Phase  1 Project EDA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "films = '/Users/Arsh/Downloads/blockbusters.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "m = pd.read_csv(films)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Main_Genre</th>\n",
       "      <th>Genre_2</th>\n",
       "      <th>Genre_3</th>\n",
       "      <th>imdb_rating</th>\n",
       "      <th>length</th>\n",
       "      <th>rank_in_year</th>\n",
       "      <th>rating</th>\n",
       "      <th>studio</th>\n",
       "      <th>title</th>\n",
       "      <th>worldwide_gross</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Action</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>Drama</td>\n",
       "      <td>7.4</td>\n",
       "      <td>135</td>\n",
       "      <td>1</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>Walt Disney Pictures</td>\n",
       "      <td>Black Panther</td>\n",
       "      <td>$700,059,566</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Action</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>Sci-Fi</td>\n",
       "      <td>8.5</td>\n",
       "      <td>156</td>\n",
       "      <td>2</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>Walt Disney Pictures</td>\n",
       "      <td>Avengers: Infinity War</td>\n",
       "      <td>$678,815,482</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Animation</td>\n",
       "      <td>Action</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>7.8</td>\n",
       "      <td>118</td>\n",
       "      <td>3</td>\n",
       "      <td>PG</td>\n",
       "      <td>Pixar</td>\n",
       "      <td>Incredibles 2</td>\n",
       "      <td>$608,581,744</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Action</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>Drama</td>\n",
       "      <td>6.2</td>\n",
       "      <td>129</td>\n",
       "      <td>4</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>Universal Pictures</td>\n",
       "      <td>Jurassic World: Fallen Kingdom</td>\n",
       "      <td>$416,769,345</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Action</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.8</td>\n",
       "      <td>119</td>\n",
       "      <td>5</td>\n",
       "      <td>R</td>\n",
       "      <td>20th Century Fox</td>\n",
       "      <td>Deadpool 2</td>\n",
       "      <td>$318,491,426</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Main_Genre    Genre_2    Genre_3  imdb_rating  length  rank_in_year rating  \\\n",
       "0     Action  Adventure      Drama          7.4     135             1  PG-13   \n",
       "1     Action  Adventure     Sci-Fi          8.5     156             2  PG-13   \n",
       "2  Animation     Action  Adventure          7.8     118             3     PG   \n",
       "3     Action  Adventure      Drama          6.2     129             4  PG-13   \n",
       "4     Action     Comedy        NaN          7.8     119             5      R   \n",
       "\n",
       "                 studio                           title worldwide_gross  year  \n",
       "0  Walt Disney Pictures                   Black Panther    $700,059,566  2018  \n",
       "1  Walt Disney Pictures          Avengers: Infinity War    $678,815,482  2018  \n",
       "2                 Pixar                   Incredibles 2    $608,581,744  2018  \n",
       "3    Universal Pictures  Jurassic World: Fallen Kingdom    $416,769,345  2018  \n",
       "4      20th Century Fox                      Deadpool 2    $318,491,426  2018  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      7.4\n",
       "1      8.5\n",
       "2      7.8\n",
       "3      6.2\n",
       "4      7.8\n",
       "      ... \n",
       "432    7.1\n",
       "433    6.4\n",
       "434    6.2\n",
       "435    6.0\n",
       "436    6.4\n",
       "Name: imdb_rating, Length: 437, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m['imdb_rating'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "m['worldwide_gross'] = m['worldwide_gross'].str.replace('$', '')\n",
    "m['worldwide_gross'] = m['worldwide_gross'].str.replace(',', '')\n",
    "m['worldwide_gross'] = m['worldwide_gross'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "m['worldwide_gross'] = m['worldwide_gross'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Main_Genre</th>\n",
       "      <th>Genre_2</th>\n",
       "      <th>Genre_3</th>\n",
       "      <th>imdb_rating</th>\n",
       "      <th>length</th>\n",
       "      <th>rank_in_year</th>\n",
       "      <th>rating</th>\n",
       "      <th>studio</th>\n",
       "      <th>title</th>\n",
       "      <th>worldwide_gross</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Action</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>Drama</td>\n",
       "      <td>7.4</td>\n",
       "      <td>135</td>\n",
       "      <td>1</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>Walt Disney Pictures</td>\n",
       "      <td>Black Panther</td>\n",
       "      <td>700059566</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Action</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>Sci-Fi</td>\n",
       "      <td>8.5</td>\n",
       "      <td>156</td>\n",
       "      <td>2</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>Walt Disney Pictures</td>\n",
       "      <td>Avengers: Infinity War</td>\n",
       "      <td>678815482</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Animation</td>\n",
       "      <td>Action</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>7.8</td>\n",
       "      <td>118</td>\n",
       "      <td>3</td>\n",
       "      <td>PG</td>\n",
       "      <td>Pixar</td>\n",
       "      <td>Incredibles 2</td>\n",
       "      <td>608581744</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Action</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>Drama</td>\n",
       "      <td>6.2</td>\n",
       "      <td>129</td>\n",
       "      <td>4</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>Universal Pictures</td>\n",
       "      <td>Jurassic World: Fallen Kingdom</td>\n",
       "      <td>416769345</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Action</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.8</td>\n",
       "      <td>119</td>\n",
       "      <td>5</td>\n",
       "      <td>R</td>\n",
       "      <td>20th Century Fox</td>\n",
       "      <td>Deadpool 2</td>\n",
       "      <td>318491426</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>Fantasy</td>\n",
       "      <td>Family</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>6.0</td>\n",
       "      <td>104</td>\n",
       "      <td>6</td>\n",
       "      <td>PG</td>\n",
       "      <td>Universal Pictures</td>\n",
       "      <td>How the Grinch Stole Christmas</td>\n",
       "      <td>345141403</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>Comedy</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>108</td>\n",
       "      <td>7</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>Universal Pictures</td>\n",
       "      <td>Meet the Parents</td>\n",
       "      <td>330444045</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>Drama</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>Action</td>\n",
       "      <td>6.4</td>\n",
       "      <td>130</td>\n",
       "      <td>8</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>Warner Bros</td>\n",
       "      <td>The Perfect Storm</td>\n",
       "      <td>328718434</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>Sci-Fi</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>Action</td>\n",
       "      <td>7.4</td>\n",
       "      <td>104</td>\n",
       "      <td>9</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>20th Century Fox</td>\n",
       "      <td>X-Men</td>\n",
       "      <td>296339527</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>Thriller</td>\n",
       "      <td>Horror</td>\n",
       "      <td>Drama</td>\n",
       "      <td>6.6</td>\n",
       "      <td>130</td>\n",
       "      <td>10</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>DreamWorks</td>\n",
       "      <td>What Lies Beneath</td>\n",
       "      <td>291420351</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>190 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Main_Genre    Genre_2    Genre_3  imdb_rating  length  rank_in_year  \\\n",
       "0       Action  Adventure      Drama          7.4     135             1   \n",
       "1       Action  Adventure     Sci-Fi          8.5     156             2   \n",
       "2    Animation     Action  Adventure          7.8     118             3   \n",
       "3       Action  Adventure      Drama          6.2     129             4   \n",
       "4       Action     Comedy        NaN          7.8     119             5   \n",
       "..         ...        ...        ...          ...     ...           ...   \n",
       "185    Fantasy     Family     Comedy          6.0     104             6   \n",
       "186     Comedy        NaN        NaN          7.0     108             7   \n",
       "187      Drama  Adventure     Action          6.4     130             8   \n",
       "188     Sci-Fi  Adventure     Action          7.4     104             9   \n",
       "189   Thriller     Horror      Drama          6.6     130            10   \n",
       "\n",
       "    rating                studio                           title  \\\n",
       "0    PG-13  Walt Disney Pictures                   Black Panther   \n",
       "1    PG-13  Walt Disney Pictures          Avengers: Infinity War   \n",
       "2       PG                 Pixar                   Incredibles 2   \n",
       "3    PG-13    Universal Pictures  Jurassic World: Fallen Kingdom   \n",
       "4        R      20th Century Fox                      Deadpool 2   \n",
       "..     ...                   ...                             ...   \n",
       "185     PG    Universal Pictures  How the Grinch Stole Christmas   \n",
       "186  PG-13    Universal Pictures                Meet the Parents   \n",
       "187  PG-13           Warner Bros               The Perfect Storm   \n",
       "188  PG-13      20th Century Fox                           X-Men   \n",
       "189  PG-13            DreamWorks               What Lies Beneath   \n",
       "\n",
       "     worldwide_gross  year  \n",
       "0          700059566  2018  \n",
       "1          678815482  2018  \n",
       "2          608581744  2018  \n",
       "3          416769345  2018  \n",
       "4          318491426  2018  \n",
       "..               ...   ...  \n",
       "185        345141403  2000  \n",
       "186        330444045  2000  \n",
       "187        328718434  2000  \n",
       "188        296339527  2000  \n",
       "189        291420351  2000  \n",
       "\n",
       "[190 rows x 11 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdf = m.loc[m['year'] >= 2000]\n",
    "newdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Main_Genre\n",
       "Fantasy      8.428503e+08\n",
       "Adventure    7.005133e+08\n",
       "Sci-Fi       6.763367e+08\n",
       "Comedy       6.506474e+08\n",
       "Thriller     6.323585e+08\n",
       "Drama        5.625680e+08\n",
       "Animation    5.269178e+08\n",
       "History      4.567590e+08\n",
       "War          4.560682e+08\n",
       "Crime        4.268867e+08\n",
       "Family       4.196656e+08\n",
       "Romance      4.125427e+08\n",
       "Action       4.032829e+08\n",
       "Music        3.518679e+08\n",
       "Horror       3.274817e+08\n",
       "Name: worldwide_gross, dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdf.groupby('Main_Genre')['worldwide_gross'].mean().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Main_Genre\n",
       "Fantasy      31185461422\n",
       "Sci-Fi       19613764811\n",
       "Comedy       15615536700\n",
       "Thriller     12647170801\n",
       "Animation    11592191168\n",
       "Action       10888637115\n",
       "Adventure     5604106108\n",
       "Drama         3375407780\n",
       "Romance       2887799069\n",
       "Crime         1707546871\n",
       "Music          703735892\n",
       "History        456758981\n",
       "War            456068181\n",
       "Family         419665568\n",
       "Horror         327481748\n",
       "Name: worldwide_gross, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdf.groupby('Main_Genre')['worldwide_gross'].sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Main_Genre\n",
       "Fantasy      829685377.0\n",
       "Adventure    785201187.0\n",
       "Sci-Fi       691247768.0\n",
       "Thriller     588547275.0\n",
       "Comedy       569114687.5\n",
       "Animation    532212922.5\n",
       "Drama        504078892.0\n",
       "History      456758981.0\n",
       "War          456068181.0\n",
       "Crime        455089318.0\n",
       "Family       419665568.0\n",
       "Romance      374111707.0\n",
       "Action       363070709.0\n",
       "Music        351867946.0\n",
       "Horror       327481748.0\n",
       "Name: worldwide_gross, dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdf.groupby('Main_Genre')['worldwide_gross'].median().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Genre_2\n",
       "Family       7.781579e+08\n",
       "Animation    6.716606e+08\n",
       "Adventure    6.457242e+08\n",
       "Crime        6.450558e+08\n",
       "Action       6.400673e+08\n",
       "Sci-Fi       6.333296e+08\n",
       "Musical      6.098277e+08\n",
       "Mystery      6.070187e+08\n",
       "Drama        5.360412e+08\n",
       "Fantasy      5.202552e+08\n",
       "Comedy       4.473255e+08\n",
       "Horror       2.914204e+08\n",
       "Music        2.703954e+08\n",
       "Name: worldwide_gross, dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdf.groupby('Genre_2')['worldwide_gross'].mean().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Genre_2\n",
       "Adventure    56178005868\n",
       "Animation    13433212714\n",
       "Family        9337894915\n",
       "Action        8960942428\n",
       "Comedy        8051859144\n",
       "Drama         6432494339\n",
       "Crime         3225278955\n",
       "Sci-Fi        3166648135\n",
       "Fantasy       2601276123\n",
       "Mystery       2428074623\n",
       "Musical        609827661\n",
       "Horror         291420351\n",
       "Music          270395425\n",
       "Name: worldwide_gross, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdf.groupby('Genre_2')['worldwide_gross'].sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Main_Genre\n",
       "Fantasy      8.428503e+08\n",
       "Adventure    7.005133e+08\n",
       "Sci-Fi       6.763367e+08\n",
       "Comedy       6.506474e+08\n",
       "Thriller     6.323585e+08\n",
       "Drama        5.625680e+08\n",
       "Animation    5.269178e+08\n",
       "History      4.567590e+08\n",
       "War          4.560682e+08\n",
       "Crime        4.268867e+08\n",
       "Name: worldwide_gross, dtype: float64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = newdf.groupby('Main_Genre')['worldwide_gross'].mean().sort_values(ascending=False)\n",
    "f = y.head(10)\n",
    "f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = f / 1000000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "s = list(f.keys())\n",
    "d = list(y)\n",
    "fig, ax = plt.subplots(figsize=(20,10))\n",
    "plt.bar(s, d, color ='blue')\n",
    "plt.xlabel(\"Genre\", fontsize = 30)\n",
    "ax.tick_params(axis='x', labelsize=20)\n",
    "ax.tick_params(axis='y', labelsize=20)\n",
    "ax.set_yticks(range(0,1000,100))\n",
    "plt.ylabel(\"Revenue in ($) Millions\", fontsize = 30)\n",
    "plt.title(\"Average Blockbuster Revenue by Genre (2000-2018)\", fontsize = 40)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5782 entries, 0 to 5781\n",
      "Data columns (total 6 columns):\n",
      " #   Column             Non-Null Count  Dtype \n",
      "---  ------             --------------  ----- \n",
      " 0   id                 5782 non-null   int64 \n",
      " 1   release_date       5782 non-null   object\n",
      " 2   movie              5782 non-null   object\n",
      " 3   production_budget  5782 non-null   object\n",
      " 4   domestic_gross     5782 non-null   object\n",
      " 5   worldwide_gross    5782 non-null   object\n",
      "dtypes: int64(1), object(5)\n",
      "memory usage: 271.2+ KB\n"
     ]
    }
   ],
   "source": [
    "budgets = pd.read_csv('/Users/Arsh/Downloads/tn.movie_budgets.csv.gz')\n",
    "budgets.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 26517 entries, 0 to 26516\n",
