RED           DISK    PA  12/15/2000 18:25\n",
       "18134           GREEN          LIGHT    WA   12/16/2000 0:20\n",
       "18141          YELLOW         CIRCLE    CA  12/17/2000 18:00\n",
       "18144          ORANGE       TRIANGLE    OH  12/17/2000 19:30\n",
       "18148            BLUE        VARIOUS    NJ   12/18/2000 2:30\n",
       "18158          ORANGE          LIGHT    CA  12/19/2000 23:30\n",
       "18167             RED         CIRCLE    AZ  12/21/2000 19:05\n",
       "18170            BLUE         CIRCLE    AZ   12/22/2000 3:00\n",
       "18172             RED           OVAL    MS   12/23/2000 4:00\n",
       "18177             RED          LIGHT    AZ  12/23/2000 22:00\n",
       "18181          ORANGE         CIRCLE    CA  12/24/2000 18:00\n",
       "18184           GREEN       FIREBALL    OH  12/24/2000 22:00\n",
       "18191             RED        VARIOUS    MD  12/25/2000 19:15\n",
       "18192          ORANGE          LIGHT    GA  12/25/2000 20:30\n",
       "18194           GREEN       FIREBALL    NH  12/26/2000 18:20\n",
       "18195           GREEN       FIREBALL    NH  12/26/2000 18:20\n",
       "18196           GREEN           OVAL    MA  12/26/2000 18:23\n",
       "18197           GREEN       FIREBALL    MA  12/26/2000 18:30\n",
       "18210            BLUE          OTHER    UT  12/28/2000 17:51\n",
       "18213           GREEN       FIREBALL    CA  12/28/2000 19:10\n",
       "18216          ORANGE          LIGHT    CA  12/29/2000 16:10\n",
       "18220            BLUE           DISK    CA  12/29/2000 20:30\n",
       "18233             RED        VARIOUS    AK  12/31/2000 21:00\n",
       "18239             RED          LIGHT    WI  12/31/2000 23:45\n",
       "\n",
       "[2490 rows x 4 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo.dropna(how='any')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "drinks = pd.read_csv('http://bit.ly/drinksbycountry')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>country</th>\n",
       "      <th>beer_servings</th>\n",
       "      <th>spirit_servings</th>\n",
       "      <th>wine_servings</th>\n",
       "      <th>total_litres_of_pure_alcohol</th>\n",
       "      <th>continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>89</td>\n",
       "      <td>132</td>\n",
       "      <td>54</td>\n",
       "      <td>4.9</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0.7</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>245</td>\n",
       "      <td>138</td>\n",
       "      <td>312</td>\n",
       "      <td>12.4</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>217</td>\n",
       "      <td>57</td>\n",
       "      <td>45</td>\n",
       "      <td>5.9</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  beer_servings  spirit_servings  wine_servings  \\\n",
       "0  Afghanistan              0                0              0   \n",
       "1      Albania             89              132             54   \n",
       "2      Algeria             25                0             14   \n",
       "3      Andorra            245              138            312   \n",
       "4       Angola            217               57             45   \n",
       "\n",
       "   total_litres_of_pure_alcohol continent  \n",
       "0                           0.0      Asia  \n",
       "1                           4.9    Europe  \n",
       "2                           0.7    Africa  \n",
       "3                          12.4    Europe  \n",
       "4                           5.9    Africa  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(193, 6)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 193 entries, 0 to 192\n",
      "Data columns (total 6 columns):\n",
      "country                         193 non-null object\n",
      "beer_servings                   193 non-null int64\n",
      "spirit_servings                 193 non-null int64\n",
      "wine_servings                   193 non-null int64\n",
      "total_litres_of_pure_alcohol    193 non-null float64\n",
      "continent                       193 non-null object\n",
      "dtypes: float64(1), int64(3), object(2)\n",
      "memory usage: 9.1+ KB\n"
     ]
    }
   ],
   "source": [
    "drinks.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>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>count</th>\n",
       "      <td>193</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>193</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>106.160622</td>\n",
       "      <td>80.994819</td>\n",
       "      <td>49.450777</td>\n",
       "      <td>4.717098</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>101.143103</td>\n",
       "      <td>88.284312</td>\n",
       "      <td>79.697598</td>\n",
       "      <td>3.773298</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>4.200000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>188.000000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>376.000000</td>\n",
       "      <td>438.000000</td>\n",
       "      <td>370.000000</td>\n",
       "      <td>14.400000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  beer_servings  spirit_servings  wine_servings  \\\n",
       "count      193     193.000000       193.000000     193.000000   \n",
       "unique     193            NaN              NaN            NaN   \n",
       "top     Zambia            NaN              NaN            NaN   \n",
       "freq         1            NaN              NaN            NaN   \n",
       "mean       NaN     106.160622        80.994819      49.450777   \n",
       "std        NaN     101.143103        88.284312      79.697598   \n",
       "min        NaN       0.000000         0.000000       0.000000   \n",
       "25%        NaN      20.000000         4.000000       1.000000   \n",
       "50%        NaN      76.000000        56.000000       8.000000   \n",
       "75%        NaN     188.000000       128.000000      59.000000   \n",
