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+{
+ "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_1000.csv` dataset contains Highest Rated IMDb \"Top 1000\" Titles."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load imdb dataset as pandas dataframe\n",
+    "imdb_df = pd.read_csv(\"data/imdb_1000.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>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()"
+   ]
+  },
+  {
+   "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": [],
+   "source": [
+    "# load bikes dataset as pandas dataframe\n",
+    "bikes_df = pd.read_csv(\"data/bikes.csv\", sep=\";\", parse_dates=['Date'], dayfirst=True, index_col='Date')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\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>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",
+       "    <tr>\n",
+       "      <th>Date</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01</th>\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>2012-01-02</th>\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>2012-01-03</th>\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": [
+       "           Unnamed: 1  Rachel / Papineau  Berri1  Maisonneuve_2  \\\n",
+       "Date                                                              \n",
+       "2012-01-01      00:00                 16      35             51   \n",
+       "2012-01-02      00:00                 43      83            153   \n",
+       "2012-01-03      00:00                 58     135            248   \n",
+       "\n",
+       "            Maisonneuve_1  Brébeuf  Parc  PierDup  \\\n",
+       "Date                                                \n",
+       "2012-01-01             38      5.0    26       10   \n",
+       "2012-01-02             68     11.0    53        6   \n",
+       "2012-01-03            104      2.0    89        3   \n",
+       "\n",
+       "            CSC (Côte Sainte-Catherine)  Pont_Jacques_Cartier  \n",
+       "Date                                                           \n",
+       "2012-01-01                            0                  27.0  \n",
+       "2012-01-02                            1                  21.0  \n",
+       "2012-01-03                            2                  15.0  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# show first 3 rows of bikes_df\n",
+    "bikes_df.head(n=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/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",
+       "Name: title, dtype: object"
+      ]
+     },
+     "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",
+       "        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>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",
+       "  </tbody>\n",
+       "</table>\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"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# show only movie title and genre\n",
+    "imdb_df[['title', 'genre']].head()"
+   ]
+  },
+  {
+   "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": [
+       "dtype('int64')"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# show the type of duration column\n",
+    "imdb_df.duration.dtype"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([142, 175, 200, 152, 154,  96, 161, 201, 195, 139])"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# show duration values of movies as numpy arrays\n",
+    "imdb_df.duration.values[:10]"
+   ]
+  },
+  {
+   "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",
+       "Name: title, dtype: object"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# convert all the movie titles to uppercase\n",
+    "to_uppercase = lambda x: x.upper()\n",
+    "imdb_df['title'].apply(to_uppercase).head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plotting a column\n",
+    "\n",
+    "Use ``.plot()`` function!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7faf733756a0>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7faf7335be80>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot the bikers travelling to Berri1 over the year\n",
+    "bikes_df['Berri1'].plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7faf71290358>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7faf71227d30>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot all the columns of bikes_df\n",
+    "bikes_df.plot(figsize=(10,7))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Value counts\n",
+    "\n",
+    "Get count of unique values in a particular column/Series."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Drama        278\n",
+       "Comedy       156\n",
+       "Action       136\n",
+       "Crime        124\n",
+       "Biography     77\n",
+       "Adventure     75\n",
+       "Animation     62\n",
+       "Horror        29\n",
+       "Mystery       16\n",
+       "Western        9\n",
+       "Sci-Fi         5\n",
+       "Thriller       5\n",
+       "Film-Noir      3\n",
+       "Family         2\n",
+       "Fantasy        1\n",
+       "History        1\n",
+       "Name: genre, dtype: int64"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# what are the unique genre in imdb_df?\n",
+    "imdb_df['genre'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7faf710ecc50>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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ACbZvbxDrSuAfuPv7qNO/RVuSfjbJZtt+eMu4twP3A+4Afg+oxn1Am7g19vqU/9M7KbW4jgY+b/tPDWLdn7IQdH9Kh/kY4ETbjXee9XP5NOBVwBMon8vP2f5J05gDc+VSguvaPluSbP8cOFTSYqBxordtSTcBN1HeVBsCX5J0lu0mh6X3tf2PTdszFdvXTdi5L5/qsSPE7PK1/6kexhrKkQ1wZ8smXgeMox7tKfWrU7Zvk/QlYF3gIGAv4K2SjrD90RHD3Wr7P7tqm6Tn2z6l3t7Q9q+7iGv7YV3EmSTu/ccRtw6LvAx4OfAj4HjgycB+wM6jxqs78aOBoyX9NfAF4PD6Pvhn20sbxDSlE3KWpF2AzwOvlfRj4GDb/zVqzIG5kuj/UMfXrlZZcXsDsF7TYJLeCLyCUjTo08Bbbf9p8DtoNv74NUnPrgvFunKdyskpS1oHeCMws7J4UxjDaz8COBV4sKT3UY5A3tWmjcA1wLmSvg78YbDR9ofaBLV9rEpJjq3rpqua9OaGSdoDeCXwCOA44Im2b1E5r3I5MGqiP0fSByg7pOHXflHDJr6LFTu3s4HHN4yzkvr63gRsYfsASVsBj7TdaIhN0ja2r5Q0aftavH4knUopnPgfwHNt31jv+qKkRQ1jzgN2p/ToFwAfpOw8nkJZKLr1lE+eOubwzuhm4PWU0jHbAycDjXeucyXRvxG4L/AG4J+Bp1L2xE09EHh+PTq4i+07JT2nRRvfIemPwCB5tD3k/DvKVbo2pezczgQObBEPOn7tto+vR1e7Ug6z97TdamcE/Hf9ulf96oSknYFjgWspbd1c0n62z2sRdi/g8IkxbP9W0qsbxBuU8x4uaGXKe74JTXG7rc9ShpcGs2RuoCSjpudS3kSpZvvBSe5r8/oBjrA96Yn4FoXDrgbOAT5g+4Kh7V+S9FcNY/4XZWe0p+3rh7YvkvTJhjGBOTJG35XpThbZ/tU91ZbVqfZGNmZoR2/7vxvGucx265OQU8RfD8D2/3UUbzHwEttX1Z+3poyl/3nDePOAb9nepaP2rQW80PZJXcSrMa+kjEuvRRkKeAlDCb9pT1m1umKX52a6Jun5q7p/MKTVMPZ6Xb0va7x5wL/ZfnNXMYfNiR69pIWUEygPZeXk9NgRQy2m9A4EbAH8ut7egNKDbDXuKOl5wGBvfm7Tw9iheA+jHL4tYOXX/bwWMV8HHEo5NByMpRsY9W+J7eWSrpK0RZMdxSra+BhKz+aB9edfAK/ooDDeOoMkD2D7J3VIrJH6+u+UtL7t1ucU6lHV2ygn4bpyIzAY8rpp6Da06yn/UdK6rDg3syVDQ01N1f/H3zP0OQI+1XCI7bmruM+0O1/zHkn/AvwO+Abl8/MPtj/fJFh9L41tDcGc6NFLugp4K7CEoRN9E4cfRoh3NHDqYDxd0rMoh0uvadHGwyhnyo+vm/YFFtl+e4uYPwY+w91f93daxOx0Rouk8ygzWX7AyjNZ2uyMLgDeOTjcrkMu77fd6oOgMt31TkrPFuClwDzbr2oRs9OZPPV99AvgixPizaqjTUlPp4z/b0sZUtwJeKXtc1vG/TSwDmWIDcp49XLXixjNFpIutr29pL2A51CGns5rOdvsE5Rh2pNZ+X/fegLBXEn059t+cofxltjebrptI8a8BNi+LvoYHIr9qMFRx3DM79vu9BKMKouGnm77jo7i/fVk21vujO42BNDFsICke1POcQzeS98FjrTduCcqadJzRbaPnWz7DOKNZdrihN9xlO0Dpn/ktHEeBOxIOSq+0Hbrqyx1+b+X9DLbn5f0psnub3NyX9Jlth9dd0xfsv2Ntu9RSZ+dvJnNOyIDc2LoBjik/kHPZuWZCE33dP8j6V2s3LP7n3ZNBMoQ0KDn1cXCnI9IOoTSY2o1A2Pozd7pjJY2CX0VrpH0bsrwDZSZCG0WyA12vMfYfikrD1+0UmfyrEuZfTLTy2GuKt5Ypi1O0PjKRZPMihnMYNmiDuE1nh1TLZe0peuaDkkPp/mU4vvV7+OYsnl6Pf/xO+DvVaYV/75NQNv7d9KyScyVRL8/sA3lkG54XLlpot+XsgDr1BrnPFas7Gzq/wE/qj1mUcYYD24ZczvKoetTWfl1NxlXHbzZO53RorLAZXBYeC/K/+g3LWcbvQp4Lyv+v9+t2xqrY6APlXQv239sE2uYpOcC/0557Q+TtD3wT02HrrqetjiFW1o8d7JZMQNtZ8dAGaI9R2U1sCjn5RolQNufqt/f27JNk8U+WNK/UdY9LJf0G2CPNjElbUaZjrtT3fRd4I0TZuA0iz1Hhm6ust3FsuWJce9n+zfTP3LG8TahjNMD/MD2TS3jLQW27TIxDcV+AOWwcOTVm6uIKcqbfUfbbXdynZN0HPAoytzk4THQNofwiynJ7Vx3sHpZ0hcpkwZeYfsxNfFfYHv7pm2ca+oQ2+DzflWbobUar/NJDTXuYyjnKO4zFPO4FvHOoiy8Gj6Sfantp7dpJ8ydHv0Fkra1fXkXwerZ7U9TFl1tIelxwGtsv7ZBrIkLPQZ734dIekjLQ9lLKcNBbXpgK6kzmD5L7eFLuhV4le3FbWO79Bq+UoebRk70kj5s+yBJp7PiKGE4fqsPJvDT+rUW3R3O/8n2rVp59XKblcFb2n6xpH3hrvn4jee/d/03HdeURU099/wvJOF2ax2+QpnUcDrtV20DUN/jO1MS/RnAs4DzKYvmmppve3ic/nOSDmoR7y5zJdHvCFxcT1T9gRX1L5qe6DwceCb1guW2f7yKN9p0xrnQYwPgSkk/ZOXx9DYJ7xjgtba/CyDpyZTE3+hvOeGDvxZl/LfpWOWgJ/PvDZ8/pTpGf3/bb+k49GWSXgLMq8MsbwAumOY5q9L1tMWu/6bjmrL41iniPRbYnHLt6aZ+b/uIFs+fzAuBx1EmXOwvaWNWnPNr6peSXgacUH/eF+hkdtxcSfS7dR3QHdWQGZq98CzbKyU4SfeZ5CmjOKTl8yezfJDkAWyfL6nNDJzhD/4dlFWnjcYqh44qtrf9keH7VEo3ND7xW8dRd5r+kSN7PWWNxx8oh93fpKzebupQyrzszSUdTxmvbXySbuhvugj43YRZYfduEG8sJwxtr7QDqf+rd1Hm/r++ZfjOJjUM+V1d93BHHQa9hbJDauNVlDH6wyk7uQso5TVamxOJfjBfXtKDGRoPa6HzGjKUf8rEGQmTbZuxMc1o+Y6kT1F6DaaU2T13MPTU4M3/advfG95QP6Rthpv2o5R+GPbKSbaN6mJJp9HtPOXdbb+TkuwBkPSi+jtGZvvMOu4/mLb4xi6mLVJmrD0NGKzmXJeS+EZamzDOKYs1/q7AuynvzffbPqtNvKrLSQ0DiyRtQClstpjyd21cdKzabOLRev0sXdcy7pw5Gfs8ytDIQygJ5KHAFbYf3TDeRpSk8TTKh+lMygdq5MMkSX9GWeQwcXn5A4BPukF5ANV1AxNmtADtS7Zq1RffsO2R3vyqZYqn2zbDWPtS/oZPpsw4GLg/cKftXUeNOSF+5/OUu3z99blnT3ydk21rEPfiiSd0J9s2gzivsf2p2kO+m6YzXCTtTtlZ3gq8z/b5TeJMEXtskxpq/AXAA2xf0jJOp++lYXOiR085FN6RUldkB5USni9rGqz2kF7aUdueSeltbkbZGQ0S/W3AO5oEdF0c5jGUbHV3dVn+ktIbnD+hd/cAmo+nXkCZl70RK5/zuB1o9SGCbocdVFZTPxvYVNLw+O8DKENYo8a7D6Vw30YqVwAb7jBs2rK5AL+R9PjBEVs9Kf+7UYPUJD8PuM324R20a+B0ykSGXwJvUykFMfx725yXGsekhrt2vravnbhtxFjj+CytZK4k+j/Z/qXKNSXXsn2OpA83DdbldCuXFZDHSnqBW1xkZJI2jq1gWO09PZqVp4X904hh7kWZtbQ2K89guY1yompkdYju55QrVnWu43nK/0MZ934e5dB94HbKhUNG9RpKLfuH1HjDHYaPNYg30UHAyZIGCwM3oQzbjaye79iXMpbclU46IFPobFLDmHbInX+WJporQzffAvakLEraiLJnfoIb1j7ReGrIvJ9Sfe5/688bAm+23bg2u0odlde724Jhn6S8UXehTDF9IWXOf5OSukh6qBvWHFpFzB0pCflRlA/BPNovwhrLPGVJ67gW3Kr/883bHMJLer1Hv1jJquI9AbjO9k31fNRrgOdTauW/xw1r6Eg6nLI4bmJNnrYrY4d/x+O7iKcOy3TUSQGDHfINrLxDPtp2453y8GdJpZLpem5xxaqVYs+RRH8/ymHmWpQhl/WB45uMqdd446ghc1e51qFtrcbXNJ6CYZfYfuzQ9/WA/7T9lIbxtgbewt2Pjhqf6FK5GMQ+lBOaCykXStnaLQrE1bidjFNPeP65lF792pSe+C2UBU5NevWDE7nfsH27SpmOxwP/0jThSboIeJrtX9UpxCdSjma3Bx5lu1GPcYpzPSOf45nmd3QyPj0OXe+Qa8wvUK5BsRz4IeUo4SO2P9A29qwfuqlDGF+rY8t3sqKqXRvjmG41T9K9XVfx1bnQI09fq899BKVe/Lsn3PUUVtQWaWowBfS3kh5CGRPdpEW8k4FPUo4OWl/mcMD2UknzXC7q/VlJPwJaJXrGM095fZdLCf4NcJztQ1QK3DX1btsnq6xveBrwAeATrLggyajmDfXaXwwcVYcYvyzp4qaN7OpczzQ6uVDKOI4QbX+0ztxbwModnDYLprat76WXAv9JWXS4mPIeaGXWJ3p3XPO7Gsd0q+MpV7D/LOUN+kqa75Q+DLzd9pLhjZJ+BbyfMuzU1Ol1WtgHgIsor/voFvHusP2JFs+fzG9VLvl3sUo9kRspR3NtTTZPue0J2rVVSl/szdAUyxYGO8vdKUn56yp1z5uaJ2ltl2qlu1IW9w00/vyrlCl4AXdPdKOe61mVrmrUfIxJjhDbBJT0H8CWwMWs+J+Zditj16nDa3sCH3O5xGcnQy6zPtFX/wcsqWOsrWt+Ay8CHt7ldCvb/1rH/p9G+Yd/kzINtImNJyb5+juW1KlcjdRxv7PreYQvS/oacJ+WO9DTJb2WUiBu+OioTf30l1N6Xa+jnNjcnJJUWqnjn23LKEz0T5T/9fm2f6hSbfHqFvFuUFnn8HTgX2tCbbOTO4GyduIXlOHPwYroR9DuAuxfrc9fTAcXHBkm6bGUHchaqiuv3bIm+xiOEBdSeuBdjn1/irLg8MfAeZIeShn7b22ujNF3XfP7K8ABtjubblXj7kCZB/4i4GfAl5ucnJF0te2tprhvqe1HtGjj3c4ltKF7oH56W5I+yiR1XgZadBiQ9KCm54qmiHdfykrwJbavrkcL29k+s0XMHSnDc2e6FvGr51bWazH237hw2zRxj6GUPbiMoaNtt1vrcB6lA/ZpykrbGykXSWlTO/5k4A1ecaHxsRg6GmtlTvToXWp+z6+3l3UQssvpVltTxnr3ZcWVgdRyDHORpL+1vdKQSh0Hblt87GxJLwBO6aI34jHUT1e5SPk/s+LSkW0Xii0auv1eui0tcWEd6/4s5aR2q7+pSxGzWyiLxq6mzMlvc4SA7Qsn2faTNjEphQa3m+zIs6UdbW/bccyXU46KujxC3Ai4XNIPaJ9DVrnamA6unzCre/SSRPlQvo7yjxLljf/RNmOBHU+3upNyOPxq20vrtmva9GhVCiSdCvyRFYl9IeVE0l5uUf5YZbXt/Sh/x9/TMolqDPXTVVYyPp/Sq+30DTqGIxpReouvopSoPgn4XNNEWicJLKT8DbeuJ8xPtj2OOj0jk3Qppae9NrAV5YIwXRQaHMT/DPBBd1CpVh1fy3hC7C5zyFhWG6/0O2Z5on8TpfznAbZ/Vrc9nDIL4RtusTKvJtPh2vGNhnEk7Uk50bMTpRjViZT6L617uiorgAeHx5fZ/nbbmF3TGOqn16l7u7oW4OrSOKfs1f/X5yk70h8DB9seqf5JPTrYAbjIK+rbX9I2gXZF0q8pUzMn5ZZrKmoCPY0yxNJqBzL8v5b0Zdutz/PMVbN96ObllOub3lXUyfY1dYrcmTRcmSdpb8qsk3Mpb6SPSnqr7S+NGsv2Vyg12O9Hqdp4EPBglQv9ntpmbNXl4tirqk3wfnX/AAAGlklEQVQzMt39UnBQTqr9vOFYYKf106u3AWdI+g4dXO5wnFSum/oyynv1Zsoc9dMoyfBkYNQd/h9tezDbor6vZpOftU3m0/gM5W+50mLGhobfh52eM+pyyqZWLqFxN23OIQ3M9kS/jiep3Gd7WZ2G1NQ7KStrbwGo4//fAkZO9ENt+g1l1eUXVFZIvgj4R8oOaTY5krIIZzC2uh2lFsj6kv6+wY6p6/rpAO+jzLS6D91f7vC+kgYzGVoXiaNULPwPYE+vXEphkcoq5FGdVGfdbCDpbylDQm2mv3btwasYS+5iZ7zM9mktYwx4ittd6HLK5vB5t67PIQGzf+hmysPsNofgkpbY3m7o57WAHw9v6ytJp1AW5VxWf96WMkXwbZQTtKNWM3wGZce5LWWnthNlRsO5Ldo4lhkd4yBJXZxHULmS0AWUtQ27AM+g7Ii+6W5K9XZC0o2UodNJj9rajidLOpIyWeJ0Vj6aG3l6paTllOnYopRl/u3gLtpXgV1ke+HwsFoX53+6Poc0MNt79I8b6n0NE+3q0n9D0jdZsULyxZTLga0Jth4keQDbl6tcDvGaJiMuHk/99DMkPaPNsNe4qdS1H9y+2/0NZl9sRlkotw3laOt7lMTf+hKPHbuxzUSIGViXkuCfMbSt0ZWrbHdS+XEK41rUN5ae96zu0XdNtbSA7e+pLMR4cr3rfym1c366+lp3z6gnT39FOWkMZSe3EWVc9HzbT5jquVPEO50yZHWaO7rQ+tDMoD8Af6KbIZZOSVpGuSDECcD3mdDDbTL7osa9F2Uo4EmUKp5/CfzvGKYcNjKuHudco7KY6WbK0OI/UOpvHTmYedci7lgmC6xpif5rTF5aYDvK1WxWdT3MXqjj6a9lxU7ue5Rx+98D97X9f1M9d4p4f03ZWexOKcR0IqU2UdPrxs4JKjWYnk5ZP/FY4OvACcNHSw3jrk9J7jvV7xtQppmO5RJ+o5L0QLdb9TxV3LEtauvSOKZsTjyHRIdDTHf9jjUs0f9wqh7rxHH7Pqu9xkdS3lxXuZbZbRlzHqVW0N8CuzWcfbCN7SunmBnUaQncLqmUKdiXMpPrvW62GvooyjUCbqccIVwIXGj71122dbbSFKvfB9xwFXzX5uqUzdk+Rt+1DVZx37r3WCtWI0k7U4qtXUvpMWwuaT/b57WIuS7lIuEvpszoafqhfBOl6NYHJ7mvbdG5ztUEvzslyS8AjqAsdGtiC0q106spdc6vpwwprhFmSyKfgbFN2RynNa1HfwLwbU9eWuDpthtdcWcuqSdOX2L7qvrz1pQhhz9vGO8k4ImUxWJfBL4zjoVOs42k4yiL2c4ATrR9aQcxRenVP6l+PYZyPuW/bHc+5W42kfRh2wfVcz53S0oNTm6PxYQe/aytlz/Rmpbox1ZaYK6YbJVlm5WXkp5JuZZvZ7Xoa9yua313SqX0xeDkc9cXcN+MMkb/JOA5wINsr+podM6T9Oe2F6vD0gLjMM4pm+O0RiX6gblQWmBcVKoD3klZqg/lil3zPGJ1wDpraUpN5j0PxZ601vdsOSE3DpLewIqe/J8oUysHX0v6fpQ0jpOcscIamejXZHVc+UBWzLr5LmVa2EirWVUusALwYEpyGuwsd6HUunlOizZeQfe1vmc1SR+izp33mEvfzkZz9STnXJFEH62oXAzmFYPkpFI//XO2n9ki5j1S6ztmj+H5+Zmr3701bdbNGkvSSbb3lrSEyU92Na2OuNmEhHwzza+sNTBZrW/b3qNl3Ji9xlmXZo2XHv0aQtImtm+sK/ruxg0rEkr6GKUu+XA5iavbjKdPOCEnykXR97H96KYxY3abqyc554ok+jWYpI2AX7YdC5e0F/BX9cdfAX9m+8CWMSdelvEU2x9tEzNiTdVFEZ6YAyTtKOlcSadI2kHlSkGXAjdL2q1l+GspV6zai7Ko6YqGbdxa0iGSrqTU+v5vSmdklyT5iObSo19DSFoEvINSfOko4Fm2L5S0DWXB1EgnvzT5tXLfYrvx+LzGcFnGiEiPfk2ytu0zbZ8M3OR6wWjbVzaMdyWl9/4c20+uPe62i6aeTyn3eo6koyXtyhR1zyNi5pLo1xzDC25+N+G+Jod1nSdl21+xvQ+lJvs5DF2WsV7gJCIayNDNGmKaWQ33sd3o0oxaca3cfSk9/ONoea3cCfEHl2V8se1du4gZsaZJoo/OJClHzE5J9BERPZcx+oiInkuij4jouST6iIieS6KPiOi5JPqIiJ77/9yKbLa9DZZ3AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7faf710fe710>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plotting value counts of unique genres as a bar chart\n",
+    "imdb_df['genre'].value_counts().plot.bar()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7faf7100ac18>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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3LGPdRpZecEV99Yo1fhsxHBlrK4T7GcLAcEKkbGtWVtnWrCy+Nb6IgOqBsxPJ+ivD4dQN7eGSefHEjKGyZaxwkbNtjs02svTAA4AVhC4YMyuEilUVAWAPUOK3LR2I6pGSZGrnZZFI/Ka29smLYvE5zhiuDUkXH4h/yrqNLGfCBebUV6/Y5bchw42xtEK4gWEkBgAqMnFfRnDi/2QU8D+FBaDaOjmV2rE0Em2/qT084cJIdG6GGfph6QMfCT7Zsja+yG8zLMMXB/hz4DN+GzLcGEuC8CG/DegRkdChYHDJEwX5PFGQD6qRca67eUk0dvzGtvbCyyLRuTmquX6bOdy5ULbZIjVLT9yFFYR3MCZcRhWrKiYB+xnpd9uqiXzVbedFY803todzrwiH5xS6GvLbrOGIdRtZesHS+uoVL/ptxHBirKwQbmakiwGASEabyIL1uTmsz80BHe9mq24/NxZvur49nHltODxzYsqd5LeZwwHrNrL0grsBKwidGCsrhF8Af+q3HUNBhuruOfH4vmvaI84N7e1lpcnUsIqbDBW2SM3SC5qAkvrqFa7fhgwXRr0gVKyqyAKagTHZZiKguv+cRKL+ynDEvbEtXDo7kTjHb5uGig/G/75mjXue7W1kORMX1FeveN1vI4YLY8FldCVjVAwAUiIlOzMzS3ZmZvL9ohCierg0mdxVGY7Gb2pvL66IxWeN1lTX+4O/Promfp7fZliGN1cDVhA8xoIgXOe3AcMJFZm0NyNj0k9CGfwkZFJdp6RS25dFouGb2tonXBCNjZpU1wulbq7NNrL0wFWYmeoWxobL6Hlgqd92jBhUw+Ncd9tFkejxG9vDoUsj0bnZqjl+m9Vf7on//abn3PNstpGlO9qBcfXVKxJ+GzIcGNUrhIpVFRnAYr/tGFGI5B4LBBb/Pj+P3+fngWq8wNWaxbFY8w3t7flXtEfmFKgW+m1mb7kv+Otjz1m3kaV78oAlwAt+GzIc6JUgiIgA7wVmqOoXROQsoFhVXx5U6wbOIiDbbyNGNCKZJwJSsTY3h7W5OTBRUzmqdQti8UPXt4ezrm0PzxrvuhP8NrM7rNvI0gsWYQUB6P0K4TuY/h9XAV8ATgA/By4cJLvSxUV+GzDqEAlEROa9kpM975WcbL40cTyZru6aE48fuDYcDtzQHj5nWjI11W8zOwiIFl/hbLRuI8uZsJloHr0VhItV9XwReQNAVY+JyEgYJ7nAbwPGAnFHZm7Ozpq5OTuLr48fR0B134xEYs/V7RG9ob29dGYiWeanffcHn2yxbiPLGbDXCY/eCkJCRAKAAojIJMyKYbgzZnLuhxMpkdIdmZmlOzIz+d64EI7qodJkctfl4Ujixrbw1AXx+CyBIXPhLBHbEttyRs7124DhQq+yjETkvcCdwPnAKuAO4LOq+r+Da97AqFhVUQfM9dsOy9sR1ZYpqdT2S8PRyI3t7RMviMbmBgc5weHe+N9tetZdbN1Glu6YVF+94ojfRvhNr9NORWQepohDgGdUtXYwDRsoFasqBIgAWX7bYukB1fYJJtX1xE1t4aKl0cjcLE1vMsBL7rw1d8b/yU5Ss3THgvrqFVv8NsJverwr81xFW1R1HlA3+CaljWKsGIwMRPKaA4Hzf5ufx29Nqmus0HVrFsdiR29sC+dfHo7MyVctGMgplsg2m21kOROTASsIPW2gqikR2SYiZ6lqw1AYlSaK/DbA0k9Eso4HAhVrcnNZk5sLqqlc1dqKWPzQ9e3t2de0R2aPc93xfTlkQLT4SudN6zaydMdkvw0YDvTWbzsO2CIiL2Mq+wBQ1XcNilXpId9vAyxpQiQQFil/KSe7/KWcbL4wQTVT2TUvHj9wXXs4cH17eEZxKlXc02HuC/762LNxW6do6ZJ+C4KIKLCx00s/BspU9aMich8QVtUfdbPvFUBcVZ8XkQeA/1TVcH9tGSi9FYR/HFQrBocx29Bu1CMicWHmpuysmZuys/i3CeMIqjbMjCcarg6Hub49fNaMRPKs03dbItvmCq6rOKOymZ9lQAzKoClV/V4Pm1wBtAHPAw9gxCTcWUhE5IfAU6r6MwARCapqcjDs7ZUgqOqawTj5IGNXCGOIpMhZ27Iyz9qWlcl3xhXhqDadlUjuvjwSSdzYFp42Px6f6RWpbXzWXWwn51hOZ0BZbqp6stBFRO4BKkRkHTALSAHvwbTI+AQwETgGxIFpwFFvdTAFWC8iU4C9wHgR+SQwHbhcRK7BtOIREVkE7AR+iykWbgbOUtVGESnErFjmqGqfejT1tnXFbcC/YpZV4j1Uh3dPGxtQHsO4IsX1mRnF9ZkZrAoVIqpH5x1Pbrli66Z9kSkT97dntDoxCbqJWKZkJ+JOTqJdszQmWSRwNEUChySOJFTUVYeUOqKuQ0AFxwVHHQkoGnAFxxUJuKjjCo6KOIoK4iiiiKiKiCKqiCAOrgAiXlGPgxp7zR+VF/NWEcHbRwX1yjbk5PucrOQQQVEUEzRHQNVscPK52fDkduYI5h9XzBP13jf7Ce/cH+28r7G+42d55zbS+TzS+bz6jvd8D/RHnewTsKLf+4tIR12WYgbvbAJmeK/lA08BUeCrmGvpdkydVAITzC7CXF8PAf/ivbcI8798ALjee2zFiEw9sAOYB1yCGRF8MfArzLzoXwDfFZGvqerW3n6O3qril4E/Ge6ppqcR89sAi/84rqaW1uobt29w4yXNsmTtJbcEb214LWPGlEq3IX9n67HJm/RoCDmelx0/Hsim3c0OnojnJyUeTElCyE4g2clEMJQ8ofmJ1mSmG5ZAKuxmaMRxNOaIxiXgxEQkjjgxxEmKSkIgqUE3JQFNBXBVkupoypVUyhU1DzSlopoSXBXUFdQF1xXBRdQVcMXBRVEcXBFJ4Ygi4uIEUxB0cQOuaMB1cFzHdVxHHBUCrqMBVxB1CLiiDgEcFcR1NKBGtEQdHHXM6ziI6yCIOOqIIII64iCAI6KOiIp5HRHBcVBBRBxwxGRuOUbMxFHFUZWTD1REzfuOqggqjqsSwEiBeR3vI4N4+yEg6iJG2pAO8VREcL1/vUpZ73WnQzBPCqnZT0Q99ekQUiPOHfIlEkxFB9rtdJP3727gCWA5cBS4BnNRHwf8Hni/t91fAfswbYCmqmqFiDRj2gFdDXwGU/x7CyZ9HiAHmIkRkXGY1v7vBf4c+DZwL0YQ7gX+UlU3d2WoiARUNdXVe70VhIMjTAygU/DbMvaY0ag77lrjHqio17kBZQlAa0HZtlT21KX7W5team74nxnXTbu3Iaftkgta9rfvqQk01O8OHJqQkPicvPx9DaGipkNFoYOJvPyjecHM2NRWKQoepLj5ACUnDlAea2SaHmFSRosW5sQTWVnJhJNJXOMSS0UklopJ1E1KLOUSTznZqRihVBsh90RGKNXmFGg4UKhhp0ja3HHSlixy2lJFwTYKaddCCUsBkUAe0WCOxDKyiGdlkswKkspxcPME8hGCcSEWFYnHRWJRceIxkXhEJBl1JBERSUZFkmHHSUVFUhFHUhFxNCLiRhzRiIhGHUeiIhoVkZh5OHETmwkkRAIJkWASAkmRYAoyXJEMFzJcyFSz+s4EsjCNLxFVN5AiGXBPPYIpUgGXVCBFMsP8mwq4pIIpUhkpdQMp3KCLG0yRCqZwOx4ZKTR46uFmpFQDKeh4LSMFAffkcwm6EDACSSClEnQRxz35uuMoEjCvOQEXx1Ecx0UcJSBqnrdlE4SVff6eiUjnxo7FwCTgAqBjdO0RTnV1WAFsxojTZqARSALTReQAkOv9Xm/DrBjehbmOvYCJNcQwsYatmFVAMfBPGFfTXGCeiPwGCKrqZhF5Dvikqr4qIm3Af2AE6q+B9V19nt4Kwqsi8jhGfU7eeavqL3q5vx/4Fqm3+EOoXY/ctsHdcuUmnZKdYB4wu/P7O2bdfgiYG8y+UKPhZyY9ufffJ1w86eY1Z+fNv6xSy8+uTJbTTvTgluS+AztONObv3xu/EDHDgoLB6LHC0OHEvKK9sYsKX3Nyco6PDwSSZSLkkglkQmteqPkgxUcamebupzTVyDQ5zORAK+Nym5hStI/gFERyAVBVknpC4m4rsVSbERI3JrFUQmKpJLFUSuJuSpLqknSVlAZRUoBkkYjnEwkXSDgcoj0SkvZoEW2xcXIiMY62VJG0uSFpd0O0UyLtTgERJ5doMEfimVnEMjNJZncWGJH++8/jEI+JxOIisagjCSNMTiIqkog4Rpg8QUpGguJGHHEj4mjEETcqohERI0yOyAlPmGIiEjeiFEggwaScFKZgSiTThQw1wtQhTlmIDDRRQGv6s5Nqs7y9tOUoxi30daAA+B5wA6bR5nTMiiCJiSF8FhNE3oJx90S89y7l1JCq16HjG8ZrmDYbF2CmHL7s7deGEYx3YeIJq7swNQ94SVUfPNPn6e0XoRBzgb0OOAvT9fQX3qNLvMDKH1T1gPf8YaBP/qwBYgVhDBBMauzqjfrGLS+6zoTjnC/QZTVyMpDVdrzwnPMAApkLzk+GnzkKjH/p8FOXN7Rt3XjZlNsnO+JMzSN7ykXJWVMuSs4iRqK1NrB/c11gf6AtwcKjzdMXHW2e3umo6ubmttaHipqaikIHY/kFzdmzMrdPnS3b5nghgXfQpvmth5hy6ADTWg8ESyONwRL3UO6UwDHG5YTJm5wgYxImKNgtsYQbORF3w43xVJvEUmGJujGJpZISS6WIuSKJVFASmk3SzSal+bgUAiHpZhJeFvFYHpG2wk4CM462eJG0JYtoSxZJW6pI2jREO4USdvKJBHKJBnIllplFIitDk9k5pHLGpVJ5ApMGIjD9JQnJk8IkYlZMRqASEXGSEUeSEZFURCQZcRw3YlZO3qrJIc91Dwzk/Kp6nohUYS7OYO7sQ8Dfcer3/hNMI71s4C3MkkSBckwQuREoxbiA2jGuptkYV1EKM+irACOCAUzcQLxt/wezAgjQdcv/FKZD9RnpbZbRvR0/eyuF9Zglz5m4x9vmgHeMv+jNudJIyxCfzzKEVOx2N9+51j02+wAVYoJqZ2TPWde/gUglgEggUwLTNmvqwHKAxshbi55s+HbzdSX3vJobLFjSsU8WGaHzUmWXnpcqI0EqvDPQ+OLWwL7UMWlfgBACccLhorJwuKis8cC8k+cKBBJtBQWH64uKmo4Vhg5pbm5rUTAYP1uEUD5toXzaQjPY1a2tEc05cYgphxuZ1nKAkvABSlKHKHaOMj67nfxQPJgxSTOCpeQFT4Zqe0XSbZO422JWJG5EYqmoRFOJVCyl4XgBR+JuQBJuJkk3G1fHkaIQKOpOSLoji3g0n0hbobSHQ4QjIWmLF9EeK5ITiXHSlhxHm1skbVr4doEJ5kg8I4tEVibJ7ACp3ABuLmYFE+jpnEEIBlWDear9TTf/aj/3A0BEtmCyh5IYl8+/YVYJezHZQDdiMoKmARMwMYcvY1YHP8HEDaZ4h2vHrAhSmBvbbZgVxizM6uGHmBjCl4CHgIjXjfoOYC0mxnA60e7iBp3pbZbRHOC7wFSM6t0HPAJ8znv/U8D7ML6y3wKvYlKsHhORCEbZfsspf9bdwKcx6rZaVT/lHacN+CZwM2b5dIuqHuyNjV3Q6Nljc85HCZNbdP+da90dl9Tp2RmpvrUs3lt6xdsKjzJyLp0YbzvVmzHmhif8eu93xl848YY15+QvvFRE3va3kUEgtzxVekl5qhQXN7HbOfz6lmBD22E5Pk/l7UVNqVRGfkvLtAUtLdPeZkN29on9odDBA0VFTeH8giOZ2dntU0Tcs0+/4OUQKTib+oKzqe/288Q0M3KYyYeamHbsACXtByhJHmSqNDMhu438wjhZExWZQGd/RtDJ16CTT26fhaS9k5CEJZaKSSwVl2hKibtIh5Ck3CxSmh91MwuiZBY1a2giQN9O9k6yiUXyT61gYp6LLD5OTiSKPIEJSRuFhLVDYPIkGszBW8GQyuqFwDT1xzYRWYq5zpwP/IP3815MllBYVed63pJxmN/Ec5jA8ePeIXbj3TQDz2Iykr6tqj/riAEA1wKVwAZvu0nev5/HuJ6yRORHwDKM0PS7TKC3S7vvY5Y+PwV+p6q/FpE8EbkAkyp1C2ZmQlhExqvqURH5KJ4AACePtJ1PAAAgAElEQVS/lyIyDZN2dYH3Yf4gIreq6q8wfq4XVfUzIvJl4C8xKthnaj5Yk6xYVdGEUWTLCCU7rm0rXtY3b3zVzS+IsEhOBet6zdGiOVvcQNbbWhw7GdPnQ2AXpGZ2elleOfK7yxva6mqWF797vCNOl+dycDJmulPOnxmfgqLufudoTU2g4Wijc+wcV/QdBXEdRKMFJdFoQcnBg7NOndBJRgvym3cUFTUdCYUOpXLzjhVmZMSmizDxTJ8pi3hOKfvOLmXf2d1tkyQYP6KTDjUy9egBStoOUJo4SLE0MzHzBIUFMbImuDiTe/S/B508DTp5/RCSsCTcFmKucW15KxKJuS6xFBJ3AyTdDDGurTxcCjArkreljEfJyomSlXNEvW40AxSYHGLhfMLhAom0h2iPFklbtFiO7vmX/h1uKiYFP+Zd48KqekBEZgDZIrIR4+Y5iLnDvxo4Tu+TXtYCXwQOe26p1zArhnMxgeUfAh/HBJcjGM9IT8Vw3dJbQchV1ZdFZBzGVwUmeHI35i7/Bx3l1qp6tIdjXQg8p6qHAUTkMUyK1q8whRpPedu9hlHGgfAWVhBGHKLqXlKnb96x3o2UHmGxwGUDOd7OWbcf6+r1QObcfan41pmnv34wWl/xRMO3Wq6bds9LeRmhi89oK+KUuhMqSr0pooekdXtNsOFAg3OkJCXu7DPtC6BuMPv48Snzjh+f8rbXMzPDhwtDh/YWFTWeKCw8EszOPjHJcVJlIvR6MFWQZGYxjaXFNJYu5vUut0nhJI/qhINNTG32RCPWxFSOMCnzOKH8KNkdotH3uEDQydWgk0tOH6/hKTdC3G2RmHvipJDEUgmiritxT0gSboYkNYuU21lIetUhN0JWboSs3MM6rmMFE66vXnGoz5/P8Adgs4hsB1qBV7zhYfcB9aq6yCsUuw+YjykYywdQ1XpggRd7AJiDufv/iog82+kcDwP3i8gsVb1ARPKAElXd7onQOlV9R08WVb2i08+9KtTt7X/yERE5H6N0D4tIjvfze4B0zkRI6Kl+3Kk+2NcdOxngxcQydJx9UHfdvcbdt+gtnRNQzk/HMRPB3Ja2vJILunovmLNsTiq+tUu3YtyNFj2173sXnz/h2jWzChYvE5Fe+dEna2jO1YmKOQAtcjKddXyc5AK6CTR3RTyeO+nI4bJJRw6XdXrVTebnH9vZOSU2MzNaKqI99nHqjgBucBKHp07i8NSKk6n0b8dF3GM6/uCptNvSk2m3rYTyo+SMSxGYjEh6ikEDTg45To7mMLVvQqIR4qlWiXtCEk1FJebGiaVcMSsSxxMSb0WiBZhg+/b+mqqqbZ6npBKowrjOazEFZtneNsc7Fa6difcCH8W0qTiZvaSqhz2303/Lqd/xZxmA3d3R2wvuX2NSmTou0tsxxv8Io4r3ishjnV1GmPSqrloWvww8JCId5dt3A98a2Mfolq6/4ZZhQ0FYj972vFtz1UadlBNnPqbwJm28dc7NmxBZ3tV74hRORXJfQ8NdCgbA681PX763vW7rFcV3FjgSmN7ddl1RpHlnVyZNOmsb0aYtwb3bdwaa8iPEF9KvTBwn2NY2YVZb24RZ+/ed8oB5KbENRUVNrYWFh5ycnBPjAoHEOSLk9v0cXZwVdSbQPGUCzVPmd9MhWkFbteiIl3Z7fD+l0Uam6WEmB1spyouQW5QkOPlk2u1gEJAccoI5mkNxn4TE1Y09b/ROROTrwB5V/QbwnIjEMNe32zDp+WtE5BOq+jVV/Tevkd0nVfUKEfm916LiVVX9C+89gDeB34C5w/fiqqjqH+lihr2qlvXH9u7o7ZfyVm/bx4BdGP/XNcCLGB/ak5hahTjmw3wa49v6XqegMgBer42VmABKR1D5ibR8mnfyyiAd1zIAgimNX7FJ37j1BZdJrd2niqaDA1OXlZ7RluzzY8lIlzU6Jzkc3Tv/Vw3far1u2gdfzM8Y12NGU1fkk118cXJ28cXJ2URJtNQF9m2pCxwItkl0IUJOf47ZQTKZPe5o8/RxXaTE7gmFDjaGippiBQXN2ZmZ4akiOr27lNiBICBFtEwsomXi3DOMTTmhBS2HmHL4ANNaD1AaaaTEPcTkQAvjcsPkhRJkTEZkQLMv+owj/Z2DsAH4kIisxlwXJ2IyhZ7E1B5cC/yVmM8T6byjql7fzTFPNrjrrRFnqjzuK70dofkTTNbQk5iL+M2Yu+8y4H9V9cvpMCbdVKyqyMUEcHpMW7MMPufucbfeudY9MmcfCxzo0zyD/nB4QsXGmor7ztjITjURjrV8K0XXq9l3sGj8lWvnFl54saTJPZIgFd4RaNy0NbDPbZH2c0066+BxppTYwTxvXwiTc+IQUw41UtJ6Ku12inOM8Tnt5IfiZE5CJJ3zTq5suvK85/q6k5cg8zomVjkFk0mUwGT7TMEEhLdiMjOPYKqPU6o6x4sD/BRTd2D6eJib6b/FBI2PqOqV3g31m5hA+y7gXs9NVY/JVLoWk756H/AScKV3ng+p6ro+f6ZeCsJa4CZVbfOe52NcSDcAr6nq/L6eeKioWFWxEbBDUXxiYqs23rnW3b60VkszU+l1B/XEixf90wvh3ClLe9oudvzR9Zo63OtY04Sskrorp96dE5BAtxk+/cGksx7atDm4t/1IF+msg8nbU2KbM7Kz26aIuGW9qQHwgxhZ4UMm7bblVNptsRxlQnYbBYVxMt+Zdts1KaCo6crz2nrYrktEZDdmhXsj5qJegmk10QpUYzKE7gA+7L3/JOYCPgm4QVX/0jtOSFVbvQv9ElU94rnVfwHcqKrtXnp/lqp+wdvuOx03416K6muq+qCI3AR8QlWv6evn6a3LaDJvbxaXAKaoasTzmw1nXsQKwpCSFdf2G1/VN1e84uYWhlkkxq04pMQyCg6HcyZ3GxvoTDB7WX6ivfdey+bY/nm/2vPQiWunfeD5wswJy/pt5GmYdNbiC2bGi1HU3ecc3VQT2HOs0WmZoaJ9il/0le5TYo/uCBU1NYdCB5N5eS2FGRnRHlNih4IsYrnT2Vs2nb3dbpMgGDuikw41Me3Y/ren3WadoKAgRvYEReqbrlrcLzHweB6zIlgGfA0jCMswgrAB093hOuANb/t8TPXxOuCrIvKvmCByV3fzl2AykzZ4upaJEZsOHj9t+47OEa9hvDd9preC8Bjwkoh0/NX8CfATb9kzVK0o+svvMZ0FLYOJql60XTe+e73bdtYhzhNTUekbu2beuoVTgboz4mTMWATOPnDPGG/oTFLjBb/d//CyinGV68pDS5d4mXdpQxBnujth4XQvnfWgtG6vCe45sNdp7lU6azowKbGT5x0/Pvltl910pMQOBRkks6bSOH0qjdMX81p3mz3CGarGe8EGjABUYDoz7AUexLiqf4BZPfyLqv7H6Tt6mZs3AV8SkWdU9QunbwI8rap3d3Pu02sZOm7O+52h2dvWFV8Ukd9y6o/8vo6CM0y20XDmaUx9w7D6so4WSg/r7rvXuHvO36mzA8p5Pe8x+CjiHpx8Ya/dUyIiTsaMnW5iZ68FoYOaY+sq97Xv2HH1tPcGAhKc0fMe/WOKhuZMSSycA3BM2vdsDjTsfitwaGKC5Ll9SWdNB31IiS0R0SFfHfaRpwe4//OYauK3vMDuUTHxjXMxhbVR4IteFmabiJRgPCxB4Kiq/lhEWoCO1j4d2ZlHMN6Nb3v1Bzs71x8M0OZu6VUMYaRTsariaUxWlCUN5If12K0vuDXXvKkTcuOc2/MeQ0vT5Atf3Tr/niU9b3kKN9W8J358Vb9jAgHJaL9m2vveKMqcPKR1Lx3prDsCjflREv1MZx08gsFYS2HhoT1FRU2thaHDYrrEpi8ldoAcByZdfdWueF92MuMVeExV3yciAUz6fAB4VlVvFjPycqmqzvW2/1tOXfDbMLUKs4CvYILFAvyZ19bnY5hahANeUPkqTGeHk/UHqvpk51iDd47nONUaaCImnbWsr7+QsSIID2AaTVn6SSClieWb9Y3bnnfdyS2cL8N4xbXhki+8HMuecFFf94u2fHsTGhtQvGl+0dL1C4oqz5fBzLfvhijxY3WB/Vvqggcy24hWDDSddfBQNze3de9QpcSegceuvmrX+/q6k1cbsBNz0Y+IyI2YKWf7VPXmPh7rHsyF/aN92GfQZiqPFUE4G9NEyvdRfSONeXu17s41qYPleznXwf9gYk9EssY3vnDJFyZj7tz6RCKyfl0q+nLlQG0oypy86+qp73ODTsaQ+Pq7olM6a6rlZHfW4U0XKbGhYDBeNogpsbdefdWuPtdAeYLwEPC614TuR5iupZWYmQTbgGVehbGDKeRdiplZ8DmMj78V47XYiWlvvR8jKk9hCnUXYLrMVqnqE55w3IYJSgeAPcAvvB5wHS2AfjrQmq4xIQgAFasqnsHMcbD0wITj2vTude62y7ZqSWaSWT3vMXyomf8Xaw5PXtyvQjd1o62x1u9k0cueOGciIMHIVVPf+9r4rGLfW6d46awbNwf3Rg7L8XnIyW6ZI4Ks7BMHQqFD+4uKGsMFBc2Z2dltk9OQEnsMKO6ruwhOCsIyTEO592F8/Q9gXDY3i8jngFZV/YaIXAd8WFVvF5EaTKrpfhEpUtWW01cIIvLPwFYvtlCEqXxeDLwb0+hzodc89HLg46p6q4iEMLUKswe6chhW/sZB5hGsIHRLZkLDN7ymb6542c0qamexmPF8IwpXnOThSYvm9nd/cbJD4ox7Xt1jA04lTWky5+kDqy6bG7pow6JxVyzyand8wUtnXXIqnbXZ6846+Oms6SAWLZh2KFow7dDBU3kCaUiJ/Wl/xKADVd0kImWY1ju/Oe3tRzBzlb+BGXbzA+/1DcAPReSndD9c7DrgXSLySe95NmYoGZiMo6Pe+deIyHdEZBJwO/DzdLiRxpIg/Bz4d0wVnwVAVS/YqRvfs85tKzvIIjF3PSOWxuJlryJOv1pLdBDMvjgzEf5dukxiW+vLlzaGd9VfO+0D+4NOZr/FKl2YdNaJFdNdc908KK3baoJ7mvY6zdOGKp01HfQqJbbgSCA7p22S4yTLRDi9svwdaaD94EnMIJwrMENvjG2qe0XkoBcQvggvE1NV7xORizGzlV/zmuKdjgC3q+q2t71o9js9zfRHmBXKXcC9pIExIwg1H6yJVqyq+G/gfr9t8ZuSI7rnrjXu7gt26qygOzxSRdPB7nNWDDjQ7WTOW0z494dA01YlfDzRXPbLhoeiVxbfvXZidkmXjfb8YoqG5k5JLJwLcEza6r3urJMSpOYPdTprOuguJTYv/9iuotDBg6GipkRebuvBFSs2vtHdMfrAI0CLqtbIO2teHsb0JHq0o8+QiMxU1ZcwNV03cmrGcue2Kb8HPiYiH1NVFZHFqtqdrT/EuJSaNE2jiceMIHh8E1NCPuamqOVFtPWWF91N176hRXkxKoC0tl3wm/acKXviGQXv6AnfV0ScgBOcXusmG9LaNsLVVPYzjT9ePrvw/BcWj7/mXOlhbrIfjNP8suXJ+WXLk/NpI9q0OdiwfWegaVims/YNJ9jeNmFme9uEmfv3zwe4a8WKgR9VVfdhgstd8STGVfSDTq99RURmY1YBz2BmIzQAK0XkTUxQ+YsYV9MmLyC9G9M7rqvzHxSRWswsmbQwZoLKHXirhLv8tmMocFxNVppU0WTxMRb3doDISOTNio+sOTrh3LR0TXWTTTviJ34yaO6TguC4hmtL7mnLcDKHbQ+wzkSJH6sN7N+yzaSzLkRG9PeoHphVVVWVlu6g3SEiS4Cvq+qAs9bOcI5coAY4X1Vb03LMMSgI52J+iSNuOdxbZu/XbXevcZvmN+h8R0dWRkl/cCUQf275N04gzoSet+4d0WMPbYPkoPn8HZz45cV3vjA556xBa/09GCRItu8INNVsDex1WyQ86N1ZB4EHqqqqvjmYJ/Da+98PvFdVz9xbvf/nuAb4L4zofCNtxx1rggBQsariZ5jI/Khh3Ak9dMd6t3b5Zp2alWSO3/YMJXumX/38rpm3pTUgngg/syYV2zjoF+sZBYteWjLh+rmS3nbOQ0IKN77bOVSzJbg3PELSWY8CZ1dVVQ2kmd2oZqwKwiJM98ERvUrISGr0utf19T95yc0c18ZiGaNzH9Ze+uWNyYy8M8496Cvqth+Otf7HOIYgzpYXLNp3fck9rRlO1rBrA9JbvHTWzTWBhmONTstMFe1zX6gh4BNVVVW2Y8EZGJOCAFCxquIJTFXhiOO8Xe6mO9e6rTOaWCiMuCV7WjmRP33nK0tWDkrxXLTl+y+jJ/rcAqM/CE6icsodzxfnlC2Xnnv4D3sOSsu2mmBD416nuTQl7nAobtwFzK+qqup37cFYYARnDgyYL2Ci9yMi42hqszbctdbdfeF2PSfo2vkOHWyfdccBGJxq6mD2hW4y8sfBOPQ7UNyMtQd/enlZ/oJXLpp400wRGfSJcoPJFC2aOyVR1Dmddc/uwKGJPqazruyrGIhIm6rmd3p+D33sOzTSGLMrBICKVRXfBP7Gbzu6Iyeqx9/1krvx+tc1lBelQka4iyvdpJzM8JrKryUwpftpRzUZi7U8FGGIixlzg4WN10+753BmIGfUCX8b0cbNwYYdOwNNBVESFUOUzrq+qqqqz9k+AxWE05vQ9bYp3WA2r+uJsbxCAPgHTNXgkI52PBOOq6llW/WN2ze48WlHWSymYZalC/acde3riAxaryCRYJYEpr6kqcYhLSYLJ49P/VXDtyZdOuVP10zLmTUqXEgd5JM99ZLknKmXJOd0pLNurQseyGgfvHTWJPCRdB/Ua1vxCKbh42HMrOMGr/V1FNN/aIOIHMdcX2YADSJyL/BdzIz6JGbU5bNdNK/zJftsTK8QACpWVSwHnsPnu++ZjbrjrufcAxV7tNzRoZulO5JZc9lXt6aC2YOay59K7NmSaPu5b8He6XnzXls66U/OEnGGewbPgEiQbN8eaNxUG9inLRJegJCuwr2vVlVVfbLnzd6JiKQwKeodjAeeVNWPisivgZ+p6ioR+XPgXV6juR9iROIWVU2JSBVmwuRlXqvsB4FzVfXPRWQe8AdgDqY26mTzun5+1gEz1lcI1HywZm3FqopvY4ZSDCmhNj18xwZ36+U1WpydYC5m1qqlF7SEZtYOthgABDLOPjeBsxvccwb7XF2xt73ugiPRfQevK7n3jexA7oArsYcrGQTzzk1NX3puanpHOuurm4MNkSNyYiDprPuAqgGYFVHVk61dOlxG3tOlmDt6gEeBL3fa73872lV4PKmqEe/nyzDtrVHVOhHZAyfTxJ/2UwzACkIHKzGzTQdtBGIHwaTGrnlTX7/lRTc4/gSLxael4Uhnx6zbjwzVuZzMuXvceK0vggAQSbVNeaLhW5OWTb7ludLcucu9lgajlgBO5iy3eMksrzvrXqd5Y02goaWpb+msCnzYp5qD05vQnf68t/sNOVYQgJoP1rRXrKr4EPBHBsl1tPAtd/Od69yjsw6wSMzdhaWfJAI5rSfyzzp/qM4XzF42Jx6vdfE3I815/tATV5Tkznnj0sm3lIg4Y8KtKIhzljtx0Vled9YmaamrCTY07XOap6fEPVPs76GqqqrT21Knk+cxbp5HMd1M1/Vyv3Xe9n8UkTmY1tbbgCH7Pp8JKwgeNR+sea5iVcV3SWMAasox3XfnWnfXxXV6dobLgnQdd6xTX3bTRkSGLNDrBELTkJw30IjvLpv94e2Ln9z7ncPXTbv3tZxgXlftk0c1xVo0rzhRNA/gqLTtrgk0NNS/M531DeDvB9mUjwE/EJG/wwsq93K/7wDf9YblJIF7VDU2XPIGxnxQuTMVqyrygU1Av90DOTE9cfPL7sYbXtP8/AiLbKpo+nmu8us73EDmkMZbkpGX1iejG3yfftYJ9+JJN687O2/+ZdKPcaGjjRNEGrcE927fGWjKiUriA1VVVdt63styOlYQTqNiVUUlpjVtRm/3EVV3aa2+ccd6N1bSzHkCQz5gfazQPH5+zcaFf10x1OdVjbfHWv5dMWmBw4apOTM2Xjbl9smOOFP9tmWYcHdpdeX/+G3ESMUKQhdUrKr4C+D7PW1X1qS77l7j7l24W+cGFPsHOQS8dOFnn2/Pm+rLZLfY8R9t0NSRS/0495nIcnKbryu5Z3dusGBJz1uPar5RWl35cb+NGMlYQeiGilUVXwPe8eUqbNfm2553t1y5USflJCj3wbQxSzwj7+j6Zf+ah8jp4xCHhFR8xxuJ9l/7HkfoBr1w4g1rz8lfeKmIjMXY4Frg6tLqSl8qfEcLY/GL01s+ickPXhFMafwqkyrqTDzOYoFhNQZxrPDWObfUIOJbmq6TMes8kP2gJX7ZcAbklSO/u7yhra5mefG7xzviDEcbB4t9wHusGAwcu0I4AxWrKgo+9kTqe5dt1RsFxvltz1hGQZ9b/s0GdYK+jv6Mtz3xnJvYdYWfNvREppPdcl3JPdvzgqEh6dTqM4eA5aXVlTaInAZGdYHLQKn5YM2Jyq36SYEWv20Z6xyadP4bfosBQDDnUt9t6Im4Gy16au/3Ltpx/PU1qprw255B5BhwrRWD9GEFoQfK62obgesxucYWn9g1413Doo+9E5h4DpK52W87esPrzU9f/mzTf+9wNbXXb1sGgRPADaXVlZv8NmQ0YQWhF5TX1e7AtLawo/d8IJpZdDCaPXHYZNAEMhf62m+mLxyO7p3/q4ZvFbYljr3oty1pJAysKK2ufNlvQ0YbVhB6SXld7auYgTon/LZlrLFr5q11DKPMmWD2hQuBmN929JaEGwut3vefl2xrfXmtqo4Yu7shBvxpaXVlb1tFWPqAFYQ+UF5Xuwa4Eus+GjIUSR2cfP6w6gIrTk6ROEVv+G1HX3nz6LPLn2l8bHdKU3v8tqWfJDHZRH/w25DRihWEPlJeV/sacCkwUv+oRhSNxRe/hgSm+W3H6QSyLx6RfzvNsf3zfrXnofHH483P+21LH3GB95dWVz7ptyGjmRH5pfYbL6awDNjity2jnbfO+ZNh+R0NZJafzwhdKSY1XvDb/Q8v29ry/LpOffqHM1HgTtuSYvAZln9sI4HyutoDmPGWI+1Oa8QQzpm4L54ZGhZtgU9HxAk6welb/bZjINQcW1f59IEf7Utp8i2/bTkDzcA1pdWVP/PbkLGAFYQBUF5Xewy4FhjMvutjlh0zb9/FMB4GE8y5tNhvGwbKsXjT7F/ueWhKS/zQer9t6YK3gGWl1ZUb/DZkrDBs/9hGCuV1tWHgFuAxv20ZTbjiJJonLBj0EZkDwQlOmwvB7X7bMVBSmsj7/f4fXFZzbN16VQ37bY/Hi8DS0urKEf/7HUlYQUgD5XW1SeD9wP/DjO6zDJD905a/yggYLB/InN/otw3pYmvL85f94cAPG5NuYofPpvwQuKK0uvKQz3aMOawgpInyulotr6v9LHADIzTYOJyoP/uGHL9t6A3BnEvmA6keNxwhtMQPzfxVw0OlR2NNfriQUsAnSqsr7y2truxXvYSI3CoiKiLzerHtwyIy4FWoiJSJyJ91er5ERB4a6HH9wApCmimvq/0DcB6wxm9bRiptedN2JzILzvPbjt4gTv4kJP91v+1IJylN5jx9YNVlbx59doOqDlV1fgtwU2l15dcHeJy7gfXev2dEVf9CVdORGFAGnBQEVX1VVf8mDccdcqwgDAJeBtLVwBcx+dOWPrBj1h0NftvQF4LZS0Zl2+VtrS9f+rv9jxxOuonBbh73R2DhQAvORCQfuAz4EHCX99oVIvKciPxMROpE5DHxBhh7ry/xfm4Tka+IyBYR+T8Ruch7/y0ReZe3TZmIrBOR171Hx6CmaqBSRN4UkY9753zK22e8iPxKRDaJyIsistB7vUpEHul0jmEhIFYQBonyutpUeV3tP2Ea4x30256RQsoJRo8VzVnktx19IZC18Hyg1W87BoPjiSPn/LLhm2cfie5fOwiHj2KGUF1TWl2ZjgZ8twC/U9XtQLOIXOC9vhh4AJgPzMAUlp5OHvBHVT0X057mS5gMwj8FvuBtcwi4VlXPB+4EOtxCK4F1qnqeqp6+wvk88IaqLgQ+Dfyo03vzMNeHi4DPiUivx/YOFlYQBpnyutr/w7iQnvXblpHA3tJrXkOkyG87+oJIMEsCxaO266arqexnGn+8/PXmp19Q1eNpOuzrwAWl1ZXfKK2uTFcixt1AR/Ha/3DKbfSyqu5TVRd4E+PiOZ048Dvv5xqgo3V4TaftM4Dvi0gN8L8YgemJy4BHAVT1j8AEESn03lutqjFVPYIRmym9+ZCDiRWEIaC8rrYJuAaoYhQFIAeDPWddE/Lbhv4QzFk2okSsP+w4/vrS3+77fkvCjdcO4DApzN33JaXVlWkr7BOR8cBVwMMiUg/8HfAeQHh7I8IUXU+KTOipaWFuxz6eiHRs/3HMan8RsATIHKDZvbFrSLGCMESU19W65XW1n8d8kUZTK+K00VpQti0VzFngtx39IZBRVgFOvd92DDYnksfO+tWeb848FGnoT9LEDuCy0urKfyytrkz34J47gEdV9WxVLVPV6cBuTDeBdBECGj2ReD8Q8F4/ARR0s8864L1g4hnAkTSustKOFYQhpryu9k1MH6QPYyY+WTx2zLp9ROedOxmzx0TDQxc389mm/778lSO/e0lVeztN8LvAeaXVlYN1M3Q38MvTXvs5vcg26gPfAT4oIhsx/v927/VNQEpENorIx0/bpwq4QEQ2YYLPH0yjPWnHzlT2kdp55ZOArzDMvyRDQTKQ1bb2sq8qIt3daQ173FTLvvjxR0owbooxQV6waN/1Jfe0ZjhZ53azSS3wN6XVlf83lHZZ+oddIfhIeV3t4fK62nuA5Yzxzqn1Z9/wxkgWAwAnUFSKZG/0246hpD3ZUvrLPQ/NaQzvXqNvv7tsBT6BSSe1YjBCsIIwDCivq12HSY37FKeWoTv9NY0AAAv8SURBVGOKfSWXT/bbhnQQzFo85sasKm7G2oM/vfzlI795xcuYeQSYU1pd+fXS6spRWaMxWrEuo2FG7bzys4BvYPKfxwRHi+ZuefO8v+nO5TCiUI23xVr+3QFy/bbFB/6YGyx88P7HfvKm34ZY+ofvaU6Wt1NeV9sA3FY7r/wSTHre1T6bNOjsnHVbbwOTwx6RzHxxJmxQt7mr4qfRyibgUw8+/tTvetzSMqyxLqNhSnld7YvldbXXYHKrR+0QnkQwt6Utr2RYDsHpL8GcpSOiMV8aaMAkRCy2YjA6sC6jEULtvPKbgH8CLvbblnSybfZ71u4vuXy533akE1V1Yy3fOAg61W9bBol9GLfmtx98/Kmo38ZY0od1GY0QyutqfwP8pnZe+TXAZ4HLfTYpLRyYuqzUbxvSjYg4TkbZdjexe7QJwsvA14GfPfj4UzZYPAqxK4QRSu288ksxwnCD37b0l8MTF75Zs+DDI6LNdV9xk4ffip94dIbfdqSBFKbg6+sPPv7UqHVdWgx2hXAGRKQYszS+ENOv/SDwgNdNsfN2z6vqsi4OMWiU19VuAG6snVdeAdwHvA8oPPNew4udM26N+G3DYOEEJ82AzC0QH6nZU8eBh4FvPfj4U/U+22IZIuwKoRu8nunPA6tU9Xvea4uAQlVd5z0PquqwWDrXzivPw5Tp3wdc0MPmvhPLKDi8Ydm/FDEMWv4OFonwmrWp2GsjLT7yFqat8yMPPv7UCb+NsQwtVhC6QUSuAqpUdflpr1+BGXxzDJinqnNEpE1V8733Po9ZTVQAP8W0z/1bIAe4VVV3icgk4HvAWd5hH1DVDemyvXZe+QWYXkl/hunzPuzYOu/9a5qKLxkVcZDuUDdyLNb63TwG3hVzKFiHiQ888eDjT9mhTmMU6zLqngXAa928dz6wQFV3d/HeIqAcOIq523pYVS8Skb8FPoYZ1PFN4Ouqul5EzgJ+7+2TFsrral8D/qp2XvknMZ0W7wMWpuv4A0UR9+DkC0eDf/2MiJMzTpzQi+q2XuK3Ld1wBNPX/5EHH3/qVb+NsfiPFYT+8XI3YgDwiqo2AojILqBjLGANcKX38zXAfG+SH0ChiOSne35teV3tcUyXye96hW5/iamAHpfO8/SVg1OWvK5OYImfNgwVgeyLnGT4ab/N6Ew7/P/27j/W6rqO4/jzdS+Xn9oPI3HqzIbCObcITG2RSohlrlTUZcrWFmUl4zbXgtXarBxbtVatrKGILUMrwBnkuG4GhYr5g0GieeVc4dIAnTWFpADBy728++PzOXK43cu9l/tT7uuxfXfO+Z7v+X6/53D5vr+fX+8PDwK/A1a7t5BVckDo2AukHOvtOVa+ocpJLw5XvK6caKMK+GhE9Fsf7mJj6Wng6VKheDMpMF0LXAP0e9fIbe+/eshUSVQPrz2v5Y01u4CxA3gazaQbk9+TqoTeGMBzsUHMAaFja4EfSPpqRCwGyBNk98aEG6tJ1Uc/zvudEhH9kv+l2FhqAdYAa0qFYh0wFbiOFCD6vBrnwMhTXnlzxLsHfaN3b5GqazTszBei5eX+bi/ZT5oScgXw0Lzl9SfknM/WuxwQOhARIela4OeSvkWaEHw78Mde2P0twMI8acYwYB2pnr9fFRtLQepJ9SQwv1QoTiYFh+tIbSi9rmn8dVuRTu+LfQ9WNaMuOrV57/L+ONTrwCpSEFg9b3n9Cdut1/qGexlZu0qF4rnATGA6aaLwHs91fFhVLY9Ou30Xqjqtp/t6uzn4+i+2Qsu5vbzbfcBTpBuKdcCTbhOwnnBAsE6VCsUqUu+paXm5BHhvd/fz8umXrN8y4cYTKhdTVx3av/qx1uaGnlYb/Rv4K0cCwKbeCgCSriGNSC5GRGM77/8GqI+IB3rjeHmf04HmiPAI6EHCVUbWqWJj6TCwKS+3A5QKxVqOBIhpwBmd7Wf72Z8+YQehdWbYqKmF1uaGVo5MzN4V/+TIxf9xoGHe8vq+uoObRQo2s4Dv9dEx2ppOKuV0OSAMpsGgJyKXEKxXlArF8aTAcCGpNPEh4KTy+/tHj9ux/sLvnEVFX9uh5uCeuzYS+zvqbnsYaKKiCmje8vqm/jgvSScBL5J6n62KiIl5pP4vgU8CL5F6Kv2adAG/KSKuz5+dDsyPiCslXU4amDkC2AZ8MSL2SdoOLAGuAmqA60ltck+TciW9RupkcRMVpZA2Az7bDgb9PKktbjiwHpgbEa199ysNDS4hWK8oNpa2kS4C9wCUCkWRei1NBibvPPOydyJdAYxniP7dDRt5fnPLgXUB7CB1a27IywtAaQBTSc8EHo6ILZJ2SzofeB8wEagFxgGbSQHhz8BiSWMiYj9wA7BM0lhSssVPRMT+3BHjG8CCfIxdEfFhSXNJAeTLkhYB+yLiJwCSbjrGOb41GFRSMR/3oog4JOkO0gDMe3vzRxmKhuR/TOt7uQdTOUisKA/DXjhnbQ1wDlCoWIrAucC7BuBU+8qrpDv+JmAr0FQ9vHZLy4F1W+Ytrx9s8y7PIlcFAsvy62HA0nzX/YqktQAR0SLpYeAqSQ8AnwG+SUrHXgs8kQuBw0mlnbIV+fFvpF5s3VU5GPQyUr6uDflYo0i/t/WQA4L1q7pFMw4BpbwcZeGctSOBU/MyrpPHsXSvPr6nmkmNurvzUvm88vUOoKlu0Yz/tr+bK/vhVLtO0imkWfkmSQrSbxqkBuaOLAO+RvrOGyNib65iWhMRszr4THmAZisdX3dayLM4Sqri6BxQlYNBRUo6+e1jnKMdBwcEGzTqFs04SJqWcWdn2y6cs7YKeA8pQJSDxBhSHXUN6WLS9nk1qa4+8mPl8wAOceTCftQFv27RjMF2V99bPgvcFxE3l1dIeoz0vW+QtIT0215KGukM8Bip+ugrpOAAqT1goaRzIqJJ0hjgjLap4tvYy9Ep27eT7vzvB64m/Zu15y/Ag5J+FhGv5qB2ckTs6OqXtvY5INjbUt2iGYdJjZGvkerh7fjMAn7UZt0fSNV4W0ltBzupqP6JiFZJ9cBs0pzKRMRrkmYDSyWNyJveChwrIKwCHpA0k9SofDfpQv8caZR1uyliImKzpFuB1bkkcQioI5XOrAfcy8jMzIBcX2dmZuaAYGZmgAOCdYGkVknPSnpO0jOSPpbXn567Hg7Ued0maf5AHd/sRONGZeuKAxExBUDSp4AfAh+PiFfoeM6IbpFU7ZGmZgPLJQTrrneQUggg6WxJDfn5SEn3SHpe0iZJl+b1oyXdL2mzpJWS1ku6IL+3T9JPc6+SqZK+K2mDpAZJi3PfdiQ9Kun2XEppkPSRivOpze//Q9ItefsFkr5e3kDS9/MUpmZ2DC4hWFeMkvQsMJI0w9qMdrapI00jMUlSgdQlcAIwF3g9ImolfRConAhoDLA+IuYBSNocEQvy8/tIo7hW5W1HR8QUSdNIfeDL8zUUSH3kTwZelHRnfn8FaS6LKuBGoDKImFk7XEKwrjgQEVMiogBcAdxbvnuvcDHwW4CcPnkHMCGvX5bXNwB/r/hMK6nPe9mluQTxPCnofKDivaV5H+tIc1CX01w8FBFvRsQuUvqCcRGxHdgt6TzgcmBTROzu0S9gNgS4hGDdEhFP5URm3Z4PoR0Hy+0GkkYCdwAXRMRLkm4jlUjeOnTbU8mPlXNYV6ZF+BVp4NRppBKDmXXCJQTrllwdVE1KbVDpcVLGSXJV0VmklMpPAJ/L62uBSR3sunzx35XTMbdtrL4h7+Ni4D8R0dkcwStJpZkLgT91sq2Z4RKCdU25DQFSYrEv5PQFldvcAdyZq3tagNkR8WZOTbxE0magkZTq+f8u5hGxR9LdpDQU/wI2tNnkoKRNpPw2X+rshCOiWdIjwB73XjLrGqeusD4lqRqoiYiDksaT8ulPjIjmbuzjUVIO/Y3d+EwV8AxwfURs7eZpmw1JLiFYXxsNPCKphlS6mNudYHA8ctVUPbDSwcCs61xCMDMzwI3KZmaWOSCYmRnggGBmZpkDgpmZAQ4IZmaWOSCYmRnggGBmZpkDgpmZAQ4IZmaWOSCYmRnggGBmZpkDgpmZAQ4IZmaWOSCYmRnggGBmZpkDgpmZAQ4IZmaW/Q9Tv5lzAnpuGgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7faf71275ba8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plotting value counts of unique genres as a pie chart\n",
+    "imdb_df['genre'].value_counts().plot.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": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DatetimeIndex(['2012-01-01', '2012-01-02', '2012-01-03', '2012-01-04',\n",
+       "               '2012-01-05', '2012-01-06', '2012-01-07', '2012-01-08',\n",
+       "               '2012-01-09', '2012-01-10',\n",
+       "               ...\n",
+       "               '2012-12-22', '2012-12-23', '2012-12-24', '2012-12-25',\n",
+       "               '2012-12-26', '2012-12-27', '2012-12-28', '2012-12-29',\n",
+       "               '2012-12-30', '2012-12-31'],\n",
+       "              dtype='datetime64[ns]', name='Date', length=366, freq=None)"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# show index of bikes_df\n",
+    "bikes_df.index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Unnamed: 1                     00:00\n",
+       "Rachel / Papineau                 16\n",
+       "Berri1                            35\n",
+       "Maisonneuve_2                     51\n",
+       "Maisonneuve_1                     38\n",
+       "Brébeuf                            5\n",
+       "Parc                              26\n",
+       "PierDup                           10\n",
+       "CSC (Côte Sainte-Catherine)        0\n",
+       "Pont_Jacques_Cartier              27\n",
+       "Name: 2012-01-01 00:00:00, dtype: object"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# get row for date 2012-01-01\n",
+    "bikes_df.loc['2012-01-01']"
+   ]
+  },
+  {
+   "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": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Unnamed: 1                     00:00\n",
+       "Rachel / Papineau                194\n",
+       "Berri1                           273\n",
+       "Maisonneuve_2                    443\n",
+       "Maisonneuve_1                    182\n",
+       "Brébeuf                            7\n",
+       "Parc                             258\n",
+       "PierDup                           12\n",
+       "CSC (Côte Sainte-Catherine)        0\n",
+       "Pont_Jacques_Cartier              20\n",
+       "Name: 2012-01-11 00:00:00, dtype: object"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# show 11th row of imdb_df using iloc\n",
+    "bikes_df.iloc[10]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Selecting rows where column has a particular value"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>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",
+       "  </tbody>\n",
+       "</table>\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",
+       "   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",
+       "                                          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...  "
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# select only those movies where genre is adventure\n",
+    "imdb_df[imdb_df['genre'] == 'Adventure'].head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Drama        36\n",
+       "Action       20\n",
+       "Adventure    17\n",
+       "Crime        13\n",
+       "Biography    12\n",
+       "Comedy        6\n",
+       "Western       6\n",
+       "Mystery       3\n",
+       "Horror        1\n",
+       "Animation     1\n",
+       "Name: genre, dtype: int64"
+      ]
+     },
+     "execution_count": 23,
+     "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",
+    "good_movies_condition = (imdb_df['star_rating'] > 8) & (imdb_df['duration'] > 130)\n",
+    "imdb_df[good_movies_condition]['genre'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Adding a new column to DataFrame"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# add a weekday column to bikes_df\n",
+    "bikes_df['weekday'] = bikes_df.index.weekday"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Deleting an existing column from DataFrame"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# remove column 'Unnamed: 1' from bikes_df\n",
+    "bikes_df.drop('Unnamed: 1', axis=1, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Deleting a row in DataFrame"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "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>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",
+       "    <tr>\n",
+       "      <th>Date</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2012-01-02</th>\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>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-03</th>\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>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-04</th>\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>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-05</th>\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>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-06</th>\n",
+       "      <td>75</td>\n",
+       "      <td>146</td>\n",
+       "      <td>244</td>\n",
+       "      <td>98</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>86</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Rachel / Papineau  Berri1  Maisonneuve_2  Maisonneuve_1  Brébeuf  \\\n",
+       "Date                                                                           \n",
+       "2012-01-02                 43      83            153             68     11.0   \n",
+       "2012-01-03                 58     135            248            104      2.0   \n",
+       "2012-01-04                 61     144            318            116      2.0   \n",
+       "2012-01-05                 95     197            330            124      6.0   \n",
+       "2012-01-06                 75     146            244             98      4.0   \n",
+       "\n",
+       "            Parc  PierDup  CSC (Côte Sainte-Catherine)  Pont_Jacques_Cartier  \\\n",
+       "Date                                                                           \n",
+       "2012-01-02    53        6                            1                  21.0   \n",
+       "2012-01-03    89        3                            2                  15.0   \n",
+       "2012-01-04   111        8                            1                  19.0   \n",
+       "2012-01-05    97       13                            2                  16.0   \n",
+       "2012-01-06    86        4                            0                  17.0   \n",
+       "\n",
+       "            weekday  \n",
+       "Date                 \n",
+       "2012-01-02        0  \n",
+       "2012-01-03        1  \n",
+       "2012-01-04        2  \n",
+       "2012-01-05        3  \n",
+       "2012-01-06        4  "
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# remove row no. 1 from bikes_df\n",
+    "bikes_df.drop(bikes_df.index[0]).head()"
+   ]
+  },
+  {
+   "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": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# group imdb_df by movie genres\n",