      "Data columns (total 10 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Unnamed: 0         26517 non-null  int64  \n",
      " 1   genre_ids          26517 non-null  object \n",
      " 2   id                 26517 non-null  int64  \n",
      " 3   original_language  26517 non-null  object \n",
      " 4   original_title     26517 non-null  object \n",
      " 5   popularity         26517 non-null  float64\n",
      " 6   release_date       26517 non-null  object \n",
      " 7   title              26517 non-null  object \n",
      " 8   vote_average       26517 non-null  float64\n",
      " 9   vote_count         26517 non-null  int64  \n",
      "dtypes: float64(2), int64(3), object(5)\n",
      "memory usage: 2.0+ MB\n"
     ]
    }
   ],
   "source": [
    "reviews = pd.read_csv('/Users/Arsh/Downloads/tmdb.movies.csv.gz')\n",
    "reviews.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3387 entries, 0 to 3386\n",
      "Data columns (total 5 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   title           3387 non-null   object \n",
      " 1   studio          3382 non-null   object \n",
      " 2   domestic_gross  3359 non-null   float64\n",
      " 3   foreign_gross   2037 non-null   object \n",
      " 4   year            3387 non-null   int64  \n",
      "dtypes: float64(1), int64(1), object(3)\n",
      "memory usage: 132.4+ KB\n"
     ]
    }
   ],
   "source": [
    "gross = pd.read_csv('/Users/Arsh/Downloads/bom.movie_gross.csv.gz')\n",
    "gross.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>domestic_gross</th>\n",
       "      <th>foreign_gross</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toy Story 3</td>\n",
       "      <td>415000000.0</td>\n",
       "      <td>652000000</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alice in Wonderland (2010)</td>\n",
       "      <td>334200000.0</td>\n",
       "      <td>691300000</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Harry Potter and the Deathly Hallows Part 1</td>\n",
       "      <td>296000000.0</td>\n",
       "      <td>664300000</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Inception</td>\n",
       "      <td>292600000.0</td>\n",
       "      <td>535700000</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Shrek Forever After</td>\n",
       "      <td>238700000.0</td>\n",
       "      <td>513900000</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         title  domestic_gross foreign_gross  \\\n",
       "0                                  Toy Story 3     415000000.0     652000000   \n",
       "1                   Alice in Wonderland (2010)     334200000.0     691300000   \n",
       "2  Harry Potter and the Deathly Hallows Part 1     296000000.0     664300000   \n",
       "3                                    Inception     292600000.0     535700000   \n",
       "4                          Shrek Forever After     238700000.0     513900000   \n",
       "\n",
       "   year  \n",
       "0  2010  \n",
       "1  2010  \n",
       "2  2010  \n",
       "3  2010  \n",
       "4  2010  "
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gross = gross.drop('studio', axis=1)\n",
    "gross.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "gross = gross.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3387 entries, 0 to 3386\n",
      "Data columns (total 4 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   title           3387 non-null   object \n",
      " 1   domestic_gross  3387 non-null   float64\n",
      " 2   foreign_gross   3387 non-null   object \n",
      " 3   year            3387 non-null   int64  \n",
      "dtypes: float64(1), int64(1), object(2)\n",
      "memory usage: 106.0+ KB\n"
     ]
    }
   ],
   "source": [
    "gross.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "gross['foreign_gross'] = gross['foreign_gross'].str.replace(',', '')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "gross['foreign_gross'] = gross['foreign_gross'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "gross['foreign_gross'] = gross['foreign_gross'].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "gross['total_gross'] = gross['domestic_gross'] + gross['foreign_gross']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title             0\n",
       "domestic_gross    0\n",
       "foreign_gross     0\n",
       "year              0\n",
       "total_gross       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gross.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>domestic_gross</th>\n",
       "      <th>foreign_gross</th>\n",
       "      <th>year</th>\n",
       "      <th>total_gross</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>727</th>\n",
       "      <td>Marvel's The Avengers</td>\n",
       "      <td>623400000.0</td>\n",
       "      <td>895500000.0</td>\n",
       "      <td>2012</td>\n",
       "      <td>1.518900e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1875</th>\n",
       "      <td>Avengers: Age of Ultron</td>\n",
       "      <td>459000000.0</td>\n",
       "      <td>946400000.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>1.405400e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3080</th>\n",
       "      <td>Black Panther</td>\n",
       "      <td>700100000.0</td>\n",
       "      <td>646900000.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>1.347000e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>Harry Potter and the Deathly Hallows Part 2</td>\n",
       "      <td>381000000.0</td>\n",
       "      <td>960500000.0</td>\n",
       "      <td>2011</td>\n",
       "      <td>1.341500e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2758</th>\n",
       "      <td>Star Wars: The Last Jedi</td>\n",
       "      <td>620200000.0</td>\n",
       "      <td>712400000.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>1.332600e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>3078</th>\n",
       "      <td>2:22</td>\n",
       "      <td>400.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>4.000000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2756</th>\n",
       "      <td>News From Planet Mars</td>\n",
       "      <td>300.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>3.000000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2757</th>\n",
       "      <td>Satanic</td>\n",
       "      <td>300.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>3.000000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2321</th>\n",
       "      <td>The Chambermaid</td>\n",
       "      <td>300.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>3.000000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1476</th>\n",
       "      <td>Storage 24</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2013</td>\n",
       "      <td>1.000000e+02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3387 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            title  domestic_gross  \\\n",
       "727                         Marvel's The Avengers     623400000.0   \n",
       "1875                      Avengers: Age of Ultron     459000000.0   \n",
       "3080                                Black Panther     700100000.0   \n",
       "328   Harry Potter and the Deathly Hallows Part 2     381000000.0   \n",
       "2758                     Star Wars: The Last Jedi     620200000.0   \n",
       "...                                           ...             ...   \n",
       "3078                                         2:22           400.0   \n",
       "2756                        News From Planet Mars           300.0   \n",
       "2757                                      Satanic           300.0   \n",
       "2321                              The Chambermaid           300.0   \n",
       "1476                                   Storage 24           100.0   \n",
       "\n",
       "      foreign_gross  year   total_gross  \n",
       "727     895500000.0  2012  1.518900e+09  \n",
       "1875    946400000.0  2015  1.405400e+09  \n",
       "3080    646900000.0  2018  1.347000e+09  \n",
       "328     960500000.0  2011  1.341500e+09  \n",
       "2758    712400000.0  2017  1.332600e+09  \n",
       "...             ...   ...           ...  \n",
       "3078            0.0  2017  4.000000e+02  \n",
       "2756            0.0  2016  3.000000e+02  \n",
       "2757            0.0  2016  3.000000e+02  \n",
       "2321            0.0  2015  3.000000e+02  \n",
       "1476            0.0  2013  1.000000e+02  \n",
       "\n",
       "[3387 rows x 5 columns]"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "moviesbygross = gross.sort_values(by='total_gross', ascending=False) \n",
    "moviesbygross"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>domestic_gross</th>\n",
       "      <th>foreign_gross</th>\n",
       "      <th>year</th>\n",
       "      <th>total_gross</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>727</th>\n",
       "      <td>Marvel's The Avengers</td>\n",
       "      <td>623400000.0</td>\n",
       "      <td>895500000.0</td>\n",
       "      <td>2012</td>\n",
       "      <td>1.518900e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1875</th>\n",
       "      <td>Avengers: Age of Ultron</td>\n",
       "      <td>459000000.0</td>\n",
       "      <td>946400000.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>1.405400e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3080</th>\n",
       "      <td>Black Panther</td>\n",
       "      <td>700100000.0</td>\n",
       "      <td>646900000.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>1.347000e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>Harry Potter and the Deathly Hallows Part 2</td>\n",
       "      <td>381000000.0</td>\n",
       "      <td>960500000.0</td>\n",
       "      <td>2011</td>\n",
       "      <td>1.341500e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2758</th>\n",
       "      <td>Star Wars: The Last Jedi</td>\n",
       "      <td>620200000.0</td>\n",
       "      <td>712400000.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>1.332600e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974</th>\n",
       "      <td>The Himalayas</td>\n",
       "      <td>697000.0</td>\n",
       "      <td>49800000.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>5.049700e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1581</th>\n",
       "      <td>Dragon Ball Z: Battle of Gods</td>\n",
       "      <td>2600000.0</td>\n",
       "      <td>47800000.0</td>\n",
       "      <td>2014</td>\n",
       "      <td>5.040000e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>827</th>\n",
       "      <td>Red Tails</td>\n",
       "      <td>49900000.0</td>\n",
       "      <td>489000.0</td>\n",
       "      <td>2012</td>\n",
       "      <td>5.038900e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>828</th>\n",
       "      <td>Monsters, Inc. (3D)</td>\n",
       "      <td>34000000.0</td>\n",
       "      <td>16200000.0</td>\n",
       "      <td>2012</td>\n",
       "      <td>5.020000e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1233</th>\n",
       "      <td>Delivery Man</td>\n",
       "      <td>30700000.0</td>\n",
       "      <td>19300000.0</td>\n",
       "      <td>2013</td>\n",
       "      <td>5.000000e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>930 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            title  domestic_gross  \\\n",
       "727                         Marvel's The Avengers     623400000.0   \n",
       "1875                      Avengers: Age of Ultron     459000000.0   \n",
       "3080                                Black Panther     700100000.0   \n",
       "328   Harry Potter and the Deathly Hallows Part 2     381000000.0   \n",
       "2758                     Star Wars: The Last Jedi     620200000.0   \n",
       "...                                           ...             ...   \n",
       "1974                                The Himalayas        697000.0   \n",
       "1581                Dragon Ball Z: Battle of Gods       2600000.0   \n",
       "827                                     Red Tails      49900000.0   \n",
       "828                           Monsters, Inc. (3D)      34000000.0   \n",
       "1233                                 Delivery Man      30700000.0   \n",
       "\n",
       "      foreign_gross  year   total_gross  \n",
       "727     895500000.0  2012  1.518900e+09  \n",
       "1875    946400000.0  2015  1.405400e+09  \n",
       "3080    646900000.0  2018  1.347000e+09  \n",
       "328     960500000.0  2011  1.341500e+09  \n",
       "2758    712400000.0  2017  1.332600e+09  \n",
       "...             ...   ...           ...  \n",
       "1974     49800000.0  2015  5.049700e+07  \n",
       "1581     47800000.0  2014  5.040000e+07  \n",
       "827        489000.0  2012  5.038900e+07  \n",
       "828      16200000.0  2012  5.020000e+07  \n",
       "1233     19300000.0  2013  5.000000e+07  \n",
       "\n",
       "[930 rows x 5 columns]"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "moviesbygross = moviesbygross.loc[moviesbygross['year'] >= 2001]\n",
    "moviesbygross = moviesbygross.loc[moviesbygross['total_gross'] >= 50000000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "moviesbygross.to_csv('moviesbygross.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "drop() got multiple values for argument 'axis'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-150-034f214adf89>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mmoviesbygross\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Foreign Gross (in millions)'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmoviesbygross\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'foreign_gross'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m1000000\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mmoviesbygross\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Total Gross (in millions)'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmoviesbygross\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'total_gross'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m1000000\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mmoviesbygross\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'domestic_gross'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'foreign_gross'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'total_gross'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Domestic gross (in millions)'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Foreign gross (in millions)'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: drop() got multiple values for argument 'axis'"
     ]
    }
   ],
   "source": [
    "moviesbygross['Domestic Gross (in millions)'] = moviesbygross['domestic_gross'] / 1000000\n",
    "moviesbygross['Foreign Gross (in millions)'] = moviesbygross['foreign_gross'] / 1000000\n",
    "moviesbygross['Total Gross (in millions)'] = moviesbygross['total_gross'] / 1000000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>year</th>\n",
       "      <th>Domestic Gross (in millions)</th>\n",
       "      <th>Foreign Gross (in millions)</th>\n",
       "      <th>Total Gross (in millions)</th>\n",
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       "      <th>727</th>\n",
       "      <td>Marvel's The Avengers</td>\n",
       "      <td>2012</td>\n",
       "      <td>623.400</td>\n",
       "      <td>895.500</td>\n",
       "      <td>1518.900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1875</th>\n",
       "      <td>Avengers: Age of Ultron</td>\n",
       "      <td>2015</td>\n",
       "      <td>459.000</td>\n",
       "      <td>946.400</td>\n",
       "      <td>1405.400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3080</th>\n",
       "      <td>Black Panther</td>\n",
       "      <td>2018</td>\n",
       "      <td>700.100</td>\n",
       "      <td>646.900</td>\n",
       "      <td>1347.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>Harry Potter and the Deathly Hallows Part 2</td>\n",
       "      <td>2011</td>\n",
       "      <td>381.000</td>\n",
       "      <td>960.500</td>\n",
       "      <td>1341.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2758</th>\n",
       "      <td>Star Wars: The Last Jedi</td>\n",
       "      <td>2017</td>\n",
       "      <td>620.200</td>\n",
       "      <td>712.400</td>\n",
       "      <td>1332.600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974</th>\n",
       "      <td>The Himalayas</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.697</td>\n",
       "      <td>49.800</td>\n",
       "      <td>50.497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1581</th>\n",
