3_pandas_part_2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# String methods in Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "URL = 'http://bit.ly/chiporders'\n",
    "orders = pd.read_table(URL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>order_id</th>\n",
       "      <th>quantity</th>\n",
       "      <th>item_name</th>\n",
       "      <th>choice_description</th>\n",
       "      <th>item_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Chips and Fresh Tomato Salsa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$2.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Izze</td>\n",
       "      <td>[Clementine]</td>\n",
       "      <td>$3.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Nantucket Nectar</td>\n",
       "      <td>[Apple]</td>\n",
       "      <td>$3.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Chips and Tomatillo-Green Chili Salsa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$2.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Chicken Bowl</td>\n",
       "      <td>[Tomatillo-Red Chili Salsa (Hot), [Black Beans...</td>\n",
       "      <td>$16.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id  quantity                              item_name  \\\n",
       "0         1         1           Chips and Fresh Tomato Salsa   \n",
       "1         1         1                                   Izze   \n",
       "2         1         1                       Nantucket Nectar   \n",
       "3         1         1  Chips and Tomatillo-Green Chili Salsa   \n",
       "4         2         2                           Chicken Bowl   \n",
       "\n",
       "                                  choice_description item_price  \n",
       "0                                                NaN     $2.39   \n",
       "1                                       [Clementine]     $3.39   \n",
       "2                                            [Apple]     $3.39   \n",
       "3                                                NaN     $2.39   \n",
       "4  [Tomatillo-Red Chili Salsa (Hot), [Black Beans...    $16.98   "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2    False\n",
       "3    False\n",
       "4     True\n",
       "Name: item_name, dtype: bool"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders.item_name.str.contains('Chicken').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\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>order_id</th>\n",
       "      <th>quantity</th>\n",
       "      <th>item_name</th>\n",
       "      <th>choice_description</th>\n",
       "      <th>item_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Chicken Bowl</td>\n",
       "      <td>[Tomatillo-Red Chili Salsa (Hot), [Black Beans...</td>\n",
       "      <td>$16.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Chicken Bowl</td>\n",
       "      <td>[Fresh Tomato Salsa (Mild), [Rice, Cheese, Sou...</td>\n",
       "      <td>$10.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>Chicken Crispy Tacos</td>\n",
       "      <td>[Roasted Chili Corn Salsa, [Fajita Vegetables,...</td>\n",
       "      <td>$8.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>Chicken Soft Tacos</td>\n",
       "      <td>[Roasted Chili Corn Salsa, [Rice, Black Beans,...</td>\n",
       "      <td>$8.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>Chicken Bowl</td>\n",
       "      <td>[Fresh Tomato Salsa, [Fajita Vegetables, Rice,...</td>\n",
       "      <td>$11.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    order_id  quantity             item_name  \\\n",
       "4          2         2          Chicken Bowl   \n",
       "5          3         1          Chicken Bowl   \n",
       "11         6         1  Chicken Crispy Tacos   \n",
       "12         6         1    Chicken Soft Tacos   \n",
       "13         7         1          Chicken Bowl   \n",
       "\n",
       "                                   choice_description item_price  \n",
       "4   [Tomatillo-Red Chili Salsa (Hot), [Black Beans...    $16.98   \n",
       "5   [Fresh Tomato Salsa (Mild), [Rice, Cheese, Sou...    $10.98   \n",
       "11  [Roasted Chili Corn Salsa, [Fajita Vegetables,...     $8.75   \n",
       "12  [Roasted Chili Corn Salsa, [Rice, Black Beans,...     $8.75   \n",
       "13  [Fresh Tomato Salsa, [Fajita Vegetables, Rice,...    $11.25   "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders[orders.item_name.str.contains('Chicken')].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0             CHIPS AND FRESH TOMATO SALSA\n",
       "1                                     IZZE\n",
       "2                         NANTUCKET NECTAR\n",
       "3    CHIPS AND TOMATILLO-GREEN CHILI SALSA\n",
       "4                             CHICKEN BOWL\n",
       "Name: item_name, dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders.item_name.str.upper().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove [,] from choice_description"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                                                  NaN\n",
       "1                                           Clementine\n",
       "2                                                Apple\n",
       "3                                                  NaN\n",
       "4    Tomatillo-Red Chili Salsa (Hot), Black Beans, ...\n",
       "Name: choice_description, dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders.choice_description.str.replace('[', '').str.replace(']', '').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Change the data type of a pandas Series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "URL = 'http://bit.ly/drinksbycountry'\n",
    "drinks = pd.read_csv(URL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "drinks['beer_servings'] = drinks.beer_servings.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country                          object\n",
       "beer_servings                   float64\n",
       "spirit_servings                   int64\n",
       "wine_servings                     int64\n",
       "total_litres_of_pure_alcohol    float64\n",
       "continent                        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define type pf column while reading the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>beer_servings</th>\n",
       "      <th>spirit_servings</th>\n",
       "      <th>wine_servings</th>\n",
       "      <th>total_litres_of_pure_alcohol</th>\n",
       "      <th>continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0.0</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>89.0</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>25.0</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>245.0</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>217.0</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              0   \n",
       "1      Albania           89.0              132             54   \n",
       "2      Algeria           25.0                0             14   \n",
       "3      Andorra          245.0              138            312   \n",
       "4       Angola          217.0               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": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks = pd.read_csv(URL, dtype={'beer_servings': float})\n",
    "drinks.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "URL = 'http://bit.ly/chiporders'\n",
    "orders = pd.read_table(URL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>quantity</th>\n",
       "      <th>item_name</th>\n",
       "      <th>choice_description</th>\n",
       "      <th>item_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Chips and Fresh Tomato Salsa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$2.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Izze</td>\n",
       "      <td>[Clementine]</td>\n",
       "      <td>$3.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Nantucket Nectar</td>\n",
       "      <td>[Apple]</td>\n",
       "      <td>$3.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Chips and Tomatillo-Green Chili Salsa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$2.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Chicken Bowl</td>\n",
       "      <td>[Tomatillo-Red Chili Salsa (Hot), [Black Beans...</td>\n",
       "      <td>$16.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id  quantity                              item_name  \\\n",
       "0         1         1           Chips and Fresh Tomato Salsa   \n",
       "1         1         1                                   Izze   \n",
       "2         1         1                       Nantucket Nectar   \n",
       "3         1         1  Chips and Tomatillo-Green Chili Salsa   \n",
       "4         2         2                           Chicken Bowl   \n",
       "\n",
       "                                  choice_description item_price  \n",
       "0                                                NaN     $2.39   \n",
       "1                                       [Clementine]     $3.39   \n",
       "2                                            [Apple]     $3.39   \n",
       "3                                                NaN     $2.39   \n",
       "4  [Tomatillo-Red Chili Salsa (Hot), [Black Beans...    $16.98   "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "order_id               int64\n",
       "quantity               int64\n",
       "item_name             object\n",
       "choice_description    object\n",
       "item_price            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    0\n",
       "2    0\n",
       "3    0\n",
       "4    1\n",
       "Name: item_name, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders.item_name.str.contains('Chicken').astype(int).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14.928671570748595"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(orders.item_price.str.replace('$', '').astype(float)*2).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# groupby in Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "URL = 'http://bit.ly/drinksbycountry'\n",
    "drinks = pd.read_csv(URL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "106.16062176165804"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.beer_servings.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.groupby('continent').beer_servings.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "61.471698113207545"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks[drinks.continent == 'Africa'].beer_servings.mean()"
   ]
  },
  {
   "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>count</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>continent</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>376</td>\n",
       "      <td>61.471698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>247</td>\n",
       "      <td>37.045455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>361</td>\n",
       "      <td>193.777778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North America</th>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>285</td>\n",
       "      <td>145.434783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>306</td>\n",
       "      <td>89.687500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South America</th>\n",
       "      <td>12</td>\n",
       "      <td>93</td>\n",
       "      <td>333</td>\n",
       "      <td>175.083333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               count  min  max        mean\n",
       "continent                                 \n",
       "Africa            53    0  376   61.471698\n",
       "Asia              44    0  247   37.045455\n",
       "Europe            45    0  361  193.777778\n",
       "North America     23    1  285  145.434783\n",
       "Oceania           16    0  306   89.687500\n",
       "South America     12   93  333  175.083333"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.groupby('continent').beer_servings.agg(['count', 'min', 'max', 'mean'])"
   ]
  },
  {
   "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>beer_servings</th>\n",
       "      <th>spirit_servings</th>\n",
       "      <th>wine_servings</th>\n",
       "      <th>total_litres_of_pure_alcohol</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>continent</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>61.471698</td>\n",
       "      <td>16.339623</td>\n",
       "      <td>16.264151</td>\n",
       "      <td>3.007547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>37.045455</td>\n",
       "      <td>60.840909</td>\n",
       "      <td>9.068182</td>\n",
       "      <td>2.170455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>193.777778</td>\n",
       "      <td>132.555556</td>\n",
       "      <td>142.222222</td>\n",
       "      <td>8.617778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North America</th>\n",
       "      <td>145.434783</td>\n",
       "      <td>165.739130</td>\n",
       "      <td>24.521739</td>\n",
       "      <td>5.995652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>89.687500</td>\n",
       "      <td>58.437500</td>\n",
       "      <td>35.625000</td>\n",
       "      <td>3.381250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South America</th>\n",
       "      <td>175.083333</td>\n",
       "      <td>114.750000</td>\n",
       "      <td>62.416667</td>\n",
       "      <td>6.308333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               beer_servings  spirit_servings  wine_servings  \\\n",
       "continent                                                      \n",
       "Africa             61.471698        16.339623      16.264151   \n",
       "Asia               37.045455        60.840909       9.068182   \n",
       "Europe            193.777778       132.555556     142.222222   \n",
       "North America     145.434783       165.739130      24.521739   \n",
       "Oceania            89.687500        58.437500      35.625000   \n",
       "South America     175.083333       114.750000      62.416667   \n",
       "\n",
       "               total_litres_of_pure_alcohol  \n",
       "continent                                    \n",
       "Africa                             3.007547  \n",
       "Asia                               2.170455  \n",
       "Europe                             8.617778  \n",
       "North America                      5.995652  \n",
       "Oceania                            3.381250  \n",
       "South America                      6.308333  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.groupby('continent').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f806e922a20>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "drinks.groupby('continent').mean().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "URL = 'http://bit.ly/imdbratings'\n",
    "movies = pd.read_csv(URL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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>duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>979.000000</td>\n",
       "      <td>979.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.889785</td>\n",
       "      <td>120.979571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.336069</td>\n",
       "      <td>26.218010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>7.400000</td>\n",
       "      <td>64.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.600000</td>\n",
       "      <td>102.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.800000</td>\n",
       "      <td>117.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.100000</td>\n",
       "      <td>134.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.300000</td>\n",
       "      <td>242.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       star_rating    duration\n",
       "count   979.000000  979.000000\n",
       "mean      7.889785  120.979571\n",
       "std       0.336069   26.218010\n",
       "min       7.400000   64.000000\n",
       "25%       7.600000  102.000000\n",
       "50%       7.800000  117.000000\n",
       "75%       8.100000  134.000000\n",
       "max       9.300000  242.000000"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count       979\n",
       "unique       16\n",
       "top       Drama\n",
       "freq        278\n",
       "Name: genre, dtype: object"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.genre.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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",
       "Sci-Fi         5\n",
       "Thriller       5\n",
       "Film-Noir      3\n",
       "Family         2\n",
       "History        1\n",
       "Fantasy        1\n",
       "Name: genre, dtype: int64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.genre.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Drama        28.396323\n",
       "Comedy       15.934627\n",
       "Action       13.891726\n",
       "Crime        12.665986\n",
       "Biography     7.865169\n",
       "Adventure     7.660878\n",
       "Animation     6.332993\n",
       "Horror        2.962206\n",
       "Mystery       1.634321\n",
       "Western       0.919305\n",
       "Sci-Fi        0.510725\n",
       "Thriller      0.510725\n",
       "Film-Noir     0.306435\n",
       "Family        0.204290\n",
       "History       0.102145\n",
       "Fantasy       0.102145\n",
       "Name: genre, dtype: float64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.genre.value_counts(normalize=True)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "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": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.genre.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.genre.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "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": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "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>content_rating</th>\n",
       "      <th>APPROVED</th>\n",
       "      <th>G</th>\n",
       "      <th>GP</th>\n",
       "      <th>NC-17</th>\n",
       "      <th>NOT RATED</th>\n",
       "      <th>PASSED</th>\n",
       "      <th>PG</th>\n",
       "      <th>PG-13</th>\n",
       "      <th>R</th>\n",
       "      <th>TV-MA</th>\n",
       "      <th>UNRATED</th>\n",
       "      <th>X</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Action</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>44</td>\n",
       "      <td>67</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adventure</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>23</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Animation</th>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Biography</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>29</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Comedy</th>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>73</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Crime</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>87</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </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": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(movies.genre, movies.content_rating)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "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": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.duration.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "120.97957099080695"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.duration.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "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": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.duration.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f806babeb38>"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "movies.duration.plot(kind='hist')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f806ac6da20>"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "movies.genre.value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Handle missing values in Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "URL = 'http://bit.ly/uforeports'\n",
    "ufo = pd.read_csv(URL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "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>Grant Park</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TRIANGLE</td>\n",
       "      <td>IL</td>\n",
       "      <td>12/31/2000 23:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18237</th>\n",
       "      <td>Spirit Lake</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DISK</td>\n",
       "      <td>IA</td>\n",
       "      <td>12/31/2000 23:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18238</th>\n",
       "      <td>Eagle River</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WI</td>\n",
       "      <td>12/31/2000 23:45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18239</th>\n",
       "      <td>Eagle River</td>\n",
       "      <td>RED</td>\n",
       "      <td>LIGHT</td>\n",
       "      <td>WI</td>\n",
       "      <td>12/31/2000 23:45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18240</th>\n",
       "      <td>Ybor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OVAL</td>\n",
       "      <td>FL</td>\n",