+    "genre_groups = imdb_df.groupby('genre')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>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",
+       "  </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",
+       "4           8.9              Pulp Fiction              R  Crime       154   \n",
+       "21          8.7               City of God              R  Crime       130   \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...  "
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# get crime movies group\n",
+    "genre_groups.get_group('Crime').head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>star_rating</th>\n",
+       "      <th>duration</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>genre</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Action</th>\n",
+       "      <td>7.884559</td>\n",
+       "      <td>126.485294</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Adventure</th>\n",
+       "      <td>7.933333</td>\n",
+       "      <td>134.840000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Animation</th>\n",
+       "      <td>7.914516</td>\n",
+       "      <td>96.596774</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Biography</th>\n",
+       "      <td>7.862338</td>\n",
+       "      <td>131.844156</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Comedy</th>\n",
+       "      <td>7.822436</td>\n",
+       "      <td>107.602564</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Crime</th>\n",
+       "      <td>7.916935</td>\n",
+       "      <td>122.298387</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Drama</th>\n",
+       "      <td>7.902518</td>\n",
+       "      <td>126.539568</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Family</th>\n",
+       "      <td>7.850000</td>\n",
+       "      <td>107.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Fantasy</th>\n",
+       "      <td>7.700000</td>\n",
+       "      <td>112.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Film-Noir</th>\n",
+       "      <td>8.033333</td>\n",
+       "      <td>97.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>History</th>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>66.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Horror</th>\n",
+       "      <td>7.806897</td>\n",
+       "      <td>102.517241</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Mystery</th>\n",
+       "      <td>7.975000</td>\n",
+       "      <td>115.625000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Sci-Fi</th>\n",
+       "      <td>7.920000</td>\n",
+       "      <td>109.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Thriller</th>\n",
+       "      <td>7.680000</td>\n",
+       "      <td>114.200000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Western</th>\n",
+       "      <td>8.255556</td>\n",
+       "      <td>136.666667</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           star_rating    duration\n",
+       "genre                             \n",
+       "Action        7.884559  126.485294\n",
+       "Adventure     7.933333  134.840000\n",
+       "Animation     7.914516   96.596774\n",
+       "Biography     7.862338  131.844156\n",
+       "Comedy        7.822436  107.602564\n",
+       "Crime         7.916935  122.298387\n",
+       "Drama         7.902518  126.539568\n",
+       "Family        7.850000  107.500000\n",
+       "Fantasy       7.700000  112.000000\n",
+       "Film-Noir     8.033333   97.333333\n",
+       "History       8.000000   66.000000\n",
+       "Horror        7.806897  102.517241\n",
+       "Mystery       7.975000  115.625000\n",
+       "Sci-Fi        7.920000  109.000000\n",
+       "Thriller      7.680000  114.200000\n",
+       "Western       8.255556  136.666667"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# get mean of movie durations for each group\n",
+    "genre_groups.aggregate('mean')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# change duration of all movies in a particular genre to mean duration of the group\n",
+    "imdb_df['new_duration'] = genre_groups['duration'].transform(lambda x:x.mean())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# drop groups/genres that do not have average movie duration greater than 120.\n",
+    "new_imdb_df = genre_groups.filter(lambda x: x['duration'].mean() > 120)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# group weekday wise bikers count\n",
+    "weekday_groups = bikes_df.groupby('weekday')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get weekday wise biker count\n",
+    "weekday_counts = weekday_groups.aggregate(sum)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# set index of the resulting aggregation by weekday names\n",
+    "weekday_counts.index = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7faf70fc00f0>"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7faf70f96390>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot weekday wise biker count for 'Berri1'\n",
+    "weekday_counts['Berri1'].plot.bar()"
+   ]
+  },
+  {
+   "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": "3.6.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Exercise.ipynb b/Exercise.ipynb
new file mode 100644
index 0000000..24f67dd
--- /dev/null
+++ b/Exercise.ipynb
@@ -0,0 +1,485 @@
+{
+ "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": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import pandas"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    ">The `imdb.csv` dataset contains Highest Rated IMDb \"Top 1000\" Titles."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load imdb dataset as pandas dataframe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# show first 5 rows of imdb_df"
+   ]
+  },
+  {
+   "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": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load bikes dataset as pandas dataframe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# show first 3 rows of bikes_df"
+   ]
+  },
+  {
+   "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": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# list columns of imdb_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# what are the datatypes of values in columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# list first 5 movie titles"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# show only movie title and 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": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# show the type of duration column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# show duration values of movies as numpy arrays"
+   ]
+  },
+  {
+   "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": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# convert all the movie titles to uppercase"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plotting a column\n",
+    "\n",
+    "Use ``.plot()`` function!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# plot the bikers travelling to Berri1 over the year"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# plot all the columns of bikes_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Value counts\n",
+    "\n",
+    "Get count of unique values in a particular column/Series."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# what are the unique genre in imdb_df?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# plotting value counts of unique genres as a bar chart"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# plotting value counts of unique genres as a pie chart"
+   ]
+  },
+  {
+   "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": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# show index of bikes_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get row for date 2012-01-01"
+   ]
+  },
+  {
+   "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": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# show 11th row of imdb_df using iloc"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Selecting rows where column has a particular value"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# select only those movies where genre is adventure"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# which genre has highest number of movies with star rating above 8 and duration more than 130 minutes?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Adding a new column to DataFrame"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# add a weekday column to bikes_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Deleting an existing column from DataFrame"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# remove column 'Unnamed: 1' from bikes_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Deleting a row in DataFrame"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# remove row no. 1 from bikes_df"
+   ]
+  },
+  {
+   "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": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# group imdb_df by movie genres"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get crime movies group"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get mean of movie durations for each group"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# change duration of all movies in a particular genre to mean duration of the group"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# drop groups/genres that do not have average movie duration greater than 120."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# group weekday wise bikers count"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get weekday wise biker count"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# plot weekday wise biker count for 'Berri1'"
+   ]
+  },
+  {
+   "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": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Solutions.ipynb b/Solutions.ipynb
new file mode 100644
index 0000000..2dea85e
--- /dev/null
+++ b/Solutions.ipynb
@@ -0,0 +1,1615 @@
+{
+ "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_1000.csv` dataset contains Highest Rated IMDb \"Top 1000\" Titles."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load imdb dataset as pandas dataframe\n",
+    "imdb_df = pd.read_csv(\"data/imdb_1000.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>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()"
+   ]
+  },
+  {
+   "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": [],
+   "source": [
+    "# load bikes dataset as pandas dataframe\n",
+    "bikes_df = pd.read_csv(\"data/bikes.csv\", sep=\";\", parse_dates=['Date'], dayfirst=True, index_col='Date')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\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>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",
+       "    <tr>\n",
+       "      <th>Date</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01</th>\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>2012-01-02</th>\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>2012-01-03</th>\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": [
+       "           Unnamed: 1  Rachel / Papineau  Berri1  Maisonneuve_2  \\\n",
+       "Date                                                              \n",
+       "2012-01-01      00:00                 16      35             51   \n",
+       "2012-01-02      00:00                 43      83            153   \n",
+       "2012-01-03      00:00                 58     135            248   \n",
+       "\n",
+       "            Maisonneuve_1  Brébeuf  Parc  PierDup  \\\n",
+       "Date                                                \n",
+       "2012-01-01             38      5.0    26       10   \n",
+       "2012-01-02             68     11.0    53        6   \n",
+       "2012-01-03            104      2.0    89        3   \n",
+       "\n",
+       "            CSC (Côte Sainte-Catherine)  Pont_Jacques_Cartier  \n",
+       "Date                                                           \n",
+       "2012-01-01                            0                  27.0  \n",
+       "2012-01-02                            1                  21.0  \n",
+       "2012-01-03                            2                  15.0  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# show first 3 rows of bikes_df\n",
+    "bikes_df.head(n=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/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",
+       "Name: title, dtype: object"
+      ]
+     },
+     "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",
+       "        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>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",
+       "  </tbody>\n",
+       "</table>\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"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# show only movie title and genre\n",
+    "imdb_df[['title', 'genre']].head()"
+   ]
+  },
+  {
+   "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": [
+       "dtype('int64')"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# show the type of duration column\n",
+    "imdb_df.duration.dtype"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([142, 175, 200, 152, 154,  96, 161, 201, 195, 139])"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# show duration values of movies as numpy arrays\n",
+    "imdb_df.duration.values[:10]"
+   ]
+  },
+  {
+   "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",
+       "Name: title, dtype: object"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# convert all the movie titles to uppercase\n",
+    "to_uppercase = lambda x: x.upper()\n",
+    "imdb_df['title'].apply(to_uppercase).head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plotting a column\n",
+    "\n",
+    "Use ``.plot()`` function!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7faf733756a0>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7faf7335be80>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot the bikers travelling to Berri1 over the year\n",
+    "bikes_df['Berri1'].plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7faf71290358>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7faf71227d30>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot all the columns of bikes_df\n",
+    "bikes_df.plot(figsize=(10,7))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Value counts\n",
+    "\n",
+    "Get count of unique values in a particular column/Series."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Drama        278\n",
+       "Comedy       156\n",
+       "Action       136\n",
+       "Crime        124\n",
+       "Biography     77\n",
+       "Adventure     75\n",
+       "Animation     62\n",
+       "Horror        29\n",
+       "Mystery       16\n",
+       "Western        9\n",
+       "Sci-Fi         5\n",
+       "Thriller       5\n",
+       "Film-Noir      3\n",
+       "Family         2\n",
+       "Fantasy        1\n",
+       "History        1\n",
+       "Name: genre, dtype: int64"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# what are the unique genre in imdb_df?\n",
+    "imdb_df['genre'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7faf710ecc50>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7faf710fe710>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plotting value counts of unique genres as a bar chart\n",
+    "imdb_df['genre'].value_counts().plot.bar()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7faf7100ac18>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7faf71275ba8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plotting value counts of unique genres as a pie chart\n",
+    "imdb_df['genre'].value_counts().plot.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": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DatetimeIndex(['2012-01-01', '2012-01-02', '2012-01-03', '2012-01-04',\n",
+       "               '2012-01-05', '2012-01-06', '2012-01-07', '2012-01-08',\n",
+       "               '2012-01-09', '2012-01-10',\n",
+       "               ...\n",
+       "               '2012-12-22', '2012-12-23', '2012-12-24', '2012-12-25',\n",
+       "               '2012-12-26', '2012-12-27', '2012-12-28', '2012-12-29',\n",
+       "               '2012-12-30', '2012-12-31'],\n",
+       "              dtype='datetime64[ns]', name='Date', length=366, freq=None)"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# show index of bikes_df\n",
+    "bikes_df.index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Unnamed: 1                     00:00\n",
+       "Rachel / Papineau                 16\n",
+       "Berri1                            35\n",
+       "Maisonneuve_2                     51\n",
+       "Maisonneuve_1                     38\n",
+       "Brébeuf                            5\n",
+       "Parc                              26\n",
+       "PierDup                           10\n",
+       "CSC (Côte Sainte-Catherine)        0\n",
+       "Pont_Jacques_Cartier              27\n",
+       "Name: 2012-01-01 00:00:00, dtype: object"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# get row for date 2012-01-01\n",
+    "bikes_df.loc['2012-01-01']"
+   ]
+  },
+  {
+   "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": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Unnamed: 1                     00:00\n",
+       "Rachel / Papineau                194\n",
+       "Berri1                           273\n",
+       "Maisonneuve_2                    443\n",
+       "Maisonneuve_1                    182\n",
+       "Brébeuf                            7\n",
+       "Parc                             258\n",
+       "PierDup                           12\n",
+       "CSC (Côte Sainte-Catherine)        0\n",
+       "Pont_Jacques_Cartier              20\n",
+       "Name: 2012-01-11 00:00:00, dtype: object"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# show 11th row of imdb_df using iloc\n",
+    "bikes_df.iloc[10]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Selecting rows where column has a particular value"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>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",
+       "  </tbody>\n",
+       "</table>\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",
+       "   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",
+       "                                          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...  "
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# select only those movies where genre is adventure\n",
+    "imdb_df[imdb_df['genre'] == 'Adventure'].head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Drama        36\n",
+       "Action       20\n",
+       "Adventure    17\n",
+       "Crime        13\n",
+       "Biography    12\n",
+       "Comedy        6\n",
+       "Western       6\n",
+       "Mystery       3\n",
+       "Horror        1\n",
+       "Animation     1\n",
+       "Name: genre, dtype: int64"
+      ]
+     },
+     "execution_count": 23,
+     "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",
+    "good_movies_condition = (imdb_df['star_rating'] > 8) & (imdb_df['duration'] > 130)\n",
+    "imdb_df[good_movies_condition]['genre'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Adding a new column to DataFrame"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# add a weekday column to bikes_df\n",
+    "bikes_df['weekday'] = bikes_df.index.weekday"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Deleting an existing column from DataFrame"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# remove column 'Unnamed: 1' from bikes_df\n",
+    "bikes_df.drop('Unnamed: 1', axis=1, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Deleting a row in DataFrame"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "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>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",
+       "    <tr>\n",
+       "      <th>Date</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2012-01-02</th>\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>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-03</th>\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>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-04</th>\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>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-05</th>\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>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-06</th>\n",
+       "      <td>75</td>\n",
+       "      <td>146</td>\n",
+       "      <td>244</td>\n",
+       "      <td>98</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>86</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Rachel / Papineau  Berri1  Maisonneuve_2  Maisonneuve_1  Brébeuf  \\\n",
+       "Date                                                                           \n",
+       "2012-01-02                 43      83            153             68     11.0   \n",
+       "2012-01-03                 58     135            248            104      2.0   \n",
+       "2012-01-04                 61     144            318            116      2.0   \n",
