Pandas_Exercise.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <center>Introduction to Pandas</center>\n",
    "\n",
    "![](https://pandas.pydata.org/_static/pandas_logo.png)\n",
    "\n",
    "\n",
    "## Installation\n",
    "\n",
    "Simply,\n",
    "```\n",
    "pip install pandas\n",
    "```\n",
    "\n",
    "\n",
    "## Reading data from a CSV file\n",
    "\n",
    "You can read data from a CSV file using the ``read_csv`` function. By default, it assumes that the fields are comma-separated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import pandas\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">The `imdb.csv` dataset contains Highest Rated IMDb \"Top 1000\" Titles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "<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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>974</th>\n",
       "      <td>7.4</td>\n",
       "      <td>Tootsie</td>\n",
       "      <td>PG</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>116</td>\n",
       "      <td>[u'Dustin Hoffman', u'Jessica Lange', u'Teri G...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>975</th>\n",
       "      <td>7.4</td>\n",
       "      <td>Back to the Future Part III</td>\n",
       "      <td>PG</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>118</td>\n",
       "      <td>[u'Michael J. Fox', u'Christopher Lloyd', u'Ma...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>976</th>\n",
       "      <td>7.4</td>\n",
       "      <td>Master and Commander: The Far Side of the World</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>Action</td>\n",
       "      <td>138</td>\n",
       "      <td>[u'Russell Crowe', u'Paul Bettany', u'Billy Bo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>977</th>\n",
       "      <td>7.4</td>\n",
       "      <td>Poltergeist</td>\n",
       "      <td>PG</td>\n",
       "      <td>Horror</td>\n",
       "      <td>114</td>\n",
       "      <td>[u'JoBeth Williams', u\"Heather O'Rourke\", u'Cr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>978</th>\n",
       "      <td>7.4</td>\n",
       "      <td>Wall Street</td>\n",
       "      <td>R</td>\n",
       "      <td>Crime</td>\n",
       "      <td>126</td>\n",
       "      <td>[u'Charlie Sheen', u'Michael Douglas', u'Tamar...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>979 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     star_rating                                            title  \\\n",
       "0            9.3                         The Shawshank Redemption   \n",
       "1            9.2                                    The Godfather   \n",
       "2            9.1                           The Godfather: Part II   \n",
       "3            9.0                                  The Dark Knight   \n",
       "4            8.9                                     Pulp Fiction   \n",
       "..           ...                                              ...   \n",
       "974          7.4                                          Tootsie   \n",
       "975          7.4                      Back to the Future Part III   \n",
       "976          7.4  Master and Commander: The Far Side of the World   \n",
       "977          7.4                                      Poltergeist   \n",
       "978          7.4                                      Wall Street   \n",
       "\n",
       "    content_rating      genre  duration  \\\n",
       "0                R      Crime       142   \n",
       "1                R      Crime       175   \n",
       "2                R      Crime       200   \n",
       "3            PG-13     Action       152   \n",
       "4                R      Crime       154   \n",
       "..             ...        ...       ...   \n",
       "974             PG     Comedy       116   \n",
       "975             PG  Adventure       118   \n",
       "976          PG-13     Action       138   \n",
       "977             PG     Horror       114   \n",
       "978              R      Crime       126   \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....  \n",
       "..                                                 ...  \n",
       "974  [u'Dustin Hoffman', u'Jessica Lange', u'Teri G...  \n",
       "975  [u'Michael J. Fox', u'Christopher Lloyd', u'Ma...  \n",
       "976  [u'Russell Crowe', u'Paul Bettany', u'Billy Bo...  \n",
       "977  [u'JoBeth Williams', u\"Heather O'Rourke\", u'Cr...  \n",
       "978  [u'Charlie Sheen', u'Michael Douglas', u'Tamar...  \n",
       "\n",
       "[979 rows x 6 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load imdb dataset as pandas dataframe\n",
    "imdb_df=pd.read_csv('imdb_1000.csv')\n",
    "imdb_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show first 5 rows of imdb_df\n",
    "imdb_df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">The `bikes.csv` dataset contains information about the number of bicycles that used certain bicycle lanes in Montreal in the year 2012."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Unnamed: 1</th>\n",
       "      <th>Rachel / Papineau</th>\n",
       "      <th>Berri1</th>\n",
       "      <th>Maisonneuve_2</th>\n",
       "      <th>Maisonneuve_1</th>\n",
       "      <th>Brébeuf</th>\n",
       "      <th>Parc</th>\n",
       "      <th>PierDup</th>\n",
       "      <th>CSC (Côte Sainte-Catherine)</th>\n",
       "      <th>Pont_Jacques_Cartier</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>00:00</td>\n",
       "      <td>16</td>\n",
       "      <td>35</td>\n",
       "      <td>51</td>\n",
       "      <td>38</td>\n",
       "      <td>5.0</td>\n",
       "      <td>26</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012-02-01</td>\n",
       "      <td>00:00</td>\n",
       "      <td>43</td>\n",
       "      <td>83</td>\n",
       "      <td>153</td>\n",
       "      <td>68</td>\n",
       "      <td>11.0</td>\n",
       "      <td>53</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012-03-01</td>\n",
       "      <td>00:00</td>\n",
       "      <td>58</td>\n",
       "      <td>135</td>\n",
       "      <td>248</td>\n",
       "      <td>104</td>\n",
       "      <td>2.0</td>\n",
       "      <td>89</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-04-01</td>\n",
       "      <td>00:00</td>\n",
       "      <td>61</td>\n",
       "      <td>144</td>\n",
       "      <td>318</td>\n",
       "      <td>116</td>\n",
       "      <td>2.0</td>\n",
       "      <td>111</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012-05-01</td>\n",
       "      <td>00:00</td>\n",
       "      <td>95</td>\n",
       "      <td>197</td>\n",
       "      <td>330</td>\n",
       "      <td>124</td>\n",
       "      <td>6.0</td>\n",
       "      <td>97</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>361</th>\n",
       "      <td>2012-12-27</td>\n",
       "      <td>00:00</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>362</th>\n",
       "      <td>2012-12-28</td>\n",
       "      <td>00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>3</td>\n",
       "      <td>38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>2012-12-29</td>\n",
       "      <td>00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>8</td>\n",
       "      <td>42</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>2012-12-30</td>\n",
       "      <td>00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>2012-12-31</td>\n",
       "      <td>00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>366 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date Unnamed: 1  Rachel / Papineau  Berri1  Maisonneuve_2  \\\n",
       "0   2012-01-01      00:00                 16      35             51   \n",
       "1   2012-02-01      00:00                 43      83            153   \n",
       "2   2012-03-01      00:00                 58     135            248   \n",
       "3   2012-04-01      00:00                 61     144            318   \n",
       "4   2012-05-01      00:00                 95     197            330   \n",
       "..         ...        ...                ...     ...            ...   \n",
       "361 2012-12-27      00:00                  8      12              7   \n",
       "362 2012-12-28      00:00                  0      35              3   \n",
       "363 2012-12-29      00:00                  0      27              8   \n",
       "364 2012-12-30      00:00                  0       5              1   \n",
       "365 2012-12-31      00:00                  0       4              3   \n",
       "\n",
       "     Maisonneuve_1  Brébeuf  Parc  PierDup  CSC (Côte Sainte-Catherine)  \\\n",
       "0               38      5.0    26       10                            0   \n",
       "1               68     11.0    53        6                            1   \n",
       "2              104      2.0    89        3                            2   \n",
       "3              116      2.0   111        8                            1   \n",
       "4              124      6.0    97       13                            2   \n",
       "..             ...      ...   ...      ...                          ...   \n",
       "361              4      NaN     1        2                            0   \n",
       "362             38      NaN     0        0                            0   \n",
       "363             42      NaN     9        0                            0   \n",
       "364              5      NaN     6        0                            0   \n",
       "365              8      NaN    12        0                            0   \n",
       "\n",
       "     Pont_Jacques_Cartier  \n",
       "0                    27.0  \n",
       "1                    21.0  \n",
       "2                    15.0  \n",
       "3                    19.0  \n",
       "4                    16.0  \n",
       "..                    ...  \n",
       "361                   NaN  \n",
       "362                   NaN  \n",
       "363                   NaN  \n",
       "364                   NaN  \n",
       "365                   NaN  \n",
       "\n",
       "[366 rows x 11 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load bikes dataset as pandas dataframe\n",
    "bikes_df=pd.DataFrame(pd.read_csv('bikes.csv',sep=';',parse_dates=['Date']))\n",