       "      <td>Dragon Ball Z: Battle of Gods</td>\n",
       "      <td>2014</td>\n",
       "      <td>2.600</td>\n",
       "      <td>47.800</td>\n",
       "      <td>50.400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>827</th>\n",
       "      <td>Red Tails</td>\n",
       "      <td>2012</td>\n",
       "      <td>49.900</td>\n",
       "      <td>0.489</td>\n",
       "      <td>50.389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>828</th>\n",
       "      <td>Monsters, Inc. (3D)</td>\n",
       "      <td>2012</td>\n",
       "      <td>34.000</td>\n",
       "      <td>16.200</td>\n",
       "      <td>50.200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1233</th>\n",
       "      <td>Delivery Man</td>\n",
       "      <td>2013</td>\n",
       "      <td>30.700</td>\n",
       "      <td>19.300</td>\n",
       "      <td>50.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>930 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            title  year  \\\n",
       "727                         Marvel's The Avengers  2012   \n",
       "1875                      Avengers: Age of Ultron  2015   \n",
       "3080                                Black Panther  2018   \n",
       "328   Harry Potter and the Deathly Hallows Part 2  2011   \n",
       "2758                     Star Wars: The Last Jedi  2017   \n",
       "...                                           ...   ...   \n",
       "1974                                The Himalayas  2015   \n",
       "1581                Dragon Ball Z: Battle of Gods  2014   \n",
       "827                                     Red Tails  2012   \n",
       "828                           Monsters, Inc. (3D)  2012   \n",
       "1233                                 Delivery Man  2013   \n",
       "\n",
       "      Domestic Gross (in millions)  Foreign Gross (in millions)  \\\n",
       "727                        623.400                      895.500   \n",
       "1875                       459.000                      946.400   \n",
       "3080                       700.100                      646.900   \n",
       "328                        381.000                      960.500   \n",
       "2758                       620.200                      712.400   \n",
       "...                            ...                          ...   \n",
       "1974                         0.697                       49.800   \n",
       "1581                         2.600                       47.800   \n",
       "827                         49.900                        0.489   \n",
       "828                         34.000                       16.200   \n",
       "1233                        30.700                       19.300   \n",
       "\n",
       "      Total Gross (in millions)  \n",
       "727                    1518.900  \n",
       "1875                   1405.400  \n",
       "3080                   1347.000  \n",
       "328                    1341.500  \n",
       "2758                   1332.600  \n",
       "...                         ...  \n",
       "1974                     50.497  \n",
       "1581                     50.400  \n",
       "827                      50.389  \n",
       "828                      50.200  \n",
       "1233                     50.000  \n",
       "\n",
       "[930 rows x 5 columns]"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "moviesbygross"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "moviesbygross = moviesbygross.drop('Total gross (in millions)', axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "ename": "UnicodeDecodeError",
     "evalue": "'utf-8' codec can't decode byte 0xa0 in position 4: invalid start byte",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mUnicodeDecodeError\u001b[0m                        Traceback (most recent call last)",
      "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._convert_tokens\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._convert_with_dtype\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._string_convert\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers._string_box_utf8\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mUnicodeDecodeError\u001b[0m: 'utf-8' codec can't decode byte 0xa0 in position 4: invalid start byte",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mUnicodeDecodeError\u001b[0m                        Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-94-1b73170a62e6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mgd4\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/Users/Arsh/Downloads/rt.reviews.tsv.gz'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdelimiter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"\\t\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mgd4\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/learn-env/lib/python3.8/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[1;32m    684\u001b[0m     )\n\u001b[1;32m    685\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 686\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    687\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    688\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/learn-env/lib/python3.8/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    456\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    457\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 458\u001b[0;31m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnrows\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    459\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    460\u001b[0m         \u001b[0mparser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/learn-env/lib/python3.8/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, nrows)\u001b[0m\n\u001b[1;32m   1194\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnrows\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1195\u001b[0m         \u001b[0mnrows\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_validate_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"nrows\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnrows\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1196\u001b[0;31m         \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnrows\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1197\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1198\u001b[0m         \u001b[0;31m# May alter columns / col_dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/learn-env/lib/python3.8/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, nrows)\u001b[0m\n\u001b[1;32m   2153\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnrows\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2154\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2155\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnrows\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2156\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2157\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_first_chunk\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.read\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._read_low_memory\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._read_rows\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._convert_column_data\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._convert_tokens\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._convert_with_dtype\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._string_convert\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers._string_box_utf8\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mUnicodeDecodeError\u001b[0m: 'utf-8' codec can't decode byte 0xa0 in position 4: invalid start byte"
     ]
    }
   ],
   "source": [
    "gd4 = pd.read_csv('/Users/Arsh/Downloads/rt.reviews.tsv.gz', delimiter=\"\\t\")\n",
    "gd4.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1560 entries, 0 to 1559\n",
      "Data columns (total 12 columns):\n",
      " #   Column        Non-Null Count  Dtype \n",
      "---  ------        --------------  ----- \n",
      " 0   id            1560 non-null   int64 \n",
      " 1   synopsis      1498 non-null   object\n",
      " 2   rating        1557 non-null   object\n",
      " 3   genre         1552 non-null   object\n",
      " 4   director      1361 non-null   object\n",
      " 5   writer        1111 non-null   object\n",
      " 6   theater_date  1201 non-null   object\n",
      " 7   dvd_date      1201 non-null   object\n",
      " 8   currency      340 non-null    object\n",
      " 9   box_office    340 non-null    object\n",
      " 10  runtime       1530 non-null   object\n",
      " 11  studio        494 non-null    object\n",
      "dtypes: int64(1), object(11)\n",
      "memory usage: 146.4+ KB\n"
     ]
    }
   ],
   "source": [
    "summary = pd.read_csv('/Users/Arsh/Downloads/rt.movie_info.tsv.gz', delimiter=\"\\t\")\n",
    "summary.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1560, 12)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>synopsis</th>\n",
       "      <th>rating</th>\n",
       "      <th>genre</th>\n",
       "      <th>director</th>\n",
       "      <th>writer</th>\n",
       "      <th>theater_date</th>\n",
       "      <th>dvd_date</th>\n",
       "      <th>currency</th>\n",
       "      <th>box_office</th>\n",
       "      <th>runtime</th>\n",
       "      <th>studio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>This gritty, fast-paced, and innovative police...</td>\n",
       "      <td>R</td>\n",
       "      <td>Action and Adventure|Classics|Drama</td>\n",
       "      <td>William Friedkin</td>\n",
       "      <td>Ernest Tidyman</td>\n",
       "      <td>Oct 9, 1971</td>\n",
       "      <td>Sep 25, 2001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>104 minutes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>New York City, not-too-distant-future: Eric Pa...</td>\n",
       "      <td>R</td>\n",
       "      <td>Drama|Science Fiction and Fantasy</td>\n",
       "      <td>David Cronenberg</td>\n",
       "      <td>David Cronenberg|Don DeLillo</td>\n",
       "      <td>Aug 17, 2012</td>\n",
       "      <td>Jan 1, 2013</td>\n",
       "      <td>$</td>\n",
       "      <td>600,000</td>\n",
       "      <td>108 minutes</td>\n",
       "      <td>Entertainment One</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>Illeana Douglas delivers a superb performance ...</td>\n",
       "      <td>R</td>\n",
       "      <td>Drama|Musical and Performing Arts</td>\n",
       "      <td>Allison Anders</td>\n",
       "      <td>Allison Anders</td>\n",
       "      <td>Sep 13, 1996</td>\n",
       "      <td>Apr 18, 2000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>116 minutes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>Michael Douglas runs afoul of a treacherous su...</td>\n",
       "      <td>R</td>\n",
       "      <td>Drama|Mystery and Suspense</td>\n",
       "      <td>Barry Levinson</td>\n",
       "      <td>Paul Attanasio|Michael Crichton</td>\n",
       "      <td>Dec 9, 1994</td>\n",
       "      <td>Aug 27, 1997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>128 minutes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NR</td>\n",
       "      <td>Drama|Romance</td>\n",
       "      <td>Rodney Bennett</td>\n",
       "      <td>Giles Cooper</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200 minutes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id                                           synopsis rating  \\\n",
       "0   1  This gritty, fast-paced, and innovative police...      R   \n",
       "1   3  New York City, not-too-distant-future: Eric Pa...      R   \n",
       "2   5  Illeana Douglas delivers a superb performance ...      R   \n",
       "3   6  Michael Douglas runs afoul of a treacherous su...      R   \n",
       "4   7                                                NaN     NR   \n",
       "\n",
       "                                 genre          director  \\\n",
       "0  Action and Adventure|Classics|Drama  William Friedkin   \n",
       "1    Drama|Science Fiction and Fantasy  David Cronenberg   \n",
       "2    Drama|Musical and Performing Arts    Allison Anders   \n",
       "3           Drama|Mystery and Suspense    Barry Levinson   \n",
       "4                        Drama|Romance    Rodney Bennett   \n",
       "\n",
       "                            writer  theater_date      dvd_date currency  \\\n",
       "0                   Ernest Tidyman   Oct 9, 1971  Sep 25, 2001      NaN   \n",
       "1     David Cronenberg|Don DeLillo  Aug 17, 2012   Jan 1, 2013        $   \n",
       "2                   Allison Anders  Sep 13, 1996  Apr 18, 2000      NaN   \n",
       "3  Paul Attanasio|Michael Crichton   Dec 9, 1994  Aug 27, 1997      NaN   \n",
       "4                     Giles Cooper           NaN           NaN      NaN   \n",
       "\n",
       "  box_office      runtime             studio  \n",
       "0        NaN  104 minutes                NaN  \n",
       "1    600,000  108 minutes  Entertainment One  \n",
       "2        NaN  116 minutes                NaN  \n",
       "3        NaN  128 minutes                NaN  \n",
       "4        NaN  200 minutes                NaN  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id              1560\n",
       "synopsis        1498\n",
       "rating          1557\n",
       "genre           1552\n",
       "director        1361\n",
       "writer          1111\n",
       "theater_date    1201\n",
       "dvd_date        1201\n",
       "currency         340\n",
       "box_office       340\n",
       "runtime         1530\n",
       "studio           494\n",
       "dtype: int64"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary.notna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "newsummary = summary.loc[m['year'] >= 2000]\n",
    "newsummary[]\n",
    "\n",
    "summary.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gd6 = pd.read_csv('/Users/Arsh/Downloads/tn.movie_budgets.csv.gz')\n",
    "gd6.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5782 entries, 0 to 5781\n",
      "Data columns (total 6 columns):\n",
      " #   Column             Non-Null Count  Dtype \n",
      "---  ------             --------------  ----- \n",
      " 0   id                 5782 non-null   int64 \n",
      " 1   release_date       5782 non-null   object\n",
      " 2   movie              5782 non-null   object\n",
      " 3   production_budget  5782 non-null   object\n",
      " 4   domestic_gross     5782 non-null   object\n",
      " 5   worldwide_gross    5782 non-null   object\n",
      "dtypes: int64(1), object(5)\n",
      "memory usage: 271.2+ KB\n"
     ]
    }
   ],
   "source": [
    "budgets = pd.read_csv('/Users/Arsh/Downloads/tn.movie_budgets.csv.gz')\n",
    "budgets.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>release_date</th>\n",
       "      <th>movie</th>\n",
       "      <th>production_budget</th>\n",
       "      <th>domestic_gross</th>\n",
       "      <th>worldwide_gross</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Dec 18, 2009</td>\n",
       "      <td>Avatar</td>\n",
       "      <td>$425,000,000</td>\n",
       "      <td>$760,507,625</td>\n",
       "      <td>$2,776,345,279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>May 20, 2011</td>\n",
       "      <td>Pirates of the Caribbean: On Stranger Tides</td>\n",
       "      <td>$410,600,000</td>\n",
       "      <td>$241,063,875</td>\n",
       "      <td>$1,045,663,875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Jun 7, 2019</td>\n",
       "      <td>Dark Phoenix</td>\n",
       "      <td>$350,000,000</td>\n",
       "      <td>$42,762,350</td>\n",
       "      <td>$149,762,350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>May 1, 2015</td>\n",
       "      <td>Avengers: Age of Ultron</td>\n",
       "      <td>$330,600,000</td>\n",
       "      <td>$459,005,868</td>\n",
       "      <td>$1,403,013,963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Dec 15, 2017</td>\n",
       "      <td>Star Wars Ep. VIII: The Last Jedi</td>\n",
       "      <td>$317,000,000</td>\n",
       "      <td>$620,181,382</td>\n",
       "      <td>$1,316,721,747</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  release_date                                        movie  \\\n",
       "0   1  Dec 18, 2009                                       Avatar   \n",
       "1   2  May 20, 2011  Pirates of the Caribbean: On Stranger Tides   \n",
       "2   3   Jun 7, 2019                                 Dark Phoenix   \n",
       "3   4   May 1, 2015                      Avengers: Age of Ultron   \n",