+       "2012-01-05                 95     197            330            124      6.0   \n",
+       "2012-01-06                 75     146            244             98      4.0   \n",
+       "\n",
+       "            Parc  PierDup  CSC (Côte Sainte-Catherine)  Pont_Jacques_Cartier  \\\n",
+       "Date                                                                           \n",
+       "2012-01-02    53        6                            1                  21.0   \n",
+       "2012-01-03    89        3                            2                  15.0   \n",
+       "2012-01-04   111        8                            1                  19.0   \n",
+       "2012-01-05    97       13                            2                  16.0   \n",
+       "2012-01-06    86        4                            0                  17.0   \n",
+       "\n",
+       "            weekday  \n",
+       "Date                 \n",
+       "2012-01-02        0  \n",
+       "2012-01-03        1  \n",
+       "2012-01-04        2  \n",
+       "2012-01-05        3  \n",
+       "2012-01-06        4  "
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# remove row no. 1 from bikes_df\n",
+    "bikes_df.drop(bikes_df.index[0]).head()"
+   ]
+  },
+  {
+   "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": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# group imdb_df by movie genres\n",
+    "genre_groups = imdb_df.groupby('genre')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>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",
+       "  </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",
+       "4           8.9              Pulp Fiction              R  Crime       154   \n",
+       "21          8.7               City of God              R  Crime       130   \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...  "
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# get crime movies group\n",
+    "genre_groups.get_group('Crime').head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>star_rating</th>\n",
+       "      <th>duration</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>genre</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Action</th>\n",
+       "      <td>7.884559</td>\n",
+       "      <td>126.485294</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Adventure</th>\n",
+       "      <td>7.933333</td>\n",
+       "      <td>134.840000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Animation</th>\n",
+       "      <td>7.914516</td>\n",
+       "      <td>96.596774</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Biography</th>\n",
+       "      <td>7.862338</td>\n",
+       "      <td>131.844156</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Comedy</th>\n",
+       "      <td>7.822436</td>\n",
+       "      <td>107.602564</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Crime</th>\n",
+       "      <td>7.916935</td>\n",
+       "      <td>122.298387</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Drama</th>\n",
+       "      <td>7.902518</td>\n",
+       "      <td>126.539568</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Family</th>\n",
+       "      <td>7.850000</td>\n",
+       "      <td>107.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Fantasy</th>\n",
+       "      <td>7.700000</td>\n",
+       "      <td>112.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Film-Noir</th>\n",
+       "      <td>8.033333</td>\n",
+       "      <td>97.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>History</th>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>66.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Horror</th>\n",
+       "      <td>7.806897</td>\n",
+       "      <td>102.517241</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Mystery</th>\n",
+       "      <td>7.975000</td>\n",
+       "      <td>115.625000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Sci-Fi</th>\n",
+       "      <td>7.920000</td>\n",
+       "      <td>109.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Thriller</th>\n",
+       "      <td>7.680000</td>\n",
+       "      <td>114.200000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Western</th>\n",
+       "      <td>8.255556</td>\n",
+       "      <td>136.666667</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           star_rating    duration\n",
+       "genre                             \n",
+       "Action        7.884559  126.485294\n",
+       "Adventure     7.933333  134.840000\n",
+       "Animation     7.914516   96.596774\n",
+       "Biography     7.862338  131.844156\n",
+       "Comedy        7.822436  107.602564\n",
+       "Crime         7.916935  122.298387\n",
+       "Drama         7.902518  126.539568\n",
+       "Family        7.850000  107.500000\n",
+       "Fantasy       7.700000  112.000000\n",
+       "Film-Noir     8.033333   97.333333\n",
+       "History       8.000000   66.000000\n",
+       "Horror        7.806897  102.517241\n",
+       "Mystery       7.975000  115.625000\n",
+       "Sci-Fi        7.920000  109.000000\n",
+       "Thriller      7.680000  114.200000\n",
+       "Western       8.255556  136.666667"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# get mean of movie durations for each group\n",
+    "genre_groups.aggregate('mean')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# change duration of all movies in a particular genre to mean duration of the group\n",
+    "imdb_df['new_duration'] = genre_groups['duration'].transform(lambda x:x.mean())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# drop groups/genres that do not have average movie duration greater than 120.\n",
+    "new_imdb_df = genre_groups.filter(lambda x: x['duration'].mean() > 120)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# group weekday wise bikers count\n",
+    "weekday_groups = bikes_df.groupby('weekday')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get weekday wise biker count\n",
+    "weekday_counts = weekday_groups.aggregate(sum)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# set index of the resulting aggregation by weekday names\n",
+    "weekday_counts.index = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7faf70fc00f0>"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7faf70f96390>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot weekday wise biker count for 'Berri1'\n",
+    "weekday_counts['Berri1'].plot.bar()"
+   ]
+  },
+  {
+   "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": "3.6.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/data/bikes.csv b/data/bikes.csv
new file mode 100644
index 0000000..151c56a
--- /dev/null
+++ b/data/bikes.csv
@@ -0,0 +1,367 @@
+Date;;Rachel / Papineau;Berri1;Maisonneuve_2;Maisonneuve_1;Brébeuf;Parc;PierDup;CSC (Côte Sainte-Catherine);Pont_Jacques_Cartier
+01/01/2012;00:00;16;35;51;38;5;26;10;0;27
+02/01/2012;00:00;43;83;153;68;11;53;6;1;21
+03/01/2012;00:00;58;135;248;104;2;89;3;2;15
+04/01/2012;00:00;61;144;318;116;2;111;8;1;19
+05/01/2012;00:00;95;197;330;124;6;97;13;2;16
+06/01/2012;00:00;75;146;244;98;4;86;4;0;17
+07/01/2012;00:00;54;98;108;80;5;53;6;2;9
+08/01/2012;00:00;63;95;98;62;13;64;11;1;23
+09/01/2012;00:00;173;244;432;165;9;198;12;2;23
+10/01/2012;00:00;241;397;563;238;6;275;18;3;46
+11/01/2012;00:00;194;273;443;182;7;258;12;0;20
+12/01/2012;00:00;63;157;261;134;3;137;9;1;15
+13/01/2012;00:00;0;75;105;41;4;64;2;0;1
+14/01/2012;00:00;1;32;56;54;0;19;0;0;3
+15/01/2012;00:00;0;54;60;33;0;18;0;0;2
+16/01/2012;00:00;0;168;312;136;8;137;1;2;1
+17/01/2012;00:00;0;155;256;86;4;74;0;0;1
+18/01/2012;00:00;0;139;188;66;2;68;3;0;20
+19/01/2012;00:00;0;191;248;104;46;79;3;1;5
+20/01/2012;00:00;1;161;217;96;4;67;1;4;13
+21/01/2012;00:00;0;53;70;47;11;32;1;0;5
+22/01/2012;00:00;0;71;73;41;20;35;5;0;29
+23/01/2012;00:00;0;210;357;114;22;91;6;6;37
+24/01/2012;00:00;0;299;444;189;9;174;4;1;20
+25/01/2012;00:00;0;334;453;217;16;180;4;1;28
+26/01/2012;00:00;1;306;495;215;3;191;0;0;21
+27/01/2012;00:00;0;91;204;79;11;65;0;5;186
+28/01/2012;00:00;1;80;123;61;3;33;9;1;34
+29/01/2012;00:00;0;87;132;65;6;40;7;1;20
+30/01/2012;00:00;0;219;371;146;6;152;2;0;38
+31/01/2012;00:00;0;186;324;109;7;122;0;1;4
+01/02/2012;00:00;0;138;271;100;10;71;5;0;4
+02/02/2012;00:00;2;217;345;134;7;128;2;5;4
+03/02/2012;00:00;1;174;301;103;2;111;1;1;8
+04/02/2012;00:00;0;84;119;53;16;44;8;0;59
+05/02/2012;00:00;0;72;133;46;11;54;7;0;58
+06/02/2012;00:00;0;248;425;136;5;167;10;1;19
+07/02/2012;00:00;0;316;516;209;12;225;9;0;22
+08/02/2012;00:00;1;271;503;202;6;215;4;0;27
+09/02/2012;00:00;0;342;471;227;6;231;11;0;24
+10/02/2012;00:00;2;303;478;206;13;216;6;1;10
+11/02/2012;00:00;0;71;112;63;12;49;3;0;22
+12/02/2012;00:00;0;78;91;36;4;53;5;0;7
+13/02/2012;00:00;0;211;408;175;13;207;4;0;3
+14/02/2012;00:00;1;318;504;186;6;243;9;0;11
+15/02/2012;00:00;1;307;491;180;8;232;5;0;26
+16/02/2012;00:00;0;386;569;212;3;295;10;1;16
+17/02/2012;00:00;1;332;496;237;15;260;11;3;6
+18/02/2012;00:00;0;220;280;159;7;134;30;1;68
+19/02/2012;00:00;2;169;205;110;7;113;13;3;38
+20/02/2012;00:00;10;303;470;224;17;243;14;2;30
+21/02/2012;00:00;6;441;503;292;12;286;10;5;10
+22/02/2012;00:00;3;375;501;263;7;264;12;10;29
+23/02/2012;00:00;13;397;528;293;12;329;22;10;21
+24/02/2012;00:00;13;243;313;187;13;137;4;4;25
+25/02/2012;00:00;5;62;52;48;1;18;2;0;2
+26/02/2012;00:00;0;78;47;76;2;35;4;0;14
+27/02/2012;00:00;5;119;298;94;9;134;3;4;8
+28/02/2012;00:00;3;195;350;158;22;168;9;0;12
+29/02/2012;00:00;4;242;446;164;15;190;7;0;11
+01/03/2012;00:00;14;92;199;56;46;63;0;0;14
+02/03/2012;00:00;1;143;283;56;31;77;7;0;2
+03/03/2012;00:00;0;82;133;65;2;47;2;0;12
+04/03/2012;00:00;0;107;138;79;0;60;15;2;6
+05/03/2012;00:00;2;155;363;114;88;157;8;1;6
+06/03/2012;00:00;0;269;437;192;4;170;10;0;11
+07/03/2012;00:00;0;438;715;290;7;266;28;3;35
+08/03/2012;00:00;9;348;530;238;2;270;9;6;34
+09/03/2012;00:00;38;371;575;279;9;268;8;8;23
+10/03/2012;00:00;58;182;296;162;16;165;0;13;89
+11/03/2012;00:00;285;380;540;253;12;289;0;60;163
+12/03/2012;00:00;548;802;1265;618;12;747;0;179;145
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+11/11/2012;00:00;1455;1080;1321;761;;769;239;502;0
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+13/11/2012;00:00;2452;2290;3303;1722;;1994;113;1177;
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diff --git a/data/imdb_1000.csv b/data/imdb_1000.csv
new file mode 100644
index 0000000..2d519da
--- /dev/null
+++ b/data/imdb_1000.csv
@@ -0,0 +1,980 @@
+star_rating,title,content_rating,genre,duration,actors_list
+9.3,The Shawshank Redemption,R,Crime,142,"[u'Tim Robbins', u'Morgan Freeman', u'Bob Gunton']"
+9.2,The Godfather,R,Crime,175,"[u'Marlon Brando', u'Al Pacino', u'James Caan']"
+9.1,The Godfather: Part II,R,Crime,200,"[u'Al Pacino', u'Robert De Niro', u'Robert Duvall']"
+9,The Dark Knight,PG-13,Action,152,"[u'Christian Bale', u'Heath Ledger', u'Aaron Eckhart']"
+8.9,Pulp Fiction,R,Crime,154,"[u'John Travolta', u'Uma Thurman', u'Samuel L. Jackson']"
+8.9,12 Angry Men,NOT RATED,Drama,96,"[u'Henry Fonda', u'Lee J. Cobb', u'Martin Balsam']"
+8.9,"The Good, the Bad and the Ugly",NOT RATED,Western,161,"[u'Clint Eastwood', u'Eli Wallach', u'Lee Van Cleef']"
+8.9,The Lord of the Rings: The Return of the King,PG-13,Adventure,201,"[u'Elijah Wood', u'Viggo Mortensen', u'Ian McKellen']"
+8.9,Schindler's List,R,Biography,195,"[u'Liam Neeson', u'Ralph Fiennes', u'Ben Kingsley']"
+8.9,Fight Club,R,Drama,139,"[u'Brad Pitt', u'Edward Norton', u'Helena Bonham Carter']"
+8.8,The Lord of the Rings: The Fellowship of the Ring,PG-13,Adventure,178,"[u'Elijah Wood', u'Ian McKellen', u'Orlando Bloom']"
+8.8,Inception,PG-13,Action,148,"[u'Leonardo DiCaprio', u'Joseph Gordon-Levitt', u'Ellen Page']"
+8.8,Star Wars: Episode V - The Empire Strikes Back,PG,Action,124,"[u'Mark Hamill', u'Harrison Ford', u'Carrie Fisher']"
+8.8,Forrest Gump,PG-13,Drama,142,"[u'Tom Hanks', u'Robin Wright', u'Gary Sinise']"
+8.8,The Lord of the Rings: The Two Towers,PG-13,Adventure,179,"[u'Elijah Wood', u'Ian McKellen', u'Viggo Mortensen']"
+8.7,Interstellar,PG-13,Adventure,169,"[u'Matthew McConaughey', u'Anne Hathaway', u'Jessica Chastain']"
+8.7,One Flew Over the Cuckoo's Nest,R,Drama,133,"[u'Jack Nicholson', u'Louise Fletcher', u'Michael Berryman']"
+8.7,Seven Samurai,UNRATED,Drama,207,"[u'Toshir\xf4 Mifune', u'Takashi Shimura', u'Keiko Tsushima']"
+8.7,Goodfellas,R,Biography,146,"[u'Robert De Niro', u'Ray Liotta', u'Joe Pesci']"
+8.7,Star Wars,PG,Action,121,"[u'Mark Hamill', u'Harrison Ford', u'Carrie Fisher']"
+8.7,The Matrix,R,Action,136,"[u'Keanu Reeves', u'Laurence Fishburne', u'Carrie-Anne Moss']"
+8.7,City of God,R,Crime,130,"[u'Alexandre Rodrigues', u'Matheus Nachtergaele', u'Leandro Firmino']"
+8.7,It's a Wonderful Life,APPROVED,Drama,130,"[u'James Stewart', u'Donna Reed', u'Lionel Barrymore']"
+8.7,The Usual Suspects,R,Crime,106,"[u'Kevin Spacey', u'Gabriel Byrne', u'Chazz Palminteri']"
+8.7,Se7en,R,Drama,127,"[u'Morgan Freeman', u'Brad Pitt', u'Kevin Spacey']"
+8.6,Life Is Beautiful,PG-13,Comedy,116,"[u'Roberto Benigni', u'Nicoletta Braschi', u'Giorgio Cantarini']"
+8.6,Once Upon a Time in the West,PG-13,Western,175,"[u'Henry Fonda', u'Charles Bronson', u'Claudia Cardinale']"
+8.6,The Silence of the Lambs,R,Drama,118,"[u'Jodie Foster', u'Anthony Hopkins', u'Lawrence A. Bonney']"
+8.6,Leon: The Professional,R,Crime,110,"[u'Jean Reno', u'Gary Oldman', u'Natalie Portman']"
+8.6,City Lights,PASSED,Comedy,87,"[u'Charles Chaplin', u'Virginia Cherrill', u'Florence Lee']"
+8.6,Spirited Away,PG,Animation,125,"[u'Daveigh Chase', u'Suzanne Pleshette', u'Miyu Irino']"
+8.6,The Intouchables,R,Biography,112,"[u'Fran\xe7ois Cluzet', u'Omar Sy', u'Anne Le Ny']"
+8.6,Casablanca,PG,Drama,102,"[u'Humphrey Bogart', u'Ingrid Bergman', u'Paul Henreid']"
+8.6,Whiplash,R,Drama,107,"[u'Miles Teller', u'J.K. Simmons', u'Melissa Benoist']"
+8.6,American History X,R,Crime,119,"[u'Edward Norton', u'Edward Furlong', u""Beverly D'Angelo""]"
+8.6,Modern Times,G,Comedy,87,"[u'Charles Chaplin', u'Paulette Goddard', u'Henry Bergman']"
+8.6,Saving Private Ryan,R,Action,169,"[u'Tom Hanks', u'Matt Damon', u'Tom Sizemore']"
+8.6,Raiders of the Lost Ark,PG,Action,115,"[u'Harrison Ford', u'Karen Allen', u'Paul Freeman']"
+8.6,Rear Window,APPROVED,Mystery,112,"[u'James Stewart', u'Grace Kelly', u'Wendell Corey']"
+8.6,Psycho,R,Horror,109,"[u'Anthony Perkins', u'Janet Leigh', u'Vera Miles']"
+8.5,The Green Mile,R,Crime,189,"[u'Tom Hanks', u'Michael Clarke Duncan', u'David Morse']"
+8.5,Sunset Blvd.,NOT RATED,Drama,110,"[u'William Holden', u'Gloria Swanson', u'Erich von Stroheim']"
+8.5,The Pianist,R,Biography,150,"[u'Adrien Brody', u'Thomas Kretschmann', u'Frank Finlay']"
+8.5,The Dark Knight Rises,PG-13,Action,165,"[u'Christian Bale', u'Tom Hardy', u'Anne Hathaway']"
+8.5,Gladiator,R,Action,155,"[u'Russell Crowe', u'Joaquin Phoenix', u'Connie Nielsen']"
+8.5,Terminator 2: Judgment Day,R,Action,137,"[u'Arnold Schwarzenegger', u'Linda Hamilton', u'Edward Furlong']"
+8.5,Memento,R,Mystery,113,"[u'Guy Pearce', u'Carrie-Anne Moss', u'Joe Pantoliano']"
+8.5,Taare Zameen Par,PG,Drama,165,"[u'Darsheel Safary', u'Aamir Khan', u'Tanay Chheda']"
+8.5,Dr. Strangelove or: How I Learned to Stop Worrying and Love the Bomb,PG,Comedy,95,"[u'Peter Sellers', u'George C. Scott', u'Sterling Hayden']"
+8.5,The Departed,R,Crime,151,"[u'Leonardo DiCaprio', u'Matt Damon', u'Jack Nicholson']"
+8.5,Cinema Paradiso,R,Drama,155,"[u'Philippe Noiret', u'Enzo Cannavale', u'Antonella Attili']"
+8.5,Apocalypse Now,R,Drama,153,"[u'Martin Sheen', u'Marlon Brando', u'Robert Duvall']"
+8.5,The Great Dictator,APPROVED,Comedy,125,"[u'Charles Chaplin', u'Paulette Goddard', u'Jack Oakie']"
+8.5,The Prestige,PG-13,Drama,130,"[u'Christian Bale', u'Hugh Jackman', u'Scarlett Johansson']"
+8.5,Back to the Future,PG,Adventure,116,"[u'Michael J. Fox', u'Christopher Lloyd', u'Lea Thompson']"
+8.5,The Lion King,G,Animation,89,"[u'Matthew Broderick', u'Jeremy Irons', u'James Earl Jones']"
+8.5,The Lives of Others,R,Drama,137,"[u'Ulrich M\xfche', u'Martina Gedeck', u'Sebastian Koch']"
+8.5,Alien,R,Horror,117,"[u'Sigourney Weaver', u'Tom Skerritt', u'John Hurt']"
+8.5,Paths of Glory,APPROVED,Drama,88,"[u'Kirk Douglas', u'Ralph Meeker', u'Adolphe Menjou']"
+8.5,Django Unchained,R,Western,165,"[u'Jamie Foxx', u'Christoph Waltz', u'Leonardo DiCaprio']"
+8.5,3 Idiots,PG-13,Comedy,170,"[u'Aamir Khan', u'Madhavan', u'Mona Singh']"
+8.5,Grave of the Fireflies,UNRATED,Animation,89,"[u'Tsutomu Tatsumi', u'Ayano Shiraishi', u'Akemi Yamaguchi']"
+8.5,The Shining,R,Horror,146,"[u'Jack Nicholson', u'Shelley Duvall', u'Danny Lloyd']"
+8.4,M,NOT RATED,Crime,99,"[u'Peter Lorre', u'Ellen Widmann', u'Inge Landgut']"
+8.4,WALL-E,G,Animation,98,"[u'Ben Burtt', u'Elissa Knight', u'Jeff Garlin']"
+8.4,Witness for the Prosecution,APPROVED,Crime,116,"[u'Tyrone Power', u'Marlene Dietrich', u'Charles Laughton']"
+8.4,Munna Bhai M.B.B.S.,NOT RATED,Comedy,156,"[u'Sunil Dutt', u'Sanjay Dutt', u'Arshad Warsi']"
+8.4,American Beauty,R,Drama,122,"[u'Kevin Spacey', u'Annette Bening', u'Thora Birch']"
+8.4,Das Boot,R,Adventure,149,"[u'J\xfcrgen Prochnow', u'Herbert Gr\xf6nemeyer', u'Klaus Wennemann']"
+8.4,Princess Mononoke,PG-13,Animation,134,"[u'Y\xf4ji Matsuda', u'Yuriko Ishida', u'Y\xfbko Tanaka']"
+8.4,Amelie,R,Comedy,122,"[u'Audrey Tautou', u'Mathieu Kassovitz', u'Rufus']"
+8.4,North by Northwest,APPROVED,Adventure,136,"[u'Cary Grant', u'Eva Marie Saint', u'James Mason']"
+8.4,Rang De Basanti,NOT RATED,Drama,157,"[u'Aamir Khan', u'Soha Ali Khan', u'Siddharth']"
+8.4,Jodaeiye Nader az Simin,PG-13,Drama,123,"[u'Peyman Moaadi', u'Leila Hatami', u'Sareh Bayat']"
+8.4,Citizen Kane,APPROVED,Drama,119,"[u'Orson Welles', u'Joseph Cotten', u'Dorothy Comingore']"
+8.4,Aliens,R,Action,137,"[u'Sigourney Weaver', u'Michael Biehn', u'Carrie Henn']"
+8.4,Vertigo,APPROVED,Mystery,128,"[u'James Stewart', u'Kim Novak', u'Barbara Bel Geddes']"
+8.4,Oldeuboi,R,Drama,120,"[u'Min-sik Choi', u'Ji-tae Yu', u'Hye-jeong Kang']"
+8.4,Once Upon a Time in America,R,Crime,229,"[u'Robert De Niro', u'James Woods', u'Elizabeth McGovern']"
+8.4,Double Indemnity,PASSED,Crime,107,"[u'Fred MacMurray', u'Barbara Stanwyck', u'Edward G. Robinson']"
+8.4,Star Wars: Episode VI - Return of the Jedi,PG,Action,134,"[u'Mark Hamill', u'Harrison Ford', u'Carrie Fisher']"
+8.4,Toy Story 3,G,Animation,103,"[u'Tom Hanks', u'Tim Allen', u'Joan Cusack']"
+8.4,Braveheart,R,Action,177,"[u'Mel Gibson', u'Sophie Marceau', u'Patrick McGoohan']"
+8.4,To Kill a Mockingbird,NOT RATED,Drama,129,"[u'Gregory Peck', u'John Megna', u'Frank Overton']"
+8.4,Requiem for a Dream,R,Drama,102,"[u'Ellen Burstyn', u'Jared Leto', u'Jennifer Connelly']"
+8.4,Lawrence of Arabia,PG,Adventure,216,"[u""Peter O'Toole"", u'Alec Guinness', u'Anthony Quinn']"
+8.4,A Clockwork Orange,X,Crime,136,"[u'Malcolm McDowell', u'Patrick Magee', u'Michael Bates']"
+8.4,Bicycle Thieves,NOT RATED,Drama,93,"[u'Lamberto Maggiorani', u'Enzo Staiola', u'Lianella Carell']"
+8.4,The Kid,NOT RATED,Comedy,68,"[u'Charles Chaplin', u'Edna Purviance', u'Jackie Coogan']"
+8.4,Swades,NOT RATED,Drama,189,"[u'Shah Rukh Khan', u'Gayatri Joshi', u'Kishori Balal']"
+8.4,Reservoir Dogs,R,Crime,99,"[u'Harvey Keitel', u'Tim Roth', u'Michael Madsen']"
+8.4,Eternal Sunshine of the Spotless Mind,R,Drama,108,"[u'Jim Carrey', u'Kate Winslet', u'Tom Wilkinson']"
+8.4,Taxi Driver,R,Crime,113,"[u'Robert De Niro', u'Jodie Foster', u'Cybill Shepherd']"
+8.4,Dilwale Dulhania Le Jayenge,NOT RATED,Comedy,181,"[u'Shah Rukh Khan', u'Kajol', u'Amrish Puri']"
+8.4,Singin' in the Rain,APPROVED,Comedy,103,"[u'Gene Kelly', u""Donald O'Connor"", u'Debbie Reynolds']"
+8.4,All About Eve,APPROVED,Drama,138,"[u'Bette Davis', u'Anne Baxter', u'George Sanders']"
+8.4,Yojimbo,UNRATED,Action,110,"[u'Toshir\xf4 Mifune', u'Eijir\xf4 T\xf4no', u'Tatsuya Nakadai']"
+8.4,The Sting,PG,Comedy,129,"[u'Paul Newman', u'Robert Redford', u'Robert Shaw']"
+8.4,Rashomon,UNRATED,Crime,88,"[u'Toshir\xf4 Mifune', u'Machiko Ky\xf4', u'Masayuki Mori']"
+8.4,Amadeus,R,Biography,160,"[u'F. Murray Abraham', u'Tom Hulce', u'Elizabeth Berridge']"
+8.3,The Treasure of the Sierra Madre,NOT RATED,Action,126,"[u'Humphrey Bogart', u'Walter Huston', u'Tim Holt']"
+8.3,Monty Python and the Holy Grail,PG,Adventure,91,"[u'Graham Chapman', u'John Cleese', u'Eric Idle']"
+8.3,Full Metal Jacket,R,Drama,116,"[u'Matthew Modine', u'R. Lee Ermey', u""Vincent D'Onofrio""]"
+8.3,The Apartment,APPROVED,Comedy,125,"[u'Jack Lemmon', u'Shirley MacLaine', u'Fred MacMurray']"
+8.3,Ikiru,NOT RATED,Drama,143,"[u'Takashi Shimura', u'Nobuo Kaneko', u""Shin'ichi Himori""]"
+8.3,The Third Man,NOT RATED,Film-Noir,93,"[u'Orson Welles', u'Joseph Cotten', u'Alida Valli']"
+8.3,Snatch.,R,Comedy,102,"[u'Jason Statham', u'Brad Pitt', u'Benicio Del Toro']"
+8.3,For a Few Dollars More,APPROVED,Western,132,"[u'Clint Eastwood', u'Lee Van Cleef', u'Gian Maria Volont\xe9']"
+8.3,Metropolis,NOT RATED,Drama,153,"[u'Brigitte Helm', u'Alfred Abel', u'Gustav Fr\xf6hlich']"
+8.3,Dil Chahta Hai,NOT RATED,Comedy,183,"[u'Aamir Khan', u'Saif Ali Khan', u'Akshaye Khanna']"
+8.3,2001: A Space Odyssey,G,Mystery,160,"[u'Keir Dullea', u'Gary Lockwood', u'William Sylvester']"
