ML Models.ipynb

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     "text": [
      "C:\\Users\\jedda\\Anaconda3\\lib\\site-packages\\tqdm\\autonotebook\\__init__.py:14: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
      "  \" (e.g. in jupyter console)\", TqdmExperimentalWarning)\n"
     ]
    }
   ],
   "source": [
    "# standard imports\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "import sqlite3\n",
    "from collections import Counter\n",
    "from tqdm.autonotebook import tqdm\n",
    "import warnings\n",
    "\n",
    "# processing and pre-processing imports\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import RobustScaler\n",
    "from sklearn.exceptions import DataConversionWarning\n",
    "from sklearn.exceptions import ConvergenceWarning\n",
    "\n",
    "# ml model selection imports\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "\n",
    "# ml models\n",
    "from sklearn.neighbors import KNeighborsRegressor, KNeighborsClassifier\n",
    "from sklearn.linear_model import LinearRegression, Ridge, Lasso, LogisticRegression\n",
    "from sklearn.svm import LinearSVC, SVC, SVR\n",
    "from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor\n",
    "from sklearn.ensemble import GradientBoostingRegressor, GradientBoostingClassifier\n",
    "import xgboost as xgb\n",
    "\n",
    "import machumachine\n",
    "\n",
    "# metric computation\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import confusion_matrix \n",
    "from sklearn.metrics import accuracy_score \n",
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
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     "end_time": "2019-08-28T08:06:02.731929Z",
     "start_time": "2019-08-28T08:06:02.699047Z"
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>actors</th>\n",
       "      <th>composer</th>\n",
       "      <th>directors</th>\n",
       "      <th>domestic_revenue</th>\n",
       "      <th>franchise</th>\n",
       "      <th>genres</th>\n",
       "      <th>link</th>\n",
       "      <th>prod_budget</th>\n",
       "      <th>producers</th>\n",
       "      <th>rating</th>\n",
       "      <th>release_date</th>\n",
       "      <th>runtime</th>\n",
       "      <th>studio</th>\n",
       "      <th>title</th>\n",
       "      <th>worldwide_revenue</th>\n",
       "      <th>writers</th>\n",
       "      <th>actor_level</th>\n",
       "      <th>director_level</th>\n",
       "      <th>res</th>\n",
       "      <th>theaters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Robert Downey, Jr., Chris Hemsworth, Mark Ruff...</td>\n",
       "      <td>Alan Silvestri</td>\n",
       "      <td>Joe Russo, Anthony Russo</td>\n",
       "      <td>8.5741e+08</td>\n",
       "      <td>1</td>\n",
       "      <td>Action / Adventure</td>\n",
       "      <td>/movies/?id=marvel2019.htm</td>\n",
       "      <td>3.56e+08</td>\n",
       "      <td>Kevin Feige</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>2019-04-26</td>\n",
       "      <td>181</td>\n",
       "      <td>Buena Vista</td>\n",
       "      <td>Avengers: Endgame</td>\n",
       "      <td>2.7945e+09</td>\n",
       "      <td>Christopher Markus, Stephen McFeely</td>\n",
       "      <td>163</td>\n",
       "      <td>8</td>\n",
       "      <td>(#1 rank, 4,662 theaters, $76,601 average)</td>\n",
       "      <td>4,662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Tom Holland, Jake Gyllenhaal, Marisa Tomei, Jo...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>3.6033e+08</td>\n",
       "      <td>1</td>\n",
       "      <td>Action / Adventure</td>\n",
       "      <td>/movies/?id=spidermanhomecoming2.htm</td>\n",
       "      <td>1.6e+08</td>\n",
       "      <td>None</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>2019-07-02</td>\n",
       "      <td>129</td>\n",
       "      <td>Sony / Columbia</td>\n",
       "      <td>Spider-Man: Far from Home</td>\n",
       "      <td>1.0753e+09</td>\n",
       "      <td>None</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>(#1 rank, 4,634 theaters, $19,978 average)</td>\n",
       "      <td>4,634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Will Smith</td>\n",
       "      <td>Alan Menken</td>\n",
       "      <td>Guy Ritchie</td>\n",
       "      <td>3.5037e+08</td>\n",
       "      <td>1</td>\n",
       "      <td>Fantasy</td>\n",
       "      <td>/movies/?id=disneyfairytale22019.htm</td>\n",
       "      <td>1.83e+08</td>\n",
       "      <td>None</td>\n",
       "      <td>PG</td>\n",
       "      <td>2019-05-24</td>\n",
       "      <td>128</td>\n",
       "      <td>Buena Vista</td>\n",
       "      <td>Aladdin (2019)</td>\n",
       "      <td>1.0256e+09</td>\n",
       "      <td>None</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>(#1 rank, 4,476 theaters, $20,443 average)</td>\n",
       "      <td>4,476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Lupita Nyong'o, Elisabeth Moss</td>\n",
       "      <td>None</td>\n",
       "      <td>Jordan Peele</td>\n",
       "      <td>1.7501e+08</td>\n",
       "      <td>0</td>\n",
       "      <td>Thriller</td>\n",
       "      <td>/movies/?id=untitledjordanpeele.htm</td>\n",
       "      <td>2e+07</td>\n",
       "      <td>Jordan Peele, Jason Blum</td>\n",
       "      <td>R</td>\n",
       "      <td>2019-03-22</td>\n",
       "      <td>116</td>\n",
       "      <td>Universal</td>\n",
       "      <td>Us</td>\n",
       "      <td>2.5466e+08</td>\n",
       "      <td>Jordan Peele</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>(#1 rank, 3,741 theaters, $19,010 average)</td>\n",
       "      <td>3,741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Jay Baruchel, America Ferrera, Cate Blanchett,...</td>\n",
       "      <td>None</td>\n",
       "      <td>Dean DeBlois</td>\n",
       "      <td>1.608e+08</td>\n",
       "      <td>1</td>\n",
       "      <td>Animation</td>\n",
       "      <td>/movies/?id=httyd3.htm</td>\n",
       "      <td>1.29e+08</td>\n",
       "      <td>None</td>\n",
       "      <td>PG</td>\n",
       "      <td>2019-02-22</td>\n",
       "      <td>104</td>\n",
       "      <td>Universal</td>\n",
       "      <td>How to Train Your Dragon: The Hidden World</td>\n",
       "      <td>5.1976e+08</td>\n",
       "      <td>None</td>\n",
       "      <td>23</td>\n",
       "      <td>4</td>\n",
       "      <td>(#1 rank, 4,259 theaters, $12,919 average)</td>\n",
       "      <td>4,259</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "                                              actors        composer  \\\n",
       "0  Robert Downey, Jr., Chris Hemsworth, Mark Ruff...  Alan Silvestri   \n",
       "4  Tom Holland, Jake Gyllenhaal, Marisa Tomei, Jo...            None   \n",
       "5                                         Will Smith     Alan Menken   \n",
       "6                     Lupita Nyong'o, Elisabeth Moss            None   \n",
       "8  Jay Baruchel, America Ferrera, Cate Blanchett,...            None   \n",
       "\n",
       "                  directors  domestic_revenue  franchise              genres  \\\n",
       "0  Joe Russo, Anthony Russo        8.5741e+08          1  Action / Adventure   \n",
       "4                      None        3.6033e+08          1  Action / Adventure   \n",
       "5               Guy Ritchie        3.5037e+08          1             Fantasy   \n",
       "6              Jordan Peele        1.7501e+08          0            Thriller   \n",
       "8              Dean DeBlois         1.608e+08          1           Animation   \n",
       "\n",
       "                                   link  prod_budget  \\\n",
       "0            /movies/?id=marvel2019.htm     3.56e+08   \n",
       "4  /movies/?id=spidermanhomecoming2.htm      1.6e+08   \n",
       "5  /movies/?id=disneyfairytale22019.htm     1.83e+08   \n",
       "6   /movies/?id=untitledjordanpeele.htm        2e+07   \n",
       "8                /movies/?id=httyd3.htm     1.29e+08   \n",
       "\n",
       "                  producers rating release_date  runtime           studio  \\\n",
       "0               Kevin Feige  PG-13   2019-04-26      181      Buena Vista   \n",
       "4                      None  PG-13   2019-07-02      129  Sony / Columbia   \n",
       "5                      None     PG   2019-05-24      128      Buena Vista   \n",
       "6  Jordan Peele, Jason Blum      R   2019-03-22      116        Universal   \n",
       "8                      None     PG   2019-02-22      104        Universal   \n",
       "\n",
       "                                        title  worldwide_revenue  \\\n",
       "0                           Avengers: Endgame         2.7945e+09   \n",
       "4                   Spider-Man: Far from Home         1.0753e+09   \n",
       "5                              Aladdin (2019)         1.0256e+09   \n",
       "6                                          Us         2.5466e+08   \n",
       "8  How to Train Your Dragon: The Hidden World         5.1976e+08   \n",
       "\n",
       "                               writers  actor_level  director_level  \\\n",
       "0  Christopher Markus, Stephen McFeely          163               8   \n",
       "4                                 None           20               0   \n",
       "5                                 None            4               4   \n",
       "6                         Jordan Peele            6               4   \n",
       "8                                 None           23               4   \n",
       "\n",
       "                                          res  theaters  \n",
       "0  (#1 rank, 4,662 theaters, $76,601 average)     4,662  \n",
       "4  (#1 rank, 4,634 theaters, $19,978 average)     4,634  \n",
       "5  (#1 rank, 4,476 theaters, $20,443 average)     4,476  \n",
       "6  (#1 rank, 3,741 theaters, $19,010 average)     3,741  \n",
       "8  (#1 rank, 4,259 theaters, $12,919 average)     4,259  "
      ]
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   ],
   "source": [
    "df = pd.read_pickle('cleaned_data.pkl')\n",
    "df.head()"
   ]
  },
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       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Taylor Schilling</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1.4856e+07</td>\n",
       "      <td>0</td>\n",
       "      <td>Horror</td>\n",
       "      <td>/movies/?id=theprodigy.htm</td>\n",
       "      <td>6e+06</td>\n",
       "      <td>None</td>\n",
       "      <td>R</td>\n",
       "      <td>2019-02-08</td>\n",
       "      <td>92</td>\n",
       "      <td>Orion Pictures</td>\n",
       "      <td>The Prodigy</td>\n",
       "      <td>1.4856e+07</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>(#6 rank, 2,530 theaters, $2,313 average)</td>\n",
       "      <td>2,530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>Leigh Whannell</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>6.7745e+07</td>\n",
       "      <td>1</td>\n",
       "      <td>Horror</td>\n",
       "      <td>/movies/?id=insidious4.htm</td>\n",
       "      <td>1e+07</td>\n",
       "      <td>Jason Blum, Oren Peli, James Wan</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>2018-01-05</td>\n",
       "      <td>103</td>\n",
       "      <td>Universal</td>\n",
       "      <td>Insidious: The Last Key</td>\n",
       "      <td>1.6789e+08</td>\n",
       "      <td>Leigh Whannell</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>(#2 rank, 3,116 theaters, $9,493 average)</td>\n",
       "      <td>3,116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>Milla Jovovich</td>\n",
       "      <td>None</td>\n",
       "      <td>Paul W.S. Anderson</td>\n",
       "      <td>2.683e+07</td>\n",
       "      <td>1</td>\n",
       "      <td>Sci-Fi Action</td>\n",
       "      <td>/movies/?id=residentevil6.htm</td>\n",
       "      <td>4e+07</td>\n",
       "      <td>None</td>\n",
       "      <td>R</td>\n",
       "      <td>2017-01-27</td>\n",
       "      <td>106</td>\n",
       "      <td>Sony / Screen Gems</td>\n",
       "      <td>Resident Evil: The Final Chapter</td>\n",
       "      <td>3.1224e+08</td>\n",
       "      <td>Paul W.S. Anderson</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>(#4 rank, 3,104 theaters, $4,382 average)</td>\n",
       "      <td>3,104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>Claire Foy</td>\n",
       "      <td>None</td>\n",
       "      <td>Fede Alvarez</td>\n",
       "      <td>1.4841e+07</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>/movies/?id=thegirlinthespidersweb.htm</td>\n",
       "      <td>4.3e+07</td>\n",
       "      <td>None</td>\n",
       "      <td>R</td>\n",
       "      <td>2018-11-09</td>\n",
       "      <td>117</td>\n",
       "      <td>Sony / Columbia</td>\n",
       "      <td>The Girl in the Spider's Web: A New Dragon Tat...</td>\n",
       "      <td>3.5165e+07</td>\n",
       "      <td>Steven Knight, Fede Alvarez</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>(#6 rank, 2,929 theaters, $2,666 average)</td>\n",
       "      <td>2,929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1527</th>\n",
       "      <td>Alicia Silverstone</td>\n",
       "      <td>None</td>\n",
       "      <td>David Bowers</td>\n",
       "      <td>2.0739e+07</td>\n",
       "      <td>1</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>/movies/?id=diaryofawimpykid4.htm</td>\n",
       "      <td>2.2e+07</td>\n",
       "      <td>Nina Jacobson</td>\n",
       "      <td>PG</td>\n",
       "      <td>2017-05-19</td>\n",
       "      <td>90</td>\n",
       "      <td>Fox</td>\n",
       "      <td>Diary of a Wimpy Kid: The Long Haul</td>\n",
       "      <td>4.0141e+07</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>(#6 rank, 3,157 theaters, $2,257 average)</td>\n",
       "      <td>3,157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2821</th>\n",
       "      <td>Bella Thorne</td>\n",
       "      <td>None</td>\n",
       "      <td>Ari Sandel</td>\n",
       "      <td>3.403e+07</td>\n",
       "      <td>0</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>/movies/?id=duff.htm</td>\n",
       "      <td>8e+06</td>\n",
       "      <td>McG</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>2015-02-20</td>\n",
       "      <td>101</td>\n",
       "      <td>Lionsgate</td>\n",
       "      <td>The DUFF</td>\n",
       "      <td>4.3529e+07</td>\n",
       "      <td>Josh A. Cagan</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>(#5 rank, 2,575 theaters, $4,198 average)</td>\n",
       "      <td>2,575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3518</th>\n",
       "      <td>Jeremy Sumpter</td>\n",
       "      <td>Brian Tyler</td>\n",
       "      <td>Steven Quale</td>\n",
       "      <td>4.7602e+07</td>\n",
       "      <td>0</td>\n",
       "      <td>Thriller</td>\n",
       "      <td>/movies/?id=intothestorm.htm</td>\n",
       "      <td>5e+07</td>\n",
       "      <td>Todd Garner</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>2014-08-08</td>\n",
       "      <td>89</td>\n",
       "      <td>Warner Bros. (New Line)</td>\n",
       "      <td>Into The Storm</td>\n",
       "      <td>1.617e+08</td>\n",
       "      <td>John Swetnam</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>(#3 rank, 3,434 theaters, $5,051 average)</td>\n",
       "      <td>3,434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3549</th>\n",
       "      <td>Ethan Hawke</td>\n",
       "      <td>None</td>\n",
       "      <td>Richard Linklater</td>\n",
       "      <td>2.5352e+07</td>\n",
       "      <td>0</td>\n",
       "      <td>Drama</td>\n",
       "      <td>/movies/?id=boyhood.htm</td>\n",
       "      <td>4e+06</td>\n",
       "      <td>Richard Linklater, John Sloss</td>\n",
       "      <td>R</td>\n",
       "      <td>2014-07-11</td>\n",
       "      <td>166</td>\n",
       "      <td>IFC</td>\n",
       "      <td>Boyhood</td>\n",
       "      <td>4.4495e+07</td>\n",
       "      <td>Richard Linklater</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>(#11 rank, 771 theaters, $2,584 average)</td>\n",
       "      <td>771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4281</th>\n",
       "      <td>Ashley Bell</td>\n",
       "      <td>Michael Wandmacher</td>\n",
       "      <td>Ed Gass-Donnelly</td>\n",
       "      <td>1.5179e+07</td>\n",
       "      <td>1</td>\n",
       "      <td>Horror</td>\n",
       "      <td>/movies/?id=lastexorcism2.htm</td>\n",
       "      <td>5e+06</td>\n",
       "      <td>Marc Abraham, Eric Newman, Eli Roth</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>2013-03-01</td>\n",
       "      <td>88</td>\n",
       "      <td>CBS Films</td>\n",
       "      <td>The Last Exorcism Part II</td>\n",
       "      <td>1.5179e+07</td>\n",
       "      <td>Damien Chazelle, Ed Gass-Donnelly</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>(#5 rank, 2,700 theaters, $2,862 average)</td>\n",
       "      <td>2,700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4290</th>\n",
       "      <td>Chris Brown</td>\n",
       "      <td>Christopher Lennertz</td>\n",
       "      <td>Benson Lee</td>\n",
       "      <td>8.8884e+06</td>\n",
       "      <td>0</td>\n",
       "      <td>Drama</td>\n",
       "      <td>/movies/?id=planetbboy3d.htm</td>\n",
       "      <td>2e+07</td>\n",
       "      <td>Beau Flynn, Will Packer</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>2013-09-20</td>\n",
       "      <td>109</td>\n",
       "      <td>Sony / Screen Gems</td>\n",
       "      <td>Battle of the Year</td>\n",
       "      <td>1.6549e+07</td>\n",
       "      <td>Brin Hill, Benson Lee, Chris Parker</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>(#5 rank, 2,008 theaters, $2,292 average)</td>\n",
       "      <td>2,008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4303</th>\n",
       "      <td>Justin Bieber</td>\n",
       "      <td>None</td>\n",
       "      <td>Jon Chu</td>\n",
       "      <td>6.2066e+06</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>/movies/?id=believe.htm</td>\n",
       "      <td>5e+06</td>\n",
       "      <td>Justin Bieber, Scooter Braun</td>\n",
       "      <td>PG</td>\n",
       "      <td>2013-12-25</td>\n",
       "      <td>91</td>\n",
       "      <td>Open Road Films</td>\n",
       "      <td>Believe</td>\n",
       "      <td>6.2066e+06</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>(#14 rank, 1,037 theaters, $1,936 average)</td>\n",
       "      <td>1,037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4913</th>\n",
       "      <td>Jeffrey Dean Morgan, Matisyahu</td>\n",
       "      <td>None</td>\n",
       "      <td>Ole Bornedal</td>\n",
       "      <td>4.913e+07</td>\n",
       "      <td>0</td>\n",
       "      <td>Horror</td>\n",
       "      <td>/movies/?id=dibbukbox.htm</td>\n",
       "      <td>1.4e+07</td>\n",
       "      <td>Sam Raimi, Peter Schlessel</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>2012-08-31</td>\n",
       "      <td>91</td>\n",
       "      <td>Lionsgate</td>\n",
       "      <td>The Possession</td>\n",
       "      <td>8.5446e+07</td>\n",
       "      <td>Juliet Snowden, Stiles White</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>(#1 rank, 2,816 theaters, $6,297 average)</td>\n",
       "      <td>2,816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4914</th>\n",
       "      <td>Zachary Gordon</td>\n",
       "      <td>Ed Shearmur</td>\n",
       "      <td>David Bowers</td>\n",
       "      <td>4.9009e+07</td>\n",
       "      <td>1</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>/movies/?id=diaryofawimpykid3.htm</td>\n",
       "      <td>2.2e+07</td>\n",
       "      <td>Nina Jacobson</td>\n",
       "      <td>PG</td>\n",
       "      <td>2012-08-03</td>\n",
       "      <td>94</td>\n",
       "      <td>Fox</td>\n",
       "      <td>Diary of a Wimpy Kid: Dog Days</td>\n",
       "      <td>7.7112e+07</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>(#3 rank, 3,391 theaters, $4,312 average)</td>\n",
       "      <td>3,391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4915</th>\n",
       "      <td>Ethan Hawke</td>\n",
       "      <td>Christopher Young</td>\n",
       "      <td>Scott Derrickson</td>\n",
       "      <td>4.8087e+07</td>\n",
       "      <td>1</td>\n",
       "      <td>Horror</td>\n",
       "      <td>/movies/?id=sinister.htm</td>\n",
       "      <td>3e+06</td>\n",
       "      <td>Jason Blum, Brian Kavanaugh-Jones</td>\n",
       "      <td>R</td>\n",
       "      <td>2012-10-12</td>\n",
       "      <td>110</td>\n",
       "      <td>Lionsgate/Summit</td>\n",
       "      <td>Sinister</td>\n",
       "      <td>7.7712e+07</td>\n",
       "      <td>C. Robert Cargill, Scott Derrickson</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>(#3 rank, 2,527 theaters, $7,126 average)</td>\n",
       "      <td>2,527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4922</th>\n",
       "      <td>Kate Upton</td>\n",
       "      <td>John Debney</td>\n",
       "      <td>The Farrelly Bros.</td>\n",
       "      <td>4.4338e+07</td>\n",
       "      <td>0</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>/movies/?id=threestooges.htm</td>\n",
       "      <td>3e+07</td>\n",
       "      <td>The Farrelly Bros., Bradley Thomas, Charles B....</td>\n",
       "      <td>PG</td>\n",
       "      <td>2012-04-13</td>\n",
       "      <td>92</td>\n",
       "      <td>Fox</td>\n",
       "      <td>The Three Stooges</td>\n",
       "      <td>5.4819e+07</td>\n",
       "      <td>Mike Cerrone, The Farrelly Bros.</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>(#2 rank, 3,477 theaters, $4,892 average)</td>\n",
       "      <td>3,477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4937</th>\n",
       "      <td>Ryan Guzman</td>\n",
       "      <td>Aaron Zigman</td>\n",
       "      <td>Scott Speer</td>\n",
       "      <td>3.5075e+07</td>\n",
       "      <td>1</td>\n",
       "      <td>Music Drama</td>\n",
       "      <td>/movies/?id=stepup4.htm</td>\n",
       "      <td>3.3e+07</td>\n",
       "      <td>Jennifer Gibgot, Adam Shankman</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>2012-07-27</td>\n",
       "      <td>100</td>\n",
       "      <td>Lionsgate/Summit</td>\n",
       "      <td>Step Up Revolution</td>\n",
       "      <td>1.4047e+08</td>\n",