       "      <td>12/31/2000 23:59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              City Colors Reported Shape Reported State              Time\n",
       "18236   Grant Park             NaN       TRIANGLE    IL  12/31/2000 23:00\n",
       "18237  Spirit Lake             NaN           DISK    IA  12/31/2000 23:00\n",
       "18238  Eagle River             NaN            NaN    WI  12/31/2000 23:45\n",
       "18239  Eagle River             RED          LIGHT    WI  12/31/2000 23:45\n",
       "18240         Ybor             NaN           OVAL    FL  12/31/2000 23:59"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "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": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo.notnull().tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<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": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo.isnull().tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "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": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<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>21</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LA</td>\n",
       "      <td>8/15/1943 0:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LIGHT</td>\n",
       "      <td>LA</td>\n",
       "      <td>8/15/1943 0:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DISK</td>\n",
       "      <td>CA</td>\n",
       "      <td>7/15/1952 12:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>NaN</td>\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>613</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DISK</td>\n",
       "      <td>NV</td>\n",
       "      <td>7/1/1960 12:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1877</th>\n",
       "      <td>NaN</td>\n",
       "      <td>YELLOW</td>\n",
       "      <td>CIRCLE</td>\n",
       "      <td>AZ</td>\n",
       "      <td>8/15/1969 1:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NH</td>\n",
       "      <td>8/1/1970 9:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2546</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FIREBALL</td>\n",
       "      <td>OH</td>\n",
       "      <td>10/25/1973 23:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3123</th>\n",
       "      <td>NaN</td>\n",
       "      <td>RED</td>\n",
       "      <td>TRIANGLE</td>\n",
       "      <td>WV</td>\n",
       "      <td>11/25/1975 23:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4736</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SPHERE</td>\n",
       "      <td>CA</td>\n",
       "      <td>6/23/1982 23:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5269</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AZ</td>\n",
       "      <td>6/30/1985 21:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6735</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FORMATION</td>\n",
       "      <td>TX</td>\n",
       "      <td>4/1/1992 2:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7208</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CIRCLE</td>\n",
       "      <td>MI</td>\n",
       "      <td>10/4/1993 17:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8828</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TRIANGLE</td>\n",
       "      <td>WA</td>\n",
       "      <td>10/30/1995 21:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8967</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>VARIOUS</td>\n",
       "      <td>CA</td>\n",
       "      <td>12/8/1995 18:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9273</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TRIANGLE</td>\n",
       "      <td>OH</td>\n",
       "      <td>5/1/1996 3:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9388</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OVAL</td>\n",
       "      <td>CA</td>\n",
       "      <td>6/12/1996 12:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9587</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EGG</td>\n",
       "      <td>FL</td>\n",
       "      <td>8/24/1996 15:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10399</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TRIANGLE</td>\n",
       "      <td>IL</td>\n",
       "      <td>6/15/1997 23:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11625</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CIRCLE</td>\n",
       "      <td>TX</td>\n",
       "      <td>6/7/1998 7:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12441</th>\n",
       "      <td>NaN</td>\n",
       "      <td>RED</td>\n",
       "      <td>FIREBALL</td>\n",
       "      <td>WA</td>\n",
       "      <td>10/26/1998 17:58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15767</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RECTANGLE</td>\n",
       "      <td>NV</td>\n",
       "      <td>1/21/2000 11:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15812</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LIGHT</td>\n",
       "      <td>NV</td>\n",
       "      <td>2/2/2000 3:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16054</th>\n",
       "      <td>NaN</td>\n",
       "      <td>GREEN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FL</td>\n",
       "      <td>3/11/2000 3:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16608</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SPHERE</td>\n",
       "      <td>NY</td>\n",
       "      <td>6/15/2000 15:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      City Colors Reported Shape Reported State              Time\n",
       "21     NaN             NaN            NaN    LA    8/15/1943 0:00\n",
       "22     NaN             NaN          LIGHT    LA    8/15/1943 0:00\n",
       "204    NaN             NaN           DISK    CA   7/15/1952 12:30\n",
       "241    NaN            BLUE           DISK    MT    7/4/1953 14:00\n",
       "613    NaN             NaN           DISK    NV    7/1/1960 12:00\n",
       "1877   NaN          YELLOW         CIRCLE    AZ    8/15/1969 1:00\n",
       "2013   NaN             NaN            NaN    NH     8/1/1970 9:30\n",
       "2546   NaN             NaN       FIREBALL    OH  10/25/1973 23:30\n",
       "3123   NaN             RED       TRIANGLE    WV  11/25/1975 23:00\n",
       "4736   NaN             NaN         SPHERE    CA   6/23/1982 23:00\n",
       "5269   NaN             NaN            NaN    AZ   6/30/1985 21:30\n",
       "6735   NaN             NaN      FORMATION    TX     4/1/1992 2:00\n",
       "7208   NaN             NaN         CIRCLE    MI   10/4/1993 17:30\n",
       "8828   NaN             NaN       TRIANGLE    WA  10/30/1995 21:30\n",
       "8967   NaN             NaN        VARIOUS    CA   12/8/1995 18:00\n",
       "9273   NaN             NaN       TRIANGLE    OH     5/1/1996 3:00\n",
       "9388   NaN             NaN           OVAL    CA   6/12/1996 12:00\n",
       "9587   NaN             NaN            EGG    FL   8/24/1996 15:00\n",
       "10399  NaN             NaN       TRIANGLE    IL   6/15/1997 23:00\n",
       "11625  NaN             NaN         CIRCLE    TX     6/7/1998 7:00\n",
       "12441  NaN             RED       FIREBALL    WA  10/26/1998 17:58\n",
       "15767  NaN             NaN      RECTANGLE    NV   1/21/2000 11:30\n",
       "15812  NaN             NaN          LIGHT    NV     2/2/2000 3:00\n",
       "16054  NaN           GREEN            NaN    FL    3/11/2000 3:30\n",
       "16608  NaN             NaN         SPHERE    NY   6/15/2000 15:00"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo[ufo.City.isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18241, 5)"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2486, 5)"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo.dropna(how='any').shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18241, 5)"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo.dropna(how='all').shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18238, 5)"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo.dropna(subset=['City', 'Colors Reported', 'Shape Reported'], how='all').shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "ufo['Shape Reported'].fillna(value = 'VARIOUS', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "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",
       "PYRAMID         1\n",
       "DOME            1\n",
       "HEXAGON         1\n",
       "Name: Shape Reported, dtype: int64"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo['Shape Reported'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "URL = 'http://bit.ly/drinksbycountry'\n",
    "drinks = pd.read_csv(URL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "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",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(193, 6)"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=193, step=1)"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "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": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "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>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": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks[drinks.continent=='South America']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "245"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "drinks.loc[23, 'beer_servings']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(193, 6)"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "drinks.set_index('country', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(193, 5)"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "245"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.loc['Brazil', 'beer_servings']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</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": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
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    "drinks.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "drinks.index.name = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "             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": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
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      ],
      "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": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.index.name = 'country'\n",
    "drinks.reset_index(inplace=True)\n",
    "drinks.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "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": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['count', 'mean', 'std', 'min', '25%', '50%', '75%', 'max'], dtype='object')"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.describe().index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['beer_servings', 'spirit_servings', 'wine_servings',\n",
       "       'total_litres_of_pure_alcohol'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.describe().columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20.0"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.describe().loc['25%', 'beer_servings']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas index - Part 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "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",
       "      <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": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.set_index('country', inplace=True)\n",
    "drinks.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "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": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.continent.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "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": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.continent.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([53, 45, 44, 23, 16, 12])"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.continent.value_counts().values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "53"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.continent.value_counts()['Africa']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "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": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.continent.value_counts().sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Albania    3000000\n",
       "Andorra      85000\n",
       "Name: population, dtype: int64"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Small Pandas Series\n",
    "people = pd.Series([3000000, 85000], index=['Albania', 'Andorra'], name='population')\n",
    "people"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Afghanistan            NaN\n",
       "Albania        267000000.0\n",
       "Algeria                NaN\n",
       "Andorra         20825000.0\n",
       "Angola                 NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(drinks.beer_servings * people).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "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",
       "      <th>continent</th>\n",
       "      <th>population</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",
       "      <td>NaN</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",
       "      <td>3000000.0</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",
       "      <td>NaN</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",
       "      <td>85000.0</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",
       "      <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     85000.0  \n",
       "Angola                                5.9    Africa         NaN  "
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([drinks, people], axis=1, sort=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Select multiple rows and columns from a pandas DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "URL = 'http://bit.ly/uforeports'\n",
    "ufo = pd.read_csv(URL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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",
       "  </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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### loc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "City                       Ithaca\n",
       "Colors Reported               NaN\n",
       "Shape Reported           TRIANGLE\n",
       "State                          NY\n",
       "Time               6/1/1930 22:00\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo.loc[0, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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",
       "  </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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo.loc[0:2, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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",
       "    </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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Willingboro</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>NJ</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abilene</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DISK</td>\n",
       "      <td>KS</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo.loc[:, 'City':'State'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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",
       "    </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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Willingboro</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>NJ</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo.head(3).drop('Time', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>1694</th>\n",
       "      <td>Oakland</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CIGAR</td>\n",
       "      <td>CA</td>\n",
       "      <td>7/21/1968 14:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2144</th>\n",
       "      <td>Oakland</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DISK</td>\n",
       "      <td>CA</td>\n",
       "      <td>8/19/1971 0:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4686</th>\n",
       "      <td>Oakland</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LIGHT</td>\n",
       "      <td>MD</td>\n",
       "      <td>6/1/1982 0:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7293</th>\n",
       "      <td>Oakland</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LIGHT</td>\n",
       "      <td>CA</td>\n",
       "      <td>3/28/1994 17:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8488</th>\n",
       "      <td>Oakland</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CA</td>\n",
       "      <td>8/10/1995 21:45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo[ufo.City=='Oakland'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1694     CA\n",
       "2144     CA\n",
       "4686     MD\n",
       "7293     CA\n",
       "8488     CA\n",
       "8768     CA\n",
       "10816    OR\n",
       "10948    CA\n",
       "11045    CA\n",
       "12322    CA\n",
       "12941    CA\n",
       "16803    MD\n",
       "17322    CA\n",
       "Name: State, dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo.loc[ufo.City=='Oakland', 'State']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['City', 'Colors Reported', 'Shape Reported', 'State', 'Time'], dtype='object')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### iloc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ithaca</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TRIANGLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Willingboro</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OTHER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Holyoke</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OVAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abilene</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DISK</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   City Colors Reported Shape Reported\n",
       "0                Ithaca             NaN       TRIANGLE\n",
       "1           Willingboro             NaN          OTHER\n",
       "2               Holyoke             NaN           OVAL\n",
       "3               Abilene             NaN           DISK\n",
       "4  New York Worlds Fair             NaN          LIGHT"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo.loc[:, 'City':'Shape Reported'].head()"
   ]
  },
  {
   "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>City</th>\n",
       "      <th>Colors Reported</th>\n",
       "      <th>Shape Reported</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Willingboro</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OTHER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Holyoke</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OVAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abilene</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DISK</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   City Colors Reported Shape Reported\n",
       "0                Ithaca             NaN       TRIANGLE\n",
       "1           Willingboro             NaN          OTHER\n",
       "2               Holyoke             NaN           OVAL\n",
       "3               Abilene             NaN           DISK\n",
       "4  New York Worlds Fair             NaN          LIGHT"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo.iloc[:, 0:3].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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",
       "  </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": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo.iloc[0:3, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### loc  - labels      - inclusive\n",
    "### iloc - integers    - exclusive"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inplace parameter - Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "URL = 'http://bit.ly/uforeports'\n",
    "ufo = pd.read_csv(URL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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",
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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>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": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "ufo.drop('City', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>NY</td>\n",
       "      <td>4/18/1933 19:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  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": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
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    "ufo.head()"
   ]
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   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2490, 4)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
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    "ufo.dropna(how='any').shape"
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   "execution_count": 63,
   "metadata": {},
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    {
     "data": {
      "text/plain": [
       "(18241, 4)"
      ]
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     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