+8.3,Some Like It Hot,NOT RATED,Comedy,120,"[u'Marilyn Monroe', u'Tony Curtis', u'Jack Lemmon']"
+8.3,L.A. Confidential,R,Crime,138,"[u'Kevin Spacey', u'Russell Crowe', u'Guy Pearce']"
+8.3,Batman Begins,PG-13,Action,140,"[u'Christian Bale', u'Michael Caine', u'Ken Watanabe']"
+8.3,Inglourious Basterds,R,Adventure,153,"[u'Brad Pitt', u'Diane Kruger', u'Eli Roth']"
+8.3,Scarface,R,Crime,170,"[u'Al Pacino', u'Michelle Pfeiffer', u'Steven Bauer']"
+8.3,Mr. Smith Goes to Washington,NOT RATED,Drama,129,"[u'James Stewart', u'Jean Arthur', u'Claude Rains']"
+8.3,Toy Story,G,Animation,81,"[u'Tom Hanks', u'Tim Allen', u'Don Rickles']"
+8.3,Indiana Jones and the Last Crusade,PG-13,Action,127,"[u'Harrison Ford', u'Sean Connery', u'Alison Doody']"
+8.3,Unforgiven,R,Western,131,"[u'Clint Eastwood', u'Gene Hackman', u'Morgan Freeman']"
+8.3,The Great Escape,UNRATED,Adventure,172,"[u'Steve McQueen', u'James Garner', u'Richard Attenborough']"
+8.3,Jagten,R,Drama,115,"[u'Mads Mikkelsen', u'Thomas Bo Larsen', u'Annika Wedderkopp']"
+8.3,On the Waterfront,NOT RATED,Crime,108,"[u'Marlon Brando', u'Karl Malden', u'Lee J. Cobb']"
+8.3,The General,UNRATED,Action,107,"[u'Buster Keaton', u'Marion Mack', u'Glen Cavender']"
+8.3,Raging Bull,R,Biography,129,"[u'Robert De Niro', u'Cathy Moriarty', u'Joe Pesci']"
+8.3,Downfall,R,Biography,156,"[u'Bruno Ganz', u'Alexandra Maria Lara', u'Ulrich Matthes']"
+8.3,Up,PG,Animation,96,"[u'Edward Asner', u'Jordan Nagai', u'John Ratzenberger']"
+8.3,Wild Strawberries,UNRATED,Drama,91,"[u'Victor Sj\xf6str\xf6m', u'Bibi Andersson', u'Ingrid Thulin']"
+8.3,The Gold Rush,NOT RATED,Adventure,95,"[u'Charles Chaplin', u'Mack Swain', u'Tom Murray']"
+8.3,Ran,R,Action,162,"[u'Tatsuya Nakadai', u'Akira Terao', u'Jinpachi Nezu']"
+8.3,Chinatown,R,Drama,130,"[u'Jack Nicholson', u'Faye Dunaway', u'John Huston']"
+8.3,My Neighbor Totoro,G,Animation,86,"[u'Hitoshi Takagi', u'Noriko Hidaka', u'Chika Sakamoto']"
+8.3,Judgment at Nuremberg,NOT RATED,Drama,186,"[u'Spencer Tracy', u'Burt Lancaster', u'Richard Widmark']"
+8.3,Barfi!,NOT RATED,Adventure,151,"[u'Ranbir Kapoor', u'Priyanka Chopra', u'Ileana']"
+8.3,The Seventh Seal,NOT RATED,Drama,96,"[u'Max von Sydow', u'Gunnar Bj\xf6rnstrand', u'Bengt Ekerot']"
+8.3,Heat,R,Action,170,"[u'Al Pacino', u'Robert De Niro', u'Val Kilmer']"
+8.3,Pan's Labyrinth,R,Drama,118,"[u'Ivana Baquero', u'Ariadna Gil', u'Sergi L\xf3pez']"
+8.3,The Bridge on the River Kwai,PG,Adventure,161,"[u'William Holden', u'Alec Guinness', u'Jack Hawkins']"
+8.3,Die Hard,R,Action,131,"[u'Bruce Willis', u'Alan Rickman', u'Bonnie Bedelia']"
+8.3,Good Will Hunting,R,Drama,126,"[u'Robin Williams', u'Matt Damon', u'Ben Affleck']"
+8.3,The Wages of Fear,NOT RATED,Adventure,131,"[u'Yves Montand', u'Charles Vanel', u'Peter van Eyck']"
+8.3,The Secret in Their Eyes,R,Drama,129,"[u'Ricardo Dar\xedn', u'Soledad Villamil', u'Pablo Rago']"
+8.3,Lagaan: Once Upon a Time in India,PG,Adventure,224,"[u'Aamir Khan', u'Gracy Singh', u'Rachel Shelley']"
+8.2,The Wolf of Wall Street,R,Biography,180,"[u'Leonardo DiCaprio', u'Jonah Hill', u'Margot Robbie']"
+8.2,It Happened One Night,UNRATED,Comedy,105,"[u'Clark Gable', u'Claudette Colbert', u'Walter Connolly']"
+8.2,Blade Runner,R,Sci-Fi,117,"[u'Harrison Ford', u'Rutger Hauer', u'Sean Young']"
+8.2,Warrior,PG-13,Drama,140,"[u'Tom Hardy', u'Nick Nolte', u'Joel Edgerton']"
+8.2,Howl's Moving Castle,PG,Animation,119,"[u'Chieko Baish\xf4', u'Takuya Kimura', u'Tatsuya Gash\xfbin']"
+8.2,The Elephant Man,PG,Biography,124,"[u'Anthony Hopkins', u'John Hurt', u'Anne Bancroft']"
+8.2,Rebecca,NOT RATED,Mystery,130,"[u'Laurence Olivier', u'Joan Fontaine', u'George Sanders']"
+8.2,Incendies,R,Drama,139,"[u'Lubna Azabal', u'M\xe9lissa D\xe9sormeaux-Poulin', u'Maxim Gaudette']"
+8.2,"Lock, Stock and Two Smoking Barrels",R,Comedy,107,"[u'Jason Flemyng', u'Dexter Fletcher', u'Nick Moran']"
+8.2,V for Vendetta,R,Action,132,"[u'Hugo Weaving', u'Natalie Portman', u'Rupert Graves']"
+8.2,The Big Lebowski,R,Comedy,117,"[u'Jeff Bridges', u'John Goodman', u'Julianne Moore']"
+8.2,Cool Hand Luke,PG,Crime,126,"[u'Paul Newman', u'George Kennedy', u'Strother Martin']"
+8.2,Relatos salvajes,R,Comedy,122,"[u'Dar\xedo Grandinetti', u'Mar\xeda Marull', u'M\xf3nica Villa']"
+8.2,Casino,R,Biography,178,"[u'Robert De Niro', u'Sharon Stone', u'Joe Pesci']"
+8.2,Gone with the Wind,G,Drama,238,"[u'Clark Gable', u'Vivien Leigh', u'Thomas Mitchell']"
+8.2,Gone Girl,R,Drama,149,"[u'Ben Affleck', u'Rosamund Pike', u'Neil Patrick Harris']"
+8.2,The Best Years of Our Lives,PASSED,Drama,172,"[u'Fredric March', u'Dana Andrews', u'Myrna Loy']"
+8.2,How to Train Your Dragon,PG,Animation,98,"[u'Jay Baruchel', u'Gerard Butler', u'Christopher Mintz-Plasse']"
+8.2,Diabolique,UNRATED,Horror,116,"[u'Simone Signoret', u'V\xe9ra Clouzot', u'Paul Meurisse']"
+8.2,Gran Torino,R,Drama,116,"[u'Clint Eastwood', u'Bee Vang', u'Christopher Carley']"
+8.2,Rush,R,Action,123,"[u'Daniel Br\xfchl', u'Chris Hemsworth', u'Olivia Wilde']"
+8.2,Into the Wild,R,Adventure,148,"[u'Emile Hirsch', u'Vince Vaughn', u'Catherine Keener']"
+8.2,My Sassy Girl,NOT RATED,Comedy,123,"[u'Tae-hyun Cha', u'Gianna Jun', u'In-mun Kim']"
+8.2,The Deer Hunter,R,Drama,182,"[u'Robert De Niro', u'Christopher Walken', u'John Cazale']"
+8.2,Mary and Max,NOT RATED,Animation,92,"[u'Toni Collette', u'Philip Seymour Hoffman', u'Eric Bana']"
+8.2,Hachi: A Dog's Tale,G,Drama,93,"[u'Richard Gere', u'Joan Allen', u'Cary-Hiroyuki Tagawa']"
+8.2,The Maltese Falcon,NOT RATED,Crime,100,"[u'Humphrey Bogart', u'Mary Astor', u'Gladys George']"
+8.2,A Beautiful Mind,PG-13,Biography,135,"[u'Russell Crowe', u'Ed Harris', u'Jennifer Connelly']"
+8.2,Dial M for Murder,PG,Crime,105,"[u'Ray Milland', u'Grace Kelly', u'Robert Cummings']"
+8.2,Trainspotting,R,Drama,94,"[u'Ewan McGregor', u'Ewen Bremner', u'Jonny Lee Miller']"
+8.2,Tae Guk Gi: The Brotherhood of War,R,Action,140,"[u'Dong-gun Jang', u'Bin Won', u'Eun-ju Lee']"
+8.2,Persona,NOT RATED,Drama,83,"[u'Bibi Andersson', u'Liv Ullmann', u'Margaretha Krook']"
+8.2,Touch of Evil,PG-13,Crime,95,"[u'Charlton Heston', u'Orson Welles', u'Janet Leigh']"
+8.2,Fargo,R,Crime,98,"[u'William H. Macy', u'Frances McDormand', u'Steve Buscemi']"
+8.2,The Avengers,PG-13,Action,143,"[u'Robert Downey Jr.', u'Chris Evans', u'Scarlett Johansson']"
+8.2,The 400 Blows,NOT RATED,Crime,99,"[u'Jean-Pierre L\xe9aud', u'Albert R\xe9my', u'Claire Maurier']"
+8.2,The Princess Bride,PG,Adventure,98,"[u'Cary Elwes', u'Mandy Patinkin', u'Robin Wright']"
+8.2,Hotel Rwanda,PG-13,Drama,121,"[u'Don Cheadle', u'Sophie Okonedo', u'Joaquin Phoenix']"
+8.2,Stalker,NOT RATED,Drama,163,"[u'Alisa Freyndlikh', u'Aleksandr Kaydanovskiy', u'Anatoliy Solonitsyn']"
+8.2,The Battle of Algiers,NOT RATED,Crime,121,"[u'Brahim Hadjadj', u'Jean Martin', u'Yacef Saadi']"
+8.2,Underground,NOT RATED,Comedy,167,"[u'Predrag Manojlovic', u'Lazar Ristovski', u'Mirjana Jokovic']"
+8.2,Fanny and Alexander,R,Drama,188,"[u'Bertil Guve', u'Pernilla Allwin', u'Kristina Adolphson']"
+8.2,Network,R,Drama,121,"[u'Faye Dunaway', u'William Holden', u'Peter Finch']"
+8.2,The Thing,R,Horror,109,"[u'Kurt Russell', u'Wilford Brimley', u'Keith David']"
+8.2,Butch Cassidy and the Sundance Kid,,Biography,110,"[u'Paul Newman', u'Robert Redford', u'Katharine Ross']"
+8.2,The Grapes of Wrath,NOT RATED,Drama,129,"[u'Henry Fonda', u'Jane Darwell', u'John Carradine']"
+8.2,"Black Cat, White Cat",R,Comedy,127,"[u'Bajram Severdzan', u'Srdjan Todorovic', u'Branka Katic']"
+8.2,Life of Brian,R,Comedy,94,"[u'Graham Chapman', u'John Cleese', u'Michael Palin']"
+8.2,The Sixth Sense,PG-13,Drama,107,"[u'Bruce Willis', u'Haley Joel Osment', u'Toni Collette']"
+8.2,Finding Nemo,G,Animation,100,"[u'Albert Brooks', u'Ellen DeGeneres', u'Alexander Gould']"
+8.2,Nausicaa of the Valley of the Wind,PG,Animation,117,"[u'Sumi Shimamoto', u'Mahito Tsujimura', u'Hisako Ky\xf4da']"
+8.1,Kingsman: The Secret Service,R,Action,129,"[u'Colin Firth', u'Taron Egerton', u'Samuel L. Jackson']"
+8.1,Platoon,R,Drama,120,"[u'Charlie Sheen', u'Tom Berenger', u'Willem Dafoe']"
+8.1,Guardians of the Galaxy,PG-13,Action,121,"[u'Chris Pratt', u'Vin Diesel', u'Bradley Cooper']"
+8.1,In the Name of the Father,R,Biography,133,"[u'Daniel Day-Lewis', u'Pete Postlethwaite', u'Alison Crosbie']"
+8.1,Kill Bill: Vol. 1,R,Action,111,"[u'Uma Thurman', u'David Carradine', u'Daryl Hannah']"
+8.1,No Country for Old Men,R,Crime,122,"[u'Tommy Lee Jones', u'Javier Bardem', u'Josh Brolin']"
+8.1,Le Samourai,PG,Crime,101,"[u'Alain Delon', u'Nathalie Delon', u'Fran\xe7ois P\xe9rier']"
+8.1,12 Years a Slave,R,Biography,134,"[u'Chiwetel Ejiofor', u'Michael Kenneth Williams', u'Michael Fassbender']"
+8.1,La leggenda del pianista sull'oceano,R,Drama,165,"[u'Tim Roth', u'Pruitt Taylor Vince', u'Bill Nunn']"
+8.1,8 1/2,NOT RATED,Drama,138,"[u'Marcello Mastroianni', u'Anouk Aim\xe9e', u'Claudia Cardinale']"
+8.1,Ben-Hur,G,Adventure,212,"[u'Charlton Heston', u'Jack Hawkins', u'Stephen Boyd']"
+8.1,Amores Perros,R,Drama,154,"[u'Emilio Echevarr\xeda', u'Gael Garc\xeda Bernal', u'Goya Toledo']"
+8.1,Stand by Me,R,Adventure,89,"[u'Wil Wheaton', u'River Phoenix', u'Corey Feldman']"
+8.1,What Ever Happened to Baby Jane?,APPROVED,Drama,134,"[u'Bette Davis', u'Joan Crawford', u'Victor Buono']"
+8.1,Annie Hall,PG,Comedy,93,"[u'Woody Allen', u'Diane Keaton', u'Tony Roberts']"
+8.1,The Imitation Game,PG-13,Biography,114,"[u'Benedict Cumberbatch', u'Keira Knightley', u'Matthew Goode']"
+8.1,Laura,APPROVED,Film-Noir,88,"[u'Gene Tierney', u'Dana Andrews', u'Clifton Webb']"
+8.1,Departures,PG-13,Drama,130,"[u'Masahiro Motoki', u'Ry\xf4ko Hirosue', u'Tsutomu Yamazaki']"
+8.1,Infernal Affairs,R,Crime,101,"[u'Andy Lau', u'Tony Chiu Wai Leung', u'Anthony Chau-Sang Wong']"
+8.1,There Will Be Blood,R,Drama,158,"[u'Daniel Day-Lewis', u'Paul Dano', u'Ciar\xe1n Hinds']"
+8.1,The Grand Budapest Hotel,R,Adventure,99,"[u'Ralph Fiennes', u'F. Murray Abraham', u'Mathieu Amalric']"
+8.1,La Strada,NOT RATED,Drama,108,"[u'Anthony Quinn', u'Giulietta Masina', u'Richard Basehart']"
+8.1,Sin City,R,Crime,124,"[u'Mickey Rourke', u'Clive Owen', u'Bruce Willis']"
+8.1,Memories of Murder,UNRATED,Crime,132,"[u'Kang-ho Song', u'Sang-kyung Kim', u'Roe-ha Kim']"
+8.1,Donnie Darko,R,Drama,113,"[u'Jake Gyllenhaal', u'Jena Malone', u'Mary McDonnell']"
+8.1,Who's Afraid of Virginia Woolf?,TV-MA,Drama,131,"[u'Elizabeth Taylor', u'Richard Burton', u'George Segal']"
+8.1,Gandhi,PG,Biography,191,"[u'Ben Kingsley', u'John Gielgud', u'Candice Bergen']"
+8.1,Solaris,PG,Drama,167,"[u'Natalya Bondarchuk', u'Donatas Banionis', u'J\xfcri J\xe4rvet']"
+8.1,Harry Potter and the Deathly Hallows: Part 2,PG-13,Adventure,130,"[u'Daniel Radcliffe', u'Emma Watson', u'Rupert Grint']"
+8.1,"Paris, Texas",R,Drama,147,"[u'Harry Dean Stanton', u'Nastassja Kinski', u'Dean Stockwell']"
+8.1,The Wizard of Oz,PASSED,Adventure,102,"[u'Judy Garland', u'Frank Morgan', u'Ray Bolger']"
+8.1,3-Iron,R,Crime,88,"[u'Seung-yeon Lee', u'Hyun-kyoon Lee', u'Hyuk-ho Kwon']"
+8.1,Boyhood,R,Drama,165,"[u'Ellar Coltrane', u'Patricia Arquette', u'Ethan Hawke']"
+8.1,Million Dollar Baby,PG-13,Drama,132,"[u'Hilary Swank', u'Clint Eastwood', u'Morgan Freeman']"
+8.1,The Last Picture Show,R,Drama,118,"[u'Timothy Bottoms', u'Jeff Bridges', u'Cybill Shepherd']"
+8.1,Strangers on a Train,APPROVED,Crime,101,"[u'Farley Granger', u'Robert Walker', u'Ruth Roman']"
+8.1,Cat on a Hot Tin Roof,APPROVED,Drama,108,"[u'Elizabeth Taylor', u'Paul Newman', u'Burl Ives']"
+8.1,La Dolce Vita,NOT RATED,Comedy,174,"[u'Marcello Mastroianni', u'Anita Ekberg', u'Anouk Aim\xe9e']"
+8.1,Chungking Express,PG-13,Drama,98,"[u'Brigitte Lin', u'Takeshi Kaneshiro', u'Tony Chiu Wai Leung']"
+8.1,The Night of the Hunter,APPROVED,Crime,92,"[u'Robert Mitchum', u'Shelley Winters', u'Lillian Gish']"
+8.1,La Haine,NOT RATED,Crime,98,"[u'Vincent Cassel', u'Hubert Kound\xe9', u'Sa\xefd Taghmaoui']"
+8.1,Yip Man,R,Action,106,"[u'Donnie Yen', u'Simon Yam', u'Siu-Wong Fan']"
+8.1,High Noon,PG,Western,85,"[u'Gary Cooper', u'Grace Kelly', u'Thomas Mitchell']"
+8.1,Notorious,APPROVED,Drama,101,"[u'Cary Grant', u'Ingrid Bergman', u'Claude Rains']"
+8.1,Before Sunrise,R,Drama,105,"[u'Ethan Hawke', u'Julie Delpy', u'Andrea Eckert']"
+8.1,Elite Squad: The Enemy Within,UNRATED,Action,115,"[u'Wagner Moura', u'Irandhir Santos', u'Andr\xe9 Ramiro']"
+8.1,The Bourne Ultimatum,PG-13,Action,115,"[u'Matt Damon', u'\xc9dgar Ram\xedrez', u'Joan Allen']"
+8.1,Castle in the Sky,PG,Animation,124,"[u'Anna Paquin', u'James Van Der Beek', u'Cloris Leachman']"
+8.1,The Celebration,R,Drama,105,"[u'Ulrich Thomsen', u'Henning Moritzen', u'Thomas Bo Larsen']"
+8.1,"Spring, Summer, Fall, Winter... and Spring",R,Drama,103,"[u'Ki-duk Kim', u'Yeong-su Oh', u'Jong-ho Kim']"
+8.1,Shutter Island,R,Mystery,138,"[u'Leonardo DiCaprio', u'Emily Mortimer', u'Mark Ruffalo']"
+8.1,Barry Lyndon,PG,Adventure,184,"[u""Ryan O'Neal"", u'Marisa Berenson', u'Patrick Magee']"
+8.1,Stalag 17,NOT RATED,Comedy,120,"[u'William Holden', u'Don Taylor', u'Otto Preminger']"
+8.1,Three Colors: Red,R,Drama,99,"[u'Ir\xe8ne Jacob', u'Jean-Louis Trintignant', u'Fr\xe9d\xe9rique Feder']"
+8.1,X-Men: Days of Future Past,PG-13,Action,131,"[u'Patrick Stewart', u'Ian McKellen', u'Hugh Jackman']"
+8.1,Sleuth,PG,Mystery,138,"[u'Laurence Olivier', u'Michael Caine', u'Alec Cawthorne']"
+8.1,In the Mood for Love,PG,Drama,98,"[u'Tony Chiu Wai Leung', u'Maggie Cheung', u'Ping Lam Siu']"
+8.1,The Man Who Shot Liberty Valance,APPROVED,Drama,123,"[u'James Stewart', u'John Wayne', u'Vera Miles']"
+8.1,Arsenic and Old Lace,NOT RATED,Comedy,118,"[u'Cary Grant', u'Priscilla Lane', u'Raymond Massey']"
+8.1,The Big Sleep,APPROVED,Crime,114,"[u'Humphrey Bogart', u'Lauren Bacall', u'John Ridgely']"
+8.1,Roman Holiday,NOT RATED,Comedy,118,"[u'Gregory Peck', u'Audrey Hepburn', u'Eddie Albert']"
+8.1,The Philadelphia Story,NOT RATED,Comedy,112,"[u'Cary Grant', u'Katharine Hepburn', u'James Stewart']"
+8.1,Akira,R,Animation,124,"[u'Nozomu Sasaki', u'Mami Koyama', u'Mitsuo Iwata']"
+8.1,Anatomy of a Murder,UNRATED,Crime,160,"[u'James Stewart', u'Lee Remick', u'Ben Gazzara']"
+8.1,The Cabinet of Dr. Caligari,UNRATED,Crime,67,"[u'Werner Krauss', u'Conrad Veidt', u'Friedrich Feher']"
+8.1,The Help,PG-13,Drama,146,"[u'Emma Stone', u'Viola Davis', u'Octavia Spencer']"
+8.1,The Sea Inside,PG-13,Biography,125,"[u'Javier Bardem', u'Bel\xe9n Rueda', u'Lola Due\xf1as']"
+8.1,Elite Squad,R,Action,115,"[u'Wagner Moura', u'Andr\xe9 Ramiro', u'Caio Junqueira']"
+8.1,The Hustler,UNRATED,Drama,134,"[u'Paul Newman', u'Jackie Gleason', u'Piper Laurie']"
+8.1,Rio Bravo,NOT RATED,Western,141,"[u'John Wayne', u'Dean Martin', u'Ricky Nelson']"
+8.1,Twelve Monkeys,R,Mystery,129,"[u'Bruce Willis', u'Madeleine Stowe', u'Brad Pitt']"
+8.1,Harvey,NOT RATED,Comedy,104,"[u'James Stewart', u'Josephine Hull', u'Peggy Dow']"
+8.1,A Christmas Story,PG,Comedy,94,"[u'Peter Billingsley', u'Melinda Dillon', u'Darren McGavin']"
+8.1,Jaws,PG,Drama,124,"[u'Roy Scheider', u'Robert Shaw', u'Richard Dreyfuss']"
+8.1,The Raid 2,R,Action,150,"[u'Iko Uwais', u'Yayan Ruhian', u'Arifin Putra']"
+8.1,Rocky,PG,Drama,119,"[u'Sylvester Stallone', u'Talia Shire', u'Burt Young']"
+8.1,Wings of Desire,PG-13,Drama,128,"[u'Bruno Ganz', u'Solveig Dommartin', u'Otto Sander']"
+8.1,Pirates of the Caribbean: The Curse of the Black Pearl,PG-13,Adventure,143,"[u'Johnny Depp', u'Geoffrey Rush', u'Orlando Bloom']"
+8.1,The Killing,APPROVED,Crime,85,"[u'Sterling Hayden', u'Coleen Gray', u'Vince Edwards']"
+8.1,Papillon,R,Biography,151,"[u'Steve McQueen', u'Dustin Hoffman', u'Victor Jory']"
+8.1,The King's Speech,R,Biography,118,"[u'Colin Firth', u'Geoffrey Rush', u'Helena Bonham Carter']"
+8.1,Groundhog Day,PG,Comedy,101,"[u'Bill Murray', u'Andie MacDowell', u'Chris Elliott']"
+8.1,A Fistful of Dollars,R,Action,99,"[u'Clint Eastwood', u'Gian Maria Volont\xe9', u'Marianne Koch']"
+8.1,"Monsters, Inc.",G,Animation,92,"[u'Billy Crystal', u'John Goodman', u'Mary Gibbs']"
+8.1,Dog Day Afternoon,R,Crime,125,"[u'Al Pacino', u'John Cazale', u'Penelope Allen']"
+8.1,The Perks of Being a Wallflower,PG-13,Drama,102,"[u'Logan Lerman', u'Emma Watson', u'Ezra Miller']"
+8.1,Young Frankenstein,PG,Comedy,106,"[u'Gene Wilder', u'Madeline Kahn', u'Marty Feldman']"
+8.1,The Terminator,R,Action,107,"[u'Arnold Schwarzenegger', u'Linda Hamilton', u'Michael Biehn']"
+8.1,Harold and Maude,GP,Comedy,91,"[u'Ruth Gordon', u'Bud Cort', u'Vivian Pickles']"
+8.1,Before Sunset,R,Drama,80,"[u'Ethan Hawke', u'Julie Delpy', u'Vernon Dobtcheff']"
+8.1,A Streetcar Named Desire,PG,Drama,122,"[u'Vivien Leigh', u'Marlon Brando', u'Kim Hunter']"
+8.1,Bringing Up Baby,APPROVED,Comedy,102,"[u'Katharine Hepburn', u'Cary Grant', u'Charles Ruggles']"
+8.1,The Diving Bell and the Butterfly,PG-13,Biography,112,"[u'Mathieu Amalric', u'Emmanuelle Seigner', u'Marie-Jos\xe9e Croze']"
+8.1,His Girl Friday,APPROVED,Comedy,92,"[u'Cary Grant', u'Rosalind Russell', u'Ralph Bellamy']"
+8.1,Sling Blade,R,Drama,135,"[u'Billy Bob Thornton', u'Dwight Yoakam', u'J.T. Walsh']"
+8.1,All Quiet on the Western Front,UNRATED,Drama,136,"[u'Lew Ayres', u'Louis Wolheim', u'John Wray']"
+8.1,Prisoners,R,Crime,153,"[u'Hugh Jackman', u'Jake Gyllenhaal', u'Viola Davis']"
+8.1,The Return,UNRATED,Drama,105,"[u'Vladimir Garin', u'Ivan Dobronravov', u'Konstantin Lavronenko']"
+8.1,The Manchurian Candidate,APPROVED,Mystery,126,"[u'Frank Sinatra', u'Laurence Harvey', u'Janet Leigh']"
+8.1,Duck Soup,PASSED,Comedy,68,"[u'Groucho Marx', u'Harpo Marx', u'Chico Marx']"
+8.1,Beauty and the Beast,G,Animation,84,"[u""Paige O'Hara"", u'Robby Benson', u'Richard White']"
+8.1,The Truman Show,PG,Drama,103,"[u'Jim Carrey', u'Ed Harris', u'Laura Linney']"
+8.1,The Wild Bunch,R,Action,145,"[u'William Holden', u'Ernest Borgnine', u'Robert Ryan']"
+8,Rope,PG,Crime,80,"[u'James Stewart', u'John Dall', u'Farley Granger']"
+8,The Graduate,APPROVED,Comedy,106,"[u'Dustin Hoffman', u'Anne Bancroft', u'Katharine Ross']"
+8,Jurassic Park,PG-13,Adventure,127,"[u'Sam Neill', u'Laura Dern', u'Jeff Goldblum']"
+8,Dogville,R,Crime,178,"[u'Nicole Kidman', u'Paul Bettany', u'Lauren Bacall']"
+8,Furious 7,PG-13,Action,137,"[u'Vin Diesel', u'Paul Walker', u'Dwayne Johnson']"
+8,Patton,GP,Biography,172,"[u'George C. Scott', u'Karl Malden', u'Stephen Young']"
+8,The Nightmare Before Christmas,PG,Animation,76,"[u'Danny Elfman', u'Chris Sarandon', u""Catherine O'Hara""]"
+8,Being There,PG,Comedy,130,"[u'Peter Sellers', u'Shirley MacLaine', u'Melvyn Douglas']"
+8,Persepolis,PG-13,Animation,96,"[u'Chiara Mastroianni', u'Catherine Deneuve', u'Gena Rowlands']"
+8,This Is Spinal Tap,R,Comedy,82,"[u'Rob Reiner', u'Michael McKean', u'Christopher Guest']"
+8,East of Eden,PG,Drama,115,"[u'James Dean', u'Raymond Massey', u'Julie Harris']"
+8,Pink Floyd The Wall,R,Animation,95,"[u'Bob Geldof', u'Christine Hargreaves', u'James Laurenson']"
+8,Blood Diamond,R,Adventure,143,"[u'Leonardo DiCaprio', u'Djimon Hounsou', u'Jennifer Connelly']"
+8,Big Fish,PG-13,Adventure,125,"[u'Ewan McGregor', u'Albert Finney', u'Billy Crudup']"
+8,Slumdog Millionaire,R,Drama,120,"[u'Dev Patel', u'Freida Pinto', u'Saurabh Shukla']"
+8,Star Trek,PG-13,Action,127,"[u'Chris Pine', u'Zachary Quinto', u'Simon Pegg']"
+8,The Straight Story,G,Biography,112,"[u'Richard Farnsworth', u'Sissy Spacek', u'Jane Galloway Heitz']"
+8,Crimes and Misdemeanors,PG-13,Comedy,104,"[u'Martin Landau', u'Woody Allen', u'Bill Bernstein']"
+8,Charade,NOT RATED,Comedy,113,"[u'Cary Grant', u'Audrey Hepburn', u'Walter Matthau']"
+8,JFK,R,Drama,189,"[u'Kevin Costner', u'Gary Oldman', u'Jack Lemmon']"
+8,Dawn of the Dead,UNRATED,Horror,127,"[u'David Emge', u'Ken Foree', u'Scott H. Reiniger']"
+8,Magnolia,R,Drama,188,"[u'Tom Cruise', u'Jason Robards', u'Julianne Moore']"
+8,Manhattan,R,Comedy,96,"[u'Woody Allen', u'Diane Keaton', u'Mariel Hemingway']"
+8,Central Station,R,Drama,113,"[u'Fernanda Montenegro', u'Vin\xedcius de Oliveira', u'Mar\xedlia P\xeara']"
+8,Brazil,R,Sci-Fi,132,"[u'Jonathan Pryce', u'Kim Greist', u'Robert De Niro']"
+8,The Exorcist,R,Horror,122,"[u'Ellen Burstyn', u'Max von Sydow', u'Linda Blair']"
+8,Her,R,Drama,126,"[u'Joaquin Phoenix', u'Amy Adams', u'Scarlett Johansson']"
+8,Dancer in the Dark,R,Crime,140,"[u'Bj\xf6rk', u'Catherine Deneuve', u'David Morse']"
+8,Rain Man,R,Drama,133,"[u'Dustin Hoffman', u'Tom Cruise', u'Valeria Golino']"
+8,Talk to Her,R,Drama,112,"[u'Rosario Flores', u'Javier C\xe1mara', u'Dar\xedo Grandinetti']"
+8,The Adventures of Robin Hood,PG,Action,102,"[u'Errol Flynn', u'Olivia de Havilland', u'Basil Rathbone']"
+8,"Aguirre, the Wrath of God",NOT RATED,Adventure,93,"[u'Klaus Kinski', u'Ruy Guerra', u'Helena Rojo']"
+8,In the Heat of the Night,APPROVED,Crime,109,"[u'Sidney Poitier', u'Rod Steiger', u'Warren Oates']"
+8,Nosferatu,UNRATED,Horror,81,"[u'Max Schreck', u'Greta Schr\xf6der', u'Ruth Landshoff']"
+8,Black Swan,R,Drama,108,"[u'Natalie Portman', u'Mila Kunis', u'Vincent Cassel']"
+8,Ratatouille,G,Animation,111,"[u'Brad Garrett', u'Lou Romano', u'Patton Oswalt']"
+8,The Searchers,APPROVED,Adventure,119,"[u'John Wayne', u'Jeffrey Hunter', u'Vera Miles']"
+8,Short Term 12,R,Drama,96,"[u'Brie Larson', u'Frantz Turner', u'John Gallagher Jr.']"