       "4   5  Dec 15, 2017            Star Wars Ep. VIII: The Last Jedi   \n",
       "\n",
       "  production_budget domestic_gross worldwide_gross  \n",
       "0      $425,000,000   $760,507,625  $2,776,345,279  \n",
       "1      $410,600,000   $241,063,875  $1,045,663,875  \n",
       "2      $350,000,000    $42,762,350    $149,762,350  \n",
       "3      $330,600,000   $459,005,868  $1,403,013,963  \n",
       "4      $317,000,000   $620,181,382  $1,316,721,747  "
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "budgets.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                   0\n",
       "release_date         0\n",
       "movie                0\n",
       "production_budget    0\n",
       "domestic_gross       0\n",
       "worldwide_gross      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "budgets.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>release_date</th>\n",
       "      <th>movie</th>\n",
       "      <th>production_budget</th>\n",
       "      <th>domestic_gross</th>\n",
       "      <th>worldwide_gross</th>\n",
       "    </tr>\n",
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       "      <td>Avatar</td>\n",
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       "      <td>$760,507,625</td>\n",
       "      <td>$2,776,345,279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>May 20, 2011</td>\n",
       "      <td>Pirates of the Caribbean: On Stranger Tides</td>\n",
       "      <td>410600000</td>\n",
       "      <td>$241,063,875</td>\n",
       "      <td>$1,045,663,875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Jun 7, 2019</td>\n",
       "      <td>Dark Phoenix</td>\n",
       "      <td>350000000</td>\n",
       "      <td>$42,762,350</td>\n",
       "      <td>$149,762,350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>May 1, 2015</td>\n",
       "      <td>Avengers: Age of Ultron</td>\n",
       "      <td>330600000</td>\n",
       "      <td>$459,005,868</td>\n",
       "      <td>$1,403,013,963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Dec 15, 2017</td>\n",
       "      <td>Star Wars Ep. VIII: The Last Jedi</td>\n",
       "      <td>317000000</td>\n",
       "      <td>$620,181,382</td>\n",
       "      <td>$1,316,721,747</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  release_date                                        movie  \\\n",
       "0   1  Dec 18, 2009                                       Avatar   \n",
       "1   2  May 20, 2011  Pirates of the Caribbean: On Stranger Tides   \n",
       "2   3   Jun 7, 2019                                 Dark Phoenix   \n",
       "3   4   May 1, 2015                      Avengers: Age of Ultron   \n",
       "4   5  Dec 15, 2017            Star Wars Ep. VIII: The Last Jedi   \n",
       "\n",
       "  production_budget domestic_gross worldwide_gross  \n",
       "0         425000000   $760,507,625  $2,776,345,279  \n",
       "1         410600000   $241,063,875  $1,045,663,875  \n",
       "2         350000000    $42,762,350    $149,762,350  \n",
       "3         330600000   $459,005,868  $1,403,013,963  \n",
       "4         317000000   $620,181,382  $1,316,721,747  "
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "budgets['production_budget'] = budgets['production_budget'].str.replace(',', '')\n",
    "budgets['production_budget'] = budgets['production_budget'].str.replace('$', '')\n",
    "budgets.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>release_date</th>\n",
       "      <th>movie</th>\n",
       "      <th>production_budget</th>\n",
       "      <th>domestic_gross</th>\n",
       "      <th>worldwide_gross</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Dec 18, 2009</td>\n",
       "      <td>Avatar</td>\n",
       "      <td>425000000</td>\n",
       "      <td>760507625</td>\n",
       "      <td>2776345279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>May 20, 2011</td>\n",
       "      <td>Pirates of the Caribbean: On Stranger Tides</td>\n",
       "      <td>410600000</td>\n",
       "      <td>241063875</td>\n",
       "      <td>1045663875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Jun 7, 2019</td>\n",
       "      <td>Dark Phoenix</td>\n",
       "      <td>350000000</td>\n",
       "      <td>42762350</td>\n",
       "      <td>149762350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>May 1, 2015</td>\n",
       "      <td>Avengers: Age of Ultron</td>\n",
       "      <td>330600000</td>\n",
       "      <td>459005868</td>\n",
       "      <td>1403013963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Dec 15, 2017</td>\n",
       "      <td>Star Wars Ep. VIII: The Last Jedi</td>\n",
       "      <td>317000000</td>\n",
       "      <td>620181382</td>\n",
       "      <td>1316721747</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  release_date                                        movie  \\\n",
       "0   1  Dec 18, 2009                                       Avatar   \n",
       "1   2  May 20, 2011  Pirates of the Caribbean: On Stranger Tides   \n",
       "2   3   Jun 7, 2019                                 Dark Phoenix   \n",
       "3   4   May 1, 2015                      Avengers: Age of Ultron   \n",
       "4   5  Dec 15, 2017            Star Wars Ep. VIII: The Last Jedi   \n",
       "\n",
       "  production_budget domestic_gross worldwide_gross  \n",
       "0         425000000      760507625      2776345279  \n",
       "1         410600000      241063875      1045663875  \n",
       "2         350000000       42762350       149762350  \n",
       "3         330600000      459005868      1403013963  \n",
       "4         317000000      620181382      1316721747  "
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "budgets['worldwide_gross'] = budgets['worldwide_gross'].str.replace(',', '')\n",
    "budgets['worldwide_gross'] = budgets['worldwide_gross'].str.replace('$', '')\n",
    "budgets['domestic_gross'] = budgets['domestic_gross'].str.replace(',', '')\n",
    "budgets['domestic_gross'] = budgets['domestic_gross'].str.replace('$', '')\n",
    "budgets.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'str'>\n"
     ]
    }
   ],
   "source": [
    "print(type(budgets['release_date'][0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [],
   "source": [
    "budgets['release_date'] = budgets['release_date'].str.replace(',', '')\n",
    "budgets['release_date'] = budgets['release_date'].str.replace('Jan', 'January')\n",
    "budgets['release_date'] = budgets['release_date'].str.replace('Feb', 'February')\n",
    "budgets['release_date'] = budgets['release_date'].str.replace('Mar', 'March')\n",
    "budgets['release_date'] = budgets['release_date'].str.replace('Apr', 'April')\n",
    "budgets['release_date'] = budgets['release_date'].str.replace('Jun', 'June')\n",
    "budgets['release_date'] = budgets['release_date'].str.replace('Jul', 'July')\n",
    "budgets['release_date'] = budgets['release_date'].str.replace('Aug', 'August')\n",
    "budgets['release_date'] = budgets['release_date'].str.replace('Sep', 'September')\n",
    "budgets['release_date'] = budgets['release_date'].str.replace('Oct', 'October')\n",
    "budgets['release_date'] = budgets['release_date'].str.replace('Nov', 'November')\n",
    "budgets['release_date'] = budgets['release_date'].str.replace('Dec', 'December')\n",
    "budgets['release_date'] = pd.to_datetime(budgets['release_date'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "time data 'December 18 2009' does not match format '%m%d%Y' (match)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m~/opt/anaconda3/envs/learn-env/lib/python3.8/site-packages/pandas/core/tools/datetimes.py\u001b[0m in \u001b[0;36m_convert_listlike_datetimes\u001b[0;34m(arg, format, name, tz, unit, errors, infer_datetime_format, dayfirst, yearfirst, exact)\u001b[0m\n\u001b[1;32m    449\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 450\u001b[0;31m                 \u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtz\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconversion\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatetime_to_datetime64\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    451\u001b[0m                 \u001b[0mdta\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDatetimeArray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtz_to_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtz\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/tslibs/conversion.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.conversion.datetime_to_datetime64\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: Unrecognized value type: <class 'str'>",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-183-9bd5544a970e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m budgets['release_date'] = pd.to_datetime(\n\u001b[0m\u001b[1;32m      2\u001b[0m                           \u001b[0mbudgets\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'release_date'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m                           format='%m%d%Y')\n",
      "\u001b[0;32m~/opt/anaconda3/envs/learn-env/lib/python3.8/site-packages/pandas/core/tools/datetimes.py\u001b[0m in \u001b[0;36mto_datetime\u001b[0;34m(arg, errors, dayfirst, yearfirst, utc, format, exact, unit, infer_datetime_format, origin, cache)\u001b[0m\n\u001b[1;32m    801\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcache_array\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    802\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 803\u001b[0;31m             \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconvert_listlike\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    804\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    805\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mABCDataFrame\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mabc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMutableMapping\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/learn-env/lib/python3.8/site-packages/pandas/core/tools/datetimes.py\u001b[0m in \u001b[0;36m_convert_listlike_datetimes\u001b[0;34m(arg, format, name, tz, unit, errors, infer_datetime_format, dayfirst, yearfirst, exact)\u001b[0m\n\u001b[1;32m    452\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mDatetimeIndex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_simple_new\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdta\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    453\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mValueError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 454\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    455\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    456\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/learn-env/lib/python3.8/site-packages/pandas/core/tools/datetimes.py\u001b[0m in \u001b[0;36m_convert_listlike_datetimes\u001b[0;34m(arg, format, name, tz, unit, errors, infer_datetime_format, dayfirst, yearfirst, exact)\u001b[0m\n\u001b[1;32m    415\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    416\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 417\u001b[0;31m                     result, timezones = array_strptime(\n\u001b[0m\u001b[1;32m    418\u001b[0m                         \u001b[0marg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexact\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mexact\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    419\u001b[0m                     )\n",
      "\u001b[0;32mpandas/_libs/tslibs/strptime.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.strptime.array_strptime\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: time data 'December 18 2009' does not match format '%m%d%Y' (match)"
     ]
    }
   ],
   "source": [
    "budgets['release_date'] = pd.to_datetime(\n",
    "                          budgets['release_date'],\n",
    "                          format='%m%d%Y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "budgets['release_date'] = pd.to_datetime(budgets['release_date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>release_date</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>2011-05-20</td>\n",
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       "      <th>2</th>\n",
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       "      <td>350000000</td>\n",
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       "      <td>149762350</td>\n",
       "      <td>350000000</td>\n",
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       "      <th>3</th>\n",
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       "      <td>Avengers: Age of Ultron</td>\n",
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       "      <td>1403013963</td>\n",
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       "      <th>4</th>\n",
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       "      <td>2017-12-15</td>\n",
       "      <td>Star Wars Ep. VIII: The Last Jedi</td>\n",
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       "      <td>620181382</td>\n",
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       "      <td>2005-07-13</td>\n",
       "      <td>Return to the Land of Wonders</td>\n",
       "      <td>5000</td>\n",
       "      <td>1338</td>\n",
       "      <td>1338</td>\n",
       "      <td>5000</td>\n",
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       "    <tr>\n",
       "      <th>5780</th>\n",
       "      <td>81</td>\n",
       "      <td>2015-09-29</td>\n",
       "      <td>A Plague So Pleasant</td>\n",
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       "      <td>181041</td>\n",
       "      <td>1100</td>\n",
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       "<p>5782 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      id release_date                                        movie  \\\n",
       "0      1   2009-12-18                                       Avatar   \n",
       "1      2   2011-05-20  Pirates of the Caribbean: On Stranger Tides   \n",
       "2      3   2019-06-07                                 Dark Phoenix   \n",
       "3      4   2015-05-01                      Avengers: Age of Ultron   \n",
       "4      5   2017-12-15            Star Wars Ep. VIII: The Last Jedi   \n",
       "...   ..          ...                                          ...   \n",
       "5777  78   2018-12-31                                       Red 11   \n",
       "5778  79   1999-04-02                                    Following   \n",
       "5779  80   2005-07-13                Return to the Land of Wonders   \n",
       "5780  81   2015-09-29                         A Plague So Pleasant   \n",
       "5781  82   2005-08-05                            My Date With Drew   \n",
       "\n",
       "     production_budget domestic_gross worldwide_gross    release  \n",
       "0            425000000      760507625      2776345279  425000000  \n",
       "1            410600000      241063875      1045663875  410600000  \n",
       "2            350000000       42762350       149762350  350000000  \n",
       "3            330600000      459005868      1403013963  330600000  \n",
       "4            317000000      620181382      1316721747  317000000  \n",
       "...                ...            ...             ...        ...  \n",
       "5777              7000              0               0       7000  \n",
       "5778              6000          48482          240495       6000  \n",
       "5779              5000           1338            1338       5000  \n",
       "5780              1400              0               0       1400  \n",
       "5781              1100         181041          181041       1100  \n",
       "\n",
       "[5782 rows x 7 columns]"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "budgets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "budgets = budgets.loc[budgets['release_date'] >= '2001-01-01']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Return to the Land of Wonders</td>\n",
       "      <td>5000</td>\n",
       "      <td>1338</td>\n",
       "      <td>1338</td>\n",
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       "    <tr>\n",
       "      <th>5780</th>\n",
       "      <td>81</td>\n",
       "      <td>2015-09-29</td>\n",
       "      <td>A Plague So Pleasant</td>\n",
       "      <td>1400</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>5781</th>\n",
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       "      <td>2005-08-05</td>\n",
       "      <td>My Date With Drew</td>\n",
       "      <td>1100</td>\n",
       "      <td>181041</td>\n",