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    "ufo.shape"
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   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "ufo.set_index('Time', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>6/30/1930 20:00</th>\n",
       "      <td>NaN</td>\n",
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       "      <th>2/15/1931 14:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>OVAL</td>\n",
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       "      <th>6/1/1931 13:00</th>\n",
       "      <td>NaN</td>\n",
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       "      <th>4/18/1933 19:00</th>\n",
       "      <td>NaN</td>\n",
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       "</table>\n",
       "</div>"
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      "text/plain": [
       "                Colors Reported Shape Reported State\n",
       "Time                                                \n",
       "6/1/1930 22:00              NaN       TRIANGLE    NY\n",
       "6/30/1930 20:00             NaN          OTHER    NJ\n",
       "2/15/1931 14:00             NaN           OVAL    CO\n",
       "6/1/1931 13:00              NaN           DISK    KS\n",
       "4/18/1933 19:00             NaN          LIGHT    NY"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>RED</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>OVAL</td>\n",
       "      <td>FL</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "                 Colors Reported Shape Reported State\n",
       "Time                                                 \n",
       "12/31/2000 23:00             RED       TRIANGLE    IL\n",
       "12/31/2000 23:00             RED           DISK    IA\n",
       "12/31/2000 23:45             RED          LIGHT    WI\n",
       "12/31/2000 23:45             RED          LIGHT    WI\n",
       "12/31/2000 23:59             NaN           OVAL    FL"
      ]
     },
     "execution_count": 66,
     "metadata": {},
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   "source": [
    "ufo.fillna(method='bfill').tail()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>RED</td>\n",
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      "text/plain": [
       "                 Colors Reported Shape Reported State\n",
       "Time                                                 \n",
       "12/31/2000 23:00             RED       TRIANGLE    IL\n",
       "12/31/2000 23:00             RED           DISK    IA\n",
       "12/31/2000 23:45             RED           DISK    WI\n",
       "12/31/2000 23:45             RED          LIGHT    WI\n",
       "12/31/2000 23:59             RED           OVAL    FL"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo.fillna(method='ffill').tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Colors Reported    15359\n",
       "Shape Reported      2644\n",
       "State                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18241, 3)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Make Pandas DataFrame smaller and faster.\n",
    "## Convert strings into category.\n",
    "## Storing strings as integers is more efficient. Pandas has a way to store in that way."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "URL = 'http://bit.ly/drinksbycountry'\n",
    "drinks = pd.read_csv(URL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": 4,
   "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": 5,
   "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.memory_usage(deep=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "31176"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.memory_usage(deep=True).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Africa', 'Asia', 'Europe', 'North America', 'Oceania', 'South America']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(drinks.continent.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(193, 6)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "drinks['continent'] = drinks.continent.astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.memory_usage(deep=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>89</td>\n",
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       "      <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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "2    0\n",
       "3    2\n",
       "4    0\n",
       "dtype: int8"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.continent.cat.codes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "drinks['country'] = drinks.country.astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index                              80\n",
       "country                         18094\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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.memory_usage(deep=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Afghanistan', 'Albania', 'Algeria', 'Andorra', 'Angola',\n",
       "       'Antigua & Barbuda', 'Argentina', 'Armenia', 'Australia', 'Austria',\n",
       "       ...\n",
       "       'United Arab Emirates', 'United Kingdom', 'Uruguay', 'Uzbekistan',\n",
       "       'Vanuatu', 'Venezuela', 'Vietnam', 'Yemen', 'Zambia', 'Zimbabwe'],\n",
       "      dtype='object', length=193)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.country.cat.categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({'ID': [100, 101, 102, 103], 'quality': ['good', 'very good', 'good', 'excellent']})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>ID</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>101</td>\n",
       "      <td>very good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>102</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>103</td>\n",
       "      <td>excellent</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ID    quality\n",
       "0  100       good\n",
       "1  101  very good\n",
       "2  102       good\n",
       "3  103  excellent"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\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>3</th>\n",
       "      <td>103</td>\n",
       "      <td>excellent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>102</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>101</td>\n",
       "      <td>very good</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ID    quality\n",
       "3  103  excellent\n",
       "0  100       good\n",
       "2  102       good\n",
       "1  101  very good"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values('quality')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/ipykernel_launcher.py:1: FutureWarning: specifying 'categories' or 'ordered' in .astype() is deprecated; pass a CategoricalDtype instead\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "df['quality'] = df.quality.astype('category', categories = ['good', 'very good', 'excellent'], ordered = True)"
   ]
  },
  {
   "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",
       "    .dataframe tbody tr th {\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>ID</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>102</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>101</td>\n",
       "      <td>very good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>103</td>\n",
       "      <td>excellent</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ID    quality\n",
       "0  100       good\n",
       "2  102       good\n",
       "1  101  very good\n",
       "3  103  excellent"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values('quality')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         good\n",
       "1    very good\n",
       "2         good\n",
       "3    excellent\n",
       "Name: quality, dtype: category\n",
       "Categories (3, object): [good < very good < excellent]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.quality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>ID</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>101</td>\n",
       "      <td>very good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>103</td>\n",
       "      <td>excellent</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ID    quality\n",
       "1  101  very good\n",
       "3  103  excellent"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df.quality > 'good', :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas with scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "URL = 'http://bit.ly/kaggletrain'\n",
    "train = pd.read_csv(URL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_cols = ['Pclass', 'Parch']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = train.loc[:, feature_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 2)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = train.Survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891,)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "logreg = LogisticRegression()\n",
    "logreg.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = pd.read_csv('http://bit.ly/kaggletest')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        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>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>3</td>\n",
       "      <td>Kelly, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330911</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>363272</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>Myles, Mr. Thomas Francis</td>\n",
       "      <td>male</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>240276</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315154</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass                                          Name     Sex  \\\n",
       "0          892       3                              Kelly, Mr. James    male   \n",
       "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
       "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
       "3          895       3                              Wirz, Mr. Albert    male   \n",
       "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
       "\n",
       "    Age  SibSp  Parch   Ticket     Fare Cabin Embarked  \n",
       "0  34.5      0      0   330911   7.8292   NaN        Q  \n",
       "1  47.0      1      0   363272   7.0000   NaN        S  \n",
       "2  62.0      0      0   240276   9.6875   NaN        Q  \n",
       "3  27.0      0      0   315154   8.6625   NaN        S  \n",
       "4  22.0      1      1  3101298  12.2875   NaN        S  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_new = test.loc[:, feature_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(418, 2)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_new.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_pred_class = logreg.predict(X_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0,\n",
       "       1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0,\n",
       "       1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0,\n",
       "       0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n",
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       "       0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0,\n",
       "       0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1,\n",
       "       1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,\n",
       "       0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0,\n",
       "       1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0,\n",
       "       1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0,\n",
       "       0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0,\n",
       "       1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1,\n",
       "       0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_pred_class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Store in a csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame({'PassengerId': test.PassengerId, 'Survived': new_pred_class}).set_index('PassengerId').to_csv('out.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.to_pickle('train.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_pickle('train.pkl').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sample large Pandas frame\n",
    "## Splitting pandas DataFrame into train and test sets effectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "URL = 'http://bit.ly/uforeports'\n",
    "ufo = pd.read_csv(URL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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>217</th>\n",
       "      <td>Norridgewock</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DISK</td>\n",
       "      <td>ME</td>\n",
       "      <td>9/15/1952 14:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12282</th>\n",
       "      <td>Ipava</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TRIANGLE</td>\n",
       "      <td>IL</td>\n",
       "      <td>10/1/1998 21:15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17933</th>\n",
       "      <td>Ellinwood</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FIREBALL</td>\n",
       "      <td>KS</td>\n",
       "      <td>11/13/2000 22:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               City Colors Reported Shape Reported State              Time\n",
       "217    Norridgewock             NaN           DISK    ME   9/15/1952 14:00\n",
       "12282         Ipava             NaN       TRIANGLE    IL   10/1/1998 21:15\n",
       "17933     Ellinwood             NaN       FIREBALL    KS  11/13/2000 22:00"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo.sample(n=3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>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>6250</th>\n",
       "      <td>Sunnyvale</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>CA</td>\n",
       "      <td>12/16/1989 0:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8656</th>\n",
       "      <td>Corpus Christi</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TX</td>\n",
       "      <td>9/13/1995 0:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2729</th>\n",
       "      <td>Mentor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DISK</td>\n",
       "      <td>OH</td>\n",
       "      <td>8/8/1974 10:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7348</th>\n",
       "      <td>Wilson</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LIGHT</td>\n",
       "      <td>WI</td>\n",
       "      <td>6/1/1994 1:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12637</th>\n",
       "      <td>Lowell</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CIRCLE</td>\n",
       "      <td>MA</td>\n",
       "      <td>11/26/1998 10:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 City Colors Reported Shape Reported State              Time\n",
       "6250        Sunnyvale             NaN          OTHER    CA   12/16/1989 0:00\n",
       "8656   Corpus Christi             NaN            NaN    TX    9/13/1995 0:10\n",
       "2729           Mentor             NaN           DISK    OH    8/8/1974 10:00\n",
       "7348           Wilson             NaN          LIGHT    WI     6/1/1994 1:00\n",
       "12637          Lowell             NaN         CIRCLE    MA  11/26/1998 10:00"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo.sample(frac=0.75, random_state=99).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = ufo.sample(frac=0.75, random_state=99)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = ufo.loc[~ufo.index.isin(train.index), :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13681, 5)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4560, 5)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18241, 5)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create dummy variables in pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "URL = 'http://bit.ly/kaggletrain'\n",
    "train = pd.read_csv(URL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>PassengerId</th>\n",
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       "      <th>Name</th>\n",
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       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
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       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
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       "      <td>Allen, Mr. William Henry</td>\n",
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       "      <td>S</td>\n",
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      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Sex_male'] = train.Sex.map({'male': 1, 'female': 0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
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       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  Sex_male  \n",
       "0      0         A/5 21171   7.2500   NaN        S         1  \n",
       "1      0          PC 17599  71.2833   C85        C         0  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S         0  \n",
       "3      0            113803  53.1000  C123        S         0  \n",
       "4      0            373450   8.0500   NaN        S         1  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      "text/plain": [
       "   Sex_male\n",
       "0         1\n",
       "1         0\n",
       "2         0\n",
       "3         0\n",
       "4         1"
      ]
     },
     "execution_count": 88,
     "metadata": {},
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   ],
   "source": [
    "pd.get_dummies(train.Sex, prefix='Sex').iloc[:, 1:].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "S    644\n",
       "C    168\n",
       "Q     77\n",
       "Name: Embarked, dtype: int64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.Embarked.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "embarked_dummies = pd.get_dummies(train.Embarked, prefix='Embarked').iloc[:, 1:].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.concat([train, embarked_dummies], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  Sex_male  Embarked_Q  \\\n",
       "0      0         A/5 21171   7.2500   NaN        S         1         0.0   \n",
       "1      0          PC 17599  71.2833   C85        C         0         0.0   \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S         0         0.0   \n",
       "3      0            113803  53.1000  C123        S         0         0.0   \n",
       "4      0            373450   8.0500   NaN        S         1         0.0   \n",
       "\n",
       "   Embarked_S  \n",
       "0         1.0  \n",
       "1         0.0  \n",
       "2         1.0  \n",
       "3         1.0  \n",
       "4         1.0  "
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "URL = 'http://bit.ly/kaggletrain'\n",
    "train = pd.read_csv(URL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
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       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Sex_female</th>\n",
       "      <th>Sex_male</th>\n",
       "      <th>Embarked_C</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
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       "      <td>Heikkinen, Miss. Laina</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name   Age  SibSp  Parch  \\\n",
       "0                            Braund, Mr. Owen Harris  22.0      1      0   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  38.0      1      0   \n",
       "2                             Heikkinen, Miss. Laina  26.0      0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  35.0      1      0   \n",
       "4                           Allen, Mr. William Henry  35.0      0      0   \n",
       "\n",
       "             Ticket     Fare Cabin  Sex_female  Sex_male  Embarked_C  \\\n",
       "0         A/5 21171   7.2500   NaN           0         1           0   \n",
       "1          PC 17599  71.2833   C85           1         0           1   \n",
       "2  STON/O2. 3101282   7.9250   NaN           1         0           0   \n",
       "3            113803  53.1000  C123           1         0           0   \n",
       "4            373450   8.0500   NaN           0         1           0   \n",
       "\n",
       "   Embarked_Q  Embarked_S  \n",
       "0           0           1  \n",
       "1           0           0  \n",
       "2           0           1  \n",
       "3           0           1  \n",
       "4           0           1  "
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_dummies(train, columns=['Sex', 'Embarked']).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
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       "      <td>1</td>\n",
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       "      <td>Heikkinen, Miss. Laina</td>\n",
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       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name   Age  SibSp  Parch  \\\n",
       "0                            Braund, Mr. Owen Harris  22.0      1      0   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  38.0      1      0   \n",
       "2                             Heikkinen, Miss. Laina  26.0      0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  35.0      1      0   \n",