+8,Doctor Zhivago,PG-13,Drama,197,"[u'Omar Sharif', u'Julie Christie', u'Geraldine Chaplin']"
+8,Life of Pi,PG,Adventure,127,"[u'Suraj Sharma', u'Irrfan Khan', u'Adil Hussain']"
+8,All the President's Men,R,Drama,138,"[u'Dustin Hoffman', u'Robert Redford', u'Jack Warden']"
+8,Battleship Potemkin,UNRATED,History,66,"[u'Aleksandr Antonov', u'Vladimir Barsky', u'Grigori Aleksandrov']"
+8,Dances with Wolves,PG-13,Adventure,181,"[u'Kevin Costner', u'Mary McDonnell', u'Graham Greene']"
+8,Catch Me If You Can,PG-13,Biography,141,"[u'Leonardo DiCaprio', u'Tom Hanks', u'Christopher Walken']"
+8,Cinderella Man,PG-13,Biography,144,"[u'Russell Crowe', u'Ren\xe9e Zellweger', u'Craig Bierko']"
+8,Dead Poets Society,PG,Drama,128,"[u'Robin Williams', u'Robert Sean Leonard', u'Ethan Hawke']"
+8,Ghost in the Shell,NOT RATED,Animation,83,"[u'Atsuko Tanaka', u'Iemasa Kayumi', u'Akio \xd4tsuka']"
+8,Head-On,R,Drama,121,"[u'Birol \xdcnel', u'Sibel Kekilli', u'G\xfcven Kira\xe7']"
+8,No Man's Land,R,Drama,98,"[u'Branko Djuric', u'Rene Bitorajac', u'Filip Sovagovic']"
+8,Dallas Buyers Club,R,Biography,117,"[u'Matthew McConaughey', u'Jennifer Garner', u'Jared Leto']"
+8,Days of Heaven,PG,Drama,94,"[u'Richard Gere', u'Brooke Adams', u'Sam Shepard']"
+8,The Sound of Music,G,Biography,174,"[u'Julie Andrews', u'Christopher Plummer', u'Eleanor Parker']"
+8,District 9,R,Action,112,"[u'Sharlto Copley', u'David James', u'Jason Cope']"
+8,Shadow of a Doubt,APPROVED,Thriller,108,"[u'Teresa Wright', u'Joseph Cotten', u'Macdonald Carey']"
+8,Frankenstein,UNRATED,Horror,70,"[u'Colin Clive', u'Mae Clarke', u'Boris Karloff']"
+8,Shaun of the Dead,R,Comedy,99,"[u'Simon Pegg', u'Nick Frost', u'Kate Ashfield']"
+8,Night of the Living Dead,UNRATED,Horror,96,"[u'Duane Jones', u""Judith O'Dea"", u'Karl Hardman']"
+8,Kill Bill: Vol. 2,R,Action,137,"[u'Uma Thurman', u'David Carradine', u'Michael Madsen']"
+8,Secrets & Lies,R,Drama,136,"[u'Timothy Spall', u'Brenda Blethyn', u'Phyllis Logan']"
+8,The Artist,PG-13,Comedy,100,"[u'Jean Dujardin', u'B\xe9r\xe9nice Bejo', u'John Goodman']"
+8,Aladdin,G,Animation,90,"[u'Scott Weinger', u'Robin Williams', u'Linda Larkin']"
+8,The Lady Vanishes,PASSED,Comedy,96,"[u'Margaret Lockwood', u'Michael Redgrave', u'Paul Lukas']"
+8,The Hobbit: The Desolation of Smaug,PG-13,Adventure,161,"[u'Ian McKellen', u'Martin Freeman', u'Richard Armitage']"
+8,Let the Right One In,R,Drama,115,"[u'K\xe5re Hedebrant', u'Lina Leandersson', u'Per Ragnar']"
+8,Fiddler on the Roof,G,Drama,181,"[u'Topol', u'Norma Crane', u'Leonard Frey']"
+8,The Hobbit: An Unexpected Journey,PG-13,Adventure,169,"[u'Martin Freeman', u'Ian McKellen', u'Richard Armitage']"
+8,Three Colors: Blue,R,Drama,98,"[u'Juliette Binoche', u'Zbigniew Zamachowski', u'Julie Delpy']"
+8,Rosemary's Baby,R,Drama,136,"[u'Mia Farrow', u'John Cassavetes', u'Ruth Gordon']"
+8,Mystic River,R,Crime,138,"[u'Sean Penn', u'Tim Robbins', u'Kevin Bacon']"
+8,Serenity,PG-13,Action,119,"[u'Nathan Fillion', u'Gina Torres', u'Chiwetel Ejiofor']"
+8,Amarcord,R,Comedy,123,"[u'Magali No\xebl', u'Bruno Zanin', u'Pupella Maggio']"
+8,Planet of the Apes,G,Adventure,112,"[u'Charlton Heston', u'Roddy McDowall', u'Kim Hunter']"
+8,True Romance,R,Crime,120,"[u'Christian Slater', u'Patricia Arquette', u'Dennis Hopper']"
+8,Hannah and Her Sisters,PG-13,Comedy,103,"[u'Mia Farrow', u'Dianne Wiest', u'Michael Caine']"
+8,Moon,R,Drama,97,"[u'Sam Rockwell', u'Kevin Spacey', u'Dominique McElligott']"
+8,Before Midnight,R,Drama,109,"[u'Ethan Hawke', u'Julie Delpy', u'Seamus Davey-Fitzpatrick']"
+8,My Name Is Khan,PG-13,Drama,165,"[u'Shah Rukh Khan', u'Kajol', u'Sheetal Menon']"
+8,Scent of a Woman,R,Drama,156,"[u'Al Pacino', u""Chris O'Donnell"", u'James Rebhorn']"
+8,Mulholland Dr.,R,Drama,147,"[u'Naomi Watts', u'Laura Harring', u'Justin Theroux']"
+8,King Kong,UNRATED,Adventure,100,"[u'Fay Wray', u'Robert Armstrong', u'Bruce Cabot']"
+8,The Fault in Our Stars,PG-13,Drama,126,"[u'Shailene Woodley', u'Ansel Elgort', u'Nat Wolff']"
+8,The Incredibles,PG,Animation,115,"[u'Craig T. Nelson', u'Samuel L. Jackson', u'Holly Hunter']"
+8,Edward Scissorhands,PG-13,Drama,105,"[u'Johnny Depp', u'Winona Ryder', u'Dianne Wiest']"
+8,Casino Royale,PG-13,Action,144,"[u'Daniel Craig', u'Eva Green', u'Judi Dench']"
+8,How to Train Your Dragon 2,PG,Animation,102,"[u'Jay Baruchel', u'Cate Blanchett', u'Gerard Butler']"
+8,In Bruges,R,Comedy,107,"[u'Colin Farrell', u'Brendan Gleeson', u'Ciar\xe1n Hinds']"
+8,The Untouchables,R,Crime,119,"[u'Kevin Costner', u'Sean Connery', u'Robert De Niro']"
+8,Breathless,UNRATED,Crime,90,"[u'Jean-Paul Belmondo', u'Jean Seberg', u'Daniel Boulanger']"
+8,Spartacus,PG-13,Action,197,"[u'Kirk Douglas', u'Laurence Olivier', u'Jean Simmons']"
+8,The Iron Giant,PG,Animation,86,"[u'Eli Marienthal', u'Harry Connick Jr.', u'Jennifer Aniston']"
+8,Midnight Cowboy,X,Drama,113,"[u'Dustin Hoffman', u'Jon Voight', u'Sylvia Miles']"
+8,The Blues Brothers,R,Action,133,"[u'John Belushi', u'Dan Aykroyd', u'Cab Calloway']"
+8,Freaks,UNRATED,Drama,64,"[u'Wallace Ford', u'Leila Hyams', u'Olga Baclanova']"
+8,Letters from Iwo Jima,R,Drama,141,"[u'Ken Watanabe', u'Kazunari Ninomiya', u'Tsuyoshi Ihara']"
+8,Edge of Tomorrow,PG-13,Action,113,"[u'Tom Cruise', u'Emily Blunt', u'Bill Paxton']"
+8,Glory,R,Drama,122,"[u'Matthew Broderick', u'Denzel Washington', u'Cary Elwes']"
+8,The African Queen,PG,Adventure,105,"[u'Humphrey Bogart', u'Katharine Hepburn', u'Robert Morley']"
+7.9,Birdman: Or (The Unexpected Virtue of Ignorance),R,Comedy,119,"[u'Michael Keaton', u'Zach Galifianakis', u'Edward Norton']"
+7.9,Big Hero 6,PG,Animation,102,"[u'Ryan Potter', u'Scott Adsit', u'Jamie Chung']"
+7.9,Almost Famous,R,Drama,122,"[u'Billy Crudup', u'Patrick Fugit', u'Kate Hudson']"
+7.9,The Notebook,PG-13,Drama,123,"[u'Gena Rowlands', u'James Garner', u'Rachel McAdams']"
+7.9,The Conversation,PG,Drama,113,"[u'Gene Hackman', u'John Cazale', u'Allen Garfield']"
+7.9,The Breakfast Club,R,Comedy,97,"[u'Emilio Estevez', u'Judd Nelson', u'Molly Ringwald']"
+7.9,The Pursuit of Happyness,PG-13,Biography,117,"[u'Will Smith', u'Thandie Newton', u'Jaden Smith']"
+7.9,The Killer,R,Action,111,"[u'Yun-Fat Chow', u'Danny Lee', u'Sally Yeh']"
+7.9,Once,R,Drama,85,"[u'Glen Hansard', u'Mark\xe9ta Irglov\xe1', u'Hugh Walsh']"
+7.9,Ying xiong,PG-13,Action,99,"[u'Jet Li', u'Tony Chiu Wai Leung', u'Maggie Cheung']"
+7.9,The Remains of the Day,PG,Drama,134,"[u'Anthony Hopkins', u'Emma Thompson', u'John Haycraft']"
+7.9,The Bride of Frankenstein,NOT RATED,Horror,75,"[u'Boris Karloff', u'Elsa Lanchester', u'Colin Clive']"
+7.9,The Wrestler,R,Drama,109,"[u'Mickey Rourke', u'Marisa Tomei', u'Evan Rachel Wood']"
+7.9,Nightcrawler,R,Crime,117,"[u'Jake Gyllenhaal', u'Rene Russo', u'Bill Paxton']"
+7.9,Gravity,PG-13,Sci-Fi,91,"[u'Sandra Bullock', u'George Clooney', u'Ed Harris']"
+7.9,Hard Boiled,R,Action,128,"[u'Yun-Fat Chow', u'Tony Chiu Wai Leung', u'Teresa Mo']"
+7.9,Bonnie and Clyde,APPROVED,Biography,111,"[u'Warren Beatty', u'Faye Dunaway', u'Michael J. Pollard']"
+7.9,The Bourne Identity,PG-13,Action,119,"[u'Franka Potente', u'Matt Damon', u'Chris Cooper']"
+7.9,Key Largo,UNRATED,Crime,100,"[u'Humphrey Bogart', u'Edward G. Robinson', u'Lauren Bacall']"
+7.9,Nine Queens,R,Crime,114,"[u'Ricardo Dar\xedn', u'Gast\xf3n Pauls', u'Leticia Br\xe9dice']"
+7.9,Toy Story 2,G,Animation,92,"[u'Tom Hanks', u'Tim Allen', u'Joan Cusack']"
+7.9,The Killing Fields,R,Drama,141,"[u'Sam Waterston', u'Haing S. Ngor', u'John Malkovich']"
+7.9,All About My Mother,R,Drama,101,"[u'Cecilia Roth', u'Marisa Paredes', u'Candela Pe\xf1a']"
+7.9,Cowboy Bebop: The Movie,R,Animation,115,"[u'Beau Billingslea', u'Melissa Fahn', u'Nicholas Guest']"
+7.9,Ed Wood,R,Biography,127,"[u'Johnny Depp', u'Martin Landau', u'Sarah Jessica Parker']"
+7.9,Stagecoach,NOT RATED,Adventure,96,"[u'John Wayne', u'Claire Trevor', u'Andy Devine']"
+7.9,The Man Who Would Be King,PG,Action,129,"[u'Sean Connery', u'Michael Caine', u'Christopher Plummer']"
+7.9,The Outlaw Josey Wales,PG,Western,135,"[u'Clint Eastwood', u'Sondra Locke', u'Chief Dan George']"
+7.9,Children of Men,R,Drama,109,"[u'Julianne Moore', u'Clive Owen', u'Chiwetel Ejiofor']"
+7.9,The Insider,R,Biography,157,"[u'Russell Crowe', u'Al Pacino', u'Christopher Plummer']"
+7.9,The Right Stuff,R,Adventure,193,"[u'Sam Shepard', u'Scott Glenn', u'Ed Harris']"
+7.9,Badlands,PG,Crime,94,"[u'Martin Sheen', u'Sissy Spacek', u'Warren Oates']"
+7.9,A Prophet,R,Crime,155,"[u'Tahar Rahim', u'Niels Arestrup', u'Adel Bencherif']"
+7.9,"4 Months, 3 Weeks and 2 Days",NOT RATED,Drama,113,"[u'Anamaria Marinca', u'Vlad Ivanov', u'Laura Vasiliu']"
+7.9,Le passe,PG-13,Drama,130,"[u'B\xe9r\xe9nice Bejo', u'Tahar Rahim', u'Ali Mosaffa']"
+7.9,The Fall,R,Adventure,117,"[u'Lee Pace', u'Catinca Untaru', u'Justine Waddell']"
+7.9,Breaking the Waves,R,Drama,159,"[u'Emily Watson', u'Stellan Skarsg\xe5rd', u'Katrin Cartlidge']"
+7.9,Mr. Nobody,R,Drama,141,"[u'Jared Leto', u'Sarah Polley', u'Diane Kruger']"
+7.9,Crash,R,Drama,112,"[u'Don Cheadle', u'Sandra Bullock', u'Thandie Newton']"
+7.9,Avatar,PG-13,Action,162,"[u'Sam Worthington', u'Zoe Saldana', u'Sigourney Weaver']"
+7.9,Iron Man,PG-13,Action,126,"[u'Robert Downey Jr.', u'Gwyneth Paltrow', u'Terrence Howard']"
+7.9,Do the Right Thing,R,Comedy,120,"[u'Danny Aiello', u'Ossie Davis', u'Ruby Dee']"
+7.9,Carlito's Way,R,Crime,144,"[u'Al Pacino', u'Sean Penn', u'Penelope Ann Miller']"
+7.9,"Crouching Tiger, Hidden Dragon",PG-13,Action,120,"[u'Yun-Fat Chow', u'Michelle Yeoh', u'Ziyi Zhang']"
+7.9,Lilya 4-Ever,R,Crime,109,"[u'Oksana Akinshina', u'Artyom Bogucharskiy', u'Pavel Ponomaryov']"
+7.9,The Chorus,PG-13,Drama,97,"[u'G\xe9rard Jugnot', u'Fran\xe7ois Berl\xe9and', u'Jean-Baptiste Maunier']"
+7.9,The Boondock Saints,R,Action,108,"[u'Willem Dafoe', u'Sean Patrick Flanery', u'Norman Reedus']"
+7.9,Miller's Crossing,R,Crime,115,"[u'Gabriel Byrne', u'Albert Finney', u'John Turturro']"
+7.9,Walk the Line,PG-13,Biography,136,"[u'Joaquin Phoenix', u'Reese Witherspoon', u'Ginnifer Goodwin']"
+7.9,Shrek,PG,Animation,90,"[u'Mike Myers', u'Eddie Murphy', u'Cameron Diaz']"
+7.9,My Fair Lady,APPROVED,Drama,170,"[u'Audrey Hepburn', u'Rex Harrison', u'Stanley Holloway']"
+7.9,The Ten Commandments,APPROVED,Adventure,220,"[u'Charlton Heston', u'Yul Brynner', u'Anne Baxter']"
+7.9,The Fighter,R,Biography,116,"[u'Mark Wahlberg', u'Christian Bale', u'Amy Adams']"
+7.9,Captain Phillips,PG-13,Biography,134,"[u'Tom Hanks', u'Barkhad Abdi', u'Barkhad Abdirahman']"
+7.9,Cabaret,PG,Drama,124,"[u'Liza Minnelli', u'Michael York', u'Helmut Griem']"
+7.9,Halloween,R,Drama,91,"[u'Donald Pleasence', u'Jamie Lee Curtis', u'Tony Moran']"
+7.9,My Left Foot,R,Biography,103,"[u'Daniel Day-Lewis', u'Brenda Fricker', u'Alison Whelan']"
+7.9,Miracle on 34th Street,APPROVED,Comedy,96,"[u'Edmund Gwenn', u""Maureen O'Hara"", u'John Payne']"
+7.9,The Man from Nowhere,R,Action,119,"[u'Bin Won', u'Sae-ron Kim', u'Tae-hoon Kim']"
+7.9,Toki o kakeru shojo,NOT RATED,Animation,98,"[u'Riisa Naka', u'Takuya Ishida', u'Mitsutaka Itakura']"
+7.9,Little Miss Sunshine,R,Adventure,101,"[u'Steve Carell', u'Toni Collette', u'Greg Kinnear']"
+7.9,Taken,PG-13,Action,93,"[u'Liam Neeson', u'Maggie Grace', u'Famke Janssen']"
+7.9,Blue Is the Warmest Color,NC-17,Drama,179,"[u'L\xe9a Seydoux', u'Ad\xe8le Exarchopoulos', u'Salim Kechiouche']"
+7.9,Boogie Nights,R,Drama,155,"[u'Mark Wahlberg', u'Julianne Moore', u'Burt Reynolds']"
+7.9,Hot Fuzz,R,Comedy,121,"[u'Simon Pegg', u'Nick Frost', u'Martin Freeman']"
+7.9,Ferris Bueller's Day Off,PG-13,Comedy,103,"[u'Matthew Broderick', u'Alan Ruck', u'Mia Sara']"
+7.9,The World's Fastest Indian,PG-13,Biography,127,"[u'Anthony Hopkins', u'Diane Ladd', u'Iain Rea']"
+7.9,Kiki's Delivery Service,G,Animation,103,"[u'Kirsten Dunst', u'Minami Takayama', u'Rei Sakuma']"
+7.9,Down by Law,R,Comedy,107,"[u'Tom Waits', u'John Lurie', u'Roberto Benigni']"
+7.9,Glengarry Glen Ross,R,Drama,100,"[u'Al Pacino', u'Jack Lemmon', u'Alec Baldwin']"
+7.9,Clerks,R,Comedy,92,"[u""Brian O'Halloran"", u'Jeff Anderson', u'Marilyn Ghigliotti']"
+7.9,Adam's Apples,R,Comedy,94,"[u'Ulrich Thomsen', u'Mads Mikkelsen', u'Nicolas Bro']"
+7.9,The Girl with the Dragon Tattoo,R,Crime,158,"[u'Daniel Craig', u'Rooney Mara', u'Christopher Plummer']"
+7.9,The 39 Steps,UNRATED,Mystery,86,"[u'Robert Donat', u'Madeleine Carroll', u'Lucie Mannheim']"
+7.9,E.T. the Extra-Terrestrial,PG,Family,115,"[u'Henry Thomas', u'Drew Barrymore', u'Peter Coyote']"
+7.8,Tombstone,R,Action,130,"[u'Kurt Russell', u'Val Kilmer', u'Sam Elliott']"
+7.8,Predator,R,Action,107,"[u'Arnold Schwarzenegger', u'Carl Weathers', u'Kevin Peter Hall']"
+7.8,Tangled,PG,Animation,100,"[u'Mandy Moore', u'Zachary Levi', u'Donna Murphy']"
+7.8,Guess Who's Coming to Dinner,UNRATED,Comedy,108,"[u'Spencer Tracy', u'Sidney Poitier', u'Katharine Hepburn']"
+7.8,Ordinary People,R,Drama,124,"[u'Donald Sutherland', u'Mary Tyler Moore', u'Judd Hirsch']"
+7.8,The Best Offer,R,Crime,131,"[u'Geoffrey Rush', u'Jim Sturgess', u'Sylvia Hoeks']"
+7.8,Silver Linings Playbook,R,Comedy,122,"[u'Bradley Cooper', u'Jennifer Lawrence', u'Robert De Niro']"
+7.8,Hamlet,PG-13,Drama,242,"[u'Kenneth Branagh', u'Julie Christie', u'Derek Jacobi']"
+7.8,Amour,PG-13,Drama,127,"[u'Jean-Louis Trintignant', u'Emmanuelle Riva', u'Isabelle Huppert']"
+7.8,I Saw the Devil,NOT RATED,Crime,141,"[u'Byung-hun Lee', u'Min-sik Choi', u'In-seo Kim']"
+7.8,The Motorcycle Diaries,R,Adventure,126,"[u'Gael Garc\xeda Bernal', u'Rodrigo De la Serna', u'M\xeda Maestro']"
+7.8,Office Space,R,Comedy,89,"[u'Ron Livingston', u'Jennifer Aniston', u'David Herman']"
+7.8,Evil,NOT RATED,Drama,113,"[u'Andreas Wilson', u'Henrik Lundstr\xf6m', u'Gustaf Skarsg\xe5rd']"
+7.8,The Girl with the Dragon Tattoo,R,Crime,152,"[u'Michael Nyqvist', u'Noomi Rapace', u'Ewa Fr\xf6ling']"
+7.8,Withnail & I,R,Comedy,107,"[u'Richard E. Grant', u'Paul McGann', u'Richard Griffiths']"
+7.8,The Day the Earth Stood Still,APPROVED,Sci-Fi,92,"[u'Michael Rennie', u'Patricia Neal', u'Hugh Marlowe']"
+7.8,Black Book,R,Drama,145,"[u'Carice van Houten', u'Sebastian Koch', u'Thom Hoffman']"
+7.8,Evil Dead II,X,Comedy,84,"[u'Bruce Campbell', u'Sarah Berry', u'Dan Hicks']"
+7.8,Kaze tachinu,PG-13,Animation,126,"[u'Hideaki Anno', u'Hidetoshi Nishijima', u'Miori Takimoto']"
+7.8,Star Trek Into Darkness,PG-13,Action,132,"[u'Chris Pine', u'Zachary Quinto', u'Zoe Saldana']"
+7.8,The Triplets of Belleville,PG-13,Animation,78,"[u'Mich\xe8le Caucheteux', u'Jean-Claude Donda', u'Michel Robin']"
+7.8,Open Your Eyes,R,Drama,117,"[u'Eduardo Noriega', u'Pen\xe9lope Cruz', u'Chete Lera']"
+7.8,The Magnificent Seven,NOT RATED,Action,128,"[u'Yul Brynner', u'Steve McQueen', u'Charles Bronson']"
+7.8,Drive,R,Crime,100,"[u'Ryan Gosling', u'Carey Mulligan', u'Bryan Cranston']"
+7.8,Mississippi Burning,R,Crime,128,"[u'Gene Hackman', u'Willem Dafoe', u'Frances McDormand']"
+7.8,The Lego Movie,PG,Animation,100,"[u'Chris Pratt', u'Will Ferrell', u'Elizabeth Banks']"
+7.8,The Day of the Jackal,PG,Crime,143,"[u'Edward Fox', u'Terence Alexander', u'Michel Auclair']"
+7.8,Misery,R,Thriller,107,"[u'James Caan', u'Kathy Bates', u'Richard Farnsworth']"
+7.8,Willy Wonka & the Chocolate Factory,G,Family,100,"[u'Gene Wilder', u'Jack Albertson', u'Peter Ostrum']"
+7.8,Fantasia,G,Animation,125,"[u'Leopold Stokowski', u'Deems Taylor', u'Corey Burton']"
+7.8,Gattaca,PG-13,Drama,106,"[u'Ethan Hawke', u'Uma Thurman', u'Jude Law']"
+7.8,American Gangster,R,Biography,157,"[u'Denzel Washington', u'Russell Crowe', u'Chiwetel Ejiofor']"
+7.8,Porco Rosso,PG,Animation,94,"[u'Sh\xfbichir\xf4 Moriyama', u'Tokiko Kat\xf4', u'Sanshi Katsura']"
+7.8,The French Connection,R,Action,104,"[u'Gene Hackman', u'Roy Scheider', u'Fernando Rey']"
+7.8,The Boy in the Striped Pajamas,PG-13,Drama,94,"[u'Asa Butterfield', u'David Thewlis', u'Rupert Friend']"
+7.8,Empire of the Sun,PG,Drama,153,"[u'Christian Bale', u'John Malkovich', u'Miranda Richardson']"
+7.8,About Time,R,Drama,123,"[u'Domhnall Gleeson', u'Rachel McAdams', u'Bill Nighy']"
+7.8,Blue Velvet,R,Crime,120,"[u'Isabella Rossellini', u'Kyle MacLachlan', u'Dennis Hopper']"
+7.8,In America,PG-13,Drama,105,"[u'Paddy Considine', u'Samantha Morton', u'Djimon Hounsou']"
+7.8,The Curious Case of Benjamin Button,PG-13,Drama,166,"[u'Brad Pitt', u'Cate Blanchett', u'Tilda Swinton']"
+7.8,The Last of the Mohicans,R,Action,112,"[u'Daniel Day-Lewis', u'Madeleine Stowe', u'Russell Means']"
+7.8,Moonrise Kingdom,PG-13,Adventure,94,"[u'Jared Gilman', u'Kara Hayward', u'Bruce Willis']"
+7.8,Rebel Without a Cause,PG-13,Drama,111,"[u'James Dean', u'Natalie Wood', u'Sal Mineo']"
+7.8,Fantastic Mr. Fox,PG,Animation,87,"[u'George Clooney', u'Meryl Streep', u'Bill Murray']"
+7.8,Invasion of the Body Snatchers,APPROVED,Horror,80,"[u'Kevin McCarthy', u'Dana Wynter', u'Larry Gates']"
+7.8,October Sky,PG,Biography,108,"[u'Jake Gyllenhaal', u'Chris Cooper', u'Laura Dern']"
+7.8,Dirty Harry,R,Action,102,"[u'Clint Eastwood', u'Andrew Robinson', u'Harry Guardino']"
+7.8,Ghostbusters,PG,Comedy,105,"[u'Bill Murray', u'Dan Aykroyd', u'Sigourney Weaver']"
+7.8,Captain America: The Winter Soldier,PG-13,Action,136,"[u'Chris Evans', u'Samuel L. Jackson', u'Scarlett Johansson']"
+7.8,Wreck-It Ralph,PG,Animation,101,"[u'John C. Reilly', u'Jack McBrayer', u'Jane Lynch']"
+7.8,The Hangover,R,Comedy,100,"[u'Zach Galifianakis', u'Bradley Cooper', u'Justin Bartha']"
+7.8,Back to the Future Part II,PG,Adventure,108,"[u'Michael J. Fox', u'Christopher Lloyd', u'Lea Thompson']"
+7.8,Belle de Jour,APPROVED,Drama,101,"[u'Catherine Deneuve', u'Jean Sorel', u'Michel Piccoli']"
+7.8,"O Brother, Where Art Thou?",PG-13,Adventure,106,"[u'George Clooney', u'John Turturro', u'Tim Blake Nelson']"
+7.8,Repulsion,UNRATED,Drama,105,"[u'Catherine Deneuve', u'Ian Hendry', u'John Fraser']"
+7.8,Airplane!,PG,Comedy,88,"[u'Robert Hays', u'Julie Hagerty', u'Leslie Nielsen']"
+7.8,Pride & Prejudice,PG,Drama,129,"[u'Keira Knightley', u'Matthew Macfadyen', u'Brenda Blethyn']"
+7.8,Zulu,UNRATED,Drama,138,"[u'Stanley Baker', u'Jack Hawkins', u'Ulla Jacobsson']"
+7.8,Night on Earth,R,Comedy,129,"[u'Winona Ryder', u'Gena Rowlands', u'Lisanne Falk']"
+7.8,From Here to Eternity,NOT RATED,Drama,118,"[u'Burt Lancaster', u'Montgomery Clift', u'Deborah Kerr']"
+7.8,Apocalypto,R,Action,139,"[u'Gerardo Taracena', u'Raoul Trujillo', u'Dalia Hern\xe1ndez']"
+7.8,Atonement,R,Drama,123,"[u'Keira Knightley', u'James McAvoy', u'Brenda Blethyn']"
+7.8,The Dirty Dozen,NOT RATED,Action,150,"[u'Lee Marvin', u'Ernest Borgnine', u'Charles Bronson']"
+7.8,X-Men: First Class,PG-13,Action,132,"[u'James McAvoy', u'Michael Fassbender', u'Jennifer Lawrence']"
+7.8,Run Lola Run,R,Action,80,"[u'Franka Potente', u'Moritz Bleibtreu', u'Herbert Knaup']"
+7.8,The Longest Day,G,Action,178,"[u'John Wayne', u'Robert Ryan', u'Richard Burton']"
+7.8,Zelig,PG,Comedy,79,"[u'Woody Allen', u'Mia Farrow', u'Patrick Horgan']"
+7.8,The Last Emperor,PG-13,Biography,163,"[u'John Lone', u'Joan Chen', u""Peter O'Toole""]"
+7.8,The Goonies,PG,Adventure,114,"[u'Sean Astin', u'Josh Brolin', u'Jeff Cohen']"
+7.8,The White Ribbon,R,Drama,144,"[u'Christian Friedel', u'Ernst Jacobi', u'Leonie Benesch']"
+7.8,The Fugitive,PG-13,Action,130,"[u'Harrison Ford', u'Tommy Lee Jones', u'Sela Ward']"
+7.8,The Color Purple,PG-13,Drama,154,"[u'Danny Glover', u'Whoopi Goldberg', u'Oprah Winfrey']"
+7.8,South Park: Bigger Longer & Uncut,R,Animation,81,"[u'Trey Parker', u'Matt Stone', u'Mary Kay Bergman']"
+7.8,(500) Days of Summer,PG-13,Comedy,95,"[u'Zooey Deschanel', u'Joseph Gordon-Levitt', u'Geoffrey Arend']"
+7.8,Lost in Translation,R,Drama,101,"[u'Bill Murray', u'Scarlett Johansson', u'Giovanni Ribisi']"
+7.8,Argo,R,Drama,120,"[u'Ben Affleck', u'Bryan Cranston', u'John Goodman']"
+7.8,Blazing Saddles,R,Comedy,93,"[u'Cleavon Little', u'Gene Wilder', u'Slim Pickens']"
+7.8,Breakfast at Tiffany's,NOT RATED,Comedy,115,"[u'Audrey Hepburn', u'George Peppard', u'Patricia Neal']"
+7.8,Finding Neverland,PG,Biography,106,"[u'Johnny Depp', u'Kate Winslet', u'Julie Christie']"
+7.8,The Experiment,R,Drama,120,"[u'Moritz Bleibtreu', u'Christian Berkel', u'Oliver Stokowski']"