       "      <td>181041</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4198 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      id release_date                                        movie  \\\n",
       "0      1   2009-12-18                                       Avatar   \n",
       "1      2   2011-05-20  Pirates of the Caribbean: On Stranger Tides   \n",
       "2      3   2019-06-07                                 Dark Phoenix   \n",
       "3      4   2015-05-01                      Avengers: Age of Ultron   \n",
       "4      5   2017-12-15            Star Wars Ep. VIII: The Last Jedi   \n",
       "...   ..          ...                                          ...   \n",
       "5776  77   2004-12-31                              The Mongol King   \n",
       "5777  78   2018-12-31                                       Red 11   \n",
       "5779  80   2005-07-13                Return to the Land of Wonders   \n",
       "5780  81   2015-09-29                         A Plague So Pleasant   \n",
       "5781  82   2005-08-05                            My Date With Drew   \n",
       "\n",
       "     production_budget domestic_gross worldwide_gross  \n",
       "0            425000000      760507625      2776345279  \n",
       "1            410600000      241063875      1045663875  \n",
       "2            350000000       42762350       149762350  \n",
       "3            330600000      459005868      1403013963  \n",
       "4            317000000      620181382      1316721747  \n",
       "...                ...            ...             ...  \n",
       "5776              7000            900             900  \n",
       "5777              7000              0               0  \n",
       "5779              5000           1338            1338  \n",
       "5780              1400              0               0  \n",
       "5781              1100         181041          181041  \n",
       "\n",
       "[4198 rows x 6 columns]"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "budgets = budgets.drop('release', axis=1)\n",
    "budgets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'>=' not supported between instances of 'str' and 'int'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-200-18ae601d59d7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mbudgets\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbudgets\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbudgets\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'worldwide_gross'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0;36m1000000\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/opt/anaconda3/envs/learn-env/lib/python3.8/site-packages/pandas/core/ops/common.py\u001b[0m in \u001b[0;36mnew_method\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m     63\u001b[0m         \u001b[0mother\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mitem_from_zerodim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     64\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 65\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     66\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mnew_method\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/learn-env/lib/python3.8/site-packages/pandas/core/ops/__init__.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m    368\u001b[0m         \u001b[0mrvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mextract_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mextract_numpy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    369\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 370\u001b[0;31m         \u001b[0mres_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcomparison_op\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    371\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    372\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_construct_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres_values\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mres_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/learn-env/lib/python3.8/site-packages/pandas/core/ops/array_ops.py\u001b[0m in \u001b[0;36mcomparison_op\u001b[0;34m(left, right, op)\u001b[0m\n\u001b[1;32m    242\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    243\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mis_object_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 244\u001b[0;31m         \u001b[0mres_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcomp_method_OBJECT_ARRAY\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    245\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    246\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/learn-env/lib/python3.8/site-packages/pandas/core/ops/array_ops.py\u001b[0m in \u001b[0;36mcomp_method_OBJECT_ARRAY\u001b[0;34m(op, x, y)\u001b[0m\n\u001b[1;32m     54\u001b[0m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlibops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvec_compare\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     55\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 56\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlibops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscalar_compare\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     57\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     58\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/ops.pyx\u001b[0m in \u001b[0;36mpandas._libs.ops.scalar_compare\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: '>=' not supported between instances of 'str' and 'int'"
     ]
    }
   ],
   "source": [
    "budgets = budgets.loc[budgets['worldwide_gross'] >= 1000000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [],
   "source": [
    "budgets['production_budget']= budgets['production_budget'].astype('int64')\n",
    "budgets['domestic_gross'] = budgets['domestic_gross'].astype('int64')\n",
    "budgets['worldwide_gross'] = budgets['worldwide_gross'].astype('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                            int64\n",
       "release_date         datetime64[ns]\n",
       "movie                        object\n",
       "production_budget             int64\n",
       "domestic_gross                int64\n",
       "worldwide_gross               int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "budgets.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [],
   "source": [
    "budgets = budgets.loc[budgets['worldwide_gross'] > 1000000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>release_date</th>\n",
       "      <th>movie</th>\n",
       "      <th>production_budget</th>\n",
       "      <th>domestic_gross</th>\n",
       "      <th>worldwide_gross</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2009-12-18</td>\n",
       "      <td>Avatar</td>\n",
       "      <td>425000000</td>\n",
       "      <td>760507625</td>\n",
       "      <td>2776345279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-05-20</td>\n",
       "      <td>Pirates of the Caribbean: On Stranger Tides</td>\n",
       "      <td>410600000</td>\n",
       "      <td>241063875</td>\n",
       "      <td>1045663875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2019-06-07</td>\n",
       "      <td>Dark Phoenix</td>\n",
       "      <td>350000000</td>\n",
       "      <td>42762350</td>\n",
       "      <td>149762350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2015-05-01</td>\n",
       "      <td>Avengers: Age of Ultron</td>\n",
       "      <td>330600000</td>\n",
       "      <td>459005868</td>\n",
       "      <td>1403013963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2017-12-15</td>\n",
       "      <td>Star Wars Ep. VIII: The Last Jedi</td>\n",
       "      <td>317000000</td>\n",
       "      <td>620181382</td>\n",
       "      <td>1316721747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5679</th>\n",
       "      <td>80</td>\n",
       "      <td>2015-07-10</td>\n",
       "      <td>The Gallows</td>\n",
       "      <td>100000</td>\n",
       "      <td>22764410</td>\n",
       "      <td>41656474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5680</th>\n",
       "      <td>81</td>\n",
       "      <td>2006-09-29</td>\n",
       "      <td>Facing the Giants</td>\n",
       "      <td>100000</td>\n",
       "      <td>10178331</td>\n",
       "      <td>10243159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5685</th>\n",
       "      <td>86</td>\n",
       "      <td>2017-07-07</td>\n",
       "      <td>A Ghost Story</td>\n",
       "      <td>100000</td>\n",
       "      <td>1594798</td>\n",
       "      <td>2769782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5709</th>\n",
       "      <td>10</td>\n",
       "      <td>2004-05-07</td>\n",
       "      <td>Super Size Me</td>\n",
       "      <td>65000</td>\n",
       "      <td>11529368</td>\n",
       "      <td>22233808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5716</th>\n",
       "      <td>17</td>\n",
       "      <td>2001-03-16</td>\n",
       "      <td>Gabriela</td>\n",
       "      <td>50000</td>\n",
       "      <td>2335352</td>\n",
       "      <td>2335352</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3328 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      id release_date                                        movie  \\\n",
       "0      1   2009-12-18                                       Avatar   \n",
       "1      2   2011-05-20  Pirates of the Caribbean: On Stranger Tides   \n",
       "2      3   2019-06-07                                 Dark Phoenix   \n",
       "3      4   2015-05-01                      Avengers: Age of Ultron   \n",
       "4      5   2017-12-15            Star Wars Ep. VIII: The Last Jedi   \n",
       "...   ..          ...                                          ...   \n",
       "5679  80   2015-07-10                                  The Gallows   \n",
       "5680  81   2006-09-29                            Facing the Giants   \n",
       "5685  86   2017-07-07                                A Ghost Story   \n",
       "5709  10   2004-05-07                                Super Size Me   \n",
       "5716  17   2001-03-16                                     Gabriela   \n",
       "\n",
       "      production_budget  domestic_gross  worldwide_gross  \n",
       "0             425000000       760507625       2776345279  \n",
       "1             410600000       241063875       1045663875  \n",
       "2             350000000        42762350        149762350  \n",
       "3             330600000       459005868       1403013963  \n",
       "4             317000000       620181382       1316721747  \n",
       "...                 ...             ...              ...  \n",
       "5679             100000        22764410         41656474  \n",
       "5680             100000        10178331         10243159  \n",
       "5685             100000         1594798          2769782  \n",
       "5709              65000        11529368         22233808  \n",
       "5716              50000         2335352          2335352  \n",
       "\n",
       "[3328 rows x 6 columns]"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "budgets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-216-7e0eebdc9a98>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  budgets['profit'] = budgets['worldwide_gross'] - budgets['production_budget']\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "      <th>id</th>\n",
       "      <th>release_date</th>\n",
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       "      <th>domestic_gross</th>\n",
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       "      <th>profit</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2009-12-18</td>\n",
       "      <td>Avatar</td>\n",
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       "      <td>2776345279</td>\n",
       "      <td>2351345279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-05-20</td>\n",
       "      <td>Pirates of the Caribbean: On Stranger Tides</td>\n",
       "      <td>410600000</td>\n",
       "      <td>241063875</td>\n",
       "      <td>1045663875</td>\n",
       "      <td>635063875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2019-06-07</td>\n",
       "      <td>Dark Phoenix</td>\n",
       "      <td>350000000</td>\n",
       "      <td>42762350</td>\n",
       "      <td>149762350</td>\n",
       "      <td>-200237650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2015-05-01</td>\n",
       "      <td>Avengers: Age of Ultron</td>\n",
       "      <td>330600000</td>\n",
       "      <td>459005868</td>\n",
       "      <td>1403013963</td>\n",
       "      <td>1072413963</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2017-12-15</td>\n",
       "      <td>Star Wars Ep. VIII: The Last Jedi</td>\n",
       "      <td>317000000</td>\n",
       "      <td>620181382</td>\n",
       "      <td>1316721747</td>\n",
       "      <td>999721747</td>\n",
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       "    <tr>\n",
       "      <th>5679</th>\n",
       "      <td>80</td>\n",
       "      <td>2015-07-10</td>\n",
       "      <td>The Gallows</td>\n",
       "      <td>100000</td>\n",
       "      <td>22764410</td>\n",
       "      <td>41656474</td>\n",
       "      <td>41556474</td>\n",
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       "    <tr>\n",
       "      <th>5680</th>\n",
       "      <td>81</td>\n",
       "      <td>2006-09-29</td>\n",
       "      <td>Facing the Giants</td>\n",
       "      <td>100000</td>\n",
       "      <td>10178331</td>\n",
       "      <td>10243159</td>\n",
       "      <td>10143159</td>\n",
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       "      <th>5685</th>\n",
       "      <td>86</td>\n",
       "      <td>2017-07-07</td>\n",
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       "      <td>100000</td>\n",
       "      <td>1594798</td>\n",
       "      <td>2769782</td>\n",
       "      <td>2669782</td>\n",
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       "      <th>5709</th>\n",
       "      <td>10</td>\n",
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       "      <td>22233808</td>\n",
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       "      <td>2335352</td>\n",
       "      <td>2285352</td>\n",
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       "<p>3328 rows × 7 columns</p>\n",
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      ],
      "text/plain": [
       "      id release_date                                        movie  \\\n",
       "0      1   2009-12-18                                       Avatar   \n",
       "1      2   2011-05-20  Pirates of the Caribbean: On Stranger Tides   \n",
       "2      3   2019-06-07                                 Dark Phoenix   \n",
       "3      4   2015-05-01                      Avengers: Age of Ultron   \n",
       "4      5   2017-12-15            Star Wars Ep. VIII: The Last Jedi   \n",
       "...   ..          ...                                          ...   \n",
       "5679  80   2015-07-10                                  The Gallows   \n",
       "5680  81   2006-09-29                            Facing the Giants   \n",
       "5685  86   2017-07-07                                A Ghost Story   \n",
       "5709  10   2004-05-07                                Super Size Me   \n",
       "5716  17   2001-03-16                                     Gabriela   \n",
       "\n",
       "      production_budget  domestic_gross  worldwide_gross      profit  \n",
       "0             425000000       760507625       2776345279  2351345279  \n",
       "1             410600000       241063875       1045663875   635063875  \n",
       "2             350000000        42762350        149762350  -200237650  \n",
       "3             330600000       459005868       1403013963  1072413963  \n",
       "4             317000000       620181382       1316721747   999721747  \n",
       "...                 ...             ...              ...         ...  \n",
       "5679             100000        22764410         41656474    41556474  \n",
       "5680             100000        10178331         10243159    10143159  \n",
       "5685             100000         1594798          2769782     2669782  \n",
       "5709              65000        11529368         22233808    22168808  \n",
       "5716              50000         2335352          2335352     2285352  \n",
       "\n",
       "[3328 rows x 7 columns]"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "budgets['profit'] = budgets['worldwide_gross'] - budgets['production_budget']\n",