       "      <td>Jenny Mayer</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>(#4 rank, 2,567 theaters, $4,570 average)</td>\n",
       "      <td>2,567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5540</th>\n",
       "      <td>Dustin Ingram</td>\n",
       "      <td>None</td>\n",
       "      <td>Henry Joost, Ariel Schulman</td>\n",
       "      <td>1.0403e+08</td>\n",
       "      <td>1</td>\n",
       "      <td>Horror</td>\n",
       "      <td>/movies/?id=paranormalactivity3.htm</td>\n",
       "      <td>5e+06</td>\n",
       "      <td>Jason Blum, Akiva Goldsman, Oren Peli, Steven ...</td>\n",
       "      <td>R</td>\n",
       "      <td>2011-10-21</td>\n",
       "      <td>81</td>\n",
       "      <td>Paramount</td>\n",
       "      <td>Paranormal Activity 3</td>\n",
       "      <td>2.0704e+08</td>\n",
       "      <td>Christopher B. Landon</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>(#1 rank, 3,321 theaters, $15,829 average)</td>\n",
       "      <td>3,321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5564</th>\n",
       "      <td>Justin Bieber</td>\n",
       "      <td>Deborah Lurie</td>\n",
       "      <td>Jon Chu</td>\n",
       "      <td>7.3014e+07</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>/movies/?id=bieber3d.htm</td>\n",
       "      <td>1.3e+07</td>\n",
       "      <td>Scooter Braun</td>\n",
       "      <td>G</td>\n",
       "      <td>2011-02-11</td>\n",
       "      <td>105</td>\n",
       "      <td>Paramount</td>\n",
       "      <td>Justin Bieber: Never Say Never</td>\n",
       "      <td>9.9037e+07</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>(#2 rank, 3,105 theaters, $9,505 average)</td>\n",
       "      <td>3,105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5580</th>\n",
       "      <td>Zachary Gordon</td>\n",
       "      <td>Ed Shearmur</td>\n",
       "      <td>David Bowers</td>\n",
       "      <td>5.2699e+07</td>\n",
       "      <td>1</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>/movies/?id=diaryofawimpykid2.htm</td>\n",
       "      <td>2.1e+07</td>\n",
       "      <td>Nina Jacobson</td>\n",
       "      <td>PG</td>\n",
       "      <td>2011-03-25</td>\n",
       "      <td>96</td>\n",
       "      <td>Fox</td>\n",
       "      <td>Diary of a Wimpy Kid: Rodrick Rules</td>\n",
       "      <td>7.2417e+07</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>(#1 rank, 3,167 theaters, $7,500 average)</td>\n",
       "      <td>3,167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5608</th>\n",
       "      <td>Alex Kendrick</td>\n",
       "      <td>None</td>\n",
       "      <td>Alex Kendrick</td>\n",
       "      <td>3.4522e+07</td>\n",
       "      <td>0</td>\n",
       "      <td>Drama</td>\n",
       "      <td>/movies/?id=courageous.htm</td>\n",
       "      <td>2e+06</td>\n",
       "      <td>Stephen Kendrick</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>2011-09-30</td>\n",
       "      <td>130</td>\n",
       "      <td>TriStar</td>\n",
       "      <td>Courageous</td>\n",
       "      <td>3.4522e+07</td>\n",
       "      <td>Alex Kendrick, Stephen Kendrick</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>(#4 rank, 1,161 theaters, $7,849 average)</td>\n",
       "      <td>1,161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5655</th>\n",
       "      <td>Aimee Teegarden</td>\n",
       "      <td>Deborah Lurie</td>\n",
       "      <td>Joe Nussbaum</td>\n",
       "      <td>1.013e+07</td>\n",
       "      <td>0</td>\n",
       "      <td>Comedy / Drama</td>\n",
       "      <td>/movies/?id=prom.htm</td>\n",
       "      <td>8e+06</td>\n",
       "      <td>None</td>\n",
       "      <td>PG</td>\n",
       "      <td>2011-04-29</td>\n",
       "      <td>103</td>\n",
       "      <td>Buena Vista</td>\n",
       "      <td>Prom</td>\n",
       "      <td>1.013e+07</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>(#5 rank, 2,730 theaters, $1,726 average)</td>\n",
       "      <td>2,730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5751</th>\n",
       "      <td>Morgan Spurlock</td>\n",
       "      <td>None</td>\n",
       "      <td>Morgan Spurlock</td>\n",
       "      <td>6.3848e+05</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>/movies/?id=greatestmovieeversold.htm</td>\n",
       "      <td>1e+06</td>\n",
       "      <td>Keith Calder, Morgan Spurlock</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>2011-04-22</td>\n",
       "      <td>90</td>\n",
       "      <td>Sony Classics</td>\n",
       "      <td>POM Wonderful Presents: The Greatest Movie Eve...</td>\n",
       "      <td>6.3848e+05</td>\n",
       "      <td>Morgan Spurlock</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>(#37 rank, 18 theaters, $6,572 average)</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5755</th>\n",
       "      <td>Miranda July</td>\n",
       "      <td>None</td>\n",
       "      <td>Miranda July</td>\n",
       "      <td>5.6866e+05</td>\n",
       "      <td>0</td>\n",
       "      <td>Drama</td>\n",
       "      <td>/movies/?id=future.htm</td>\n",
       "      <td>1e+06</td>\n",
       "      <td>None</td>\n",
       "      <td>R</td>\n",
       "      <td>2011-07-29</td>\n",
       "      <td>91</td>\n",
       "      <td>Roadside Attractions</td>\n",
       "      <td>The Future</td>\n",
       "      <td>5.6866e+05</td>\n",
       "      <td>Miranda July</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>(#54 rank, 1 theaters, $27,137 average)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6192</th>\n",
       "      <td>Ashley Bell</td>\n",
       "      <td>Nathan Barr</td>\n",
       "      <td>Daniel Stamm</td>\n",
       "      <td>4.1034e+07</td>\n",
       "      <td>1</td>\n",
       "      <td>Horror</td>\n",
       "      <td>/movies/?id=lastexorcism.htm</td>\n",
       "      <td>1e+06</td>\n",
       "      <td>Marc Abraham, Eric Newman, Eli Roth</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>2010-08-27</td>\n",
       "      <td>100</td>\n",
       "      <td>Lionsgate</td>\n",
       "      <td>The Last Exorcism</td>\n",
       "      <td>6.7738e+07</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>(#2 rank, 2,874 theaters, $7,087 average)</td>\n",
       "      <td>2,874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6227</th>\n",
       "      <td>Crystal Reed</td>\n",
       "      <td>None</td>\n",
       "      <td>Greg Strause</td>\n",
       "      <td>2.1394e+07</td>\n",
       "      <td>0</td>\n",
       "      <td>Thriller</td>\n",
       "      <td>/movies/?id=skyline.htm</td>\n",
       "      <td>1e+07</td>\n",
       "      <td>Ryan Kavanaugh, Brian Kavanaugh-Jones, Brett R...</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>2010-11-12</td>\n",
       "      <td>100</td>\n",
       "      <td>Universal</td>\n",
       "      <td>Skyline</td>\n",
       "      <td>6.6821e+07</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>(#4 rank, 2,880 theaters, $4,060 average)</td>\n",
       "      <td>2,880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6240</th>\n",
       "      <td>Max Thieriot</td>\n",
       "      <td>Marco Beltrami</td>\n",
       "      <td>Wes Craven</td>\n",
       "      <td>1.4744e+07</td>\n",
       "      <td>0</td>\n",
       "      <td>Horror</td>\n",
       "      <td>/movies/?id=mysoultotake.htm</td>\n",
       "      <td>2.5e+07</td>\n",
       "      <td>Ryan Kavanaugh, Tucker Tooley</td>\n",
       "      <td>R</td>\n",
       "      <td>2010-10-08</td>\n",
       "      <td>107</td>\n",
       "      <td>Universal</td>\n",
       "      <td>My Soul to Take</td>\n",
       "      <td>2.0976e+07</td>\n",
       "      <td>Wes Craven</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>(#5 rank, 2,572 theaters, $2,660 average)</td>\n",
       "      <td>2,572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6427</th>\n",
       "      <td>Kang-ho Song</td>\n",
       "      <td>None</td>\n",
       "      <td>Jee-woon Kim</td>\n",
       "      <td>1.2849e+05</td>\n",
       "      <td>0</td>\n",
       "      <td>Action / Adventure</td>\n",
       "      <td>/movies/?id=thegoodthebadtheweird.htm</td>\n",
       "      <td>1e+07</td>\n",
       "      <td>None</td>\n",
       "      <td>R</td>\n",
       "      <td>2010-04-23</td>\n",
       "      <td>130</td>\n",
       "      <td>IFC</td>\n",
       "      <td>The Good, the Bad, the Weird</td>\n",
       "      <td>4.4261e+07</td>\n",
       "      <td>Jee-woon Kim</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>(#83 rank, 1 theaters, $4,775 average)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6759</th>\n",
       "      <td>Naturi Naughton</td>\n",
       "      <td>Mark Isham</td>\n",
       "      <td>None</td>\n",
       "      <td>2.2456e+07</td>\n",
       "      <td>0</td>\n",
       "      <td>Music Drama</td>\n",
       "      <td>/movies/?id=fame09.htm</td>\n",
       "      <td>1.8e+07</td>\n",
       "      <td>Mark Canton, Gary Lucchesi, Tom Rosenberg, Ric...</td>\n",
       "      <td>PG</td>\n",
       "      <td>2009-09-25</td>\n",
       "      <td>107</td>\n",
       "      <td>MGM</td>\n",
       "      <td>Fame (2009)</td>\n",
       "      <td>7.7212e+07</td>\n",
       "      <td>Allison Burnett</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>(#3 rank, 3,096 theaters, $3,234 average)</td>\n",
       "      <td>3,096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6779</th>\n",
       "      <td>Hayden Panettiere</td>\n",
       "      <td>Christophe Beck</td>\n",
       "      <td>Chris Columbus</td>\n",
       "      <td>1.4801e+07</td>\n",
       "      <td>0</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>/movies/?id=iloveyoubethcooper.htm</td>\n",
       "      <td>1.8e+07</td>\n",
       "      <td>Chris Columbus</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>2009-07-10</td>\n",
       "      <td>102</td>\n",
       "      <td>Fox</td>\n",
       "      <td>I Love You Beth Cooper</td>\n",
       "      <td>1.5822e+07</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>(#7 rank, 1,858 theaters, $2,648 average)</td>\n",
       "      <td>1,858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6816</th>\n",
       "      <td>Abbie Cornish</td>\n",
       "      <td>None</td>\n",
       "      <td>Jane Campion</td>\n",
       "      <td>4.4446e+06</td>\n",
       "      <td>0</td>\n",
       "      <td>Period Drama</td>\n",
       "      <td>/movies/?id=brightstar.htm</td>\n",
       "      <td>8e+06</td>\n",
       "      <td>None</td>\n",
       "      <td>PG</td>\n",
       "      <td>2009-09-16</td>\n",
       "      <td>119</td>\n",
       "      <td>Apparition</td>\n",
       "      <td>Bright Star</td>\n",
       "      <td>1.4351e+07</td>\n",
       "      <td>Jane Campion</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>(#34 rank, 19 theaters, $9,984 average)</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7258</th>\n",
       "      <td>Alex Kendrick</td>\n",
       "      <td>None</td>\n",
       "      <td>Alex Kendrick</td>\n",
       "      <td>3.3456e+07</td>\n",
       "      <td>0</td>\n",
       "      <td>Drama</td>\n",
       "      <td>/movies/?id=fireproof.htm</td>\n",
       "      <td>5e+05</td>\n",
       "      <td>Alex Kendrick, Stephen Kendrick</td>\n",
       "      <td>PG</td>\n",
       "      <td>2008-09-26</td>\n",
       "      <td>122</td>\n",
       "      <td>Samuel Goldwyn</td>\n",
       "      <td>Fireproof</td>\n",
       "      <td>3.3473e+07</td>\n",
       "      <td>Alex Kendrick, Stephen Kendrick</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>(#4 rank, 839 theaters, $8,148 average)</td>\n",
       "      <td>839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7459</th>\n",
       "      <td>Uma Thurman</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>3.0344e+05</td>\n",
       "      <td>0</td>\n",
       "      <td>Drama</td>\n",
       "      <td>/movies/?id=lifebeforehereyes.htm</td>\n",
       "      <td>1.3e+07</td>\n",
       "      <td>None</td>\n",
       "      <td>R</td>\n",
       "      <td>2008-04-18</td>\n",
       "      <td>90</td>\n",
       "      <td>Magnolia</td>\n",
       "      <td>The Life Before Her Eyes</td>\n",
       "      <td>7.2485e+06</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>(#70 rank, 8 theaters, $2,527 average)</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7823</th>\n",
       "      <td>Ne-Yo, Chris Brown</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>6.1356e+07</td>\n",
       "      <td>0</td>\n",
       "      <td>Music Drama</td>\n",
       "      <td>/movies/?id=stomptheyard.htm</td>\n",
       "      <td>1.3e+07</td>\n",
       "      <td>Rob Hardy, Will Packer</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>2007-01-12</td>\n",
       "      <td>115</td>\n",
       "      <td>Sony / Screen Gems</td>\n",
       "      <td>Stomp the Yard</td>\n",
       "      <td>7.5511e+07</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>(#1 rank, 2,051 theaters, $10,645 average)</td>\n",
       "      <td>2,051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7969</th>\n",
       "      <td>Matthias Schoenaerts</td>\n",
       "      <td>None</td>\n",
       "      <td>Paul Verhoeven</td>\n",
       "      <td>4.3985e+06</td>\n",
       "      <td>0</td>\n",
       "      <td>Foreign</td>\n",
       "      <td>/movies/?id=blackbook.htm</td>\n",
       "      <td>2.1e+07</td>\n",
       "      <td>None</td>\n",
       "      <td>R</td>\n",
       "      <td>2007-04-04</td>\n",
       "      <td>145</td>\n",
       "      <td>Sony Classics</td>\n",
       "      <td>Black Book</td>\n",
       "      <td>2.6769e+07</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>(#35 rank, 9 theaters, $12,502 average)</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8486</th>\n",
       "      <td>Emilie de Ravin</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>4.1779e+07</td>\n",
       "      <td>0</td>\n",
       "      <td>Horror</td>\n",
       "      <td>/movies/?id=hillshaveeyes06.htm</td>\n",
       "      <td>1.5e+07</td>\n",
       "      <td>None</td>\n",
       "      <td>R</td>\n",
       "      <td>2006-03-10</td>\n",
       "      <td>105</td>\n",
       "      <td>Fox Searchlight</td>\n",
       "      <td>The Hills Have Eyes</td>\n",
       "      <td>6.9624e+07</td>\n",
       "      <td>Gregory Levasseur</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>(#3 rank, 2,620 theaters, $5,995 average)</td>\n",
       "      <td>2,620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8534</th>\n",
       "      <td>Milla Jovovich</td>\n",
       "      <td>Klaus Badelt</td>\n",
       "      <td>Kurt Wimmer</td>\n",
       "      <td>1.8536e+07</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>/movies/?id=ultraviolet.htm</td>\n",
       "      <td>3e+07</td>\n",
       "      <td>None</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>2006-03-03</td>\n",
       "      <td>85</td>\n",
       "      <td>Sony / Screen Gems</td>\n",
       "      <td>Ultraviolet</td>\n",
       "      <td>3.107e+07</td>\n",
       "      <td>Kurt Wimmer</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>(#4 rank, 2,558 theaters, $3,543 average)</td>\n",
       "      <td>2,558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8569</th>\n",
       "      <td>Alex Kendrick</td>\n",
       "      <td>None</td>\n",
       "      <td>Alex Kendrick</td>\n",
       "      <td>1.0178e+07</td>\n",
       "      <td>0</td>\n",
       "      <td>Sports Drama</td>\n",
       "      <td>/movies/?id=facingthegiants.htm</td>\n",
       "      <td>1e+05</td>\n",
       "      <td>Alex Kendrick, Stephen Kendrick</td>\n",
       "      <td>PG</td>\n",
       "      <td>2006-09-29</td>\n",
       "      <td>111</td>\n",
       "      <td>IDP</td>\n",
       "      <td>Facing the Giants</td>\n",
       "      <td>1.0243e+07</td>\n",
       "      <td>Alex Kendrick, Stephen Kendrick</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>(#12 rank, 441 theaters, $3,046 average)</td>\n",
       "      <td>441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9104</th>\n",
       "      <td>AnnaSophia Robb</td>\n",
       "      <td>Rachel Portman</td>\n",
       "      <td>None</td>\n",
       "      <td>3.2647e+07</td>\n",
       "      <td>0</td>\n",
       "      <td>Family</td>\n",
       "      <td>/movies/?id=winndixie.htm</td>\n",
       "      <td>1.4e+07</td>\n",
       "      <td>None</td>\n",
       "      <td>PG</td>\n",
       "      <td>2005-02-18</td>\n",
       "      <td>106</td>\n",
       "      <td>Fox</td>\n",
       "      <td>Because of Winn-Dixie</td>\n",
       "      <td>3.3589e+07</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>(#3 rank, 3,188 theaters, $3,202 average)</td>\n",
       "      <td>3,188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9665</th>\n",
       "      <td>Frankie Muniz</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2.363e+07</td>\n",
       "      <td>0</td>\n",
       "      <td>Family Adventure</td>\n",
       "      <td>/movies/?id=agentcodybanks2.htm</td>\n",
       "      <td>2.6e+07</td>\n",
       "      <td>Dylan Sellers, Bob Yari</td>\n",
       "      <td>PG</td>\n",
       "      <td>2004-03-12</td>\n",
       "      <td>101</td>\n",
       "      <td>MGM</td>\n",
       "      <td>Agent Cody Banks 2: Destination London</td>\n",
       "      <td>2.8819e+07</td>\n",
       "      <td>Don Rhymer</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>(#5 rank, 2,973 theaters, $2,695 average)</td>\n",
       "      <td>2,973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9694</th>\n",
       "      <td>Mary-Kate Olsen</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1.4071e+07</td>\n",
       "      <td>0</td>\n",
       "      <td>Family Comedy</td>\n",
       "      <td>/movies/?id=newyorkminute.htm</td>\n",
       "      <td>3e+07</td>\n",
       "      <td>Denise Di Novi, Alison Greenspan, Mary-Kate Olsen</td>\n",
       "      <td>PG</td>\n",
       "      <td>2004-05-07</td>\n",
       "      <td>86</td>\n",
       "      <td>Warner Bros.</td>\n",
       "      <td>New York Minute</td>\n",
       "      <td>2.129e+07</td>\n",
       "      <td>Bill Collage, Adam Cooper</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>(#4 rank, 3,006 theaters, $1,983 average)</td>\n",
       "      <td>3,006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10176</th>\n",
       "      <td>Jeremy Sumpter</td>\n",
       "      <td>James Newton Howard</td>\n",
       "      <td>P.J. Hogan</td>\n",
       "      <td>4.8463e+07</td>\n",
       "      <td>0</td>\n",
       "      <td>Family Adventure</td>\n",
       "      <td>/movies/?id=peterpan03.htm</td>\n",
       "      <td>1e+08</td>\n",
       "      <td>Lucy Fisher, Gail Lyon, Douglas Wick</td>\n",
       "      <td>PG</td>\n",
       "      <td>2003-12-25</td>\n",
       "      <td>113</td>\n",
       "      <td>Universal</td>\n",
       "      <td>Peter Pan</td>\n",
       "      <td>1.2198e+08</td>\n",
       "      <td>Michael Goldenberg</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>(#7 rank, 2,813 theaters, $3,960 average)</td>\n",
       "      <td>2,813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10282</th>\n",
       "      <td>Snoop Dogg*</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>3.8254e+06</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>/movies/?id=realcancun.htm</td>\n",
       "      <td>7e+06</td>\n",
       "      <td>None</td>\n",
       "      <td>R</td>\n",
       "      <td>2003-04-25</td>\n",
       "      <td>97</td>\n",
       "      <td>New Line</td>\n",
       "      <td>The Real Cancun</td>\n",
       "      <td>5.3451e+06</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>(#10 rank, 2,261 theaters, $932 average)</td>\n",
       "      <td>2,261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10713</th>\n",
       "      <td>Erika Christensen</td>\n",
       "      <td>John Debney</td>\n",
       "      <td>None</td>\n",
       "      <td>2.8565e+07</td>\n",
       "      <td>0</td>\n",
       "      <td>Thriller</td>\n",
       "      <td>/movies/?id=swimfan.htm</td>\n",
       "      <td>1e+07</td>\n",
       "      <td>Marcy Drogin, Allison Lyon Segan</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>2002-09-06</td>\n",
       "      <td>86</td>\n",
       "      <td>Fox</td>\n",
       "      <td>SwimFan</td>\n",
       "      <td>3.4411e+07</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>(#1 rank, 2,856 theaters, $3,965 average)</td>\n",
       "      <td>2,856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11236</th>\n",
       "      <td>Snoop Dogg</td>\n",
       "      <td>None</td>\n",
       "      <td>Ernest R. Dickerson</td>\n",
       "      <td>7.3167e+06</td>\n",
       "      <td>0</td>\n",
       "      <td>Horror</td>\n",
       "      <td>/movies/?id=bones.htm</td>\n",
       "      <td>1.6e+07</td>\n",
       "      <td>None</td>\n",
       "      <td>R</td>\n",
       "      <td>2001-10-26</td>\n",
       "      <td>92</td>\n",
       "      <td>New Line</td>\n",
       "      <td>Bones</td>\n",
       "      <td>8.3789e+06</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>(#10 rank, 847 theaters, $3,333 average)</td>\n",
       "      <td>847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11469</th>\n",
       "      <td>Hayden Panettiere*</td>\n",
       "      <td>James Newton Howard</td>\n",
       "      <td>None</td>\n",
       "      <td>1.3775e+08</td>\n",
       "      <td>0</td>\n",
       "      <td>Animation</td>\n",
       "      <td>/movies/?id=dinosaur.htm</td>\n",
       "      <td>1.27e+08</td>\n",
       "      <td>None</td>\n",
       "      <td>PG</td>\n",
       "      <td>2000-05-19</td>\n",
       "      <td>82</td>\n",
       "      <td>Buena Vista</td>\n",
       "      <td>Dinosaur</td>\n",
       "      <td>3.4982e+08</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>(#1 rank, 3,257 theaters, $11,929 average)</td>\n",
       "      <td>3,257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11494</th>\n",
       "      <td>Breckin Meyer</td>\n",
       "      <td>None</td>\n",
       "      <td>Todd Phillips</td>\n",
       "      <td>6.8541e+07</td>\n",
       "      <td>0</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>/movies/?id=roadtrip.htm</td>\n",
       "      <td>1.6e+07</td>\n",
       "      <td>Joe Medjuck, Tom Pollock, Ivan Reitman</td>\n",
       "      <td>R</td>\n",