+7.8,Lucky Number Slevin,R,Crime,110,"[u'Josh Hartnett', u'Ben Kingsley', u'Morgan Freeman']"
+7.8,The Theory of Everything,PG-13,Biography,123,"[u'Eddie Redmayne', u'Felicity Jones', u'Tom Prior']"
+7.8,Harry Potter and the Prisoner of Azkaban,PG,Adventure,142,"[u'Daniel Radcliffe', u'Emma Watson', u'Rupert Grint']"
+7.8,Kung Fu Hustle,R,Action,99,"[u'Stephen Chow', u'Wah Yuen', u'Qiu Yuen']"
+7.8,Being John Malkovich,R,Comedy,112,"[u'John Cusack', u'Cameron Diaz', u'Catherine Keener']"
+7.8,The Social Network,PG-13,Biography,120,"[u'Jesse Eisenberg', u'Andrew Garfield', u'Justin Timberlake']"
+7.8,3:10 to Yuma,R,Adventure,122,"[u'Russell Crowe', u'Christian Bale', u'Ben Foster']"
+7.8,The Name of the Rose,R,Crime,130,"[u'Sean Connery', u'Christian Slater', u'Helmut Qualtinger']"
+7.8,Mary Poppins,APPROVED,Comedy,139,"[u'Julie Andrews', u'Dick Van Dyke', u'David Tomlinson']"
+7.8,The Game,R,Drama,129,"[u'Michael Douglas', u'Deborah Kara Unger', u'Sean Penn']"
+7.8,Changeling,R,Drama,141,"[u'Angelina Jolie', u'Colm Feore', u'Amy Ryan']"
+7.8,Donnie Brasco,R,Biography,127,"[u'Al Pacino', u'Johnny Depp', u'Michael Madsen']"
+7.8,Serpico,R,Biography,130,"[u'Al Pacino', u'John Randolph', u'Jack Kehoe']"
+7.8,What's Eating Gilbert Grape,PG-13,Drama,118,"[u'Johnny Depp', u'Leonardo DiCaprio', u'Juliette Lewis']"
+7.8,Goldfinger,APPROVED,Action,110,"[u'Sean Connery', u'Gert Fr\xf6be', u'Honor Blackman']"
+7.8,Nebraska,R,Adventure,115,"[u'Bruce Dern', u'Will Forte', u'June Squibb']"
+7.8,The Sandlot,PG,Comedy,101,"[u'Tom Guiry', u'Mike Vitar', u'Patrick Renna']"
+7.8,Boyz n the Hood,R,Crime,112,"[u'Cuba Gooding Jr.', u'Laurence Fishburne', u'Hudhail Al-Amir']"
+7.8,The Bourne Supremacy,PG-13,Action,108,"[u'Matt Damon', u'Franka Potente', u'Joan Allen']"
+7.8,Skyfall,PG-13,Action,143,"[u'Daniel Craig', u'Javier Bardem', u'Naomie Harris']"
+7.8,Ray,PG-13,Biography,152,"[u'Jamie Foxx', u'Regina King', u'Kerry Washington']"
+7.8,300,R,Action,117,"[u'Gerard Butler', u'Lena Headey', u'David Wenham']"
+7.8,A Bronx Tale,R,Crime,121,"[u'Robert De Niro', u'Chazz Palminteri', u'Lillo Brancato']"
+7.8,The Birds,PG-13,Horror,119,"[u'Rod Taylor', u'Tippi Hedren', u'Suzanne Pleshette']"
+7.8,Battle Royale,NOT RATED,Action,114,"[u'Tatsuya Fujiwara', u'Aki Maeda', u'Tar\xf4 Yamamoto']"
+7.8,Control,R,Biography,122,"[u'Sam Riley', u'Samantha Morton', u'Craig Parkinson']"
+7.8,Kramer vs. Kramer,APPROVED,Drama,105,"[u'Dustin Hoffman', u'Meryl Streep', u'Jane Alexander']"
+7.8,Deliverance,R,Adventure,110,"[u'Jon Voight', u'Burt Reynolds', u'Ned Beatty']"
+7.8,Ocean's Eleven,PG-13,Crime,116,"[u'George Clooney', u'Brad Pitt', u'Julia Roberts']"
+7.8,As Good as It Gets,PG-13,Comedy,139,"[u'Jack Nicholson', u'Helen Hunt', u'Greg Kinnear']"
+7.8,Awakenings,PG-13,Biography,121,"[u'Robert De Niro', u'Robin Williams', u'Julie Kavner']"
+7.8,The King of Comedy,PG,Comedy,109,"[u'Robert De Niro', u'Jerry Lewis', u'Diahnne Abbott']"
+7.8,Man on Fire,R,Action,146,"[u'Denzel Washington', u'Christopher Walken', u'Dakota Fanning']"
+7.8,Sabrina,UNRATED,Comedy,113,"[u'Humphrey Bogart', u'Audrey Hepburn', u'William Holden']"
+7.8,Dark City,R,Mystery,100,"[u'Rufus Sewell', u'Kiefer Sutherland', u'Jennifer Connelly']"
+7.8,Good Bye Lenin!,R,Comedy,121,"[u'Daniel Br\xfchl', u'Katrin Sa\xdf', u'Chulpan Khamatova']"
+7.7,Remember the Titans,PG,Biography,113,"[u'Denzel Washington', u'Will Patton', u'Wood Harris']"
+7.7,Star Trek II: The Wrath of Khan,PG,Action,113,"[u'William Shatner', u'Leonard Nimoy', u'DeForest Kelley']"
+7.7,Road to Perdition,R,Crime,117,"[u'Tom Hanks', u'Tyler Hoechlin', u'Rob Maxey']"
+7.7,Rushmore,R,Comedy,93,"[u'Jason Schwartzman', u'Bill Murray', u'Olivia Williams']"
+7.7,The Machinist,R,Drama,101,"[u'Christian Bale', u'Jennifer Jason Leigh', u'Aitana S\xe1nchez-Gij\xf3n']"
+7.7,Flipped,PG,Comedy,90,"[u'Madeline Carroll', u'Callan McAuliffe', u'Rebecca De Mornay']"
+7.7,The Count of Monte Cristo,PG-13,Action,131,"[u'Jim Caviezel', u'Guy Pearce', u'Richard Harris']"
+7.7,Detachment,NOT RATED,Drama,98,"[u'Adrien Brody', u'Christina Hendricks', u'Marcia Gay Harden']"
+7.7,Love Me If You Dare,R,Comedy,93,"[u'Guillaume Canet', u'Marion Cotillard', u'Thibault Verhaeghe']"
+7.7,Dead Man,R,Drama,121,"[u'Johnny Depp', u'Gary Farmer', u'Crispin Glover']"
+7.7,The Purple Rose of Cairo,PG,Comedy,82,"[u'Mia Farrow', u'Jeff Daniels', u'Danny Aiello']"
+7.7,21 Grams,R,Crime,124,"[u'Sean Penn', u'Benicio Del Toro', u'Naomi Watts']"
+7.7,50/50,R,Comedy,100,"[u'Joseph Gordon-Levitt', u'Seth Rogen', u'Anna Kendrick']"
+7.7,Kick-Ass,R,Action,117,"[u'Aaron Taylor-Johnson', u'Nicolas Cage', u'Chlo\xeb Grace Moretz']"
+7.7,Delicatessen,R,Comedy,99,"[u'Marie-Laure Dougnac', u'Dominique Pinon', u'Pascal Benezech']"
+7.7,Barton Fink,R,Drama,116,"[u'John Turturro', u'John Goodman', u'Judy Davis']"
+7.7,The Last King of Scotland,R,Biography,123,"[u'James McAvoy', u'Forest Whitaker', u'Gillian Anderson']"
+7.7,Adaptation.,R,Comedy,114,"[u'Nicolas Cage', u'Meryl Streep', u'Chris Cooper']"
+7.7,A Very Long Engagement,R,Drama,133,"[u'Audrey Tautou', u'Gaspard Ulliel', u'Jodie Foster']"
+7.7,Mysterious Skin,NC-17,Drama,105,"[u'Brady Corbet', u'Joseph Gordon-Levitt', u'Elisabeth Shue']"
+7.7,Stardust,PG-13,Adventure,127,"[u'Charlie Cox', u'Claire Danes', u'Sienna Miller']"
+7.7,Black Hawk Down,R,Drama,144,"[u'Josh Hartnett', u'Ewan McGregor', u'Tom Sizemore']"
+7.7,Paprika,R,Animation,90,"[u'Megumi Hayashibara', u'T\xf4ru Emori', u'Katsunosuke Hori']"
+7.7,Fear and Loathing in Las Vegas,R,Comedy,118,"[u'Johnny Depp', u'Benicio Del Toro', u'Tobey Maguire']"
+7.7,Frost/Nixon,R,Drama,122,"[u'Frank Langella', u'Michael Sheen', u'Kevin Bacon']"
+7.7,Enter the Dragon,R,Action,102,"[u'Bruce Lee', u'John Saxon', u'Jim Kelly']"
+7.7,Short Cuts,R,Comedy,187,"[u'Andie MacDowell', u'Julianne Moore', u'Tim Robbins']"
+7.7,A Hard Day's Night,APPROVED,Comedy,87,"[u'John Lennon', u'Paul McCartney', u'George Harrison']"
+7.7,The Last Samurai,R,Action,154,"[u'Tom Cruise', u'Ken Watanabe', u'Billy Connolly']"
+7.7,The Station Agent,R,Comedy,89,"[u'Peter Dinklage', u'Patricia Clarkson', u'Bobby Cannavale']"
+7.7,Zombieland,R,Comedy,88,"[u'Jesse Eisenberg', u'Emma Stone', u'Woody Harrelson']"
+7.7,Sympathy for Mr. Vengeance,R,Crime,129,"[u'Kang-ho Song', u'Ha-kyun Shin', u'Doona Bae']"
+7.7,Despicable Me,PG,Animation,95,"[u'Steve Carell', u'Jason Segel', u'Russell Brand']"
+7.7,Shane,APPROVED,Drama,118,"[u'Alan Ladd', u'Jean Arthur', u'Van Heflin']"
+7.7,Forbidden Planet,PASSED,Action,98,"[u'Walter Pidgeon', u'Anne Francis', u'Leslie Nielsen']"
+7.7,Titanic,PG-13,Drama,194,"[u'Leonardo DiCaprio', u'Kate Winslet', u'Billy Zane']"
+7.7,Gone Baby Gone,R,Crime,114,"[u'Morgan Freeman', u'Ed Harris', u'Casey Affleck']"
+7.7,The Dinner Game,PG-13,Comedy,80,"[u'Thierry Lhermitte', u'Jacques Villeret', u'Francis Huster']"
+7.7,Dawn of the Planet of the Apes,PG-13,Action,130,"[u'Gary Oldman', u'Keri Russell', u'Andy Serkis']"
+7.7,25th Hour,R,Crime,135,"[u'Edward Norton', u'Barry Pepper', u'Philip Seymour Hoffman']"
+7.7,Ponyo,G,Animation,101,"[u'Cate Blanchett', u'Matt Damon', u'Liam Neeson']"
+7.7,Shine,PG-13,Biography,105,"[u'Geoffrey Rush', u'Armin Mueller-Stahl', u'Justin Braine']"
+7.7,The Blind Side,PG-13,Biography,129,"[u'Quinton Aaron', u'Sandra Bullock', u'Tim McGraw']"
+7.7,Brokeback Mountain,R,Drama,134,"[u'Jake Gyllenhaal', u'Heath Ledger', u'Michelle Williams']"
+7.7,Cast Away,PG-13,Adventure,143,"[u'Tom Hanks', u'Helen Hunt', u'Paul Sanchez']"
+7.7,Malcolm X,PG-13,Biography,202,"[u'Denzel Washington', u'Angela Bassett', u'Delroy Lindo']"
+7.7,Show Me Love,UNRATED,Comedy,89,"[u'Alexandra Dahlstr\xf6m', u'Rebecka Liljeberg', u'Erica Carlson']"
+7.7,Boy A,R,Drama,106,"[u'Andrew Garfield', u'Peter Mullan', u'Shaun Evans']"
+7.7,The Warriors,R,Action,92,"[u'Michael Beck', u'James Remar', u'Dorsey Wright']"
+7.7,Close Encounters of the Third Kind,PG,Drama,137,"[u'Richard Dreyfuss', u'Fran\xe7ois Truffaut', u'Teri Garr']"
+7.7,Match Point,R,Drama,124,"[u'Scarlett Johansson', u'Jonathan Rhys Meyers', u'Emily Mortimer']"
+7.7,Training Day,R,Crime,122,"[u'Denzel Washington', u'Ethan Hawke', u'Scott Glenn']"
+7.7,The Butterfly Effect,R,Sci-Fi,113,"[u'Ashton Kutcher', u'Amy Smart', u'Melora Walters']"
+7.7,The City of Lost Children,R,Fantasy,112,"[u'Ron Perlman', u'Daniel Emilfork', u'Judith Vittet']"
+7.7,Billy Elliot,PG-13,Comedy,110,"[u'Jamie Bell', u'Julie Walters', u'Jean Heywood']"
+7.7,Love Actually,R,Comedy,135,"[u'Hugh Grant', u'Martine McCutcheon', u'Liam Neeson']"
+7.7,The Producers,PG,Comedy,88,"[u'Zero Mostel', u'Gene Wilder', u'Dick Shawn']"
+7.7,Harry Potter and the Deathly Hallows: Part 1,PG-13,Adventure,146,"[u'Daniel Radcliffe', u'Emma Watson', u'Rupert Grint']"
+7.7,The Visitor,PG-13,Drama,104,"[u'Richard Jenkins', u'Haaz Sleiman', u'Danai Gurira']"
+7.7,Philadelphia,PG-13,Drama,125,"[u'Tom Hanks', u'Denzel Washington', u'Roberta Maxwell']"
+7.7,Sense and Sensibility,PG,Drama,131,"[u'Emma Thompson', u'Kate Winslet', u'James Fleet']"
+7.7,Three Colors: White,R,Comedy,91,"[u'Zbigniew Zamachowski', u'Julie Delpy', u'Janusz Gajos']"
+7.7,La grande bellezza,NOT RATED,Drama,142,"[u'Toni Servillo', u'Carlo Verdone', u'Sabrina Ferilli']"
+7.7,Primal Fear,R,Crime,129,"[u'Richard Gere', u'Laura Linney', u'Edward Norton']"
+7.7,Where Eagles Dare,,Action,158,"[u'Richard Burton', u'Clint Eastwood', u'Mary Ure']"
+7.7,Dazed and Confused,R,Comedy,102,"[u'Jason London', u'Wiley Wiggins', u'Matthew McConaughey']"
+7.7,Blood Simple.,R,Crime,99,"[u'John Getz', u'Frances McDormand', u'Dan Hedaya']"
+7.7,This Is England,UNRATED,Crime,101,"[u'Thomas Turgoose', u'Stephen Graham', u'Jo Hartley']"
+7.7,Fearless,PG-13,Action,104,"[u'Jet Li', u'Li Sun', u'Yong Dong']"
+7.7,Dangerous Liaisons,R,Drama,119,"[u'Glenn Close', u'John Malkovich', u'Michelle Pfeiffer']"
+7.7,Saw,R,Horror,103,"[u'Cary Elwes', u'Leigh Whannell', u'Danny Glover']"
+7.7,Snow White and the Seven Dwarfs,APPROVED,Animation,88,"[u'Adriana Caselotti', u'Harry Stockwell', u'Lucille La Verne']"
+7.7,Coraline,PG,Animation,100,"[u'Dakota Fanning', u'Teri Hatcher', u'John Hodgman']"
+7.7,The Hunger Games: Catching Fire,PG-13,Adventure,146,"[u'Jennifer Lawrence', u'Josh Hutcherson', u'Liam Hemsworth']"
+7.7,Sophie's Choice,R,Drama,150,"[u'Meryl Streep', u'Kevin Kline', u'Peter MacNicol']"
+7.7,La Vie en Rose,PG-13,Biography,140,"[u'Marion Cotillard', u'Sylvie Testud', u'Pascal Greggory']"
+7.7,Star Wars: Episode III - Revenge of the Sith,PG-13,Action,140,"[u'Hayden Christensen', u'Natalie Portman', u'Ewan McGregor']"
+7.7,True Grit,PG-13,Adventure,110,"[u'Jeff Bridges', u'Matt Damon', u'Hailee Steinfeld']"
+7.7,After Hours,R,Comedy,97,"[u'Griffin Dunne', u'Rosanna Arquette', u'Verna Bloom']"
+7.7,Frozen,PG,Animation,102,"[u'Kristen Bell', u'Idina Menzel', u'Jonathan Groff']"
+7.7,Lolita,NOT RATED,Drama,152,"[u'James Mason', u'Shelley Winters', u'Sue Lyon']"
+7.7,Eastern Promises,R,Crime,100,"[u'Naomi Watts', u'Viggo Mortensen', u'Armin Mueller-Stahl']"
+7.7,Midnight in Paris,PG-13,Comedy,94,"[u'Owen Wilson', u'Rachel McAdams', u'Kathy Bates']"
+7.7,The Muppet Christmas Carol,G,Comedy,85,"[u'Michael Caine', u'Dave Goelz', u'Steve Whitmire']"
+7.7,Who Framed Roger Rabbit,PG,Animation,104,"[u'Bob Hoskins', u'Christopher Lloyd', u'Joanna Cassidy']"
+7.7,Sympathy for Lady Vengeance,R,Crime,112,"[u'Yeong-ae Lee', u'Min-sik Choi', u'Shi-hoo Kim']"
+7.7,Grindhouse,R,Action,191,"[u'Kurt Russell', u'Rose McGowan', u'Danny Trejo']"
+7.7,In a Better World,R,Drama,119,"[u'Mikael Persbrandt', u'Trine Dyrholm', u'Markus Rygaard']"
+7.7,Blow-Up,NOT RATED,Drama,111,"[u'David Hemmings', u'Vanessa Redgrave', u'Sarah Miles']"
+7.7,The Secret World of Arrietty,G,Animation,94,"[u'Bridgit Mendler', u'Amy Poehler', u'Will Arnett']"
+7.7,Kiss Kiss Bang Bang,R,Action,103,"[u'Robert Downey Jr.', u'Val Kilmer', u'Michelle Monaghan']"
+7.7,Lost Highway,R,Drama,134,"[u'Bill Pullman', u'Patricia Arquette', u'John Roselius']"
+7.7,Zodiac,R,Crime,157,"[u'Jake Gyllenhaal', u'Robert Downey Jr.', u'Mark Ruffalo']"
+7.7,Les Miserables,PG-13,Drama,158,"[u'Hugh Jackman', u'Russell Crowe', u'Anne Hathaway']"
+7.7,Moulin Rouge!,PG-13,Drama,127,"[u'Nicole Kidman', u'Ewan McGregor', u'John Leguizamo']"
+7.7,Whale Rider,PG-13,Drama,101,"[u'Keisha Castle-Hughes', u'Rawiri Paratene', u'Vicky Haughton']"
+7.7,End of Watch,R,Crime,109,"[u'Jake Gyllenhaal', u'Michael Pe\xf1a', u'Anna Kendrick']"
+7.7,Philomena,PG-13,Biography,98,"[u'Judi Dench', u'Steve Coogan', u'Sophie Kennedy Clark']"
+7.7,Fury,R,Action,134,"[u'Brad Pitt', u'Shia LaBeouf', u'Logan Lerman']"
+7.7,The Big Blue,PG-13,Action,168,"[u'Jean-Marc Barr', u'Jean Reno', u'Rosanna Arquette']"
+7.7,First Blood,R,Action,93,"[u'Sylvester Stallone', u'Brian Dennehy', u'Richard Crenna']"
+7.7,Minority Report,PG-13,Action,145,"[u'Tom Cruise', u'Colin Farrell', u'Samantha Morton']"
+7.7,Spellbound,UNRATED,Film-Noir,111,"[u'Ingrid Bergman', u'Gregory Peck', u'Michael Chekhov']"
+7.7,The Wicker Man,R,Horror,88,"[u'Edward Woodward', u'Christopher Lee', u'Diane Cilento']"
+7.7,Seven Pounds,PG-13,Drama,123,"[u'Will Smith', u'Rosario Dawson', u'Woody Harrelson']"
+7.7,Midnight Express,R,Biography,121,"[u'Brad Davis', u'Irene Miracle', u'Bo Hopkins']"
+7.7,Kelly's Heroes,GP,Action,144,"[u'Clint Eastwood', u'Telly Savalas', u'Don Rickles']"
+7.7,MASH,R,Comedy,116,"[u'Donald Sutherland', u'Elliott Gould', u'Tom Skerritt']"
+7.7,The Player,R,Comedy,124,"[u'Tim Robbins', u'Greta Scacchi', u'Fred Ward']"
+7.7,Traffic,R,Crime,147,"[u'Michael Douglas', u'Benicio Del Toro', u'Catherine Zeta-Jones']"
+7.7,Y Tu Mama Tambien,R,Drama,106,"[u'Maribel Verd\xfa', u'Gael Garc\xeda Bernal', u'Daniel Gim\xe9nez Cacho']"
+7.7,Stranger Than Fiction,PG-13,Comedy,113,"[u'Will Ferrell', u'Emma Thompson', u'Dustin Hoffman']"
+7.7,Inside Man,R,Crime,129,"[u'Denzel Washington', u'Clive Owen', u'Jodie Foster']"
+7.6,127 Hours,R,Adventure,94,"[u'James Franco', u'Amber Tamblyn', u'Kate Mara']"
+7.6,The Fifth Element,PG-13,Action,126,"[u'Bruce Willis', u'Milla Jovovich', u'Gary Oldman']"
+7.6,Milk,R,Biography,128,"[u'Sean Penn', u'Josh Brolin', u'Emile Hirsch']"
+7.6,United 93,R,Action,111,"[u'David Alan Basche', u'Olivia Thirlby', u'Liza Col\xf3n-Zayas']"
+7.6,Jin ling shi san chai,R,Drama,146,"[u'Christian Bale', u'Ni Ni', u'Xinyi Zhang']"
+7.6,Dracula,APPROVED,Horror,85,"[u'Bela Lugosi', u'Helen Chandler', u'David Manners']"
+7.6,High Plains Drifter,R,Western,105,"[u'Clint Eastwood', u'Verna Bloom', u'Marianna Hill']"
+7.6,I Am Sam,PG-13,Drama,132,"[u'Sean Penn', u'Michelle Pfeiffer', u'Dakota Fanning']"
+7.6,Fried Green Tomatoes,PG-13,Drama,130,"[u'Kathy Bates', u'Jessica Tandy', u'Mary Stuart Masterson']"
+7.6,Watchmen,R,Action,162,"[u'Jackie Earle Haley', u'Patrick Wilson', u'Carla Gugino']"
+7.6,28 Days Later...,R,Horror,113,"[u'Cillian Murphy', u'Naomie Harris', u'Christopher Eccleston']"
+7.6,The Illusionist,PG-13,Drama,110,"[u'Edward Norton', u'Jessica Biel', u'Paul Giamatti']"
+7.6,Superbad,R,Comedy,113,"[u'Michael Cera', u'Jonah Hill', u'Christopher Mintz-Plasse']"
+7.6,The Godfather: Part III,R,Crime,162,"[u'Al Pacino', u'Diane Keaton', u'Andy Garcia']"
+7.6,The Royal Tenenbaums,R,Comedy,110,"[u'Gene Hackman', u'Gwyneth Paltrow', u'Anjelica Huston']"
+7.6,The Jungle Book,APPROVED,Animation,78,"[u'Phil Harris', u'Sebastian Cabot', u'Louis Prima']"
+7.6,Thank You for Smoking,R,Comedy,92,"[u'Aaron Eckhart', u'Cameron Bright', u'Maria Bello']"
+7.6,Man Bites Dog,NC-17,Comedy,95,"[u'Beno\xeet Poelvoorde', u'Jacqueline Poelvoorde-Pappaert', u'Nelly Pappaert']"
+7.6,A Few Good Men,R,Drama,138,"[u'Tom Cruise', u'Jack Nicholson', u'Demi Moore']"
+7.6,The Legend of Drunken Master,R,Action,102,"[u'Jackie Chan', u'Ho-Sung Pak', u'Lung Ti']"
+7.6,The Kite Runner,PG-13,Drama,128,"[u'Khalid Abdalla', u'Ahmad Khan Mahmoodzada', u'Atossa Leoni']"
+7.6,When Harry Met Sally...,R,Comedy,96,"[u'Billy Crystal', u'Meg Ryan', u'Carrie Fisher']"
+7.6,Munich,R,Drama,164,"[u'Eric Bana', u'Daniel Craig', u'Marie-Jos\xe9e Croze']"
+7.6,House of Sand and Fog,R,Drama,126,"[u'Jennifer Connelly', u'Ben Kingsley', u'Ron Eldard']"
+7.6,Lethal Weapon,R,Action,110,"[u'Mel Gibson', u'Danny Glover', u'Gary Busey']"
+7.6,Animal House,R,Comedy,109,"[u'John Belushi', u'Karen Allen', u'Tom Hulce']"
+7.6,Lord of War,R,Crime,122,"[u'Nicolas Cage', u'Ethan Hawke', u'Jared Leto']"
+7.6,Blow,R,Biography,124,"[u'Johnny Depp', u'Pen\xe9lope Cruz', u'Franka Potente']"
+7.6,Sherlock Holmes,PG-13,Action,128,"[u'Robert Downey Jr.', u'Jude Law', u'Rachel McAdams']"
+7.6,Harry Potter and the Goblet of Fire,PG-13,Adventure,157,"[u'Daniel Radcliffe', u'Emma Watson', u'Rupert Grint']"
+7.6,La piel que habito,R,Thriller,120,"[u'Antonio Banderas', u'Elena Anaya', u'Jan Cornet']"
+7.6,The Crow,R,Action,102,"[u'Brandon Lee', u'Michael Wincott', u'Rochelle Davis']"
+7.6,West Side Story,UNRATED,Crime,152,"[u'Natalie Wood', u'George Chakiris', u'Richard Beymer']"
+7.6,The Thin Red Line,R,Drama,170,"[u'Jim Caviezel', u'Sean Penn', u'Nick Nolte']"
+7.6,American Psycho,R,Crime,102,"[u'Christian Bale', u'Justin Theroux', u'Josh Lucas']"
+7.6,The Blind Swordsman: Zatoichi,R,Action,116,"[u'Takeshi Kitano', u'Tadanobu Asano', u'Yui Natsukawa']"
+7.6,The Last Temptation of Christ,R,Drama,164,"[u'Willem Dafoe', u'Harvey Keitel', u'Barbara Hershey']"
+7.6,The Naked Gun: From the Files of Police Squad!,PG-13,Comedy,85,"[u'Leslie Nielsen', u'Priscilla Presley', u'O.J. Simpson']"
+7.6,The Others,PG-13,Horror,104,"[u'Nicole Kidman', u'Christopher Eccleston', u'Fionnula Flanagan']"
+7.6,The Party,PG,Comedy,99,"[u'Peter Sellers', u'Claudine Longet', u'Natalia Borisova']"
+7.6,Army of Darkness,R,Comedy,81,"[u'Bruce Campbell', u'Embeth Davidtz', u'Marcus Gilbert']"
+7.6,The Hurt Locker,R,Drama,131,"[u'Jeremy Renner', u'Anthony Mackie', u'Brian Geraghty']"
+7.6,The Raid: Redemption,R,Action,101,"[u'Iko Uwais', u'Ananda George', u'Ray Sahetapy']"
+7.6,Volver,R,Comedy,121,"[u'Pen\xe9lope Cruz', u'Carmen Maura', u'Lola Due\xf1as']"
+7.6,Following,R,Mystery,69,"[u'Jeremy Theobald', u'Alex Haw', u'Lucy Russell']"
+7.6,Kung Fu Panda,PG,Animation,92,"[u'Jack Black', u'Ian McShane', u'Angelina Jolie']"
+7.6,Mad Max 2: The Road Warrior,R,Action,94,"[u'Mel Gibson', u'Bruce Spence', u'Michael Preston']"
+7.6,Jusan-nin no shikaku,R,Action,141,"[u'K\xf4ji Yakusho', u'Takayuki Yamada', u'Y\xfbsuke Iseya']"
+7.6,The Man Who Wasn't There,R,Crime,116,"[u'Billy Bob Thornton', u'Frances McDormand', u'Michael Badalucco']"
+7.6,The Counterfeiters,R,Crime,98,"[u'Karl Markovics', u'August Diehl', u'Devid Striesow']"
+7.6,The Hunt for Red October,PG,Action,135,"[u'Sean Connery', u'Alec Baldwin', u'Scott Glenn']"
+7.6,Lone Survivor,R,Action,121,"[u'Mark Wahlberg', u'Taylor Kitsch', u'Emile Hirsch']"
+7.6,A Fish Called Wanda,R,Comedy,108,"[u'John Cleese', u'Jamie Lee Curtis', u'Kevin Kline']"
+7.6,The Impossible,PG-13,Drama,114,"[u'Naomi Watts', u'Ewan McGregor', u'Tom Holland']"
+7.6,Garden State,R,Comedy,102,"[u'Zach Braff', u'Peter Sarsgaard', u'Natalie Portman']"
+7.6,Escape from Alcatraz,PG,Action,112,"[u'Clint Eastwood', u'Patrick McGoohan', u'Roberts Blossom']"
+7.6,Apollo 13,PG,Adventure,140,"[u'Tom Hanks', u'Bill Paxton', u'Kevin Bacon']"
+7.6,"Lust, Caution",NC-17,Drama,157,"[u'Tony Chiu Wai Leung', u'Wei Tang', u'Joan Chen']"
+7.6,Selma,PG-13,Biography,128,"[u'David Oyelowo', u'Carmen Ejogo', u'Tim Roth']"
+7.6,Funny Games,UNRATED,Crime,108,"[u'Susanne Lothar', u'Ulrich M\xfche', u'Arno Frisch']"
+7.6,The Abyss,PG-13,Adventure,139,"[u'Ed Harris', u'Mary Elizabeth Mastrantonio', u'Michael Biehn']"
+7.6,Robin Hood,G,Animation,83,"[u'Brian Bedford', u'Phil Harris', u'Roger Miller']"
+7.6,Enemy at the Gates,R,Drama,131,"[u'Jude Law', u'Ed Harris', u'Joseph Fiennes']"
+7.6,Falling Down,R,Crime,113,"[u'Michael Douglas', u'Robert Duvall', u'Barbara Hershey']"
+7.6,Little Children,R,Drama,136,"[u'Kate Winslet', u'Jennifer Connelly', u'Patrick Wilson']"
+7.6,Hunger,NOT RATED,Biography,96,"[u'Stuart Graham', u'Laine Megaw', u'Brian Milligan']"