       "4                           Allen, Mr. William Henry  35.0      0      0   \n",
       "\n",
       "             Ticket     Fare Cabin  Sex_male  Embarked_Q  Embarked_S  \n",
       "0         A/5 21171   7.2500   NaN         1           0           1  \n",
       "1          PC 17599  71.2833   C85         0           0           0  \n",
       "2  STON/O2. 3101282   7.9250   NaN         0           0           1  \n",
       "3            113803  53.1000  C123         0           0           1  \n",
       "4            373450   8.0500   NaN         1           0           1  "
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_dummies(train, columns=['Sex', 'Embarked'], drop_first=True).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dates and times in Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "URL = 'http://bit.ly/uforeports'\n",
    "ufo = pd.read_csv(URL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\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>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": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "City               object\n",
       "Colors Reported    object\n",
       "Shape Reported     object\n",
       "State              object\n",
       "Time               object\n",
       "dtype: object"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    22\n",
       "1    20\n",
       "2    14\n",
       "3    13\n",
       "4    19\n",
       "Name: Time, dtype: int64"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo.Time.str.slice(-5, -3).astype(int).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "ufo['Time'] = pd.to_datetime(ufo.Time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
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       "      <th>State</th>\n",
       "      <th>Time</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ithaca</td>\n",
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       "      <td>1930-06-01 22:00:00</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Willingboro</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>NJ</td>\n",
       "      <td>1930-06-30 20:00:00</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>1931-02-15 14:00:00</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>1931-06-01 13:00:00</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>1933-04-18 19:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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   \n",
       "\n",
       "                 Time  \n",
       "0 1930-06-01 22:00:00  \n",
       "1 1930-06-30 20:00:00  \n",
       "2 1931-02-15 14:00:00  \n",
       "3 1931-06-01 13:00:00  \n",
       "4 1933-04-18 19:00:00  "
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "City                       object\n",
       "Colors Reported            object\n",
       "Shape Reported             object\n",
       "State                      object\n",
       "Time               datetime64[ns]\n",
       "dtype: object"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     Sunday\n",
       "1     Monday\n",
       "2     Sunday\n",
       "3     Monday\n",
       "4    Tuesday\n",
       "Name: Time, dtype: object"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo.Time.dt.weekday_name.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts = pd.to_datetime('1/1/1999')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "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>12832</th>\n",
       "      <td>Loma Rica</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LIGHT</td>\n",
       "      <td>CA</td>\n",
       "      <td>1999-01-01 02:30:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12833</th>\n",
       "      <td>Bauxite</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AR</td>\n",
       "      <td>1999-01-01 03:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12834</th>\n",
       "      <td>Florence</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CYLINDER</td>\n",
       "      <td>SC</td>\n",
       "      <td>1999-01-01 14:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12835</th>\n",
       "      <td>Lake Henshaw</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CIGAR</td>\n",
       "      <td>CA</td>\n",
       "      <td>1999-01-01 15:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12836</th>\n",
       "      <td>Wilmington Island</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LIGHT</td>\n",
       "      <td>GA</td>\n",
       "      <td>1999-01-01 17:15:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    City Colors Reported Shape Reported State  \\\n",
       "12832          Loma Rica             NaN          LIGHT    CA   \n",
       "12833            Bauxite             NaN            NaN    AR   \n",
       "12834           Florence             NaN       CYLINDER    SC   \n",
       "12835       Lake Henshaw             NaN          CIGAR    CA   \n",
       "12836  Wilmington Island             NaN          LIGHT    GA   \n",
       "\n",
       "                     Time  \n",
       "12832 1999-01-01 02:30:00  \n",
       "12833 1999-01-01 03:00:00  \n",
       "12834 1999-01-01 14:00:00  \n",
       "12835 1999-01-01 15:00:00  \n",
       "12836 1999-01-01 17:15:00  "
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo.loc[ufo.Time >= ts, :].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_delta = ufo.Time.max() - ufo.Time.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25781"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_delta.days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "ufo['Year'] = ufo.Time.dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "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",
       "      <th>Year</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>1930-06-01 22:00:00</td>\n",
       "      <td>1930</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>1930-06-30 20:00:00</td>\n",
       "      <td>1930</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>1931-02-15 14:00:00</td>\n",
       "      <td>1931</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>1931-06-01 13:00:00</td>\n",
       "      <td>1931</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>1933-04-18 19:00:00</td>\n",
       "      <td>1933</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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   \n",
       "\n",
       "                 Time  Year  \n",
       "0 1930-06-01 22:00:00  1930  \n",
       "1 1930-06-30 20:00:00  1930  \n",
       "2 1931-02-15 14:00:00  1931  \n",
       "3 1931-06-01 13:00:00  1931  \n",
       "4 1933-04-18 19:00:00  1933  "
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5c75af1630>"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ufo.Year.value_counts().sort_index().plot(kind = 'line')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Find and remove duplicate rows in Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "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>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>occupation</th>\n",
       "      <th>zip_code</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>24</td>\n",
       "      <td>M</td>\n",
       "      <td>technician</td>\n",
       "      <td>85711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>53</td>\n",
       "      <td>F</td>\n",
       "      <td>other</td>\n",
       "      <td>94043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23</td>\n",
       "      <td>M</td>\n",
       "      <td>writer</td>\n",
       "      <td>32067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24</td>\n",
       "      <td>M</td>\n",
       "      <td>technician</td>\n",
       "      <td>43537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>33</td>\n",
       "      <td>F</td>\n",
       "      <td>other</td>\n",
       "      <td>15213</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         age gender  occupation zip_code\n",
       "user_id                                 \n",
       "1         24      M  technician    85711\n",
       "2         53      F       other    94043\n",
       "3         23      M      writer    32067\n",
       "4         24      M  technician    43537\n",
       "5         33      F       other    15213"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "URL = 'http://bit.ly/movieusers'\n",
    "user_cols = ['user_id', 'age', 'gender', 'occupation', 'zip_code']\n",
    "users = pd.read_table(URL, delimiter='|', header=None, names=user_cols, index_col='user_id')\n",
    "users.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "148"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users.zip_code.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(943, 4)"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "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>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>occupation</th>\n",
       "      <th>zip_code</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>21</td>\n",
       "      <td>F</td>\n",
       "      <td>student</td>\n",
       "      <td>55414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>572</th>\n",
       "      <td>51</td>\n",
       "      <td>M</td>\n",
       "      <td>educator</td>\n",
       "      <td>20003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>621</th>\n",
       "      <td>17</td>\n",
       "      <td>M</td>\n",
       "      <td>student</td>\n",
       "      <td>60402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>684</th>\n",
       "      <td>28</td>\n",
       "      <td>M</td>\n",
       "      <td>student</td>\n",
       "      <td>55414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>733</th>\n",
       "      <td>44</td>\n",
       "      <td>F</td>\n",
       "      <td>other</td>\n",
       "      <td>60630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>805</th>\n",
       "      <td>27</td>\n",
       "      <td>F</td>\n",
       "      <td>other</td>\n",
       "      <td>20009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>32</td>\n",
       "      <td>M</td>\n",
       "      <td>student</td>\n",
       "      <td>97301</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         age gender occupation zip_code\n",
       "user_id                                \n",
       "496       21      F    student    55414\n",
       "572       51      M   educator    20003\n",
       "621       17      M    student    60402\n",
       "684       28      M    student    55414\n",
       "733       44      F      other    60630\n",
       "805       27      F      other    20009\n",
       "890       32      M    student    97301"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users.loc[users.duplicated(keep='first'), :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "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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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>occupation</th>\n",
       "      <th>zip_code</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>17</td>\n",
       "      <td>M</td>\n",
       "      <td>student</td>\n",
       "      <td>60402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>51</td>\n",
       "      <td>M</td>\n",
       "      <td>educator</td>\n",
       "      <td>20003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>21</td>\n",
       "      <td>F</td>\n",
       "      <td>student</td>\n",
       "      <td>55414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>32</td>\n",
       "      <td>M</td>\n",
       "      <td>student</td>\n",
       "      <td>97301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>428</th>\n",
       "      <td>28</td>\n",
       "      <td>M</td>\n",
       "      <td>student</td>\n",
       "      <td>55414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>437</th>\n",
       "      <td>27</td>\n",
       "      <td>F</td>\n",
       "      <td>other</td>\n",
       "      <td>20009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>460</th>\n",
       "      <td>44</td>\n",
       "      <td>F</td>\n",
       "      <td>other</td>\n",
       "      <td>60630</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         age gender occupation zip_code\n",
       "user_id                                \n",
       "67        17      M    student    60402\n",
       "85        51      M   educator    20003\n",
       "198       21      F    student    55414\n",
       "350       32      M    student    97301\n",
       "428       28      M    student    55414\n",
       "437       27      F      other    20009\n",
       "460       44      F      other    60630"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users.loc[users.duplicated(keep='last'), :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "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>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>occupation</th>\n",
       "      <th>zip_code</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>17</td>\n",
       "      <td>M</td>\n",
       "      <td>student</td>\n",
       "      <td>60402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>51</td>\n",
       "      <td>M</td>\n",
       "      <td>educator</td>\n",
       "      <td>20003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>21</td>\n",
       "      <td>F</td>\n",
       "      <td>student</td>\n",
       "      <td>55414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>32</td>\n",
       "      <td>M</td>\n",
       "      <td>student</td>\n",
       "      <td>97301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>428</th>\n",
       "      <td>28</td>\n",
       "      <td>M</td>\n",
       "      <td>student</td>\n",
       "      <td>55414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>437</th>\n",
       "      <td>27</td>\n",
       "      <td>F</td>\n",
       "      <td>other</td>\n",
       "      <td>20009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>460</th>\n",
       "      <td>44</td>\n",
       "      <td>F</td>\n",
       "      <td>other</td>\n",
       "      <td>60630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>21</td>\n",
       "      <td>F</td>\n",
       "      <td>student</td>\n",
       "      <td>55414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>572</th>\n",
       "      <td>51</td>\n",
       "      <td>M</td>\n",
       "      <td>educator</td>\n",
       "      <td>20003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>621</th>\n",
       "      <td>17</td>\n",
       "      <td>M</td>\n",
       "      <td>student</td>\n",
       "      <td>60402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>684</th>\n",
       "      <td>28</td>\n",
       "      <td>M</td>\n",
       "      <td>student</td>\n",
       "      <td>55414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>733</th>\n",
       "      <td>44</td>\n",
       "      <td>F</td>\n",
       "      <td>other</td>\n",
       "      <td>60630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>805</th>\n",
       "      <td>27</td>\n",
       "      <td>F</td>\n",
       "      <td>other</td>\n",
       "      <td>20009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>32</td>\n",
       "      <td>M</td>\n",
       "      <td>student</td>\n",
       "      <td>97301</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         age gender occupation zip_code\n",
       "user_id                                \n",
       "67        17      M    student    60402\n",
       "85        51      M   educator    20003\n",
       "198       21      F    student    55414\n",
       "350       32      M    student    97301\n",
       "428       28      M    student    55414\n",
       "437       27      F      other    20009\n",
       "460       44      F      other    60630\n",
       "496       21      F    student    55414\n",
       "572       51      M   educator    20003\n",
       "621       17      M    student    60402\n",
       "684       28      M    student    55414\n",
       "733       44      F      other    60630\n",
       "805       27      F      other    20009\n",
       "890       32      M    student    97301"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users.loc[users.duplicated(keep=False), :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(943, 4)"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(936, 4)"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users.drop_duplicates(keep='first').shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(943,)"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users.duplicated(subset=['age', 'zip_code']).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(943, 4)"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(927, 4)"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users.drop_duplicates(subset=['age', 'zip_code']).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## It's better if we use 'loc'/'iloc' functions more.\n",
    "## Use .copy() method when we are assigning and creating a new DataFrame from a old DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Display options in Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "URL = 'http://bit.ly/drinksbycountry'\n",
    "drinks = pd.read_csv(URL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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",
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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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_option('display.max_rows')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_rows', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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",
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       "    }\n",
       "\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>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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Antigua &amp; Barbuda</td>\n",
       "      <td>102</td>\n",
       "      <td>128</td>\n",
       "      <td>45</td>\n",
       "      <td>4.9</td>\n",
       "      <td>North America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>193</td>\n",
       "      <td>25</td>\n",
       "      <td>221</td>\n",
       "      <td>8.3</td>\n",
       "      <td>South America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Armenia</td>\n",
       "      <td>21</td>\n",
       "      <td>179</td>\n",
       "      <td>11</td>\n",
       "      <td>3.8</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Australia</td>\n",
       "      <td>261</td>\n",
       "      <td>72</td>\n",
       "      <td>212</td>\n",
       "      <td>10.4</td>\n",
       "      <td>Oceania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Austria</td>\n",
       "      <td>279</td>\n",
       "      <td>75</td>\n",
       "      <td>191</td>\n",
       "      <td>9.7</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>21</td>\n",
       "      <td>46</td>\n",
       "      <td>5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Bahamas</td>\n",
       "      <td>122</td>\n",
       "      <td>176</td>\n",
       "      <td>51</td>\n",
       "      <td>6.3</td>\n",
       "      <td>North America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Bahrain</td>\n",
       "      <td>42</td>\n",
       "      <td>63</td>\n",
       "      <td>7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Bangladesh</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>14</th>\n",
       "      <td>Barbados</td>\n",
       "      <td>143</td>\n",
       "      <td>173</td>\n",
       "      <td>36</td>\n",
       "      <td>6.3</td>\n",
       "      <td>North America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Belarus</td>\n",
       "      <td>142</td>\n",
       "      <td>373</td>\n",
       "      <td>42</td>\n",
       "      <td>14.4</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>295</td>\n",
       "      <td>84</td>\n",
       "      <td>212</td>\n",
       "      <td>10.5</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Belize</td>\n",
       "      <td>263</td>\n",
       "      <td>114</td>\n",
       "      <td>8</td>\n",
       "      <td>6.8</td>\n",
       "      <td>North America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Benin</td>\n",
       "      <td>34</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>1.1</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Bhutan</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Asia</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>21</th>\n",
       "      <td>Bosnia-Herzegovina</td>\n",
       "      <td>76</td>\n",
       "      <td>173</td>\n",
       "      <td>8</td>\n",
       "      <td>4.6</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Botswana</td>\n",
       "      <td>173</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>5.4</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>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>24</th>\n",
       "      <td>Brunei</td>\n",
       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Bulgaria</td>\n",
       "      <td>231</td>\n",
       "      <td>252</td>\n",
       "      <td>94</td>\n",
       "      <td>10.3</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Burkina Faso</td>\n",