    "bikes_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Unnamed: 1</th>\n",
       "      <th>Rachel / Papineau</th>\n",
       "      <th>Berri1</th>\n",
       "      <th>Maisonneuve_2</th>\n",
       "      <th>Maisonneuve_1</th>\n",
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       "      <th>Parc</th>\n",
       "      <th>PierDup</th>\n",
       "      <th>CSC (Côte Sainte-Catherine)</th>\n",
       "      <th>Pont_Jacques_Cartier</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>00:00</td>\n",
       "      <td>16</td>\n",
       "      <td>35</td>\n",
       "      <td>51</td>\n",
       "      <td>38</td>\n",
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       "      <td>0</td>\n",
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       "      <td>2012-02-01</td>\n",
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       "      <td>43</td>\n",
       "      <td>83</td>\n",
       "      <td>153</td>\n",
       "      <td>68</td>\n",
       "      <td>11.0</td>\n",
       "      <td>53</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012-03-01</td>\n",
       "      <td>00:00</td>\n",
       "      <td>58</td>\n",
       "      <td>135</td>\n",
       "      <td>248</td>\n",
       "      <td>104</td>\n",
       "      <td>2.0</td>\n",
       "      <td>89</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date Unnamed: 1  Rachel / Papineau  Berri1  Maisonneuve_2  \\\n",
       "0 2012-01-01      00:00                 16      35             51   \n",
       "1 2012-02-01      00:00                 43      83            153   \n",
       "2 2012-03-01      00:00                 58     135            248   \n",
       "\n",
       "   Maisonneuve_1  Brébeuf  Parc  PierDup  CSC (Côte Sainte-Catherine)  \\\n",
       "0             38      5.0    26       10                            0   \n",
       "1             68     11.0    53        6                            1   \n",
       "2            104      2.0    89        3                            2   \n",
       "\n",
       "   Pont_Jacques_Cartier  \n",
       "0                  27.0  \n",
       "1                  21.0  \n",
       "2                  15.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show first 3 rows of bikes_df\n",
    "bikes_df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selecting columns\n",
    "\n",
    "When you read a CSV, you get a kind of object called a DataFrame, which is made up of rows and columns. You get columns out of a DataFrame the same way you get elements out of a dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['star_rating', 'title', 'content_rating', 'genre', 'duration',\n",
       "       'actors_list'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# list columns of imdb_df\n",
    "imdb_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# what are the datatypes of values in columns\n",
    "imdb_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The Godfather</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>The Godfather: Part II</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>The Dark Knight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pulp Fiction</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      title\n",
       "0  The Shawshank Redemption\n",
       "1             The Godfather\n",
       "2    The Godfather: Part II\n",
       "3           The Dark Knight\n",
       "4              Pulp Fiction"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# list first 5 movie titles\n",
    "imdb_df[['title']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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",
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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>title</th>\n",
       "      <th>genre</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "      <td>Crime</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The Godfather</td>\n",
       "      <td>Crime</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>The Godfather: Part II</td>\n",
       "      <td>Crime</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>The Dark Knight</td>\n",
       "      <td>Action</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pulp Fiction</td>\n",
       "      <td>Crime</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>974</th>\n",
       "      <td>Tootsie</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>975</th>\n",
       "      <td>Back to the Future Part III</td>\n",
       "      <td>Adventure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>976</th>\n",
       "      <td>Master and Commander: The Far Side of the World</td>\n",
       "      <td>Action</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>977</th>\n",
       "      <td>Poltergeist</td>\n",
       "      <td>Horror</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>978</th>\n",
       "      <td>Wall Street</td>\n",
       "      <td>Crime</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>979 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               title      genre\n",
       "0                           The Shawshank Redemption      Crime\n",
       "1                                      The Godfather      Crime\n",
       "2                             The Godfather: Part II      Crime\n",
       "3                                    The Dark Knight     Action\n",
       "4                                       Pulp Fiction      Crime\n",
       "..                                               ...        ...\n",
       "974                                          Tootsie     Comedy\n",
       "975                      Back to the Future Part III  Adventure\n",
       "976  Master and Commander: The Far Side of the World     Action\n",
       "977                                      Poltergeist     Horror\n",
       "978                                      Wall Street      Crime\n",
       "\n",
       "[979 rows x 2 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show only movie title and genre\n",
    "imdb_df[['title','genre']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Understanding columns\n",
    "\n",
    "On the inside, the type of a column is ``pd.Series`` and pandas Series are internally numpy arrays. If you add ``.values`` to the end of any Series, you'll get its internal **numpy array**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show the type of duration column\n",
    "type(imdb_df['duration'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       [126]], dtype=int64)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show duration values of movies as numpy arrays\n",
    "imdb_df[['duration']].to_numpy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Applying functions to columns\n",
    "\n",
    "Use `.apply` function to apply any function to each element of a column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                             THE SHAWSHANK REDEMPTION\n",
       "1                                        THE GODFATHER\n",
       "2                               THE GODFATHER: PART II\n",
       "3                                      THE DARK KNIGHT\n",
       "4                                         PULP FICTION\n",
       "                            ...                       \n",
       "974                                            TOOTSIE\n",
       "975                        BACK TO THE FUTURE PART III\n",
       "976    MASTER AND COMMANDER: THE FAR SIDE OF THE WORLD\n",
       "977                                        POLTERGEIST\n",
       "978                                        WALL STREET\n",
       "Name: title, Length: 979, dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert all the movie titles to uppercase\n",
    "imdb_df['title'].str.upper() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting a column\n",
    "\n",
    "Use ``.plot()`` function!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1d5d38c5e48>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5fmPXYbz0fV+xXnfns+PYdWACLoo8fSZ7PdClmaDRSrG6KhdIniPMSp2vSMzP7qkj0tqJAiqwd7zSXw7wpO+x6EDGFKwpVdzE6l8TpyJ9JlU3hVGIrGCLZ+kmqbBsjoAMW0ddMTQTgU9844ncOZnE2EhSS+nHYYDVAxHGVCC3qsh0sBJZSp/PcVXVPnczC8nFa95/By75v/+Ve39jBf4/nyP/5d79G1VR1RV/eScOT9RzpG/aO08ckqS/fcOQXujqrQTVKNQLpO+0ubThSd9j0cEM5LLSZwuCuW9Q2TtMnPnsnczeMfvrmIQVEiFWKZvTHaphTWXfaKXWIhAFUumPz7S0KpbnGDqevvw7pJQ3p3OWWUomvq+ayHEwdbRI6TvZOy7pA5Kw+XG+IjHtoIPjMnPnxJEBW+nHhr3j8/SXNDzpeyw6mKTPSp/VaWaRKLWsiNNNoRTIFK9l7xiERZTZO9MO8bpEbHr69VZi5elHYYDV1QjjM01F+iqQW40KG66tUkVO04280jcJGsgKpG556BmkRkxibLrI0+fsHfm3iPRbiWhr7zTU8atRmPP0Y+/pLwt40vdYMAghrDbIDDKKs1jpNxI7YLvKUcuu/ZGmotjTN0g/DLLiLFfpu+MMzUWl0XLtHcLqgQgTMy2V6SKPWXOUfmbvqHNv8jCYbB83FZSrYA9N1LX3DxTbOzp7R/3lDB+T9JtJmg/kGqTPqr8aBfp86yojKZ5lywqPxQVP+h4Lhk/d9SQu+8AduGv3YWu7Ls4SQnvirC7NYCiQtRkomo3L29jeKST9EqXvFkqZh3ftnTCQpD8+00LDsHeGKiEaSWpcpUBtd+ydVoJf+NHn4JIzT8i1fubzbSXCqu6drb0jZwqUK30zRTbz9G2l79swLG140vdYMDywR3aL5IwRBgdyW6nQ9khDqVzuqeMqfTe2KLN35O2K0T7Z9PTJKM5y4RZKuYFcc5GJg0B5+ra9U3PI3V2wphuJTiOtxSFqlTCXM885/M0kta4aigq5+LluIHdYzQHm4/ACqj19i/SzCmg7eyfUi6e3d5Y2/LhEjwUD98fhVsoMnbKZCq3kdV+ZnNK3CZWRCmF4+qS3uYFcIkIYUC7l0+14aT5ebzqBXGXvTDYSEJFW+hWnmImfw5aNOXRlIA4RB5RLh+Q++M1UWHGGIuJlL7/lePqmMm8mWf+hTOlnj7fSFGFACEPKlL5qw+BTNpcHPOl7LBiYp3Nam4uz2gZyWS231LHygVzeFhsjEV17h/+6pL9/dAatJNUVu+bh64lL+oG+8piotzLSd9oW8DFMa0qTfhRY5/Hk4Ul87t69+nybrdSyd4oGueuF0VD6q6sRzt22xnoed+EciIo8fYEoIARkNLZT2TuRb8OwLODtHY8FA5NK4HwLTaU/6bQGZtKvRoHVurgoZbOb7B3AbubG+I3PPoBfv+kBfd8N5Nr2DukUUj4383WbLTsescrIPOK2DwNxiCgMNHH/949/Gx/6yi5t77TSVMcdZKygSOnLbVmGTxMjgzHO37YWf/9zL9aPccolB5xd66oSBgjIKM5qyjx9/px8nv7Shid9jwUDc41r7wSGp+8qfebaMCDUjNbFxRW58rbl6VtKX/5lbxsAfuK8bK7PzQ9knb9Th/Rde6dWyY7BxVmxQ5JpTuk79k5I2prZc8Rux9BIhCb94VqcU/p7jkzpnjmm0h9Rg0/4Nc1BMkVtGFqJQBSSIn25zQ3k9jplzGNxwds7HgsGJlJyPX1jZuxEozh7JyC7+ClfkSvy2TsFnj4AHXh9zoYh/OkbX6h76m9Rg8ABuzir3kqsRSAKA02gQLbIuB54pvSzwrKM9ANEQVBKqC0jkDs8EOdm3l78Z1/N9lULx/hMS8cPzHNpl6ffSqWlRSTPt6WKuapRiFDZPs0khRBCdzj1WFrwSt9jwSC00re3c57+kcmG3sdNeySSlsqkTtl0jo2C3jtJWmjvcB+fOAz0AgEAJ63NWhfn8vQNoR0HZJEf2yYVNRxFW1O6EjZEQNLTr2urRbY5aKVp4VQsM3tnuBa1zaBhEp9qtrTtxFlKZiCX37c5aavRkm2rCQQhsqsUfk+xamn9sTsfxxm/d0tutKLH4ocnfY8Fg1b6KFb6hycbetv3nxnH9nf+h57nGhBZSt+1d8w2DObkrKJALts7bvO1zSMZ6Zv1SI2CQK6p9HOevrNghWrBsuydKEQUEFqJKBzk0jTsndUDcdsCKV4QphuJtaAB0pppOYHcvNLPArn1ZlasBcirmGZL6KuhZ8dmcNbv34Lf/