+7.6,Indiana Jones and the Temple of Doom,PG,Action,118,"[u'Harrison Ford', u'Kate Capshaw', u'Jonathan Ke Quan']"
+7.6,Ip Man 2,R,Action,108,"[u'Donnie Yen', u'Xiaoming Huang', u'Sammo Kam-Bo Hung']"
+7.6,The Little Mermaid,G,Animation,83,"[u'Jodi Benson', u'Samuel E. Wright', u'Rene Auberjonois']"
+7.6,"It's a Mad, Mad, Mad, Mad World",APPROVED,Action,205,"[u'Spencer Tracy', u'Milton Berle', u'Ethel Merman']"
+7.6,The Reader,R,Drama,124,"[u'Kate Winslet', u'Ralph Fiennes', u'Bruno Ganz']"
+7.6,The Omen,R,Horror,111,"[u'Gregory Peck', u'Lee Remick', u'Harvey Stephens']"
+7.6,A Royal Affair,R,Drama,137,"[u'Alicia Vikander', u'Mads Mikkelsen', u'Mikkel Boe F\xf8lsgaard']"
+7.6,Braindead,R,Comedy,104,"[u'Timothy Balme', u'Diana Pe\xf1alver', u'Elizabeth Moody']"
+7.6,Secondhand Lions,PG,Comedy,109,"[u'Haley Joel Osment', u'Michael Caine', u'Robert Duvall']"
+7.6,Disconnect,R,Drama,115,"[u'Jason Bateman', u'Jonah Bobo', u'Haley Ramm']"
+7.6,A Single Man,R,Drama,99,"[u'Colin Firth', u'Julianne Moore', u'Matthew Goode']"
+7.6,Hoosiers,PG,Drama,114,"[u'Gene Hackman', u'Barbara Hershey', u'Dennis Hopper']"
+7.6,Moneyball,PG-13,Biography,133,"[u'Brad Pitt', u'Robin Wright', u'Jonah Hill']"
+7.6,The Book Thief,PG-13,Drama,131,"[u'Sophie N\xe9lisse', u'Geoffrey Rush', u'Emily Watson']"
+7.6,Die Hard: With a Vengeance,R,Action,131,"[u'Bruce Willis', u'Jeremy Irons', u'Samuel L. Jackson']"
+7.6,Interview with the Vampire: The Vampire Chronicles,R,Horror,123,"[u'Brad Pitt', u'Tom Cruise', u'Antonio Banderas']"
+7.6,Midnight Run,R,Action,126,"[u'Robert De Niro', u'Charles Grodin', u'Yaphet Kotto']"
+7.6,An American Werewolf in London,R,Comedy,97,"[u'David Naughton', u'Jenny Agutter', u'Joe Belcher']"
+7.6,The Great Debaters,PG-13,Biography,126,"[u'Denzel Washington', u'Forest Whitaker', u'Kimberly Elise']"
+7.6,Tucker and Dale vs. Evil,R,Comedy,89,"[u'Tyler Labine', u'Alan Tudyk', u'Katrina Bowden']"
+7.6,Dead Man Walking,R,Crime,122,"[u'Susan Sarandon', u'Sean Penn', u'Robert Prosky']"
+7.6,The Piano,R,Drama,121,"[u'Holly Hunter', u'Harvey Keitel', u'Sam Neill']"
+7.6,The Town,R,Crime,125,"[u'Ben Affleck', u'Rebecca Hall', u'Jon Hamm']"
+7.6,Rise of the Planet of the Apes,PG-13,Action,105,"[u'James Franco', u'Andy Serkis', u'Freida Pinto']"
+7.6,Boys Don't Cry,R,Biography,118,"[u'Hilary Swank', u'Chlo\xeb Sevigny', u'Peter Sarsgaard']"
+7.6,Ice Age,PG,Animation,81,"[u'Denis Leary', u'John Leguizamo', u'Ray Romano']"
+7.6,Of Mice and Men,PG-13,Drama,115,"[u'John Malkovich', u'Gary Sinise', u'Ray Walston']"
+7.6,Sideways,R,Comedy,126,"[u'Paul Giamatti', u'Thomas Haden Church', u'Virginia Madsen']"
+7.6,High Fidelity,R,Comedy,113,"[u'John Cusack', u'Iben Hjejle', u'Todd Louiso']"
+7.6,The Meaning of Life,R,Comedy,107,"[u'John Cleese', u'Terry Gilliam', u'Eric Idle']"
+7.6,Hugo,PG,Adventure,126,"[u'Asa Butterfield', u'Chlo\xeb Grace Moretz', u'Christopher Lee']"
+7.6,Leaving Las Vegas,R,Drama,111,"[u'Nicolas Cage', u'Elisabeth Shue', u'Julian Sands']"
+7.6,The Evil Dead,NC-17,Horror,85,"[u'Bruce Campbell', u'Ellen Sandweiss', u'Richard DeManincor']"
+7.6,Collateral,R,Crime,120,"[u'Tom Cruise', u'Jamie Foxx', u'Jada Pinkett Smith']"
+7.6,"Planes, Trains & Automobiles",R,Comedy,93,"[u'Steve Martin', u'John Candy', u'Laila Robins']"
+7.6,Hodejegerne,R,Crime,100,"[u'Aksel Hennie', u'Synn\xf8ve Macody Lund', u'Nikolaj Coster-Waldau']"
+7.6,Field of Dreams,PG,Drama,107,"[u'Kevin Costner', u'James Earl Jones', u'Ray Liotta']"
+7.6,Batman,PG-13,Action,126,"[u'Michael Keaton', u'Jack Nicholson', u'Kim Basinger']"
+7.6,Tell No One,UNRATED,Crime,131,"[u'Fran\xe7ois Cluzet', u'Marie-Jos\xe9e Croze', u'Andr\xe9 Dussollier']"
+7.6,Ghost Dog: The Way of the Samurai,R,Action,116,"[u'Forest Whitaker', u'Henry Silva', u'John Tormey']"
+7.6,Chaplin,PG-13,Biography,143,"[u'Robert Downey Jr.', u'Geraldine Chaplin', u'Paul Rhys']"
+7.6,Keith,PG-13,Drama,93,"[u'Elisabeth Harnois', u'Jesse McCartney', u'Margo Harshman']"
+7.6,Juno,PG-13,Comedy,96,"[u'Ellen Page', u'Michael Cera', u'Jennifer Garner']"
+7.6,The Assassination of Jesse James by the Coward Robert Ford,R,Biography,160,"[u'Brad Pitt', u'Casey Affleck', u'Sam Shepard']"
+7.6,The Fisher King,R,Comedy,137,"[u'Jeff Bridges', u'Robin Williams', u'Adam Bryant']"
+7.6,House of Flying Daggers,PG-13,Action,119,"[u'Ziyi Zhang', u'Takeshi Kaneshiro', u'Andy Lau']"
+7.6,The Hours,PG-13,Drama,114,"[u'Meryl Streep', u'Nicole Kidman', u'Julianne Moore']"
+7.6,The Damned United,R,Biography,98,"[u'Colm Meaney', u'Henry Goodman', u'David Roper']"
+7.6,Star Trek: First Contact,PG-13,Action,111,"[u'Patrick Stewart', u'Jonathan Frakes', u'Brent Spiner']"
+7.6,The Natural,PG,Drama,138,"[u'Robert Redford', u'Robert Duvall', u'Glenn Close']"
+7.6,The Hobbit: The Battle of the Five Armies,PG-13,Adventure,144,"[u'Ian McKellen', u'Martin Freeman', u'Richard Armitage']"
+7.6,Equilibrium,R,Action,107,"[u'Christian Bale', u'Sean Bean', u'Emily Watson']"
+7.6,August Rush,PG,Drama,114,"[u'Freddie Highmore', u'Keri Russell', u'Jonathan Rhys Meyers']"
+7.6,Saving Mr. Banks,PG-13,Biography,125,"[u'Emma Thompson', u'Tom Hanks', u'Annie Rose Buckley']"
+7.6,The Hurricane,R,Biography,146,"[u'Denzel Washington', u'Vicellous Reon Shannon', u'Deborah Kara Unger']"
+7.6,Jacob's Ladder,R,Drama,113,"[u'Tim Robbins', u'Elizabeth Pe\xf1a', u'Danny Aiello']"
+7.6,The Curse of the Were-Rabbit,G,Animation,85,"[u'Peter Sallis', u'Helena Bonham Carter', u'Ralph Fiennes']"
+7.6,Christmas Vacation,PG-13,Comedy,97,"[u'Chevy Chase', u""Beverly D'Angelo"", u'Juliette Lewis']"
+7.5,"Good Night, and Good Luck.",PG,Drama,93,"[u'David Strathairn', u'George Clooney', u'Patricia Clarkson']"
+7.5,Straw Dogs,UNRATED,Crime,113,"[u'Dustin Hoffman', u'Susan George', u'Peter Vaughan']"
+7.5,42,PG-13,Biography,128,"[u'Chadwick Boseman', u'T.R. Knight', u'Harrison Ford']"
+7.5,Rudy,PG,Biography,114,"[u'Sean Astin', u'Jon Favreau', u'Ned Beatty']"
+7.5,Sleepers,R,Crime,147,"[u'Robert De Niro', u'Kevin Bacon', u'Brad Pitt']"
+7.5,The Wind That Shakes the Barley,NOT RATED,Drama,127,"[u'Cillian Murphy', u'Padraic Delaney', u'Liam Cunningham']"
+7.5,Gangs of New York,R,Crime,167,"[u'Leonardo DiCaprio', u'Cameron Diaz', u'Daniel Day-Lewis']"
+7.5,Elizabeth,R,Biography,124,"[u'Cate Blanchett', u'Geoffrey Rush', u'Christopher Eccleston']"
+7.5,Green Street Hooligans,R,Crime,109,"[u'Elijah Wood', u'Charlie Hunnam', u'Claire Forlani']"
+7.5,Reign Over Me,R,Drama,124,"[u'Adam Sandler', u'Don Cheadle', u'Jada Pinkett Smith']"
+7.5,RoboCop,R,Action,102,"[u'Peter Weller', u'Nancy Allen', u""Dan O'Herlihy""]"
+7.5,The Life of David Gale,R,Crime,130,"[u'Kevin Spacey', u'Kate Winslet', u'Laura Linney']"
+7.5,Life as a House,R,Drama,125,"[u'Hayden Christensen', u'Kevin Kline', u'Kristin Scott Thomas']"
+7.5,Despicable Me 2,PG,Animation,98,"[u'Steve Carell', u'Kristen Wiig', u'Benjamin Bratt']"
+7.5,Jackie Brown,R,Crime,154,"[u'Pam Grier', u'Samuel L. Jackson', u'Robert Forster']"
+7.5,Menace II Society,R,Crime,97,"[u'Tyrin Turner', u'Larenz Tate', u'June Kyoto Lu']"
+7.5,The Class,PG-13,Drama,128,"[u'Fran\xe7ois B\xe9gaudeau', u'Agame Malembo-Emene', u'Ang\xe9lica Sancio']"
+7.5,Bullets Over Broadway,R,Comedy,98,"[u'John Cusack', u'Dianne Wiest', u'Jennifer Tilly']"
+7.5,American Splendor,R,Biography,101,"[u'Paul Giamatti', u'Shari Springer Berman', u'Harvey Pekar']"
+7.5,Perfume: The Story of a Murderer,R,Crime,147,"[u'Ben Whishaw', u'Dustin Hoffman', u'Alan Rickman']"
+7.5,Cloud Atlas,R,Drama,172,"[u'Tom Hanks', u'Halle Berry', u'Hugh Grant']"
+7.5,Marathon Man,R,Crime,125,"[u'Dustin Hoffman', u'Laurence Olivier', u'Roy Scheider']"
+7.5,Pinocchio,APPROVED,Animation,88,"[u'Dickie Jones', u'Christian Rub', u'Mel Blanc']"
+7.5,The Painted Veil,PG-13,Drama,125,"[u'Naomi Watts', u'Edward Norton', u'Liev Schreiber']"
+7.5,A Simple Plan,R,Crime,121,"[u'Bill Paxton', u'Billy Bob Thornton', u'Bridget Fonda']"
+7.5,The Conjuring,R,Horror,112,"[u'Patrick Wilson', u'Vera Farmiga', u'Ron Livingston']"
+7.5,The Man Who Knew Too Much,PG,Thriller,120,"[u'James Stewart', u'Doris Day', u'Brenda de Banzie']"
+7.5,Quiz Show,PG-13,Drama,133,"[u'Ralph Fiennes', u'John Turturro', u'Rob Morrow']"
+7.5,American Graffiti,PG,Comedy,110,"[u'Richard Dreyfuss', u'Ron Howard', u'Paul Le Mat']"
+7.5,The Bridges of Madison County,PG-13,Drama,135,"[u'Clint Eastwood', u'Meryl Streep', u'Annie Corley']"
+7.5,Gallipoli,PG,Adventure,110,"[u'Mel Gibson', u'Mark Lee', u'Bill Kerr']"
+7.5,L'illusionniste,PG,Animation,80,"[u'Jean-Claude Donda', u'Eilidh Rankin', u'Duncan MacNeil']"
+7.5,Miracle,PG,Drama,135,"[u'Kurt Russell', u'Patricia Clarkson', u'Nathan West']"
+7.5,Maria Full of Grace,R,Crime,101,"[u'Catalina Sandino Moreno', u'Guilied Lopez', u'Orlando Tob\xf3n']"
+7.5,Scott Pilgrim vs. the World,PG-13,Action,112,"[u'Michael Cera', u'Mary Elizabeth Winstead', u'Kieran Culkin']"
+7.5,Pleasantville,PG-13,Comedy,124,"[u'Tobey Maguire', u'Jeff Daniels', u'Joan Allen']"
+7.5,Sherlock Holmes: A Game of Shadows,PG-13,Action,129,"[u'Robert Downey Jr.', u'Jude Law', u'Jared Harris']"
+7.5,Doubt,PG-13,Drama,104,"[u'Meryl Streep', u'Philip Seymour Hoffman', u'Amy Adams']"
+7.5,The Fly,R,Horror,96,"[u'Jeff Goldblum', u'Geena Davis', u'John Getz']"
+7.5,The Orphanage,R,Drama,105,"[u'Bel\xe9n Rueda', u'Fernando Cayo', u'Roger Pr\xedncep']"
+7.5,Malena,R,Drama,109,"[u'Monica Bellucci', u'Giuseppe Sulfaro', u'Luciano Federico']"
+7.5,Three Days of the Condor,R,Mystery,117,"[u'Robert Redford', u'Faye Dunaway', u'Cliff Robertson']"
+7.5,[Rec],R,Horror,78,"[u'Manuela Velasco', u'Ferran Terraza', u'Jorge-Yamam Serrano']"
+7.5,A Perfect World,PG-13,Crime,138,"[u'Kevin Costner', u'Clint Eastwood', u'Laura Dern']"
+7.5,To Catch a Thief,APPROVED,Mystery,106,"[u'Cary Grant', u'Grace Kelly', u'Jessie Royce Landis']"
+7.5,Best in Show,PG-13,Comedy,90,"[u'Fred Willard', u'Eugene Levy', u""Catherine O'Hara""]"
+7.5,Everything Is Illuminated,PG-13,Comedy,106,"[u'Elijah Wood', u'Eugene Hutz', u'Boris Leskin']"
+7.5,A Nightmare on Elm Street,R,Horror,91,"[u'Heather Langenkamp', u'Johnny Depp', u'Robert Englund']"
+7.5,The Ice Storm,R,Drama,112,"[u'Kevin Kline', u'Joan Allen', u'Sigourney Weaver']"
+7.5,X2,PG-13,Action,134,"[u'Patrick Stewart', u'Hugh Jackman', u'Halle Berry']"
+7.5,We Need to Talk About Kevin,R,Drama,112,"[u'Tilda Swinton', u'John C. Reilly', u'Ezra Miller']"
+7.5,Felon,R,Crime,104,"[u'Stephen Dorff', u'Marisol Nichols', u'Vincent Miller']"
+7.5,Still Alice,PG-13,Drama,101,"[u'Julianne Moore', u'Alec Baldwin', u'Kristen Stewart']"
+7.5,Rust and Bone,R,Drama,120,"[u'Marion Cotillard', u'Matthias Schoenaerts', u'Armand Verdure']"
+7.5,Freedom Writers,PG-13,Biography,123,"[u'Hilary Swank', u'Imelda Staunton', u'Patrick Dempsey']"
+7.5,Legends of the Fall,R,Drama,133,"[u'Brad Pitt', u'Anthony Hopkins', u'Aidan Quinn']"
+7.5,Buffalo '66,R,Comedy,110,"[u'Vincent Gallo', u'Christina Ricci', u'Ben Gazzara']"
+7.5,The Constant Gardener,R,Drama,129,"[u'Ralph Fiennes', u'Rachel Weisz', u'Hubert Kound\xe9']"
+7.5,My Cousin Vinny,R,Comedy,120,"[u'Joe Pesci', u'Marisa Tomei', u'Ralph Macchio']"
+7.5,The Texas Chain Saw Massacre,R,Horror,83,"[u'Marilyn Burns', u'Edwin Neal', u'Allen Danziger']"
+7.5,Mulan,G,Animation,88,"[u'Ming-Na Wen', u'Eddie Murphy', u'BD Wong']"
+7.5,A History of Violence,R,Crime,96,"[u'Viggo Mortensen', u'Maria Bello', u'Ed Harris']"
+7.5,Open Range,R,Action,139,"[u'Kevin Costner', u'Robert Duvall', u'Diego Luna']"
+7.5,Total Recall,R,Action,113,"[u'Arnold Schwarzenegger', u'Sharon Stone', u'Michael Ironside']"
+7.5,Pi,R,Drama,84,"[u'Sean Gullette', u'Mark Margolis', u'Ben Shenkman']"
+7.5,The Devil's Backbone,R,Drama,106,"[u'Marisa Paredes', u'Eduardo Noriega', u'Federico Luppi']"
+7.5,The Aviator,PG-13,Biography,170,"[u'Leonardo DiCaprio', u'Cate Blanchett', u'Kate Beckinsale']"
+7.5,Devil's Advocate,R,Drama,144,"[u'Keanu Reeves', u'Al Pacino', u'Charlize Theron']"
+7.5,"The Adventures of Priscilla, Queen of the Desert",R,Comedy,104,"[u'Hugo Weaving', u'Guy Pearce', u'Terence Stamp']"
+7.5,Harry Potter and the Half-Blood Prince,PG,Adventure,153,"[u'Daniel Radcliffe', u'Emma Watson', u'Rupert Grint']"
+7.5,Fruitvale Station,R,Biography,85,"[u'Michael B. Jordan', u'Melonie Diaz', u'Octavia Spencer']"
+7.5,In the Bedroom,R,Crime,130,"[u'Tom Wilkinson', u'Sissy Spacek', u'Nick Stahl']"
+7.5,Source Code,PG-13,Mystery,93,"[u'Jake Gyllenhaal', u'Michelle Monaghan', u'Vera Farmiga']"
+7.5,Biutiful,R,Drama,148,"[u'Javier Bardem', u'Maricel \xc1lvarez', u'Hanaa Bouchaib']"
+7.5,From Russia with Love,APPROVED,Action,115,"[u'Sean Connery', u'Robert Shaw', u'Lotte Lenya']"
+7.5,Calvary,R,Drama,102,"[u'Brendan Gleeson', u""Chris O'Dowd"", u'Kelly Reilly']"
+7.5,The Mission,PG,Adventure,125,"[u'Robert De Niro', u'Jeremy Irons', u'Ray McAnally']"
+7.5,In the Loop,NOT RATED,Comedy,106,"[u'Tom Hollander', u'Peter Capaldi', u'James Gandolfini']"
+7.5,The Three Burials of Melquiades Estrada,R,Adventure,121,"[u'Tommy Lee Jones', u'Barry Pepper', u'Dwight Yoakam']"
+7.5,Babel,R,Drama,143,"[u'Brad Pitt', u'Cate Blanchett', u'Gael Garc\xeda Bernal']"
+7.5,Frenzy,R,Thriller,116,"[u'Jon Finch', u'Barry Foster', u'Alec McCowen']"
+7.5,Heavenly Creatures,R,Biography,99,"[u'Melanie Lynskey', u'Kate Winslet', u'Sarah Peirse']"
+7.5,Sweeney Todd: The Demon Barber of Fleet Street,R,Drama,116,"[u'Johnny Depp', u'Helena Bonham Carter', u'Alan Rickman']"
+7.5,Dracula,R,Horror,128,"[u'Gary Oldman', u'Winona Ryder', u'Anthony Hopkins']"
+7.5,Looper,R,Action,119,"[u'Joseph Gordon-Levitt', u'Bruce Willis', u'Emily Blunt']"
+7.5,The Proposition,R,Crime,104,"[u'Ray Winstone', u'Guy Pearce', u'Emily Watson']"
+7.5,Bullitt,PG,Action,114,"[u'Steve McQueen', u'Jacqueline Bisset', u'Robert Vaughn']"
+7.5,Harry Potter and the Sorcerer's Stone,PG,Adventure,152,"[u'Daniel Radcliffe', u'Rupert Grint', u'Richard Harris']"
+7.5,2046,R,Drama,129,"[u'Tony Chiu Wai Leung', u'Ziyi Zhang', u'Faye Wong']"
+7.5,Transamerica,R,Adventure,103,"[u'Felicity Huffman', u'Kevin Zegers', u'Fionnula Flanagan']"
+7.5,Smoke,R,Comedy,112,"[u'Harvey Keitel', u'William Hurt', u'Giancarlo Esposito']"
+7.5,Suspiria,X,Horror,92,"[u'Jessica Harper', u'Stefania Casini', u'Flavio Bucci']"
+7.5,The Judge,R,Drama,141,"[u'Robert Downey Jr.', u'Robert Duvall', u'Vera Farmiga']"
+7.5,Bad Education,NC-17,Crime,106,"[u'Gael Garc\xeda Bernal', u'Fele Mart\xednez', u'Javier C\xe1mara']"
+7.5,Up in the Air,R,Drama,109,"[u'George Clooney', u'Vera Farmiga', u'Anna Kendrick']"
+7.5,Begin Again,R,Drama,104,"[u'Keira Knightley', u'Mark Ruffalo', u'Adam Levine']"
+7.5,Running Scared,R,Action,122,"[u'Paul Walker', u'Cameron Bright', u'Chazz Palminteri']"
+7.5,Ghost World,R,Comedy,111,"[u'Steve Buscemi', u'Thora Birch', u'Scarlett Johansson']"
+7.5,Witness,R,Crime,112,"[u'Harrison Ford', u'Kelly McGillis', u'Lukas Haas']"
+7.5,Trading Places,R,Comedy,116,"[u'Eddie Murphy', u'Dan Aykroyd', u'Ralph Bellamy']"
+7.5,Mud,PG-13,Drama,130,"[u'Matthew McConaughey', u'Tye Sheridan', u'Jacob Lofland']"
+7.5,Across the Universe,PG-13,Drama,133,"[u'Evan Rachel Wood', u'Jim Sturgess', u'Joe Anderson']"
+7.5,Les Miserables,PG-13,Crime,134,"[u'Liam Neeson', u'Geoffrey Rush', u'Uma Thurman']"
+7.5,Notes on a Scandal,R,Drama,92,"[u'Cate Blanchett', u'Judi Dench', u'Andrew Simpson']"
+7.5,Inside Llewyn Davis,R,Drama,104,"[u'Oscar Isaac', u'Carey Mulligan', u'John Goodman']"
+7.5,Brick,R,Crime,110,"[u'Joseph Gordon-Levitt', u'Lukas Haas', u'Emilie de Ravin']"
+7.5,Clerks II,R,Comedy,97,"[u""Brian O'Halloran"", u'Jeff Anderson', u'Rosario Dawson']"
+7.5,Say Anything...,PG-13,Comedy,100,"[u'John Cusack', u'Ione Skye', u'John Mahoney']"
+7.4,Man on the Moon,R,Biography,118,"[u'Jim Carrey', u'Danny DeVito', u'Gerry Becker']"
+7.4,Mean Streets,R,Crime,112,"[u'Robert De Niro', u'Harvey Keitel', u'David Proval']"
+7.4,Harry Potter and the Order of the Phoenix,PG-13,Adventure,138,"[u'Daniel Radcliffe', u'Emma Watson', u'Rupert Grint']"
+7.4,Beetlejuice,PG,Comedy,92,"[u'Alec Baldwin', u'Geena Davis', u'Michael Keaton']"
+7.4,"Crazy, Stupid, Love.",PG-13,Comedy,118,"[u'Steve Carell', u'Ryan Gosling', u'Julianne Moore']"
+7.4,Excalibur,R,Adventure,140,"[u'Nigel Terry', u'Helen Mirren', u'Nicholas Clay']"
+7.4,True Grit,,Adventure,128,"[u'John Wayne', u'Kim Darby', u'Glen Campbell']"
+7.4,Labyrinth,PG,Adventure,101,"[u'David Bowie', u'Jennifer Connelly', u'Toby Froud']"
+7.4,Alice in Wonderland,G,Animation,75,"[u'Kathryn Beaumont', u'Ed Wynn', u'Richard Haydn']"
+7.4,Predestination,R,Drama,97,"[u'Ethan Hawke', u'Sarah Snook', u'Noah Taylor']"
+7.4,Much Ado About Nothing,PG-13,Comedy,111,"[u'Kenneth Branagh', u'Emma Thompson', u'Keanu Reeves']"
+7.4,A Bridge Too Far,PG,Drama,175,"[u'Sean Connery', u""Ryan O'Neal"", u'Michael Caine']"
+7.4,Raising Arizona,PG-13,Comedy,94,"[u'Nicolas Cage', u'Holly Hunter', u'Trey Wilson']"
+7.4,The Bucket List,PG-13,Adventure,97,"[u'Jack Nicholson', u'Morgan Freeman', u'Sean Hayes']"
+7.4,Terms of Endearment,R,Comedy,132,"[u'Shirley MacLaine', u'Debra Winger', u'Jack Nicholson']"
+7.4,Take Shelter,R,Drama,120,"[u'Michael Shannon', u'Jessica Chastain', u'Shea Whigham']"
+7.4,Far from Heaven,PG-13,Drama,107,"[u'Julianne Moore', u'Dennis Quaid', u'Dennis Haysbert']"
+7.4,Eraserhead,UNRATED,Drama,89,"[u'Jack Nance', u'Charlotte Stewart', u'Allen Joseph']"
+7.4,Frances Ha,R,Comedy,86,"[u'Greta Gerwig', u'Mickey Sumner', u'Adam Driver']"
+7.4,Home Alone,PG,Comedy,103,"[u'Macaulay Culkin', u'Joe Pesci', u'Daniel Stern']"
+7.4,Bound,R,Crime,108,"[u'Jennifer Tilly', u'Gina Gershon', u'Joe Pantoliano']"
+7.4,Sleepy Hollow,R,Drama,105,"[u'Johnny Depp', u'Christina Ricci', u'Miranda Richardson']"
+7.4,Pirate Radio,R,Comedy,117,"[u'Philip Seymour Hoffman', u'Bill Nighy', u'Nick Frost']"
+7.4,The NeverEnding Story,PG,Adventure,102,"[u'Noah Hathaway', u'Barret Oliver', u'Tami Stronach']"
+7.4,X-Men,PG-13,Action,104,"[u'Patrick Stewart', u'Hugh Jackman', u'Ian McKellen']"
+7.4,Zero Dark Thirty,R,Drama,157,"[u'Jessica Chastain', u'Joel Edgerton', u'Chris Pratt']"
+7.4,Manhattan Murder Mystery,PG,Comedy,104,"[u'Woody Allen', u'Diane Keaton', u'Jerry Adler']"
+7.4,National Lampoon's Vacation,R,Comedy,98,"[u'Chevy Chase', u""Beverly D'Angelo"", u'Imogene Coca']"
+7.4,My Sister's Keeper,PG-13,Drama,109,"[u'Cameron Diaz', u'Abigail Breslin', u'Alec Baldwin']"
+7.4,Deconstructing Harry,R,Comedy,96,"[u'Woody Allen', u'Judy Davis', u'Julia Louis-Dreyfus']"
+7.4,The Way Way Back,PG-13,Comedy,103,"[u'Steve Carell', u'Toni Collette', u'Allison Janney']"
+7.4,Capote,R,Biography,114,"[u'Philip Seymour Hoffman', u'Clifton Collins Jr.', u'Catherine Keener']"
+7.4,Driving Miss Daisy,PG,Comedy,99,"[u'Morgan Freeman', u'Jessica Tandy', u'Dan Aykroyd']"
+7.4,La Femme Nikita,R,Action,118,"[u'Anne Parillaud', u'Marc Duret', u'Patrick Fontana']"
+7.4,Lincoln,PG-13,Biography,150,"[u'Daniel Day-Lewis', u'Sally Field', u'David Strathairn']"
+7.4,Limitless,PG-13,Mystery,105,"[u'Bradley Cooper', u'Anna Friel', u'Abbie Cornish']"
+7.4,The Simpsons Movie,PG-13,Animation,87,"[u'Dan Castellaneta', u'Julie Kavner', u'Nancy Cartwright']"
+7.4,The Rock,R,Action,136,"[u'Sean Connery', u'Nicolas Cage', u'Ed Harris']"
+7.4,The English Patient,R,Drama,162,"[u'Ralph Fiennes', u'Juliette Binoche', u'Willem Dafoe']"
+7.4,Law Abiding Citizen,R,Crime,109,"[u'Gerard Butler', u'Jamie Foxx', u'Leslie Bibb']"
+7.4,Wonder Boys,R,Drama,107,"[u'Michael Douglas', u'Tobey Maguire', u'Frances McDormand']"
+7.4,Death at a Funeral,R,Comedy,90,"[u'Matthew Macfadyen', u'Peter Dinklage', u'Ewen Bremner']"
+7.4,Blue Valentine,NC-17,Drama,112,"[u'Ryan Gosling', u'Michelle Williams', u'John Doman']"
+7.4,The Cider House Rules,PG-13,Drama,126,"[u'Tobey Maguire', u'Charlize Theron', u'Michael Caine']"
+7.4,Tootsie,PG,Comedy,116,"[u'Dustin Hoffman', u'Jessica Lange', u'Teri Garr']"
+7.4,Back to the Future Part III,PG,Adventure,118,"[u'Michael J. Fox', u'Christopher Lloyd', u'Mary Steenburgen']"
+7.4,Master and Commander: The Far Side of the World,PG-13,Action,138,"[u'Russell Crowe', u'Paul Bettany', u'Billy Boyd']"
+7.4,Poltergeist,PG,Horror,114,"[u'JoBeth Williams', u""Heather O'Rourke"", u'Craig T. Nelson']"
+7.4,Wall Street,R,Crime,126,"[u'Charlie Sheen', u'Michael Douglas', u'Tamara Tunie']"