       "      <td>25</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>4.3</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Burundi</td>\n",
       "      <td>88</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.3</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Cote d'Ivoire</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Cabo Verde</td>\n",
       "      <td>144</td>\n",
       "      <td>56</td>\n",
       "      <td>16</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Cambodia</td>\n",
       "      <td>57</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>2.2</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Cameroon</td>\n",
       "      <td>147</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5.8</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Canada</td>\n",
       "      <td>240</td>\n",
       "      <td>122</td>\n",
       "      <td>100</td>\n",
       "      <td>8.2</td>\n",
       "      <td>North America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Central African Republic</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Chad</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Africa</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>36</th>\n",
       "      <td>China</td>\n",
       "      <td>79</td>\n",
       "      <td>192</td>\n",
       "      <td>8</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Asia</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>38</th>\n",
       "      <td>Comoros</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Congo</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Cook Islands</td>\n",
       "      <td>0</td>\n",
       "      <td>254</td>\n",
       "      <td>74</td>\n",
       "      <td>5.9</td>\n",
       "      <td>Oceania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Costa Rica</td>\n",
       "      <td>149</td>\n",
       "      <td>87</td>\n",
       "      <td>11</td>\n",
       "      <td>4.4</td>\n",
       "      <td>North America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Croatia</td>\n",
       "      <td>230</td>\n",
       "      <td>87</td>\n",
       "      <td>254</td>\n",
       "      <td>10.2</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Cuba</td>\n",
       "      <td>93</td>\n",
       "      <td>137</td>\n",
       "      <td>5</td>\n",
       "      <td>4.2</td>\n",
       "      <td>North America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Cyprus</td>\n",
       "      <td>192</td>\n",
       "      <td>154</td>\n",
       "      <td>113</td>\n",
       "      <td>8.2</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>361</td>\n",
       "      <td>170</td>\n",
       "      <td>134</td>\n",
       "      <td>11.8</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>North Korea</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>47</th>\n",
       "      <td>DR Congo</td>\n",
       "      <td>32</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>224</td>\n",
       "      <td>81</td>\n",
       "      <td>278</td>\n",
       "      <td>10.4</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Djibouti</td>\n",
       "      <td>15</td>\n",
       "      <td>44</td>\n",
       "      <td>3</td>\n",
       "      <td>1.1</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Dominica</td>\n",
       "      <td>52</td>\n",
       "      <td>286</td>\n",
       "      <td>26</td>\n",
       "      <td>6.6</td>\n",
       "      <td>North America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Dominican Republic</td>\n",
       "      <td>193</td>\n",
       "      <td>147</td>\n",
       "      <td>9</td>\n",
       "      <td>6.2</td>\n",
       "      <td>North 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>53</th>\n",
       "      <td>Egypt</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>El Salvador</td>\n",
       "      <td>52</td>\n",
       "      <td>69</td>\n",
       "      <td>2</td>\n",
       "      <td>2.2</td>\n",
       "      <td>North America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Equatorial Guinea</td>\n",
       "      <td>92</td>\n",
       "      <td>0</td>\n",
       "      <td>233</td>\n",
       "      <td>5.8</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Eritrea</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Estonia</td>\n",
       "      <td>224</td>\n",
       "      <td>194</td>\n",
       "      <td>59</td>\n",
       "      <td>9.5</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Fiji</td>\n",
       "      <td>77</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Oceania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>Finland</td>\n",
       "      <td>263</td>\n",
       "      <td>133</td>\n",
       "      <td>97</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>France</td>\n",
       "      <td>127</td>\n",
       "      <td>151</td>\n",
       "      <td>370</td>\n",
       "      <td>11.8</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>Gabon</td>\n",
       "      <td>347</td>\n",
       "      <td>98</td>\n",
       "      <td>59</td>\n",
       "      <td>8.9</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Gambia</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.4</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Georgia</td>\n",
       "      <td>52</td>\n",
       "      <td>100</td>\n",
       "      <td>149</td>\n",
       "      <td>5.4</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Germany</td>\n",
       "      <td>346</td>\n",
       "      <td>117</td>\n",
       "      <td>175</td>\n",
       "      <td>11.3</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Ghana</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>Greece</td>\n",
       "      <td>133</td>\n",
       "      <td>112</td>\n",
       "      <td>218</td>\n",
       "      <td>8.3</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Grenada</td>\n",
       "      <td>199</td>\n",
       "      <td>438</td>\n",
       "      <td>28</td>\n",
       "      <td>11.9</td>\n",
       "      <td>North America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>Guatemala</td>\n",
       "      <td>53</td>\n",
       "      <td>69</td>\n",
       "      <td>2</td>\n",
       "      <td>2.2</td>\n",
       "      <td>North America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>Guinea</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>Guinea-Bissau</td>\n",
       "      <td>28</td>\n",
       "      <td>31</td>\n",
       "      <td>21</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Africa</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>73</th>\n",
       "      <td>Haiti</td>\n",
       "      <td>1</td>\n",
       "      <td>326</td>\n",
       "      <td>1</td>\n",
       "      <td>5.9</td>\n",
       "      <td>North America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>Honduras</td>\n",
       "      <td>69</td>\n",
       "      <td>98</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>North America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Hungary</td>\n",
       "      <td>234</td>\n",
       "      <td>215</td>\n",
       "      <td>185</td>\n",
       "      <td>11.3</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>233</td>\n",
       "      <td>61</td>\n",
       "      <td>78</td>\n",
       "      <td>6.6</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>India</td>\n",
       "      <td>9</td>\n",
       "      <td>114</td>\n",
       "      <td>0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>Indonesia</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>Iran</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>80</th>\n",
       "      <td>Iraq</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>Ireland</td>\n",
       "      <td>313</td>\n",
       "      <td>118</td>\n",
       "      <td>165</td>\n",
       "      <td>11.4</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>Israel</td>\n",
       "      <td>63</td>\n",
       "      <td>69</td>\n",
       "      <td>9</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>Italy</td>\n",
       "      <td>85</td>\n",
       "      <td>42</td>\n",
       "      <td>237</td>\n",
       "      <td>6.5</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Jamaica</td>\n",
       "      <td>82</td>\n",
       "      <td>97</td>\n",
       "      <td>9</td>\n",
       "      <td>3.4</td>\n",
       "      <td>North America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>Japan</td>\n",
       "      <td>77</td>\n",
       "      <td>202</td>\n",
       "      <td>16</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>Jordan</td>\n",
       "      <td>6</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>Kazakhstan</td>\n",
       "      <td>124</td>\n",
       "      <td>246</td>\n",
       "      <td>12</td>\n",
       "      <td>6.8</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>Kenya</td>\n",
       "      <td>58</td>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>Kiribati</td>\n",
       "      <td>21</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Oceania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>Kuwait</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>91</th>\n",
       "      <td>Kyrgyzstan</td>\n",
       "      <td>31</td>\n",
       "      <td>97</td>\n",
       "      <td>6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>Laos</td>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>123</td>\n",
       "      <td>6.2</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>Latvia</td>\n",
       "      <td>281</td>\n",
       "      <td>216</td>\n",
       "      <td>62</td>\n",
       "      <td>10.5</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Lebanon</td>\n",
       "      <td>20</td>\n",
       "      <td>55</td>\n",
       "      <td>31</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>Lesotho</td>\n",
       "      <td>82</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>2.8</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>Liberia</td>\n",
       "      <td>19</td>\n",
       "      <td>152</td>\n",
       "      <td>2</td>\n",
       "      <td>3.1</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>Libya</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>Lithuania</td>\n",
       "      <td>343</td>\n",
       "      <td>244</td>\n",
       "      <td>56</td>\n",
       "      <td>12.9</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>Luxembourg</td>\n",
       "      <td>236</td>\n",
       "      <td>133</td>\n",
       "      <td>271</td>\n",
       "      <td>11.4</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>Madagascar</td>\n",
       "      <td>26</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>0.8</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>Malawi</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>Malaysia</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>Maldives</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>104</th>\n",
       "      <td>Mali</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>Malta</td>\n",
       "      <td>149</td>\n",
       "      <td>100</td>\n",
       "      <td>120</td>\n",
       "      <td>6.6</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>Marshall Islands</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Oceania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>Mauritania</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>Mauritius</td>\n",
       "      <td>98</td>\n",
       "      <td>31</td>\n",
       "      <td>18</td>\n",
       "      <td>2.6</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>Mexico</td>\n",
       "      <td>238</td>\n",
       "      <td>68</td>\n",
       "      <td>5</td>\n",
       "      <td>5.5</td>\n",
       "      <td>North America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>Micronesia</td>\n",
       "      <td>62</td>\n",
       "      <td>50</td>\n",
       "      <td>18</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Oceania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>Monaco</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>Mongolia</td>\n",
       "      <td>77</td>\n",
       "      <td>189</td>\n",
       "      <td>8</td>\n",
       "      <td>4.9</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>Montenegro</td>\n",
       "      <td>31</td>\n",
       "      <td>114</td>\n",
       "      <td>128</td>\n",
       "      <td>4.9</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>Morocco</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>0.5</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>Mozambique</td>\n",
       "      <td>47</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>Myanmar</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>Namibia</td>\n",
       "      <td>376</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>6.8</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>Nauru</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Oceania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>Nepal</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>251</td>\n",
       "      <td>88</td>\n",
       "      <td>190</td>\n",
       "      <td>9.4</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>203</td>\n",
       "      <td>79</td>\n",
       "      <td>175</td>\n",
       "      <td>9.3</td>\n",
       "      <td>Oceania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>Nicaragua</td>\n",
       "      <td>78</td>\n",
       "      <td>118</td>\n",
       "      <td>1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>North America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>Niger</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>Nigeria</td>\n",
       "      <td>42</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>9.1</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>Niue</td>\n",
       "      <td>188</td>\n",
       "      <td>200</td>\n",
       "      <td>7</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Oceania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>Norway</td>\n",
       "      <td>169</td>\n",
       "      <td>71</td>\n",
       "      <td>129</td>\n",
       "      <td>6.7</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>Oman</td>\n",
       "      <td>22</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>0.7</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>Pakistan</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>129</th>\n",
       "      <td>Palau</td>\n",
       "      <td>306</td>\n",
       "      <td>63</td>\n",
       "      <td>23</td>\n",
       "      <td>6.9</td>\n",
       "      <td>Oceania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>Panama</td>\n",
       "      <td>285</td>\n",
       "      <td>104</td>\n",
       "      <td>18</td>\n",
       "      <td>7.2</td>\n",
       "      <td>North America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>Papua New Guinea</td>\n",
       "      <td>44</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Oceania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>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>134</th>\n",
       "      <td>Philippines</td>\n",
       "      <td>71</td>\n",
       "      <td>186</td>\n",
       "      <td>1</td>\n",
       "      <td>4.6</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>Poland</td>\n",
       "      <td>343</td>\n",
       "      <td>215</td>\n",
       "      <td>56</td>\n",
       "      <td>10.9</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>194</td>\n",
       "      <td>67</td>\n",
       "      <td>339</td>\n",
       "      <td>11.0</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>Qatar</td>\n",
       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "      <td>7</td>\n",
       "      <td>0.9</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>South Korea</td>\n",
       "      <td>140</td>\n",
       "      <td>16</td>\n",
       "      <td>9</td>\n",
       "      <td>9.8</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>Moldova</td>\n",
       "      <td>109</td>\n",
       "      <td>226</td>\n",
       "      <td>18</td>\n",
       "      <td>6.3</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>Romania</td>\n",
       "      <td>297</td>\n",
       "      <td>122</td>\n",
       "      <td>167</td>\n",
       "      <td>10.4</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>Russian Federation</td>\n",
       "      <td>247</td>\n",
       "      <td>326</td>\n",
       "      <td>73</td>\n",
       "      <td>11.5</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>Rwanda</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>St. Kitts &amp; Nevis</td>\n",
       "      <td>194</td>\n",
       "      <td>205</td>\n",
       "      <td>32</td>\n",
       "      <td>7.7</td>\n",
       "      <td>North America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>St. Lucia</td>\n",
       "      <td>171</td>\n",
       "      <td>315</td>\n",
       "      <td>71</td>\n",
       "      <td>10.1</td>\n",
       "      <td>North America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>St. Vincent &amp; the Grenadines</td>\n",
       "      <td>120</td>\n",
       "      <td>221</td>\n",
       "      <td>11</td>\n",
       "      <td>6.3</td>\n",
       "      <td>North America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>Samoa</td>\n",
       "      <td>105</td>\n",
       "      <td>18</td>\n",
       "      <td>24</td>\n",
       "      <td>2.6</td>\n",
       "      <td>Oceania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>San Marino</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>Sao Tome &amp; Principe</td>\n",
       "      <td>56</td>\n",
       "      <td>38</td>\n",
       "      <td>140</td>\n",
       "      <td>4.2</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>Saudi Arabia</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>Senegal</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>Serbia</td>\n",
       "      <td>283</td>\n",
       "      <td>131</td>\n",
       "      <td>127</td>\n",
       "      <td>9.6</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>Seychelles</td>\n",
       "      <td>157</td>\n",
       "      <td>25</td>\n",
       "      <td>51</td>\n",
       "      <td>4.1</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>Sierra Leone</td>\n",
       "      <td>25</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>6.7</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>60</td>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>Slovakia</td>\n",
       "      <td>196</td>\n",
       "      <td>293</td>\n",
       "      <td>116</td>\n",
       "      <td>11.4</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>Slovenia</td>\n",
       "      <td>270</td>\n",
       "      <td>51</td>\n",
       "      <td>276</td>\n",
       "      <td>10.6</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>Solomon Islands</td>\n",
       "      <td>56</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>Oceania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>Somalia</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>225</td>\n",
       "      <td>76</td>\n",
       "      <td>81</td>\n",
       "      <td>8.2</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>Spain</td>\n",
       "      <td>284</td>\n",
       "      <td>157</td>\n",
       "      <td>112</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>16</td>\n",
       "      <td>104</td>\n",
       "      <td>0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>Sudan</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>Africa</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>164</th>\n",
       "      <td>Swaziland</td>\n",
       "      <td>90</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4.7</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>152</td>\n",
       "      <td>60</td>\n",
       "      <td>186</td>\n",
       "      <td>7.2</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>185</td>\n",
       "      <td>100</td>\n",
       "      <td>280</td>\n",
       "      <td>10.2</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>Syria</td>\n",
       "      <td>5</td>\n",
       "      <td>35</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>Tajikistan</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>Thailand</td>\n",
       "      <td>99</td>\n",
       "      <td>258</td>\n",
       "      <td>1</td>\n",
       "      <td>6.4</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>Macedonia</td>\n",
       "      <td>106</td>\n",
       "      <td>27</td>\n",
       "      <td>86</td>\n",
       "      <td>3.9</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>Timor-Leste</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>Togo</td>\n",
       "      <td>36</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>1.3</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>Tonga</td>\n",
       "      <td>36</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>1.1</td>\n",
       "      <td>Oceania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>Trinidad &amp; Tobago</td>\n",
       "      <td>197</td>\n",