       "      <td>2000-05-19</td>\n",
       "      <td>94</td>\n",
       "      <td>DreamWorks</td>\n",
       "      <td>Road Trip</td>\n",
       "      <td>1.1975e+08</td>\n",
       "      <td>Todd Phillips</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>(#3 rank, 2,530 theaters, $6,120 average)</td>\n",
       "      <td>2,530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11507</th>\n",
       "      <td>Ali Larter</td>\n",
       "      <td>None</td>\n",
       "      <td>James Wong</td>\n",
       "      <td>5.3331e+07</td>\n",
       "      <td>1</td>\n",
       "      <td>Horror</td>\n",
       "      <td>/movies/?id=finaldestination.htm</td>\n",
       "      <td>2.3e+07</td>\n",
       "      <td>Chris Bender*</td>\n",
       "      <td>R</td>\n",
       "      <td>2000-03-17</td>\n",
       "      <td>98</td>\n",
       "      <td>New Line</td>\n",
       "      <td>Final Destination</td>\n",
       "      <td>1.1288e+08</td>\n",
       "      <td>James Wong</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>(#3 rank, 2,587 theaters, $3,871 average)</td>\n",
       "      <td>2,587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11542</th>\n",
       "      <td>Wesley Snipes</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>3.0209e+07</td>\n",
       "      <td>0</td>\n",
       "      <td>Action Thriller</td>\n",
       "      <td>/movies/?id=artofwar.htm</td>\n",
       "      <td>6e+07</td>\n",
       "      <td>Don Carmody, Dan Halsted*</td>\n",
       "      <td>R</td>\n",
       "      <td>2000-08-25</td>\n",
       "      <td>117</td>\n",
       "      <td>Warner Bros.</td>\n",
       "      <td>The Art of War</td>\n",
       "      <td>4.04e+07</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>(#2 rank, 2,630 theaters, $3,958 average)</td>\n",
       "      <td>2,630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11893</th>\n",
       "      <td>Hugh Grant</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>3.3864e+07</td>\n",
       "      <td>0</td>\n",
       "      <td>Romantic Comedy</td>\n",
       "      <td>/movies/?id=mickeyblueeyes.htm</td>\n",
       "      <td>7.5e+07</td>\n",
       "      <td>None</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>1999-08-20</td>\n",
       "      <td>102</td>\n",
       "      <td>Warner Bros.</td>\n",
       "      <td>Mickey Blue Eyes</td>\n",
       "      <td>5.4264e+07</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>(#3 rank, 2,573 theaters, $3,955 average)</td>\n",
       "      <td>2,573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11922</th>\n",
       "      <td>Ali Larter</td>\n",
       "      <td>None</td>\n",
       "      <td>John Schultz</td>\n",
       "      <td>1.7845e+07</td>\n",
       "      <td>0</td>\n",
       "      <td>Romantic Comedy</td>\n",
       "      <td>/movies/?id=drivemecrazy.htm</td>\n",
       "      <td>8e+06</td>\n",
       "      <td>None</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>1999-10-01</td>\n",
       "      <td>91</td>\n",
       "      <td>Fox</td>\n",
       "      <td>Drive Me Crazy</td>\n",
       "      <td>2.2593e+07</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>(#6 rank, 2,222 theaters, $3,081 average)</td>\n",
       "      <td>2,222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11932</th>\n",
       "      <td>Ethan Hawke</td>\n",
       "      <td>James Newton Howard</td>\n",
       "      <td>Scott Hicks</td>\n",
       "      <td>1.4418e+07</td>\n",
       "      <td>0</td>\n",
       "      <td>Period Drama</td>\n",
       "      <td>/movies/?id=snowfallingoncedars.htm</td>\n",
       "      <td>3.5e+07</td>\n",
       "      <td>Kerry Heysen, Kathleen Kennedy, Frank Marshall...</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>1999-12-22</td>\n",
       "      <td>127</td>\n",
       "      <td>Universal</td>\n",
       "      <td>Snow Falling on Cedars</td>\n",
       "      <td>2.305e+07</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>(#10 rank, 1,150 theaters, $3,400 average)</td>\n",
       "      <td>1,150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13466</th>\n",
       "      <td>Hugh Grant</td>\n",
       "      <td>None</td>\n",
       "      <td>Mike Newell</td>\n",
       "      <td>5.2701e+07</td>\n",
       "      <td>0</td>\n",
       "      <td>Romantic Comedy</td>\n",
       "      <td>/movies/?id=fourweddingsandafuneral.htm</td>\n",
       "      <td>4e+06</td>\n",
       "      <td>Tim Bevan, Eric Fellner</td>\n",
       "      <td>R</td>\n",
       "      <td>1994-03-11</td>\n",
       "      <td>118</td>\n",
       "      <td>Gramercy</td>\n",
       "      <td>Four Weddings and a Funeral</td>\n",
       "      <td>2.457e+08</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>(#1 rank, 721 theaters, $5,773 average)</td>\n",
       "      <td>721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14257</th>\n",
       "      <td>Christina Ricci</td>\n",
       "      <td>Marc Shaiman</td>\n",
       "      <td>Barry Sonnenfeld</td>\n",
       "      <td>1.135e+08</td>\n",
       "      <td>0</td>\n",
       "      <td>Horror Comedy</td>\n",
       "      <td>/movies/?id=addamsfamily.htm</td>\n",
       "      <td>3e+07</td>\n",
       "      <td>Scott Rudin</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>1991-11-22</td>\n",
       "      <td>102</td>\n",
       "      <td>Paramount</td>\n",
       "      <td>The Addams Family</td>\n",
       "      <td>1.915e+08</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>(#1 rank, 2,411 theaters, $10,038 average)</td>\n",
       "      <td>2,411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14328</th>\n",
       "      <td>Madonna</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1.5013e+07</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>/movies/?id=truthordare.htm</td>\n",
       "      <td>4e+06</td>\n",
       "      <td>None</td>\n",
       "      <td>R</td>\n",
       "      <td>1991-05-10</td>\n",
       "      <td>114</td>\n",
       "      <td>Miramax</td>\n",
       "      <td>Madonna: Truth or Dare</td>\n",
       "      <td>2.9013e+07</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>(#9 rank, 652 theaters, $3,866 average)</td>\n",
       "      <td>652</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               actors              composer  \\\n",
       "59                   Taylor Schilling                  None   \n",
       "144                    Leigh Whannell                  None   \n",
       "289                    Milla Jovovich                  None   \n",
       "765                        Claire Foy                  None   \n",
       "1527               Alicia Silverstone                  None   \n",
       "2821                     Bella Thorne                  None   \n",
       "3518                   Jeremy Sumpter           Brian Tyler   \n",
       "3549                      Ethan Hawke                  None   \n",
       "4281                      Ashley Bell    Michael Wandmacher   \n",
       "4290                      Chris Brown  Christopher Lennertz   \n",
       "4303                    Justin Bieber                  None   \n",
       "4913   Jeffrey Dean Morgan, Matisyahu                  None   \n",
       "4914                   Zachary Gordon           Ed Shearmur   \n",
       "4915                      Ethan Hawke     Christopher Young   \n",
       "4922                       Kate Upton           John Debney   \n",
       "4937                      Ryan Guzman          Aaron Zigman   \n",
       "5540                    Dustin Ingram                  None   \n",
       "5564                    Justin Bieber         Deborah Lurie   \n",
       "5580                   Zachary Gordon           Ed Shearmur   \n",
       "5608                    Alex Kendrick                  None   \n",
       "5655                  Aimee Teegarden         Deborah Lurie   \n",
       "5751                  Morgan Spurlock                  None   \n",
       "5755                     Miranda July                  None   \n",
       "6192                      Ashley Bell           Nathan Barr   \n",
       "6227                     Crystal Reed                  None   \n",
       "6240                     Max Thieriot        Marco Beltrami   \n",
       "6427                     Kang-ho Song                  None   \n",
       "6759                  Naturi Naughton            Mark Isham   \n",
       "6779                Hayden Panettiere       Christophe Beck   \n",
       "6816                    Abbie Cornish                  None   \n",
       "7258                    Alex Kendrick                  None   \n",
       "7459                      Uma Thurman                  None   \n",
       "7823               Ne-Yo, Chris Brown                  None   \n",
       "7969             Matthias Schoenaerts                  None   \n",
       "8486                  Emilie de Ravin                  None   \n",
       "8534                   Milla Jovovich          Klaus Badelt   \n",
       "8569                    Alex Kendrick                  None   \n",
       "9104                  AnnaSophia Robb        Rachel Portman   \n",
       "9665                    Frankie Muniz                  None   \n",
       "9694                  Mary-Kate Olsen                  None   \n",
       "10176                  Jeremy Sumpter   James Newton Howard   \n",
       "10282                     Snoop Dogg*                  None   \n",
       "10713               Erika Christensen           John Debney   \n",
       "11236                      Snoop Dogg                  None   \n",
       "11469              Hayden Panettiere*   James Newton Howard   \n",
       "11494                   Breckin Meyer                  None   \n",
       "11507                      Ali Larter                  None   \n",
       "11542                   Wesley Snipes                  None   \n",
       "11893                      Hugh Grant                  None   \n",
       "11922                      Ali Larter                  None   \n",
       "11932                     Ethan Hawke   James Newton Howard   \n",
       "13466                      Hugh Grant                  None   \n",
       "14257                 Christina Ricci          Marc Shaiman   \n",
       "14328                         Madonna                  None   \n",
       "\n",
       "                         directors  domestic_revenue  franchise  \\\n",
       "59                            None        1.4856e+07          0   \n",
       "144                           None        6.7745e+07          1   \n",
       "289             Paul W.S. Anderson         2.683e+07          1   \n",
       "765                   Fede Alvarez        1.4841e+07          0   \n",
       "1527                  David Bowers        2.0739e+07          1   \n",
       "2821                    Ari Sandel         3.403e+07          0   \n",
       "3518                  Steven Quale        4.7602e+07          0   \n",
       "3549             Richard Linklater        2.5352e+07          0   \n",
       "4281              Ed Gass-Donnelly        1.5179e+07          1   \n",
       "4290                    Benson Lee        8.8884e+06          0   \n",
       "4303                       Jon Chu        6.2066e+06          0   \n",
       "4913                  Ole Bornedal         4.913e+07          0   \n",
       "4914                  David Bowers        4.9009e+07          1   \n",
       "4915              Scott Derrickson        4.8087e+07          1   \n",
       "4922            The Farrelly Bros.        4.4338e+07          0   \n",
       "4937                   Scott Speer        3.5075e+07          1   \n",
       "5540   Henry Joost, Ariel Schulman        1.0403e+08          1   \n",
       "5564                       Jon Chu        7.3014e+07          0   \n",
       "5580                  David Bowers        5.2699e+07          1   \n",
       "5608                 Alex Kendrick        3.4522e+07          0   \n",
       "5655                  Joe Nussbaum         1.013e+07          0   \n",
       "5751               Morgan Spurlock        6.3848e+05          0   \n",
       "5755                  Miranda July        5.6866e+05          0   \n",
       "6192                  Daniel Stamm        4.1034e+07          1   \n",
       "6227                  Greg Strause        2.1394e+07          0   \n",
       "6240                    Wes Craven        1.4744e+07          0   \n",
       "6427                  Jee-woon Kim        1.2849e+05          0   \n",
       "6759                          None        2.2456e+07          0   \n",
       "6779                Chris Columbus        1.4801e+07          0   \n",
       "6816                  Jane Campion        4.4446e+06          0   \n",
       "7258                 Alex Kendrick        3.3456e+07          0   \n",
       "7459                          None        3.0344e+05          0   \n",
       "7823                          None        6.1356e+07          0   \n",
       "7969                Paul Verhoeven        4.3985e+06          0   \n",
       "8486                          None        4.1779e+07          0   \n",
       "8534                   Kurt Wimmer        1.8536e+07          0   \n",
       "8569                 Alex Kendrick        1.0178e+07          0   \n",
       "9104                          None        3.2647e+07          0   \n",
       "9665                          None         2.363e+07          0   \n",
       "9694                          None        1.4071e+07          0   \n",
       "10176                   P.J. Hogan        4.8463e+07          0   \n",
       "10282                         None        3.8254e+06          0   \n",
       "10713                         None        2.8565e+07          0   \n",
       "11236          Ernest R. Dickerson        7.3167e+06          0   \n",
       "11469                         None        1.3775e+08          0   \n",
       "11494                Todd Phillips        6.8541e+07          0   \n",
       "11507                   James Wong        5.3331e+07          1   \n",
       "11542                         None        3.0209e+07          0   \n",
       "11893                         None        3.3864e+07          0   \n",
       "11922                 John Schultz        1.7845e+07          0   \n",
       "11932                  Scott Hicks        1.4418e+07          0   \n",
       "13466                  Mike Newell        5.2701e+07          0   \n",
       "14257             Barry Sonnenfeld         1.135e+08          0   \n",
       "14328                         None        1.5013e+07          0   \n",
       "\n",
       "                   genres                                     link  \\\n",
       "59                 Horror               /movies/?id=theprodigy.htm   \n",
       "144                Horror               /movies/?id=insidious4.htm   \n",
       "289         Sci-Fi Action            /movies/?id=residentevil6.htm   \n",
       "765                  None   /movies/?id=thegirlinthespidersweb.htm   \n",
       "1527               Comedy        /movies/?id=diaryofawimpykid4.htm   \n",
       "2821               Comedy                     /movies/?id=duff.htm   \n",
       "3518             Thriller             /movies/?id=intothestorm.htm   \n",
       "3549                Drama                  /movies/?id=boyhood.htm   \n",
       "4281               Horror            /movies/?id=lastexorcism2.htm   \n",
       "4290                Drama             /movies/?id=planetbboy3d.htm   \n",
       "4303                 None                  /movies/?id=believe.htm   \n",
       "4913               Horror                /movies/?id=dibbukbox.htm   \n",
       "4914               Comedy        /movies/?id=diaryofawimpykid3.htm   \n",
       "4915               Horror                 /movies/?id=sinister.htm   \n",
       "4922               Comedy             /movies/?id=threestooges.htm   \n",
       "4937          Music Drama                  /movies/?id=stepup4.htm   \n",
       "5540               Horror      /movies/?id=paranormalactivity3.htm   \n",
       "5564                 None                 /movies/?id=bieber3d.htm   \n",
       "5580               Comedy        /movies/?id=diaryofawimpykid2.htm   \n",
       "5608                Drama               /movies/?id=courageous.htm   \n",
       "5655       Comedy / Drama                     /movies/?id=prom.htm   \n",
       "5751                 None    /movies/?id=greatestmovieeversold.htm   \n",
       "5755                Drama                   /movies/?id=future.htm   \n",
       "6192               Horror             /movies/?id=lastexorcism.htm   \n",
       "6227             Thriller                  /movies/?id=skyline.htm   \n",
       "6240               Horror             /movies/?id=mysoultotake.htm   \n",
       "6427   Action / Adventure    /movies/?id=thegoodthebadtheweird.htm   \n",
       "6759          Music Drama                   /movies/?id=fame09.htm   \n",
       "6779               Comedy       /movies/?id=iloveyoubethcooper.htm   \n",
       "6816         Period Drama               /movies/?id=brightstar.htm   \n",
       "7258                Drama                /movies/?id=fireproof.htm   \n",
       "7459                Drama        /movies/?id=lifebeforehereyes.htm   \n",
       "7823          Music Drama             /movies/?id=stomptheyard.htm   \n",
       "7969              Foreign                /movies/?id=blackbook.htm   \n",
       "8486               Horror          /movies/?id=hillshaveeyes06.htm   \n",
       "8534                 None              /movies/?id=ultraviolet.htm   \n",
       "8569         Sports Drama          /movies/?id=facingthegiants.htm   \n",
       "9104               Family                /movies/?id=winndixie.htm   \n",
       "9665     Family Adventure          /movies/?id=agentcodybanks2.htm   \n",
       "9694        Family Comedy            /movies/?id=newyorkminute.htm   \n",
       "10176    Family Adventure               /movies/?id=peterpan03.htm   \n",
       "10282                None               /movies/?id=realcancun.htm   \n",
       "10713            Thriller                  /movies/?id=swimfan.htm   \n",
       "11236              Horror                    /movies/?id=bones.htm   \n",
       "11469           Animation                 /movies/?id=dinosaur.htm   \n",
       "11494              Comedy                 /movies/?id=roadtrip.htm   \n",
       "11507              Horror         /movies/?id=finaldestination.htm   \n",
       "11542     Action Thriller                 /movies/?id=artofwar.htm   \n",
       "11893     Romantic Comedy           /movies/?id=mickeyblueeyes.htm   \n",
       "11922     Romantic Comedy             /movies/?id=drivemecrazy.htm   \n",
       "11932        Period Drama      /movies/?id=snowfallingoncedars.htm   \n",
       "13466     Romantic Comedy  /movies/?id=fourweddingsandafuneral.htm   \n",
       "14257       Horror Comedy             /movies/?id=addamsfamily.htm   \n",
       "14328                None              /movies/?id=truthordare.htm   \n",
       "\n",
       "       prod_budget                                          producers rating  \\\n",
       "59           6e+06                                               None      R   \n",
       "144          1e+07                   Jason Blum, Oren Peli, James Wan  PG-13   \n",
       "289          4e+07                                               None      R   \n",
       "765        4.3e+07                                               None      R   \n",
       "1527       2.2e+07                                      Nina Jacobson     PG   \n",
       "2821         8e+06                                                McG  PG-13   \n",
       "3518         5e+07                                        Todd Garner  PG-13   \n",
       "3549         4e+06                      Richard Linklater, John Sloss      R   \n",
       "4281         5e+06                Marc Abraham, Eric Newman, Eli Roth  PG-13   \n",
       "4290         2e+07                            Beau Flynn, Will Packer  PG-13   \n",
       "4303         5e+06                       Justin Bieber, Scooter Braun     PG   \n",
       "4913       1.4e+07                         Sam Raimi, Peter Schlessel  PG-13   \n",
       "4914       2.2e+07                                      Nina Jacobson     PG   \n",
       "4915         3e+06                  Jason Blum, Brian Kavanaugh-Jones      R   \n",
       "4922         3e+07  The Farrelly Bros., Bradley Thomas, Charles B....     PG   \n",
       "4937       3.3e+07                     Jennifer Gibgot, Adam Shankman  PG-13   \n",
       "5540         5e+06  Jason Blum, Akiva Goldsman, Oren Peli, Steven ...      R   \n",
       "5564       1.3e+07                                      Scooter Braun      G   \n",
       "5580       2.1e+07                                      Nina Jacobson     PG   \n",
       "5608         2e+06                                   Stephen Kendrick  PG-13   \n",
       "5655         8e+06                                               None     PG   \n",
       "5751         1e+06                      Keith Calder, Morgan Spurlock  PG-13   \n",
       "5755         1e+06                                               None      R   \n",
       "6192         1e+06                Marc Abraham, Eric Newman, Eli Roth  PG-13   \n",
       "6227         1e+07  Ryan Kavanaugh, Brian Kavanaugh-Jones, Brett R...  PG-13   \n",
       "6240       2.5e+07                      Ryan Kavanaugh, Tucker Tooley      R   \n",
       "6427         1e+07                                               None      R   \n",
       "6759       1.8e+07  Mark Canton, Gary Lucchesi, Tom Rosenberg, Ric...     PG   \n",
       "6779       1.8e+07                                     Chris Columbus  PG-13   \n",
       "6816         8e+06                                               None     PG   \n",
       "7258         5e+05                    Alex Kendrick, Stephen Kendrick     PG   \n",
       "7459       1.3e+07                                               None      R   \n",
       "7823       1.3e+07                             Rob Hardy, Will Packer  PG-13   \n",
       "7969       2.1e+07                                               None      R   \n",
       "8486       1.5e+07                                               None      R   \n",
       "8534         3e+07                                               None  PG-13   \n",