    "budgets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-217-c66d7af56fef>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  budgets['profit_margin'] = 100 * ((budgets['worldwide_gross'] - budgets['production_budget']) / budgets['worldwide_gross'])\n"
     ]
    },
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      ],
      "text/plain": [
       "      id release_date                                        movie  \\\n",
       "0      1   2009-12-18                                       Avatar   \n",
       "1      2   2011-05-20  Pirates of the Caribbean: On Stranger Tides   \n",
       "2      3   2019-06-07                                 Dark Phoenix   \n",
       "3      4   2015-05-01                      Avengers: Age of Ultron   \n",
       "4      5   2017-12-15            Star Wars Ep. VIII: The Last Jedi   \n",
       "...   ..          ...                                          ...   \n",
       "5679  80   2015-07-10                                  The Gallows   \n",
       "5680  81   2006-09-29                            Facing the Giants   \n",
       "5685  86   2017-07-07                                A Ghost Story   \n",
       "5709  10   2004-05-07                                Super Size Me   \n",
       "5716  17   2001-03-16                                     Gabriela   \n",
       "\n",
       "      production_budget  domestic_gross  worldwide_gross      profit  \\\n",
       "0             425000000       760507625       2776345279  2351345279   \n",
       "1             410600000       241063875       1045663875   635063875   \n",
       "2             350000000        42762350        149762350  -200237650   \n",
       "3             330600000       459005868       1403013963  1072413963   \n",
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       "5680             100000        10178331         10243159    10143159   \n",
       "5685             100000         1594798          2769782     2669782   \n",
       "5709              65000        11529368         22233808    22168808   \n",
       "5716              50000         2335352          2335352     2285352   \n",
       "\n",
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       "1         60.733080  \n",
       "2       -133.703598  \n",
       "3         76.436443  \n",
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       "\n",
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     "metadata": {},
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    }
   ],
   "source": [
    "budgets['profit_margin'] = 100 * ((budgets['worldwide_gross'] - budgets['production_budget']) / budgets['worldwide_gross'])\n",
    "budgets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [],
   "source": [
    "budgets = budgets.sort_values(by='profit_margin', ascending=False) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
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       "      <th>5211</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>5062</th>\n",
       "      <td>63</td>\n",
       "      <td>2011-04-01</td>\n",
       "      <td>Insidious</td>\n",
       "      <td>1500000</td>\n",
       "      <td>54009150</td>\n",
       "      <td>99870886</td>\n",
       "      <td>98370886</td>\n",
       "      <td>98.498061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5213</th>\n",
       "      <td>14</td>\n",
       "      <td>2015-04-17</td>\n",
       "      <td>Unfriended</td>\n",
       "      <td>1000000</td>\n",
       "      <td>32789645</td>\n",
       "      <td>64364198</td>\n",
       "      <td>63364198</td>\n",
       "      <td>98.446341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4664</th>\n",
       "      <td>65</td>\n",
       "      <td>2010-10-20</td>\n",
       "      <td>Paranormal Activity 2</td>\n",
       "      <td>3000000</td>\n",
       "      <td>84752907</td>\n",
       "      <td>177512032</td>\n",
       "      <td>174512032</td>\n",
       "      <td>98.309974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4249</th>\n",
       "      <td>50</td>\n",
       "      <td>2017-01-20</td>\n",
       "      <td>Split</td>\n",
       "      <td>5000000</td>\n",
       "      <td>138141585</td>\n",
       "      <td>278964806</td>\n",
       "      <td>273964806</td>\n",
       "      <td>98.207659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5189</th>\n",
       "      <td>90</td>\n",
       "      <td>2014-03-21</td>\n",
       "      <td>Godâs Not Dead</td>\n",
       "      <td>1150000</td>\n",
       "      <td>60755732</td>\n",
       "      <td>63777092</td>\n",
       "      <td>62627092</td>\n",
       "      <td>98.196845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5215</th>\n",
       "      <td>16</td>\n",
       "      <td>2006-05-24</td>\n",
       "      <td>An Inconvenient Truth</td>\n",
       "      <td>1000000</td>\n",
       "      <td>24146161</td>\n",
       "      <td>53365925</td>\n",
       "      <td>52365925</td>\n",
       "      <td>98.126145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4248</th>\n",
       "      <td>49</td>\n",
       "      <td>2017-02-24</td>\n",
       "      <td>Get Out</td>\n",
       "      <td>5000000</td>\n",
       "      <td>176040665</td>\n",
       "      <td>255367951</td>\n",
       "      <td>250367951</td>\n",
       "      <td>98.042041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5716</th>\n",
       "      <td>17</td>\n",
       "      <td>2001-03-16</td>\n",
       "      <td>Gabriela</td>\n",
       "      <td>50000</td>\n",
       "      <td>2335352</td>\n",
       "      <td>2335352</td>\n",
       "      <td>2285352</td>\n",
       "      <td>97.858995</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id release_date                     movie  production_budget  \\\n",
       "5492  93   2009-09-25       Paranormal Activity             450000   \n",
       "5679  80   2015-07-10               The Gallows             100000   \n",
       "5709  10   2004-05-07             Super Size Me              65000   \n",
       "5656  57   2007-05-16                      Once             150000   \n",
       "5507   8   2004-06-11         Napoleon Dynamite             400000   \n",
       "5430  31   2004-08-06                Open Water             500000   \n",
       "5680  81   2006-09-29         Facing the Giants             100000   \n",
       "5211  12   2012-01-06          The Devil Inside            1000000   \n",
       "5459  60   2009-04-23                      Home             500000   \n",
       "5156  57   2004-10-29                       Saw            1200000   \n",
       "4247  48   2002-04-19  My Big Fat Greek Wedding            5000000   \n",
       "5428  29   2008-09-26                 Fireproof             500000   \n",
       "5062  63   2011-04-01                 Insidious            1500000   \n",
       "5213  14   2015-04-17                Unfriended            1000000   \n",
       "4664  65   2010-10-20     Paranormal Activity 2            3000000   \n",
       "4249  50   2017-01-20                     Split            5000000   \n",
       "5189  90   2014-03-21          Godâs Not Dead            1150000   \n",
       "5215  16   2006-05-24     An Inconvenient Truth            1000000   \n",
       "4248  49   2017-02-24                   Get Out            5000000   \n",
       "5716  17   2001-03-16                  Gabriela              50000   \n",
       "\n",
       "      domestic_gross  worldwide_gross     profit  profit_margin  \n",
       "5492       107918810        194183034  193733034      99.768260  \n",
       "5679        22764410         41656474   41556474      99.759941  \n",
       "5709        11529368         22233808   22168808      99.707652  \n",
       "5656         9445857         23323631   23173631      99.356875  \n",
       "5507        44540956         46122713   45722713      99.132748  \n",
       "5430        30500882         55518641   55018641      99.099402  \n",
       "5680        10178331         10243159   10143159      99.023739  \n",
       "5211        53262945        101759490  100759490      99.017291  \n",
       "5459           15433         44793168   44293168      98.883758  \n",
       "5156        55968727        103880027  102680027      98.844821  \n",
       "4247       241438208        374890034  369890034      98.666276  \n",
       "5428        33456317         33473297   32973297      98.506272  \n",
       "5062        54009150         99870886   98370886      98.498061  \n",
       "5213        32789645         64364198   63364198      98.446341  \n",
       "4664        84752907        177512032  174512032      98.309974  \n",
       "4249       138141585        278964806  273964806      98.207659  \n",
       "5189        60755732         63777092   62627092      98.196845  \n",
       "5215        24146161         53365925   52365925      98.126145  \n",
       "4248       176040665        255367951  250367951      98.042041  \n",
       "5716         2335352          2335352    2285352      97.858995  "
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "budgets.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>46122713</td>\n",
       "      <td>45722713</td>\n",
       "      <td>99.132748</td>\n",
       "      <td>0.991020</td>\n",
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       "    <tr>\n",
       "      <th>5680</th>\n",
       "      <td>81</td>\n",
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       "      <td>10143159</td>\n",
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       "      <th>5558</th>\n",
       "      <td>59</td>\n",
       "      <td>2014-10-31</td>\n",
       "      <td>All You Need Is Love</td>\n",
       "      <td>300000</td>\n",
       "      <td>0</td>\n",
       "      <td>3565259</td>\n",
       "      <td>3265259</td>\n",
       "      <td>91.585464</td>\n",
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       "    <tr>\n",
       "      <th>2451</th>\n",
       "      <td>52</td>\n",
       "      <td>2013-12-31</td>\n",
       "      <td>Metegol</td>\n",
       "      <td>22000000</td>\n",
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       "      <td>66</td>\n",
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       "      <td>Flood</td>\n",
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       "      <td>56</td>\n",
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       "      <td>3000000</td>\n",
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       "      <td>0</td>\n",
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       "<p>3328 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      id release_date                 movie  production_budget  \\\n",
       "5492  93   2009-09-25   Paranormal Activity             450000   \n",
       "5679  80   2015-07-10           The Gallows             100000   \n",
       "5709  10   2004-05-07         Super Size Me              65000   \n",
       "5507   8   2004-06-11     Napoleon Dynamite             400000   \n",
       "5680  81   2006-09-29     Facing the Giants             100000   \n",
       "...   ..          ...                   ...                ...   \n",
       "5558  59   2014-10-31  All You Need Is Love             300000   \n",
       "2451  52   2013-12-31               Metegol           22000000   \n",
       "2365  66   2007-12-31                 Flood           24000000   \n",
       "4755  56   2017-10-10               Demonic            3000000   \n",
       "619   20   2019-01-22             Renegades           77500000   \n",
       "\n",
       "      domestic_gross  worldwide_gross     profit  profit_margin  \\\n",
       "5492       107918810        194183034  193733034      99.768260   \n",
       "5679        22764410         41656474   41556474      99.759941   \n",
       "5709        11529368         22233808   22168808      99.707652   \n",
       "5507        44540956         46122713   45722713      99.132748   \n",
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       "...              ...              ...        ...            ...   \n",
       "5558               0          3565259    3265259      91.585464   \n",
       "2451               0         34061097   12061097      35.410184   \n",
       "2365               0          4084232  -19915768    -487.625776   \n",
       "4755               0          4668109    1668109      35.734148   \n",
       "619                0          1521672  -75978328   -4993.081821   \n",
       "\n",
       "      domestic_profit_margin  \n",
       "5492                0.995830  \n",
       "5679                0.995607  \n",
       "5709                0.994362  \n",
       "5507                0.991020  \n",
       "5680                0.990175  \n",
       "...                      ...  \n",
       "5558                    -inf  \n",
       "2451                    -inf  \n",
       "2365                    -inf  \n",
       "4755                    -inf  \n",
       "619                     -inf  \n",
       "\n",
       "[3328 rows x 9 columns]"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "budgets = budgets.drop('domestic_profit_margin', axis=1)\n",
    "budgets['domestic_profit_margin'] = (budgets['domestic_gross'] - budgets['production_budget']) / budgets['domestic_gross']\n",
    "budgets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>1537</th>\n",
       "      <td>38</td>\n",
       "      <td>2013-04-16</td>\n",
       "      <td>Un monstre Ã Paris</td>\n",
       "      <td>40000000</td>\n",
       "      <td>0</td>\n",
       "      <td>27000381</td>\n",
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       "    <tr>\n",
       "      <th>3938</th>\n",
       "      <td>39</td>\n",
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       "      <td>Don Gato, el inicio de la pandilla</td>\n",
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       "      <th>4855</th>\n",
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       "      <td>NiktÃ©</td>\n",
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       "      <td>15</td>\n",
       "      <td>2016-02-05</td>\n",
       "      <td>Misconduct</td>\n",
       "      <td>11000000</td>\n",
       "      <td>0</td>\n",
       "      <td>2352511</td>\n",
       "      <td>-8647489</td>\n",
       "      <td>-0.036759</td>\n",
       "      <td>-inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>619</th>\n",
       "      <td>20</td>\n",
       "      <td>2019-01-22</td>\n",
       "      <td>Renegades</td>\n",
       "      <td>77500000</td>\n",
       "      <td>0</td>\n",
       "      <td>1521672</td>\n",
       "      <td>-75978328</td>\n",
       "      <td>-0.499308</td>\n",
       "      <td>-inf</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3328 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      id release_date                               movie  production_budget  \\\n",
       "5492  93   2009-09-25                 Paranormal Activity             450000   \n",
       "5679  80   2015-07-10                         The Gallows             100000   \n",
       "5709  10   2004-05-07                       Super Size Me              65000   \n",
       "5507   8   2004-06-11                   Napoleon Dynamite             400000   \n",
       "5680  81   2006-09-29                   Facing the Giants             100000   \n",
       "...   ..          ...                                 ...                ...   \n",
       "1537  38   2013-04-16                  Un monstre Ã Paris           40000000   \n",
       "3938  39   2017-09-29  Don Gato, el inicio de la pandilla            8000000   \n",
       "4855  56   2009-12-31                              NiktÃ©            2400000   \n",
       "3514  15   2016-02-05                          Misconduct           11000000   \n",
       "619   20   2019-01-22                           Renegades           77500000   \n",
       "\n",
       "      domestic_gross  worldwide_gross     profit  profit_margin  \\\n",
       "5492       107918810        194183034  193733034       0.009977   \n",
       "5679        22764410         41656474   41556474       0.009976   \n",
       "5709        11529368         22233808   22168808       0.009971   \n",
       "5507        44540956         46122713   45722713       0.009913   \n",
       "5680        10178331         10243159   10143159       0.009902   \n",
       "...              ...              ...        ...            ...   \n",
       "1537               0         27000381  -12999619      -0.004815   \n",
       "3938               0          4604656   -3395344      -0.007374   \n",
