new_datasets.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from functools import reduce\n",
    "import sys\n",
    "import csv\n",
    "from time import strptime\n",
    "import json\n",
    "from datetime import datetime as dt\n",
    "from datetime import date\n",
    "from statistics import mean\n",
    "import math\n",
    "import pickle\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# csv.field_size_limit(sys.maxsize)\n",
    "# np.set_printoptions(threshold=sys.maxsize)\n",
    "pd.options.display.max_columns = None\n",
    "pd.options.display.float_format = '{:20,.15f}'.format\n",
    "# pd.options.display.max_rows = 10000\n",
    "# pd.set_option('display.max_colwidth', -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_smooth_mean(df, by, on, m):\n",
    "    mean = df[on].mean()\n",
    "    agg = df.groupby(by)[on].agg(['count', 'mean'])\n",
    "    counts = agg['count']\n",
    "    means = agg['mean']\n",
    "    smooth = (counts * means + m * mean) / (counts + m)\n",
    "    return df[by].map(smooth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_file = pd.read_csv('savedcsv/17_homepage.csv')\n",
    "df_file = df_file.drop(df_file[df_file['META__id'] == 6].index)\n",
    "df_file.to_csv('datasets/df_file.csv', index=False)\n",
    "df_file = pd.read_csv('datasets/df_file.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_raw = reduce_mem_usage(pd.read_csv('savedcsv/17_homepage.csv'))\n",
    "df_raw = pd.read_csv('savedcsv/17_homepage.csv')\n",
    "df_raw['META__profitability'] = df_raw['profitability']\n",
    "df_raw['META__year_avg_profitability'] = df_raw['year_avg_profitability']\n",
    "df_raw = df_raw.drop(['profitability', 'year_avg_profitability', 'PROCESS__original_language'], axis=1)\n",
    "drop_cols = [c for c in df_raw.columns if 'META' not in c and ('avg' in c or 'experience' in c or 'movies_before' in c)]\n",
    "df_raw = df_raw.drop(drop_cols, axis=1)\n",
    "df_raw = df_raw.drop(df_raw[df_raw['META__id'] == 6].index)\n",
    "df_raw.to_csv('datasets/df_raw.csv', index=False)\n",
    "df_raw = pd.read_csv('datasets/df_raw.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_raw = pd.read_csv('datasets/df_raw.csv')\n",
    "# for i in range(1, 9):\n",
    "#     df_raw.rename(columns={f'META__cast_{i}_name': f'META__cast_{i}'}, inplace=True)\n",
    "# df_raw.rename(columns={\n",
    "#     'META__crew__editing__editor__1': 'META__crew__editing__editor', \n",
    "#     'META__crew__writing__screenplay__1': 'META__crew__writing__screenplay',\n",
    "#     'META__crew__production__executive_producer__1': 'META__crew__production__executive_producer',\n",
    "#     'META__crew__directing__director__1': 'META__crew__directing__director'\n",
    "#     }, inplace=True)\n",
    "# df_raw.rename(columns={\n",
    "#     'META__crew__production__producer__1': 'META__crew__production__producer_1',\n",
    "#     'META__crew__production__producer__2': 'META__crew__production__producer_2'\n",
    "#     }, inplace=True)\n",
    "# df_raw.to_csv('datasets/df_raw.csv', index=False)\n",
    "df_raw['META__date'] = pd.to_datetime((df_raw.META__year*10000 + df_raw.META__month*100 + df_raw.META__day).apply(str), format='%Y%m%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "get_group_avg_revenue = lambda group: np.rint(group['META__revenue'].mean()) if not group.empty else np.nan\n",
    "get_group_avg_profit = lambda group: np.rint((group['META__revenue'] - group['budget']).mean()) if not group.empty else np.nan\n",
    "\n",
    "\n",
    "class CalculateInfo:\n",
    "    def __init__(self, df, movie, movie_index, df_ref):\n",
    "        self.df_ref = df_ref\n",
    "        self.df = df\n",
    "        self.movie = movie\n",
    "        self.movie_index = movie_index\n",
    "        self.movies_before = df_ref[df_ref.META__date < movie.META__date]\n",
    "\n",
    "    def _get_col_index(self, col):\n",
    "        if col not in self.df:\n",
    "            self.df[col] = np.nan\n",
    "        return self.df.columns.get_loc(col)\n",
    "\n",
    "    def _set_column(self, column, value):\n",
    "        self.df.iat[self.movie_index, self._get_col_index(column)] = value\n",
    "\n",
    "    def _set_avg_profit(self, column, group): \n",
    "        self._set_column(f'{column}_avg_profit', get_group_avg_profit(group))\n",
    "    def _set_avg_revenue(self, column, group): \n",
    "        self._set_column(f'{column}_avg_revenue', get_group_avg_revenue(group))\n",
    "    def _set_movies_before(self, column, group): \n",
    "        self._set_column(f'{column}_movies_before', group.shape[0])\n",
    "    def _set_experience(self, column, group): \n",
    "        self._set_column(f'{column}_experience', self.movie.META__year - group['META__year'].min() if not group.empty else 0)\n",
    "\n",
    "\n",
    "\n",
    "    def _get_groups(self, column, iteration, df_ref): \n",
    "        def concat_df(df, column, iteration):\n",
    "            def_columns = ['budget', 'META__revenue', 'META__year']\n",
    "            if len(iteration) > 1:\n",
    "                return pd.concat([df[[f'{column}_{i}'] + def_columns].rename(columns={f'{column}_{i}': column}) for i in iteration])\n",
    "            return df[list(set([column] + def_columns))]\n",
    "\n",
    "        df = self.movies_before if not df_ref else self.df_ref\n",
    "        return concat_df(df, column, iteration).groupby(column)\n",
    "\n",
    "    def _apply_set_functions(self, column, group, *argv):\n",
    "        for func in argv:\n",
    "            func(column, group)\n",
    "\n",
    "    def _set_info(self, column_to_iterate, new_column, iteration, df_ref, *argv):\n",
    "        groups = self._get_groups(column_to_iterate, iteration, df_ref)\n",
    "        if len(iteration) == 1:\n",
    "            value = self.movie.get(column_to_iterate)\n",
    "            group = groups.get_group(value) if value in groups.groups.keys() else pd.DataFrame()\n",
    "            self._apply_set_functions(new_column, group, *argv)\n",
    "        for i in iteration:\n",
    "            value = self.movie.get(f'{column_to_iterate}_{i}')\n",
    "            group = groups.get_group(value) if value in groups.groups.keys() else pd.DataFrame()\n",
    "            self._apply_set_functions(f'{new_column}_{i}', group, *argv)\n",
    "        return self\n",
    "\n",
    "    def set_info_year(self):\n",
    "        return self._set_info('META__year', 'year', range(1), True, self._set_avg_profit, self._set_avg_revenue)\n",
    "\n",
    "    def set_info_production_company(self):\n",
    "        return self._set_info('META__production_company', 'production_company', range(1,4), False, self._set_avg_profit, self._set_avg_revenue)\n",
    "\n",
    "    def set_info_cast(self):\n",
    "        return self._set_info('META__cast', 'cast', range(1,9), False, self._set_avg_profit, self._set_avg_revenue, self._set_experience, self._set_movies_before)\n",
    "\n",
    "    def set_info_collections(self):\n",
    "        return self._set_info('META__collection_name', 'collection', range(1), False, self._set_avg_profit, self._set_avg_revenue)\n",
    "\n",
    "    def set_info_crew(self):\n",
    "        crew_columns = [column for column in self.df.columns if 'META__crew' in column]\n",
    "        producer_passed = False\n",
    "        for column in crew_columns:\n",
    "            if 'production__producer' in column and not producer_passed:\n",
    "                column_to_iterate, new_column, iteration = column[:-2], column[6:-2], range(1,3)\n",
    "                producer_passed = True\n",
    "            else:\n",
    "                column_to_iterate, new_column, iteration = column, column[6:], range(1)\n",
    "            self._set_info(column_to_iterate, new_column, iteration, False, self._set_avg_profit, self._set_avg_revenue, self._set_experience)\n",
    "        return self\n",
    "\n",
    "\n",
    "    def set_info_cast_avg(self):\n",
    "        key_columns = ['avg_revenue', 'avg_profit', 'experience', 'movies_before']\n",
    "        for key_column in key_columns:\n",
    "            df_columns = [f'cast_{i}_{key_column}' for i in range(1,9)]\n",
    "            movie = self.df.iloc[self.movie_index]\n",
    "            self._set_column(f'cast_avg_{key_column}', np.mean([movie[col] for col in df_columns if not pd.isna(movie[col])]))\n",
    "        return self"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "4.335583209991455\n"
    }
   ],
   "source": [
    "start = time.time()\n",
    "\n",
    "\n",
    "dff = df_raw[:10].copy()\n",
    "for row_index, row in dff.iterrows():\n",
    "    CalculateInfo(dff, row, row_index, df_ref=df_raw) \\\n",
    "        .set_info_cast() \\\n",
    "        .set_info_collections() \\\n",
    "        .set_info_crew() \\\n",
    "        .set_info_production_company() \\\n",
    "        .set_info_year() \\\n",
    "        .set_info_cast_avg()\n",
    "\n",
    "print(time.time() - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "0\n100\n200\n300\n400\n500\n600\n700\n800\n900\n1000\n1100\n1200\n1300\n1400\n1500\n1600\n1700\n1800\n1900\n2000\n2100\n2200\n2300\n2400\n2500\n2600\n2700\n2800\n2900\n3000\n3100\n3200\n3300\n3400\n3500\n3600\n3700\n3800\n3900\n4000\n4100\n4200\n4300\n4400\n4500\n4600\n4700\n4800\n4900\n5000\n5100\n5200\n5300\n5400\n5500\n5600\n5700\n5800\n5900\n6000\n6100\n6200\n6300\n6400\n6500\n6600\n6700\n6800\n6900\n7000\n7100\n7200\n7300\n7400\n126.9867513179779\n"
    }
   ],
   "source": [
    "start = time.time()\n",
    "\n",
    "d = df_raw.copy()\n",
    "d = set_all_info(d)\n",
    "\n",
    "print(time.time() - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_compare = pd.read_csv('datasets/dataset_all.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "'1.0.5'"
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "source": [
    "import pandas as pd\n",
    "pd.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "(10, 293)"
     },
     "metadata": {},
     "execution_count": 166
    }
   ],
   "source": [
    "d.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "\nPlease wait a moment while I gather a list of all available modules...\n\nUpdate LANGUAGE_CODES (inside config/base.py) if a new translation has been added to Spyder\nCython              cgi                 lief                scipy\nIPython             cgitb               linecache           scripts\nOpenSSL             chardet             llvmlite            seaborn\nPIL                 chunk               locale              secrets\nPyQt5               click               locket              secretstorage\n__future__          cloudpickle         logging             select\n_abc                clyent              lxml                selectors\n_ast                cmath               lzma                send2trash\n_asyncio            cmd                 mailbox             setuptools\n_bisect             code                mailcap             shell_exec\n_blake2             codecs              markupsafe          shelve\n_bootlocale         codeop              marshal             shlex\n_bz2                collections         math                shutil\n_cffi_backend       colorama            matplotlib          signal\n_codecs             colorsys            mccabe              simplegeneric\n_codecs_cn          compileall          mimetypes           singledispatch\n_codecs_hk          completion          mistune             singledispatch_helpers\n_codecs_iso2022     concurrent          mkl                 sipbuild\n_codecs_jp          conda               mkl_fft             site\n_codecs_kr          conda_build         mkl_random          six\n_codecs_tw          conda_env           mmap                skimage\n_collections        conda_package_handling mock                sklearn\n_collections_abc    conda_verify        modin               smtpd\n_compat_pickle      configparser        modulefinder        smtplib\n_compression        contextlib          more_itertools      sndhdr\n_contextvars        contextlib2         mpmath              snowballstemmer\n_crypt              contextvars         msgpack             socket\n_csv                copy                multipledispatch    socketserver\n_ctypes             copyreg             multiprocessing     socks\n_ctypes_test        crypt               navigator_updater   sockshandler\n_curses             cryptography        nbclient            sortImports\n_curses_panel       csv                 nbconvert           sortedcollections\n_datetime           ctypes              nbformat            sortedcontainers\n_decimal            curl                nest_asyncio        soupsieve\n_distutils_hack     curses              netrc               sphinx\n_dummy_thread       cycler              networkx            sphinxcontrib\n_elementtree        cython              nis                 spwd\n_functools          cythonmagic         nltk                spyder\n_hashlib            cytoolz             nntplib             spyder_kernels\n_heapq              dask                normalizeForInterpreter sqlalchemy\n_imp                dataclasses         nose                sqlite3\n_io                 datetime            notebook            sre_compile\n_json               dateutil            ntpath              sre_constants\n_locale             dbm                 nturl2path          sre_parse\n_lsprof             debugpy             numbers             ssl\n_lzma               decimal             numexpr             stat\n_markupbase         decorator           numpy               statistics\n_md5                defusedxml          numpydoc            statsmodels\n_multibytecodec     diff_match_patch    olefile             storemagic\n_multiprocessing    difflib             opcode              string\n_opcode             dis                 openpyxl            stringprep\n_operator           distributed         operator            struct\n_osx_support        distutils           optparse            subprocess\n_pickle             doctest             os                  sunau\n_posixshmem         docutils            ossaudiodev         symbol\n_posixsubprocess    dummy_threading     output              symbolProvider\n_py_abc             easy_install        packaging           sympy\n_pydecimal          email               pandas              sympyprinting\n_pyio               encodings           pandocfilters       symtable\n_pyrsistent_version ensurepip           parser              sys\n_pytest             entrypoints         parso               sysconfig\n_queue              enum                partd               syslog\n_random             errno               past                tables\n_ruamel_yaml        et_xmlfile          path                tabnanny\n_sha1               fastcache           pathlib             tarfile\n_sha256             faulthandler        pathlib2            tblib\n_sha3               fcntl               pathtools           telnetlib\n_sha512             filecmp             patsy               tempfile\n_signal             fileinput           pdb                 terminado\n_sitebuiltins       filelock            pep8                termios\n_socket             flake8              pexpect             test\n_sqlite3            flask               pickle              test_data\n_sre                fnmatch             pickleshare         test_pycosat\n_ssl                formatter           pickletools         testing_tools\n_stat               fractions           pip                 testlauncher\n_statistics         fsspec              pipes               testpath\n_string             ftplib              pkg_resources       tests\n_strptime           functools           pkginfo             textwrap\n_struct             future              pkgutil             this\n_symtable           gc                  platform            threading\n_sysconfigdata__linux_x86_64-linux-gnu genericpath         plistlib            threadpoolctl\n_sysconfigdata_aarch64_conda_cos7_linux_gnu getopt              pluggy              tifffile\n_sysconfigdata_i686_conda_cos6_linux_gnu getpass             ply                 time\n_sysconfigdata_powerpc64le_conda_cos7_linux_gnu gettext             poplib              timeit\n_sysconfigdata_x86_64_apple_darwin13_4_0 gevent              posix               tkinter\n_sysconfigdata_x86_64_conda_cos6_linux_gnu glob                posixpath           tlz\n_testbuffer         glob2               pprint              token\n_testcapi           gmpy2               printEnvVariables   tokenize\n_testimportmultiple greenlet            printEnvVariablesToFile toml\n_testinternalcapi   grp                 process_class       toolz\n_testmultiphase     gzip                profile             tornado\n_thread             h5py                prometheus_client   tqdm\n_threading_local    hashlib             prompt_toolkit      trace\n_tkinter            heapdict            pstats              traceback\n_tracemalloc        heapq               psutil              tracemalloc\n_warnings           helpers             pty                 traitlets\n_weakref            hmac                ptyprocess          tty\n_weakrefset         