bfvlZ6Px+KCJ32PBYNp1ZhgBX7ImdUKAPftOaafY44jzI1LFGbDtSxgadoibO8w+cVOQNdcA/j4UUD5QG5ImlyBzC7Ke/psZ2XVurwIVaIAURiglQo9xMSEacsMVkJL6btXBkzi042szbM5AD6Xp+96+gEhCKSnX2/ZVlAUyrRS871MNRL8w11P5c7ZY3HCk77HgsG0akzwPR5huMmoKL37cTlsLSBCrRJhSnfZdI+d9/Tf9/++jycMQuWXZcJ2s3iahgLmYw3EIeq5hmuOp59T+lk8goitKblgMeHGISFWq19x2+TsyqUahVbapNtbnwO1081Ez7XVmTetVAeBi3vvpIgNT58XSfPqRXr66jPMJ9yWYnSq2VVzOY/5hSd9jwVDmafPKZyHJqS9Y7YR+LYifSJgMA51nn6u4ZrIyCwqSMkEMntHK3PlwX/lN34Ug5XQypBJDD/e7b1Tq4S2p68Ikv82jXRTvrqoxZL0zcHvoVLjU04foGoU6OApIBcVk/SPTTWs/bnYaqpQ6WdN26oFFbktRfoBSU9fxxzUZxSpWgIurHMDyibqrcS6Cjnn3V/GT/7NN0v39zg+8KTvsWDQefo5pS/vHy5Q+tyyITDUMlBi7zievgvX02dl/pyNq3DqhiFLAfPxByuhNUTlty8/A2972XaH9G17x/T0+b3K/vtNfTURh4Fu8zzZsJX7qmqEZppdXVQjO8vnqDM6kadk1VupvorhxnJWcVZhRa7Qnn5a4OmHoSR9/mjKWlJP1Fs4/XdvwQdv32Vtf2T/WOH+HscPXZE+ET1BRN8jovuJ6B61bR0R3UZEO9XftWo7EdEHiWgXET1IROcbx7lG7b+TiK6Zn7fksVRg+sIm+P6hiQaigLBuqJJ7rrR3zJRNuf39bzpH3ocwfPggdzUhXydT3YDt6UcBWb45k9xAHFh5+hfv2IAta2q2p68XEXvoCNs7ALB9/RB2H5zUVwFxmDV/49oExlA1QrNl2Dux9P550TzqKP2WEfTN3ls+e6eoIrfRypS+ae+wZRUFAZpGB1O3Gykg21Lzgv3Jbz2Re9xjYdGL0n+lEOJcIQQPSH8ngNuFEDsA3K7uA8AVAHaof9cC+DAgFwkA1wN4CYALAVzPC4XHykTm6dvb+W4jSbFmMLbSKBkcyGVVzCS8qhrrY5uBYla6rzpjE04/QQ415wuAAYcYAWkJmUpfD1lXnn7qXKUMGOeYz9NXnn4q9P47TliFQxN1HFTkGIeBtqFcIh2shGil2WtWnePm7Z2M9NnTj4yrjlaSLTQBudk7AnEoexKlRsomvxfZKiLz9N1upABwwR/9J370z78GIN+91GPhMRd750oAN6rbNwK4ytj+SSFxF4A1RHQigMsA3CaEOCKEOArgNgCXz+H1PZY4mMRyaenGKrBmsJLLqpG7yEAuz5o1s2vkMbM2DEFAOk6wbd0gBlUbBLZ33LRGfsz0zc1Arjl9io9hxg2yPP28vcP771ALz6PK7ojCbEj7VIG902hlnj5X/PJxj0669k6W0+8uaK1UoGkQexQEOU8/CgK98JqBZv58ZVBZbi/q628uWp70Fx+6JX0B4MtEdC8RXau2nSCE2A8A6u8mtf0kAHuM5+5V28q2WyCia4noHiK65+DBg92/E48lC7ewyhT+a0uUfhiQnpM71UxyVw2pMAPF2bCUahzoYCo5Kj0ymgDJlgiGvaP4nxcI9rqLYsS5lM1W3t55niL9R/ZJ0q+EgSb9yUbe3mmlQheEVY0GckAnpa8qcgNb6fN7jUJysneEtnd4f/k+ydpfk37BBC8TBXVmHguMbtswvFwIsY+INgG4jYi+32bfoqiZaLPd3iDERwF8FAAuuOAC/5VZxmDicIOwpt0zPBAXKv2AjB429ZayTsygcKb0QyLt6VejEAF31+Q2DEoNC+PrGAYBWqkx31Zk9g4AnSrqBqHla9ievtk3iPffMjKAgTjAzgMTAOSVAr/PqXpBINfIjedFkK2XY9P5QC4HuLknUKBaKMhxiULHD0Kl3PVzk1TbPua58yIRqisDXgRZ6Rc1rSuDECKXputx/NDV/5QQYp/6ewDA5yE9+WeVbQP194DafS+Ak42nbwWwr812jxUKJo5835zs9kAcFrYlCIisdsapEAgDspS+GSjmI1SjLKir7Z2CfPU4ICtl07R3AGCmUU76bp4+k3OiFiZ5Ttn5y32zQO5EPbHiC1yMxVcKurpWfYBuBk0rEXpbLc5eI1I59q001ceIArKqe2X2TqAXRu7mGRn2jvk58TAXfs9F/1eA3bvIWz4Li46kT0RDRLSabwO4FMBDAG4GwBk41wD4grp9M4C3qiyeiwCMKvvnVgCXEtFaFcC9VG3zWKEo8/RN4qhGgc4VN0GG0p+sS3uHiHS6pzBI31SzVdW3no8BZERu2jmu7WH2zQEy4uJjmTBbFgB2a2Vzf1Mdx0FgefpsEQHZ+2wkKQKiXMtm9/ORbZjl1YKZVVRRrZubLaFfK3Q8fZm9ky2ebE1l+5MVVOZALpN+0f8VYNdRdLKEPOYX3dg7JwD4vLociwD8kxDiFiK6G8BNRPR2AE8BuFrt/yUArwWwC8AUgJ8DACHEESJ6D4C71X7vFkIc6ds78VhyYB7I98LPblfjoLCKMyDCUDXLdhGC7R0+RpbiGBBZOe6szrNALjdks6ts7UCu/FtzSD8oJP1QPxapgPDb//5u3LnzEEYGs9GFTJShansQ6Tz9RD6mepnxFUG9mSIgwzZSCt0tkEoMe2fQIH1u6NZ0lL75vltpijho4+k7bSjY3uFzKqom5nNijM00sdnoYOpxfNGR9IUQuwGcU7D9MIBXF2wXAK4rOdYNAG7o/TQ9liPYQ3c9fZMgqlFYqB5Ne2ey3lLWCenIkan0gyBTmhVD6bu9d0w1GjmBXJ2yqRYItk/CAnvHtGa4bcFduw8rpZ7tZ1a58mvy+6kawWtW+vWWo/STVG830UpNe8cg/SDQefqx6em7k7MKPH29SIQBphpZ4JxVO38+Rdk8M83Eeg1zYWgmKV73wTvxrivOxCvP2JR7rkf/4StyPRYMqVb6wtnu2DvGxCiGGcidbLRkOiSRVqgCQlsyttIP9T4cTOR0S9N3dgOcqRPI/ftvPiGPXfALMoOUcUhWiqcZA6g4NpAZyDUzllitNxTp8yLB58eZRAwh5MIB2PZOrJqlcc98+d7Jqsjl3jt8nmzvmEo/MQrD2NPn/UYLrJux6aZ1NTFqkP6hiTp+8OwE3vmvD+ae5zE/8KTvsWAo9/Sz25UowOteuAUA8GOnZ0qQCgK5htC32jCYRGvbO3KbJlErkBtYAU4+161rB61zLVL6JrhPTtKG9M1MGkDaO6bS58Wg3kqkvRO5Sj/JxRbYazeVfhwGsg1DIhyP3lb6nMMPZEFod3/+P+Jmb3pEY4HSH51uWp+luQ9/Hq7Fl51PWtrqwWN28KTvsWDgH7pAe6V/4anr8MSfvA7P3bRKb7eUviL9QHnjgN1wLXSIlsmeiZKfkzr2TlFF7vNPGsHvv/4svb0okGsiDmXbBq30jV+cq/DZcplq2Eqft2t7R+fcZ4FcnpDFGK/nST8KCc1U6KZqQD4bR0/OUvc5WBxZ+xuevlLtvAAVefqj003b0zeuBvi/piyf/7p/+i7O/P1bih/0mBX8uESPBQNbBCwC3/LRu/DTL95qeetmFotJ3kFAGFSENlFPdAdL3sNsrUwG0RbZO3zclhXItXvvmLN5zSCkGci99R2v0A3hGHEYYNqwX4qUfkbAGZmb7ztT+imCgLLqWkW0M80EwwMxjhmN18ZnZFzAPL84CPT4w4BzmtYAACAASURBVCwFM8veEULm8Et7B+pcbHuHlT5zOD+Xm7wVtWWYbNieflG/nrJUz1sffrZwu8fs4UnfY8GQZe/IG9/afRjf2n0Yp24Y0vtUjaHlZv1PQJJwhyqhUvoqZVN7+kbKpmnvxEGuOIsJzZ2GVdRa2bzCcI99+ubVOH3zaus9xiFZ9kQx6duBXEBeBdz5W6/E3qPTOKImiDWUvcOqu2EEcnkAOuPYVMM6Tz5+MxFoJqm+WjCvaJiYY+OKqenYO24bBhONJC20d9JUWJ+t2WaCF/yi43nMD7y947FgMD19U+m59g7DDJAyeQ5VI0n6qvApK84SpZ6+S/YvOmUtXv/CE/G+N75A7xc5XjdffcisIePqowt7x0w5NXfPPP18G4hqHODkdYN46XPX68WA7R22hVqGvTM8YJP+Y8+MW4snn0uTPf2C7J2WLsQK8p6+XpgCFcjNv9dmkhbm4KdCWOmvk/V8pXOZp+/Rf3jS91gwmJ6++aM3Sd/0tk2CZSJfVY2silxN8Ianb/noRkUu71qJAvzVz5yP527MYgZuyqbu/R+QpaCLKnJNVKPAUrbWAuR4+q7Sd2/Xm6lskua0d6g3k5ynv/vQZOFVRyvJe/p8RZN11MxSNrPiLLuCt3h4uyisqUjaKv3itF2P+YMnfY8FgzBUnpUpY2Qglnr6rtIXcpvp6RcNaSnqvVOEKAis1EQWqiERhiqGvdOF0u/s6cttsaX07QAswFk6ZnGWofQdewcAznBIP1IZSc2SPP2WkZPvFmeZrSuSVOQmlfG+RQPbUyGsBdRsKFfaadVj3uBJ32PBYDZcK5pSBdj2jhmUZO4cqoayDUMqUzZ1nr6wido8nmvvFMFN4zQLvVZZSr/9e3TtHXOdyQVyS5Q+q+x8GwapuIvsHQB6boA+TkhoJEJm6ASZpeR6+nZxlp3CybZXkR0jh8vktyepHS+Ztkif/7Zn/bJAr0fv8KTvsWDIfvBAs6AQCrCVvkmwZfaO7emr7B2HaN3snSJEjm9uDk0x7Z1O3SLjKLBIzlxoeEEzg6T6MSOAXVGze2UbBsrOLU21JePaOwBy9k5FBadbJZ5+o5Upfe3pt1LrnMMgkIVWRaSfpFahFyOn9I3snaRLe8dzfv/gSd9jwaB/6Dmln+1T6umrzauqctastncySz8r2HKLs3T6Yfm5ZUo/65AJyKuGov7+ZaiEVOrp64pcJ6Arn5dX+vVWqrpscpBV6BYMRaS/ZtAeMxkpTz/Xe0e9R529E2axkYbRe5+P0TTy9IFsUZVFaPnPQF7JZfGCqQJ7p1Mgt8hO8pgdPOl7LBwMpW96wW6XTYZJmHz7uRtXYe/RaRydaqiKXNLH4Nx9E9UozIqz2ip9O3e/KBOoG0hPvyR7J5RXMVk7iGKlb1bkhkFWnNVKUm0dmTEAwP7csvcUoJkqpW/m3Se2p29OzmomqXVeug2DcVwuAJNzfPPknKTZXN7hgdga/J55+sXPc/fzmDs86XssGMo8favhWlyi9NXNC7avAwDc/cQR2XtH7c5tGFySNlVsV/YO2w8FmUDdIFbtjBlUoPSZ8KxAbmT3zAGy3jtmGwbuu+OS/LZ1drsIeXxSKZtZ753YmAXM5xmHgX6fzSS1bCf29M3/I90byOgxZMLM3hmuxdbg98zeyT3NWhw85/cPnvQ9Fgymp283N8v2KfP0mTzPPXkNooAwo/xuVvqpUIPInW84GU3ZugvkZqMOOz2nCO7Ur6I8fUaR328eQxagZefWNOydahTg8rM341WqU2Uh6YcBWonA2HQLw7VIv2ZG+mbKpnqNlsh5+i4B8/9RM0mtpnXZopYtnsMDUSGZFyl50/svevy8d38Z7/KN2nqGJ32POWGmmeAzdz81q+wK89K+q+ydAnunVglxmurJI7N35OMy918U2jFmS4EyuJ0szeKsXtCO2Pkxfr9mS2bzfZv2ittamXvpV6MQH/nZF+EVOzYAAE4uIP0oDHBsqolGkmL9UEW/z6yVQqr3ozaevoua0QU0SYW2e3j2cCIcpd9IjFTY8pTNooCviaNTTfzzd/bktnu0hyd9jznhz299DL/9L9/DV75/oPPODow4rlMIle1TXpyV7cPZNGYgN1Upm0W+vTs5qwja03dIqWfSd0jSCirrls75x8z3bQZ1QyJVhCYXJFb6A8oG+4nzt+Kqc7fgHZfsyJ1LLQ51fGHdUFUez1D6u9S83jW12CrOMom+aKHk124qe4ftHl4MklQYSj9Gkmbn3S5AO1HPp3YyfArn7OFJ32NOeProNIAs3a8XiBJPv2GkgHRS+kBGijJl0w7k8m4/ctoGvT9v61ScBWTBTbP3Ti/oxt4psi52bMrSLU2i5VOOVVA28/QlwY7UYnzgzeflMncAYPNIVd/WSj/MKmw//vXHccbm1Xjh1hGrOCt0PH0XOpCrhrfzSEn+a2bvsK305GHZmK4deZtKn/d7cO8xXPXX38DRqeIJXR6d4UnfY07Q9kLc+1fJ8vTNQR4tk/QNT99K2cyr4oDcfvqZH/3J/3Ehdr33CgD5RmtFcIuzdMpmr55+5JJ+QSC34HkXnrpO3y5qPxGHAZot0dPnf8Jw1h10nSJ9zt45OC6HmVx9wckq7iH3M5uzuefCqFV4hq+wlD5PJEsdpQ8Al33gDhyZbBSmeDLMjp38Xfnuk0dx/55j+P7+sY7v16MYvsumx5yQBRLDDnvmUZa9Y8L0ue02DNk+TJ52l03b0w8CAiciZvZO98VZwsn5/+Y7X4WD4/WO79FV+kWD0V21+8KtI05NQr4+IVJTsMxAbiecOFLTt5n0hwdijE43cVh18ty0Wl4NmMVZg0bbiaiguIG9+2ZLBnLZ1mHbJxGwPH3GVKNV+v8OFHv6vBDsVVeYHr2ja3lGRCER3UdEX1T3TyWibxPRTiL6DBFV1Paqur9LPb7dOMa71PbHiOiyfr8Zj+OPujNOrxeYv/eiVD/AJma7tXJeMbPXDWRtGIoGl2d99MvPTTc1SzPv2XzNLWtqOOfkNeUH4HNr5+nHWXYL477few0++z9faj3HXOz4NvfB13n6XSy6JxpzANavkqS/ff0QWqnAo0o5MylnxVnCWngL7R0jZTNJhV6AtL1j5OmbLSzc7qouzLGKvB/34t97VNpDPYZYPNCbvfOrAB417v8pgPcLIXYAOArg7Wr72wEcFUKcBuD9aj8Q0VkA3gzgbACXA/gbIupdHnosKrC9kLZRbGXIhqiUK30TJmGaP3YOiAZGcVaqCKVoLerF3jHzyDuNRixCW08/zHv6a4cqOQK3PX22d2QlbS9Kf9Nw5umzet+2Xmb5PLh3FAB0X36zOMtO2Wzv6SeptNQqUaBJPxFC23fmIlzWuI1xeDK7kuKvx5ij9AdjTyG9oivSJ6KtAF4H4GPqPgF4FYDPqV1uBHCVun2lug/1+KvV/lcC+LQQoi6EeBzALgAX9uNNeCwcOJA4mzJ5fobsvdM5EFzUZRNw7R11bGUZFadsQu9fhmyKlcrTT8WsVGWe9A3VHLIV1R5FWUvsxWvS78LTL7oa2L5e9tz/3tM26dvFWWZLiKLsHSZ9SeJREKAaBtreMXvvXHLmJl1LYM48KMLhiYa+zQsjD2Jn0q9VvEPdK7pV+h8A8FsA+Je5HsAxIQSbbnsBnKRunwRgDwCox0fV/np7wXM0iOhaIrqHiO45ePBgD2/FYyHApFNmz7RDN56+iaKAJmDYOxbpF1fkAoa9007ph7bSZwXbK9oFcvk8Or11y94JskBuKxVoqs+/UuC1d4NNq6sYiAM8vE+S/rDq4cPn5rZhKPoM2P7hgG2glH7NsHf4c4zDAFedJ3/2qRBtrxAPGaTvevpPH1NKv+KVfq/o+E0hotcDOCCEuNfcXLCr6PBYu+dkG4T4qBDiAiHEBRs3bux0eh4LjLnYO1lhjsgtGm+5cBu+9CsXW9tM/i6ySYLAaK0M2Qu/qG2CLs5qq/RV9k4yN3un2sbe0Tc7XCUFBfZOqIaZmAVV3eADbzoX73/TOdaxT1k3hJkmp1Tann4zEU7KZv51KkYRVpoKhAS84dwteKVS9EmafY5xkLW2dlsuuzDtHf6IWOl70p89urk2ejmANxDRawEMABiGVP5riChSan4rgH1q/70ATgawl4giACMAjhjbGeZzPJYomCxmo/Q16QOyXa+BrWtrOGvLsLWtyNsGzJRNpziroOEakJF3Ow5nW8bspz8reyeyn1RUa9DNR8dFVPwR8Kxa7pdTZLsUgVW2iW3rB/HYs+MYqoT6fZuHs3rvFES/+TncYycMAlz/42dDCIFf/fT9VkVuGJK215K0uEEbo9jesccx1jzp94yO8kAI8S4hxFYhxHbIQOxXhBD/DcBXAfyU2u0aAF9Qt29W96Ee/4qQEbubAbxZZfecCmAHgO/07Z14LAhY6Xdjz7gwCdVdNIoItixl0/TNzSEq5faOOl4bogy10s9678zK3nGVvnF3rSqgev5J9uLW7nz4/fA4x5bRJG222K6CuWY6pR17aO/p6yIzTfpyO8dYzCu5KCBjsetE+nVsWCWDzxwzmqjbpD+bq6+VjrlEQX4bwKeJ6I8A3Afg42r7xwF8ioh2QSr8NwOAEOJhIroJwCMAWgCuE0LkB2p6LClwIdVsSD+bj5p/fhFZBx08/VYirHGJqfKXXXTTe8cdSVgWFO6EdoHcbesH8flfehnOPLEL0nfiEJyy2Uplj/3ZLEiMU1Qwd8QgffOtmkRf9DoV46qIA7nmecsrgCy11yb94nOabiSYbCQ4Zf0QDk3UdaaXq/R9n/3e0RPpCyG+BuBr6vZuFGTfCCFmAFxd8vz3AnhvryfpsXjBP9q5K307e6dIwZmEWdSRMkmFUZwFVZyVf91uirPCgI+ZKf2iBaQT3ACru3Cct21tV8dxz5kHmjcTYVXMzgacwVOm9Dt5+pGqj+CAvFs5nRhKn3sHAfmB6Y/sG9OWHvv5G1ZXgf2ZMHCV/mxiSSsdvg2DR1/QDekfm2rgv35wUPbFMfcvUPqF9k5Q/Dgr/aZSvUD7lE3dWrmLQC575mlJ87ZOcJX+bN0I5lHt6Wt7Jy302XvBKcreGSkhffM9FCn9MKBsYHoq7CsDIm37BKQqo4MslmHaO6//0J26ypn9/A2qiCwVGeGbn+FsYkkrHZ70PfqCbkj/H+56Etfc8B382a2PWT/WIk+/HVm7tytGILEbTz/rp19+rty5k1sBJCVXDZ0QuxW5vR8CQIGnH6h5tw7JzgZb1tQQh2QNVzc/NkvpFywwYSDnDicFC628AoA6TztI7Hr6qQCOTkmyP6aqcbWnnwqdufPWi07B2162HUOVcFZXmCsdnvQ9ZoVGKxvVB3TnrTaUar75/n25mbh5Tz///FLS15OkHE+/NGVT/m1n74zUYhBBd3Ocrb3j5unPlqJCTZhZIDdJhWyINocgrjw24R2XPA8/YWT2lGbvFHwGkVL6biAXUPaO3m5fYcnt9rFYzXMLhjWDciESIvPzX/Kc9fiDN5yNi3ds9GMUZwFfzuYxK7z0fbdbP7hOl9n7R6e1pdNKU2uRKFT6bQKwQHHDtSRNHaVfnLLZjb0TBlL5HlPKM51lINf19GcrTPkwZspmM5HZO3O1dwDguleeZt03F0R7Rm5+gQkCUgFblSbrdOVMhbDm8mb2Tj57Z9Ihfc5wSoVAvSFXCE7TNGcBeHQPT/oeswJ3ZWQkbdooHByv4+I//aqecNVopVZevhD553eyd6jA3mklQvsnHFQsUvPdjEsEgLWDsVb6iZhdhow7OWu2ypQXKNPeSVKBZpoWEvFcYaVsdmitHCmfnj9zc53j7B1CijC030Oa5gOxTPpjmvSl0jf9f3Oou+f83uHtHY++IGnz4xudbqCVCjwzNgMg69HCKKrIbTfm0IUZyDV3ESVE3c3kLABYM1jJlP6sPX3nJzZLkgq0Spb3w5DQTFO0nC6Y/YL5Xjt5+kEukJu9ZyIyPH07lpKIfMM1npY1Ot1E1WzaZmT6mOmrXun3Dk/6HrPCahXo5AlMSVqu9DkDhpuzNVqplaLJpGCi2NMvPr62dxJh9LPJMkZyx+kiTx9gpT83e8clZDFL1tfWCHfZVITXStOuWzD0gqLGcEAbpa8Cue7nFAZm0Zaj9Avy9LW9M9XESC3WzzHnKJvH8aTfOzzpe8wKQUC4/OzN+Nr//jEAaDsBiatGZ1T1biNJcfVHvqUfL2q41qk4ywTbO02D5NnTL7aJ5N9OKZhrBys4OpkFcmdl77iefu9TJQEYSl+r3EC3YZhr9k4Ryoqzil5LpmxmDddy9o7g88zmHgByf9feMQO5I7XYalXhkn4YzK4+ZKXDk77HrNBopdi6tqbb9bZV+uox80qeZ6QCSumrhUHnoxeRSwlJZxW5qdNPv8Qm0r132pPlmsGKDigmaef9i+DaO3P39Pm4quFaH7J3imCrdaMNQ8FrhURZ5a0TyA10Vk+aU/pucRZgB3I5gwpQwkB9duaVmq/I7R2e9D1mhUaSohoHuVmyReisxiQpENnzbl108vRbadYUTYAJqPyKoRt7Z6LeQqOVKqXf4W0UoH8pm67Slw3XWml/sndcWMVZnZR+SLryNnVTNol0dpZrUbnFWUCe9LOgb3ZVYHr6viK3d3jS9+gZrNAqYYggkE212v34Og1ISdMs0Bc7+egmyoS2WZyVVeSWd8YMHNVchjUqXnFsulGa/tkJOU9/toFcPmd10rqfvjO4vF+wirOM98DnYdpWZp6+tHfsbB+ekcuLk7Z3ClI2zUCu6ekX2jvklf5s4Enfo2scGJ/Bb33uAV0ZaQ4vaaf0WyWpPe+58mxsGRkwUv1IE0NxG4ZO9o7Q9o7QqrM48NjueAxOF3x2tI6jk43Z2TsOIbebCdsOUWgvVKHqvdOvPH0XZcVZTPavfcFmvY3tHQ7Yu91Q3cWADydbK9uva6ZsDtdiq3o3F8j12Tuzgs/T9+ga7/2PR/GF+/fh3JNlkzBzIHk7xVX2w6xGIYgIP3h2HA0V6GPPuJ0X7yKzd1InkFt8nJc8Zx3ecckOPP+kkdJzBqDbErzxI99Eo5XiRad01xzNRBAQ4jAjxNl6+tnVicrTV713mqnA4Dx4+vZA+uz4I4Mxvvxrr8D29UP4t/v3qcelvdNIuJMmjOfme/KYxVk5T7/Rki0X6i3l6Sv/3/D0LaXvSb9neNL36BpcMMNphxbpt0nUL7N3qnEAIuABYyh3OxVeprSroRrLJ+wRhGUpm4OVCO+45Hml58vg98fto/n994o4DNBMpG0xV0/f6rKpGq7FHa5YZgO7t5F9/OedsDp3blLpM+nb8w3Y03fbMBSNS5yotzDVkGp/VTUyKqyzBcKMa3jS7x3e3vHoGtOq187YtPxR8ijAMOhg75Qq/cCp/KScjWGik71jPk9AlKZsdgu3mnbXwYlZHcfMrpmtBZ0FL+V9rsg9HvaO+znkzk15+rw4FgVyi7N38m0pJusty8bRC0SaXSWZ9o7vvdM7POl7dI3phiR9bn/LZBB1+PGVk35okUtgBHKLVH0ne8d8HmeGzIn0Q9ePn91xTNKfdcqmQ5h8RTTTSua9OKtTSmgUBAiDYqVv2js67VQ9nKb5itzJeqIXgoBgp2yqC8bQ+Ay80u8dnvQ9usYUk/6ETfodlX6JvVNxlH4zyatBE2VJKu4VABEAVe05l4lS1Q4Kt1u49shskB+XKM9tupHMi71DPSj9IJALtlb6zkKeCDWPwLHuUiFyge0JR+mb1bt6+pbRw0fWY3ji7wWe9D26BjdZO6B66FQMe6ddymZZ9k41CqwG89ONxCC3/P6dVPsvq06RBKX009kNM2e4ZHfF8zeX7NkeZq7+rAO52tOX97XSby4SpU9Z62y3G6pQk9Fy9k5BIJdrIoCspw/AMRq5jzs60ov93tDx20JEA0T0HSJ6gIgeJqI/VNtPJaJvE9FOIvoMEVXU9qq6v0s9vt041rvU9seI6LL5elMe/cdMM8ERRfo5pd8pZdN4rKYaaAFs72QEUW9l1aWdWiu7eOJPXoffvOx0+Vwi7enPRembpH/xjg348H9/0ayO0x9Pn/9m2TsAMNNM56XhWi9KPwxQbu/oSt18KwnX3jlj82o0k9RqrGalbOrFgF9XPuiO2vRoj24kQh3Aq4QQ5wA4F8DlRHQRgD8F8H4hxA4ARwG8Xe3/dgBHhRCnAXi/2g9EdBbkkPSzAVwO4G+IKITHkgD7+OZtTfphB6Vv/CgHKwbpx0FO0UdO+10T3frzREoZ9tHT53YTs0FfPf3A9vQbyfy3Vq52UPo8OaswkKt89zQV2cJlKHQhpBD43h9cisufv1kOV08zpW/FaJyKXLNFs0f36PhtERKcthCrfwLAqwB8Tm2/EcBV6vaV6j7U468m+T93JYBPCyHqQojHAexCwWB1j8UJnlpk3s4CuUFbpd807J0BS+kHOVKOnQIeE92KdiKSefpp9wtFEUyFOxd/vxIFVqXwbOC2gzYtnfnI3jE/NreVhAuuyC1T+mbxHW8DssrugIDVA7FeHOs6NmAofWMxyBq3ycd6rcr9/H178YlvPN7Tc5YTuvomE1FIRPcDOADgNgA/BHBMCMFMsBcAz1o7CcAeAFCPjwJYb24veI7HIsd0U6VpGgRQCTOCNr1ZIQS+/8yYvm8Gci2lX6CedUVuwTTZbq0aUucgZtkDn2GSfieLox3OOnEYZ24eBjB30i8aADPfDdfcLKbcvu1SNoMsk0ovXJy9w+2vdWsJ+beuurEGgR30za4AoF4na7/RC37tMw/gD//9kZ6es5zQ1bdFCJEIIc4FsBVSnZ9ZtJv6W/QzE222WyCia4noHiK65+DBg92cnsdxwLQaVbd5ZEBvqxpK3/zhfebuPbj8A3fi6zsPAbA9fZP03ewdoL1P220bBOnp99fe6UR87fC+n3wB/uANZwOYfT99d8Sj6ePPR2vl3gK5ZNk77izjJC0vzhLG/xEr+JlmdhyrtXKuIle+hm+61ht6+iYLIY4B+BqAiwCsISKu6N0KYJ+6vRfAyQCgHh8BcMTcXvAc8zU+KoS4QAhxwcaNG3s5PY95BFdJnjCckb7uiOmkbO45Ktsm3/3EEQB29k6t4tg7zjeQCaYs46cbcAO41Agezu44pMl+Lkqfzwnoh9Ln++3bHc8VvRRnBaSUvrqic8crcuWte5WSzdRVC1nE9k6i97Pz9PMVuQBw75NHcfkH7sB//cCLxG7QTfbORiJao27XAFwC4FEAXwXwU2q3awB8Qd2+Wd2HevwrQibS3gzgzSq751QAOwB8p19vxGN+wdW4J47kSd8tzto8UgMgh6EDbiA36/xR5OlHbZR+t2Cln5a0YegFfDUzV9I3s1BmA3cGgJmbPz95+t3bO1rpF/XeIbPPvh2X4MlZgfOeeMJaaCp9s7WyE9R+aN8ovv/MOK654Ts+Z78LdNN750QAN6pMmwDATUKILxLRIwA+TUR/BOA+AB9X+38cwKeIaBekwn8zAAghHiaimwA8AqAF4DohRNLft+Ph4sD4DB56ehSvOuOEOR2Hq3G3rq3pbZWwWOkzce8flfn8VsqmUvqyo2aQs2xY6TfnovSRte2dS8omoMi+PnfShzHcZVbPdjJW7Lm1C6v0w1BOzmK+Na9CeEauWZEbOkTOr5UFctnTt/P0+XvkHodtJQA4MtnA+lXVrt6jEKJry3A5oSPpCyEeBHBewfbdKMi+EULMALi65FjvBfDe3k/TY7Z489/ehd2HJrHzvVfMKeDHSn/bukG9raoycWQ5fPbD4yyOfceU0jcDueo5rKDdnxwHcudSXk8kCShJ5+bpAxnhzcXT53MCZt9wjcGnYf5fzs9gdNPTb3/8kMhahOwumwa5OxW5iZPVY9Ye8HHtqwJZbOcep26Q/v7Rma5Jv5kIVKKVR/q+IneZY/ehSQBZC4XZgp9/8tqM9M2KXJOkWXntOyaVfrPA02fSd4U4/5A7DV5pByLCvz+wD2Mzrb6RfjWeq70zN1Ofn05FSn+B2zCY7RLkfaf3jjM5i0ylb9g7lZzSN6ds2f17+NiArfRZaHSD6Tn+JpYqPOmvEHAgdraYaSYgArasyewdVoBuX3P2dqebCRqt1HosCgLEIRljEbMf8Yu3r9WLynAtnvW5EmUtI548PDnr4wAZEc1V6T/vhFXYPDyA37r8jFk9nz8ls58+Y77bMHTbZVPfd7J3JLkLK6guA7yqKR53DnXy9K3sHRUXcI8h98/Imy3FbsBXrysNvp/+CsFkfe5KvxaH2LA6u3Qmg4DqrbzSB+QPywzKxqHMiOEcff5Rv/H8rfj/fvocNJMUO05YhUvPmn0MwiSsi56zftbHAQylP0dPf7AS4a7/8+pZP5/fEnOemSEz320YOo1jdO0dc/estbKj0knN1DXGUOo8fbZ3zNbKqiLXXVAAM8UT2DfavdKfqxBaqvCkv0Iw1y/4dDPBYCXEUCVfUBW4St8g/ZlmYtk7YUCIoyAjUU1i/MMP8PoXbpnTuTItXHLmJvz8xafO6ViVPmXvzBVcrOb63wDmvQ1Dp7TXwCBn93zY3jG9e/mcrMpWZ++EnKevUjaJrEIu0yLiYwOZ0j9pbQ37j3ml3wne3lkhmKvSn24kGIjDwmyHKLDHJZp+/HQjse2dMJBKP7Y9/bCPapXPsex8e0G/8vTnCtfTN8lvfoao9HbMskCutHfyoytZKJi2T6RJXCl3p7WyGQw2z7HRShEFhC0jNZ0m3A28p++x7GCq+zkr/UZiVdOakEO6y+0dcxGIAkJcYO+UDUiZDfhQ/WhPkGXvLGxvwPb2zvymbHa1v0X6ptLPeuyYi1OoUjnT1Kgyjhyl79g7iZOCay4SUUjYunYQe450T/r/cNeTeKaHGMBygSf9ZYzDEw19e3KOqma6mVhtkU3ksneMBWC6mVgLQhgQqoa9Mm5ANgAAIABJREFUU5R3PldkVatzP2a/irPmDjtPP5rnNgy9XiG5fj3D9PRdy4jbJetFOnADufnJWXaWUEb6cRBg27pBPDM2oxcNQKYLmyLExL/dvw833bOn8LHljIX+JnvMIziDBZCzR+eC6Uai0y3/8edfgr+4+hz9WOjYO5bSb+QDuXEYaBLVXSP7SFzsf/fjmP0K5M4VeaVv5tEvzLmdvWVY3y4N5Brkbts+yFou6zYMtkdvZu8IFciNAnvh4P3DkHDKepn5tVe1AQGAH/+rb+BFf3Rb6Xs4PFEvfWy5wgdylzHML/ScSb+ZYMOqCgDg5adtsB5zlb7r6ZsVuWEQ4PLnb8Z6dSydd95HX7qfSn/RePrO/cjqvbMwBUY3/cJLMTrdBGAr8MhprezOyAWMnjwFDdd0a+Uga63sNm3jYwPs6Qc4WRUOPnVkCqdtWg0AeHR/1u21CKYwWinwpL+MMTbT1Ld7Kc46MtnA6oHIUpBTjRZqlVrh/kXFWVwV69o7cUj4tdc8T9/XBN1XT7//Sn/BSV+9Ff4kTaKfy4CXuWCoGmGoKinErcLNbmctOtwgrLwCyLZXnOwdbuQGZBW59lUE1P5yehgr/ScPZ0qfcXSygbVDldx20wJdKfCkv4xhEv1kF4HcP7j5YXzn8SN4ZP8Yrn7RVvy5YeHMNFPU4uKvi1uc1UxSjNRiHJtq5gK5rvoO+kjQDNJKv5+B3MXlhJqf1wu3jizgmUiUBXKJsiu/yCH9JJWtlXn9inQ//Uzpm5OzinL95f4JKlGA9UMVDFZCTfqmt7/70CRepEg/oKwH0pEVqPQX1zfZo6+YUmmacUj6djv8/TefwCPqcvjLjzxrH6vRQq1S/HWJQrvhWiNJMTwgK2pnmnbKZlmhTz8ImkEOicwFnLWz0J6+6W0DwIZVVbzlwm344v/6EWsa2UKhLJAbEuk6jaKK3LZ5+kY7aSHyFbn8/8v2DhFh4+oqjk5JIn92LMvMeeJQVpltWlHe3vFYVmB1v2FVtSulb+KEYbtplSzOKlH6gT0jt9GSSh+Qnn4ztbN3TOgReH319JehvaP+cmvmICC87ydfsHAn5KAskGtaf1ZWj1mcpQvznDx9HbzO5uy6Vwu8P2+vxaHOvzdbMjx+aBIT9RZWVSMr6eDoVCOX/7/c4ZX+MsZ0I0E1CrB6IOpK6Zswh6Ucm2pgppli7WDeEwXkjzmn9GtygZCevpGn75C7JrF+evr8WsuJ9B2lv9hQlEoJ5NW9uX8iBITIyF23VjYmZ/ExuLVyWcom9+2pxqGutOUc/EoU4KN37sbzr78V9zxxxPoMk1ToYPRKgSf9ZYzJRgtD1QiDlahnpc/2DAA8uHcUAPCCk4q94zAILKXfTFIMRCEqUYBpx94paxnQLUF/852vwt2/c0lX+/bDMqouUk9/scEkdDOwbP63Bo7t4xZcZZ5+sb2TOtk7fLzEuAKoxYFeNFjpX//jZ+k04scNm4frTlaaxeO/ycsYU6qKdqgadpW9Y/rWZhDswb3HAAAvKAkYhoE9KKXRShGHAWpxiJlG+0AuK/1u0yu3rKlh4+r2/dL58r0fltFJa2oYHsiyVBYK/erHP18w///MIj6rJ09oXwHo7pvs6RfMyOW/Wa5/8ZUDH7tmKP0jk3XU4hBvfvE2/MR5JwHI7MTfvvwM/N1bLwCw8nL1PekvY0zVJekPViKMz7S/hG20UmsYxbRF+qM4dcOQ9uldhEHg9N4RqESB/gGaC4LbEZLrtvpZkdvPY77hnC341rteveDBUi44W6zjAE0Vb4qHol45QJbxZc7O5UlZZvYOPy9J80NxrAI1tWDUKhnpNxOBahwgDAi/ednpALLK9CggXSviZvDcv+cYXvuXd865tmWxwpP+MsZko4XBSoSztwxj54EJPN1mwAQvCr/+mufh/G1rLKX/xOFJnLZpVelzo4I8/TgM1A8wzbVhMNGr0u8GfMx+ePpBQAuu8gG71fFiRFGlLIBcQRaDCIXqPQ4J9YLsHW7lUPY6rPQHjEBuI0l1nIAXoilF5GFAWK9SOA85pP97//YQHtk/hu8/M97bh7BE4El/GYObpP3keVshBPD57+4t3XdsRv4YTl5Xw7qhKqab5vhD0VbpBkGWcw3IwFolCvQP0G7DYH/lWLj2M09fZ4ssw4yM4y30z9i8uqv93BGG7nYgvwDIzpnOaMYg0Epft54ISKZsOlk2RQtKLQ61YGm2Uj1snUlfK/2QdLHWEadA65Cye2Y7xH6xoyPpE9HJRPRVInqUiB4mol9V29cR0W1EtFP9Xau2ExF9kIh2EdGDRHS+caxr1P47ieia+XtbHoD8gg9WImxbP4jnbhzC954eLd13TGUwDA/EqFVCS+k3k7TtoA4mbCbbZpKiGgWoxQFmVEXuQJyNVjSx2JX+YgG/E3EcXf27f+cS/Osvvayrffnr4X5Pyvrycyqn9PSz/eMo0JPXQsPTdweu8DH085SYGDBJP0l1504OLnO32VB1ex0eiHBk0vb0uUp3bJlm9XSj9FsAfkMIcSaAiwBcR0RnAXgngNuFEDsA3K7uA8AVAHaof9cC+DAgFwkA1wN4CeRA9et5ofCYH0w1Whiqyi/7QBxaNosLbtkwXIs1WTOaSdp2ehL/+Ni7l/YOaX+1lWZXCi4Rzw/py7/zMUZwocCEeTzF58bV1dLaDBf8/+dmOZW1ZyAiJGpcYtm8X9PT1xW5ZfaOmaffTCCEQDMRejGIQzlknedK8P4bVlVz9g4vOmMd4mBLFR1/FUKI/UKI76rb4wAeBXASgCsB3Kh2uxHAVer2lQA+KSTuArCGiE4EcBmA24QQR4QQRwHcBuDyvr4bDwtTRg/8OMwUVBHGpqUCGh6IrQwIQNo73AGxCKzuWobSl4HcSNo7SaozOlwiZoLuJ+kvR3snU/qLE7p/jlPPUDownatsneCsaf8FmvSBf/r2U/juU8dKZ/Hy82qVEKmQxG16+kSypXem9OX2dUMVy945aiwA/JtYbuhJChHRdgDnAfg2gBOEEPsBuTAA2KR2OwmA2aR6r9pWtt19jWuJ6B4iuufgwYO9nJ6Hg6l6Syu1OCQrddJFpvQjKxgGsL1T/lXh3PtWkiJV3RCzQK6syF2lgqFuOwOhrZj+qXKuGVhO9g5jsdrMTMCu0nczdvRtbe/Yit20h0x7x3xeu9t8RTnTSKX4cBrTmdk7gCJ9g+gffSbryrmS7R0AABGtAvAvAN4hhGjXr7TolybabLc3CPFRIcQFQogLNm7c2O3peRgQQuB9X3pUefqZ0m9n73D2zvBAjIE4RL2VavLsRPr8Q20mQl9NVKIA6wZjPDs2g0YrxWVnb8YH3nRuLjA4H0p/PiyjBYfO01+crF+m9MvGKJIxLtEMA5jfMzN7R79OyQJg5ukD2cQ283jVKLCydwBg/aoKDhue/jd2HdKPrVh7BwCIKIYk/H8UQvyr2vyssm2g/h5Q2/cCONl4+lYA+9ps9+gzklTgb+/YDQCG0g/aKn32OgcroR6WwlkU0httE8hVP6xWmmakHwbYNDygi8K2rq3hqvNOyk1kmg+CTubh6mGhkeXpL/CJlEArfdfeaZOnL0RB7r1B0u5cYMCJEThZPwB0wsBMM0GzJWzSj4Oc0l8/VMXRqaYWOF/feQjnnrwGJwxXV669Q/IT/ziAR4UQ/9d46GYAnIFzDYAvGNvfqrJ4LgIwquyfWwFcSkRrVQD3UrXNo89oGoo+U/pkjTHMPydFJZSdCgfUD3e6meimWO2UPhP23qPTeNbod7LJqJw9YWSg8Lk66DoPgdzlpPQXe56+nn7lBnLbWDNJwbDzSoG9Y6b8ljZ2K1D6DSN7B5D2jpm9A0h7h/vvTDVaePDpUbz8uesxPBAvW6XfTWj+5QB+FsD3iOh+te3/APgTADcR0dsBPAXgavXYlwC8FsAuAFMAfg4AhBBHiOg9AO5W+71bCHGkL+/Cw4IZsOX2stLeKVf6ZlomK/2ZZoJmNbOHysDP+8V/uFcXccVK6TM2DxeTvpiP7J1l6OmXxUQWC8rsHfO+m3mTpMIKtgK20jcbqmXbssfNY3M+/kDFtndsTz/Asammeh25fc2grDI/Nt3EzFgCIYDnbV6Nb/zw8MolfSHE11HsxwPAqwv2FwCuKznWDQBu6OUEPXqHaeO89LnrAXS2d2SGTpbrDPAPR6jnt8vTl887NNHAcE36o5UwsNozn1BC+vORU9/P3juLBT9/8akQQuCtL92+0KdSiLJAbinpkyTzw5MN63vC3zOizNapG4WC5n9pNQpRUVlpvFjUdCC3xNN3snf4u15vJXjysGzGtn39EIYHIhxaplO1Fqds8JgTmNz/+CdegIt3yGB4FJJl+7gwFZcm/UbWFrmdP24uCNyvJI4CbFotib4SBVg7WNy3h62YfvYzz6p8l8/XuxqF+OVX7VjwFs9lYG51z8+8MnGHqzwzOg0h7KtA/g6a+860skwy93vCdSjFgVzH04+yxoMsMrIYQIrHD8mJW6esH8RwbfnaO4vzG+QxJzRbkvXMH1ylk9JvpVql1Qz1w1ZR3IZsTHLlgHAllEQfh4QThqu5AC5DzGP17HLy9Bc7+P/XtZ/MRSBw7J2jymrZbMR7+Dto7msGr91ZyqsGpFmRkThbk2le6cf5qw6u1J1pSqW/YVUFqwdi6emv9JRNj6WDIqLubO/kPf3pRqqvDipts3cMpa8un6uRDApvWj1Q6ucD8xt0XU72zmJHS9uAbZS+Y+8wTNLn55d9HdzvyVCFSd8WLNrTN4oKzXNxlX69leKJw5M4Zf2QPG41woS6ap1pJnjflx7F6NTyWAQWvn2gR9/R1GmTdh5zuzx981LY/OF0Z+9kj7Eq422vf+GJbfvfp/OYXumV/vFDI1FXeDl7x+itX1JYdeJwTd9moeIqeoZ7xViNs+w0ABioZJlnRfYOIyhQ+k8fm8b529bq99FMZHbRF+5/Gn97x24kqcDvvv6skk9g6cCT/jIEk775heeAVxmKPP2pRis7Vlt7J/8D5R//u157ZttzzWa+tt1tVlhO2TuLHWwpuoHcMk+fc/MH4kCP1jSfXxbjca9WOb04dJT+TCORXTadQC4j7+knmG4kuo0279tIUj1O0ZwLsZTh7Z1liCLSj0Jq+6U1OxLysJSxmRYarW7snfzXqF22j4n5DLp6pX/8UC8RB+X2jry9eXjAUu9sx5T93zVaDum7St/N0+/S06+3Ukw3Er1o8OLTSNJlN07Rk/4yBBO1+YWPwwCJKrQqgpnTzKQ/OtXQhTHdZu8wus0ySeexOdpyyt5Z7GAybpeyWdRCYbNTtFcpyN4xUW/ZYz95UWHlHocB4pBkjYnTEty0dyI3ZbOZYLppkD4r/VaKpw7LrJ6Dy2Ssov9VLENk/W+MMnX1YyoL5pol65UowFAlxLGpZpf2Tv6xbgeJz0dFLsMHco8fTlojfXm3t1KZp28qfRNxB3unTOmbV5sDUYjJegupQKm9Ezr2ztiM3J/vM+k3kxRPKtI/oAodlzq8p78M0Wzl7R23/bGLRpKiVsly6dcMVnBsutlVcdaclP48Nkfznv7xw2Vnn4B/+cWX4fxta6zt7YqzAGDzSK1w/7L/OjcuxSRtXkUMVEI9Ca4skBs59g779gOuvdNKddHWs2Ne6XssUhR5+lrpt0qUvuN/jtRiW+m3a61c6Ol399XiJWg+SN97+scPRIQXnbI2n11TQvra3hm2M7uylM3elH7DsH1qcahz7C17p8DTj0NCQFkffU5X5sVndLqpm7Q9MzazaAfT9wJP+ssQjXakX2bvODnNawZjHJtqdEf6bbJ3OmE+i7O8p7/wKM3e0Z5+sdIviz25pM/HN/vz1OJQV9Oa30Mre0e3eyAMxCGOqUXC9fTH1RXDtnWDaLSyTJ6lDP+rWIbICqry9k6zzY8pp/S7tHeKvPNulT7/uPvZhoHhlf7Cw7wKtLpisqdfEsgtG0peL1H6M0Z/noFKqNsil9k75nejGgW68GogR/py+5Y18jyXQyaPJ/1lCF2cFfVi79iFLFLpd2nvFCjqbrtB6orceegd7D39xYVCTz8XyJUPdKv0Nekb9s5AFGiyNr+33KcHsL+zA3GIo1PK3lHHq6rn8eKxdrACANZEuaUKT/rLEBlR57N3zN7kJtyc5pFaBaPT3dk7RVcBXXv6XJw1D6Qf+uydRQW3tXIYUK5au6LUeFnCgav0eV5EauxfswK52WuaC4x5Lqa94yp9tom4BTPPjm4lKT7xjcd16/KlBJ+9swzBasjuvUPqsc55+oD8kjcToS97u5mcxQjVD7ob8FU8zYP8iL2nv6hgXs294ZwtOHFkIPc9KVL677nq+Xj66DQ+8l8/xMtUq3DGWy7chicPT+GXXnma3laLQ903x7Q4TzTiB5Fj7zx9lEnfTtlkT3+NUvrcpfP7z4zjD//9Efzhvz+C97/pHAxWIlx29ubuP4wFhCf9ZYhiT79Tnr6t9NeoAi0uSOklkNttjj4AXP78zfjsvXvnZTiI9/QXF8z/j/O2rcV5qs+NCSZbU+n/7EWnAAB+5sJtuRjAQBziD95wtrWNLRrA/t5uMjKFLE8/DnXyg5u9w6TPrcGnVUNBVvwA8GufeQAAsOu9VxRmsi02LP4z9OgZxW0Y2ts75hAVANiiim1ueeiZ3LFcuI9124IBAP74J1+Au3/nEivI1i94T39xoZtgvQ7kFtg729YPdpUVxtOzAPtqdyDO5+kDWf8eIFsw+Ds9ru0dW+nzYJfnbhzSz71vz7GO57YY4El/GaKZpAjIVjNF9o4QAq/74J343L17c57+xTs24EdO24AfHpy0nl+EMCBrhmulBwKPw6BtF865YD4ygjxmj26C9Vnsafb58LbSL35N19N3n1sJXaWvArlK4XM7iD/7qXPwn7/+CkQB4fZHD8z6nI8nPOkvQ7gEDmRfYtPeOTbVxMP7xvCbn31A7ZP9EIgIl559gr7fKTBr+uftmrN5rFx0Y7f1YzLYQGx+F4uPZxaRmdYit2rmbeN1qfQze4dJX/6OhqohTtu0GqesH8SeI1NzPvfjgY6fMBHdQEQHiOghY9s6IrqNiHaqv2vVdiKiDxLRLiJ6kIjON55zjdp/JxFdMz9vxwOQfXTcL3uRvfOMk3ngEvuGVdXSx1yYufqLdaSfx8KiG9LvNuurHco8/TIUKv1cIFeSvrZ3lNJnW7JWCS2ffzGjm0/47wFc7mx7J4DbhRA7ANyu7gPAFQB2qH/XAvgwIBcJANcDeAmACwFczwuFR//RSJJcgzTT3rnpnj346b/9Fp4Z7Z70O/1gI8tK8qTvkUc39k5/lH456X/+l16G/33Z6c7+cp8wIP07cUl/qBohDinn6fMVQS0Ol0wOf8dPWAhxB4AjzuYrAdyobt8I4Cpj+yeFxF0A1hDRiQAuA3CbEOKIEOIogNuQX0g8+gTZMbM4o6aVpvitzz2I7zx+BI8fmrT2cReKDasqXb+mNbDFK32PAvQSyJ0LapVyT/+8bWtxnZHeCRhqPQ617VNxArnVKEQtDjHTtO0dJv2BeHkp/SKcIITYDwDq7ya1/SQAe4z99qptZdtzIKJriegeIrrn4MGDszy9lQ23eRqQ2TuP7BvT2354cMLax/Xi16/qPsAaFRSCeXj0in4Ihl7tHV4k7N48AQKCLvKqRgEGKxGmVMqmtnfibMGYWeakX4aipVy02Z7fKMRHhRAXCCEu2LhxY19PbqWgkaQ5xcSK534jrcwlffcHMjzQfRmHWdbulb7HbNFLum8ZTHuHs27a4awThwHk++pUokAXOkrSD8vtnWXm6RfhWWXbQP3lXKW9AE429tsKYF+b7R7zgCKlz4vAIWP6zw8PTuLsLcP6vvsct01uO5hKfz4KrTxWBvodyB0ZjNvsKfGqMzcVbtdTvAJCFAbSwjGydwLKYlnLytMvwc0AOAPnGgBfMLa/VWXxXARgVNk/twK4lIjWqgDupWqbxzygmYic2o406Wdq5uB43SpNn8sPbjEFcm/+5Zfjr3/m/M47eiw69EMwcNLB6Ses7rCnxPBAjNM2rcKOTaus7Vxvwuc0aKj5eitBNcpiAEvJ0+94/U5E/wzgxwBsIKK9kFk4fwLgJiJ6O4CnAFytdv8SgNcC2AVgCsDPAYAQ4ggRvQfA3Wq/dwsh3OCwR5/gzgYFssvmI84l7LqhTAmZ/fR7hRXIXWDSf+HWNXjh1jWdd/Q4LviPX/kRfG/vaFf79kMwrFa25JtefHKHPTPc8qsX55r+MdmbFg5n8zRaqTWUpVZZOp5+R9IXQryl5KFXF+wrAFxXcpwbANzQ09l5dESaClzy/v/CdT92Gt74oq0A8r3xgfIf00gtxqpqhIl6q3Cfj731Auwfne54HlYg19s7HgbO3jKCs7eMdLVvP+JB521bi//89VfguRtXdd5ZoahnTsXIzAGk0j+gRibWW6l1VVKLQzQTgVaSLvr+O4v77Dw6Yrzewu6Dk/je05mSklOwyknfDNCO1GKMqOZqRaR/yVkn4Gdfur3jeViB3EX+pfdYvOiXNXjaptU9xaSKz4Xn6Ga5+FNNzt5JrX5RHEeYcVo/7zkyteiuAPyvc4mDWx8fmWzgq48dwP/81L2FbRjCgLCqKsmem6kBwMhgRVcbzqVBWWxV5Po2DB6zQz+yd/qFirZ3uOo2wnRDkrr09I1mbirt0wzmHhifwcV/9lX82S2PHa9T7gqe9Jc4jk1Lj/7wZB1fefQAbnn4GRyeaBT+eLaulWRvkX4tzpWYzwZREOima17pe8wWc1Xn/QR/j9m7r8UhDk3U8Y5P34d60/H09djG7Df0xQf2AwB2OanRCw3/61ziOKqU/uGJBvYdk977/tEZrKrmU9W2rh0EAGw0iq5GajFecqocTLGqh7x8F1FIGHTa0np4LGVUnEDuK8+QdUNf+8HBUnuHM3iEEPjsvXsBABuGuq9sPx7wQ1SWOI6p2Z5uVs5JawZy+568Tip8M3d5TS3GL7/yNPzIjg04v2CoRbeIVR5zIgSGqv5r5bH0kaVsyr8X79iIt71sO/7l3r05e6dWkbfZ3rnloWfw6H5Z/c7zdxcL/K9ziWN0OvP0zTxh08JhbFotFwJzFN1ILUYQ0JwIH5DxgDgM8Mn/8RJrsISHx1LF5uF8G5K1gxWM11uYqCfYPJzR54Cj9D9yx248Z+MQNg8P6KvxxQJ/Hb7EcUx9oVqp0DnEQDHpD9fkl3RsOvsScubOXBGHAeKIcOGp63rq2ePh4eITb3sxvvxrr1jo08AbzpHtwb6+65DetlbVtRwYmym1d3YdmMADe47hLS/ehvWrqvpqfLHAK/0ljmMlKqKI9M9UPUbOODFrvTDcJ9J/8fa1fTuWx8rGK88obotwvPFSNYT9Fc/LeoBxL5/Dkw0rkMtKf6aR4P99bz+IgCvP24I9R6cWndL3pL/Ewdk7LrYUePrnb1uLW95xMZ63aTXe88VHAPRvePjbXn5qX47j4bFYEAaE+3//NaUN3NziLEAq/SePTGHz8AA2rR7AmsEK/v/2zj06qure459f3iaEvEMgJIFgeMo7POShSxRBrkrtjV7aW6/X1rpqr3257BWXXSy9rbVqb+3y2lWL9VEqyxe2F5bKVSgilPIKrxAIYUISQshjyAMySchz9v3j7JlMIA/EZGbI7M9as+acffbM+c5v5vxmn9/+7b0bWtrpdKoBu9a+Kia842Oe21zAN9buwRrM3D/1TW3c+T87ydeDsc43t3cbiDU9LZbYyFAiw3r+P5+YMtysHWswXCGxkWHdnH6sRxJEt/COztP/5ScnOFHVQEqM1eiKiwxFqa6+t06nYvWHeRw47btZaALS6b+fe4bXdhT75Ny7T9Vystrh3v/DF8XsLq7l5he3c7Csvt/X/+NULflnG/jPDXmAlb2TnRHHr++dzuE1S7llQhKLs/qfknr9Q/P4zX3Tr/6DGAwBSFxUzy191x9DTWMr+WcbGOl2+lZ9VwbPvpI63t1/hjUbj3lL8mUEZHjn9Z0lVJy/yIMLx1zxPBlKqS81cKSptYMOp+rWUdrc1sE3XtsDwMlf3EFDS1esr6yumfV7yvrNoimpsQZ6HK9s4JZfb6e908nM9Dhy9Lw7P75t/BXpW3h94hV/FoPBYBHn2dL3iOlHeazWBbhnr3XdGbg6cz/Ks2aUT4r2XbJDwLX0L7Z1YrM7cLR2cLyyof8XAP8oqiH7F1tZt7u037qOlnY2HCjnX/+4l+nPfEa1Xny8yN7I3a/sctfbcKCcQ2XWgiYv5kzj+uRh3e4AeqOgqqtOSU0T5fUXGZMQeUWfw2AwfDU85+r3DO+EBAdx7JllpOoECldLP17fGdQ0tuF0Kj49VgXg087dgHP6xysbcKWp7y3uP67W1NrBI+sP0tDSzpqNxyivb+6z/q82n+DxD464V6h6actJAF7ZZqPI3khmUhRjEiLZWlDNobJ6QoKEu6aP4tZJyRRWOdwr9fRGYZWDpZNH8N7D891lYxNNXrzB4A087/Zd2T0uosJD3PNbuWL6rmvTVu0gv+ICNY1thAUHuUfP+4KAc/rHKqwO0LjIUPYU1/Zbf9sJOxcutrvDJjZ77/NoNLV2sOlw14JgE1OisdkbcToVO201LM5KZMP3FrAoK5E9xbXsK6lj8qjhRIQGc8OoGNo6ndjsXS35LcersTtaur1/SU0Tk1KiGe+xQETml5hC1mAwfDVezJnGmw/OYc6Y+MuOuaYyGR5hhXWiI0LJSIjkUNl5frvVhgismpvGOUere51dbxMQTv/CxXbaO60W9NHyCyREhbH8hhT2ldR1G53qwjOT5uO8SpKjw90x87LaZjqdikfePsDHeZXstJ1j9Yd5tHc6OVR2HkdrB289OIfcn93G1NQYztQ1c6yigdqmNu6ZmUp8VBiLrk+iua2T3NP1zEzfaKjwAAAKOklEQVSzFvuYrJctLKi0nP7nJ+x8d10uv/y4wK1lX6mld+7YBOKiwkjQt46ZZgSsweA17s1O45YJPY8l+OmyCcRGhjI1tWv9gMkjh/O3E3a2nbCzcFyi+1jVhZYe32OwGfIduY6Wdpa9tIMJKdG89eAcjp69wJTUGOZnJvDOvjMcr2hg6ugYTtc2YXe0EhESzL+/uY+fLB3PN+ems6uohjunjyI5OpzIsGBKa5uw2R1szq9ic36V+zwjY65zr9hzQ2oMicPCSYuPxO5oZXuhtYTwIt15ujirqxN1pu64zYiPJCw4CJvdgdOp+C+dR19W1xVO2mWrISwkiOwx1mvGJQ9DzjW5WxUGg8G3zM9M4PCa27uVuTp178sezXNfn8beEivCcLb+IhkJ3m+wDWmnf6aumTUb86lqaKGqoYW/HjqLzd7IrZOSmZ9pxeN2Fp1j6ugYHv/gCPtL64kIDaKl3cnP/jef5OhwHK0dzM6IQ0TISIjidG0zB05fnlr5+t+LuW3yCOKjwkjU0xC4JjjbdKSC9PhIkodbcb6o8BBeyJnG6g/z3DpCgoPITIqiqLqRPcW1lNQ0ER0ewsGy8/z8o+P8cEkWWwuqmZ0e504Pe2jRWKodrZdpMRgM/sO/zEnjZLWDJ5ZPJDhIyEqORgT2l9azwAdZdEM2vNPpVHx//UH2FNfxw1uzmDJqOI+9f4ROp2JqagwjhkcwMz2WjYcqaGzt4FDZeeZnxpOZOIzVd0wE4LdbbQDM0CGYjPhISmua2H2qlsRhYaz79lzGjxjGC/88jYaWDv5y8CzjR3TF19Pjrawam72R2RndUzHvy07D9uwKd4cPQNaIaAqrHby6o5jhESH8dPkEAF7/ewkzfv4ZpbXNfHtR18jX26ekcP/8jEGwnsFgGCgmpETz9kPz3HNSJUWHMys9jo/yKnD2EF4ebLzu9EVkuYgUikiRiKwerPO8s6+Mo2cv8HzONB5bOp5n75lKcJAQGizukMrXZ6ZSWO1g7Ren6HAqfrAki09+tJjv3TyOtPjrOF7ZQHRECJm6B37KqOEU1zTxUV4l2Rnx3DQ+ic9+cjNfm5nqDu14drCmxXWlUs5Kv3yh7kuHZWclD6O8/iI7Tp7jsaXjWeCRHaAU5MwezdLJIwbOSAaDwScsn5KCzd7Iwue3UVLT5O5z9AZeDe+ISDDwO2ApUA7sF5FNSqnjA3meuqY2Xvy0kBszE7hr2kjAaq0fffp22juUez75u2ek8vK2Il7eVkRUWHC31vjSSSm8sauEb83PcE9b8PDNmSRFh3P+Yjt3Tx/lrhsWEsTa+7P5855SVs5IdZcnRYdzz8xUBFgxdWS/um8cl0DE9iC+NS+DBxaMQUTYtXoJwSLsLanlrmmj+n0Pg8Hg/3xzXjrBQcIrnxdx76u7aWnvZMnEZMJ1n929s9MGbboUudI5XwbkZCI3Ak8rpZbp/ScBlFLP9VQ/Oztb5ebmfunz5J+9wA/eOcQf7p/dreXdE7uKanjh00KeWD6BBeO64mvNbR2U11/s9/UGg8FwtdiqHXx3XS7NbZ3YHa2EhwTR2uEkNfY6VkxN4al/mnxV7ysiB5RS2T0e87LTzwGWK6Ue0vv3A/OUUo961HkYeBggPT199unTp6/qXE6nMhOLGQwGv6fTqVBK8X/Hqlh8fRJbCqr54uQ5ZqTF8p1FVzd7bV9O39vZOz154W7/OkqptcBasFr6V3si4/ANBsO1gNW3J9ypw7c5s0e7xwUNBt7uyC0H0jz2RwMVvdQ1GAwGwwDjbae/H8gSkbEiEgasAjZ5WYPBYDAELF4N7yilOkTkUeBTIBh4Qynlu4mlDQaDIcDw+ohcpdQnwCfePq/BYDAYhvCIXIPBYDBcjnH6BoPBEEAYp28wGAwBhHH6BoPBEEB4dUTul0VEzgFXNyTXIhGoGSA5g8W1oBGMzoHkWtAIRudA4m2NGUqppJ4O+LXT/6qISG5vQ5H9hWtBIxidA8m1oBGMzoHEnzSa8I7BYDAEEMbpGwwGQwAx1J3+Wl8LuAKuBY1gdA4k14JGMDoHEr/ROKRj+gaDwWDozlBv6RsMBoPBA+P0DQaDIYAYkk7fW4uvXw0iUioiR0XksIjk6rJ4EdkiIjb9HNff+wyCrjdExC4i+R5lPeoSi5e1ffNEZJYPNT4tIme1PQ+LyAqPY09qjYUisswbGvV500TkcxEpEJFjIvIjXe439uxDo1/ZU0QiRGSfiBzROp/R5WNFZK+25Xt6qnZEJFzvF+njY3ys8y0RKfGw5wxd7pNrCACl1JB6YE3ZfArIBMKAI8BkX+vy0FcKJF5S9gKwWm+vBp73ga6bgFlAfn+6gBXAZqyV0OYDe32o8Wng8R7qTtbffTgwVv8mgr2kcyQwS29HAye1Hr+xZx8a/cqe2ibD9HYosFfb6H1glS5/FXhEb38feFVvrwLe89J33pvOt4CcHur75BpSSg3Jlv5coEgpVayUagPeBVb6WFN/rAT+pLf/BHzN2wKUUjuAukuKe9O1ElinLPYAsSIy0kcae2Ml8K5SqlUpVQIUYf02Bh2lVKVS6qDedgAFQCp+ZM8+NPaGT+ypbdKod0P1QwFLgA26/FJbumy8AbhVRAZ97dQ+dPaGT64hGJrhnVTgjMd+OX3/mL2NAj4TkQN6EXiAEUqpSrAuRiDZZ+q605suf7Pxo/oW+Q2P0JhfaNThhZlYLT+/tOclGsHP7CkiwSJyGLADW7DuMs4rpTp60OLWqY9fABJ8oVMp5bLns9qeL4lI+KU6NV6z51B0+v0uvu5jFiqlZgF3AP8hIjf5WtBV4E82/j0wDpgBVAL/rct9rlFEhgEfAj9WSjX0VbWHMq9o7UGj39lTKdWplJqBtab2XGBSH1r8RqeI3AA8CUwE5gDxwBO+1jkUnb5fL76ulKrQz3bgr1g/4mrXrZ1+tvtOYTd60+U3NlZKVeuLzQm8RlfIwacaRSQUy5muV0r9RRf7lT170uiv9tTazgPbsWLgsSLiWvnPU4tbpz4ew5WHBAda53IdRlNKqVbgTfzAnkPR6fvt4usiEiUi0a5t4HYgH0vfA7raA8BG3yi8jN50bQL+TWcgzAcuuMIW3uaSOOg9WPYES+Mqnc0xFsgC9nlJkwCvAwVKqd94HPIbe/am0d/sKSJJIhKrt68DbsPqf/gcyNHVLrWly8Y5wDale059oPOEx5+8YPU7eNrTN9eQt3qMvfnA6hk/iRX7e8rXejx0ZWJlQBwBjrm0YcUc/wbY9HO8D7S9g3U7347VCvlOb7qwbk1/p+17FMj2ocY/aw15WBfSSI/6T2mNhcAdXrTlIqxb9TzgsH6s8Cd79qHRr+wJTAMOaT35wBpdnon1p1MEfACE6/IIvV+kj2f6WOc2bc984G26Mnx8cg0ppcw0DAaDwRBIDMXwjsFgMBh6wTh9g8FgCCCM0zcYDIYAwjh9g8FgCCCM0zcYDIYAwjh9g8FgCCCM0zcYDIYA4v8BgEUpUepdkUAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the bikers travelling to Berri1 over the year\n",
    "import matplotlib.pyplot as plt\n",
    "plt.plot(bikes_df['Berri1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x000001D5D30ABDC8>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x000001D5D39DAD08>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x000001D5D39A7408>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x000001D5D3A43808>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x000001D5D3A7C7C8>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x000001D5D3AB4788>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x000001D5D3AEC788>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x000001D5D3B25748>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x000001D5D3B312C8>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x000001D5D3B6C348>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot all the columns of bikes_df\n",
    "bikes_df.plot(subplots=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Value counts\n",
    "\n",
    "Get count of unique values in a particular column/Series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# what are the unique genre in imdb_df?\n",
    "imdb_df['genre'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d5d3daf408>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting value counts of unique genres as a bar chart\n",
    "imdb_df['genre'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d5d3e60288>"
      ]
     },
     "execution_count": 17,
     "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": [
    "# plotting value counts of unique genres as a pie chart\n",
    "imdb_df['genre'].value_counts().plot(kind='pie')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Index\n",
    "\n",
    "### DATAFRAME = COLUMNS + INDEX + ND DATA\n",
    "\n",
    "### SERIES = INDEX + 1-D DATA\n",
    "\n",