       "4855               0          1333891   -1066109      -0.007992   \n",
       "3514               0          2352511   -8647489      -0.036759   \n",
       "619                0          1521672  -75978328      -0.499308   \n",
       "\n",
       "      domestic_profit_margin  \n",
       "5492                0.995830  \n",
       "5679                0.995607  \n",
       "5709                0.994362  \n",
       "5507                0.991020  \n",
       "5680                0.990175  \n",
       "...                      ...  \n",
       "1537                    -inf  \n",
       "3938                    -inf  \n",
       "4855                    -inf  \n",
       "3514                    -inf  \n",
       "619                     -inf  \n",
       "\n",
       "[3328 rows x 9 columns]"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "budgets = budgets.sort_values(by='domestic_profit_margin', ascending=False) \n",
    "budgets['profit_margin'] = budgets['profit_margin'] / 100\n",
    "budgets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>release_date</th>\n",
       "      <th>movie</th>\n",
       "      <th>production_budget</th>\n",
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       "      <th>5492</th>\n",
       "      <td>93</td>\n",
       "      <td>2009-09-25</td>\n",
       "      <td>Paranormal Activity</td>\n",
       "      <td>450000</td>\n",
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       "    <tr>\n",
       "      <th>5709</th>\n",
       "      <td>10</td>\n",
       "      <td>2004-05-07</td>\n",
       "      <td>Super Size Me</td>\n",
       "      <td>65000</td>\n",
       "      <td>11529368</td>\n",
       "      <td>22233808</td>\n",
       "      <td>22168808</td>\n",
       "      <td>0.009971</td>\n",
       "      <td>0.994362</td>\n",
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       "    <tr>\n",
       "      <th>5507</th>\n",
       "      <td>8</td>\n",
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       "      <td>400000</td>\n",
       "      <td>44540956</td>\n",
       "      <td>46122713</td>\n",
       "      <td>45722713</td>\n",
       "      <td>0.009913</td>\n",
       "      <td>0.991020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5680</th>\n",
       "      <td>81</td>\n",
       "      <td>2006-09-29</td>\n",
       "      <td>Facing the Giants</td>\n",
       "      <td>100000</td>\n",
       "      <td>10178331</td>\n",
       "      <td>10243159</td>\n",
       "      <td>10143159</td>\n",
       "      <td>0.009902</td>\n",
       "      <td>0.990175</td>\n",
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       "    <tr>\n",
       "      <th>1620</th>\n",
       "      <td>21</td>\n",
       "      <td>2011-05-06</td>\n",
       "      <td>There Be Dragons</td>\n",
       "      <td>36000000</td>\n",
       "      <td>1069334</td>\n",
       "      <td>4020990</td>\n",
       "      <td>-31979010</td>\n",
       "      <td>-0.079530</td>\n",
       "      <td>-32.665814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>42</td>\n",
       "      <td>2019-06-14</td>\n",
       "      <td>Men in Black: International</td>\n",
       "      <td>110000000</td>\n",
       "      <td>3100000</td>\n",
       "      <td>3100000</td>\n",
       "      <td>-106900000</td>\n",
       "      <td>-0.344839</td>\n",
       "      <td>-34.483871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1201</th>\n",
       "      <td>2</td>\n",
       "      <td>2015-04-17</td>\n",
       "      <td>Child 44</td>\n",
       "      <td>50000000</td>\n",
       "      <td>1224330</td>\n",
       "      <td>8004221</td>\n",
       "      <td>-41995779</td>\n",
       "      <td>-0.052467</td>\n",
       "      <td>-39.838663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>607</th>\n",
       "      <td>8</td>\n",
       "      <td>2005-09-02</td>\n",
       "      <td>A Sound of Thunder</td>\n",
       "      <td>80000000</td>\n",
       "      <td>1900451</td>\n",
       "      <td>6300451</td>\n",
       "      <td>-73699549</td>\n",
       "      <td>-0.116975</td>\n",
       "      <td>-41.095271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>618</th>\n",
       "      <td>19</td>\n",
       "      <td>2016-02-12</td>\n",
       "      <td>The Little Prince</td>\n",
       "      <td>77500000</td>\n",
       "      <td>1311213</td>\n",
       "      <td>102028919</td>\n",
       "      <td>24528919</td>\n",
       "      <td>0.002404</td>\n",
       "      <td>-58.105576</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3020 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      id release_date                        movie  production_budget  \\\n",
       "5492  93   2009-09-25          Paranormal Activity             450000   \n",
       "5679  80   2015-07-10                  The Gallows             100000   \n",
       "5709  10   2004-05-07                Super Size Me              65000   \n",
       "5507   8   2004-06-11            Napoleon Dynamite             400000   \n",
       "5680  81   2006-09-29            Facing the Giants             100000   \n",
       "...   ..          ...                          ...                ...   \n",
       "1620  21   2011-05-06             There Be Dragons           36000000   \n",
       "341   42   2019-06-14  Men in Black: International          110000000   \n",
       "1201   2   2015-04-17                     Child 44           50000000   \n",
       "607    8   2005-09-02           A Sound of Thunder           80000000   \n",
       "618   19   2016-02-12            The Little Prince           77500000   \n",
       "\n",
       "      domestic_gross  worldwide_gross     profit  profit_margin  \\\n",
       "5492       107918810        194183034  193733034       0.009977   \n",
       "5679        22764410         41656474   41556474       0.009976   \n",
       "5709        11529368         22233808   22168808       0.009971   \n",
       "5507        44540956         46122713   45722713       0.009913   \n",
       "5680        10178331         10243159   10143159       0.009902   \n",
       "...              ...              ...        ...            ...   \n",
       "1620         1069334          4020990  -31979010      -0.079530   \n",
       "341          3100000          3100000 -106900000      -0.344839   \n",
       "1201         1224330          8004221  -41995779      -0.052467   \n",
       "607          1900451          6300451  -73699549      -0.116975   \n",
       "618          1311213        102028919   24528919       0.002404   \n",
       "\n",
       "      domestic_profit_margin  \n",
       "5492                0.995830  \n",
       "5679                0.995607  \n",
       "5709                0.994362  \n",
       "5507                0.991020  \n",
       "5680                0.990175  \n",
       "...                      ...  \n",
       "1620              -32.665814  \n",
       "341               -34.483871  \n",
       "1201              -39.838663  \n",
       "607               -41.095271  \n",
       "618               -58.105576  \n",
       "\n",
       "[3020 rows x 9 columns]"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "budgets = budgets.loc[budgets['domestic_gross'] > 1000000]\n",
    "budgets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [],
   "source": [
    "budgets1 = budgets.loc[budgets['profit'] > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>5492</th>\n",
       "      <td>93</td>\n",
       "      <td>2009-09-25</td>\n",
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       "      <td>450000</td>\n",
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       "      <th>5679</th>\n",
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       "    <tr>\n",
       "      <th>5709</th>\n",
       "      <td>10</td>\n",
       "      <td>2004-05-07</td>\n",
       "      <td>Super Size Me</td>\n",
       "      <td>65000</td>\n",
       "      <td>11529368</td>\n",
       "      <td>22233808</td>\n",
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       "      <th>5507</th>\n",
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       "      <td>44540956</td>\n",
       "      <td>46122713</td>\n",
       "      <td>45722713</td>\n",
       "      <td>0.009913</td>\n",
       "      <td>0.991020</td>\n",
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       "    <tr>\n",
       "      <th>5680</th>\n",
       "      <td>81</td>\n",
       "      <td>2006-09-29</td>\n",
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       "      <td>10178331</td>\n",
       "      <td>10243159</td>\n",
       "      <td>10143159</td>\n",
       "      <td>0.009902</td>\n",
       "      <td>0.990175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1619</th>\n",
       "      <td>20</td>\n",
       "      <td>2016-01-22</td>\n",
       "      <td>Yip Man 3</td>\n",
       "      <td>36000000</td>\n",
       "      <td>2679437</td>\n",
       "      <td>157300954</td>\n",
       "      <td>121300954</td>\n",
       "      <td>0.007711</td>\n",
       "      <td>-12.435658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>841</th>\n",
       "      <td>42</td>\n",
       "      <td>2016-02-19</td>\n",
       "      <td>Mei Ren Yu</td>\n",
       "      <td>60720000</td>\n",
       "      <td>3229457</td>\n",
       "      <td>554516671</td>\n",
       "      <td>493796671</td>\n",
       "      <td>0.008905</td>\n",
       "      <td>-17.801922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2450</th>\n",
       "      <td>51</td>\n",
       "      <td>2006-03-03</td>\n",
       "      <td>Joyeux NoÃ«l</td>\n",
       "      <td>22000000</td>\n",
       "      <td>1054361</td>\n",
       "      <td>23134075</td>\n",
       "      <td>1134075</td>\n",
       "      <td>0.000490</td>\n",
       "      <td>-19.865719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>823</th>\n",
       "      <td>24</td>\n",
       "      <td>2006-12-27</td>\n",
       "      <td>Perfume: The Story of a Murderer</td>\n",
       "      <td>63700000</td>\n",
       "      <td>2223293</td>\n",
       "      <td>133603463</td>\n",
       "      <td>69903463</td>\n",
       "      <td>0.005232</td>\n",
       "      <td>-27.651194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>618</th>\n",
       "      <td>19</td>\n",
       "      <td>2016-02-12</td>\n",
       "      <td>The Little Prince</td>\n",
       "      <td>77500000</td>\n",
       "      <td>1311213</td>\n",
       "      <td>102028919</td>\n",
       "      <td>24528919</td>\n",
       "      <td>0.002404</td>\n",
       "      <td>-58.105576</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2378 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      id release_date                             movie  production_budget  \\\n",
       "5492  93   2009-09-25               Paranormal Activity             450000   \n",
       "5679  80   2015-07-10                       The Gallows             100000   \n",
       "5709  10   2004-05-07                     Super Size Me              65000   \n",
       "5507   8   2004-06-11                 Napoleon Dynamite             400000   \n",
       "5680  81   2006-09-29                 Facing the Giants             100000   \n",
       "...   ..          ...                               ...                ...   \n",
       "1619  20   2016-01-22                         Yip Man 3           36000000   \n",
       "841   42   2016-02-19                        Mei Ren Yu           60720000   \n",
       "2450  51   2006-03-03                      Joyeux NoÃ«l           22000000   \n",
       "823   24   2006-12-27  Perfume: The Story of a Murderer           63700000   \n",
       "618   19   2016-02-12                 The Little Prince           77500000   \n",
       "\n",
       "      domestic_gross  worldwide_gross     profit  profit_margin  \\\n",
       "5492       107918810        194183034  193733034       0.009977   \n",
       "5679        22764410         41656474   41556474       0.009976   \n",
       "5709        11529368         22233808   22168808       0.009971   \n",
       "5507        44540956         46122713   45722713       0.009913   \n",
       "5680        10178331         10243159   10143159       0.009902   \n",
       "...              ...              ...        ...            ...   \n",
       "1619         2679437        157300954  121300954       0.007711   \n",
       "841          3229457        554516671  493796671       0.008905   \n",
       "2450         1054361         23134075    1134075       0.000490   \n",
       "823          2223293        133603463   69903463       0.005232   \n",
       "618          1311213        102028919   24528919       0.002404   \n",
       "\n",
       "      domestic_profit_margin  \n",
       "5492                0.995830  \n",
       "5679                0.995607  \n",
       "5709                0.994362  \n",
       "5507                0.991020  \n",
       "5680                0.990175  \n",
       "...                      ...  \n",
       "1619              -12.435658  \n",
       "841               -17.801922  \n",
       "2450              -19.865719  \n",
       "823               -27.651194  \n",
       "618               -58.105576  \n",
       "\n",
       "[2378 rows x 9 columns]"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "budgets1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [],
   "source": [
    "budgets2 = budgets.sort_values(by='domestic_profit_margin', ascending=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [],
   "source": [
    "budgets2 = budgets.loc[budgets['profit'] <= 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [],
   "source": [
    "unprofitable = budgets2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [],
   "source": [
    "unprofitable.to_csv('unprofitable.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [],
   "source": [
    "profitable= budgets1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [],
   "source": [
    "profitable.to_csv('profitable.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
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       "      <th>production_budget</th>\n",
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       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "      id release_date                movie  production_budget  domestic_gross  \\\n",
       "5492  93   2009-09-25  Paranormal Activity             450000       107918810   \n",
       "5679  80   2015-07-10          The Gallows             100000        22764410   \n",
       "5709  10   2004-05-07        Super Size Me              65000        11529368   \n",
       "5507   8   2004-06-11    Napoleon Dynamite             400000        44540956   \n",
       "5680  81   2006-09-29    Facing the Giants             100000        10178331   \n",
       "\n",
       "      worldwide_gross     profit  profit_margin  domestic_profit_margin  \n",
       "5492        194183034  193733034       0.009977                0.995830  \n",
       "5679         41656474   41556474       0.009976                0.995607  \n",
       "5709         22233808   22168808       0.009971                0.994362  \n",
       "5507         46122713   45722713       0.009913                0.991020  \n",
       "5680         10243159   10143159       0.009902                0.990175  "
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profitable.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-258-9748b2801c8d>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  profitable['profit_margin'] = profitable['profit_margin'] * 100\n"
     ]
    }
   ],
   "source": [
    "profitable['profit_margin'] = profitable['profit_margin'] * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.990237</td>\n",
       "      <td>0.990175</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "      id release_date                movie  production_budget  domestic_gross  \\\n",
       "5492  93   2009-09-25  Paranormal Activity             450000       107918810   \n",
       "5679  80   2015-07-10          The Gallows             100000        22764410   \n",
       "5709  10   2004-05-07        Super Size Me              65000        11529368   \n",
       "5507   8   2004-06-11    Napoleon Dynamite             400000        44540956   \n",
       "5680  81   2006-09-29    Facing the Giants             100000        10178331   \n",
       "\n",
       "      worldwide_gross     profit  profit_margin  domestic_profit_margin  \n",
       "5492        194183034  193733034       0.997683                0.995830  \n",