       "      <td>156</td>\n",
       "      <td>7</td>\n",
       "      <td>6.4</td>\n",
       "      <td>North America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>Tunisia</td>\n",
       "      <td>51</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>1.3</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>Turkey</td>\n",
       "      <td>51</td>\n",
       "      <td>22</td>\n",
       "      <td>7</td>\n",
       "      <td>1.4</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>Turkmenistan</td>\n",
       "      <td>19</td>\n",
       "      <td>71</td>\n",
       "      <td>32</td>\n",
       "      <td>2.2</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>Tuvalu</td>\n",
       "      <td>6</td>\n",
       "      <td>41</td>\n",
       "      <td>9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Oceania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>Uganda</td>\n",
       "      <td>45</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>Ukraine</td>\n",
       "      <td>206</td>\n",
       "      <td>237</td>\n",
       "      <td>45</td>\n",
       "      <td>8.9</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>16</td>\n",
       "      <td>135</td>\n",
       "      <td>5</td>\n",
       "      <td>2.8</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>219</td>\n",
       "      <td>126</td>\n",
       "      <td>195</td>\n",
       "      <td>10.4</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>Tanzania</td>\n",
       "      <td>36</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>5.7</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>USA</td>\n",
       "      <td>249</td>\n",
       "      <td>158</td>\n",
       "      <td>84</td>\n",
       "      <td>8.7</td>\n",
       "      <td>North 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>186</th>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>25</td>\n",
       "      <td>101</td>\n",
       "      <td>8</td>\n",
       "      <td>2.4</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>Vanuatu</td>\n",
       "      <td>21</td>\n",
       "      <td>18</td>\n",
       "      <td>11</td>\n",
       "      <td>0.9</td>\n",
       "      <td>Oceania</td>\n",
       "    </tr>\n",
       "    <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",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>111</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>32</td>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>64</td>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "      <td>4.7</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          country  beer_servings  spirit_servings  \\\n",
       "0                     Afghanistan              0                0   \n",
       "1                         Albania             89              132   \n",
       "2                         Algeria             25                0   \n",
       "3                         Andorra            245              138   \n",
       "4                          Angola            217               57   \n",
       "5               Antigua & Barbuda            102              128   \n",
       "6                       Argentina            193               25   \n",
       "7                         Armenia             21              179   \n",
       "8                       Australia            261               72   \n",
       "9                         Austria            279               75   \n",
       "10                     Azerbaijan             21               46   \n",
       "11                        Bahamas            122              176   \n",
       "12                        Bahrain             42               63   \n",
       "13                     Bangladesh              0                0   \n",
       "14                       Barbados            143              173   \n",
       "15                        Belarus            142              373   \n",
       "16                        Belgium            295               84   \n",
       "17                         Belize            263              114   \n",
       "18                          Benin             34                4   \n",
       "19                         Bhutan             23                0   \n",
       "20                        Bolivia            167               41   \n",
       "21             Bosnia-Herzegovina             76              173   \n",
       "22                       Botswana            173               35   \n",
       "23                         Brazil            245              145   \n",
       "24                         Brunei             31                2   \n",
       "25                       Bulgaria            231              252   \n",
       "26                   Burkina Faso             25                7   \n",
       "27                        Burundi             88                0   \n",
       "28                  Cote d'Ivoire             37                1   \n",
       "29                     Cabo Verde            144               56   \n",
       "30                       Cambodia             57               65   \n",
       "31                       Cameroon            147                1   \n",
       "32                         Canada            240              122   \n",
       "33       Central African Republic             17                2   \n",
       "34                           Chad             15                1   \n",
       "35                          Chile            130              124   \n",
       "36                          China             79              192   \n",
       "37                       Colombia            159               76   \n",
       "38                        Comoros              1                3   \n",
       "39                          Congo             76                1   \n",
       "40                   Cook Islands              0              254   \n",
       "41                     Costa Rica            149               87   \n",
       "42                        Croatia            230               87   \n",
       "43                           Cuba             93              137   \n",
       "44                         Cyprus            192              154   \n",
       "45                 Czech Republic            361              170   \n",
       "46                    North Korea              0                0   \n",
       "47                       DR Congo             32                3   \n",
       "48                        Denmark            224               81   \n",
       "49                       Djibouti             15               44   \n",
       "50                       Dominica             52              286   \n",
       "51             Dominican Republic            193              147   \n",
       "52                        Ecuador            162               74   \n",
       "53                          Egypt              6                4   \n",
       "54                    El Salvador             52               69   \n",
       "55              Equatorial Guinea             92                0   \n",
       "56                        Eritrea             18                0   \n",
       "57                        Estonia            224              194   \n",
       "58                       Ethiopia             20                3   \n",
       "59                           Fiji             77               35   \n",
       "60                        Finland            263              133   \n",
       "61                         France            127              151   \n",
       "62                          Gabon            347               98   \n",
       "63                         Gambia              8                0   \n",
       "64                        Georgia             52              100   \n",
       "65                        Germany            346              117   \n",
       "66                          Ghana             31                3   \n",
       "67                         Greece            133              112   \n",
       "68                        Grenada            199              438   \n",
       "69                      Guatemala             53               69   \n",
       "70                         Guinea              9                0   \n",
       "71                  Guinea-Bissau             28               31   \n",
       "72                         Guyana             93              302   \n",
       "73                          Haiti              1              326   \n",
       "74                       Honduras             69               98   \n",
       "75                        Hungary            234              215   \n",
       "76                        Iceland            233               61   \n",
       "77                          India              9              114   \n",
       "78                      Indonesia              5                1   \n",
       "79                           Iran              0                0   \n",
       "80                           Iraq              9                3   \n",
       "81                        Ireland            313              118   \n",
       "82                         Israel             63               69   \n",
       "83                          Italy             85               42   \n",
       "84                        Jamaica             82               97   \n",
       "85                          Japan             77              202   \n",
       "86                         Jordan              6               21   \n",
       "87                     Kazakhstan            124              246   \n",
       "88                          Kenya             58               22   \n",
       "89                       Kiribati             21               34   \n",
       "90                         Kuwait              0                0   \n",
       "91                     Kyrgyzstan             31               97   \n",
       "92                           Laos             62                0   \n",
       "93                         Latvia            281              216   \n",
       "94                        Lebanon             20               55   \n",
       "95                        Lesotho             82               29   \n",
       "96                        Liberia             19              152   \n",
       "97                          Libya              0                0   \n",
       "98                      Lithuania            343              244   \n",
       "99                     Luxembourg            236              133   \n",
       "100                    Madagascar             26               15   \n",
       "101                        Malawi              8               11   \n",
       "102                      Malaysia             13                4   \n",
       "103                      Maldives              0                0   \n",
       "104                          Mali              5                1   \n",
       "105                         Malta            149              100   \n",
       "106              Marshall Islands              0                0   \n",
       "107                    Mauritania              0                0   \n",
       "108                     Mauritius             98               31   \n",
       "109                        Mexico            238               68   \n",
       "110                    Micronesia             62               50   \n",
       "111                        Monaco              0                0   \n",
       "112                      Mongolia             77              189   \n",
       "113                    Montenegro             31              114   \n",
       "114                       Morocco             12                6   \n",
       "115                    Mozambique             47               18   \n",
       "116                       Myanmar              5                1   \n",
       "117                       Namibia            376                3   \n",
       "118                         Nauru             49                0   \n",
       "119                         Nepal              5                6   \n",
       "120                   Netherlands            251               88   \n",
       "121                   New Zealand            203               79   \n",
       "122                     Nicaragua             78              118   \n",
       "123                         Niger              3                2   \n",
       "124                       Nigeria             42                5   \n",
       "125                          Niue            188              200   \n",
       "126                        Norway            169               71   \n",
       "127                          Oman             22               16   \n",
       "128                      Pakistan              0                0   \n",
       "129                         Palau            306               63   \n",
       "130                        Panama            285              104   \n",
       "131              Papua New Guinea             44               39   \n",
       "132                      Paraguay            213              117   \n",
       "133                          Peru            163              160   \n",
       "134                   Philippines             71              186   \n",
       "135                        Poland            343              215   \n",
       "136                      Portugal            194               67   \n",
       "137                         Qatar              1               42   \n",
       "138                   South Korea            140               16   \n",
       "139                       Moldova            109              226   \n",
       "140                       Romania            297              122   \n",
       "141            Russian Federation            247              326   \n",
       "142                        Rwanda             43                2   \n",
       "143             St. Kitts & Nevis            194              205   \n",
       "144                     St. Lucia            171              315   \n",
       "145  St. Vincent & the Grenadines            120              221   \n",
       "146                         Samoa            105               18   \n",
       "147                    San Marino              0                0   \n",
       "148           Sao Tome & Principe             56               38   \n",
       "149                  Saudi Arabia              0                5   \n",
       "150                       Senegal              9                1   \n",
       "151                        Serbia            283              131   \n",
       "152                    Seychelles            157               25   \n",
       "153                  Sierra Leone             25                3   \n",
       "154                     Singapore             60               12   \n",
       "155                      Slovakia            196              293   \n",
       "156                      Slovenia            270               51   \n",
       "157               Solomon Islands             56               11   \n",
       "158                       Somalia              0                0   \n",
       "159                  South Africa            225               76   \n",
       "160                         Spain            284              157   \n",
       "161                     Sri Lanka             16              104   \n",
       "162                         Sudan              8               13   \n",
       "163                      Suriname            128              178   \n",
       "164                     Swaziland             90                2   \n",
       "165                        Sweden            152               60   \n",
       "166                   Switzerland            185              100   \n",
       "167                         Syria              5               35   \n",
       "168                    Tajikistan              2               15   \n",
       "169                      Thailand             99              258   \n",
       "170                     Macedonia            106               27   \n",
       "171                   Timor-Leste              1                1   \n",
       "172                          Togo             36                2   \n",
       "173                         Tonga             36               21   \n",
       "174             Trinidad & Tobago            197              156   \n",
       "175                       Tunisia             51                3   \n",
       "176                        Turkey             51               22   \n",
       "177                  Turkmenistan             19               71   \n",
       "178                        Tuvalu              6               41   \n",
       "179                        Uganda             45                9   \n",
       "180                       Ukraine            206              237   \n",
       "181          United Arab Emirates             16              135   \n",
       "182                United Kingdom            219              126   \n",
       "183                      Tanzania             36                6   \n",
       "184                           USA            249              158   \n",
       "185                       Uruguay            115               35   \n",
       "186                    Uzbekistan             25              101   \n",
       "187                       Vanuatu             21               18   \n",
       "188                     Venezuela            333              100   \n",
       "189                       Vietnam            111                2   \n",
       "190                         Yemen              6                0   \n",
       "191                        Zambia             32               19   \n",
       "192                      Zimbabwe             64               18   \n",
       "\n",
       "     wine_servings  total_litres_of_pure_alcohol      continent  \n",
       "0                0                           0.0           Asia  \n",
       "1               54                           4.9         Europe  \n",
       "2               14                           0.7         Africa  \n",
       "3              312                          12.4         Europe  \n",
       "4               45                           5.9         Africa  \n",
       "5               45                           4.9  North America  \n",
       "6              221                           8.3  South America  \n",
       "7               11                           3.8         Europe  \n",
       "8              212                          10.4        Oceania  \n",
       "9              191                           9.7         Europe  \n",
       "10               5                           1.3         Europe  \n",
       "11              51                           6.3  North America  \n",
       "12               7                           2.0           Asia  \n",
       "13               0                           0.0           Asia  \n",
       "14              36                           6.3  North America  \n",
       "15              42                          14.4         Europe  \n",
       "16             212                          10.5         Europe  \n",
       "17               8                           6.8  North America  \n",
       "18              13                           1.1         Africa  \n",
       "19               0                           0.4           Asia  \n",
       "20               8                           3.8  South America  \n",
       "21               8                           4.6         Europe  \n",
       "22              35                           5.4         Africa  \n",
       "23              16                           7.2  South America  \n",
       "24               1                           0.6           Asia  \n",
       "25              94                          10.3         Europe  \n",
       "26               7                           4.3         Africa  \n",
       "27               0                           6.3         Africa  \n",
       "28               7                           4.0         Africa  \n",
       "29              16                           4.0         Africa  \n",
       "30               1                           2.2           Asia  \n",
       "31               4                           5.8         Africa  \n",
       "32             100                           8.2  North America  \n",
       "33               1                           1.8         Africa  \n",
       "34               1                           0.4         Africa  \n",
       "35             172                           7.6  South America  \n",
       "36               8                           5.0           Asia  \n",
       "37               3                           4.2  South America  \n",
       "38               1                           0.1         Africa  \n",
       "39               9                           1.7         Africa  \n",