html                pvectorc            turtle\n_xxsubinterpreters  html5lib            pwd                 turtledemo\n_xxtestfuzz         http                py                  types\n_yaml               idlelib             py_compile          typing\nabc                 idna                pyclbr              typing_extensions\naifc                imageio             pycodestyle         ujson\nalabaster           imagesize           pycosat             unicodecsv\nanaconda_navigator  imaplib             pycparser           unicodedata\nanaconda_project    imghdr              pycurl              unittest\nantigravity         imp                 pydoc               urllib\nargh                importlib           pydoc_data          urllib3\nargon2              importlib_metadata  pydocstyle          uu\nargparse            iniconfig           pyexpat             uuid\narray               inspect             pyflakes            venv\nasn1crypto          install_debugpy     pygls               visualstudio_py_testlauncher\nast                 interpreterInfo     pygments            vscode_datascience_helpers\nastroid             intervaltree        pylab               warnings\nastropy             io                  pylint              watchdog\nasync_generator     ipaddress           pyls                wave\nasynchat            ipykernel           pyls_jsonrpc        wcwidth\nasyncio             ipykernel_launcher  pyodbc              weakref\nasyncore            ipython_genutils    pyparsing           webbrowser\natexit              ipywidgets          pyrsistent          webencodings\natomicwrites        isort               pytest              werkzeug\nattr                isympy              pytz                wheel\naudioop             itertools           pyvsc-run-isolated  widgetsnbextension\nautopep8            itsdangerous        pywt                wrapt\nautoreload          jdcal               pyximport           wsgiref\nbabel               jedi                qdarkstyle          wurlitzer\nbackcall            jedi_language_server qtawesome           xdg\nbackports           jeepney             qtconsole           xdrlib\nbase64              jinja2              qtpy                xlrd\nbdb                 joblib              queue               xlsxwriter\nbinascii            json                quopri              xlwt\nbinhex              json5               random              xml\nbinstar_client      jsonschema          re                  xmlrpc\nbisect              jupyter             readline            xmltodict\nbitarray            jupyter_client      refactor            xxlimited\nbkcharts            jupyter_console     regex               xxsubtype\nbleach              jupyter_core        reprlib             yaml\nbokeh               jupyterlab          requests            yapf\nboto                jupyterlab_pygments resource            yapftests\nbottleneck          jupyterlab_server   rlcompleter         zict\nbrotli              keyring             rmagic              zipapp\nbs4                 keyword             rope                zipfile\nbuiltins            kiwisolver          rtree               zipimport\nbz2                 lazy_object_proxy   ruamel_yaml         zipp\ncProfile            lib2to3             runJediLanguageServer zlib\ncalendar            libarchive          run_models          zmq\ncertifi             libfuturize         runpy               zope\ncffi                libpasteurize       sched               \n\nEnter any module name to get more help.  Or, type \"modules spam\" to search\nfor modules whose name or summary contain the string \"spam\".\n\n"
    }
   ],
   "source": [
    "help('modules')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "coll = [col for col in d.columns if (col.startswith('cast') or col.startswith('crew') or col.startswith('production_company') or col.startswith('collection') or col.startswith('year')) and not 'gender' in col]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "                      nan   \n\n   crew__production__producer_1_avg_revenue  \\\n5                                       nan   \n6                     7,235.000000000000000   \n7                45,284,974.000000000000000   \n8                                       nan   \n\n   crew__production__producer_1_movies_before  \\\n5                                           0   \n6                                           1   \n7                                           1   \n8                                           0   \n\n   crew__production__producer_2_avg_profit  \\\n5                                      nan   \n6                                      nan   \n7                                      nan   \n8                                      nan   \n\n   crew__production__producer_2_avg_revenue  \\\n5                                       nan   \n6                                       nan   \n7                                       nan   \n8                                       nan   \n\n   crew__production__producer_2_movies_before  \\\n5                                           0   \n6                                           0   \n7                                           0   \n8                                           0   \n\n   crew__sound__music_editor_avg_profit  \\\n5                                   nan   \n6                                   nan   \n7                                   nan   \n8                                   nan   \n\n   crew__sound__music_editor_avg_revenue  \\\n5                                    nan   \n6                                    nan   \n7                                    nan   \n8                                    nan   \n\n   crew__sound__music_editor_movies_before  \\\n5                                        0   \n6                                        0   \n7                                        0   \n8                                        0   \n\n   crew__sound__original_music_composer_avg_profit  \\\n5                                              nan   \n6                                              nan   \n7                       79,582,741.000000000000000   \n8                                              nan   \n\n   crew__sound__original_music_composer_avg_revenue  \\\n5                                               nan   \n6                                               nan   \n7                       100,717,417.000000000000000   \n8                                               nan   \n\n   crew__sound__original_music_composer_movies_before  \\\n5                                                  0    \n6                                                  0    \n7                                                  4    \n8                                                  0    \n\n   crew__sound__sound_designer_avg_profit  \\\n5                                     nan   \n6                                     nan   \n7             276,101,666.000000000000000   \n8                                     nan   \n\n   crew__sound__sound_designer_avg_revenue  \\\n5                                      nan   \n6                                      nan   \n7              366,101,666.000000000000000   \n8                                      nan   \n\n   crew__sound__sound_designer_movies_before  \\\n5                                          0   \n6                                          0   \n7                                          1   \n8                                          0   \n\n   crew__sound__sound_effects_editor_avg_profit  \\\n5                                           nan   \n6                                           nan   \n7                                           nan   \n8                                           nan   \n\n   crew__sound__sound_effects_editor_avg_revenue  \\\n5                                            nan   \n6                                            nan   \n7                                            nan   \n8                                            nan   \n\n   crew__sound__sound_effects_editor_movies_before  \\\n5                                                0   \n6                                                0   \n7                                                0   \n8                                                0   \n\n   crew__sound__sound_re_recording_mixer_avg_profit  \\\n5                         2,593,000.000000000000000   \n6                                               nan   \n7                        64,548,284.000000000000000   \n8                                               nan   \n\n   crew__sound__sound_re_recording_mixer_avg_revenue  \\\n5                          3,202,000.000000000000000   \n6                                                nan   \n7                         95,148,284.000000000000000   \n8                                                nan   \n\n   crew__sound__sound_re_recording_mixer_movies_before  \\\n5                                                  1     \n6                                                  0     \n7                                                  2     \n8                                                  0     \n\n   crew__sound__supervising_sound_editor_avg_profit  \\\n5                                               nan   \n6                                               nan   \n7                                               nan   \n8                                               nan   \n\n   crew__sound__supervising_sound_editor_avg_revenue  \\\n5                                                nan   \n6                                                nan   \n7                                                nan   \n8                                                nan   \n\n   crew__sound__supervising_sound_editor_movies_before  \\\n5                                                  0     \n6                                                  0     \n7                                                  0     \n8                                                  0     \n\n   crew__directing__director_avg_profit  \\\n5                                   nan   \n6            -2,492,765.000000000000000   \n7             8,045,643.000000000000000   \n8                                   nan   \n\n   crew__directing__director_avg_revenue  \\\n5                                    nan   \n6                  7,235.000000000000000   \n7             16,891,878.000000000000000   \n8                                    nan   \n\n   crew__directing__director_movies_before  \\\n5                                        0   \n6                                        1   \n7                                        3   \n8                                        0   \n\n   crew__directing__script_supervisor_avg_profit  \\\n5                                            nan   \n6                                            nan   \n7                     34,514,260.000000000000000   \n8                                            nan   \n\n   crew__directing__script_supervisor_avg_revenue  \\\n5                                             nan   \n6                                             nan   \n7                      44,014,260.000000000000000   \n8                                             nan   \n\n   crew__directing__script_supervisor_movies_before  \\\n5                                                 0   \n6                                                 0   \n7                                                 2   \n8                                                 0   \n\n   crew__production__casting_avg_profit  \\\n5                                   nan   \n6            42,300,225.000000000000000   \n7                                   nan   \n8                                   nan   \n\n   crew__production__casting_avg_revenue  \\\n5                                    nan   \n6             66,800,225.000000000000000   \n7                                    nan   \n8                                    nan   \n\n   crew__production__casting_movies_before  \\\n5                                        0   \n6                                       14   \n7                                        0   \n8                                        0   \n\n   crew__production__executive_producer_avg_profit  \\\n5                                              nan   \n6                       -2,492,765.000000000000000   \n7                                              nan   \n8                                              nan   \n\n   crew__production__executive_producer_avg_revenue  \\\n5                                               nan   \n6                             7,235.000000000000000   \n7                                               nan   \n8                                               nan   \n\n   crew__production__executive_producer_movies_before  \\\n5                                                  0    \n6                                                  1    \n7                                                  0    \n8                                                  0    \n\n   crew__editing__editor_avg_profit  crew__editing__editor_avg_revenue  \\\n5         5,077,900.000000000000000          6,877,900.000000000000000   \n6                               nan                                nan   \n7        29,284,974.000000000000000         45,284,974.000000000000000   \n8                               nan                                nan   \n\n   crew__editing__editor_movies_before  \\\n5                                    1   \n6                                    0   \n7                                    1   \n8                                    0   \n\n   crew__costume__costume_designer_avg_profit  \\\n5                     892,000.000000000000000   \n6                                         nan   \n7                 -16,261,389.000000000000000   \n8                                         nan   \n\n   crew__costume__costume_designer_avg_revenue  \\\n5                    2,807,000.000000000000000   \n6                                          nan   \n7                    1,738,611.000000000000000   \n8                                          nan   \n\n   crew__costume__costume_designer_movies_before  \\\n5                                              1   \n6                                              0   \n7                                              1   \n8                                              0   \n\n   crew__costume__costume_supervisor_avg_profit  \\\n5                                           nan   \n6                                           nan   \n7                                           nan   \n8                                           nan   \n\n   crew__costume__costume_supervisor_avg_revenue  \\\n5                                            nan   \n6                                            nan   \n7                                            nan   \n8                                            nan   \n\n   crew__costume__costume_supervisor_movies_before  \\\n5                                                0   \n6                                                0   \n7                                                0   \n8                                                0   \n\n   crew__costume__makeup_artist_avg_profit  \\\n5                                      nan   \n6                                      nan   \n7               80,134,932.000000000000000   \n8                                      nan   \n\n   crew__costume__makeup_artist_avg_revenue  \\\n5                                       nan   \n6                                       nan   \n7               133,634,932.000000000000000   \n8                                       nan   \n\n   crew__costume__makeup_artist_movies_before  \\\n5                                           0   \n6                                           0   \n7                                           2   \n8                                           0   \n\n   crew__crew__stunt_coordinator_avg_profit  \\\n5                                       nan   \n6                                       nan   \n7               109,773,545.000000000000000   \n8                                       nan   \n\n   crew__crew__stunt_coordinator_avg_revenue  \\\n5                                        nan   \n6                                        nan   \n7                159,773,545.000000000000000   \n8                                        nan   \n\n   crew__crew__stunt_coordinator_movies_before  \\\n5                                            0   \n6                                            0   \n7                                            1   \n8                                            0   \n\n   crew__writing__screenplay_avg_profit  \\\n5                                   nan   \n6                                   nan   \n7            29,284,974.000000000000000   \n8                                   nan   \n\n   crew__writing__screenplay_avg_revenue  \\\n5                                    nan   \n6                                    nan   \n7             45,284,974.000000000000000   \n8                                    nan   \n\n   crew__writing__screenplay_movies_before  \\\n5                                        0   \n6                                        0   \n7                                        1   \n8                                        0   \n\n   crew__art__art_direction_avg_profit  crew__art__art_direction_avg_revenue  \\\n5            1,413,738.000000000000000             2,374,112.000000000000000   \n6                                  nan                                   nan   \n7                                  nan                                   nan   \n8                                  nan                                   nan   \n\n   crew__art__art_direction_movies_before  \\\n5                                       8   \n6                                       0   \n7                                       0   \n8                                       0   \n\n   crew__art__production_design_avg_profit  \\\n5                                      nan   \n6                                      nan   \n7               12,873,136.000000000000000   \n8                                      nan   \n\n   crew__art__production_design_avg_revenue  \\\n5                                       nan   \n6                                       nan   \n7                25,142,487.000000000000000   \n8                                       nan   \n\n   crew__art__production_design_movies_before  \\\n5                                           0   \n6                                           0   \n7                                           2   \n8                                           0   \n\n   crew__art__property_master_avg_profit  \\\n5                                    nan   \n6                                    nan   \n7            305,000,000.000000000000000   \n8                                    nan   \n\n   crew__art__property_master_avg_revenue  \\\n5                                     nan   \n6                                     nan   \n7             333,000,000.000000000000000   \n8                                     nan   \n\n   crew__art__property_master_movies_before  \\\n5                                         0   \n6                                         0   \n7                                         1   \n8                                         0   \n\n   crew__art__set_decoration_avg_profit  \\\n5             2,984,950.000000000000000   \n6                                   nan   \n7           -36,562,432.000000000000000   \n8                                   nan   \n\n   crew__art__set_decoration_avg_revenue  \\\n5              4,842,450.000000000000000   \n6                                    nan   \n7             20,437,568.000000000000000   \n8                                    nan   \n\n   crew__art__set_decoration_movies_before  \\\n5                                        2   \n6                                        0   \n7                                        2   \n8                                        0   \n\n   crew__visualeffects__visual_effects_supervisor_avg_profit  \\\n5                                                nan           \n6                                                nan           \n7                                                nan           \n8                                                nan           \n\n   crew__visualeffects__visual_effects_supervisor_avg_revenue  \\\n5                                                nan            \n6                                                nan            \n7                                                nan            \n8                                                nan            \n\n   crew__visualeffects__visual_effects_supervisor_movies_before  \\\n5                                                  0              \n6                                                  0              \n7                                                  0              \n8                                                  0              \n\n   crew__camera__director_of_photography_avg_profit  \\\n5                                               nan   \n6                        14,676,795.000000000000000   \n7                         1,312,525.000000000000000   \n8                                               nan   \n\n   crew__camera__director_of_photography_avg_revenue  \\\n5                                                nan   \n6                         17,697,790.000000000000000   \n7                         16,947,201.000000000000000   \n8                                                nan   \n\n   crew__camera__director_of_photography_movies_before  \\\n5                                                  0     \n6                                                  7     \n7                                                  4     \n8                                                  0     \n\n   crew__camera__steadicam_operator_avg_profit  \\\n5                                          nan   \n6                                          nan   \n7                  185,925,792.000000000000000   \n8                                          nan   \n\n   crew__camera__steadicam_operator_avg_revenue  \\\n5                                           nan   \n6                                           nan   \n7                   218,425,792.000000000000000   \n8                                           nan   \n\n   crew__camera__steadicam_operator_movies_before  \\\n5                                               0   \n6                                               0   \n7                                               2   \n8                                               0   \n\n   crew__camera__still_photographer_avg_profit  \\\n5                                          nan   \n6                                          nan   \n7                                          nan   \n8                                          nan   \n\n   crew__camera__still_photographer_avg_revenue  \\\n5                                           nan   \n6                                           nan   \n7                                           nan   \n8                                           nan   \n\n   crew__camera__still_photographer_movies_before             year_avg_profit  \\\n5                                               0   6,927,690.000000000000000   \n6                                               0  40,044,968.000000000000000   \n7                                               0  55,279,438.000000000000000   \n8                                               0 -45,712,462.000000000000000   \n\n            year_avg_revenue       cast_avg_avg_revenue  \\\n5  7,868,508.000000000000000                        nan   \n6 74,682,780.000000000000000 53,746,749.000000000000000   \n7 91,347,406.000000000000000 42,299,413.250000000000000   \n8    839,090.000000000000000                        nan   \n\n         cast_avg_avg_profit  cast_avg_experience  cast_avg_movies_before  \n5                        nan    0.000000000000000       0.000000000000000  \n6 31,682,102.333333332091570    5.625000000000000       2.000000000000000  \n7 26,873,002.375000000000000   10.000000000000000       5.625000000000000  \n8                        nan    0.000000000000000       0.000000000000000  ",
      "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>budget</th>\n      <th>META__id</th>\n      <th>runtime</th>\n      <th>spoken_languages</th>\n      <th>META__year</th>\n      <th>META__month</th>\n      <th>META__day</th>\n      <th>weekend</th>\n      <th>META__collection_name</th>\n      <th>META__cast_1</th>\n      <th>META__cast_2</th>\n      <th>META__cast_3</th>\n      <th>META__cast_4</th>\n      <th>META__cast_5</th>\n      <th>META__cast_6</th>\n      <th>META__cast_7</th>\n      <th>META__cast_8</th>\n      <th>META__crew__sound__music_editor</th>\n      <th>META__crew__sound__original_music_composer</th>\n      <th>META__crew__sound__sound_designer</th>\n      <th>META__crew__sound__sound_effects_editor</th>\n      <th>META__crew__sound__sound_re_recording_mixer</th>\n      <th>META__crew__sound__supervising_sound_editor</th>\n      <th>META__crew__directing__director</th>\n      <th>META__crew__directing__script_supervisor</th>\n      <th>META__crew__production__casting</th>\n      <th>META__crew__production__executive_producer</th>\n      <th>META__crew__production__producer_1</th>\n      <th>META__crew__production__producer_2</th>\n      <th>META__crew__editing__editor</th>\n      <th>META__crew__costume__costume_designer</th>\n      <th>META__crew__costume__costume_supervisor</th>\n      <th>META__crew__costume__makeup_artist</th>\n      <th>META__crew__crew__stunt_coordinator</th>\n      <th>META__crew__writing__screenplay</th>\n      <th>META__crew__art__art_direction</th>\n      <th>META__crew__art__production_design</th>\n      <th>META__crew__art__property_master</th>\n      <th>META__crew__art__set_decoration</th>\n      <th>META__crew__visualeffects__visual_effects_supervisor</th>\n      <th>META__crew__camera__director_of_photography</th>\n      <th>META__crew__camera__steadicam_operator</th>\n      <th>META__crew__camera__still_photographer</th>\n      <th>genre__war</th>\n      <th>genre__western</th>\n      <th>genre__mystery</th>\n      <th>genre__music</th>\n      <th>genre__crime</th>\n      <th>genre__romance</th>\n      <th>genre__action</th>\n      <th>genre__adventure</th>\n      <th>genre__thriller</th>\n      <th>genre__animation</th>\n      <th>genre__family</th>\n      <th>genre__drama</th>\n      <th>genre__comedy</th>\n      <th>genre__documentary</th>\n      <th>genre__history</th>\n      <th>genre__fantasy</th>\n      <th>genre__horror</th>\n      <th>META__production_company_1</th>\n      <th>META__production_company_2</th>\n      <th>META__production_company_3</th>\n      <th>country__es</th>\n      <th>country__jp</th>\n      <th>country__us</th>\n      <th>country__ca</th>\n      <th>country__de</th>\n      <th>country__cn</th>\n      <th>country__in</th>\n      <th>country__fr</th>\n      <th>country__ru</th>\n      <th>country__it</th>\n      <th>country__au</th>\n      <th>country__gb</th>\n      <th>day_sin</th>\n      <th>day_cos</th>\n      <th>month_sin</th>\n      <th>month_cos</th>\n      <th>competition</th>\n      <th>rating__g</th>\n      <th>rating__pg</th>\n      <th>rating__pg-13</th>\n      <th>rating__r</th>\n      <th>rating__nc-17</th>\n      <th>META__original_title</th>\n      <th>META__title</th>\n      <th>META__keywords</th>\n      <th>META__plot</th>\n      <th>country__other</th>\n      <th>META__imdb_id</th>\n      <th>tag__murder</th>\n      <th>tag__violence</th>\n      <th>tag__flashback</th>\n      <th>tag__romantic</th>\n      <th>tag__cult</th>\n      <th>tag__revenge</th>\n      <th>tag__psychedelic</th>\n      <th>tag__comedy</th>\n      <th>tag__suspenseful</th>\n      <th>tag__good_versus_evil</th>\n      <th>tag__humor</th>\n      <th>tag__satire</th>\n      <th>tag__entertaining</th>\n      <th>tag__neo_noir</th>\n      <th>tag__action</th>\n      <th>tag__sadist</th>\n      <th>tag__insanity</th>\n      <th>tag__tragedy</th>\n      <th>tag__fantasy</th>\n      <th>tag__paranormal</th>\n      <th>tag__boring</th>\n      <th>tag__mystery</th>\n      <th>tag__horror</th>\n      <th>tag__melodrama</th>\n      <th>tag__cruelty</th>\n      <th>tag__gothic</th>\n      <th>tag__dramatic</th>\n      <th>tag__dark</th>\n      <th>tag__atmospheric</th>\n      <th>tag__storytelling</th>\n      <th>tag__sci_fi</th>\n      <th>tag__psychological</th>\n      <th>tag__historical</th>\n      <th>tag__absurd</th>\n      <th>tag__prank</th>\n      <th>tag__sentimental</th>\n      <th>tag__philosophical</th>\n      <th>tag__bleak</th>\n      <th>tag__alternate_reality</th>\n      <th>tag__depressing</th>\n      <th>tag__plot_twist</th>\n      <th>tag__realism</th>\n      <th>tag__cute</th>\n      <th>tag__stupid</th>\n      <th>tag__home_movie</th>\n      <th>tag__thought_provoking</th>\n      <th>tag__inspiring</th>\n      <th>tag__other</th>\n      <th>cast_1_gender</th>\n      <th>cast_2_gender</th>\n      <th>cast_3_gender</th>\n      <th>cast_4_gender</th>\n      <th>cast_5_gender</th>\n      <th>cast_6_gender</th>\n      <th>cast_7_gender</th>\n      <th>cast_8_gender</th>\n      <th>META__cast_1_male</th>\n      <th>META__cast_1_female</th>\n      <th>META__cast_2_male</th>\n      <th>META__cast_2_female</th>\n      <th>META__cast_3_male</th>\n      <th>META__cast_3_female</th>\n      <th>META__cast_4_male</th>\n      <th>META__cast_4_female</th>\n      <th>META__cast_5_male</th>\n      <th>META__cast_5_female</th>\n      <th>META__cast_6_male</th>\n      <th>META__cast_6_female</th>\n      <th>META__cast_7_male</th>\n      <th>META__cast_7_female</th>\n      <th>META__cast_8_male</th>\n      <th>META__cast_8_female</th>\n      <th>META__revenue</th>\n      <th>homepage_exists</th>\n      <th>homepage_repeats</th>\n      <th>META__profitability</th>\n      <th>META__year_avg_profitability</th>\n      <th>META__date</th>\n      <th>cast_1_avg_profit</th>\n      <th>cast_1_avg_revenue</th>\n      <th>cast_1_movies_before</th>\n      <th>cast_1_experience</th>\n      <th>cast_2_avg_profit</th>\n      <th>cast_2_avg_revenue</th>\n      <th>cast_2_movies_before</th>\n      <th>cast_2_experience</th>\n      <th>cast_3_avg_profit</th>\n      <th>cast_3_avg_revenue</th>\n      <th>cast_3_movies_before</th>\n      <th>cast_3_experience</th>\n      <th>cast_4_avg_profit</th>\n      <th>cast_4_avg_revenue</th>\n      <th>cast_4_movies_before</th>\n      <th>cast_4_experience</th>\n      <th>cast_5_avg_profit</th>\n      <th>cast_5_avg_revenue</th>\n      <th>cast_5_movies_before</th>\n      <th>cast_5_experience</th>\n      <th>cast_6_avg_profit</th>\n      <th>cast_6_avg_revenue</th>\n      <th>cast_6_movies_before</th>\n      <th>cast_6_experience</th>\n      <th>cast_7_avg_profit</th>\n      <th>cast_7_avg_revenue</th>\n      <th>cast_7_movies_before</th>\n      <th>cast_7_experience</th>\n      <th>cast_8_avg_profit</th>\n      <th>cast_8_avg_revenue</th>\n      <th>cast_8_movies_before</th>\n      <th>cast_8_experience</th>\n      <th>production_company_1_avg_profit</th>\n      <th>production_company_1_avg_revenue</th>\n      <th>production_company_2_avg_profit</th>\n      <th>production_company_2_avg_revenue</th>\n      <th>production_company_3_avg_profit</th>\n      <th>production_company_3_avg_revenue</th>\n      <th>collection_avg_profit</th>\n      <th>collection_avg_revenue</th>\n      <th>crew__production__producer_1_avg_profit</th>\n      <th>crew__production__producer_1_avg_revenue</th>\n      <th>crew__production__producer_1_movies_before</th>\n      <th>crew__production__producer_2_avg_profit</th>\n      <th>crew__production__producer_2_avg_revenue</th>\n      <th>crew__production__producer_2_movies_before</th>\n      <th>crew__sound__music_editor_avg_profit</th>\n      <th>crew__sound__music_editor_avg_revenue</th>\n      <th>crew__sound__music_editor_movies_before</th>\n      <th>crew__sound__original_music_composer_avg_profit</th>\n      <th>crew__sound__original_music_composer_avg_revenue</th>\n      <th>crew__sound__original_music_composer_movies_before</th>\n      <th>crew__sound__sound_designer_avg_profit</th>\n      <th>crew__sound__sound_designer_avg_revenue</th>\n      <th>crew__sound__sound_designer_movies_before</th>\n      <th>crew__sound__sound_effects_editor_avg_profit</th>\n      <th>crew__sound__sound_effects_editor_avg_revenue</th>\n      <th>crew__sound__sound_effects_editor_movies_before</th>\n      <th>crew__sound__sound_re_recording_mixer_avg_profit</th>\n      <th>crew__sound__sound_re_recording_mixer_avg_revenue</th>\n      <th>crew__sound__sound_re_recording_mixer_movies_before</th>\n      <th>crew__sound__supervising_sound_editor_avg_profit</th>\n      <th>crew__sound__supervising_sound_editor_avg_revenue</th>\n      <th>crew__sound__supervising_sound_editor_movies_before</th>\n      <th>crew__directing__director_avg_profit</th>\n      <th>crew__directing__director_avg_revenue</th>\n      <th>crew__directing__director_movies_before</th>\n      <th>crew__directing__script_supervisor_avg_profit</th>\n      <th>crew__directing__script_supervisor_avg_revenue</th>\n      <th>crew__directing__script_supervisor_movies_before</th>\n      <th>crew__production__casting_avg_profit</th>\n      <th>crew__production__casting_avg_revenue</th>\n      <th>crew__production__casting_movies_before</th>\n      <th>crew__production__executive_producer_avg_profit</th>\n      <th>crew__production__executive_producer_avg_revenue</th>\n      <th>crew__production__executive_producer_movies_before</th>\n      <th>crew__editing__editor_avg_profit</th>\n      <th>crew__editing__editor_avg_revenue</th>\n      <th>crew__editing__editor_movies_before</th>\n      <th>crew__costume__costume_designer_avg_profit</th>\n      <th>crew__costume__costume_designer_avg_revenue</th>\n      <th>crew__costume__costume_designer_movies_before</th>\n      <th>crew__costume__costume_supervisor_avg_profit</th>\n      <th>crew__costume__costume_supervisor_avg_revenue</th>\n      <th>crew__costume__costume_supervisor_movies_before</th>\n      <th>crew__costume__makeup_artist_avg_profit</th>\n      <th>crew__costume__makeup_artist_avg_revenue</th>\n      <th>crew__costume__makeup_artist_movies_before</th>\n      <th>crew__crew__stunt_coordinator_avg_profit</th>\n      <th>crew__crew__stunt_coordinator_avg_revenue</th>\n      <th>crew__crew__stunt_coordinator_movies_before</th>\n      <th>crew__writing__screenplay_avg_profit</th>\n      <th>crew__writing__screenplay_avg_revenue</th>\n      <th>crew__writing__screenplay_movies_before</th>\n      <th>crew__art__art_direction_avg_profit</th>\n      <th>crew__art__art_direction_avg_revenue</th>\n      <th>crew__art__art_direction_movies_before</th>\n      <th>crew__art__production_design_avg_profit</th>\n      <th>crew__art__production_design_avg_revenue</th>\n      <th>crew__art__production_design_movies_before</th>\n      <th>crew__art__property_master_avg_profit</th>\n      <th>crew__art__property_master_avg_revenue</th>\n      <th>crew__art__property_master_movies_before</th>\n      <th>crew__art__set_decoration_avg_profit</th>\n      <th>crew__art__set_decoration_avg_revenue</th>\n      <th>crew__art__set_decoration_movies_before</th>\n      <th>crew__visualeffects__visual_effects_supervisor_avg_profit</th>\n      <th>crew__visualeffects__visual_effects_supervisor_avg_revenue</th>\n      <th>crew__visualeffects__visual_effects_supervisor_movies_before</th>\n      <th>crew__camera__director_of_photography_avg_profit</th>\n      <th>crew__camera__director_of_photography_avg_revenue</th>\n      <th>crew__camera__director_of_photography_movies_before</th>\n      <th>crew__camera__steadicam_operator_avg_profit</th>\n      <th>crew__camera__steadicam_operator_avg_revenue</th>\n      <th>crew__camera__steadicam_operator_movies_before</th>\n      <th>crew__camera__still_photographer_avg_profit</th>\n      <th>crew__camera__still_photographer_avg_revenue</th>\n      <th>crew__camera__still_photographer_movies_before</th>\n      <th>year_avg_profit</th>\n      <th>year_avg_revenue</th>\n      <th>cast_avg_avg_revenue</th>\n      <th>cast_avg_avg_profit</th>\n      <th>cast_avg_experience</th>\n      <th>cast_avg_movies_before</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>5</th>\n      <td>839727</td>\n      <td>15</td>\n      <td>119</td>\n      <td>1</td>\n      <td>1941</td>\n      <td>4</td>\n      <td>30</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>orson welles</td>\n      <td>joseph cotten</td>\n      <td>dorothy comingore</td>\n      <td>ray collins</td>\n      <td>george coulouris</td>\n      <td>agnes moorehead</td>\n      <td>paul stewart</td>\n      <td>ruth warrick</td>\n      <td>NaN</td>\n      <td>bernard herrmann</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>clem portman</td>\n      <td>john aalberg</td>\n      <td>orson welles</td>\n      <td>NaN</td>\n      <td>rufus le maire</td>\n      <td>george schaefer</td>\n      <td>orson welles</td>\n      <td>NaN</td>\n      <td>robert wise</td>\n      <td>edward stevenson</td>\n      <td>NaN</td>\n      <td>layne britton</td>\n      <td>NaN</td>\n      <td>orson welles</td>\n      <td>van nest polglase</td>\n      <td>NaN</td>\n      <td>charles sayers</td>\n      <td>darrell silvera</td>\n      <td>NaN</td>\n      <td>gregg toland</td>\n      <td>NaN</td>\n      <td>phil stern</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>rko radio pictures</td>\n      <td>mercury productions</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>-0.207911690817759</td>\n      <td>0.978147600733806</td>\n      <td>0.989821441880933</td>\n      <td>-0.142314838273285</td>\n      <td>0.000000000000000</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>Citizen Kane</td>\n      <td>Citizen Kane</td>\n      <td>journalist newspaper florida capitalist banker...</td>\n      <td>Charles Foster Kane , an enormously wealthy me...</td>\n      <td>False</td>\n      <td>tt0033467</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1.000000000000000</td>\n      <td>1.000000000000000</td>\n      <td>0.000000000000000</td>\n      <td>1.000000000000000</td>\n      <td>1.000000000000000</td>\n      <td>0.000000000000000</td>\n      <td>1.000000000000000</td>\n      <td>0.000000000000000</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>23217674</td>\n      <td>0</td>\n      <td>0</td>\n      <td>26.649074044302488</td>\n      <td>4.834435714362336</td>\n      <td>1941-04-30</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2,041,900.000000000000000</td>\n      <td>2,872,445.000000000000000</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>2,593,000.000000000000000</td>\n      <td>3,202,000.000000000000000</td>\n      <td>1</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>5,077,900.000000000000000</td>\n      <td>6,877,900.000000000000000</td>\n      <td>1</td>\n      <td>892,000.000000000000000</td>\n      <td>2,807,000.000000000000000</td>\n      <td>1</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>1,413,738.000000000000000</td>\n      <td>2,374,112.000000000000000</td>\n      <td>8</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>2,984,950.000000000000000</td>\n      <td>4,842,450.000000000000000</td>\n      <td>2</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>6,927,690.000000000000000</td>\n      <td>7,868,508.000000000000000</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>0.000000000000000</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>12800000</td>\n      <td>16</td>\n      <td>141</td>\n      <td>1</td>\n      <td>2000</td>\n      <td>5</td>\n      <td>17</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>bjrk</td>\n      <td>catherine deneuve</td>\n      <td>david morse</td>\n      <td>peter stormare</td>\n      <td>joel grey</td>\n      <td>cara seymour</td>\n      <td>vladica kostic</td>\n      <td>jean-marc barr</td>\n      <td>NaN</td>\n      <td>bjrk</td>\n      <td>kristian eidnes andersen</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>lars von trier</td>\n      <td>NaN</td>\n      <td>avy kaufman</td>\n      <td>peter aalbk jensen</td>\n      <td>vibeke windelv</td>\n      <td>finn gjerdrum</td>\n      <td>franois gdigier</td>\n      <td>manon rasmussen</td>\n      <td>NaN</td>\n      <td>cdric grard</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>peter grant</td>\n      <td>karl jlusson</td>\n      <td>jesper lorents</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>robby mller</td>\n      <td>NaN</td>\n      <td>david koskas</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>lantia cinema &amp; audiovisivi</td>\n      <td>fine line features</td>\n      <td>zentropa entertainments</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>-0.207911690817759</td>\n      <td>-0.978147600733806</td>\n      <td>0.755749574354258</td>\n      <td>-0.654860733945285</td>\n      <td>2.150000000000000</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>Dancer in the Dark</td>\n      <td>Dancer in the Dark</td>\n      <td>individual dancing robbery factory worker secr...</td>\n      <td>The film is set in Washington state in 1964 an...</td>\n      <td>True</td>\n      <td>tt0168629</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.000000000000000</td>\n      <td>0.000000000000000</td>\n      <td>1.000000000000000</td>\n      <td>1.000000000000000</td>\n      <td>1.000000000000000</td>\n      <td>0.000000000000000</td>\n      <td>0.000000000000000</td>\n      <td>1.000000000000000</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>40031879</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2.127490546875000</td>\n      <td>-1.162769204436023</td>\n      <td>2000-05-17</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>0</td>\n      <td>-2,486,102.000000000000000</td>\n      <td>1,813,898.000000000000000</td>\n      <td>2</td>\n      <td>31</td>\n      <td>63,455,518.000000000000000</td>\n      <td>105,682,791.000000000000000</td>\n      <td>11</td>\n      <td>10</td>\n      <td>34,076,891.000000000000000</td>\n      <td>53,743,558.000000000000000</td>\n      <td>3</td>\n      <td>4</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>878,739.000000000000000</td>\n      <td>7,103,151.000000000000000</td>\n      <td>-2,492,765.000000000000000</td>\n      <td>7,235.000000000000000</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>-2,492,765.000000000000000</td>\n      <td>7,235.000000000000000</td>\n      <td>1</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>-2,492,765.000000000000000</td>\n      <td>7,235.000000000000000</td>\n      <td>1</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>42,300,225.000000000000000</td>\n      <td>66,800,225.000000000000000</td>\n      <td>14</td>\n      <td>-2,492,765.000000000000000</td>\n      <td>7,235.000000000000000</td>\n      <td>1</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>14,676,795.000000000000000</td>\n      <td>17,697,790.000000000000000</td>\n      <td>7</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>40,044,968.000000000000000</td>\n      <td>74,682,780.000000000000000</td>\n      <td>53,746,749.000000000000000</td>\n      <td>31,682,102.333333332091570</td>\n      <td>5.625000000000000</td>\n      <td>2.000000000000000</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>90000000</td>\n      <td>18</td>\n      <td>126</td>\n      <td>3</td>\n      <td>1997</td>\n      <td>5</td>\n      <td>2</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>bruce willis</td>\n      <td>gary oldman</td>\n      <td>ian holm</td>\n      <td>milla jovovich</td>\n      <td>chris tucker</td>\n      <td>luke perry</td>\n      <td>brion james</td>\n      <td>tom lister jr.</td>\n      <td>NaN</td>\n      <td>ric serra</td>\n      <td>mark a. mangini</td>\n      <td>julia evershade</td>\n      <td>ron bartlett</td>\n      <td>NaN</td>\n      <td>luc besson</td>\n      <td>jean bourne</td>\n      <td>lucinda syson</td>\n      <td>NaN</td>\n      <td>patrice ledoux</td>\n      <td>NaN</td>\n      <td>sylvie landra</td>\n      <td>jean paul gaultier</td>\n      <td>janet tebrooke</td>\n      <td>amanda knight</td>\n      <td>marc boyle</td>\n      <td>luc besson</td>\n      <td>ira gilford</td>\n      <td>dan weil</td>\n      <td>barry wilkinson</td>\n      <td>maggie gray</td>\n      <td>mark stetson</td>\n      <td>thierry arbogast</td>\n      <td>john ward</td>\n      <td>jack english</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>columbia pictures</td>\n      <td>gaumont</td>\n      <td>sony pictures</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.207911690817759</td>\n      <td>0.978147600733806</td>\n      <td>0.755749574354258</td>\n      <td>-0.654860733945285</td>\n      <td>1.366666666666667</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>The Fifth Element</td>\n      <td>The Fifth Element</td>\n      <td>new york city clone taxi cyborg egypt future s...</td>\n      <td>In 1914, at the outbreak of World War I, extra...</td>\n      <td>False</td>\n      <td>tt0119116</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1.000000000000000</td>\n      <td>1.000000000000000</td>\n      <td>1.000000000000000</td>\n      <td>0.000000000000000</td>\n      <td>1.000000000000000</td>\n      <td>1.000000000000000</td>\n      <td>1.000000000000000</td>\n      <td>1.000000000000000</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>263920180</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1.932446444444444</td>\n      <td>-0.169600900518265</td>\n      <td>1997-05-02</td>\n      <td>83,959,284.000000000000000</td>\n      <td>122,759,284.000000000000000</td>\n      <td>15</td>\n      <td>10</td>\n      <td>40,171,047.000000000000000</td>\n      <td>62,291,303.000000000000000</td>\n      <td>8</td>\n      <td>11</td>\n      <td>34,815,533.000000000000000</td>\n      <td>48,172,675.000000000000000</td>\n      <td>7</td>\n      <td>21</td>\n      <td>-8,192,146.000000000000000</td>\n      <td>2,807,854.000000000000000</td>\n      <td>1</td>\n      <td>6</td>\n      <td>24,715,918.000000000000000</td>\n      <td>28,215,918.000000000000000</td>\n      <td>1</td>\n      <td>2</td>\n      <td>9,624,456.000000000000000</td>\n      <td>16,624,456.000000000000000</td>\n      <td>1</td>\n      <td>5</td>\n      <td>19,268,503.000000000000000</td>\n      <td>42,635,725.000000000000000</td>\n      <td>9</td>\n      <td>15</td>\n      <td>10,621,424.000000000000000</td>\n      <td>14,888,091.000000000000000</td>\n      <td>3</td>\n      <td>10</td>\n      <td>27,823,833.000000000000000</td>\n      <td>46,696,953.000000000000000</td>\n      <td>13,617.000000000000000</td>\n      <td>18,721,357.000000000000000</td>\n      <td>19,383,263.000000000000000</td>\n      <td>35,870,406.000000000000000</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>29,284,974.000000000000000</td>\n      <td>45,284,974.000000000000000</td>\n      <td>1</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>79,582,741.000000000000000</td>\n      <td>100,717,417.000000000000000</td>\n      <td>4</td>\n      <td>276,101,666.000000000000000</td>\n      <td>366,101,666.000000000000000</td>\n      <td>1</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>64,548,284.000000000000000</td>\n      <td>95,148,284.000000000000000</td>\n      <td>2</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>8,045,643.000000000000000</td>\n      <td>16,891,878.000000000000000</td>\n      <td>3</td>\n      <td>34,514,260.000000000000000</td>\n      <td>44,014,260.000000000000000</td>\n      <td>2</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>29,284,974.000000000000000</td>\n      <td>45,284,974.000000000000000</td>\n      <td>1</td>\n      <td>-16,261,389.000000000000000</td>\n      <td>1,738,611.000000000000000</td>\n      <td>1</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>80,134,932.000000000000000</td>\n      <td>133,634,932.000000000000000</td>\n      <td>2</td>\n      <td>109,773,545.000000000000000</td>\n      <td>159,773,545.000000000000000</td>\n      <td>1</td>\n      <td>29,284,974.000000000000000</td>\n      <td>45,284,974.000000000000000</td>\n      <td>1</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>12,873,136.000000000000000</td>\n      <td>25,142,487.000000000000000</td>\n      <td>2</td>\n      <td>305,000,000.000000000000000</td>\n      <td>333,000,000.000000000000000</td>\n      <td>1</td>\n      <td>-36,562,432.000000000000000</td>\n      <td>20,437,568.000000000000000</td>\n      <td>2</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>1,312,525.000000000000000</td>\n      <td>16,947,201.000000000000000</td>\n      <td>4</td>\n      <td>185,925,792.000000000000000</td>\n      <td>218,425,792.000000000000000</td>\n      <td>2</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>55,279,438.000000000000000</td>\n      <td>91,347,406.000000000000000</td>\n      <td>42,299,413.250000000000000</td>\n      <td>26,873,002.375000000000000</td>\n      <td>10.000000000000000</td>\n      <td>5.625000000000000</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>92620000</td>\n      <td>19</td>\n      <td>153</td>\n      <td>1</td>\n      <td>1927</td>\n      <td>1</td>\n      <td>10</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>gustav frhlich</td>\n      <td>brigitte helm</td>\n      <td>alfred abel</td>\n      <td>rudolf klein-rogge</td>\n      <td>theodor loos</td>\n      <td>fritz rasp</td>\n      <td>erwin biswanger</td>\n      <td>heinrich george</td>\n      <td>frank strobel</td>\n      <td>gottfried huppertz</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>fritz lang</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>erich pommer</td>\n      <td>NaN</td>\n      <td>fritz lang</td>\n      <td>aenne willkomm</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>fritz lang</td>\n      <td>karl vollbrecht</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>karl freund</td>\n      <td>NaN</td>\n      <td>horst von harbou</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>universum film (ufa)</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.951056516295154</td>\n      <td>-0.309016994374947</td>\n      <td>0.000000000000000</td>\n      <td>1.000000000000000</td>\n      <td>0.000000000000000</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>Metropolis</td>\n      <td>Metropolis</td>\n      <td>man vs machine based on novel or book undergro...</td>\n      <td>Set in the year 2026, Metropolis takes place i...</td>\n      <td>False</td>\n      <td>tt0017136</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1.000000000000000</td>\n      <td>0.000000000000000</td>\n      <td>1.000000000000000</td>\n      <td>1.000000000000000</td>\n      <td>1.000000000000000</td>\n      <td>1.000000000000000</td>\n      <td>1.000000000000000</td>\n      <td>1.000000000000000</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>650422</td>\n      <td>0</td>\n      <td>0</td>\n      <td>-141.399857323399260</td>\n      <td>-18.884395158298162</td>\n      <td>1927-01-10</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0</td>\n      <td>-45,712,462.000000000000000</td>\n      <td>839,090.000000000000000</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>0.000000000000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 168
    }
   ],
   "source": [