       "max        NaN     376.000000       438.000000     370.000000   \n",
       "\n",
       "        total_litres_of_pure_alcohol continent  \n",
       "count                     193.000000       193  \n",
       "unique                           NaN         6  \n",
       "top                              NaN    Africa  \n",
       "freq                             NaN        53  \n",
       "mean                        4.717098       NaN  \n",
       "std                         3.773298       NaN  \n",
       "min                         0.000000       NaN  \n",
       "25%                         1.300000       NaN  \n",
       "50%                         4.200000       NaN  \n",
       "75%                         7.200000       NaN  \n",
       "max                        14.400000       NaN  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 193 entries, 0 to 192\n",
      "Data columns (total 6 columns):\n",
      "country                         193 non-null object\n",
      "beer_servings                   193 non-null int64\n",
      "spirit_servings                 193 non-null int64\n",
      "wine_servings                   193 non-null int64\n",
      "total_litres_of_pure_alcohol    193 non-null float64\n",
      "continent                       193 non-null object\n",
      "dtypes: float64(1), int64(3), object(2)\n",
      "memory usage: 30.4 KB\n"
     ]
    }
   ],
   "source": [
    "drinks.info(memory_usage='deep')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index                             80\n",
       "country                         1544\n",
       "beer_servings                   1544\n",
       "spirit_servings                 1544\n",
       "wine_servings                   1544\n",
       "total_litres_of_pure_alcohol    1544\n",
       "continent                       1544\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.memory_usage()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index                              80\n",
       "country                         12588\n",
       "beer_servings                    1544\n",
       "spirit_servings                  1544\n",
       "wine_servings                    1544\n",
       "total_litres_of_pure_alcohol     1544\n",
       "continent                       12332\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.memory_usage(deep=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "31176"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.memory_usage(deep=True).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Africa', 'Asia', 'Europe', 'North America', 'Oceania', 'South America']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(drinks.continent.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "drinks['continent'] = drinks.continent.astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>country</th>\n",
       "      <th>beer_servings</th>\n",
       "      <th>spirit_servings</th>\n",
       "      <th>wine_servings</th>\n",
       "      <th>total_litres_of_pure_alcohol</th>\n",
       "      <th>continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>89</td>\n",
       "      <td>132</td>\n",
       "      <td>54</td>\n",
       "      <td>4.9</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0.7</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>245</td>\n",
       "      <td>138</td>\n",
       "      <td>312</td>\n",
       "      <td>12.4</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>217</td>\n",
       "      <td>57</td>\n",
       "      <td>45</td>\n",
       "      <td>5.9</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  beer_servings  spirit_servings  wine_servings  \\\n",
       "0  Afghanistan              0                0              0   \n",
       "1      Albania             89              132             54   \n",
       "2      Algeria             25                0             14   \n",
       "3      Andorra            245              138            312   \n",
       "4       Angola            217               57             45   \n",
       "\n",
       "   total_litres_of_pure_alcohol continent  \n",
       "0                           0.0      Asia  \n",
       "1                           4.9    Europe  \n",
       "2                           0.7    Africa  \n",
       "3                          12.4    Europe  \n",
       "4                           5.9    Africa  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country                           object\n",
       "beer_servings                      int64\n",
       "spirit_servings                    int64\n",
       "wine_servings                      int64\n",
       "total_litres_of_pure_alcohol     float64\n",
       "continent                       category\n",
       "dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "2    0\n",
       "3    2\n",
       "4    0\n",
       "dtype: int8"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.continent.cat.codes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index                              80\n",
       "country                         12588\n",
       "beer_servings                    1544\n",
       "spirit_servings                  1544\n",
       "wine_servings                    1544\n",
       "total_litres_of_pure_alcohol     1544\n",
       "continent                         744\n",
       "dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.memory_usage(deep=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 193 entries, 0 to 192\n",
      "Data columns (total 6 columns):\n",
      "country                         193 non-null object\n",
      "beer_servings                   193 non-null int64\n",
      "spirit_servings                 193 non-null int64\n",
      "wine_servings                   193 non-null int64\n",
      "total_litres_of_pure_alcohol    193 non-null float64\n",
      "continent                       193 non-null category\n",