       "40              74                           5.9        Oceania  \n",
       "41              11                           4.4  North America  \n",
       "42             254                          10.2         Europe  \n",
       "43               5                           4.2  North America  \n",
       "44             113                           8.2         Europe  \n",
       "45             134                          11.8         Europe  \n",
       "46               0                           0.0           Asia  \n",
       "47               1                           2.3         Africa  \n",
       "48             278                          10.4         Europe  \n",
       "49               3                           1.1         Africa  \n",
       "50              26                           6.6  North America  \n",
       "51               9                           6.2  North America  \n",
       "52               3                           4.2  South America  \n",
       "53               1                           0.2         Africa  \n",
       "54               2                           2.2  North America  \n",
       "55             233                           5.8         Africa  \n",
       "56               0                           0.5         Africa  \n",
       "57              59                           9.5         Europe  \n",
       "58               0                           0.7         Africa  \n",
       "59               1                           2.0        Oceania  \n",
       "60              97                          10.0         Europe  \n",
       "61             370                          11.8         Europe  \n",
       "62              59                           8.9         Africa  \n",
       "63               1                           2.4         Africa  \n",
       "64             149                           5.4         Europe  \n",
       "65             175                          11.3         Europe  \n",
       "66              10                           1.8         Africa  \n",
       "67             218                           8.3         Europe  \n",
       "68              28                          11.9  North America  \n",
       "69               2                           2.2  North America  \n",
       "70               2                           0.2         Africa  \n",
       "71              21                           2.5         Africa  \n",
       "72               1                           7.1  South America  \n",
       "73               1                           5.9  North America  \n",
       "74               2                           3.0  North America  \n",
       "75             185                          11.3         Europe  \n",
       "76              78                           6.6         Europe  \n",
       "77               0                           2.2           Asia  \n",
       "78               0                           0.1           Asia  \n",
       "79               0                           0.0           Asia  \n",
       "80               0                           0.2           Asia  \n",
       "81             165                          11.4         Europe  \n",
       "82               9                           2.5           Asia  \n",
       "83             237                           6.5         Europe  \n",
       "84               9                           3.4  North America  \n",
       "85              16                           7.0           Asia  \n",
       "86               1                           0.5           Asia  \n",
       "87              12                           6.8           Asia  \n",
       "88               2                           1.8         Africa  \n",
       "89               1                           1.0        Oceania  \n",
       "90               0                           0.0           Asia  \n",
       "91               6                           2.4           Asia  \n",
       "92             123                           6.2           Asia  \n",
       "93              62                          10.5         Europe  \n",
       "94              31                           1.9           Asia  \n",
       "95               0                           2.8         Africa  \n",
       "96               2                           3.1         Africa  \n",
       "97               0                           0.0         Africa  \n",
       "98              56                          12.9         Europe  \n",
       "99             271                          11.4         Europe  \n",
       "100              4                           0.8         Africa  \n",
       "101              1                           1.5         Africa  \n",
       "102              0                           0.3           Asia  \n",
       "103              0                           0.0           Asia  \n",
       "104              1                           0.6         Africa  \n",
       "105            120                           6.6         Europe  \n",
       "106              0                           0.0        Oceania  \n",
       "107              0                           0.0         Africa  \n",
       "108             18                           2.6         Africa  \n",
       "109              5                           5.5  North America  \n",
       "110             18                           2.3        Oceania  \n",
       "111              0                           0.0         Europe  \n",
       "112              8                           4.9           Asia  \n",
       "113            128                           4.9         Europe  \n",
       "114             10                           0.5         Africa  \n",
       "115              5                           1.3         Africa  \n",
       "116              0                           0.1           Asia  \n",
       "117              1                           6.8         Africa  \n",
       "118              8                           1.0        Oceania  \n",
       "119              0                           0.2           Asia  \n",
       "120            190                           9.4         Europe  \n",
       "121            175                           9.3        Oceania  \n",
       "122              1                           3.5  North America  \n",
       "123              1                           0.1         Africa  \n",
       "124              2                           9.1         Africa  \n",
       "125              7                           7.0        Oceania  \n",
       "126            129                           6.7         Europe  \n",
       "127              1                           0.7           Asia  \n",
       "128              0                           0.0           Asia  \n",
       "129             23                           6.9        Oceania  \n",
       "130             18                           7.2  North America  \n",
       "131              1                           1.5        Oceania  \n",
       "132             74                           7.3  South America  \n",
       "133             21                           6.1  South America  \n",
       "134              1                           4.6           Asia  \n",
       "135             56                          10.9         Europe  \n",
       "136            339                          11.0         Europe  \n",
       "137              7                           0.9           Asia  \n",
       "138              9                           9.8           Asia  \n",
       "139             18                           6.3         Europe  \n",
       "140            167                          10.4         Europe  \n",
       "141             73                          11.5           Asia  \n",
       "142              0                           6.8         Africa  \n",
       "143             32                           7.7  North America  \n",
       "144             71                          10.1  North America  \n",
       "145             11                           6.3  North America  \n",
       "146             24                           2.6        Oceania  \n",
       "147              0                           0.0         Europe  \n",
       "148            140                           4.2         Africa  \n",
       "149              0                           0.1           Asia  \n",
       "150              7                           0.3         Africa  \n",
       "151            127                           9.6         Europe  \n",
       "152             51                           4.1         Africa  \n",
       "153              2                           6.7         Africa  \n",
       "154             11                           1.5           Asia  \n",
       "155            116                          11.4         Europe  \n",
       "156            276                          10.6         Europe  \n",
       "157              1                           1.2        Oceania  \n",
       "158              0                           0.0         Africa  \n",
       "159             81                           8.2         Africa  \n",
       "160            112                          10.0         Europe  \n",
       "161              0                           2.2           Asia  \n",
       "162              0                           1.7         Africa  \n",
       "163              7                           5.6  South America  \n",
       "164              2                           4.7         Africa  \n",
       "165            186                           7.2         Europe  \n",
       "166            280                          10.2         Europe  \n",
       "167             16                           1.0           Asia  \n",
       "168              0                           0.3           Asia  \n",
       "169              1                           6.4           Asia  \n",
       "170             86                           3.9         Europe  \n",
       "171              4                           0.1           Asia  \n",
       "172             19                           1.3         Africa  \n",
       "173              5                           1.1        Oceania  \n",
       "174              7                           6.4  North America  \n",
       "175             20                           1.3         Africa  \n",
       "176              7                           1.4           Asia  \n",
       "177             32                           2.2           Asia  \n",
       "178              9                           1.0        Oceania  \n",
       "179              0                           8.3         Africa  \n",
       "180             45                           8.9         Europe  \n",
       "181              5                           2.8           Asia  \n",
       "182            195                          10.4         Europe  \n",
       "183              1                           5.7         Africa  \n",
       "184             84                           8.7  North America  \n",
       "185            220                           6.6  South America  \n",
       "186              8                           2.4           Asia  \n",
       "187             11                           0.9        Oceania  \n",
       "188              3                           7.7  South America  \n",
       "189              1                           2.0           Asia  \n",
       "190              0                           0.1           Asia  \n",
       "191              4                           2.5         Africa  \n",
       "192              4                           4.7         Africa  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.reset_option('display.max_rows')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Afghanistan</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>89</td>\n",
       "      <td>132</td>\n",
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       "      <td>Andorra</td>\n",
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       "      <td>138</td>\n",
       "      <td>312</td>\n",
       "      <td>12.4</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
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       "      <td>57</td>\n",
       "      <td>45</td>\n",
       "      <td>5.9</td>\n",
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      ],
      "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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "URL = 'http://bit.ly/kaggletrain'\n",
    "train = pd.read_csv(URL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
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    "pd.get_option('display.max_colwidth')"
   ]
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  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_colwidth', 1000)"
   ]
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  {
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   "metadata": {},
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       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
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       "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
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       "4                             Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
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     "metadata": {},
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   "source": [
    "train.head()"
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  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.precision', 2)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
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       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                  Name     Sex   Age  SibSp  \\\n",
       "0                              Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Thayer)  female  38.0      1   \n",
       "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                             Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket   Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.25   NaN        S  \n",
       "1      0          PC 17599  71.28   C85        C  \n",
       "2      0  STON/O2. 3101282   7.92   NaN        S  \n",
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     "execution_count": 24,
     "metadata": {},
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   ],
   "source": [
    "train.head()"
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  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      "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": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "drinks['x'] = drinks.wine_servings * 1000\n",
    "drinks['y'] = drinks.total_litres_of_pure_alcohol * 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
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      "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       x        y  \n",
       "0                           0.0      Asia       0      0.0  \n",
       "1                           4.9    Europe   54000   4900.0  \n",
       "2                           0.7    Africa   14000    700.0  \n",
       "3                          12.4    Europe  312000  12400.0  \n",
       "4                           5.9    Africa   45000   5900.0  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "compute.use_bottleneck : bool\n",
      "    Use the bottleneck library to accelerate if it is installed,\n",
      "    the default is True\n",
      "    Valid values: False,True\n",
      "    [default: True] [currently: True]\n",
      "\n",
      "compute.use_numexpr : bool\n",
      "    Use the numexpr library to accelerate computation if it is installed,\n",
      "    the default is True\n",
      "    Valid values: False,True\n",
      "    [default: True] [currently: True]\n",
      "\n",
      "display.chop_threshold : float or None\n",
      "    if set to a float value, all float values smaller then the given threshold\n",
      "    will be displayed as exactly 0 by repr and friends.\n",
      "    [default: None] [currently: None]\n",
      "\n",
      "display.colheader_justify : 'left'/'right'\n",
      "    Controls the justification of column headers. used by DataFrameFormatter.\n",
      "    [default: right] [currently: right]\n",
      "\n",
      "display.column_space No description available.\n",
      "    [default: 12] [currently: 12]\n",
      "\n",
      "display.date_dayfirst : boolean\n",
      "    When True, prints and parses dates with the day first, eg 20/01/2005\n",
      "    [default: False] [currently: False]\n",
      "\n",
      "display.date_yearfirst : boolean\n",
      "    When True, prints and parses dates with the year first, eg 2005/01/20\n",
      "    [default: False] [currently: False]\n",
      "\n",
      "display.encoding : str/unicode\n",
      "    Defaults to the detected encoding of the console.\n",
      "    Specifies the encoding to be used for strings returned by to_string,\n",
      "    these are generally strings meant to be displayed on the console.\n",
      "    [default: UTF-8] [currently: UTF-8]\n",
      "\n",
      "display.expand_frame_repr : boolean\n",
      "    Whether to print out the full DataFrame repr for wide DataFrames across\n",
      "    multiple lines, `max_columns` is still respected, but the output will\n",
      "    wrap-around across multiple \"pages\" if its width exceeds `display.width`.\n",
      "    [default: True] [currently: True]\n",
      "\n",
      "display.float_format : callable\n",
      "    The callable should accept a floating point number and return\n",
      "    a string with the desired format of the number. This is used\n",
      "    in some places like SeriesFormatter.\n",
      "    See formats.format.EngFormatter for an example.\n",
      "    [default: None] [currently: None]\n",
      "\n",
      "display.html.border : int\n",
      "    A ``border=value`` attribute is inserted in the ``<table>`` tag\n",
      "    for the DataFrame HTML repr.\n",
      "    [default: 1] [currently: 1]\n",
      "\n",
      "display.html.table_schema : boolean\n",
      "    Whether to publish a Table Schema representation for frontends\n",
      "    that support it.\n",
      "    (default: False)\n",
      "    [default: False] [currently: False]\n",
      "\n",
      "display.html.use_mathjax : boolean\n",
      "    When True, Jupyter notebook will process table contents using MathJax,\n",
      "    rendering mathematical expressions enclosed by the dollar symbol.\n",
      "    (default: True)\n",
      "    [default: True] [currently: True]\n",
      "\n",
      "display.large_repr : 'truncate'/'info'\n",
      "    For DataFrames exceeding max_rows/max_cols, the repr (and HTML repr) can\n",
      "    show a truncated table (the default from 0.13), or switch to the view from\n",
      "    df.info() (the behaviour in earlier versions of pandas).\n",
      "    [default: truncate] [currently: truncate]\n",
      "\n",
      "display.latex.escape : bool\n",
      "    This specifies if the to_latex method of a Dataframe uses escapes special\n",
      "    characters.\n",
      "    Valid values: False,True\n",
      "    [default: True] [currently: True]\n",
      "\n",
      "display.latex.longtable :bool\n",
      "    This specifies if the to_latex method of a Dataframe uses the longtable\n",
      "    format.\n",
      "    Valid values: False,True\n",
      "    [default: False] [currently: False]\n",
      "\n",
      "display.latex.multicolumn : bool\n",
      "    This specifies if the to_latex method of a Dataframe uses multicolumns\n",
      "    to pretty-print MultiIndex columns.\n",
      "    Valid values: False,True\n",
      "    [default: True] [currently: True]\n",
      "\n",
      "display.latex.multicolumn_format : bool\n",
      "    This specifies if the to_latex method of a Dataframe uses multicolumns\n",
      "    to pretty-print MultiIndex columns.\n",
      "    Valid values: False,True\n",
      "    [default: l] [currently: l]\n",
      "\n",
      "display.latex.multirow : bool\n",
      "    This specifies if the to_latex method of a Dataframe uses multirows\n",
      "    to pretty-print MultiIndex rows.\n",
      "    Valid values: False,True\n",
      "    [default: False] [currently: False]\n",
      "\n",
      "display.latex.repr : boolean\n",
      "    Whether to produce a latex DataFrame representation for jupyter\n",
      "    environments that support it.\n",
      "    (default: False)\n",
      "    [default: False] [currently: False]\n",
      "\n",
      "display.max_categories : int\n",
      "    This sets the maximum number of categories pandas should output when\n",
      "    printing out a `Categorical` or a Series of dtype \"category\".\n",
      "    [default: 8] [currently: 8]\n",
      "\n",
      "display.max_columns : int\n",
      "    If max_cols is exceeded, switch to truncate view. Depending on\n",
      "    `large_repr`, objects are either centrally truncated or printed as\n",