    "d[5:9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "coll2 = [col for col in df_compare.columns if (col.startswith('cast') or col.startswith('crew') or col.startswith('production_company') or col.startswith('collection') or col.startswith('year')) and not 'gender' in col]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "                                    nan   \n7                                      nan   \n8                        0.000000000000000   \n\n   crew__sound__original_music_composer_avg_profit  \\\n5                                              nan   \n6                                              nan   \n7                       79,582,741.000000000000000   \n8                                              nan   \n\n   crew__sound__original_music_composer_avg_revenue  \\\n5                                               nan   \n6                                               nan   \n7                       100,717,416.750000000000000   \n8                                               nan   \n\n   crew__sound__original_music_composer_movies_before  \\\n5                                  0.000000000000000    \n6                                  0.000000000000000    \n7                                  4.000000000000000    \n8                                  0.000000000000000    \n\n   crew__sound__sound_designer_avg_profit  \\\n5                                     nan   \n6                                     nan   \n7             276,101,666.000000000000000   \n8                                     nan   \n\n   crew__sound__sound_designer_avg_revenue  \\\n5                                      nan   \n6                                      nan   \n7              366,101,666.000000000000000   \n8                                      nan   \n\n   crew__sound__sound_designer_movies_before  \\\n5                                        nan   \n6                          0.000000000000000   \n7                          1.000000000000000   \n8                                        nan   \n\n   crew__sound__sound_effects_editor_avg_profit  \\\n5                                           nan   \n6                                           nan   \n7                                           nan   \n8                                           nan   \n\n   crew__sound__sound_effects_editor_avg_revenue  \\\n5                                            nan   \n6                                            nan   \n7                                            nan   \n8                                            nan   \n\n   crew__sound__sound_effects_editor_movies_before  \\\n5                                              nan   \n6                                              nan   \n7                                0.000000000000000   \n8                                              nan   \n\n   crew__sound__sound_re_recording_mixer_avg_profit  \\\n5                         2,593,000.000000000000000   \n6                                               nan   \n7                        64,548,284.000000000000000   \n8                                               nan   \n\n   crew__sound__sound_re_recording_mixer_avg_revenue  \\\n5                          3,202,000.000000000000000   \n6                                                nan   \n7                         95,148,284.000000000000000   \n8                                                nan   \n\n   crew__sound__sound_re_recording_mixer_movies_before  \\\n5                                  1.000000000000000     \n6                                                nan     \n7                                  2.000000000000000     \n8                                                nan     \n\n   crew__sound__supervising_sound_editor_avg_profit  \\\n5                                               nan   \n6                                               nan   \n7                                               nan   \n8                                               nan   \n\n   crew__sound__supervising_sound_editor_avg_revenue  \\\n5                                                nan   \n6                                                nan   \n7                                                nan   \n8                                                nan   \n\n   crew__sound__supervising_sound_editor_movies_before  \\\n5                                  0.000000000000000     \n6                                                nan     \n7                                                nan     \n8                                                nan     \n\n   crew__directing__director__1_avg_profit  \\\n5                                      nan   \n6               -2,492,765.000000000000000   \n7                8,045,643.333333332091570   \n8                                      nan   \n\n   crew__directing__director__1_avg_revenue  \\\n5                                       nan   \n6                     7,235.000000000000000   \n7                16,891,877.666666667908430   \n8                                       nan   \n\n   crew__directing__director__1_movies_before  \\\n5                           0.000000000000000   \n6                           1.000000000000000   \n7                           3.000000000000000   \n8                           0.000000000000000   \n\n   crew__directing__script_supervisor_avg_profit  \\\n5                                            nan   \n6                                            nan   \n7                     34,514,260.000000000000000   \n8                                            nan   \n\n   crew__directing__script_supervisor_avg_revenue  \\\n5                                             nan   \n6                                             nan   \n7                      44,014,260.000000000000000   \n8                                             nan   \n\n   crew__directing__script_supervisor_movies_before  \\\n5                                               nan   \n6                                               nan   \n7                                 2.000000000000000   \n8                                               nan   \n\n   crew__production__casting_avg_profit  \\\n5                                   nan   \n6            42,300,225.285714283585548   \n7                                   nan   \n8                                   nan   \n\n   crew__production__casting_avg_revenue  \\\n5                                    nan   \n6             66,800,225.285714283585548   \n7                                    nan   \n8                                    nan   \n\n   crew__production__casting_movies_before  \\\n5                        0.000000000000000   \n6                       14.000000000000000   \n7                        0.000000000000000   \n8                                      nan   \n\n   crew__production__executive_producer__1_avg_profit  \\\n5                                                nan    \n6                         -2,492,765.000000000000000    \n7                                                nan    \n8                                                nan    \n\n   crew__production__executive_producer__1_avg_revenue  \\\n5                                                nan     \n6                              7,235.000000000000000     \n7                                                nan     \n8                                                nan     \n\n   crew__production__executive_producer__1_movies_before  \\\n5                                  0.000000000000000       \n6                                  1.000000000000000       \n7                                                nan       \n8                                                nan       \n\n   crew__production__producer__1_avg_profit  \\\n5                                       nan   \n6                -2,492,765.000000000000000   \n7                29,284,974.000000000000000   \n8                                       nan   \n\n   crew__production__producer__1_avg_revenue  \\\n5                                        nan   \n6                      7,235.000000000000000   \n7                 45,284,974.000000000000000   \n8                                        nan   \n\n   crew__production__producer__1_movies_before  \\\n5                            0.000000000000000   \n6                            1.000000000000000   \n7                            1.000000000000000   \n8                            0.000000000000000   \n\n   crew__production__producer__2_avg_profit  \\\n5                                       nan   \n6                                       nan   \n7                                       nan   \n8                                       nan   \n\n   crew__production__producer__2_avg_revenue  \\\n5                                        nan   \n6                                        nan   \n7                                        nan   \n8                                        nan   \n\n   crew__production__producer__2_movies_before  \\\n5                                          nan   \n6                            0.000000000000000   \n7                                          nan   \n8                                          nan   \n\n   crew__editing__editor__1_avg_profit  crew__editing__editor__1_avg_revenue  \\\n5            5,077,900.000000000000000             6,877,900.000000000000000   \n6                                  nan                                   nan   \n7           29,284,974.000000000000000            45,284,974.000000000000000   \n8                                  nan                                   nan   \n\n   crew__editing__editor__1_movies_before  \\\n5                       1.000000000000000   \n6                       0.000000000000000   \n7                       1.000000000000000   \n8                       0.000000000000000   \n\n   crew__costume__costume_designer_avg_profit  \\\n5                     892,000.000000000000000   \n6                                         nan   \n7                 -16,261,389.000000000000000   \n8                                         nan   \n\n   crew__costume__costume_designer_avg_revenue  \\\n5                    2,807,000.000000000000000   \n6                                          nan   \n7                    1,738,611.000000000000000   \n8                                          nan   \n\n   crew__costume__costume_designer_movies_before  \\\n5                              1.000000000000000   \n6                              0.000000000000000   \n7                              1.000000000000000   \n8                              0.000000000000000   \n\n   crew__costume__costume_supervisor_avg_profit  \\\n5                                           nan   \n6                                           nan   \n7                                           nan   \n8                                           nan   \n\n   crew__costume__costume_supervisor_avg_revenue  \\\n5                                            nan   \n6                                            nan   \n7                                            nan   \n8                                            nan   \n\n   crew__costume__costume_supervisor_movies_before  \\\n5                                              nan   \n6                                              nan   \n7                                0.000000000000000   \n8                                              nan   \n\n   crew__costume__makeup_artist_avg_profit  \\\n5                                      nan   \n6                                      nan   \n7               80,134,931.500000000000000   \n8                                      nan   \n\n   crew__costume__makeup_artist_avg_revenue  \\\n5                                       nan   \n6                                       nan   \n7               133,634,931.500000000000000   \n8                                       nan   \n\n   crew__costume__makeup_artist_movies_before  \\\n5                           0.000000000000000   \n6                           0.000000000000000   \n7                           2.000000000000000   \n8                                         nan   \n\n   crew__crew__stunt_coordinator_avg_profit  \\\n5                                       nan   \n6                                       nan   \n7               109,773,545.000000000000000   \n8                                       nan   \n\n   crew__crew__stunt_coordinator_avg_revenue  \\\n5                                        nan   \n6                                        nan   \n7                159,773,545.000000000000000   \n8                                        nan   \n\n   crew__crew__stunt_coordinator_movies_before  \\\n5                                          nan   \n6                                          nan   \n7                            1.000000000000000   \n8                                          nan   \n\n   crew__writing__screenplay__1_avg_profit  \\\n5                                      nan   \n6                                      nan   \n7               29,284,974.000000000000000   \n8                                      nan   \n\n   crew__writing__screenplay__1_avg_revenue  \\\n5                                       nan   \n6                                       nan   \n7                45,284,974.000000000000000   \n8                                       nan   \n\n   crew__writing__screenplay__1_movies_before  \\\n5                           0.000000000000000   \n6                                         nan   \n7                           1.000000000000000   \n8                           0.000000000000000   \n\n   crew__art__art_direction_avg_profit  crew__art__art_direction_avg_revenue  \\\n5            1,413,737.500000000000000             2,374,112.500000000000000   \n6                                  nan                                   nan   \n7                                  nan                                   nan   \n8                                  nan                                   nan   \n\n   crew__art__art_direction_movies_before  \\\n5                       8.000000000000000   \n6                       0.000000000000000   \n7                       0.000000000000000   \n8                       0.000000000000000   \n\n   crew__art__production_design_avg_profit  \\\n5                                      nan   \n6                                      nan   \n7               12,873,135.500000000000000   \n8                                      nan   \n\n   crew__art__production_design_avg_revenue  \\\n5                                       nan   \n6                                       nan   \n7                25,142,487.000000000000000   \n8                                       nan   \n\n   crew__art__production_design_movies_before  \\\n5                                         nan   \n6                           0.000000000000000   \n7                           2.000000000000000   \n8                                         nan   \n\n   crew__art__property_master_avg_profit  \\\n5                                    nan   \n6                                    nan   \n7            305,000,000.000000000000000   \n8                                    nan   \n\n   crew__art__property_master_avg_revenue  \\\n5                                     nan   \n6                                     nan   \n7             333,000,000.000000000000000   \n8                                     nan   \n\n   crew__art__property_master_movies_before  \\\n5                         0.000000000000000   \n6                         0.000000000000000   \n7                         1.000000000000000   \n8                                       nan   \n\n   crew__art__set_decoration_avg_profit  \\\n5             2,984,950.000000000000000   \n6                                   nan   \n7           -36,562,432.000000000000000   \n8                                   nan   \n\n   crew__art__set_decoration_avg_revenue  \\\n5              4,842,450.000000000000000   \n6                                    nan   \n7             20,437,568.000000000000000   \n8                                    nan   \n\n   crew__art__set_decoration_movies_before  \\\n5                        2.000000000000000   \n6                                      nan   \n7                        2.000000000000000   \n8                                      nan   \n\n   crew__visualeffects__visual_effects_supervisor_avg_profit  \\\n5                                                nan           \n6                                                nan           \n7                                                nan           \n8                                                nan           \n\n   crew__visualeffects__visual_effects_supervisor_avg_revenue  \\\n5                                                nan            \n6                                                nan            \n7                                                nan            \n8                                                nan            \n\n   crew__visualeffects__visual_effects_supervisor_movies_before  \\\n5                                                nan              \n6                                                nan              \n7                                  0.000000000000000              \n8                                                