       "8569         1e+05                    Alex Kendrick, Stephen Kendrick     PG   \n",
       "9104       1.4e+07                                               None     PG   \n",
       "9665       2.6e+07                            Dylan Sellers, Bob Yari     PG   \n",
       "9694         3e+07  Denise Di Novi, Alison Greenspan, Mary-Kate Olsen     PG   \n",
       "10176        1e+08               Lucy Fisher, Gail Lyon, Douglas Wick     PG   \n",
       "10282        7e+06                                               None      R   \n",
       "10713        1e+07                   Marcy Drogin, Allison Lyon Segan  PG-13   \n",
       "11236      1.6e+07                                               None      R   \n",
       "11469     1.27e+08                                               None     PG   \n",
       "11494      1.6e+07             Joe Medjuck, Tom Pollock, Ivan Reitman      R   \n",
       "11507      2.3e+07                                      Chris Bender*      R   \n",
       "11542        6e+07                          Don Carmody, Dan Halsted*      R   \n",
       "11893      7.5e+07                                               None  PG-13   \n",
       "11922        8e+06                                               None  PG-13   \n",
       "11932      3.5e+07  Kerry Heysen, Kathleen Kennedy, Frank Marshall...  PG-13   \n",
       "13466        4e+06                            Tim Bevan, Eric Fellner      R   \n",
       "14257        3e+07                                        Scott Rudin  PG-13   \n",
       "14328        4e+06                                               None      R   \n",
       "\n",
       "      release_date  runtime                   studio  \\\n",
       "59      2019-02-08       92           Orion Pictures   \n",
       "144     2018-01-05      103                Universal   \n",
       "289     2017-01-27      106       Sony / Screen Gems   \n",
       "765     2018-11-09      117          Sony / Columbia   \n",
       "1527    2017-05-19       90                      Fox   \n",
       "2821    2015-02-20      101                Lionsgate   \n",
       "3518    2014-08-08       89  Warner Bros. (New Line)   \n",
       "3549    2014-07-11      166                      IFC   \n",
       "4281    2013-03-01       88                CBS Films   \n",
       "4290    2013-09-20      109       Sony / Screen Gems   \n",
       "4303    2013-12-25       91          Open Road Films   \n",
       "4913    2012-08-31       91                Lionsgate   \n",
       "4914    2012-08-03       94                      Fox   \n",
       "4915    2012-10-12      110         Lionsgate/Summit   \n",
       "4922    2012-04-13       92                      Fox   \n",
       "4937    2012-07-27      100         Lionsgate/Summit   \n",
       "5540    2011-10-21       81                Paramount   \n",
       "5564    2011-02-11      105                Paramount   \n",
       "5580    2011-03-25       96                      Fox   \n",
       "5608    2011-09-30      130                  TriStar   \n",
       "5655    2011-04-29      103              Buena Vista   \n",
       "5751    2011-04-22       90            Sony Classics   \n",
       "5755    2011-07-29       91     Roadside Attractions   \n",
       "6192    2010-08-27      100                Lionsgate   \n",
       "6227    2010-11-12      100                Universal   \n",
       "6240    2010-10-08      107                Universal   \n",
       "6427    2010-04-23      130                      IFC   \n",
       "6759    2009-09-25      107                      MGM   \n",
       "6779    2009-07-10      102                      Fox   \n",
       "6816    2009-09-16      119               Apparition   \n",
       "7258    2008-09-26      122           Samuel Goldwyn   \n",
       "7459    2008-04-18       90                 Magnolia   \n",
       "7823    2007-01-12      115       Sony / Screen Gems   \n",
       "7969    2007-04-04      145            Sony Classics   \n",
       "8486    2006-03-10      105          Fox Searchlight   \n",
       "8534    2006-03-03       85       Sony / Screen Gems   \n",
       "8569    2006-09-29      111                      IDP   \n",
       "9104    2005-02-18      106                      Fox   \n",
       "9665    2004-03-12      101                      MGM   \n",
       "9694    2004-05-07       86             Warner Bros.   \n",
       "10176   2003-12-25      113                Universal   \n",
       "10282   2003-04-25       97                 New Line   \n",
       "10713   2002-09-06       86                      Fox   \n",
       "11236   2001-10-26       92                 New Line   \n",
       "11469   2000-05-19       82              Buena Vista   \n",
       "11494   2000-05-19       94               DreamWorks   \n",
       "11507   2000-03-17       98                 New Line   \n",
       "11542   2000-08-25      117             Warner Bros.   \n",
       "11893   1999-08-20      102             Warner Bros.   \n",
       "11922   1999-10-01       91                      Fox   \n",
       "11932   1999-12-22      127                Universal   \n",
       "13466   1994-03-11      118                 Gramercy   \n",
       "14257   1991-11-22      102                Paramount   \n",
       "14328   1991-05-10      114                  Miramax   \n",
       "\n",
       "                                                   title  worldwide_revenue  \\\n",
       "59                                           The Prodigy         1.4856e+07   \n",
       "144                              Insidious: The Last Key         1.6789e+08   \n",
       "289                     Resident Evil: The Final Chapter         3.1224e+08   \n",
       "765    The Girl in the Spider's Web: A New Dragon Tat...         3.5165e+07   \n",
       "1527                 Diary of a Wimpy Kid: The Long Haul         4.0141e+07   \n",
       "2821                                            The DUFF         4.3529e+07   \n",
       "3518                                      Into The Storm          1.617e+08   \n",
       "3549                                             Boyhood         4.4495e+07   \n",
       "4281                           The Last Exorcism Part II         1.5179e+07   \n",
       "4290                                  Battle of the Year         1.6549e+07   \n",
       "4303                                             Believe         6.2066e+06   \n",
       "4913                                      The Possession         8.5446e+07   \n",
       "4914                      Diary of a Wimpy Kid: Dog Days         7.7112e+07   \n",
       "4915                                            Sinister         7.7712e+07   \n",
       "4922                                   The Three Stooges         5.4819e+07   \n",
       "4937                                  Step Up Revolution         1.4047e+08   \n",
       "5540                               Paranormal Activity 3         2.0704e+08   \n",
       "5564                      Justin Bieber: Never Say Never         9.9037e+07   \n",
       "5580                 Diary of a Wimpy Kid: Rodrick Rules         7.2417e+07   \n",
       "5608                                          Courageous         3.4522e+07   \n",
       "5655                                                Prom          1.013e+07   \n",
       "5751   POM Wonderful Presents: The Greatest Movie Eve...         6.3848e+05   \n",
       "5755                                          The Future         5.6866e+05   \n",
       "6192                                   The Last Exorcism         6.7738e+07   \n",
       "6227                                             Skyline         6.6821e+07   \n",
       "6240                                     My Soul to Take         2.0976e+07   \n",
       "6427                        The Good, the Bad, the Weird         4.4261e+07   \n",
       "6759                                         Fame (2009)         7.7212e+07   \n",
       "6779                              I Love You Beth Cooper         1.5822e+07   \n",
       "6816                                         Bright Star         1.4351e+07   \n",
       "7258                                           Fireproof         3.3473e+07   \n",
       "7459                            The Life Before Her Eyes         7.2485e+06   \n",
       "7823                                      Stomp the Yard         7.5511e+07   \n",
       "7969                                          Black Book         2.6769e+07   \n",
       "8486                                 The Hills Have Eyes         6.9624e+07   \n",
       "8534                                         Ultraviolet          3.107e+07   \n",
       "8569                                   Facing the Giants         1.0243e+07   \n",
       "9104                               Because of Winn-Dixie         3.3589e+07   \n",
       "9665              Agent Cody Banks 2: Destination London         2.8819e+07   \n",
       "9694                                     New York Minute          2.129e+07   \n",
       "10176                                          Peter Pan         1.2198e+08   \n",
       "10282                                    The Real Cancun         5.3451e+06   \n",
       "10713                                            SwimFan         3.4411e+07   \n",
       "11236                                              Bones         8.3789e+06   \n",
       "11469                                           Dinosaur         3.4982e+08   \n",
       "11494                                          Road Trip         1.1975e+08   \n",
       "11507                                  Final Destination         1.1288e+08   \n",
       "11542                                     The Art of War           4.04e+07   \n",
       "11893                                   Mickey Blue Eyes         5.4264e+07   \n",
       "11922                                     Drive Me Crazy         2.2593e+07   \n",
       "11932                             Snow Falling on Cedars          2.305e+07   \n",
       "13466                        Four Weddings and a Funeral          2.457e+08   \n",
       "14257                                  The Addams Family          1.915e+08   \n",
       "14328                             Madonna: Truth or Dare         2.9013e+07   \n",
       "\n",
       "                                   writers  actor_level  director_level  \\\n",
       "59                                    None            1               0   \n",
       "144                         Leigh Whannell            1               0   \n",
       "289                     Paul W.S. Anderson            1               4   \n",
       "765            Steven Knight, Fede Alvarez            1               4   \n",
       "1527                                  None            1               4   \n",
       "2821                         Josh A. Cagan            1               3   \n",
       "3518                          John Swetnam            1               3   \n",
       "3549                     Richard Linklater            1               4   \n",
       "4281     Damien Chazelle, Ed Gass-Donnelly            1               2   \n",
       "4290   Brin Hill, Benson Lee, Chris Parker            1               2   \n",
       "4303                                  None            1               4   \n",
       "4913          Juliet Snowden, Stiles White            1               3   \n",
       "4914                                  None            1               4   \n",
       "4915   C. Robert Cargill, Scott Derrickson            1               4   \n",
       "4922      Mike Cerrone, The Farrelly Bros.            1               4   \n",
       "4937                           Jenny Mayer            1               3   \n",
       "5540                 Christopher B. Landon            1               8   \n",
       "5564                                  None            1               4   \n",
       "5580                                  None            1               4   \n",
       "5608       Alex Kendrick, Stephen Kendrick            1               4   \n",
       "5655                                  None            1               2   \n",
       "5751                       Morgan Spurlock            1               3   \n",
       "5755                          Miranda July            1               2   \n",
       "6192                                  None            1               3   \n",
       "6227                                  None            1               3   \n",
       "6240                            Wes Craven            1               4   \n",
       "6427                          Jee-woon Kim            1               2   \n",
       "6759                       Allison Burnett            1               0   \n",
       "6779                                  None            1               4   \n",
       "6816                          Jane Campion            1               3   \n",
       "7258       Alex Kendrick, Stephen Kendrick            1               4   \n",
       "7459                                  None            1               0   \n",
       "7823                                  None            1               0   \n",
       "7969                                  None            1               4   \n",
       "8486                     Gregory Levasseur            1               0   \n",
       "8534                           Kurt Wimmer            1               2   \n",
       "8569       Alex Kendrick, Stephen Kendrick            1               4   \n",
       "9104                                  None            1               0   \n",
       "9665                            Don Rhymer            1               0   \n",
       "9694             Bill Collage, Adam Cooper            1               0   \n",
       "10176                   Michael Goldenberg            1               4   \n",
       "10282                                 None            1               0   \n",
       "10713                                 None            1               0   \n",
       "11236                                 None            1               2   \n",
       "11469                                 None            1               0   \n",
       "11494                        Todd Phillips            1               4   \n",
       "11507                           James Wong            1               3   \n",
       "11542                                 None            1               0   \n",
       "11893                                 None            1               0   \n",
       "11922                                 None            1               3   \n",
       "11932                                 None            1               4   \n",
       "13466                                 None            1               4   \n",
       "14257                                 None            1               4   \n",
       "14328                                 None            1               0   \n",
       "\n",
       "                                              res  theaters  \n",
       "59      (#6 rank, 2,530 theaters, $2,313 average)     2,530  \n",
       "144     (#2 rank, 3,116 theaters, $9,493 average)     3,116  \n",
       "289     (#4 rank, 3,104 theaters, $4,382 average)     3,104  \n",
       "765     (#6 rank, 2,929 theaters, $2,666 average)     2,929  \n",
       "1527    (#6 rank, 3,157 theaters, $2,257 average)     3,157  \n",
       "2821    (#5 rank, 2,575 theaters, $4,198 average)     2,575  \n",
       "3518    (#3 rank, 3,434 theaters, $5,051 average)     3,434  \n",
       "3549     (#11 rank, 771 theaters, $2,584 average)       771  \n",
       "4281    (#5 rank, 2,700 theaters, $2,862 average)     2,700  \n",
       "4290    (#5 rank, 2,008 theaters, $2,292 average)     2,008  \n",
       "4303   (#14 rank, 1,037 theaters, $1,936 average)     1,037  \n",
       "4913    (#1 rank, 2,816 theaters, $6,297 average)     2,816  \n",
       "4914    (#3 rank, 3,391 theaters, $4,312 average)     3,391  \n",
       "4915    (#3 rank, 2,527 theaters, $7,126 average)     2,527  \n",
       "4922    (#2 rank, 3,477 theaters, $4,892 average)     3,477  \n",
       "4937    (#4 rank, 2,567 theaters, $4,570 average)     2,567  \n",
       "5540   (#1 rank, 3,321 theaters, $15,829 average)     3,321  \n",
       "5564    (#2 rank, 3,105 theaters, $9,505 average)     3,105  \n",
       "5580    (#1 rank, 3,167 theaters, $7,500 average)     3,167  \n",
       "5608    (#4 rank, 1,161 theaters, $7,849 average)     1,161  \n",
       "5655    (#5 rank, 2,730 theaters, $1,726 average)     2,730  \n",
       "5751      (#37 rank, 18 theaters, $6,572 average)        18  \n",
       "5755      (#54 rank, 1 theaters, $27,137 average)         1  \n",
       "6192    (#2 rank, 2,874 theaters, $7,087 average)     2,874  \n",
       "6227    (#4 rank, 2,880 theaters, $4,060 average)     2,880  \n",
       "6240    (#5 rank, 2,572 theaters, $2,660 average)     2,572  \n",
       "6427       (#83 rank, 1 theaters, $4,775 average)         1  \n",
       "6759    (#3 rank, 3,096 theaters, $3,234 average)     3,096  \n",
       "6779    (#7 rank, 1,858 theaters, $2,648 average)     1,858  \n",
       "6816      (#34 rank, 19 theaters, $9,984 average)        19  \n",
       "7258      (#4 rank, 839 theaters, $8,148 average)       839  \n",
       "7459       (#70 rank, 8 theaters, $2,527 average)         8  \n",
       "7823   (#1 rank, 2,051 theaters, $10,645 average)     2,051  \n",
       "7969      (#35 rank, 9 theaters, $12,502 average)         9  \n",
       "8486    (#3 rank, 2,620 theaters, $5,995 average)     2,620  \n",
       "8534    (#4 rank, 2,558 theaters, $3,543 average)     2,558  \n",
       "8569     (#12 rank, 441 theaters, $3,046 average)       441  \n",
       "9104    (#3 rank, 3,188 theaters, $3,202 average)     3,188  \n",
       "9665    (#5 rank, 2,973 theaters, $2,695 average)     2,973  \n",
       "9694    (#4 rank, 3,006 theaters, $1,983 average)     3,006  \n",
       "10176   (#7 rank, 2,813 theaters, $3,960 average)     2,813  \n",
       "10282    (#10 rank, 2,261 theaters, $932 average)     2,261  \n",
       "10713   (#1 rank, 2,856 theaters, $3,965 average)     2,856  \n",
       "11236    (#10 rank, 847 theaters, $3,333 average)       847  \n",
       "11469  (#1 rank, 3,257 theaters, $11,929 average)     3,257  \n",
       "11494   (#3 rank, 2,530 theaters, $6,120 average)     2,530  \n",
       "11507   (#3 rank, 2,587 theaters, $3,871 average)     2,587  \n",
       "11542   (#2 rank, 2,630 theaters, $3,958 average)     2,630  \n",
       "11893   (#3 rank, 2,573 theaters, $3,955 average)     2,573  \n",
       "11922   (#6 rank, 2,222 theaters, $3,081 average)     2,222  \n",
       "11932  (#10 rank, 1,150 theaters, $3,400 average)     1,150  \n",
       "13466     (#1 rank, 721 theaters, $5,773 average)       721  \n",
       "14257  (#1 rank, 2,411 theaters, $10,038 average)     2,411  \n",
       "14328     (#9 rank, 652 theaters, $3,866 average)       652  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.actor_level==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-22T10:48:14.884934Z",
     "start_time": "2019-08-22T10:48:14.628421Z"
    }
   },
   "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>actors</th>\n",
       "      <th>composer</th>\n",
       "      <th>directors</th>\n",
       "      <th>domestic_revenue</th>\n",
       "      <th>franchise</th>\n",
       "      <th>genres</th>\n",
       "      <th>link</th>\n",
       "      <th>prod_budget</th>\n",
       "      <th>producers</th>\n",
       "      <th>rating</th>\n",
       "      <th>release_date</th>\n",
       "      <th>runtime</th>\n",
       "      <th>studio</th>\n",
       "      <th>title</th>\n",
       "      <th>worldwide_revenue</th>\n",
       "      <th>writers</th>\n",
       "      <th>actor_level</th>\n",
       "      <th>director_level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Robert Downey, Jr., Chris Hemsworth, Mark Ruff...</td>\n",
       "      <td>Alan Silvestri</td>\n",
       "      <td>Joe Russo, Anthony Russo</td>\n",
       "      <td>857409335</td>\n",
       "      <td>1</td>\n",
       "      <td>Action / Adventure</td>\n",
       "      <td>/movies/?id=marvel2019.htm</td>\n",
       "      <td>356000000</td>\n",
       "      <td>Kevin Feige</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>2019-04-26</td>\n",
       "      <td>181</td>\n",
       "      <td>Buena Vista</td>\n",
       "      <td>Avengers: Endgame</td>\n",
       "      <td>2794509335</td>\n",
       "      <td>Christopher Markus, Stephen McFeely</td>\n",
       "      <td>163</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Donald Glover, James Earl Jones, Beyonce Knowl...</td>\n",
       "      <td>Hans Zimmer</td>\n",
       "      <td>Jon Favreau</td>\n",
       "      <td>430889078</td>\n",
       "      <td>1</td>\n",
       "      <td>Action / Adventure</td>\n",
       "      <td>/movies/?id=lionkingliveaction.htm</td>\n",
       "      <td>None</td>\n",
       "      <td>Jon Favreau, Jeffrey Silver</td>\n",
       "      <td>PG</td>\n",
       "      <td>2019-07-19</td>\n",
       "      <td>118</td>\n",
       "      <td>Buena Vista</td>\n",
       "      <td>The Lion King (2019)</td>\n",
       "      <td>1195789078</td>\n",
       "      <td>None</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Brie Larson, Samuel L. Jackson, Ben Mendelsohn...</td>\n",
       "      <td>None</td>\n",
       "      <td>Anna Boden, Ryan Fleck</td>\n",
       "      <td>426829839</td>\n",
       "      <td>1</td>\n",
       "      <td>Action / Adventure</td>\n",
       "      <td>/movies/?id=marvel2018a.htm</td>\n",
       "      <td>None</td>\n",
       "      <td>Kevin Feige</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>2019-03-08</td>\n",
       "      <td>128</td>\n",
       "      <td>Buena Vista</td>\n",
       "      <td>Captain Marvel</td>\n",
       "      <td>1128274794</td>\n",
       "      <td>None</td>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tom Hanks, Tim Allen, Jordan Peele, Keanu Reeves</td>\n",
       "      <td>Randy Newman</td>\n",
       "      <td>Josh Cooley</td>\n",
       "      <td>410050743</td>\n",
       "      <td>1</td>\n",
       "      <td>Animation</td>\n",
       "      <td>/movies/?id=pixar2017.htm</td>\n",
       "      <td>None</td>\n",