    "**Index** or (**row labels**) is one of the fundamental data structure of pandas. It can be thought of as an **immutable array** and an **ordered set**.\n",
    "\n",
    "> Every row is uniquely identified by its index value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=366, step=1)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show index of bikes_df\n",
    "bikes_df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>Date</th>\n",
       "      <th>Unnamed: 1</th>\n",
       "      <th>Rachel / Papineau</th>\n",
       "      <th>Berri1</th>\n",
       "      <th>Maisonneuve_2</th>\n",
       "      <th>Maisonneuve_1</th>\n",
       "      <th>Brébeuf</th>\n",
       "      <th>Parc</th>\n",
       "      <th>PierDup</th>\n",
       "      <th>CSC (Côte Sainte-Catherine)</th>\n",
       "      <th>Pont_Jacques_Cartier</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>00:00</td>\n",
       "      <td>16</td>\n",
       "      <td>35</td>\n",
       "      <td>51</td>\n",
       "      <td>38</td>\n",
       "      <td>5.0</td>\n",
       "      <td>26</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date Unnamed: 1  Rachel / Papineau  Berri1  Maisonneuve_2  \\\n",
       "0 2012-01-01      00:00                 16      35             51   \n",
       "\n",
       "   Maisonneuve_1  Brébeuf  Parc  PierDup  CSC (Côte Sainte-Catherine)  \\\n",
       "0             38      5.0    26       10                            0   \n",
       "\n",
       "   Pont_Jacques_Cartier  \n",
       "0                  27.0  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get row for date 2012-01-01\n",
    "bikes_df[bikes_df['Date']=='01/01/2012']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### To get row by integer index:\n",
    "\n",
    "Use ``.iloc[]`` for purely integer-location based indexing for selection by position."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "star_rating                                                     8.8\n",
       "title                                                     Inception\n",
       "content_rating                                                PG-13\n",
       "genre                                                        Action\n",
       "duration                                                        148\n",
       "actors_list       [u'Leonardo DiCaprio', u'Joseph Gordon-Levitt'...\n",
       "Name: 11, dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show 11th row of imdb_df using iloc\n",
    "imdb_df.iloc[11]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selecting rows where column has a particular value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>7</th>\n",
       "      <td>8.9</td>\n",
       "      <td>The Lord of the Rings: The Return of the King</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>201</td>\n",
       "      <td>[u'Elijah Wood', u'Viggo Mortensen', u'Ian McK...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>8.8</td>\n",
       "      <td>The Lord of the Rings: The Fellowship of the Ring</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>178</td>\n",
       "      <td>[u'Elijah Wood', u'Ian McKellen', u'Orlando Bl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>8.8</td>\n",
       "      <td>The Lord of the Rings: The Two Towers</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>179</td>\n",
       "      <td>[u'Elijah Wood', u'Ian McKellen', u'Viggo Mort...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>8.7</td>\n",
       "      <td>Interstellar</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>169</td>\n",
       "      <td>[u'Matthew McConaughey', u'Anne Hathaway', u'J...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>8.5</td>\n",
       "      <td>Back to the Future</td>\n",
       "      <td>PG</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>116</td>\n",
       "      <td>[u'Michael J. Fox', u'Christopher Lloyd', u'Le...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>936</th>\n",
       "      <td>7.4</td>\n",
       "      <td>True Grit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>128</td>\n",
       "      <td>[u'John Wayne', u'Kim Darby', u'Glen Campbell']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>937</th>\n",
       "      <td>7.4</td>\n",
       "      <td>Labyrinth</td>\n",
       "      <td>PG</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>101</td>\n",
       "      <td>[u'David Bowie', u'Jennifer Connelly', u'Toby ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>943</th>\n",
       "      <td>7.4</td>\n",
       "      <td>The Bucket List</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>97</td>\n",
       "      <td>[u'Jack Nicholson', u'Morgan Freeman', u'Sean ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>953</th>\n",
       "      <td>7.4</td>\n",
       "      <td>The NeverEnding Story</td>\n",
       "      <td>PG</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>102</td>\n",
       "      <td>[u'Noah Hathaway', u'Barret Oliver', u'Tami St...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>975</th>\n",
       "      <td>7.4</td>\n",
       "      <td>Back to the Future Part III</td>\n",
       "      <td>PG</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>118</td>\n",
       "      <td>[u'Michael J. Fox', u'Christopher Lloyd', u'Ma...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>75 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     star_rating                                              title  \\\n",
       "7            8.9      The Lord of the Rings: The Return of the King   \n",
       "10           8.8  The Lord of the Rings: The Fellowship of the Ring   \n",
       "14           8.8              The Lord of the Rings: The Two Towers   \n",
       "15           8.7                                       Interstellar   \n",
       "54           8.5                                 Back to the Future   \n",
       "..           ...                                                ...   \n",
       "936          7.4                                          True Grit   \n",
       "937          7.4                                          Labyrinth   \n",
       "943          7.4                                    The Bucket List   \n",
       "953          7.4                              The NeverEnding Story   \n",
       "975          7.4                        Back to the Future Part III   \n",
       "\n",
       "    content_rating      genre  duration  \\\n",
       "7            PG-13  Adventure       201   \n",
       "10           PG-13  Adventure       178   \n",
       "14           PG-13  Adventure       179   \n",
       "15           PG-13  Adventure       169   \n",
       "54              PG  Adventure       116   \n",
       "..             ...        ...       ...   \n",
       "936            NaN  Adventure       128   \n",
       "937             PG  Adventure       101   \n",
       "943          PG-13  Adventure        97   \n",
       "953             PG  Adventure       102   \n",
       "975             PG  Adventure       118   \n",
       "\n",
       "                                           actors_list  \n",
       "7    [u'Elijah Wood', u'Viggo Mortensen', u'Ian McK...  \n",
       "10   [u'Elijah Wood', u'Ian McKellen', u'Orlando Bl...  \n",
       "14   [u'Elijah Wood', u'Ian McKellen', u'Viggo Mort...  \n",
       "15   [u'Matthew McConaughey', u'Anne Hathaway', u'J...  \n",
       "54   [u'Michael J. Fox', u'Christopher Lloyd', u'Le...  \n",
       "..                                                 ...  \n",
       "936    [u'John Wayne', u'Kim Darby', u'Glen Campbell']  \n",
       "937  [u'David Bowie', u'Jennifer Connelly', u'Toby ...  \n",
       "943  [u'Jack Nicholson', u'Morgan Freeman', u'Sean ...  \n",
       "953  [u'Noah Hathaway', u'Barret Oliver', u'Tami St...  \n",
       "975  [u'Michael J. Fox', u'Christopher Lloyd', u'Ma...  \n",
       "\n",
       "[75 rows x 6 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select only those movies where genre is adventure\n",
    "imdb_df[imdb_df.genre=='Adventure']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Western'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# which genre has highest number of movies with star rating above 8 and duration more than 130 minutes?\n",
    "imdb_df[(imdb_df.star_rating>8.0) & (imdb_df.duration>130)]['genre'].max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding a new column to DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>Date</th>\n",
       "      <th>Unnamed: 1</th>\n",
       "      <th>Rachel / Papineau</th>\n",
       "      <th>Berri1</th>\n",
       "      <th>Maisonneuve_2</th>\n",
       "      <th>Maisonneuve_1</th>\n",
       "      <th>Brébeuf</th>\n",
       "      <th>Parc</th>\n",
       "      <th>PierDup</th>\n",
       "      <th>CSC (Côte Sainte-Catherine)</th>\n",
       "      <th>Pont_Jacques_Cartier</th>\n",
       "      <th>weekday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>00:00</td>\n",
       "      <td>16</td>\n",
       "      <td>35</td>\n",
       "      <td>51</td>\n",
       "      <td>38</td>\n",
       "      <td>5.0</td>\n",
       "      <td>26</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012-02-01</td>\n",
       "      <td>00:00</td>\n",
       "      <td>43</td>\n",
       "      <td>83</td>\n",
       "      <td>153</td>\n",
       "      <td>68</td>\n",
       "      <td>11.0</td>\n",
       "      <td>53</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012-03-01</td>\n",
       "      <td>00:00</td>\n",
       "      <td>58</td>\n",
       "      <td>135</td>\n",
       "      <td>248</td>\n",
       "      <td>104</td>\n",
       "      <td>2.0</td>\n",
       "      <td>89</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>15.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-04-01</td>\n",
       "      <td>00:00</td>\n",
       "      <td>61</td>\n",
       "      <td>144</td>\n",
       "      <td>318</td>\n",
       "      <td>116</td>\n",
       "      <td>2.0</td>\n",
       "      <td>111</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>19.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012-05-01</td>\n",
       "      <td>00:00</td>\n",
       "      <td>95</td>\n",
       "      <td>197</td>\n",
       "      <td>330</td>\n",