nan              \n\n   crew__camera__director_of_photography_avg_profit  \\\n5                                               nan   \n6                        14,676,794.857142854481936   \n7                         1,312,525.000000000000000   \n8                                               nan   \n\n   crew__camera__director_of_photography_avg_revenue  \\\n5                                                nan   \n6                         17,697,789.714285712689161   \n7                         16,947,200.750000000000000   \n8                                                nan   \n\n   crew__camera__director_of_photography_movies_before  \\\n5                                  0.000000000000000     \n6                                  7.000000000000000     \n7                                  4.000000000000000     \n8                                  0.000000000000000     \n\n   crew__camera__steadicam_operator_avg_profit  \\\n5                                          nan   \n6                                          nan   \n7                  185,925,792.000000000000000   \n8                                          nan   \n\n   crew__camera__steadicam_operator_avg_revenue  \\\n5                                           nan   \n6                                           nan   \n7                   218,425,792.000000000000000   \n8                                           nan   \n\n   crew__camera__steadicam_operator_movies_before  \\\n5                                             nan   \n6                                             nan   \n7                               2.000000000000000   \n8                                             nan   \n\n   crew__camera__still_photographer_avg_profit  \\\n5                                          nan   \n6                                          nan   \n7                                          nan   \n8                                          nan   \n\n   crew__camera__still_photographer_avg_revenue  \\\n5                                           nan   \n6                                           nan   \n7                                           nan   \n8                                           nan   \n\n   crew__camera__still_photographer_movies_before  collection_avg_profit  \\\n5                               0.000000000000000                    nan   \n6                               0.000000000000000                    nan   \n7                               0.000000000000000                    nan   \n8                               0.000000000000000                    nan   \n\n   collection_avg_revenue           cast_avg_revenue  \\\n5                     nan                        nan   \n6                     nan 53,746,748.712121210992336   \n7                     nan 42,299,413.277529761195183   \n8                     nan                        nan   \n\n             cast_avg_profit  cast_avg_experience  cast_avg_movies_before  \\\n5                        nan    0.000000000000000       0.000000000000000   \n6 31,682,102.247474748641253    5.625000000000000       2.000000000000000   \n7 26,873,002.262400794774294   10.000000000000000       5.625000000000000   \n8                        nan    0.000000000000000       0.000000000000000   \n\n            year_avg_revenue  \n5 37,038,641.948785535991192  \n6 74,772,500.535873189568520  \n7 90,873,089.713981837034225  \n8 55,866,046.197918057441711  ",
      "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>budget</th>\n      <th>META__id</th>\n      <th>runtime</th>\n      <th>spoken_languages</th>\n      <th>META__year</th>\n      <th>META__month</th>\n      <th>META__day</th>\n      <th>weekend</th>\n      <th>META__collection_name</th>\n      <th>META__cast_1_name</th>\n      <th>META__cast_2_name</th>\n      <th>META__cast_3_name</th>\n      <th>META__cast_4_name</th>\n      <th>META__cast_5_name</th>\n      <th>META__cast_6_name</th>\n      <th>META__cast_7_name</th>\n      <th>META__cast_8_name</th>\n      <th>META__crew__sound__music_editor</th>\n      <th>META__crew__sound__original_music_composer</th>\n      <th>META__crew__sound__sound_designer</th>\n      <th>META__crew__sound__sound_effects_editor</th>\n      <th>META__crew__sound__sound_re_recording_mixer</th>\n      <th>META__crew__sound__supervising_sound_editor</th>\n      <th>META__crew__directing__director__1</th>\n      <th>META__crew__directing__script_supervisor</th>\n      <th>META__crew__production__casting</th>\n      <th>META__crew__production__executive_producer__1</th>\n      <th>META__crew__production__producer__1</th>\n      <th>META__crew__production__producer__2</th>\n      <th>META__crew__editing__editor__1</th>\n      <th>META__crew__costume__costume_designer</th>\n      <th>META__crew__costume__costume_supervisor</th>\n      <th>META__crew__costume__makeup_artist</th>\n      <th>META__crew__crew__stunt_coordinator</th>\n      <th>META__crew__writing__screenplay__1</th>\n      <th>META__crew__art__art_direction</th>\n      <th>META__crew__art__production_design</th>\n      <th>META__crew__art__property_master</th>\n      <th>META__crew__art__set_decoration</th>\n      <th>META__crew__visualeffects__visual_effects_supervisor</th>\n      <th>META__crew__camera__director_of_photography</th>\n      <th>META__crew__camera__steadicam_operator</th>\n      <th>META__crew__camera__still_photographer</th>\n      <th>genre__war</th>\n      <th>genre__western</th>\n      <th>genre__mystery</th>\n      <th>genre__music</th>\n      <th>genre__crime</th>\n      <th>genre__romance</th>\n      <th>genre__action</th>\n      <th>genre__adventure</th>\n      <th>genre__thriller</th>\n      <th>genre__animation</th>\n      <th>genre__family</th>\n      <th>genre__drama</th>\n      <th>genre__comedy</th>\n      <th>genre__documentary</th>\n      <th>genre__history</th>\n      <th>genre__fantasy</th>\n      <th>genre__horror</th>\n      <th>META__production_company_1</th>\n      <th>META__production_company_2</th>\n      <th>META__production_company_3</th>\n      <th>country__es</th>\n      <th>country__jp</th>\n      <th>country__us</th>\n      <th>country__ca</th>\n      <th>country__de</th>\n      <th>country__cn</th>\n      <th>country__in</th>\n      <th>country__fr</th>\n      <th>country__ru</th>\n      <th>country__it</th>\n      <th>country__au</th>\n      <th>country__gb</th>\n      <th>day_sin</th>\n      <th>day_cos</th>\n      <th>month_sin</th>\n      <th>month_cos</th>\n      <th>competition</th>\n      <th>rating__g</th>\n      <th>rating__pg</th>\n      <th>rating__pg-13</th>\n      <th>rating__r</th>\n      <th>rating__nc-17</th>\n      <th>META__original_title</th>\n      <th>META__title</th>\n      <th>META__keywords</th>\n      <th>META__plot</th>\n      <th>country__other</th>\n      <th>META__imdb_id</th>\n      <th>tag__murder</th>\n      <th>tag__violence</th>\n      <th>tag__flashback</th>\n      <th>tag__romantic</th>\n      <th>tag__cult</th>\n      <th>tag__revenge</th>\n      <th>tag__psychedelic</th>\n      <th>tag__comedy</th>\n      <th>tag__suspenseful</th>\n      <th>tag__good_versus_evil</th>\n      <th>tag__humor</th>\n      <th>tag__satire</th>\n      <th>tag__entertaining</th>\n      <th>tag__neo_noir</th>\n      <th>tag__action</th>\n      <th>tag__sadist</th>\n      <th>tag__insanity</th>\n      <th>tag__tragedy</th>\n      <th>tag__fantasy</th>\n      <th>tag__paranormal</th>\n      <th>tag__boring</th>\n      <th>tag__mystery</th>\n      <th>tag__horror</th>\n      <th>tag__melodrama</th>\n      <th>tag__cruelty</th>\n      <th>tag__gothic</th>\n      <th>tag__dramatic</th>\n      <th>tag__dark</th>\n      <th>tag__atmospheric</th>\n      <th>tag__storytelling</th>\n      <th>tag__sci_fi</th>\n      <th>tag__psychological</th>\n      <th>tag__historical</th>\n      <th>tag__absurd</th>\n      <th>tag__prank</th>\n      <th>tag__sentimental</th>\n      <th>tag__philosophical</th>\n      <th>tag__bleak</th>\n      <th>tag__alternate_reality</th>\n      <th>tag__depressing</th>\n      <th>tag__plot_twist</th>\n      <th>tag__realism</th>\n      <th>tag__cute</th>\n      <th>tag__stupid</th>\n      <th>tag__home_movie</th>\n      <th>tag__thought_provoking</th>\n      <th>tag__inspiring</th>\n      <th>tag__other</th>\n      <th>cast_1_gender</th>\n      <th>cast_2_gender</th>\n      <th>cast_3_gender</th>\n      <th>cast_4_gender</th>\n      <th>cast_5_gender</th>\n      <th>cast_6_gender</th>\n      <th>cast_7_gender</th>\n      <th>cast_8_gender</th>\n      <th>META__cast_1_male</th>\n      <th>META__cast_1_female</th>\n      <th>META__cast_2_male</th>\n      <th>META__cast_2_female</th>\n      <th>META__cast_3_male</th>\n      <th>META__cast_3_female</th>\n      <th>META__cast_4_male</th>\n      <th>META__cast_4_female</th>\n      <th>META__cast_5_male</th>\n      <th>META__cast_5_female</th>\n      <th>META__cast_6_male</th>\n      <th>META__cast_6_female</th>\n      <th>META__cast_7_male</th>\n      <th>META__cast_7_female</th>\n      <th>META__cast_8_male</th>\n      <th>META__cast_8_female</th>\n      <th>META__revenue</th>\n      <th>homepage_exists</th>\n      <th>homepage_repeats</th>\n      <th>META__profitability</th>\n      <th>META__year_avg_profitability</th>\n      <th>production_company_1_avg_profit</th>\n      <th>production_company_1_avg_revenue</th>\n      <th>production_company_2_avg_profit</th>\n      <th>production_company_2_avg_revenue</th>\n      <th>production_company_3_avg_profit</th>\n      <th>production_company_3_avg_revenue</th>\n      <th>cast_1_avg_profit</th>\n      <th>cast_1_experience</th>\n      <th>cast_1_avg_revenue</th>\n      <th>cast_1_movies_before</th>\n      <th>cast_2_avg_profit</th>\n      <th>cast_2_experience</th>\n      <th>cast_2_avg_revenue</th>\n      <th>cast_2_movies_before</th>\n      <th>cast_3_avg_profit</th>\n      <th>cast_3_experience</th>\n      <th>cast_3_avg_revenue</th>\n      <th>cast_3_movies_before</th>\n      <th>cast_4_avg_profit</th>\n      <th>cast_4_experience</th>\n      <th>cast_4_avg_revenue</th>\n      <th>cast_4_movies_before</th>\n      <th>cast_5_avg_profit</th>\n      <th>cast_5_experience</th>\n      <th>cast_5_avg_revenue</th>\n      <th>cast_5_movies_before</th>\n      <th>cast_6_avg_profit</th>\n      <th>cast_6_experience</th>\n      <th>cast_6_avg_revenue</th>\n      <th>cast_6_movies_before</th>\n      <th>cast_7_avg_profit</th>\n      <th>cast_7_experience</th>\n      <th>cast_7_avg_revenue</th>\n      <th>cast_7_movies_before</th>\n      <th>cast_8_avg_profit</th>\n      <th>cast_8_experience</th>\n      <th>cast_8_avg_revenue</th>\n      <th>cast_8_movies_before</th>\n      <th>crew__sound__music_editor_avg_profit</th>\n      <th>crew__sound__music_editor_avg_revenue</th>\n      <th>crew__sound__music_editor_movies_before</th>\n      <th>crew__sound__original_music_composer_avg_profit</th>\n      <th>crew__sound__original_music_composer_avg_revenue</th>\n      <th>crew__sound__original_music_composer_movies_before</th>\n      <th>crew__sound__sound_designer_avg_profit</th>\n      <th>crew__sound__sound_designer_avg_revenue</th>\n      <th>crew__sound__sound_designer_movies_before</th>\n      <th>crew__sound__sound_effects_editor_avg_profit</th>\n      <th>crew__sound__sound_effects_editor_avg_revenue</th>\n      <th>crew__sound__sound_effects_editor_movies_before</th>\n      <th>crew__sound__sound_re_recording_mixer_avg_profit</th>\n      <th>crew__sound__sound_re_recording_mixer_avg_revenue</th>\n      <th>crew__sound__sound_re_recording_mixer_movies_before</th>\n      <th>crew__sound__supervising_sound_editor_avg_profit</th>\n      <th>crew__sound__supervising_sound_editor_avg_revenue</th>\n      <th>crew__sound__supervising_sound_editor_movies_before</th>\n      <th>crew__directing__director__1_avg_profit</th>\n      <th>crew__directing__director__1_avg_revenue</th>\n      <th>crew__directing__director__1_movies_before</th>\n      <th>crew__directing__script_supervisor_avg_profit</th>\n      <th>crew__directing__script_supervisor_avg_revenue</th>\n      <th>crew__directing__script_supervisor_movies_before</th>\n      <th>crew__production__casting_avg_profit</th>\n      <th>crew__production__casting_avg_revenue</th>\n      <th>crew__production__casting_movies_before</th>\n      <th>crew__production__executive_producer__1_avg_profit</th>\n      <th>crew__production__executive_producer__1_avg_revenue</th>\n      <th>crew__production__executive_producer__1_movies_before</th>\n      <th>crew__production__producer__1_avg_profit</th>\n      <th>crew__production__producer__1_avg_revenue</th>\n      <th>crew__production__producer__1_movies_before</th>\n      <th>crew__production__producer__2_avg_profit</th>\n      <th>crew__production__producer__2_avg_revenue</th>\n      <th>crew__production__producer__2_movies_before</th>\n      <th>crew__editing__editor__1_avg_profit</th>\n      <th>crew__editing__editor__1_avg_revenue</th>\n      <th>crew__editing__editor__1_movies_before</th>\n      <th>crew__costume__costume_designer_avg_profit</th>\n      <th>crew__costume__costume_designer_avg_revenue</th>\n      <th>crew__costume__costume_designer_movies_before</th>\n      <th>crew__costume__costume_supervisor_avg_profit</th>\n      <th>crew__costume__costume_supervisor_avg_revenue</th>\n      <th>crew__costume__costume_supervisor_movies_before</th>\n      <th>crew__costume__makeup_artist_avg_profit</th>\n      <th>crew__costume__makeup_artist_avg_revenue</th>\n      <th>crew__costume__makeup_artist_movies_before</th>\n      <th>crew__crew__stunt_coordinator_avg_profit</th>\n      <th>crew__crew__stunt_coordinator_avg_revenue</th>\n      <th>crew__crew__stunt_coordinator_movies_before</th>\n      <th>crew__writing__screenplay__1_avg_profit</th>\n      <th>crew__writing__screenplay__1_avg_revenue</th>\n      <th>crew__writing__screenplay__1_movies_before</th>\n      <th>crew__art__art_direction_avg_profit</th>\n      <th>crew__art__art_direction_avg_revenue</th>\n      <th>crew__art__art_direction_movies_before</th>\n      <th>crew__art__production_design_avg_profit</th>\n      <th>crew__art__production_design_avg_revenue</th>\n      <th>crew__art__production_design_movies_before</th>\n      <th>crew__art__property_master_avg_profit</th>\n      <th>crew__art__property_master_avg_revenue</th>\n      <th>crew__art__property_master_movies_before</th>\n      <th>crew__art__set_decoration_avg_profit</th>\n      <th>crew__art__set_decoration_avg_revenue</th>\n      <th>crew__art__set_decoration_movies_before</th>\n      <th>crew__visualeffects__visual_effects_supervisor_avg_profit</th>\n      <th>crew__visualeffects__visual_effects_supervisor_avg_revenue</th>\n      <th>crew__visualeffects__visual_effects_supervisor_movies_before</th>\n      <th>crew__camera__director_of_photography_avg_profit</th>\n      <th>crew__camera__director_of_photography_avg_revenue</th>\n      <th>crew__camera__director_of_photography_movies_before</th>\n      <th>crew__camera__steadicam_operator_avg_profit</th>\n      <th>crew__camera__steadicam_operator_avg_revenue</th>\n      <th>crew__camera__steadicam_operator_movies_before</th>\n      <th>crew__camera__still_photographer_avg_profit</th>\n      <th>crew__camera__still_photographer_avg_revenue</th>\n      <th>crew__camera__still_photographer_movies_before</th>\n      <th>collection_avg_profit</th>\n      <th>collection_avg_revenue</th>\n      <th>cast_avg_revenue</th>\n      <th>cast_avg_profit</th>\n      <th>cast_avg_experience</th>\n      <th>cast_avg_movies_before</th>\n      <th>year_avg_revenue</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>5</th>\n      <td>839727</td>\n      <td>15</td>\n      <td>119</td>\n      <td>1</td>\n      <td>1941</td>\n      <td>4</td>\n      <td>30</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>orson welles</td>\n      <td>joseph cotten</td>\n      <td>dorothy comingore</td>\n      <td>ray collins</td>\n      <td>george coulouris</td>\n      <td>agnes moorehead</td>\n      <td>paul stewart</td>\n      <td>ruth warrick</td>\n      <td>NaN</td>\n      <td>bernard herrmann</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>clem portman</td>\n      <td>john aalberg</td>\n      <td>orson welles</td>\n      <td>NaN</td>\n      <td>rufus le maire</td>\n      <td>george schaefer</td>\n      <td>orson welles</td>\n      <td>NaN</td>\n      <td>robert wise</td>\n      <td>edward stevenson</td>\n      <td>NaN</td>\n      <td>layne britton</td>\n      <td>NaN</td>\n      <td>orson welles</td>\n      <td>van nest polglase</td>\n      <td>NaN</td>\n      <td>charles sayers</td>\n      <td>darrell silvera</td>\n      <td>NaN</td>\n      <td>gregg toland</td>\n      <td>NaN</td>\n      <td>phil stern</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>rko radio pictures</td>\n      <td>mercury productions</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>-0.207911690817759</td>\n      <td>0.978147600733806</td>\n      <td>0.989821441880933</td>\n      <td>-0.142314838273285</td>\n      <td>0.000000000000000</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>Citizen Kane</td>\n      <td>Citizen Kane</td>\n      <td>journalist newspaper florida capitalist banker...</td>\n      <td>Charles Foster Kane , an enormously wealthy me...