       "      <td>Jonas Rivera</td>\n",
       "      <td>G</td>\n",
       "      <td>2019-06-21</td>\n",
       "      <td>100</td>\n",
       "      <td>Buena Vista</td>\n",
       "      <td>Toy Story 4</td>\n",
       "      <td>959250743</td>\n",
       "      <td>None</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Tom Holland, Jake Gyllenhaal, Marisa Tomei, Jo...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>360328925</td>\n",
       "      <td>1</td>\n",
       "      <td>Action / Adventure</td>\n",
       "      <td>/movies/?id=spidermanhomecoming2.htm</td>\n",
       "      <td>160000000</td>\n",
       "      <td>None</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>2019-07-02</td>\n",
       "      <td>129</td>\n",
       "      <td>Sony / Columbia</td>\n",
       "      <td>Spider-Man: Far from Home</td>\n",
       "      <td>1075328925</td>\n",
       "      <td>None</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              actors        composer  \\\n",
       "0  Robert Downey, Jr., Chris Hemsworth, Mark Ruff...  Alan Silvestri   \n",
       "1  Donald Glover, James Earl Jones, Beyonce Knowl...     Hans Zimmer   \n",
       "2  Brie Larson, Samuel L. Jackson, Ben Mendelsohn...            None   \n",
       "3   Tom Hanks, Tim Allen, Jordan Peele, Keanu Reeves    Randy Newman   \n",
       "4  Tom Holland, Jake Gyllenhaal, Marisa Tomei, Jo...            None   \n",
       "\n",
       "                  directors domestic_revenue  franchise              genres  \\\n",
       "0  Joe Russo, Anthony Russo        857409335          1  Action / Adventure   \n",
       "1               Jon Favreau        430889078          1  Action / Adventure   \n",
       "2    Anna Boden, Ryan Fleck        426829839          1  Action / Adventure   \n",
       "3               Josh Cooley        410050743          1           Animation   \n",
       "4                      None        360328925          1  Action / Adventure   \n",
       "\n",
       "                                   link prod_budget  \\\n",
       "0            /movies/?id=marvel2019.htm   356000000   \n",
       "1    /movies/?id=lionkingliveaction.htm        None   \n",
       "2           /movies/?id=marvel2018a.htm        None   \n",
       "3             /movies/?id=pixar2017.htm        None   \n",
       "4  /movies/?id=spidermanhomecoming2.htm   160000000   \n",
       "\n",
       "                     producers rating release_date  runtime           studio  \\\n",
       "0                  Kevin Feige  PG-13   2019-04-26      181      Buena Vista   \n",
       "1  Jon Favreau, Jeffrey Silver     PG   2019-07-19      118      Buena Vista   \n",
       "2                  Kevin Feige  PG-13   2019-03-08      128      Buena Vista   \n",
       "3                 Jonas Rivera      G   2019-06-21      100      Buena Vista   \n",
       "4                         None  PG-13   2019-07-02      129  Sony / Columbia   \n",
       "\n",
       "                       title worldwide_revenue  \\\n",
       "0          Avengers: Endgame        2794509335   \n",
       "1       The Lion King (2019)        1195789078   \n",
       "2            Captain Marvel         1128274794   \n",
       "3                Toy Story 4         959250743   \n",
       "4  Spider-Man: Far from Home        1075328925   \n",
       "\n",
       "                               writers  actor_level  director_level  \n",
       "0  Christopher Markus, Stephen McFeely          163               8  \n",
       "1                                 None           20               4  \n",
       "2                                 None           32               8  \n",
       "3                                 None           16               4  \n",
       "4                                 None           20               0  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sqlite3\n",
    "conn = sqlite3.connect('movies.db')\n",
    "df2 = pd.read_sql('select * from moves', conn)\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-22T10:48:15.341135Z",
     "start_time": "2019-08-22T10:48:15.283731Z"
    }
   },
   "outputs": [],
   "source": [
    "df = pd.read_pickle('cleaned_data.pkl')\n",
    "df = df.drop(['res','composer', 'link','writers',\n",
    "              'producers', 'worldwide_revenue'],axis=1).copy()\n",
    "df.dropna(subset=['genres','release_date','theaters'], inplace=True)\n",
    "df['runtime'] = df['runtime'].fillna(int(df['runtime'].mean()))\n",
    "df['profit'] = df['domestic_revenue'] - df['prod_budget']\n",
    "df['month'] = [i.month for i in df['release_date']]\n",
    "df['year'] = [i.year for i in df['release_date']]\n",
    "df = df[df['year']>2009]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-22T10:48:15.384935Z",
     "start_time": "2019-08-22T10:48:15.373774Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 981 entries, 0 to 6472\n",
      "Data columns (total 17 columns):\n",
      "actors              883 non-null object\n",
      "directors           790 non-null object\n",
      "domestic_revenue    981 non-null float64\n",
      "franchise           981 non-null int64\n",
      "genres              981 non-null object\n",
      "prod_budget         981 non-null float64\n",
      "rating              981 non-null object\n",
      "release_date        981 non-null datetime64[ns]\n",
      "runtime             981 non-null float64\n",
      "studio              981 non-null object\n",
      "title               981 non-null object\n",
      "actor_level         981 non-null int64\n",
      "director_level      981 non-null int64\n",
      "theaters            981 non-null float64\n",
      "profit              981 non-null float64\n",
      "month               981 non-null int64\n",
      "year                981 non-null int64\n",
      "dtypes: datetime64[ns](1), float64(5), int64(5), object(6)\n",
      "memory usage: 138.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-22T10:48:15.848859Z",
     "start_time": "2019-08-22T10:48:15.487635Z"
    }
   },
   "outputs": [],
   "source": [
    "df['roi'] = df['profit']/df['prod_budget']\n",
    "df['category'] = df['roi'].apply(lambda x: 0 if x < 0 else (1 if x < 3 else 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-22T10:48:16.736423Z",
     "start_time": "2019-08-22T10:48:16.731561Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({1: 463, 2: 121, 0: 397})"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Counter(df['category'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-22T10:48:16.873290Z",
     "start_time": "2019-08-22T10:48:16.866494Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40.17, 50.22)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "machumachine.pcc(df['category'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-22T10:48:17.006299Z",
     "start_time": "2019-08-22T10:48:16.987726Z"
    }
   },
   "outputs": [],
   "source": [
    "dummy = df.drop(['domestic_revenue', 'release_date', 'title', 'actors', 'directors', 'profit', 'category', 'year', 'roi'],axis=1).copy()\n",
    "X = pd.get_dummies(dummy, drop_first=True)\n",
    "target = df['category']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-22T10:48:17.130508Z",
     "start_time": "2019-08-22T10:48:17.126730Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['franchise', 'genres', 'prod_budget', 'rating', 'runtime', 'studio',\n",
       "       'actor_level', 'director_level', 'theaters', 'month'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummy.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-22T10:48:19.746680Z",
     "start_time": "2019-08-22T10:48:19.740278Z"
    }
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, target, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-22T10:48:22.488501Z",
     "start_time": "2019-08-22T10:48:20.146099Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7195121951219512\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.78      0.68      0.73       104\n",
      "           1       0.68      0.77      0.72       114\n",
      "           2       0.72      0.64      0.68        28\n",
      "\n",
      "    accuracy                           0.72       246\n",
      "   macro avg       0.73      0.70      0.71       246\n",
      "weighted avg       0.73      0.72      0.72       246\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model = xgb.XGBClassifier(learning_rate=0.1, n_estimators=200, max_features=3, min_samples_leaf=3)\n",
    "model.fit(X_train, y_train)\n",
    "print(model.score(X_test, y_test))\n",
    "print(classification_report(y_test, model.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-22T10:51:13.960641Z",
     "start_time": "2019-08-22T10:51:13.952543Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['franchise', 'prod_budget', 'runtime', 'actor_level', 'director_level',\n",
       "       'theaters', 'month', 'genres_Action / Adventure', 'genres_Action Drama',\n",
       "       'genres_Action Thriller',\n",
       "       ...\n",
       "       'studio_Universal', 'studio_Unknown', 'studio_Vivendi Entertainment',\n",
       "       'studio_Warner Bros.', 'studio_Warner Bros. (New Line)',\n",
       "       'studio_Weinstein / Dimension', 'studio_Weinstein Company',\n",
       "       'studio_Well Go USA', 'studio_Wrekin Hill', 'studio_Yash Raj'],\n",
       "      dtype='object', length=115)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-22T10:48:22.505959Z",
     "start_time": "2019-08-22T10:48:22.490863Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[71, 33,  0],\n",
       "       [19, 88,  7],\n",
       "       [ 1,  9, 18]])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, model.predict(X_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Run Iterations on GridSearch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-20T17:14:37.911082Z",
     "start_time": "2019-08-20T17:14:37.895615Z"
    }
   },
   "outputs": [],
   "source": [
    "def grid_search(model, parameters, X, target, cv=3, random_state=None, scaler=None, return_score=False):\n",
    "    \n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, target, random_state=random_state)\n",
    "    \n",
    "    if scaler is not None:\n",
    "        X_train = scaler.fit_transform(X_train)\n",
    "        X_test = scaler.transform(X_test)\n",
    "    gs = GridSearchCV(model, parameters, n_jobs=-1, verbose=8, \n",
    "                      cv=StratifiedKFold(n_splits=cv, shuffle=True)).fit(X_train, y_train)\n",
    "    if return_score:\n",
    "        return gs, gs.score(X_test, y_test)\n",
    "    else:\n",
    "        return gs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-20T17:14:38.225667Z",
     "start_time": "2019-08-20T17:14:38.205730Z"
    }
   },
   "outputs": [],
   "source": [
    "param_dict = {'knn':{'n_neighbors':range(1,30)},\n",
    "             'linearsvcl1':{'penalty':['l1',], 'C':[0.001,0.01,0.1,0.5,1,10,100],'loss':['squared_hinge',],\n",
    "                           'dual':[False,]},\n",
    "              'linearsvcl2':{'penalty':['l2',], 'C':[0.001,0.01, 0.1, 0.5, 1, 10, 100]},\n",
    "              'logisticregression':{'penalty':['l1', 'l2'], 'C':[0.001,0.01, 0.1, 0.5, 1, 10, 100]},\n",
    "              'svcrbf':{'kernel':['rbf',], 'gamma':[0.01, 0.1, 0.5, 1], 'coef0':[2,3]},\n",
    "              'decisiontree': {'max_depth':[2,4,6], 'max_features':[3,4,5]},\n",
    "             'randomforest':{'max_depth':[2,3,6], 'max_features':[3,4,5], 'n_estimators':[200,400,600,800], 'n_jobs':[-1]},\n",
    "             'gbm':{'learning_rate':[0.1,0.5,0.2,0.01], 'n_estimators':[100,200,400,600], 'min_samples_leaf':[3,4,5],'max_features':[3,4,5]},\n",
    "             'xgb':{'learning_rate':[0.1,], 'n_estimators':[100,200,], 'min_samples_leaf':[3,],'max_features':[3,]},\n",
    "            }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-20T17:19:18.361074Z",
     "start_time": "2019-08-20T17:18:00.066248Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "556ba252af624b589784e0d938da1f41",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=9), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "knn\n",
      "Fitting 3 folds for each of 29 candidates, totalling 87 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done  87 out of  87 | elapsed:    0.5s finished\n",
      "/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:814: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   3 out of  21 | elapsed:    0.0s remaining:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done   6 out of  21 | elapsed:    0.0s remaining:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done   9 out of  21 | elapsed:    0.0s remaining:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done  12 out of  21 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done  15 out of  21 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done  21 out of  21 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done  21 out of  21 | elapsed:    0.2s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5528455284552846\n",
      "linearsvcl1\n",
      "Fitting 3 folds for each of 7 candidates, totalling 21 fits\n",
      "0.6666666666666666\n",
      "linearsvcl2\n",
      "Fitting 3 folds for each of 7 candidates, totalling 21 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done   6 out of  21 | elapsed:    0.1s remaining:    0.3s\n",
      "[Parallel(n_jobs=-1)]: Done   9 out of  21 | elapsed:    0.2s remaining:    0.2s\n",
      "[Parallel(n_jobs=-1)]: Done  12 out of  21 | elapsed:    0.2s remaining:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done  15 out of  21 | elapsed:    0.2s remaining:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done  21 out of  21 | elapsed:    0.4s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done  21 out of  21 | elapsed:    0.4s finished\n",
      "/anaconda3/lib/python3.7/site-packages/sklearn/svm/base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   9 out of  42 | elapsed:    0.0s remaining:    0.2s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.11382113821138211\n",
      "logisticregression\n",
      "Fitting 3 folds for each of 14 candidates, totalling 42 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  21 out of  42 | elapsed:    0.7s remaining:    0.7s\n",
      "[Parallel(n_jobs=-1)]: Done  42 out of  42 | elapsed:    1.9s finished\n",
      "/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done   5 out of  24 | elapsed:    0.1s remaining:    0.4s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6666666666666666\n",
      "svcrbf\n",
      "Fitting 3 folds for each of 8 candidates, totalling 24 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done   9 out of  24 | elapsed:    0.2s remaining:    0.3s\n",
      "[Parallel(n_jobs=-1)]: Done  13 out of  24 | elapsed:    0.2s remaining:    0.2s\n",
      "[Parallel(n_jobs=-1)]: Done  24 out of  24 | elapsed:    0.4s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   4 out of  27 | elapsed:    0.0s remaining:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done   8 out of  27 | elapsed:    0.0s remaining:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done  12 out of  27 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done  16 out of  27 | elapsed:    0.0s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.483739837398374\n",
      "decisiontree\n",
      "Fitting 3 folds for each of 9 candidates, totalling 27 fits\n",
      "0.4878048780487805\n",
      "randomforest\n",
      "Fitting 3 folds for each of 36 candidates, totalling 108 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  27 out of  27 | elapsed:    0.1s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 tasks      | elapsed:    0.4s\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    3.7s\n",
      "[Parallel(n_jobs=-1)]: Done  56 tasks      | elapsed:    7.6s\n",
      "[Parallel(n_jobs=-1)]: Done 108 out of 108 | elapsed:   15.7s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5813008130081301\n",
      "gbm\n",
      "Fitting 3 folds for each of 144 candidates, totalling 432 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    2.9s\n",
      "[Parallel(n_jobs=-1)]: Done  56 tasks      | elapsed:    6.9s\n",
      "[Parallel(n_jobs=-1)]: Done  97 tasks      | elapsed:   12.3s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:   18.2s\n",
      "[Parallel(n_jobs=-1)]: Done 205 tasks      | elapsed:   26.8s\n",
      "[Parallel(n_jobs=-1)]: Done 272 tasks      | elapsed:   34.3s\n",
      "[Parallel(n_jobs=-1)]: Done 349 tasks      | elapsed:   42.9s\n",
      "[Parallel(n_jobs=-1)]: Done 432 out of 432 | elapsed:   52.6s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6626016260162602\n",
      "xgb\n",
      "Fitting 3 folds for each of 2 candidates, totalling 6 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   6 | elapsed:    1.2s remaining:    2.4s\n",
      "[Parallel(n_jobs=-1)]: Done   3 out of   6 | elapsed:    1.2s remaining:    1.2s\n",
      "[Parallel(n_jobs=-1)]: Done   4 out of   6 | elapsed:    2.0s remaining:    1.0s\n",
      "[Parallel(n_jobs=-1)]: Done   6 out of   6 | elapsed:    2.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   6 out of   6 | elapsed:    2.1s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7195121951219512\n",
      "\n"
     ]
    }
   ],
   "source": [
    "all_models = [KNeighborsClassifier(), LinearSVC(), LinearSVC(), LogisticRegression(), \n",
    "              SVC(), DecisionTreeClassifier(), RandomForestClassifier(), \n",
    "             GradientBoostingClassifier(), xgb.XGBClassifier()]\n",
    "models = []\n",
    "accuracy = []\n",
    "\n",
    "for i in tqdm(range(len(param_dict.keys()))):\n",
    "    print(list(param_dict.keys())[i])\n",
    "    model, score = grid_search(all_models[i], param_dict[list(param_dict.keys())[i]], \n",
    "                X=X, target=target, random_state=1, return_score=True)\n",
    "    \n",
    "    models.append(model)\n",
    "    accuracy.append(score)\n",
    "    print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-20T17:21:44.431506Z",
     "start_time": "2019-08-20T17:21:44.172976Z"
    }
   },
   "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>model</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>model_object</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KNeighborsClassifier(algorithm='auto', leaf_si...</td>\n",
       "      <td>0.55285</td>\n",
       "      <td>GridSearchCV(cv=StratifiedKFold(n_splits=3, ra...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LinearSVC(C=0.1, class_weight=None, dual=False...</td>\n",
       "      <td>0.66667</td>\n",
       "      <td>GridSearchCV(cv=StratifiedKFold(n_splits=3, ra...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LinearSVC(C=100, class_weight=None, dual=True,...</td>\n",
       "      <td>0.11382</td>\n",
       "      <td>GridSearchCV(cv=StratifiedKFold(n_splits=3, ra...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LogisticRegression(C=1, class_weight=None, dua...</td>\n",
       "      <td>0.66667</td>\n",
       "      <td>GridSearchCV(cv=StratifiedKFold(n_splits=3, ra...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SVC(C=1.0, cache_size=200, class_weight=None, ...</td>\n",
       "      <td>0.48374</td>\n",
       "      <td>GridSearchCV(cv=StratifiedKFold(n_splits=3, ra...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>DecisionTreeClassifier(class_weight=None, crit...</td>\n",
       "      <td>0.4878</td>\n",
       "      <td>GridSearchCV(cv=StratifiedKFold(n_splits=3, ra...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(DecisionTreeClassifier(class_weight=None, cri...</td>\n",
       "      <td>0.5813</td>\n",
       "      <td>GridSearchCV(cv=StratifiedKFold(n_splits=3, ra...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>([DecisionTreeRegressor(criterion='friedman_ms...</td>\n",
       "      <td>0.6626</td>\n",
       "      <td>GridSearchCV(cv=StratifiedKFold(n_splits=3, ra...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>XGBClassifier(base_score=0.5, booster='gbtree'...</td>\n",
       "      <td>0.71951</td>\n",
       "      <td>GridSearchCV(cv=StratifiedKFold(n_splits=3, ra...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               model accuracy  \\\n",
       "0  KNeighborsClassifier(algorithm='auto', leaf_si...  0.55285   \n",
       "1  LinearSVC(C=0.1, class_weight=None, dual=False...  0.66667   \n",
       "2  LinearSVC(C=100, class_weight=None, dual=True,...  0.11382   \n",
       "3  LogisticRegression(C=1, class_weight=None, dua...  0.66667   \n",
       "4  SVC(C=1.0, cache_size=200, class_weight=None, ...  0.48374   \n",
       "5  DecisionTreeClassifier(class_weight=None, crit...   0.4878   \n",
       "6  (DecisionTreeClassifier(class_weight=None, cri...   0.5813   \n",
       "7  ([DecisionTreeRegressor(criterion='friedman_ms...   0.6626   \n",
       "8  XGBClassifier(base_score=0.5, booster='gbtree'...  0.71951   \n",
       "\n",
       "                                        model_object  \n",
       "0  GridSearchCV(cv=StratifiedKFold(n_splits=3, ra...  \n",
       "1  GridSearchCV(cv=StratifiedKFold(n_splits=3, ra...  \n",
       "2  GridSearchCV(cv=StratifiedKFold(n_splits=3, ra...  \n",
       "3  GridSearchCV(cv=StratifiedKFold(n_splits=3, ra...  \n",
       "4  GridSearchCV(cv=StratifiedKFold(n_splits=3, ra...  \n",
       "5  GridSearchCV(cv=StratifiedKFold(n_splits=3, ra...  \n",
       "6  GridSearchCV(cv=StratifiedKFold(n_splits=3, ra...  \n",
       "7  GridSearchCV(cv=StratifiedKFold(n_splits=3, ra...  \n",
       "8  GridSearchCV(cv=StratifiedKFold(n_splits=3, ra...  "
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res = pd.DataFrame([[i.best_estimator_ for i in models], accuracy, models]).transpose()\n",
    "res.columns = ['model', 'accuracy', 'model_object']\n",
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-19T13:28:37.399496Z",
     "start_time": "2019-08-19T13:28:37.381192Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'learning_rate': 0.1,\n",
       " 'max_features': 3,\n",
       " 'min_samples_leaf': 3,\n",
       " 'n_estimators': 100}"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models[-1].best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-20T17:16:01.902950Z",
     "start_time": "2019-08-20T17:16:01.897255Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Comedy', 161), ('Drama', 136)]"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Counter(dummy['genres']).most_common(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-20T17:31:27.135217Z",