      "dtypes: category(1), float64(1), int64(3), object(1)\n",
      "memory usage: 19.1 KB\n"
     ]
    }
   ],
   "source": [
    "drinks.info(memory_usage='deep')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0               Afghanistan\n",
       "1                   Albania\n",
       "2                   Algeria\n",
       "3                   Andorra\n",
       "4                    Angola\n",
       "5         Antigua & Barbuda\n",
       "6                 Argentina\n",
       "7                   Armenia\n",
       "8                 Australia\n",
       "9                   Austria\n",
       "10               Azerbaijan\n",
       "11                  Bahamas\n",
       "12                  Bahrain\n",
       "13               Bangladesh\n",
       "14                 Barbados\n",
       "15                  Belarus\n",
       "16                  Belgium\n",
       "17                   Belize\n",
       "18                    Benin\n",
       "19                   Bhutan\n",
       "20                  Bolivia\n",
       "21       Bosnia-Herzegovina\n",
       "22                 Botswana\n",
       "23                   Brazil\n",
       "24                   Brunei\n",
       "25                 Bulgaria\n",
       "26             Burkina Faso\n",
       "27                  Burundi\n",
       "28            Cote d'Ivoire\n",
       "29               Cabo Verde\n",
       "               ...         \n",
       "163                Suriname\n",
       "164               Swaziland\n",
       "165                  Sweden\n",
       "166             Switzerland\n",
       "167                   Syria\n",
       "168              Tajikistan\n",
       "169                Thailand\n",
       "170               Macedonia\n",
       "171             Timor-Leste\n",
       "172                    Togo\n",
       "173                   Tonga\n",
       "174       Trinidad & Tobago\n",
       "175                 Tunisia\n",
       "176                  Turkey\n",
       "177            Turkmenistan\n",
       "178                  Tuvalu\n",
       "179                  Uganda\n",
       "180                 Ukraine\n",
       "181    United Arab Emirates\n",
       "182          United Kingdom\n",
       "183                Tanzania\n",
       "184                     USA\n",
       "185                 Uruguay\n",
       "186              Uzbekistan\n",
       "187                 Vanuatu\n",
       "188               Venezuela\n",
       "189                 Vietnam\n",
       "190                   Yemen\n",
       "191                  Zambia\n",
       "192                Zimbabwe\n",
       "Name: country, Length: 193, dtype: object"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({'ID':[1,2,3,4,5], 'quality' : ['good','bad', 'poor', 'very good', 'excellent']})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>ID</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>poor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>very good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>excellent</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID    quality\n",
       "0   1       good\n",
       "1   2        bad\n",
       "2   3       poor\n",
       "3   4  very good\n",
       "4   5  excellent"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
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       "   ID    quality\n",
       "1   2        bad\n",
       "4   5  excellent\n",
       "0   1       good\n",
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       "3   4  very good"
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     "metadata": {},
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    "df.sort_values('quality')"
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   "execution_count": 38,
   "metadata": {},
   "outputs": [
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     "text": [
      "C:\\Users\\91961\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3296: FutureWarning: specifying 'categories' or 'ordered' in .astype() is deprecated; pass a CategoricalDtype instead\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
     ]
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   "source": [
    "df['quality'] = df.quality.astype('category', categories = ['good','bad','poor','very good', 'excellent'], ordered = True)"
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   "execution_count": 39,
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      "text/plain": [
       "   ID    quality\n",
       "0   1       good\n",
       "1   2        bad\n",
       "2   3       poor\n",
       "3   4  very good\n",
       "4   5  excellent"
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    "df.head()"
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   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         good\n",
       "1          bad\n",
       "2         poor\n",
       "3    very good\n",
       "4    excellent\n",
       "Name: quality, dtype: category\n",
       "Categories (5, object): [good < bad < poor < very good < excellent]"
      ]
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     "execution_count": 40,
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       "   ID    quality\n",
       "0   1       good\n",
       "1   2        bad\n",
       "2   3       poor\n",
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   "execution_count": 43,
   "metadata": {},
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       "   ID    quality\n",
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      ]
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   "source": [
    "df.loc[df.quality > 'poor', :]"
   ]
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   "execution_count": null,
   "metadata": {},
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   "source": []
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