</td>\n      <td>False</td>\n      <td>tt0033467</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1.000000000000000</td>\n      <td>1.000000000000000</td>\n      <td>0.000000000000000</td>\n      <td>1.000000000000000</td>\n      <td>1.000000000000000</td>\n      <td>0.000000000000000</td>\n      <td>1.000000000000000</td>\n      <td>0.000000000000000</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>23217674</td>\n      <td>0</td>\n      <td>0</td>\n      <td>26.649074044302488</td>\n      <td>4.834435714362336</td>\n      <td>2,041,900.000000000000000</td>\n      <td>2,872,445.454545454587787</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>2,593,000.000000000000000</td>\n      <td>3,202,000.000000000000000</td>\n      <td>1.000000000000000</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>5,077,900.000000000000000</td>\n      <td>6,877,900.000000000000000</td>\n      <td>1.000000000000000</td>\n      <td>892,000.000000000000000</td>\n      <td>2,807,000.000000000000000</td>\n      <td>1.000000000000000</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>1,413,737.500000000000000</td>\n      <td>2,374,112.500000000000000</td>\n      <td>8.000000000000000</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>2,984,950.000000000000000</td>\n      <td>4,842,450.000000000000000</td>\n      <td>2.000000000000000</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>0.000000000000000</td>\n      <td>37,038,641.948785535991192</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>12800000</td>\n      <td>16</td>\n      <td>141</td>\n      <td>1</td>\n      <td>2000</td>\n      <td>5</td>\n      <td>17</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>bjrk</td>\n      <td>catherine deneuve</td>\n      <td>david morse</td>\n      <td>peter stormare</td>\n      <td>joel grey</td>\n      <td>cara seymour</td>\n      <td>vladica kostic</td>\n      <td>jean-marc barr</td>\n      <td>NaN</td>\n      <td>bjrk</td>\n      <td>kristian eidnes andersen</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>lars von trier</td>\n      <td>NaN</td>\n      <td>avy kaufman</td>\n      <td>peter aalbk jensen</td>\n      <td>vibeke windelv</td>\n      <td>finn gjerdrum</td>\n      <td>franois gdigier</td>\n      <td>manon rasmussen</td>\n      <td>NaN</td>\n      <td>cdric grard</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>peter grant</td>\n      <td>karl jlusson</td>\n      <td>jesper lorents</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>robby mller</td>\n      <td>NaN</td>\n      <td>david koskas</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>lantia cinema &amp; audiovisivi</td>\n      <td>fine line features</td>\n      <td>zentropa entertainments</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>-0.207911690817759</td>\n      <td>-0.978147600733806</td>\n      <td>0.755749574354258</td>\n      <td>-0.654860733945285</td>\n      <td>2.150000000000000</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>Dancer in the Dark</td>\n      <td>Dancer in the Dark</td>\n      <td>individual dancing robbery factory worker secr...</td>\n      <td>The film is set in Washington state in 1964 an...</td>\n      <td>True</td>\n      <td>tt0168629</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.000000000000000</td>\n      <td>0.000000000000000</td>\n      <td>1.000000000000000</td>\n      <td>1.000000000000000</td>\n      <td>1.000000000000000</td>\n      <td>0.000000000000000</td>\n      <td>0.000000000000000</td>\n      <td>1.000000000000000</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>40031879</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2.127490546875000</td>\n      <td>-1.162769204436023</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>878,739.388888888875954</td>\n      <td>7,103,150.555555555969477</td>\n      <td>-2,492,765.000000000000000</td>\n      <td>7,235.000000000000000</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>-2,486,102.500000000000000</td>\n      <td>31.000000000000000</td>\n      <td>1,813,897.500000000000000</td>\n      <td>2.000000000000000</td>\n      <td>63,455,517.909090906381607</td>\n      <td>10.000000000000000</td>\n      <td>105,682,790.636363640427589</td>\n      <td>11.000000000000000</td>\n      <td>34,076,891.333333335816860</td>\n      <td>4.000000000000000</td>\n      <td>53,743,558.000000000000000</td>\n      <td>3.000000000000000</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>-2,492,765.000000000000000</td>\n      <td>7,235.000000000000000</td>\n      <td>1.000000000000000</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>42,300,225.285714283585548</td>\n      <td>66,800,225.285714283585548</td>\n      <td>14.000000000000000</td>\n      <td>-2,492,765.000000000000000</td>\n      <td>7,235.000000000000000</td>\n      <td>1.000000000000000</td>\n      <td>-2,492,765.000000000000000</td>\n      <td>7,235.000000000000000</td>\n      <td>1.000000000000000</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>14,676,794.857142854481936</td>\n      <td>17,697,789.714285712689161</td>\n      <td>7.000000000000000</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>53,746,748.712121210992336</td>\n      <td>31,682,102.247474748641253</td>\n      <td>5.625000000000000</td>\n      <td>2.000000000000000</td>\n      <td>74,772,500.535873189568520</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>90000000</td>\n      <td>18</td>\n      <td>126</td>\n      <td>3</td>\n      <td>1997</td>\n      <td>5</td>\n      <td>2</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>bruce willis</td>\n      <td>gary oldman</td>\n      <td>ian holm</td>\n      <td>milla jovovich</td>\n      <td>chris tucker</td>\n      <td>luke perry</td>\n      <td>brion james</td>\n      <td>tom lister jr.</td>\n      <td>NaN</td>\n      <td>ric serra</td>\n      <td>mark a. mangini</td>\n      <td>julia evershade</td>\n      <td>ron bartlett</td>\n      <td>NaN</td>\n      <td>luc besson</td>\n      <td>jean bourne</td>\n      <td>lucinda syson</td>\n      <td>NaN</td>\n      <td>patrice ledoux</td>\n      <td>NaN</td>\n      <td>sylvie landra</td>\n      <td>jean paul gaultier</td>\n      <td>janet tebrooke</td>\n      <td>amanda knight</td>\n      <td>marc boyle</td>\n      <td>luc besson</td>\n      <td>ira gilford</td>\n      <td>dan weil</td>\n      <td>barry wilkinson</td>\n      <td>maggie gray</td>\n      <td>mark stetson</td>\n      <td>thierry arbogast</td>\n      <td>john ward</td>\n      <td>jack english</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>columbia pictures</td>\n      <td>gaumont</td>\n      <td>sony pictures</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.207911690817759</td>\n      <td>0.978147600733806</td>\n      <td>0.755749574354258</td>\n      <td>-0.654860733945285</td>\n      <td>1.366666666666667</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>The Fifth Element</td>\n      <td>The Fifth Element</td>\n      <td>new york city clone taxi cyborg egypt future s...</td>\n      <td>In 1914, at the outbreak of World War I, extra...</td>\n      <td>False</td>\n      <td>tt0119116</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1.000000000000000</td>\n      <td>1.000000000000000</td>\n      <td>1.000000000000000</td>\n      <td>0.000000000000000</td>\n      <td>1.000000000000000</td>\n      <td>1.000000000000000</td>\n      <td>1.000000000000000</td>\n      <td>1.000000000000000</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>263920180</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1.932446444444444</td>\n      <td>-0.169600900518265</td>\n      <td>27,823,832.951219514012337</td>\n      <td>46,696,952.756097562611103</td>\n      <td>13,616.799999999999272</td>\n      <td>18,721,357.399999998509884</td>\n      <td>19,383,262.714285716414452</td>\n      <td>35,870,405.571428567171097</td>\n      <td>83,959,283.666666671633720</td>\n      <td>10.000000000000000</td>\n      <td>122,759,283.666666686534882</td>\n      <td>15.000000000000000</td>\n      <td>40,171,046.750000000000000</td>\n      <td>11.000000000000000</td>\n      <td>62,291,303.125000000000000</td>\n      <td>8.000000000000000</td>\n      <td>34,815,532.571428567171097</td>\n      <td>21.000000000000000</td>\n      <td>48,172,675.428571432828903</td>\n      <td>7.000000000000000</td>\n      <td>-8,192,146.000000000000000</td>\n      <td>6.000000000000000</td>\n      <td>2,807,854.000000000000000</td>\n      <td>1.000000000000000</td>\n      <td>24,715,918.000000000000000</td>\n      <td>2.000000000000000</td>\n      <td>28,215,918.000000000000000</td>\n      <td>1.000000000000000</td>\n      <td>9,624,456.000000000000000</td>\n      <td>5.000000000000000</td>\n      <td>16,624,456.000000000000000</td>\n      <td>1.000000000000000</td>\n      <td>19,268,502.777777776122093</td>\n      <td>15.000000000000000</td>\n      <td>42,635,725.000000000000000</td>\n      <td>9.000000000000000</td>\n      <td>10,621,424.333333333954215</td>\n      <td>10.000000000000000</td>\n      <td>14,888,091.000000000000000</td>\n      <td>3.000000000000000</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>79,582,741.000000000000000</td>\n      <td>100,717,416.750000000000000</td>\n      <td>4.000000000000000</td>\n      <td>276,101,666.000000000000000</td>\n      <td>366,101,666.000000000000000</td>\n      <td>1.000000000000000</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>64,548,284.000000000000000</td>\n      <td>95,148,284.000000000000000</td>\n      <td>2.000000000000000</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>8,045,643.333333332091570</td>\n      <td>16,891,877.666666667908430</td>\n      <td>3.000000000000000</td>\n      <td>34,514,260.000000000000000</td>\n      <td>44,014,260.000000000000000</td>\n      <td>2.000000000000000</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>29,284,974.000000000000000</td>\n      <td>45,284,974.000000000000000</td>\n      <td>1.000000000000000</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>29,284,974.000000000000000</td>\n      <td>45,284,974.000000000000000</td>\n      <td>1.000000000000000</td>\n      <td>-16,261,389.000000000000000</td>\n      <td>1,738,611.000000000000000</td>\n      <td>1.000000000000000</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>80,134,931.500000000000000</td>\n      <td>133,634,931.500000000000000</td>\n      <td>2.000000000000000</td>\n      <td>109,773,545.000000000000000</td>\n      <td>159,773,545.000000000000000</td>\n      <td>1.000000000000000</td>\n      <td>29,284,974.000000000000000</td>\n      <td>45,284,974.000000000000000</td>\n      <td>1.000000000000000</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>12,873,135.500000000000000</td>\n      <td>25,142,487.000000000000000</td>\n      <td>2.000000000000000</td>\n      <td>305,000,000.000000000000000</td>\n      <td>333,000,000.000000000000000</td>\n      <td>1.000000000000000</td>\n      <td>-36,562,432.000000000000000</td>\n      <td>20,437,568.000000000000000</td>\n      <td>2.000000000000000</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>1,312,525.000000000000000</td>\n      <td>16,947,200.750000000000000</td>\n      <td>4.000000000000000</td>\n      <td>185,925,792.000000000000000</td>\n      <td>218,425,792.000000000000000</td>\n      <td>2.000000000000000</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>42,299,413.277529761195183</td>\n      <td>26,873,002.262400794774294</td>\n      <td>10.000000000000000</td>\n      <td>5.625000000000000</td>\n      <td>90,873,089.713981837034225</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>92620000</td>\n      <td>19</td>\n      <td>153</td>\n      <td>1</td>\n      <td>1927</td>\n      <td>1</td>\n      <td>10</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>gustav frhlich</td>\n      <td>brigitte helm</td>\n      <td>alfred abel</td>\n      <td>rudolf klein-rogge</td>\n      <td>theodor loos</td>\n      <td>fritz rasp</td>\n      <td>erwin biswanger</td>\n      <td>heinrich george</td>\n      <td>frank strobel</td>\n      <td>gottfried huppertz</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>fritz lang</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>erich pommer</td>\n      <td>NaN</td>\n      <td>fritz lang</td>\n      <td>aenne willkomm</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>fritz lang</td>\n      <td>karl vollbrecht</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>karl freund</td>\n      <td>NaN</td>\n      <td>horst von harbou</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>universum film (ufa)</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.951056516295154</td>\n      <td>-0.309016994374947</td>\n      <td>0.000000000000000</td>\n      <td>1.000000000000000</td>\n      <td>0.000000000000000</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>Metropolis</td>\n      <td>Metropolis</td>\n      <td>man vs machine based on novel or book undergro...</td>\n      <td>Set in the year 2026, Metropolis takes place i...</td>\n      <td>False</td>\n      <td>tt0017136</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1.000000000000000</td>\n      <td>0.000000000000000</td>\n      <td>1.000000000000000</td>\n      <td>1.000000000000000</td>\n      <td>1.000000000000000</td>\n      <td>1.000000000000000</td>\n      <td>1.000000000000000</td>\n      <td>1.000000000000000</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>650422</td>\n      <td>0</td>\n      <td>0</td>\n      <td>-141.399857323399260</td>\n      <td>-18.884395158298162</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>nan</td>\n      <td>0.000000000000000</td>\n      <td>0.000000000000000</td>\n      <td>55,866,046.197918057441711</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 170
    }
   ],
   "source": [
    "df_compare[5:9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "0.050000000000000       111,229.200000000011642\n0.950000000000000   348,514,752.599999785423279\nName: META__revenue, dtype: float64"
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "source": [
    "df_raw.META__revenue.quantile([0.05, 0.95])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "4600\n0.6139063125583878\n"
    }
   ],
   "source": [
    "num = df_raw[\n",
    "    (df_raw.META__profitability > df_raw.META__profitability.quantile(0.01)) & \n",
    "    (df_raw.META__profitability < df_raw.META__profitability.quantile(0.99)) &\n",
    "    (df_raw.META__year >= 2000) &\n",
    "    (df_raw.META__year <= 2018) &\n",
    "    (df_raw.budget >= 250000)\n",
    "].shape[0]\n",
    "\n",
    "print(num)\n",
    "print(num/df_raw.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "4660\n0.6219137862004538\n"
    }
   ],
   "source": [
    "num = df_raw[\n",
    "    (df_raw.META__revenue > df_raw.META__profitability.quantile(0.01)) & \n",
    "    (df_raw.META__profitability < df_raw.META__profitability.quantile(0.99)) &\n",
    "    (df_raw.META__year >= 2000) &\n",
    "    (df_raw.META__year <= 2018) &\n",
    "    (df_raw.budget >= 250000)\n",
    "].shape[0]\n",
    "\n",
    "print(num)\n",
    "print(num/df_raw.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_real_columns(name_prefix, name_suffix, num_columns):\n",
    "    return [f'{name_prefix}{j}{name_suffix}' for j in range(1, num_columns+1)]\n",
    "\n",
    "def get_date_from_movie(movie):\n",
    "    return date(movie['META__year'], movie['META__month'], movie['META__day'])\n",
    "\n",
    "\n",
    "def get_info_df_movies_per_value_range_columns(df, columns):\n",
    "    result = {}\n",
    "    for i, row in df.iterrows():\n",
    "        for col in columns:\n",
    "            value = row[col]\n",
    "            if not pd.isna(value):\n",
    "                if value in list(result.keys()):\n",
    "                    result[value]['movies'] = result[value]['movies'].append(df.iloc[[i]])\n",
    "                else:\n",
    "                    result[value] = {'movies': df.iloc[[i]]}\n",
    "    return result\n",
    "\n",
    "# def get_info_df_movies_per_value_range_columns(df, columns):\n",
    "#     def rows_with_value(value):\n",
    "#         return df[df.apply(lambda row: value in [row[column] for column in columns], axis=1)]\n",
    "\n",
    "#     unique_values = list(set([movie[column] for i, movie in df.iterrows() for column in columns]))\n",
    "#     return {value: rows_with_value(value) for value in unique_values}\n",
    "\n",
    "def calculate_info_per_previous_movies_per_value(df, \n",
    "                                                 info_dict, \n",
    "                                                 calc_budget=False, \n",
    "                                                 calc_movies_before=False, \n",
    "                                                 calc_experience=False, \n",
    "                                                 calc_revenue=False,\n",
    "                                                ):\n",
    "    \n",
    "    def get_average_profit(df):\n",
    "        return (df['META__revenue'] - df['budget']).mean()\n",
    "    \n",
    "    def get_experience(df, row):\n",
    "        return (row['META__year'] - df.META__year.min())\n",
    "    \n",
    "    def is_movie_before(movie, compare_date):\n",