     "start_time": "2019-08-20T17:31:27.126571Z"
    }
   },
   "outputs": [],
   "source": [
    "comedy = df[df['genres']=='Comedy'].drop(['domestic_revenue', 'genres', 'release_date', 'profit', 'year', 'roi'],axis=1)\n",
    "drama = df[df['genres']=='Drama'].drop(['domestic_revenue', 'genres', 'release_date', 'profit', 'year', 'roi'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-20T17:32:40.184874Z",
     "start_time": "2019-08-20T17:32:40.160656Z"
    }
   },
   "outputs": [],
   "source": [
    "Xc = pd.get_dummies(comedy.drop(['category'],axis=1))\n",
    "yc = comedy['category']\n",
    "Xd = pd.get_dummies(drama.drop(['category'],axis=1))\n",
    "yd = drama['category']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-20T17:51:52.053818Z",
     "start_time": "2019-08-20T17:50:49.335508Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "905e36b7b9ca408b92650ec518560580",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=9), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "knn\n",
      "Fitting 3 folds for each of 29 candidates, totalling 87 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 tasks      | elapsed:    2.6s\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    2.7s\n",
      "[Parallel(n_jobs=-1)]: Done  50 out of  87 | elapsed:    2.8s remaining:    2.0s\n",
      "[Parallel(n_jobs=-1)]: Done  72 out of  87 | elapsed:    2.8s remaining:    0.6s\n",
      "[Parallel(n_jobs=-1)]: Done  87 out of  87 | elapsed:    2.8s finished\n",
      "/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:814: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   3 out of  21 | elapsed:    0.0s remaining:    0.1s\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5609756097560976\n",
      "linearsvcl1\n",
      "Fitting 3 folds for each of 7 candidates, totalling 21 fits\n",
      "0.5365853658536586\n",
      "linearsvcl2\n",
      "Fitting 3 folds for each of 7 candidates, totalling 21 fits\n",
      "0.4878048780487805\n",
      "logisticregression\n",
      "Fitting 3 folds for each of 14 candidates, totalling 42 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  21 out of  21 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done  21 out of  21 | elapsed:    0.1s finished\n",
      "/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:814: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 tasks      | elapsed:    0.0s\n",
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      "/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:814: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 tasks      | elapsed:    0.0s\n",
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      "[Parallel(n_jobs=-1)]: Done  42 out of  42 | elapsed:    0.1s finished\n",
      "/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:814: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n",
      "/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   5 out of  24 | elapsed:    0.0s remaining:    0.0s\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5121951219512195\n",
      "svcrbf\n",
      "Fitting 3 folds for each of 8 candidates, totalling 24 fits\n",
      "0.4878048780487805\n",
      "decisiontree\n",
      "Fitting 3 folds for each of 9 candidates, totalling 27 fits\n",
      "0.43902439024390244\n",
      "randomforest\n",
      "Fitting 3 folds for each of 36 candidates, totalling 108 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  13 out of  24 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done  24 out of  24 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   4 out of  27 | elapsed:    0.0s remaining:    0.1s\n",
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      "[Parallel(n_jobs=-1)]: Done  16 out of  27 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done  27 out of  27 | elapsed:    0.0s finished\n",
      "/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:814: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 tasks      | elapsed:    0.5s\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    3.2s\n",
      "[Parallel(n_jobs=-1)]: Done  56 tasks      | elapsed:    6.7s\n",
      "[Parallel(n_jobs=-1)]: Done 108 out of 108 | elapsed:   14.2s finished\n",
      "/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:814: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5121951219512195\n",
      "gbm\n",
      "Fitting 3 folds for each of 144 candidates, totalling 432 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 tasks      | elapsed:    0.3s\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    2.6s\n",
      "[Parallel(n_jobs=-1)]: Done  56 tasks      | elapsed:    6.3s\n",
      "[Parallel(n_jobs=-1)]: Done  97 tasks      | elapsed:   10.8s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:   15.0s\n",
      "[Parallel(n_jobs=-1)]: Done 205 tasks      | elapsed:   19.9s\n",
      "[Parallel(n_jobs=-1)]: Done 272 tasks      | elapsed:   26.3s\n",
      "[Parallel(n_jobs=-1)]: Done 349 tasks      | elapsed:   34.5s\n",
      "[Parallel(n_jobs=-1)]: Done 432 out of 432 | elapsed:   43.5s finished\n",
      "/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:814: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5853658536585366\n",
      "xgb\n",
      "Fitting 3 folds for each of 2 candidates, totalling 6 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   6 | elapsed:    0.2s remaining:    0.4s\n",
      "[Parallel(n_jobs=-1)]: Done   3 out of   6 | elapsed:    0.2s remaining:    0.2s\n",
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      "[Parallel(n_jobs=-1)]: Done   6 out of   6 | elapsed:    0.4s finished\n",
      "/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:814: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6097560975609756\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>model_object</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KNeighborsClassifier(algorithm='auto', leaf_si...</td>\n",
       "      <td>0.56098</td>\n",
       "      <td>GridSearchCV(cv=StratifiedKFold(n_splits=3, ra...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LinearSVC(C=0.5, class_weight=None, dual=False...</td>\n",
       "      <td>0.53659</td>\n",
       "      <td>GridSearchCV(cv=StratifiedKFold(n_splits=3, ra...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LinearSVC(C=0.01, class_weight=None, dual=True...</td>\n",
       "      <td>0.4878</td>\n",
       "      <td>GridSearchCV(cv=StratifiedKFold(n_splits=3, ra...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LogisticRegression(C=1, class_weight=None, dua...</td>\n",
       "      <td>0.5122</td>\n",
       "      <td>GridSearchCV(cv=StratifiedKFold(n_splits=3, ra...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SVC(C=1.0, cache_size=200, class_weight=None, ...</td>\n",
       "      <td>0.4878</td>\n",
       "      <td>GridSearchCV(cv=StratifiedKFold(n_splits=3, ra...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>DecisionTreeClassifier(class_weight=None, crit...</td>\n",
       "      <td>0.43902</td>\n",
       "      <td>GridSearchCV(cv=StratifiedKFold(n_splits=3, ra...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(DecisionTreeClassifier(class_weight=None, cri...</td>\n",
       "      <td>0.5122</td>\n",
       "      <td>GridSearchCV(cv=StratifiedKFold(n_splits=3, ra...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>([DecisionTreeRegressor(criterion='friedman_ms...</td>\n",
       "      <td>0.58537</td>\n",
       "      <td>GridSearchCV(cv=StratifiedKFold(n_splits=3, ra...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>XGBClassifier(base_score=0.5, booster='gbtree'...</td>\n",
       "      <td>0.60976</td>\n",
       "      <td>GridSearchCV(cv=StratifiedKFold(n_splits=3, ra...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               model accuracy  \\\n",
       "0  KNeighborsClassifier(algorithm='auto', leaf_si...  0.56098   \n",
       "1  LinearSVC(C=0.5, class_weight=None, dual=False...  0.53659   \n",
       "2  LinearSVC(C=0.01, class_weight=None, dual=True...   0.4878   \n",
       "3  LogisticRegression(C=1, class_weight=None, dua...   0.5122   \n",
       "4  SVC(C=1.0, cache_size=200, class_weight=None, ...   0.4878   \n",
       "5  DecisionTreeClassifier(class_weight=None, crit...  0.43902   \n",
       "6  (DecisionTreeClassifier(class_weight=None, cri...   0.5122   \n",
       "7  ([DecisionTreeRegressor(criterion='friedman_ms...  0.58537   \n",
       "8  XGBClassifier(base_score=0.5, booster='gbtree'...  0.60976   \n",
       "\n",
       "                                        model_object  \n",
       "0  GridSearchCV(cv=StratifiedKFold(n_splits=3, ra...  \n",
       "1  GridSearchCV(cv=StratifiedKFold(n_splits=3, ra...  \n",
       "2  GridSearchCV(cv=StratifiedKFold(n_splits=3, ra...  \n",
       "3  GridSearchCV(cv=StratifiedKFold(n_splits=3, ra...  \n",
       "4  GridSearchCV(cv=StratifiedKFold(n_splits=3, ra...  \n",
       "5  GridSearchCV(cv=StratifiedKFold(n_splits=3, ra...  \n",
       "6  GridSearchCV(cv=StratifiedKFold(n_splits=3, ra...  \n",
       "7  GridSearchCV(cv=StratifiedKFold(n_splits=3, ra...  \n",
       "8  GridSearchCV(cv=StratifiedKFold(n_splits=3, ra...  "
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_models = [KNeighborsClassifier(), LinearSVC(), LinearSVC(), LogisticRegression(), \n",
    "              SVC(), DecisionTreeClassifier(), RandomForestClassifier(), \n",
    "             GradientBoostingClassifier(), xgb.XGBClassifier()]\n",
    "modelsc = []\n",
    "accuracyc = []\n",
    "\n",
    "for i in tqdm(range(len(param_dict.keys()))):\n",
    "    print(list(param_dict.keys())[i])\n",
    "    model, score = grid_search(all_models[i], param_dict[list(param_dict.keys())[i]], \n",
    "                X=Xc, target=yc, random_state=1, return_score=True, scaler=RobustScaler())\n",
    "    \n",
    "    modelsc.append(model)\n",
    "    accuracyc.append(score)\n",
    "    print(score)\n",
    "resc = pd.DataFrame([[i.best_estimator_ for i in modelsc], accuracyc, modelsc]).transpose()\n",
    "resc.columns = ['model', 'accuracy', 'model_object']\n",
    "resc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-20T17:52:50.149379Z",
     "start_time": "2019-08-20T17:51:52.059001Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6d36e317407e4a7095ca18da98364a05",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=9), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "knn\n",
      "Fitting 3 folds for each of 29 candidates, totalling 87 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   6 out of  87 | elapsed:    0.0s remaining:    0.4s\n",
      "[Parallel(n_jobs=-1)]: Done  87 out of  87 | elapsed:    0.2s finished\n",
      "/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:814: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   3 out of  21 | elapsed:    0.0s remaining:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done   6 out of  21 | elapsed:    0.0s remaining:    0.0s\n",
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      "[Parallel(n_jobs=-1)]: Done  21 out of  21 | elapsed:    0.1s finished\n",
      "/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:814: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   3 out of  21 | elapsed:    0.0s remaining:    0.1s\n",
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      "[Parallel(n_jobs=-1)]: Done  21 out of  21 | elapsed:    0.1s finished\n",
      "/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:814: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   3 out of  42 | elapsed:    0.0s remaining:    0.2s\n",
      "[Parallel(n_jobs=-1)]: Done   9 out of  42 | elapsed:    0.0s remaining:    0.1s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.47058823529411764\n",
      "linearsvcl1\n",
      "Fitting 3 folds for each of 7 candidates, totalling 21 fits\n",
      "0.4117647058823529\n",
      "linearsvcl2\n",
      "Fitting 3 folds for each of 7 candidates, totalling 21 fits\n",
      "0.4411764705882353\n",
      "logisticregression\n",
      "Fitting 3 folds for each of 14 candidates, totalling 42 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  15 out of  42 | elapsed:    0.1s remaining:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done  42 out of  42 | elapsed:    0.2s finished\n",
      "/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:814: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n",
      "/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   5 out of  24 | elapsed:    0.0s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5\n",
      "svcrbf\n",
      "Fitting 3 folds for each of 8 candidates, totalling 24 fits\n",
      "0.38235294117647056\n",
      "decisiontree\n",
      "Fitting 3 folds for each of 9 candidates, totalling 27 fits\n",
      "0.38235294117647056\n",
      "randomforest\n",
      "Fitting 3 folds for each of 36 candidates, totalling 108 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done   9 out of  24 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done  13 out of  24 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done  24 out of  24 | elapsed:    0.0s finished\n",
      "/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:814: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   4 out of  27 | elapsed:    0.0s remaining:    0.1s\n",
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      "[Parallel(n_jobs=-1)]: Done  12 out of  27 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done  16 out of  27 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done  27 out of  27 | elapsed:    0.0s finished\n",
      "/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:814: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 tasks      | elapsed:    0.4s\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    3.5s\n",
      "[Parallel(n_jobs=-1)]: Done  56 tasks      | elapsed:    7.6s\n",
      "[Parallel(n_jobs=-1)]: Done 108 out of 108 | elapsed:   15.4s finished\n",
      "/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:814: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5\n",
      "gbm\n",
      "Fitting 3 folds for each of 144 candidates, totalling 432 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    2.5s\n",
      "[Parallel(n_jobs=-1)]: Done  56 tasks      | elapsed:    5.6s\n",
      "[Parallel(n_jobs=-1)]: Done  97 tasks      | elapsed:    9.5s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:   13.8s\n",
      "[Parallel(n_jobs=-1)]: Done 205 tasks      | elapsed:   18.7s\n",
      "[Parallel(n_jobs=-1)]: Done 272 tasks      | elapsed:   24.6s\n",
      "[Parallel(n_jobs=-1)]: Done 349 tasks      | elapsed:   31.5s\n",
      "[Parallel(n_jobs=-1)]: Done 432 out of 432 | elapsed:   39.4s finished\n",
      "/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:814: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5\n",
      "xgb\n",
      "Fitting 3 folds for each of 2 candidates, totalling 6 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   6 | elapsed:    0.2s remaining:    0.3s\n",
      "[Parallel(n_jobs=-1)]: Done   3 out of   6 | elapsed:    0.2s remaining:    0.2s\n",
      "[Parallel(n_jobs=-1)]: Done   4 out of   6 | elapsed:    0.3s remaining:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done   6 out of   6 | elapsed:    0.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   6 out of   6 | elapsed:    0.3s finished\n",
      "/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:814: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5\n"
     ]
    },
    {
     "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>model</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>model_object</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KNeighborsClassifier(algorithm='auto', leaf_si...</td>\n",
       "      <td>0.47059</td>\n",
       "      <td>GridSearchCV(cv=StratifiedKFold(n_splits=3, ra...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LinearSVC(C=0.1, class_weight=None, dual=False...</td>\n",
       "      <td>0.41176</td>\n",
       "      <td>GridSearchCV(cv=StratifiedKFold(n_splits=3, ra...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LinearSVC(C=0.01, class_weight=None, dual=True...</td>\n",
       "      <td>0.44118</td>\n",
       "      <td>GridSearchCV(cv=StratifiedKFold(n_splits=3, ra...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LogisticRegression(C=0.5, class_weight=None, d...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>GridSearchCV(cv=StratifiedKFold(n_splits=3, ra...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SVC(C=1.0, cache_size=200, class_weight=None, ...</td>\n",
       "      <td>0.38235</td>\n",
       "      <td>GridSearchCV(cv=StratifiedKFold(n_splits=3, ra...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>DecisionTreeClassifier(class_weight=None, crit...</td>\n",
       "      <td>0.38235</td>\n",
       "      <td>GridSearchCV(cv=StratifiedKFold(n_splits=3, ra...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(DecisionTreeClassifier(class_weight=None, cri...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>GridSearchCV(cv=StratifiedKFold(n_splits=3, ra...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>([DecisionTreeRegressor(criterion='friedman_ms...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>GridSearchCV(cv=StratifiedKFold(n_splits=3, ra...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>XGBClassifier(base_score=0.5, booster='gbtree'...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>GridSearchCV(cv=StratifiedKFold(n_splits=3, ra...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               model accuracy  \\\n",
       "0  KNeighborsClassifier(algorithm='auto', leaf_si...  0.47059   \n",
       "1  LinearSVC(C=0.1, class_weight=None, dual=False...  0.41176   \n",
       "2  LinearSVC(C=0.01, class_weight=None, dual=True...  0.44118   \n",
       "3  LogisticRegression(C=0.5, class_weight=None, d...      0.5   \n",
       "4  SVC(C=1.0, cache_size=200, class_weight=None, ...  0.38235   \n",
       "5  DecisionTreeClassifier(class_weight=None, crit...  0.38235   \n",
       "6  (DecisionTreeClassifier(class_weight=None, cri...      0.5   \n",
       "7  ([DecisionTreeRegressor(criterion='friedman_ms...      0.5   \n",
       "8  XGBClassifier(base_score=0.5, booster='gbtree'...      0.5   \n",
       "\n",
       "                                        model_object  \n",
       "0  GridSearchCV(cv=StratifiedKFold(n_splits=3, ra...  \n",
       "1  GridSearchCV(cv=StratifiedKFold(n_splits=3, ra...  \n",
       "2  GridSearchCV(cv=StratifiedKFold(n_splits=3, ra...  \n",
       "3  GridSearchCV(cv=StratifiedKFold(n_splits=3, ra...  \n",
       "4  GridSearchCV(cv=StratifiedKFold(n_splits=3, ra...  \n",
       "5  GridSearchCV(cv=StratifiedKFold(n_splits=3, ra...  \n",
       "6  GridSearchCV(cv=StratifiedKFold(n_splits=3, ra...  \n",
       "7  GridSearchCV(cv=StratifiedKFold(n_splits=3, ra...  \n",
       "8  GridSearchCV(cv=StratifiedKFold(n_splits=3, ra...  "
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_models = [KNeighborsClassifier(), LinearSVC(), LinearSVC(), LogisticRegression(), \n",
    "              SVC(), DecisionTreeClassifier(), RandomForestClassifier(), \n",
    "             GradientBoostingClassifier(), xgb.XGBClassifier()]\n",
    "modelsd = []\n",
    "accuracyd = []\n",
    "\n",
    "for i in tqdm(range(len(param_dict.keys()))):\n",
    "    print(list(param_dict.keys())[i])\n",
    "    model, score = grid_search(all_models[i], param_dict[list(param_dict.keys())[i]], \n",
    "                X=Xd, target=yd, random_state=1, return_score=True, scaler=RobustScaler())\n",
    "    \n",
    "    modelsd.append(model)\n",
    "    accuracyd.append(score)\n",
    "    print(score)\n",
    "resd = pd.DataFrame([[i.best_estimator_ for i in modelsd], accuracyd, modelsd]).transpose()\n",
    "resd.columns = ['model', 'accuracy', 'model_object']\n",
    "resd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-21T02:29:50.940150Z",
     "start_time": "2019-08-21T02:29:50.763345Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2491c4e0>"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df[df['category']==0]['prod_budget'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-21T02:29:40.860619Z",
     "start_time": "2019-08-21T02:29:40.629284Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a24a3b0b8>"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df[df['category']==1]['prod_budget'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-21T02:30:00.700453Z",
     "start_time": "2019-08-21T02:29:59.471627Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a24b164e0>"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df[df['category']==2]['prod_budget'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-21T05:52:49.786536Z",
     "start_time": "2019-08-21T05:52:49.751608Z"
    }
   },
   "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>director_level</th>\n",
       "      <th>profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1.0547e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>-8.4411e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>-4.5563e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3.1847e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>3.254e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>-6.1538e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>1.1442e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>-4.3924e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>6.1753e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12</td>\n",
       "      <td>1.9776e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>16</td>\n",
       "      <td>7.1391e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>20</td>\n",
       "      <td>2.8405e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    director_level      profit\n",
       "0                0  1.0547e+07\n",
       "1                1 -8.4411e+06\n",
       "2                2 -4.5563e+06\n",
       "3                3  3.1847e+06\n",
       "4                4   3.254e+07\n",
       "5                5 -6.1538e+07\n",
       "6                6  1.1442e+07\n",
       "7                7 -4.3924e+07\n",
       "8                8  6.1753e+07\n",
       "9               12  1.9776e+08\n",
       "10              16  7.1391e+07\n",
       "11              20  2.8405e+06"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('director_level')['profit'].mean().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-21T06:08:00.268348Z",