       "5679         41656474   41556474       0.997599                0.995607  \n",
       "5709         22233808   22168808       0.997077                0.994362  \n",
       "5507         46122713   45722713       0.991327                0.991020  \n",
       "5680         10243159   10143159       0.990237                0.990175  "
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profitable.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                                 int64\n",
       "release_date              datetime64[ns]\n",
       "movie                             object\n",
       "production_budget                  int64\n",
       "domestic_gross                     int64\n",
       "worldwide_gross                    int64\n",
       "profit                             int64\n",
       "profit_margin                    float64\n",
       "domestic_profit_margin           float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profitable.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [],
   "source": [
    "profitable['cost_revenue_ratio_in_%'] = 100 * (profitable['domestic_gross'] / profitable['production_budget'])\n",
    "profitable = profitable.sort_values(by='cost_revenue_ratio_in_%', ascending=False) \n",
    "profitable.to_csv('profitable.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>5492</th>\n",
       "      <td>93</td>\n",
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       "    <tr>\n",
       "      <th>5507</th>\n",
       "      <td>8</td>\n",
       "      <td>2004-06-11</td>\n",
       "      <td>Napoleon Dynamite</td>\n",
       "      <td>400000</td>\n",
       "      <td>44540956</td>\n",
       "      <td>46122713</td>\n",
       "      <td>45722713</td>\n",
       "      <td>0.991327</td>\n",
       "      <td>0.991020</td>\n",
       "      <td>11135.239000</td>\n",
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       "    <tr>\n",
       "      <th>5680</th>\n",
       "      <td>81</td>\n",
       "      <td>2006-09-29</td>\n",
       "      <td>Facing the Giants</td>\n",
       "      <td>100000</td>\n",
       "      <td>10178331</td>\n",
       "      <td>10243159</td>\n",
       "      <td>10143159</td>\n",
       "      <td>0.990237</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>5428</th>\n",
       "      <td>29</td>\n",
       "      <td>2008-09-26</td>\n",
       "      <td>Fireproof</td>\n",
       "      <td>500000</td>\n",
       "      <td>33456317</td>\n",
       "      <td>33473297</td>\n",
       "      <td>32973297</td>\n",
       "      <td>0.985063</td>\n",
       "      <td>0.985055</td>\n",
       "      <td>6691.263400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5656</th>\n",
       "      <td>57</td>\n",
       "      <td>2007-05-16</td>\n",
       "      <td>Once</td>\n",
       "      <td>150000</td>\n",
       "      <td>9445857</td>\n",
       "      <td>23323631</td>\n",
       "      <td>23173631</td>\n",
       "      <td>0.993569</td>\n",
       "      <td>0.984120</td>\n",
       "      <td>6297.238000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5430</th>\n",
       "      <td>31</td>\n",
       "      <td>2004-08-06</td>\n",
       "      <td>Open Water</td>\n",
       "      <td>500000</td>\n",
       "      <td>30500882</td>\n",
       "      <td>55518641</td>\n",
       "      <td>55018641</td>\n",
       "      <td>0.990994</td>\n",
       "      <td>0.983607</td>\n",
       "      <td>6100.176400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5211</th>\n",
       "      <td>12</td>\n",
       "      <td>2012-01-06</td>\n",
       "      <td>The Devil Inside</td>\n",
       "      <td>1000000</td>\n",
       "      <td>53262945</td>\n",
       "      <td>101759490</td>\n",
       "      <td>100759490</td>\n",
       "      <td>0.990173</td>\n",
       "      <td>0.981225</td>\n",
       "      <td>5326.294500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5189</th>\n",
       "      <td>90</td>\n",
       "      <td>2014-03-21</td>\n",
       "      <td>Godâs Not Dead</td>\n",
       "      <td>1150000</td>\n",
       "      <td>60755732</td>\n",
       "      <td>63777092</td>\n",
       "      <td>62627092</td>\n",
       "      <td>0.981968</td>\n",
       "      <td>0.981072</td>\n",
       "      <td>5283.107130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4247</th>\n",
       "      <td>48</td>\n",
       "      <td>2002-04-19</td>\n",
       "      <td>My Big Fat Greek Wedding</td>\n",
       "      <td>5000000</td>\n",
       "      <td>241438208</td>\n",
       "      <td>374890034</td>\n",
       "      <td>369890034</td>\n",
       "      <td>0.986663</td>\n",
       "      <td>0.979291</td>\n",
       "      <td>4828.764160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5716</th>\n",
       "      <td>17</td>\n",
       "      <td>2001-03-16</td>\n",
       "      <td>Gabriela</td>\n",
       "      <td>50000</td>\n",
       "      <td>2335352</td>\n",
       "      <td>2335352</td>\n",
       "      <td>2285352</td>\n",
       "      <td>0.978590</td>\n",
       "      <td>0.978590</td>\n",
       "      <td>4670.704000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5156</th>\n",
       "      <td>57</td>\n",
       "      <td>2004-10-29</td>\n",
       "      <td>Saw</td>\n",
       "      <td>1200000</td>\n",
       "      <td>55968727</td>\n",
       "      <td>103880027</td>\n",
       "      <td>102680027</td>\n",
       "      <td>0.988448</td>\n",
       "      <td>0.978559</td>\n",
       "      <td>4664.060583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5062</th>\n",
       "      <td>63</td>\n",
       "      <td>2011-04-01</td>\n",
       "      <td>Insidious</td>\n",
       "      <td>1500000</td>\n",
       "      <td>54009150</td>\n",
       "      <td>99870886</td>\n",
       "      <td>98370886</td>\n",
       "      <td>0.984981</td>\n",
       "      <td>0.972227</td>\n",
       "      <td>3600.610000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4248</th>\n",
       "      <td>49</td>\n",
       "      <td>2017-02-24</td>\n",
       "      <td>Get Out</td>\n",
       "      <td>5000000</td>\n",
       "      <td>176040665</td>\n",
       "      <td>255367951</td>\n",
       "      <td>250367951</td>\n",
       "      <td>0.980420</td>\n",
       "      <td>0.971597</td>\n",
       "      <td>3520.813300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5213</th>\n",
       "      <td>14</td>\n",
       "      <td>2015-04-17</td>\n",
       "      <td>Unfriended</td>\n",
       "      <td>1000000</td>\n",
       "      <td>32789645</td>\n",
       "      <td>64364198</td>\n",
       "      <td>63364198</td>\n",
       "      <td>0.984463</td>\n",
       "      <td>0.969503</td>\n",
       "      <td>3278.964500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4664</th>\n",
       "      <td>65</td>\n",
       "      <td>2010-10-20</td>\n",
       "      <td>Paranormal Activity 2</td>\n",
       "      <td>3000000</td>\n",
       "      <td>84752907</td>\n",
       "      <td>177512032</td>\n",
       "      <td>174512032</td>\n",
       "      <td>0.983100</td>\n",
       "      <td>0.964603</td>\n",
       "      <td>2825.096900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4249</th>\n",
       "      <td>50</td>\n",
       "      <td>2017-01-20</td>\n",
       "      <td>Split</td>\n",
       "      <td>5000000</td>\n",
       "      <td>138141585</td>\n",
       "      <td>278964806</td>\n",
       "      <td>273964806</td>\n",
       "      <td>0.982077</td>\n",
       "      <td>0.963805</td>\n",
       "      <td>2762.831700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5540</th>\n",
       "      <td>41</td>\n",
       "      <td>2003-11-14</td>\n",
       "      <td>Tupac Resurrection</td>\n",
       "      <td>300000</td>\n",
       "      <td>7718961</td>\n",
       "      <td>7808524</td>\n",
       "      <td>7508524</td>\n",
       "      <td>0.961580</td>\n",
       "      <td>0.961135</td>\n",
       "      <td>2572.987000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5215</th>\n",
       "      <td>16</td>\n",
       "      <td>2006-05-24</td>\n",
       "      <td>An Inconvenient Truth</td>\n",
       "      <td>1000000</td>\n",
       "      <td>24146161</td>\n",
       "      <td>53365925</td>\n",
       "      <td>52365925</td>\n",
       "      <td>0.981261</td>\n",
       "      <td>0.958586</td>\n",
       "      <td>2414.616100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5014</th>\n",
       "      <td>15</td>\n",
       "      <td>2010-08-27</td>\n",
       "      <td>The Last Exorcism</td>\n",
       "      <td>1800000</td>\n",
       "      <td>41034350</td>\n",
       "      <td>70165900</td>\n",
       "      <td>68365900</td>\n",
       "      <td>0.974347</td>\n",
       "      <td>0.956134</td>\n",
       "      <td>2279.686111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4634</th>\n",
       "      <td>35</td>\n",
       "      <td>2005-06-24</td>\n",
       "      <td>La marche de l'empereur</td>\n",
       "      <td>3400000</td>\n",
       "      <td>77437223</td>\n",
       "      <td>133276359</td>\n",
       "      <td>129876359</td>\n",
       "      <td>0.974489</td>\n",
       "      <td>0.956093</td>\n",
       "      <td>2277.565382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4665</th>\n",
       "      <td>66</td>\n",
       "      <td>2015-08-28</td>\n",
       "      <td>War Room</td>\n",
       "      <td>3000000</td>\n",
       "      <td>67790117</td>\n",
       "      <td>73975239</td>\n",
       "      <td>70975239</td>\n",
       "      <td>0.959446</td>\n",
       "      <td>0.955746</td>\n",
       "      <td>2259.670567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4666</th>\n",
       "      <td>67</td>\n",
       "      <td>2013-06-07</td>\n",
       "      <td>The Purge</td>\n",
       "      <td>3000000</td>\n",
       "      <td>64473115</td>\n",
       "      <td>91266581</td>\n",
       "      <td>88266581</td>\n",
       "      <td>0.967129</td>\n",
       "      <td>0.953469</td>\n",
       "      <td>2149.103833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5038</th>\n",
       "      <td>39</td>\n",
       "      <td>2001-11-23</td>\n",
       "      <td>In the Bedroom</td>\n",
       "      <td>1700000</td>\n",
       "      <td>35930604</td>\n",
       "      <td>42137871</td>\n",
       "      <td>40437871</td>\n",
       "      <td>0.959656</td>\n",
       "      <td>0.952687</td>\n",
       "      <td>2113.564941</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id release_date                     movie  production_budget  \\\n",
       "5492  93   2009-09-25       Paranormal Activity             450000   \n",
       "5679  80   2015-07-10               The Gallows             100000   \n",
       "5709  10   2004-05-07             Super Size Me              65000   \n",
       "5507   8   2004-06-11         Napoleon Dynamite             400000   \n",
       "5680  81   2006-09-29         Facing the Giants             100000   \n",
       "5428  29   2008-09-26                 Fireproof             500000   \n",
       "5656  57   2007-05-16                      Once             150000   \n",
       "5430  31   2004-08-06                Open Water             500000   \n",
       "5211  12   2012-01-06          The Devil Inside            1000000   \n",
       "5189  90   2014-03-21          Godâs Not Dead            1150000   \n",
       "4247  48   2002-04-19  My Big Fat Greek Wedding            5000000   \n",
       "5716  17   2001-03-16                  Gabriela              50000   \n",
       "5156  57   2004-10-29                       Saw            1200000   \n",
       "5062  63   2011-04-01                 Insidious            1500000   \n",
       "4248  49   2017-02-24                   Get Out            5000000   \n",
       "5213  14   2015-04-17                Unfriended            1000000   \n",
       "4664  65   2010-10-20     Paranormal Activity 2            3000000   \n",
       "4249  50   2017-01-20                     Split            5000000   \n",
       "5540  41   2003-11-14        Tupac Resurrection             300000   \n",
       "5215  16   2006-05-24     An Inconvenient Truth            1000000   \n",
       "5014  15   2010-08-27         The Last Exorcism            1800000   \n",
       "4634  35   2005-06-24   La marche de l'empereur            3400000   \n",
       "4665  66   2015-08-28                  War Room            3000000   \n",
       "4666  67   2013-06-07                 The Purge            3000000   \n",
       "5038  39   2001-11-23            In the Bedroom            1700000   \n",
       "\n",
       "      domestic_gross  worldwide_gross     profit  profit_margin  \\\n",
       "5492       107918810        194183034  193733034       0.997683   \n",
       "5679        22764410         41656474   41556474       0.997599   \n",
       "5709        11529368         22233808   22168808       0.997077   \n",
       "5507        44540956         46122713   45722713       0.991327   \n",
       "5680        10178331         10243159   10143159       0.990237   \n",
       "5428        33456317         33473297   32973297       0.985063   \n",
       "5656         9445857         23323631   23173631       0.993569   \n",
       "5430        30500882         55518641   55018641       0.990994   \n",
       "5211        53262945        101759490  100759490       0.990173   \n",
       "5189        60755732         63777092   62627092       0.981968   \n",
       "4247       241438208        374890034  369890034       0.986663   \n",
       "5716         2335352          2335352    2285352       0.978590   \n",
       "5156        55968727        103880027  102680027       0.988448   \n",
       "5062        54009150         99870886   98370886       0.984981   \n",
       "4248       176040665        255367951  250367951       0.980420   \n",
       "5213        32789645         64364198   63364198       0.984463   \n",
       "4664        84752907        177512032  174512032       0.983100   \n",
       "4249       138141585        278964806  273964806       0.982077   \n",
       "5540         7718961          7808524    7508524       0.961580   \n",
       "5215        24146161         53365925   52365925       0.981261   \n",
       "5014        41034350         70165900   68365900       0.974347   \n",
       "4634        77437223        133276359  129876359       0.974489   \n",
       "4665        67790117         73975239   70975239       0.959446   \n",
       "4666        64473115         91266581   88266581       0.967129   \n",
       "5038        35930604         42137871   40437871       0.959656   \n",
       "\n",
       "      domestic_profit_margin  cost_revenue_ratio_in_%  \n",
       "5492                0.995830             23981.957778  \n",
       "5679                0.995607             22764.410000  \n",
       "5709                0.994362             17737.489231  \n",
       "5507                0.991020             11135.239000  \n",
       "5680                0.990175             10178.331000  \n",
       "5428                0.985055              6691.263400  \n",
       "5656                0.984120              6297.238000  \n",
       "5430                0.983607              6100.176400  \n",
       "5211                0.981225              5326.294500  \n",
       "5189                0.981072              5283.107130  \n",
       "4247                0.979291              4828.764160  \n",
       "5716                0.978590              4670.704000  \n",
       "5156                0.978559              4664.060583  \n",
       "5062                0.972227              3600.610000  \n",
       "4248                0.971597              3520.813300  \n",
       "5213                0.969503              3278.964500  \n",
       "4664                0.964603              2825.096900  \n",
       "4249                0.963805              2762.831700  \n",
       "5540                0.961135              2572.987000  \n",
       "5215                0.958586              2414.616100  \n",
       "5014                0.956134              2279.686111  \n",
       "4634                0.956093              2277.565382  \n",
       "4665                0.955746              2259.670567  \n",
       "4666                0.953469              2149.103833  \n",
       "5038                0.952687              2113.564941  "
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profitable.head(25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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