      "    a summary view. 'None' value means unlimited.\n",
      "\n",
      "    In case python/IPython is running in a terminal and `large_repr`\n",
      "    equals 'truncate' this can be set to 0 and pandas will auto-detect\n",
      "    the width of the terminal and print a truncated object which fits\n",
      "    the screen width. The IPython notebook, IPython qtconsole, or IDLE\n",
      "    do not run in a terminal and hence it is not possible to do\n",
      "    correct auto-detection.\n",
      "    [default: 20] [currently: 20]\n",
      "\n",
      "display.max_colwidth : int\n",
      "    The maximum width in characters of a column in the repr of\n",
      "    a pandas data structure. When the column overflows, a \"...\"\n",
      "    placeholder is embedded in the output.\n",
      "    [default: 50] [currently: 1000]\n",
      "\n",
      "display.max_info_columns : int\n",
      "    max_info_columns is used in DataFrame.info method to decide if\n",
      "    per column information will be printed.\n",
      "    [default: 100] [currently: 100]\n",
      "\n",
      "display.max_info_rows : int or None\n",
      "    df.info() will usually show null-counts for each column.\n",
      "    For large frames this can be quite slow. max_info_rows and max_info_cols\n",
      "    limit this null check only to frames with smaller dimensions than\n",
      "    specified.\n",
      "    [default: 1690785] [currently: 1690785]\n",
      "\n",
      "display.max_rows : int\n",
      "    If max_rows is exceeded, switch to truncate view. Depending on\n",
      "    `large_repr`, objects are either centrally truncated or printed as\n",
      "    a summary view. 'None' value means unlimited.\n",
      "\n",
      "    In case python/IPython is running in a terminal and `large_repr`\n",
      "    equals 'truncate' this can be set to 0 and pandas will auto-detect\n",
      "    the height of the terminal and print a truncated object which fits\n",
      "    the screen height. The IPython notebook, IPython qtconsole, or\n",
      "    IDLE do not run in a terminal and hence it is not possible to do\n",
      "    correct auto-detection.\n",
      "    [default: 60] [currently: 60]\n",
      "\n",
      "display.max_seq_items : int or None\n",
      "    when pretty-printing a long sequence, no more then `max_seq_items`\n",
      "    will be printed. If items are omitted, they will be denoted by the\n",
      "    addition of \"...\" to the resulting string.\n",
      "\n",
      "    If set to None, the number of items to be printed is unlimited.\n",
      "    [default: 100] [currently: 100]\n",
      "\n",
      "display.memory_usage : bool, string or None\n",
      "    This specifies if the memory usage of a DataFrame should be displayed when\n",
      "    df.info() is called. Valid values True,False,'deep'\n",
      "    [default: True] [currently: True]\n",
      "\n",
      "display.multi_sparse : boolean\n",
      "    \"sparsify\" MultiIndex display (don't display repeated\n",
      "    elements in outer levels within groups)\n",
      "    [default: True] [currently: True]\n",
      "\n",
      "display.notebook_repr_html : boolean\n",
      "    When True, IPython notebook will use html representation for\n",
      "    pandas objects (if it is available).\n",
      "    [default: True] [currently: True]\n",
      "\n",
      "display.pprint_nest_depth : int\n",
      "    Controls the number of nested levels to process when pretty-printing\n",
      "    [default: 3] [currently: 3]\n",
      "\n",
      "display.precision : int\n",
      "    Floating point output precision (number of significant digits). This is\n",
      "    only a suggestion\n",
      "    [default: 6] [currently: 2]\n",
      "\n",
      "display.show_dimensions : boolean or 'truncate'\n",
      "    Whether to print out dimensions at the end of DataFrame repr.\n",
      "    If 'truncate' is specified, only print out the dimensions if the\n",
      "    frame is truncated (e.g. not display all rows and/or columns)\n",
      "    [default: truncate] [currently: truncate]\n",
      "\n",
      "display.unicode.ambiguous_as_wide : boolean\n",
      "    Whether to use the Unicode East Asian Width to calculate the display text\n",
      "    width.\n",
      "    Enabling this may affect to the performance (default: False)\n",
      "    [default: False] [currently: False]\n",
      "\n",
      "display.unicode.east_asian_width : boolean\n",
      "    Whether to use the Unicode East Asian Width to calculate the display text\n",
      "    width.\n",
      "    Enabling this may affect to the performance (default: False)\n",
      "    [default: False] [currently: False]\n",
      "\n",
      "display.width : int\n",
      "    Width of the display in characters. In case python/IPython is running in\n",
      "    a terminal this can be set to None and pandas will correctly auto-detect\n",
      "    the width.\n",
      "    Note that the IPython notebook, IPython qtconsole, or IDLE do not run in a\n",
      "    terminal and hence it is not possible to correctly detect the width.\n",
      "    [default: 80] [currently: 80]\n",
      "\n",
      "html.border : int\n",
      "    A ``border=value`` attribute is inserted in the ``<table>`` tag\n",
      "    for the DataFrame HTML repr.\n",
      "    [default: 1] [currently: 1]\n",
      "    (Deprecated, use `display.html.border` instead.)\n",
      "\n",
      "io.excel.xls.writer : string\n",
      "    The default Excel writer engine for 'xls' files. Available options:\n",
      "    auto, xlwt.\n",
      "    [default: auto] [currently: auto]\n",
      "\n",
      "io.excel.xlsm.writer : string\n",
      "    The default Excel writer engine for 'xlsm' files. Available options:\n",
      "    auto, openpyxl.\n",
      "    [default: auto] [currently: auto]\n",
      "\n",
      "io.excel.xlsx.writer : string\n",
      "    The default Excel writer engine for 'xlsx' files. Available options:\n",
      "    auto, openpyxl, xlsxwriter.\n",
      "    [default: auto] [currently: auto]\n",
      "\n",
      "io.hdf.default_format : format\n",
      "    default format writing format, if None, then\n",
      "    put will default to 'fixed' and append will default to 'table'\n",
      "    [default: None] [currently: None]\n",
      "\n",
      "io.hdf.dropna_table : boolean\n",
      "    drop ALL nan rows when appending to a table\n",
      "    [default: False] [currently: False]\n",
      "\n",
      "io.parquet.engine : string\n",
      "    The default parquet reader/writer engine. Available options:\n",
      "    'auto', 'pyarrow', 'fastparquet', the default is 'auto'\n",
      "    [default: auto] [currently: auto]\n",
      "\n",
      "mode.chained_assignment : string\n",
      "    Raise an exception, warn, or no action if trying to use chained assignment,\n",
      "    The default is warn\n",
      "    [default: warn] [currently: warn]\n",
      "\n",
      "mode.sim_interactive : boolean\n",
      "    Whether to simulate interactive mode for purposes of testing\n",
      "    [default: False] [currently: False]\n",
      "\n",
      "mode.use_inf_as_na : boolean\n",
      "    True means treat None, NaN, INF, -INF as NA (old way),\n",
      "    False means None and NaN are null, but INF, -INF are not NA\n",
      "    (new way).\n",
      "    [default: False] [currently: False]\n",
      "\n",
      "mode.use_inf_as_null : boolean\n",
      "    use_inf_as_null had been deprecated and will be removed in a future\n",
      "    version. Use `use_inf_as_na` instead.\n",
      "    [default: False] [currently: False]\n",
      "    (Deprecated, use `mode.use_inf_as_na` instead.)\n",
      "\n",
      "plotting.matplotlib.register_converters : bool\n",
      "    Whether to register converters with matplotlib's units registry for\n",
      "    dates, times, datetimes, and Periods. Toggling to False will remove\n",
      "    the converters, restoring any converters that pandas overwrote.\n",
      "    [default: True] [currently: True]\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pd.describe_option()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "display.max_info_rows : int or None\n",
      "    df.info() will usually show null-counts for each column.\n",
      "    For large frames this can be quite slow. max_info_rows and max_info_cols\n",
      "    limit this null check only to frames with smaller dimensions than\n",
      "    specified.\n",
      "    [default: 1690785] [currently: 1690785]\n",
      "\n",
      "display.max_rows : int\n",
      "    If max_rows is exceeded, switch to truncate view. Depending on\n",
      "    `large_repr`, objects are either centrally truncated or printed as\n",
      "    a summary view. 'None' value means unlimited.\n",
      "\n",
      "    In case python/IPython is running in a terminal and `large_repr`\n",
      "    equals 'truncate' this can be set to 0 and pandas will auto-detect\n",
      "    the height of the terminal and print a truncated object which fits\n",
      "    the screen height. The IPython notebook, IPython qtconsole, or\n",
      "    IDLE do not run in a terminal and hence it is not possible to do\n",
      "    correct auto-detection.\n",
      "    [default: 60] [currently: 60]\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pd.describe_option('rows')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({'id': [100, 101, 102], 'color': ['red', 'blue', 'red']}, columns=['id', 'color'], index=['a', 'b', 'c'])"
   ]
  },
  {
   "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",
       "    }\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>color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>101</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>102</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    id color\n",
       "0  100   red\n",
       "1  101  blue\n",
       "2  102   red"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame([[100, 'red'], [101, 'blue'], ['102', 'red']], columns=['id', 'color'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.8800615 , 0.4381623 ],\n",
       "       [0.22117345, 0.36564884],\n",
       "       [0.8629659 , 0.72857763],\n",
       "       [0.31065179, 0.18066846]])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.random.rand(4,2)\n",
    "arr"
   ]
  },
  {
   "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",
       "    }\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>one</th>\n",
       "      <th>two</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.880061</td>\n",
       "      <td>0.438162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.221173</td>\n",
       "      <td>0.365649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.862966</td>\n",
       "      <td>0.728578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.310652</td>\n",
       "      <td>0.180668</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        one       two\n",
       "0  0.880061  0.438162\n",
       "1  0.221173  0.365649\n",
       "2  0.862966  0.728578\n",
       "3  0.310652  0.180668"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(arr, columns=['one', 'two'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        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>student</th>\n",
       "      <th>test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100</td>\n",
       "      <td>34</td>\n",
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       "      <th>1</th>\n",
       "      <td>101</td>\n",
       "      <td>98</td>\n",
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       "      <th>2</th>\n",
       "      <td>102</td>\n",
       "      <td>50</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>103</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>104</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>105</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>106</td>\n",
       "      <td>43</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>107</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>108</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>109</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   student  test\n",
       "0      100    34\n",
       "1      101    98\n",
       "2      102    50\n",
       "3      103    63\n",
       "4      104    50\n",
       "5      105    50\n",
       "6      106    43\n",
       "7      107    99\n",
       "8      108    51\n",
       "9      109    37"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({'student': np.arange(100, 110, 1), 'test': np.random.randint(16, 101, 10)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
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       "      <th>100</th>\n",
       "      <td>48</td>\n",
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       "      <th>101</th>\n",
       "      <td>87</td>\n",
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       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>25</td>\n",
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       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         test\n",
       "student      \n",
       "100        48\n",
       "101        87\n",
       "102        47\n",
       "103        25\n",
       "104        37\n",
       "105        66\n",
       "106        41\n",
       "107        43\n",
       "108        60\n",
       "109        74"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({'student': np.arange(100, 110, 1), 'test': np.random.randint(16, 101, 10)}).set_index('student')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "c     round\n",
       "b    square\n",
       "Name: shape, dtype: object"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series(['round', 'square'], index=['c', 'b'], name='shape')\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>100</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>101</td>\n",
       "      <td>blue</td>\n",
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       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>102</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    id color\n",
       "a  100   red\n",
       "b  101  blue\n",
       "c  102   red"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th>id</th>\n",
       "      <th>color</th>\n",
       "      <th>shape</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>100</td>\n",
       "      <td>red</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>101</td>\n",
       "      <td>blue</td>\n",
       "      <td>square</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>102</td>\n",
       "      <td>red</td>\n",
       "      <td>round</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    id color   shape\n",
       "a  100   red     NaN\n",
       "b  101  blue  square\n",
       "c  102   red   round"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([df, s], axis=1, sort=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Apply function to a Pandas Series or DataFrame\n",
    "# map, applymap, apply"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "URL = 'http://bit.ly/kaggletrain'\n",
    "train = pd.read_csv(URL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Sex_num'] = train.Sex.map({'female': 0, 'male':1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Sex</th>\n",
       "      <th>Sex_num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
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       "      <th>1</th>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Sex  Sex_num\n",
       "0    male        1\n",
       "1  female        0\n",
       "2  female        0\n",
       "3  female        0\n",
       "4    male        1"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.loc[0:4, ['Sex', 'Sex_num']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['name_length'] = train.Name.apply(len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Name</th>\n",
       "      <th>name_length</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>23</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Name  name_length\n",
       "0                            Braund, Mr. Owen Harris           23\n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...           51\n",
       "2                             Heikkinen, Miss. Laina           22\n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)           44\n",
       "4                           Allen, Mr. William Henry           24"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.loc[0:4, ['Name', 'name_length']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "train['Fare_ceil'] = train.Fare.apply(np.ceil)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>Fare</th>\n",
       "      <th>Fare_ceil</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.2500</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>71.2833</td>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.9250</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53.1000</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.0500</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Fare  Fare_ceil\n",
       "0   7.2500        8.0\n",
       "1  71.2833       72.0\n",
       "2   7.9250        8.0\n",
       "3  53.1000       54.0\n",
       "4   8.0500        9.0"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.loc[0:4, ['Fare', 'Fare_ceil']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                           [Braund,  Mr. Owen Harris]\n",
       "1    [Cumings,  Mrs. John Bradley (Florence Briggs ...\n",
       "2                            [Heikkinen,  Miss. Laina]\n",
       "3      [Futrelle,  Mrs. Jacques Heath (Lily May Peel)]\n",
       "4                          [Allen,  Mr. William Henry]\n",
       "Name: Name, dtype: object"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.Name.str.split(',').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_elememt(my_list, position):\n",
    "    return my_list[position]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       Braund\n",
       "1      Cumings\n",
       "2    Heikkinen\n",
       "3     Futrelle\n",
       "4        Allen\n",
       "Name: Name, dtype: object"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.Name.str.split(',').apply(get_elememt, position=0).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       Braund\n",
       "1      Cumings\n",
       "2    Heikkinen\n",
       "3     Futrelle\n",
       "4        Allen\n",
       "Name: Name, dtype: object"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.Name.str.split(',').apply(lambda x: x[0]).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "URL = 'http://bit.ly/drinksbycountry'\n",
    "drinks = pd.read_csv(URL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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>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": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "beer_servings      376\n",
       "spirit_servings    438\n",
       "wine_servings      370\n",
       "dtype: int64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.loc[:, 'beer_servings':'wine_servings'].apply(max, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/abhishek/.local/lib/python3.5/site-packages/numpy/core/fromnumeric.py:52: FutureWarning: 'argmax' is deprecated, use 'idxmax' instead. The behavior of 'argmax'\n",
      "will be corrected to return the positional maximum in the future.\n",
      "Use 'series.values.argmax' to get the position of the maximum now.\n",
      "  return getattr(obj, method)(*args, **kwds)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0      beer_servings\n",
       "1    spirit_servings\n",
       "2      beer_servings\n",
       "3      wine_servings\n",
       "4      beer_servings\n",
       "dtype: object"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.loc[:, 'beer_servings':'wine_servings'].apply(np.argmax, axis=1).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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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       "        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>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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>89.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>245.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>312.0</td>\n",
       "      <td>12.4</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>217.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>45.0</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              0.0            0.0   \n",
       "1      Albania           89.0            132.0           54.0   \n",
       "2      Algeria           25.0              0.0           14.0   \n",
       "3      Andorra          245.0            138.0          312.0   \n",
       "4       Angola          217.0             57.0           45.0   \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": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drinks.loc[:, 'beer_servings':'wine_servings'] = drinks.loc[:, 'beer_servings':'wine_servings'].applymap(float)\n",
    "drinks.head()"
   ]
  }
 ],
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