     "start_time": "2019-08-21T06:07:57.766299Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "theaters 0.059744123\n",
      "prod_budget 0.048783872\n",
      "franchise 0.04595904\n",
      "genres_Thriller 0.04002892\n",
      "rating_R 0.038883638\n",
      "director_level 0.03332414\n",
      "genres_Horror 0.0322342\n",
      "actor_level 0.031273276\n",
      "studio_Universal 0.030204566\n",
      "rating_PG 0.030103799\n",
      "genres_Animation 0.028966904\n",
      "month 0.027581034\n",
      "genres_Comedy 0.027295997\n",
      "studio_Sony / Columbia 0.026814144\n",
      "studio_Fox 0.02664297\n",
      "studio_Paramount 0.02357904\n",
      "studio_STX Entertainment 0.02274991\n",
      "genres_Musical 0.022632275\n",
      "studio_Warner Bros. (New Line) 0.02217415\n",
      "runtime 0.022158343\n",
      "studio_Buena Vista 0.022076257\n",
      "genres_Sci-Fi Action 0.019118555\n",
      "studio_Lionsgate 0.019076878\n",
      "studio_Roadside Attractions 0.017798932\n",
      "rating_PG-13 0.0176391\n",
      "studio_IFC 0.01747228\n",
      "genres_Comedy / Drama 0.016631134\n",
      "studio_Weinstein Company 0.015776517\n",
      "genres_Sci-Fi Adventure 0.015191802\n",
      "genres_Music Drama 0.013811084\n",
      "studio_TriStar 0.013642071\n",
      "studio_Sony / Screen Gems 0.01316086\n",
      "studio_Focus Features 0.01307434\n",
      "genres_Sci-Fi Horror 0.012774653\n",
      "genres_Action / Adventure 0.01223276\n",
      "genres_Romantic Comedy 0.012145741\n",
      "studio_Warner Bros. 0.011127026\n",
      "genres_Action Thriller 0.010965794\n",
      "studio_Open Road Films 0.010773878\n",
      "genres_Family 0.009950357\n",
      "genres_Drama 0.008588883\n",
      "studio_Weinstein / Dimension 0.008067629\n",
      "studio_Summit Entertainment 0.007945178\n",
      "studio_Paramount (DreamWorks) 0.007926708\n",
      "genres_Romance 0.0072164237\n",
      "studio_Sony Classics 0.0066998764\n",
      "genres_Fantasy 0.0066655045\n",
      "studio_CBS Films 0.005716785\n",
      "studio_Relativity 0.0055986233\n",
      "genres_Sci-Fi Thriller 0.0\n",
      "genres_Romantic Thriller 0.0\n",
      "genres_Sci-Fi Fantasy 0.0\n",
      "genres_Sports Drama 0.0\n",
      "studio_Strand 0.0\n",
      "genres_Period Drama 0.0\n",
      "genres_Foreign 0.0\n",
      "genres_Period Adventure 0.0\n",
      "genres_Crime Drama 0.0\n",
      "studio_Well Go USA 0.0\n",
      "studio_Vivendi Entertainment 0.0\n",
      "studio_Unknown 0.0\n",
      "genres_Action Drama 0.0\n",
      "studio_United Artists Releasing 0.0\n",
      "genres_Crime Comedy 0.0\n",
      "genres_Family Adventure 0.0\n",
      "studio_Studio 8 0.0\n",
      "genres_Family Comedy 0.0\n",
      "genres_Fantasy Comedy 0.0\n",
      "genres_War Drama 0.0\n",
      "genres_Historical Drama 0.0\n",
      "genres_Historical Epic 0.0\n",
      "genres_Horror Comedy 0.0\n",
      "genres_War 0.0\n",
      "rating_Unrated 0.0\n",
      "rating_Not Yet Rated 0.0\n",
      "studio_Freestyle Releasing 0.0\n",
      "studio_H Collective 0.0\n",
      "studio_Hannover House 0.0\n",
      "studio_Kino 0.0\n",
      "studio_Lionsgate Premiere 0.0\n",
      "studio_Lionsgate/Summit 0.0\n",
      "studio_MGM 0.0\n",
      "studio_Magnolia 0.0\n",
      "studio_Millennium Ent. 0.0\n",
      "studio_Miramax 0.0\n",
      "studio_Music Box Films 0.0\n",
      "studio_Newmarket 0.0\n",
      "studio_Orion Pictures 0.0\n",
      "studio_Overture Films 0.0\n",
      "studio_Paladin 0.0\n",
      "studio_Pantelion 0.0\n",
      "studio_Samuel Goldwyn 0.0\n",
      "studio_Picturehouse (II) 0.0\n",
      "studio_GKIDS 0.0\n",
      "studio_Fox Searchlight 0.0\n",
      "studio_ATO Pictures 0.0\n",
      "studio_Sony / AFFIRM Films 0.0\n",
      "studio_AfterLight 0.0\n",
      "studio_Alchemy 0.0\n",
      "studio_Anchor Bay Films 0.0\n",
      "studio_Annapurna Pictures 0.0\n",
      "studio_Apparition 0.0\n",
      "studio_Aviron 0.0\n",
      "studio_BH Tilt 0.0\n",
      "studio_Bleecker Street 0.0\n",
      "studio_Broad Green Pictures 0.0\n",
      "studio_Wrekin Hill 0.0\n",
      "studio_China Lion 0.0\n",
      "studio_Clarius Entertainment 0.0\n",
      "studio_Crimson Forest 0.0\n",
      "studio_Entertainment Studios 0.0\n",
      "studio_EuropaCorp 0.0\n",
      "studio_FilmDistrict 0.0\n",
      "studio_First Look 0.0\n",
      "studio_Yash Raj 0.0\n"
     ]
    }
   ],
   "source": [
    "for i in np.argsort(model.feature_importances_)[::-1]:\n",
    "        print(X.columns[i], model.feature_importances_[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-21T06:23:30.518043Z",
     "start_time": "2019-08-21T06:23:30.471592Z"
    }
   },
   "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>theaters</th>\n",
       "      <th>prod_budget</th>\n",
       "      <th>franchise</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>category</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2,307.5</td>\n",
       "      <td>6.0207e+07</td>\n",
       "      <td>0.12846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3,034.8</td>\n",
       "      <td>6.054e+07</td>\n",
       "      <td>0.2743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2,650.8</td>\n",
       "      <td>1.7672e+07</td>\n",
       "      <td>0.36364</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          theaters  prod_budget  franchise\n",
       "category                                  \n",
       "0          2,307.5   6.0207e+07    0.12846\n",
       "1          3,034.8    6.054e+07     0.2743\n",
       "2          2,650.8   1.7672e+07    0.36364"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('category')[['theaters', 'prod_budget', 'franchise']].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-21T06:26:37.379670Z",
     "start_time": "2019-08-21T06:26:37.004985Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Distribution of Theaters')"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(dpi=200)\n",
    "for i in df['category'].unique():\n",
    "    sns.distplot(df[df['category']==i]['theaters'], label=i, ax=ax)\n",
    "ax.legend()\n",
    "ax.set_title('Distribution of Theaters')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-21T08:07:25.515913Z",
     "start_time": "2019-08-21T08:07:25.502866Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[60206599.49622166, 60539956.803455725, 17671900.82644628]\n"
     ]
    }
   ],
   "source": [
    "print(df.groupby('category')['prod_budget'].mean().reset_index()['prod_budget'].to_list())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-21T06:27:04.797848Z",
     "start_time": "2019-08-21T06:27:04.426831Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Distribution of Prod Budget (Log Scale)')"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kEWnK/EHS53goB9/nWeq66A0/tgbH8WWS3iFpZA/K/FionL2S5udyH0izjfcE63wqTb5Lszgmzg3leUZJgbmY/JeF8v8yi7pODOVvlVSa7++HB4+ePhgIERg8wk2YezIIT0JtaLneOef6WJ++aJa/e5MPTtJnXYp+9UH6Z4N8kjTPzA7LU10K5d/V1cx1mfzFVCzn3HZJXwklfTDdgFSBVkWn6Uous1ld00pJXV1Xeus0+fEnEuv+TKr9NUj/lPwPVclfPJ8al7fYzOxE+Wb84T7RmZr0r1N23T1yug8EfXv/LZT0ZefcnhTldUi6SF3HVD7sJ7+dkx/nS3qT/J3OVvkLstnOz1JRSKera5C5JqU5vznn6uXvIqdlZodKOjrxNklfSHMc7JW/Q5upzCGSPh087ZD0cedcW4a3fU1d2/ZDmdaRB9NCy2sLvO4PygfcJd/N7ZupMjrnWtT13UrSyWYWbqqf6OrwkVDSpc65lAOayl+cpnu9x5xzd8p3JUs+nkdJerv8vvlnSfXmB5v9pJml6kqQ8MXQ8lWuAONfOOfWyHf9kfz/8do02TMJ/3/7jPMzsaRzhbq2yweC7ZrORnXN2DNE0QEpgX6BoAEweIT/ifVm9N0t6vqRMMfM+jp2QF/cH1y45MPjzrlX02Vwzr2i6Aj2J+epLoVySmj5hiwCQneqazTnCknHZ8j/mHMu06Byz4SWp2bIm0n48/zVObchXWbn3Dr51iMJxdyex5qfZSD8WGhmz8r3aw3/WPyhc+6pDOX9IYuLOin3+8AJQbrkL5b+lq4w59xKFX8O93L5C+eHzOyQAq87vM/dk8X57Xb57gnpLAgtP5XFee2f8nf80zlaXWOQPOGcey1Dfjnn1qtr/I9DLTS7TYGMCy3XF3jd4ePqtlSBswTn3CJJz4WSks9FB8uPBSL5QGfaMWWcH6Qw59NLOj9Q40xJP5VvYRDH5Aea/W9Jr1iK2S/MbIq6xoqR0gQse8r8zDn/z/zMMT8ws5+Fz63qCiiZfCuu3qxjgqTDg6frnHMZx8wIAuWJ3xDD5cf7SZe/TdHgT7cBIIFiYyBEYPAIBwp6PE2Sc26vmd0pf+ekVNLfzewO+R8tDwd3vwrl6TyWnW4AxrAn1DUTwBHpMvZnwcjqh4eSHs/0nmBfWCTpjCDpSEUvupM9l+a1hPD+k6nlQibh7ZHx84TyJUZYP7KP6++LWYr+gI7TKt9kNpvZHjIeK3naB8LlLU7VcifJk/J3/fPhYefcguREM6uQH8xvvqQvSZor35z9CTM72TmXz3NNWPj7yhQIknOu0cyelx8ULpsy4wabjbNY6YN24eDQ6ODCKxuJQIHJD5KY07vfGYRnF0h70Z4HvT0XzQmWk89F4W36knMubsDIZIvlW9TklHNuraSLzOyL8mMazJdvJXakoq07JD844d/M7Ezn3P1Jr4WnPlwRlNsnZna8/PlxvuJnUYjT26lfw8eE9eCYODC0PEm+hVc64X23R1PeAoVA0AAYPMIXYr2ablF+YLQj5S9qhsiPPPwBSc7MlsvfCb1f/k5Zcx/qmsmWPJb9epb53ggtj0mZq/8bLr8tEzLeOQysCS1n+rGVzZRfe0PLvZqHPiS8PfLxeQqtSX6axRfk+8bfkEXLjYRsjpV87APhbdCbY6oggibhayStMbPb5PtknyofZL3FzA4Nmu7nW2+/r3RBg/A2yfZCLNMMBfuFlg9SdKT7bPWme1yuFLpbXa7PRfnYpn0S3AX/p0IthYKZEN4j/5thepBcJukmMzvARac4DLcEWdXX+pjZRyVdp+yDBQm9aYEpRY+JRDeonsrmmOjp5wEKiu4JwCAQzL0ebtbcq/nHnXOb5e8kXCo/EnbnKuSnGfoP+ZYH683sq2aWPB91ruTzblG28zWH56bu7Y+N/qAm6Xm2c2735PMX+od6+DPl4/Pk043OOUt6VDvnJjrnTnfOXdGDgIGU3bGSj30gXGZvjqmCC4ID4R/8M9U1zVm+5eP7ykeZfW0FJBX+hlT4M1UVeN25PhcNiOPKObfeOfcL+Wb3t4deGifpfUnZw58x01gAaQVTgf6Pui6wl8mPQTRPvlvN0PC5VX6QzoTeXvMU6pgIjwtR1HMlEIegATA4HK3o4GnZNsHvxjnX6Jy7TL453Tz5AYzuUrRf40j5gX7+EDR9Hkiy/VEZbh6YTRPRTIp1vk3+kZZts8dcf/5cCn+mwfB58i0f+0C4zN4cU0XhnFshPzNEwltyVHSm4zsf31dvLpZ7UuY1MQGubB4PZVmXXAkH2QrdiijX56J8bNO8CVryXCjfUiphflK28GdMDmD21OfVdQH+F/lpD3/mnFvinNsS0wIyFwHi8Da5q5fHxMJ0KzCzMnV18ZGktOP0AMVA0AAYHM4JLXco+76VKTnnOoJ/xFc7586Sv4NwoqKDLr1b0nv7uq4Cm9yLfHEDQYWbNGdzFyEXdyt6Y6eidc32808JLacaCKtYwk3yB8Pnybd87AO92QaTssyXb+Ef5FNS5OkcXDL4QZ9JpuM7H99XeJtkO9r6/hle3xRanpFlmcW2JrRc6FHnc30uysc2zatgJoHwbAgTkrKE96nksRB6Khzk+1YWXYtSHd89UYhjYoK6rsn2Ks9dToDeIGgADHBmNlrSeaGk+zJM0dQrQRDhcflp1u4LvfSuuOy5Xn8OZZoJICE8eNPSmNfDd09GZVHenMxZcv+9BaPkPxtKOiFV3oTgIumYUFLc5y+m8EwMGT9PIDwAX3/7PHmVp30gXN68LKYUk6LHVDGF7+CmGsAx6+PbzMrluzqkE/6+Mn4PZlajDCOuJ5V5TMpcUZmmOw0P0nhSMJBkLuTzf8Jz6tqOE8yskN2Pcn0uCm/T2cF+kElfp7DNhfAd/uRZP8ItH2eaWV8CO+HxBV5IlzGYJnZuH9aVEK7/IX2sfyrhwXGfd86152EdQJ8QNAAGsKBrwI2KNvn7Xj7XGVyA3BNKGheTLfwDoq+D3uXaCWZ2YLoMZjZT0eDCgzHZwk2cD495Pdk5mbPk7Xt7ILT8kSy6lLxLXRdK4amj+ovw53m7mY1NmVOSmY1X1ywAye/fV+R6H/in/CwPkj8HnJquMDObruwvqvLGzIbKj8+SkOqOXk+O73cp2h85Tvgc8nYzq8uQ/33qmtIylYdCy8ea2QHpMpvZCco83enj6pr5oEbSv2fIn628/U8Iph18MXhqys2FYrbCx9X7zSztfmBmRypav+T/LS+oq/XCEElnZyivWn5AwmILHyORgT6DaTtfCiX1ZiDBhHCQL1P3jY8pB/uac261ovX/XF/LjBHeJzLOrgIUA0EDYIAK7kDcJuntoeTfBq0BelPesOCOWTbCzTDjRm8PT69X1KaTMUzST1PdGQ3Sf6qugZaWOOf+FZN1sbrunh0bDNAUv0KzT0rKZl74Her6UTTWzHL14/raULlHyg9oGSu4O/ODUNKtzrlsZkcopPvVdVFXIemaVBmDi+OfSkrs269K+ntea9c/5XQfcM5tl/THUNIPgwvyuPJM0k/UP0YH/4SiF+P/SJEvPIXh+akKM7NaZTc15n3qClBUSfp+mjJHSbosU4HOuefUNeWmSbo6VTAoaDnywyzKbFH0eLrczLJpJZVYT1wQWcr//4S/hZZPzEP5qdyirlYpEyR9O1XG4P/rz0JJDzrnXg7nCaYuvSmUdKmZhfu6J/u2on3h+8TMjjWzLwXBiGzf8xFFW9rETc97dWj5i2aWPO5BtsKzL6QcxNTMZijNtuiF8PF6kZm9Nds3BkHrTMLfR/KUlUC/QNAAGGDMbLyZfUn+zkr47vVT8pH13jpK0mtmdpmZxV7gmlmpmX1I0mdCyX+JyfpcaPn04CKkv2iVD7TclFyv4PlvJJ0eSv5aXCHBCPeJu0Qm6dbkZotmVhbMcf1TdW+yGVdmi6RXgqdlks7K+Gmy4Jx7VX7E6YSfm9mnkgMnQQuM+9U1v/QuSf+VizrkUvDD+quhpA+Y2bXJTXmDZsq/VvRu3VeC9+9T8rQPfEdd+/UcSX9K/oEcbIMb5Y+5VhWJmQ0JgnfhC/xVkv6U4i23hJbfb2afjilzlvyd5gOV4fgOmhtfEkr6mJn9MDlQG7QWuE/+wjqb7+tboeV3S7ou5jiok/R7+ZYeGc9Dkq5SV9PvYZIeM7N/TxVUNrNRZvYxM3ta0pdTlBn+n5BNq6ue+nNo+ZQ8lB/LObdL0ndDSV81s/+K2a7jJN2prtY2bUrxv0X++0+09pgs6V4zi4wFEPxv+Zb8953NNs3WSPng0hozu8bMjk4TYK8N6nBdKPkZRbsvJixU15SNQ+Q/0yfjAuNmVm5m7zSzO2PKuTu0fLWZnZ6cwczeIt8KZ5hyNwvBb9TVqqRM0j1m9pVUwRUzqzGzD5jZA4oGiuLylqoraNCsfTOojQGg0NPiAMjs7cE4BQklkmrl7yYcrPiBhK6XdFHMyME9NV7+h+0lZrZJ/gfARvkfOOPlAwvhQY4elW/tkGyxfBPFycH7lpvZ/fKDPCXuzi92zv2uj/XtjcslXSTpQ5LeHfxT3yTfxPoURbt6XO2cS/cP/OvyP4RKJB0m6RUz+4f8dJV1kt4sPw1Uo/wPxLQ/HgJ/kPSNYPk3wV2clQoNZOec+1IW5ST7kvwsG/Pkz/0/l/+B+1hQvwOD+iZm4WiTdGHQNLPfcc7dbmZvVldT149Jep+ZPSi/PcfKb89w/+ZrnHN/KGxN+5Wc7gPOuZfM7GL5VgSS76KwJjim1srPYX+K/PlrR5Avl3f/wmaY2c9j0svlz1nHKTqyfpOkc4NAXTfOuUfN7B5JZwZJPzOzT8n3b3aSDgrKLJG/IJom6aR0FXTOXW9mZ8qPCyP57XF+sM/uDJVRJn8OXSHpgxnK/KuZ/ULSJ4Okj0r6f8E22CLfB/xk+dYNq+WDJInm1bHBM+dco5m9S/7iZZr89vtf+dYkT8i3mHDy57jZwXeRuLCM68ol+fPax4PlTwTN9Jc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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(dpi=200)\n",
    "for i in df['category'].unique():\n",
    "    sns.distplot(np.log(df[df['category']==i]['prod_budget']), label=i, ax=ax)\n",
    "ax.legend()\n",
    "ax.set_title('Distribution of Prod Budget (Log Scale)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-21T06:28:34.481667Z",
     "start_time": "2019-08-21T06:28:34.472318Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 Counter({0: 336, 1: 127})\n",
      "2 Counter({0: 77, 1: 44})\n",
      "0 Counter({0: 346, 1: 51})\n"
     ]
    }
   ],
   "source": [
    "for i in df['category'].unique():\n",
    "    print(i, Counter(df[df['category']==i]['franchise']))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-21T08:20:53.557924Z",
     "start_time": "2019-08-21T08:20:53.521252Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0: 0.12846347607052896\n",
      "1: 0.27429805615550756\n",
      "2: 0.36363636363636365\n"
     ]
    }
   ],
   "source": [
    "print(f'0: {51/(51+346)}')\n",
    "print(f'1: {127/(127+336)}')\n",
    "print(f'2: {44/(77+44)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-21T06:46:33.417685Z",
     "start_time": "2019-08-21T06:46:33.406489Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 [('Comedy', 96), ('Animation', 50), ('Drama', 49), ('Action / Adventure', 41), ('Horror', 32), ('Thriller', 22), ('Action', 19), ('Comedy / Drama', 17), ('Romantic Comedy', 16), ('Romance', 13), ('Sci-Fi Action', 11), ('Action Thriller', 10), ('Fantasy', 9), ('Musical', 9), ('Family', 8), ('Crime Drama', 8), ('Sci-Fi Thriller', 7), ('Sports Drama', 7), ('Action Drama', 7), ('Family Comedy', 6), ('Sci-Fi Adventure', 4), ('Horror Comedy', 4), ('Music Drama', 4), ('Family Adventure', 3), ('Sci-Fi Fantasy', 3), ('War', 3), ('War Drama', 1), ('Historical Drama', 1), ('Period Drama', 1), ('Fantasy Comedy', 1), ('Sci-Fi Horror', 1)] \n",
      "\n",
      "2 [('Horror', 30), ('Drama', 22), ('Comedy', 22), ('Thriller', 18), ('Animation', 5), ('Comedy / Drama', 5), ('Romantic Comedy', 4), ('Action', 2), ('Action / Adventure', 2), ('Romance', 2), ('Crime Drama', 2), ('Music Drama', 1), ('Musical', 1), ('Horror Comedy', 1), ('Sci-Fi Horror', 1), ('Sci-Fi Action', 1), ('Action Drama', 1), ('Historical Drama', 1)] \n",
      "\n",
      "0 [('Drama', 65), ('Comedy', 43), ('Action', 39), ('Action / Adventure', 29), ('Thriller', 29), ('Animation', 27), ('Sci-Fi Action', 19), ('Comedy / Drama', 17), ('Horror', 16), ('Fantasy', 15), ('Action Thriller', 12), ('Foreign', 10), ('Sci-Fi Adventure', 7), ('Crime Drama', 7), ('Sci-Fi Thriller', 7), ('Action Drama', 7), ('Romantic Comedy', 5), ('Historical Drama', 5), ('Sci-Fi Horror', 4), ('Musical', 4), ('Family Comedy', 4), ('Romance', 4), ('Period Adventure', 3), ('Horror Comedy', 3), ('Family Adventure', 3), ('Sports Drama', 3), ('Family', 2), ('Romantic Thriller', 2), ('Crime Comedy', 2), ('Period Drama', 2), ('Historical Epic', 1), ('War Drama', 1)] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i in df['category'].unique():\n",
    "    print(i, Counter(df[df['category']==i]['genres']).most_common(), '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-21T06:47:12.085914Z",
     "start_time": "2019-08-21T06:47:12.072346Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 [('PG-13', 205), ('R', 154), ('PG', 98), ('G', 6)] \n",
      "\n",
      "2 [('R', 67), ('PG-13', 43), ('PG', 11)] \n",
      "\n",
      "0 [('PG-13', 175), ('R', 147), ('PG', 54), ('Unrated', 18), ('G', 2), ('Not Yet Rated', 1)] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i in df['category'].unique():\n",
    "    print(i, Counter(df[df['category']==i]['rating']).most_common(), '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-21T07:44:15.468092Z",
     "start_time": "2019-08-21T07:44:15.337469Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: RuntimeWarning: invalid value encountered in log\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1a25c387b8>"
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(np.log(df['profit']), df['theaters'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-21T07:44:55.183453Z",
     "start_time": "2019-08-21T07:44:55.106305Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "category\n",
       "0   2,307.5\n",
       "1   3,034.8\n",
       "2   2,650.8\n",
       "Name: theaters, dtype: float64"
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('category')['theaters'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-21T10:57:03.410573Z",
     "start_time": "2019-08-21T10:57:03.372010Z"
    },
    "scrolled": false
   },
   "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>category</th>\n",
       "      <th>title</th>\n",
       "      <th>profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>Us</td>\n",
       "      <td>1.5501e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Glass</td>\n",
       "      <td>9.1035e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Escape Room</td>\n",
       "      <td>4.8006e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>The Curse of La Llorona</td>\n",
       "      <td>4.5734e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>Ma (2019)</td>\n",
       "      <td>4.0373e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>The Intruder (2019)</td>\n",
       "      <td>2.7419e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>Bohemian Rhapsody</td>\n",
       "      <td>1.6443e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>A Star is Born (2018)</td>\n",
       "      <td>1.7929e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>Crazy Rich Asians</td>\n",
       "      <td>1.4453e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>Halloween (2018)</td>\n",
       "      <td>1.4934e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2</td>\n",
       "      <td>The Nun</td>\n",
       "      <td>9.545e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2</td>\n",
       "      <td>The First Purge</td>\n",
       "      <td>5.6489e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2</td>\n",
       "      <td>Insidious: The Last Key</td>\n",
       "      <td>5.7745e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2</td>\n",
       "      <td>Overboard (2018)</td>\n",
       "      <td>3.8316e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2</td>\n",
       "      <td>Breaking In (2018)</td>\n",
       "      <td>4.0841e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2</td>\n",
       "      <td>Truth or Dare</td>\n",
       "      <td>3.8411e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji: Welcome to the Jungle</td>\n",
       "      <td>3.1452e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2</td>\n",
       "      <td>It</td>\n",
       "      <td>2.9248e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2</td>\n",
       "      <td>Get Out</td>\n",
       "      <td>1.7204e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2</td>\n",
       "      <td>Girls Trip</td>\n",
       "      <td>9.6172e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2</td>\n",
       "      <td>Annabelle: Creation</td>\n",
       "      <td>8.7092e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2</td>\n",
       "      <td>Happy Death Day</td>\n",
       "      <td>5.1684e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2</td>\n",
       "      <td>The Secret Life of Pets</td>\n",
       "      <td>2.9338e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2</td>\n",
       "      <td>Deadpool</td>\n",
       "      <td>3.0507e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2</td>\n",
       "      <td>Hidden Figures</td>\n",
       "      <td>1.4461e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2</td>\n",
       "      <td>La La Land</td>\n",
       "      <td>1.211e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2</td>\n",
       "      <td>Bad Moms</td>\n",
       "      <td>9.3257e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2</td>\n",
       "      <td>Sausage Party</td>\n",
       "      <td>7.8686e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2</td>\n",
       "      <td>The Purge: Election Year</td>\n",
       "      <td>6.9213e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2</td>\n",
       "      <td>Lights Out</td>\n",
       "      <td>6.3269e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>2</td>\n",
       "      <td>The Vow</td>\n",
       "      <td>9.5014e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>2</td>\n",
       "      <td>Magic Mike</td>\n",
       "      <td>1.0672e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>2</td>\n",
       "      <td>Think Like a Man</td>\n",
       "      <td>7.9547e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>2</td>\n",
       "      <td>Act of Valor</td>\n",
       "      <td>5.8013e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>2</td>\n",
       "      <td>Chronicle (2012)</td>\n",
       "      <td>5.2575e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>2</td>\n",
       "      <td>Paranormal Activity 4</td>\n",
       "      <td>4.89e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>2</td>\n",
       "      <td>The Devil Inside</td>\n",
       "      <td>5.2262e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>2</td>\n",
       "      <td>Sinister</td>\n",
       "      <td>4.5087e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>2</td>\n",
       "      <td>The Best Exotic Marigold Hotel</td>\n",
       "      <td>3.6412e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>2</td>\n",
       "      <td>End of Watch</td>\n",
       "      <td>3.4003e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>2</td>\n",
       "      <td>The Help</td>\n",
       "      <td>1.4471e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>2</td>\n",
       "      <td>Bridesmaids</td>\n",
       "      <td>1.3711e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>2</td>\n",
       "      <td>Paranormal Activity 3</td>\n",
       "      <td>9.9029e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>2</td>\n",
       "      <td>Bad Teacher</td>\n",
       "      <td>8.0293e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>2</td>\n",
       "      <td>Insidious</td>\n",
       "      <td>5.3009e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumping the Broom</td>\n",
       "      <td>3.1295e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>2</td>\n",
       "      <td>50/50</td>\n",
       "      <td>2.7014e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>2</td>\n",
       "      <td>Courageous</td>\n",
       "      <td>3.2522e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>2</td>\n",
       "      <td>Our Idiot Brother</td>\n",