       "      <td>124</td>\n",
       "      <td>6.0</td>\n",
       "      <td>97</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>361</th>\n",
       "      <td>2012-12-27</td>\n",
       "      <td>00:00</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>362</th>\n",
       "      <td>2012-12-28</td>\n",
       "      <td>00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>3</td>\n",
       "      <td>38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>2012-12-29</td>\n",
       "      <td>00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>8</td>\n",
       "      <td>42</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>2012-12-30</td>\n",
       "      <td>00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>2012-12-31</td>\n",
       "      <td>00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>366 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date Unnamed: 1  Rachel / Papineau  Berri1  Maisonneuve_2  \\\n",
       "0   2012-01-01      00:00                 16      35             51   \n",
       "1   2012-02-01      00:00                 43      83            153   \n",
       "2   2012-03-01      00:00                 58     135            248   \n",
       "3   2012-04-01      00:00                 61     144            318   \n",
       "4   2012-05-01      00:00                 95     197            330   \n",
       "..         ...        ...                ...     ...            ...   \n",
       "361 2012-12-27      00:00                  8      12              7   \n",
       "362 2012-12-28      00:00                  0      35              3   \n",
       "363 2012-12-29      00:00                  0      27              8   \n",
       "364 2012-12-30      00:00                  0       5              1   \n",
       "365 2012-12-31      00:00                  0       4              3   \n",
       "\n",
       "     Maisonneuve_1  Brébeuf  Parc  PierDup  CSC (Côte Sainte-Catherine)  \\\n",
       "0               38      5.0    26       10                            0   \n",
       "1               68     11.0    53        6                            1   \n",
       "2              104      2.0    89        3                            2   \n",
       "3              116      2.0   111        8                            1   \n",
       "4              124      6.0    97       13                            2   \n",
       "..             ...      ...   ...      ...                          ...   \n",
       "361              4      NaN     1        2                            0   \n",
       "362             38      NaN     0        0                            0   \n",
       "363             42      NaN     9        0                            0   \n",
       "364              5      NaN     6        0                            0   \n",
       "365              8      NaN    12        0                            0   \n",
       "\n",
       "     Pont_Jacques_Cartier  weekday  \n",
       "0                    27.0        6  \n",
       "1                    21.0        2  \n",
       "2                    15.0        3  \n",
       "3                    19.0        6  \n",
       "4                    16.0        1  \n",
       "..                    ...      ...  \n",
       "361                   NaN        3  \n",
       "362                   NaN        4  \n",
       "363                   NaN        5  \n",
       "364                   NaN        6  \n",
       "365                   NaN        0  \n",
       "\n",
       "[366 rows x 12 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add a weekday column to bikes_df\n",
    "bikes_df['weekday']=bikes_df['Date'].apply(lambda y:y.dayofweek)\n",
    "bikes_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deleting an existing column from DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove column 'Unnamed: 1' from bikes_df\n",
    "bikes_df.drop('Unnamed: 1',inplace=True,axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deleting a row in DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove row no. 1 from bikes_df\n",
    "bikes_df.drop(1,inplace=True,axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Group By\n",
    "\n",
    "Any groupby operation involves one of the following operations on the original object. They are −\n",
    "\n",
    "- Splitting the Object\n",
    "\n",
    "- Applying a function\n",
    "\n",
    "- Combining the results\n",
    "\n",
    "In many situations, we split the data into sets and we apply some functionality on each subset. In the apply functionality, we can perform the following operations −\n",
    "\n",
    "- **Aggregation** − computing a summary statistic\n",
    "\n",
    "- **Transformation** − perform some group-specific operation\n",
    "\n",
    "- **Filtration** − discarding the data with some condition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x000001D5D4ECB948>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# group imdb_df by movie genres\n",
    "imdb_df.groupby(['genre'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>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>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",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>8.7</td>\n",
       "      <td>City of God</td>\n",
       "      <td>R</td>\n",
       "      <td>Crime</td>\n",
       "      <td>130</td>\n",
       "      <td>[u'Alexandre Rodrigues', u'Matheus Nachtergael...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>927</th>\n",
       "      <td>7.5</td>\n",
       "      <td>Brick</td>\n",
       "      <td>R</td>\n",
       "      <td>Crime</td>\n",
       "      <td>110</td>\n",
       "      <td>[u'Joseph Gordon-Levitt', u'Lukas Haas', u'Emi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>931</th>\n",
       "      <td>7.4</td>\n",
       "      <td>Mean Streets</td>\n",
       "      <td>R</td>\n",
       "      <td>Crime</td>\n",
       "      <td>112</td>\n",
       "      <td>[u'Robert De Niro', u'Harvey Keitel', u'David ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>950</th>\n",
       "      <td>7.4</td>\n",
       "      <td>Bound</td>\n",
       "      <td>R</td>\n",
       "      <td>Crime</td>\n",
       "      <td>108</td>\n",
       "      <td>[u'Jennifer Tilly', u'Gina Gershon', u'Joe Pan...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>969</th>\n",
       "      <td>7.4</td>\n",
       "      <td>Law Abiding Citizen</td>\n",
       "      <td>R</td>\n",
       "      <td>Crime</td>\n",
       "      <td>109</td>\n",
       "      <td>[u'Gerard Butler', u'Jamie Foxx', u'Leslie Bibb']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>978</th>\n",
       "      <td>7.4</td>\n",
       "      <td>Wall Street</td>\n",
       "      <td>R</td>\n",
       "      <td>Crime</td>\n",
       "      <td>126</td>\n",
       "      <td>[u'Charlie Sheen', u'Michael Douglas', u'Tamar...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>124 rows × 6 columns</p>\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",
       "4            8.9              Pulp Fiction              R  Crime       154   \n",
       "21           8.7               City of God              R  Crime       130   \n",
       "..           ...                       ...            ...    ...       ...   \n",
       "927          7.5                     Brick              R  Crime       110   \n",
       "931          7.4              Mean Streets              R  Crime       112   \n",
       "950          7.4                     Bound              R  Crime       108   \n",
       "969          7.4       Law Abiding Citizen              R  Crime       109   \n",
       "978          7.4               Wall Street              R  Crime       126   \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",
       "4    [u'John Travolta', u'Uma Thurman', u'Samuel L....  \n",
       "21   [u'Alexandre Rodrigues', u'Matheus Nachtergael...  \n",
       "..                                                 ...  \n",
       "927  [u'Joseph Gordon-Levitt', u'Lukas Haas', u'Emi...  \n",
       "931  [u'Robert De Niro', u'Harvey Keitel', u'David ...  \n",
       "950  [u'Jennifer Tilly', u'Gina Gershon', u'Joe Pan...  \n",
       "969  [u'Gerard Butler', u'Jamie Foxx', u'Leslie Bibb']  \n",
       "978  [u'Charlie Sheen', u'Michael Douglas', u'Tamar...  \n",
       "\n",
       "[124 rows x 6 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get crime movies group\n",
    "imdb_df.groupby(imdb_df['genre']).get_group('Crime')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "genre\n",
       "Action       126.485294\n",
       "Adventure    134.840000\n",
       "Animation     96.596774\n",
       "Biography    131.844156\n",
       "Comedy       107.602564\n",
       "Crime        122.298387\n",
       "Drama        126.539568\n",
       "Family       107.500000\n",
       "Fantasy      112.000000\n",
       "Film-Noir     97.333333\n",
       "History       66.000000\n",
       "Horror       102.517241\n",
       "Mystery      115.625000\n",
       "Sci-Fi       109.000000\n",
       "Thriller     114.200000\n",
       "Western      136.666667\n",
       "Name: duration, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get mean of movie durations for each group\n",
    "a=imdb_df.groupby(['genre']).mean()\n",
    "a['duration']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Mypc\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>The Shawshank Redemption</td>\n",
       "      <td>122.298387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>122.298387</td>\n",
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       "      <th>2</th>\n",
       "      <td>The Godfather: Part II</td>\n",
       "      <td>122.298387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pulp Fiction</td>\n",
       "      <td>122.298387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>City of God</td>\n",
       "      <td>122.298387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>927</th>\n",
       "      <td>Brick</td>\n",
       "      <td>122.298387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>931</th>\n",
       "      <td>Mean Streets</td>\n",
       "      <td>122.298387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>950</th>\n",
       "      <td>Bound</td>\n",