       "      <td>1.9816e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>2</td>\n",
       "      <td>The Twilight Saga: Eclipse</td>\n",
       "      <td>2.3253e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>2</td>\n",
       "      <td>The Karate Kid</td>\n",
       "      <td>1.3659e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>2</td>\n",
       "      <td>The King's Speech</td>\n",
       "      <td>1.2045e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>2</td>\n",
       "      <td>Jackass 3-D</td>\n",
       "      <td>9.723e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>2</td>\n",
       "      <td>Paranormal Activity 2</td>\n",
       "      <td>8.1753e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>2</td>\n",
       "      <td>Diary of a Wimpy Kid</td>\n",
       "      <td>4.9004e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>2</td>\n",
       "      <td>Easy A</td>\n",
       "      <td>5.0401e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>2</td>\n",
       "      <td>The Last Exorcism</td>\n",
       "      <td>4.0034e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>2</td>\n",
       "      <td>The Kids Are All Right</td>\n",
       "      <td>1.6811e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>2</td>\n",
       "      <td>Blue Valentine</td>\n",
       "      <td>8.7063e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>2</td>\n",
       "      <td>To Save a Life</td>\n",
       "      <td>3.2772e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>121 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     category                           title     profit\n",
       "0           2                              Us 1.5501e+08\n",
       "1           2                           Glass 9.1035e+07\n",
       "2           2                     Escape Room 4.8006e+07\n",
       "3           2        The Curse of La Llorona  4.5734e+07\n",
       "4           2                       Ma (2019) 4.0373e+07\n",
       "5           2            The Intruder (2019)  2.7419e+07\n",
       "6           2               Bohemian Rhapsody 1.6443e+08\n",
       "7           2           A Star is Born (2018) 1.7929e+08\n",
       "8           2               Crazy Rich Asians 1.4453e+08\n",
       "9           2                Halloween (2018) 1.4934e+08\n",
       "10          2                         The Nun  9.545e+07\n",
       "11          2                 The First Purge 5.6489e+07\n",
       "12          2         Insidious: The Last Key 5.7745e+07\n",
       "13          2                Overboard (2018) 3.8316e+07\n",
       "14          2              Breaking In (2018) 4.0841e+07\n",
       "15          2                   Truth or Dare 3.8411e+07\n",
       "16          2  Jumanji: Welcome to the Jungle 3.1452e+08\n",
       "17          2                              It 2.9248e+08\n",
       "18          2                         Get Out 1.7204e+08\n",
       "19          2                      Girls Trip 9.6172e+07\n",
       "20          2             Annabelle: Creation 8.7092e+07\n",
       "21          2                 Happy Death Day 5.1684e+07\n",
       "22          2         The Secret Life of Pets 2.9338e+08\n",
       "23          2                        Deadpool 3.0507e+08\n",
       "24          2                  Hidden Figures 1.4461e+08\n",
       "25          2                      La La Land  1.211e+08\n",
       "26          2                        Bad Moms 9.3257e+07\n",
       "27          2                   Sausage Party 7.8686e+07\n",
       "28          2        The Purge: Election Year 6.9213e+07\n",
       "29          2                      Lights Out 6.3269e+07\n",
       "..        ...                             ...        ...\n",
       "91          2                         The Vow 9.5014e+07\n",
       "92          2                      Magic Mike 1.0672e+08\n",
       "93          2                Think Like a Man 7.9547e+07\n",
       "94          2                    Act of Valor 5.8013e+07\n",
       "95          2                Chronicle (2012) 5.2575e+07\n",
       "96          2           Paranormal Activity 4   4.89e+07\n",
       "97          2                The Devil Inside 5.2262e+07\n",
       "98          2                        Sinister 4.5087e+07\n",
       "99          2  The Best Exotic Marigold Hotel 3.6412e+07\n",
       "100         2                    End of Watch 3.4003e+07\n",
       "101         2                        The Help 1.4471e+08\n",
       "102         2                     Bridesmaids 1.3711e+08\n",
       "103         2           Paranormal Activity 3 9.9029e+07\n",
       "104         2                     Bad Teacher 8.0293e+07\n",
       "105         2                       Insidious 5.3009e+07\n",
       "106         2               Jumping the Broom 3.1295e+07\n",
       "107         2                           50/50 2.7014e+07\n",
       "108         2                      Courageous 3.2522e+07\n",
       "109         2               Our Idiot Brother 1.9816e+07\n",
       "110         2      The Twilight Saga: Eclipse 2.3253e+08\n",
       "111         2                  The Karate Kid 1.3659e+08\n",
       "112         2               The King's Speech 1.2045e+08\n",
       "113         2                     Jackass 3-D  9.723e+07\n",
       "114         2           Paranormal Activity 2 8.1753e+07\n",
       "115         2            Diary of a Wimpy Kid 4.9004e+07\n",
       "116         2                          Easy A 5.0401e+07\n",
       "117         2               The Last Exorcism 4.0034e+07\n",
       "118         2          The Kids Are All Right 1.6811e+07\n",
       "119         2                  Blue Valentine 8.7063e+06\n",
       "120         2                  To Save a Life 3.2772e+06\n",
       "\n",
       "[121 rows x 3 columns]"
      ]
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat2 = df[df['category']==2]\n",
    "cat2.merge(df2, left_on=cat2.index, right_on=df2.index)[['category', 'title', 'profit']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-22T18:20:02.320865Z",
     "start_time": "2019-08-22T18:20:02.025001Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(dpi=200)\n",
    "sns.distplot(df[df['category']==0]['roi'], ax=ax, color='red', hist_kws={'alpha':0.5})\n",
    "ax.axvline(0, color='black', alpha=0.5)\n",
    "fig.savefig('profit categorynloss.png', transparent=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-22T18:27:44.562339Z",
     "start_time": "2019-08-22T18:27:44.509172Z"
    }
   },
   "outputs": [],
   "source": [
    "actors = pd.read_sql('select * from actors', conn)\n",
    "directors = pd.read_sql('select * from directors', conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-22T18:36:20.036173Z",
     "start_time": "2019-08-22T18:36:20.020626Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>actor</th>\n",
       "      <th>total_gross</th>\n",
       "      <th>num_movies</th>\n",
       "      <th>average</th>\n",
       "      <th>top_movie</th>\n",
       "      <th>gross</th>\n",
       "      <th>actor_level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>John Boyega</td>\n",
       "      <td>1.655e+09</td>\n",
       "      <td>6</td>\n",
       "      <td>2.758e+08</td>\n",
       "      <td>Star Wars: The Force Awakens</td>\n",
       "      <td>9.367e+08</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>Daisy Ridley</td>\n",
       "      <td>1.775e+09</td>\n",
       "      <td>5</td>\n",
       "      <td>3.55e+08</td>\n",
       "      <td>Star Wars: The Force Awakens</td>\n",
       "      <td>9.367e+08</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>469</th>\n",
       "      <td>Andy Serkis</td>\n",
       "      <td>4.3577e+09</td>\n",
       "      <td>26</td>\n",
       "      <td>1.676e+08</td>\n",
       "      <td>Star Wars: The Force Awakens</td>\n",
       "      <td>9.367e+08</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325</th>\n",
       "      <td>Mark Hamill</td>\n",
       "      <td>2.8263e+09</td>\n",
       "      <td>14</td>\n",
       "      <td>2.019e+08</td>\n",
       "      <td>Star Wars: The Force Awakens</td>\n",
       "      <td>9.367e+08</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>741</th>\n",
       "      <td>Peter Mayhew</td>\n",
       "      <td>1.9974e+09</td>\n",
       "      <td>4</td>\n",
       "      <td>4.994e+08</td>\n",
       "      <td>Star Wars: The Force Awakens</td>\n",
       "      <td>9.367e+08</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>Adam Driver</td>\n",
       "      <td>1.6997e+09</td>\n",
       "      <td>13</td>\n",
       "      <td>1.307e+08</td>\n",
       "      <td>Star Wars: The Force Awakens</td>\n",
       "      <td>9.367e+08</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>838</th>\n",
       "      <td>Simon Pegg</td>\n",
       "      <td>2.978e+09</td>\n",
       "      <td>24</td>\n",
       "      <td>1.241e+08</td>\n",
       "      <td>Star Wars: The Force Awakens</td>\n",
       "      <td>9.367e+08</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>808</th>\n",
       "      <td>Lupita Nyong'o</td>\n",
       "      <td>2.9536e+09</td>\n",
       "      <td>8</td>\n",
       "      <td>3.692e+08</td>\n",
       "      <td>Star Wars: The Force Awakens</td>\n",
       "      <td>9.367e+08</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>Harrison Ford</td>\n",
       "      <td>5.1196e+09</td>\n",
       "      <td>43</td>\n",
       "      <td>1.191e+08</td>\n",
       "      <td>Star Wars: The Force Awakens</td>\n",
       "      <td>9.367e+08</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>Carrie Fisher</td>\n",
       "      <td>2.8906e+09</td>\n",
       "      <td>19</td>\n",
       "      <td>1.521e+08</td>\n",
       "      <td>Star Wars: The Force Awakens</td>\n",
       "      <td>9.367e+08</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>Anthony Daniels</td>\n",
       "      <td>4.0409e+09</td>\n",
       "      <td>9</td>\n",
       "      <td>4.49e+08</td>\n",
       "      <td>Star Wars: The Force Awakens</td>\n",
       "      <td>9.367e+08</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392</th>\n",
       "      <td>Oscar Isaac</td>\n",
       "      <td>2.0248e+09</td>\n",
       "      <td>22</td>\n",
       "      <td>9.2e+07</td>\n",
       "      <td>Star Wars: The Force Awakens</td>\n",
       "      <td>9.367e+08</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>707</th>\n",
       "      <td>Evangeline Lilly</td>\n",
       "      <td>1.8534e+09</td>\n",
       "      <td>6</td>\n",
       "      <td>3.089e+08</td>\n",
       "      <td>Avengers: Endgame</td>\n",
       "      <td>8.576e+08</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>Zoe Saldana</td>\n",
       "      <td>4.1928e+09</td>\n",
       "      <td>31</td>\n",
       "      <td>1.353e+08</td>\n",
       "      <td>Avengers: Endgame</td>\n",
       "      <td>8.576e+08</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>Paul Rudd</td>\n",
       "      <td>2.9047e+09</td>\n",
       "      <td>36</td>\n",
       "      <td>8.07e+07</td>\n",
       "      <td>Avengers: Endgame</td>\n",
       "      <td>8.576e+08</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>Michael Douglas</td>\n",
       "      <td>2.8511e+09</td>\n",
       "      <td>38</td>\n",
       "      <td>7.5e+07</td>\n",
       "      <td>Avengers: Endgame</td>\n",
       "      <td>8.576e+08</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>Benedict Cumberbatch</td>\n",
       "      <td>3.5847e+09</td>\n",
       "      <td>21</td>\n",
       "      <td>1.707e+08</td>\n",
       "      <td>Avengers: Endgame</td>\n",
       "      <td>8.576e+08</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>443</th>\n",
       "      <td>Rene Russo</td>\n",
       "      <td>2.3876e+09</td>\n",
       "      <td>24</td>\n",
       "      <td>9.95e+07</td>\n",
       "      <td>Avengers: Endgame</td>\n",
       "      <td>8.576e+08</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>Bradley Cooper</td>\n",
       "      <td>4.803e+09</td>\n",
       "      <td>30</td>\n",
       "      <td>1.601e+08</td>\n",
       "      <td>Avengers: Endgame</td>\n",
       "      <td>8.576e+08</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>Mark Ruffalo</td>\n",
       "      <td>3.764e+09</td>\n",
       "      <td>37</td>\n",
       "      <td>1.017e+08</td>\n",
       "      <td>Avengers: Endgame</td>\n",
       "      <td>8.576e+08</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    actor  total_gross  num_movies   average  \\\n",
       "69            John Boyega    1.655e+09           6 2.758e+08   \n",
       "415          Daisy Ridley    1.775e+09           5  3.55e+08   \n",
       "469           Andy Serkis   4.3577e+09          26 1.676e+08   \n",
       "325           Mark Hamill   2.8263e+09          14 2.019e+08   \n",
       "741          Peter Mayhew   1.9974e+09           4 4.994e+08   \n",
       "205           Adam Driver   1.6997e+09          13 1.307e+08   \n",
       "838            Simon Pegg    2.978e+09          24 1.241e+08   \n",
       "808        Lupita Nyong'o   2.9536e+09           8 3.692e+08   \n",
       "255         Harrison Ford   5.1196e+09          43 1.191e+08   \n",
       "251         Carrie Fisher   2.8906e+09          19 1.521e+08   \n",
       "167       Anthony Daniels   4.0409e+09           9  4.49e+08   \n",
       "392           Oscar Isaac   2.0248e+09          22   9.2e+07   \n",
       "707      Evangeline Lilly   1.8534e+09           6 3.089e+08   \n",
       "449           Zoe Saldana   4.1928e+09          31 1.353e+08   \n",
       "438             Paul Rudd   2.9047e+09          36  8.07e+07   \n",
       "203       Michael Douglas   2.8511e+09          38   7.5e+07   \n",
       "159  Benedict Cumberbatch   3.5847e+09          21 1.707e+08   \n",
       "443            Rene Russo   2.3876e+09          24  9.95e+07   \n",
       "134        Bradley Cooper    4.803e+09          30 1.601e+08   \n",
       "439          Mark Ruffalo    3.764e+09          37 1.017e+08   \n",
       "\n",
       "                        top_movie     gross  actor_level  \n",
       "69   Star Wars: The Force Awakens 9.367e+08            4  \n",
       "415  Star Wars: The Force Awakens 9.367e+08            4  \n",
       "469  Star Wars: The Force Awakens 9.367e+08            4  \n",
       "325  Star Wars: The Force Awakens 9.367e+08            4  \n",
       "741  Star Wars: The Force Awakens 9.367e+08            4  \n",
       "205  Star Wars: The Force Awakens 9.367e+08            4  \n",
       "838  Star Wars: The Force Awakens 9.367e+08            4  \n",
       "808  Star Wars: The Force Awakens 9.367e+08            4  \n",
       "255  Star Wars: The Force Awakens 9.367e+08            4  \n",
       "251  Star Wars: The Force Awakens 9.367e+08            4  \n",
       "167  Star Wars: The Force Awakens 9.367e+08            4  \n",
       "392  Star Wars: The Force Awakens 9.367e+08            4  \n",
       "707             Avengers: Endgame 8.576e+08            4  \n",
       "449             Avengers: Endgame 8.576e+08            4  \n",
       "438             Avengers: Endgame 8.576e+08            4  \n",
       "203             Avengers: Endgame 8.576e+08            4  \n",
       "159             Avengers: Endgame 8.576e+08            4  \n",
       "443             Avengers: Endgame 8.576e+08            4  \n",
       "134             Avengers: Endgame 8.576e+08            4  \n",
       "439             Avengers: Endgame 8.576e+08            4  "
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actors[actors['actor_level']==4].sort_values('gross', ascending=False).head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-22T18:39:44.147403Z",
     "start_time": "2019-08-22T18:39:44.121425Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>director</th>\n",
       "      <th>total_gross</th>\n",
       "      <th>num_movies</th>\n",
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       "      <th>top_movie</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3683</th>\n",
       "      <td>Christopher Hampton</td>\n",
       "      <td>3.4e+06</td>\n",
       "      <td>3</td>\n",
       "      <td>1.1e+06</td>\n",
       "      <td>Carrington</td>\n",
       "      <td>3.2e+06</td>\n",
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       "    <tr>\n",
       "      <th>4154</th>\n",
       "      <td>Rowan Joffe</td>\n",
       "      <td>3.5e+06</td>\n",
       "      <td>2</td>\n",
       "      <td>1.7e+06</td>\n",
       "      <td>Before I Go To Sleep</td>\n",
       "      <td>3.2e+06</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4153</th>\n",
       "      <td>Jeremy Saulnier</td>\n",
       "      <td>3.5e+06</td>\n",
       "      <td>2</td>\n",
       "      <td>1.7e+06</td>\n",
       "      <td>Green Room</td>\n",
       "      <td>3.2e+06</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3361</th>\n",
       "      <td>Gillian Robespierre</td>\n",
       "      <td>4.1e+06</td>\n",
       "      <td>2</td>\n",
       "      <td>2e+06</td>\n",
       "      <td>Obvious Child</td>\n",
       "      <td>3.1e+06</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3364</th>\n",
       "      <td>Andrew Jarecki</td>\n",
       "      <td>3.7e+06</td>\n",
       "      <td>2</td>\n",
       "      <td>1.9e+06</td>\n",
       "      <td>Capturing the Friedmans</td>\n",
       "      <td>3.1e+06</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3649</th>\n",
       "      <td>Atom Egoyan</td>\n",
       "      <td>6.8e+06</td>\n",
       "      <td>5</td>\n",
       "      <td>1.4e+06</td>\n",
       "      <td>Chloe</td>\n",
       "      <td>3.1e+06</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3646</th>\n",
       "      <td>John Cameron Mitchell</td>\n",
       "      <td>7.4e+06</td>\n",
       "      <td>4</td>\n",
       "      <td>1.8e+06</td>\n",
       "      <td>Hedwig and the Angry Inch</td>\n",
       "      <td>3.1e+06</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2599</th>\n",
       "      <td>Damian Szifron</td>\n",
       "      <td>3.1e+06</td>\n",
       "      <td>1</td>\n",
       "      <td>3.1e+06</td>\n",
       "      <td>Wild Tales</td>\n",
       "      <td>3.1e+06</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3372</th>\n",
       "      <td>Lake Bell</td>\n",
       "      <td>3.2e+06</td>\n",
       "      <td>2</td>\n",
       "      <td>1.6e+06</td>\n",
       "      <td>In a World</td>\n",
       "      <td>3e+06</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4161</th>\n",
       "      <td>Clark Gregg</td>\n",
       "      <td>2.9e+06</td>\n",
       "      <td>1</td>\n",
       "      <td>2.9e+06</td>\n",
       "      <td>Choke</td>\n",
       "      <td>2.9e+06</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4327</th>\n",
       "      <td>Gregory Levasseur</td>\n",
       "      <td>2.8e+06</td>\n",
       "      <td>1</td>\n",
       "      <td>2.8e+06</td>\n",
       "      <td>The Pyramid</td>\n",
       "      <td>2.8e+06</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3016</th>\n",
       "      <td>Robert B. Weide</td>\n",
       "      <td>2.8e+06</td>\n",
       "      <td>1</td>\n",
       "      <td>2.8e+06</td>\n",
       "      <td>How to Lose Friends and Alienate People</td>\n",
       "      <td>2.8e+06</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3015</th>\n",
       "      <td>Dustin Marcellino</td>\n",
       "      <td>2.8e+06</td>\n",
       "      <td>1</td>\n",
       "      <td>2.8e+06</td>\n",
       "      <td>The Identical</td>\n",
       "      <td>2.8e+06</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   director  total_gross  num_movies  average  \\\n",
       "3683    Christopher Hampton      3.4e+06           3  1.1e+06   \n",
       "4154            Rowan Joffe      3.5e+06           2  1.7e+06   \n",
       "4153        Jeremy Saulnier      3.5e+06           2  1.7e+06   \n",
       "3361    Gillian Robespierre      4.1e+06           2    2e+06   \n",
       "3364         Andrew Jarecki      3.7e+06           2  1.9e+06   \n",
       "3649            Atom Egoyan      6.8e+06           5  1.4e+06   \n",
       "3646  John Cameron Mitchell      7.4e+06           4  1.8e+06   \n",
       "2599         Damian Szifron      3.1e+06           1  3.1e+06   \n",
       "3372              Lake Bell      3.2e+06           2  1.6e+06   \n",
       "4161            Clark Gregg      2.9e+06           1  2.9e+06   \n",
       "4327      Gregory Levasseur      2.8e+06           1  2.8e+06   \n",
       "3016        Robert B. Weide      2.8e+06           1  2.8e+06   \n",
       "3015      Dustin Marcellino      2.8e+06           1  2.8e+06   \n",
       "\n",
       "                                    top_movie   gross  director_level  \n",
       "3683                               Carrington 3.2e+06               1  \n",
       "4154                     Before I Go To Sleep 3.2e+06               1  \n",
       "4153                               Green Room 3.2e+06               1  \n",
       "3361                            Obvious Child 3.1e+06               1  \n",
       "3364                  Capturing the Friedmans 3.1e+06               1  \n",
       "3649                                    Chloe 3.1e+06               1  \n",
       "3646                Hedwig and the Angry Inch 3.1e+06               1  \n",
       "2599                               Wild Tales 3.1e+06               1  \n",
       "3372                               In a World   3e+06               1  \n",
       "4161                                    Choke 2.9e+06               1  \n",
       "4327                              The Pyramid 2.8e+06               1  \n",
       "3016  How to Lose Friends and Alienate People 2.8e+06               1  \n",
       "3015                            The Identical 2.8e+06               1  "
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "directors[directors['director_level']==1].sort_values('gross', ascending=False).head(100).drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
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     "end_time": "2019-08-22T18:33:22.225246Z",
     "start_time": "2019-08-22T18:33:22.220376Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "142240000.0"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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