       "      <td>122.298387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>969</th>\n",
       "      <td>Law Abiding Citizen</td>\n",
       "      <td>122.298387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>978</th>\n",
       "      <td>Wall Street</td>\n",
       "      <td>122.298387</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>124 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        title    duration\n",
       "0    The Shawshank Redemption  122.298387\n",
       "1               The Godfather  122.298387\n",
       "2      The Godfather: Part II  122.298387\n",
       "4                Pulp Fiction  122.298387\n",
       "21                City of God  122.298387\n",
       "..                        ...         ...\n",
       "927                     Brick  122.298387\n",
       "931              Mean Streets  122.298387\n",
       "950                     Bound  122.298387\n",
       "969       Law Abiding Citizen  122.298387\n",
       "978               Wall Street  122.298387\n",
       "\n",
       "[124 rows x 2 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# change duration of all movies in a particular genre to mean duration of the group\n",
    "x=imdb_df.groupby('genre').get_group('Crime')\n",
    "x['duration']=x['duration'].mean()\n",
    "x[['title','duration']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>title</th>\n",
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       "      <td>9.3</td>\n",
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       "      <td>The Godfather</td>\n",
       "      <td>R</td>\n",
       "      <td>Crime</td>\n",
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       "      <td>[u'Marlon Brando', u'Al Pacino', u'James Caan']</td>\n",
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       "      <th>2</th>\n",
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       "      <td>R</td>\n",
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       "      <th>3</th>\n",
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       "      <td>152</td>\n",
       "      <td>[u'Christian Bale', u'Heath Ledger', u'Aaron E...</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>154</td>\n",
       "      <td>[u'John Travolta', u'Uma Thurman', u'Samuel L....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>972</th>\n",
       "      <td>7.4</td>\n",
       "      <td>Blue Valentine</td>\n",
       "      <td>NC-17</td>\n",
       "      <td>Drama</td>\n",
       "      <td>112</td>\n",
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       "    <tr>\n",
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       "      <td>7.4</td>\n",
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       "      <td>PG-13</td>\n",
       "      <td>Drama</td>\n",
       "      <td>126</td>\n",
       "      <td>[u'Tobey Maguire', u'Charlize Theron', u'Micha...</td>\n",
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       "    <tr>\n",
       "      <th>975</th>\n",
       "      <td>7.4</td>\n",
       "      <td>Back to the Future Part III</td>\n",
       "      <td>PG</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>118</td>\n",
       "      <td>[u'Michael J. Fox', u'Christopher Lloyd', u'Ma...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>976</th>\n",
       "      <td>7.4</td>\n",
       "      <td>Master and Commander: The Far Side of the World</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>Action</td>\n",
       "      <td>138</td>\n",
       "      <td>[u'Russell Crowe', u'Paul Bettany', u'Billy Bo...</td>\n",
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       "      <td>R</td>\n",
       "      <td>Crime</td>\n",
       "      <td>126</td>\n",
       "      <td>[u'Charlie Sheen', u'Michael Douglas', u'Tamar...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>699 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     star_rating                                            title  \\\n",
       "0            9.3                         The Shawshank Redemption   \n",
       "1            9.2                                    The Godfather   \n",
       "2            9.1                           The Godfather: Part II   \n",
       "3            9.0                                  The Dark Knight   \n",
       "4            8.9                                     Pulp Fiction   \n",
       "..           ...                                              ...   \n",
       "972          7.4                                   Blue Valentine   \n",
       "973          7.4                            The Cider House Rules   \n",
       "975          7.4                      Back to the Future Part III   \n",
       "976          7.4  Master and Commander: The Far Side of the World   \n",
       "978          7.4                                      Wall Street   \n",
       "\n",
       "    content_rating      genre  duration  \\\n",
       "0                R      Crime       142   \n",
       "1                R      Crime       175   \n",
       "2                R      Crime       200   \n",
       "3            PG-13     Action       152   \n",
       "4                R      Crime       154   \n",
       "..             ...        ...       ...   \n",
       "972          NC-17      Drama       112   \n",
       "973          PG-13      Drama       126   \n",
       "975             PG  Adventure       118   \n",
       "976          PG-13     Action       138   \n",
       "978              R      Crime       126   \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....  \n",
       "..                                                 ...  \n",
       "972  [u'Ryan Gosling', u'Michelle Williams', u'John...  \n",
       "973  [u'Tobey Maguire', u'Charlize Theron', u'Micha...  \n",
       "975  [u'Michael J. Fox', u'Christopher Lloyd', u'Ma...  \n",
       "976  [u'Russell Crowe', u'Paul Bettany', u'Billy Bo...  \n",
       "978  [u'Charlie Sheen', u'Michael Douglas', u'Tamar...  \n",
       "\n",
       "[699 rows x 6 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop groups/genres that do not have average movie duration greater than 120.\n",
    "imdb_df.groupby('genre').filter(lambda a:a['duration'].mean()>float(120))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# group weekday wise bikers count\n",
    "a=bikes_df.groupby('weekday').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
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       "      <td>58033</td>\n",
       "      <td>51967.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>141436</td>\n",
       "      <td>156540</td>\n",
       "      <td>190065</td>\n",
       "      <td>106485</td>\n",
       "      <td>14935.0</td>\n",
       "      <td>104338</td>\n",
       "      <td>45891</td>\n",
       "      <td>68486</td>\n",
       "      <td>61058.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>147325</td>\n",
       "      <td>158044</td>\n",
       "      <td>190903</td>\n",
       "      <td>109796</td>\n",
       "      <td>16227.0</td>\n",
       "      <td>101395</td>\n",
       "      <td>47487</td>\n",
       "      <td>66057</td>\n",
       "      <td>63069.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>149402</td>\n",
       "      <td>155651</td>\n",
       "      <td>188838</td>\n",
       "      <td>106005</td>\n",
       "      <td>10019.0</td>\n",
       "      <td>99924</td>\n",
       "      <td>47731</td>\n",
       "      <td>63381</td>\n",
       "      <td>66798.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>130158</td>\n",
       "      <td>123719</td>\n",
       "      <td>132358</td>\n",
       "      <td>77071</td>\n",
       "      <td>10171.0</td>\n",
       "      <td>66223</td>\n",
       "      <td>56076</td>\n",
       "      <td>44808</td>\n",
       "      <td>54014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>122230</td>\n",
       "      <td>113428</td>\n",
       "      <td>122742</td>\n",
       "      <td>67324</td>\n",
       "      <td>14098.0</td>\n",
       "      <td>63731</td>\n",
       "      <td>55296</td>\n",
       "      <td>43843</td>\n",
       "      <td>89473.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Rachel / Papineau  Berri1  Maisonneuve_2  Maisonneuve_1  Brébeuf  \\\n",
       "weekday                                                                     \n",
       "0                   134966  140206         173675          96247  15976.0   \n",
       "1                   128109  132563         162262          89861  10644.0   \n",
       "2                   141436  156540         190065         106485  14935.0   \n",
       "3                   147325  158044         190903         109796  16227.0   \n",
       "4                   149402  155651         188838         106005  10019.0   \n",
       "5                   130158  123719         132358          77071  10171.0   \n",
       "6                   122230  113428         122742          67324  14098.0   \n",
       "\n",
       "           Parc  PierDup  CSC (Côte Sainte-Catherine)  Pont_Jacques_Cartier  \n",
       "weekday                                                                      \n",
       "0         95962    42152                        61397               68785.0  \n",
       "1         89884    37330                        58033               51967.0  \n",
       "2        104338    45891                        68486               61058.0  \n",
       "3        101395    47487                        66057               63069.0  \n",
       "4         99924    47731                        63381               66798.0  \n",
       "5         66223    56076                        44808               54014.0  \n",
       "6         63731    55296                        43843               89473.0  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get weekday wise biker count\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1d5d4f0df88>]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot weekday wise biker count for 'Berri1'\n",
    "plt.plot(a['Berri1'],color='blue')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://memegenerator.net/img/instances/500x/73988569/pythonpandas-is-easy-import-and-go.jpg)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}