    "        return date(movie.META__year, movie.META__month, movie.META__day) < compare_date\n",
    "\n",
    "    for value_name in info_dict:\n",
    "        value = info_dict[value_name]\n",
    "        value['avg_profit'] = {}\n",
    "        if calc_budget:\n",
    "            value['avg_budget'] = {}\n",
    "        if calc_experience:\n",
    "            value['experience'] = {}\n",
    "        if calc_revenue:\n",
    "            value['avg_revenue'] = {}\n",
    "        if calc_movies_before:\n",
    "            value['movies_before'] = {}\n",
    "\n",
    "        movies = value['movies']\n",
    "        for movie_index, movie in movies.iterrows():\n",
    "            current_date = get_date_from_movie(movie)\n",
    "            str_date = str(current_date)\n",
    "            if calc_experience and str_date not in list(value['experience'].keys()):\n",
    "                value['experience'][str_date] = get_experience(movies, movie)\n",
    "            if str_date not in list(value['avg_profit'].keys()):\n",
    "                movies_before = movies[movies.apply(lambda row: is_movie_before(row, current_date), axis=1)]\n",
    "                if not movies_before.empty:\n",
    "                    value['avg_profit'][str_date] = get_average_profit(movies_before)\n",
    "                    if calc_movies_before:\n",
    "                        value['movies_before'][str_date] = movies_before.shape[0]\n",
    "                    if calc_revenue:\n",
    "                        value['avg_revenue'][str_date] = movies_before['META__revenue'].mean()                        \n",
    "                    if calc_budget:\n",
    "                        value['avg_budget'][str_date] = movies_before['budget'].mean()\n",
    "                else:\n",
    "                    if calc_movies_before:\n",
    "                        value['movies_before'][str_date] = 0\n",
    "                    \n",
    "\n",
    "def get_columns_from_info_dict(df, info_dict, real_columns, new_column_prefix='', nested_info=False):\n",
    "    def get_first_key(d):\n",
    "        return d[list(d.keys())[0]]\n",
    "    \n",
    "    new_columns_data = {}\n",
    "    info_names = [key for key in get_first_key(info_dict).keys() if key != 'movies']\n",
    "    print(info_names)\n",
    "    for index, real_column in enumerate(real_columns):\n",
    "        print(real_column)\n",
    "        new_column_names = [f'{new_column_prefix}_{key}' if len(real_columns) == 1 else f'{new_column_prefix}_{index+1}_{key}' for key in info_names] \n",
    "        print(new_column_names)\n",
    "            \n",
    "        for new_column_name in new_column_names:\n",
    "            new_columns_data[new_column_name] = []\n",
    "\n",
    "        for movie_index, movie in df.iterrows():\n",
    "            value = movie[real_column]\n",
    "            str_date = str(get_date_from_movie(movie))\n",
    "            if pd.isna(value):\n",
    "                for col in new_column_names:\n",
    "                    new_columns_data[col].append(np.nan)\n",
    "            else:\n",
    "                for name_index, new_column_name in enumerate(new_column_names):\n",
    "                    year_info = info_dict[value][info_names[name_index]].get(str_date, np.nan)\n",
    "                    new_columns_data[new_column_name].append(year_info)\n",
    "    return new_columns_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_features(df, \n",
    "                 columns,\n",
    "                 new_column_prefix,\n",
    "                 calc_movies_before=False, \n",
    "                 calc_budget=False, \n",
    "                 calc_experience=False,\n",
    "                 calc_revenue=False):\n",
    "    print('obtaine movies')\n",
    "    \n",
    "    t = time.process_time()\n",
    "    data_info = get_info_df_movies_per_value_range_columns(df, columns)\n",
    "    print(f'time: {time.process_time() - t}')\n",
    "    \n",
    "    data_info_copy = data_info.copy()\n",
    "    print('calculate info')\n",
    "    t = time.process_time()\n",
    "    calculate_info_per_previous_movies_per_value(df, \n",
    "                                                 data_info_copy, \n",
    "                                                 calc_movies_before=calc_movies_before, \n",
    "                                                 calc_budget=calc_budget, \n",
    "                                                 calc_experience=calc_experience,\n",
    "                                                 calc_revenue=calc_revenue)\n",
    "    print(f'time: {time.process_time() - t}')\n",
    "    \n",
    "    print('get columns')\n",
    "    t = time.process_time()\n",
    "    feature_columns = get_columns_from_info_dict(df, data_info_copy, columns, new_column_prefix=new_column_prefix)\n",
    "    print(f'time: {time.process_time() - t}')\n",
    "    for column in feature_columns.keys():\n",
    "        df[column] = feature_columns[column]\n",
    "    return df\n",
    "\n",
    "\n",
    "def add_companies_features(df):\n",
    "    columns = get_real_columns('META__production_company_', '', 3)\n",
    "    return add_features(df, columns, 'production_company', calc_revenue=True)\n",
    "\n",
    "\n",
    "def add_cast_features(df):\n",
    "    columns = get_real_columns('META__cast_', '_name', 8)\n",
    "    return add_features(df, columns, 'cast', calc_movies_before=True, calc_revenue=True, calc_experience=True)\n",
    "\n",
    "\n",
    "def add_crew_features(df):\n",
    "    for column in [c for c in list(df.columns) if 'META__crew' in c]:\n",
    "        df = add_features(df, [column], column.replace('META__', ''), calc_movies_before=True, calc_revenue=True)\n",
    "    return df\n",
    "\n",
    "\n",
    "def add_collection_features(df):\n",
    "    return add_features(df, ['META__collection_name'], 'collection', calc_revenue=True)\n",
    "\n",
    "\n",
    "def get_avg_cast_info(df):\n",
    "    def get_average_cast_movies(row, columns):\n",
    "        avgs = [row[col] for col in columns if not pd.isna(row[col])]\n",
    "        return mean(avgs) if len(avgs) else np.nan\n",
    "    \n",
    "    def get_cast_average(df, column_name):\n",
    "        columns = [col for col in df.columns if 'cast_' in col and column_name in col and not 'cast_avg' in col]\n",
    "        return [get_average_cast_movies(row, columns) for i, row in df.iterrows()]\n",
    "    \n",
    "    for column in ['revenue', 'profit', 'experience', 'movies_before']:\n",
    "        df[f'cast_avg_{column}'] = get_cast_average(df, column)\n",
    "    return df\n",
    "\n",
    "\n",
    "def prepare_df(df_original):\n",
    "    overall_time = time.process_time()\n",
    "    df = df_original.copy()\n",
    "    df = add_companies_features(df)\n",
    "    df = add_cast_features(df)\n",
    "    df = add_crew_features(df)\n",
    "    df = add_collection_features(df)\n",
    "    df = get_avg_cast_info(df)\n",
    "    df['year_avg_revenue'] = calc_smooth_mean(df, by='META__year', on='META__revenue', m=5)\n",
    "    print(f'overall time: {time.process_time() - overall_time}')\n",
    "    return df\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_datasets = {\n",
    "    'all': df_raw.copy(),\n",
    "    'us': df_raw[\n",
    "        df_raw.country__us == 1].copy(),\n",
    "    'gb': df_raw[\n",
    "        df_raw.country__gb == 1].copy(),\n",
    "    'years2000-2018': df_raw[\n",
    "        (df_raw.META__year >= 2000) &\n",
    "        (df_raw.META__year <= 2018)].copy(),\n",
    "    'years1970-1999': df_raw[\n",
    "        (df_raw.META__year >= 1970) &\n",
    "        (df_raw.META__year <= 1999)].copy(),\n",
    "    'profitability_positive': df_raw[\n",
    "        df_raw.META__profitability > 0].copy(),\n",
    "    'profitability_negative': df_raw[\n",
    "        df_raw.META__profitability < 0].copy(),\n",
    "    'budget_start_1percent': df_raw[\n",
    "        df_raw.budget >= 8875].copy(),\n",
    "    'budget_start_5percent': df_raw[\n",
    "        df_raw.budget >= 250000].copy(),\n",
    "    'profitability_1percentile': df_raw[\n",
    "        (df_raw.META__profitability >= -138) &\n",
    "        (df_raw.META__profitability <= 65)].copy(),\n",
    "    'profitability_5percentile': df_raw[\n",
    "        (df_raw.META__profitability >= -14.9) &\n",
    "        (df_raw.META__profitability <= 13.9)].copy(),\n",
    "    'profitability_10percentile': df_raw[\n",
    "        (df_raw.META__profitability >= -4.42) &\n",
    "        (df_raw.META__profitability <= 7.4)].copy(),\n",
    "    'profitability_1percentile_years2000-2018': df_raw[\n",
    "        (df_raw.META__profitability >= -138) &\n",
    "        (df_raw.META__profitability <= 65) &\n",
    "        (df_raw.META__year >= 2000) &\n",
    "        (df_raw.META__year <= 2018)].copy(),\n",
    "    'profitability_1percentile_years2000-2018_budget_start_1percent': df_raw[\n",
    "        (df_raw.META__profitability >= -138) &\n",
    "        (df_raw.META__profitability <= 65) &\n",
    "        (df_raw.META__year >= 2000) &\n",
    "        (df_raw.META__year <= 2018) &\n",
    "        (df_raw.budget >= 8875)].copy(),\n",
    "    'profitability_1percentile_years2000-2018_budget_start_5percent': df_raw[\n",
    "        (df_raw.META__profitability >= -138) &\n",
    "        (df_raw.META__profitability <= 65) &\n",
    "        (df_raw.META__year >= 2000) &\n",
    "        (df_raw.META__year <= 2018) &\n",
    "        (df_raw.budget >= 250000)].copy(),\n",
    "    'revenue_1percentile': df_raw[\n",
    "        (df_raw.META__revenue >= 10000) &\n",
    "        (df_raw.META__revenue <= 854060072)].copy(),\n",
    "    'revenue_5percentile': df_raw[\n",
    "        (df_raw.META__revenue >= 111229) &\n",
    "        (df_raw.META__revenue <= 348514752)].copy(),\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7493\n",
      "5695\n",
      "893\n",
      "4903\n",
      "2042\n",
      "5107\n",
      "2335\n",
      "7418\n",
      "7129\n",
      "7342\n",
      "6744\n",
      "5994\n",
      "4814\n",
      "4754\n",
      "4599\n",
      "7354\n",
      "6743\n",
      "100.0\n",
      "76.00427065260911\n",
      "11.917789937274788\n",
      "65.43440544508208\n",
      "27.252101961831045\n",
      "68.1569464833845\n",
      "31.162418257039903\n",
      "98.99906579474175\n",
      "95.14213265714668\n",
      "97.98478580008008\n",
      "90.00400373682103\n",
      "79.99466168423862\n",
      "64.24663018817563\n",
      "63.445882823969036\n",
      "61.37728546643534\n",
      "98.1449352729214\n",
      "89.99065794741759\n"
     ]
    }
   ],
   "source": [
    "for d in raw_datasets.keys():\n",
    "    print(raw_datasets[d].shape[0])\n",
    "for d in raw_datasets.keys():\n",
    "    print(raw_datasets[d].shape[0]/df_raw.shape[0]*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------------------------------\n",
      "all\n",
      "-------------------------------------------------------\n",
      "obtaine movies\n",
      "time: 32.76154373700001\n",
      "calculate info\n",
      "time: 51.697076400000014\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue']\n",
      "META__production_company_1\n",
      "['production_company_1_avg_profit', 'production_company_1_avg_revenue']\n",
      "META__production_company_2\n",
      "['production_company_2_avg_profit', 'production_company_2_avg_revenue']\n",
      "META__production_company_3\n",
      "['production_company_3_avg_profit', 'production_company_3_avg_revenue']\n",
      "time: 2.0308234389999598\n",
      "obtaine movies\n",
      "time: 106.78814797400003\n",
      "calculate info\n",
      "time: 80.79361381900003\n",
      "get columns\n",
      "['avg_profit', 'experience', 'avg_revenue', 'movies_before']\n",
      "META__cast_1_name\n",
      "['cast_1_avg_profit', 'cast_1_experience', 'cast_1_avg_revenue', 'cast_1_movies_before']\n",
      "META__cast_2_name\n",
      "['cast_2_avg_profit', 'cast_2_experience', 'cast_2_avg_revenue', 'cast_2_movies_before']\n",
      "META__cast_3_name\n",
      "['cast_3_avg_profit', 'cast_3_experience', 'cast_3_avg_revenue', 'cast_3_movies_before']\n",
      "META__cast_4_name\n",
      "['cast_4_avg_profit', 'cast_4_experience', 'cast_4_avg_revenue', 'cast_4_movies_before']\n",
      "META__cast_5_name\n",
      "['cast_5_avg_profit', 'cast_5_experience', 'cast_5_avg_revenue', 'cast_5_movies_before']\n",
      "META__cast_6_name\n",
      "['cast_6_avg_profit', 'cast_6_experience', 'cast_6_avg_revenue', 'cast_6_movies_before']\n",
      "META__cast_7_name\n",
      "['cast_7_avg_profit', 'cast_7_experience', 'cast_7_avg_revenue', 'cast_7_movies_before']\n",
      "META__cast_8_name\n",
      "['cast_8_avg_profit', 'cast_8_experience', 'cast_8_avg_revenue', 'cast_8_movies_before']\n",
      "time: 5.255951309000011\n",
      "obtaine movies\n",
      "time: 3.9199712829999953\n",
      "calculate info\n",
      "time: 2.3422554840000203\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__music_editor\n",
      "['crew__sound__music_editor_avg_profit', 'crew__sound__music_editor_avg_revenue', 'crew__sound__music_editor_movies_before']\n",
      "time: 0.6588012419999814\n",
      "obtaine movies\n",
      "time: 10.286566214999993\n",
      "calculate info\n",
      "time: 8.310948107000058\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__original_music_composer\n",
      "['crew__sound__original_music_composer_avg_profit', 'crew__sound__original_music_composer_avg_revenue', 'crew__sound__original_music_composer_movies_before']\n",
      "time: 0.6582412310000336\n",
      "obtaine movies\n",
      "time: 2.8145269660000167\n",
      "calculate info\n",
      "time: 1.8767070149999654\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__sound_designer\n",
      "['crew__sound__sound_designer_avg_profit', 'crew__sound__sound_designer_avg_revenue', 'crew__sound__sound_designer_movies_before']\n",
      "time: 0.655423060999965\n",
      "obtaine movies\n",
      "time: 2.9857148710000274\n",
      "calculate info\n",
      "time: 1.9288419419999627\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__sound_effects_editor\n",
      "['crew__sound__sound_effects_editor_avg_profit', 'crew__sound__sound_effects_editor_avg_revenue', 'crew__sound__sound_effects_editor_movies_before']\n",
      "time: 0.6678296990000945\n",
      "obtaine movies\n",
      "time: 4.443051231000027\n",
      "calculate info\n",
      "time: 2.9093420939999532\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__sound_re_recording_mixer\n",
      "['crew__sound__sound_re_recording_mixer_avg_profit', 'crew__sound__sound_re_recording_mixer_avg_revenue', 'crew__sound__sound_re_recording_mixer_movies_before']\n",
      "time: 0.6497723589999396\n",
      "obtaine movies\n",
      "time: 4.053153496999926\n",
      "calculate info\n",
      "time: 2.602560525000058\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__supervising_sound_editor\n",
      "['crew__sound__supervising_sound_editor_avg_profit', 'crew__sound__supervising_sound_editor_avg_revenue', 'crew__sound__supervising_sound_editor_movies_before']\n",
      "time: 0.6554601809999667\n",
      "obtaine movies\n",
      "time: 11.848743722999984\n",
      "calculate info\n",
      "time: 9.228264457000023\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__directing__director__1\n",
      "['crew__directing__director__1_avg_profit', 'crew__directing__director__1_avg_revenue', 'crew__directing__director__1_movies_before']\n",
      "time: 0.6650845219999155\n",
      "obtaine movies\n",
      "time: 5.166065677000006\n",
      "calculate info\n",
      "time: 3.3582655589999604\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__directing__script_supervisor\n",
      "['crew__directing__script_supervisor_avg_profit', 'crew__directing__script_supervisor_avg_revenue', 'crew__directing__script_supervisor_movies_before']\n",
      "time: 0.6514933180000071\n",
      "obtaine movies\n",
      "time: 9.849201416000028\n",
      "calculate info\n",
      "time: 8.302811806000022\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__production__casting\n",
      "['crew__production__casting_avg_profit', 'crew__production__casting_avg_revenue', 'crew__production__casting_movies_before']\n",
      "time: 0.6631683379998776\n",
      "obtaine movies\n",
      "time: 5.892678045000139\n",
      "calculate info\n",
      "time: 5.588438407000012\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__production__executive_producer__1\n",
      "['crew__production__executive_producer__1_avg_profit', 'crew__production__executive_producer__1_avg_revenue', 'crew__production__executive_producer__1_movies_before']\n",
      "time: 0.6562371800000619\n",
      "obtaine movies\n",
      "time: 10.748242634999997\n",
      "calculate info\n",
      "time: 8.608885332\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__production__producer__1\n",
      "['crew__production__producer__1_avg_profit', 'crew__production__producer__1_avg_revenue', 'crew__production__producer__1_movies_before']\n",
      "time: 0.6648947849998876\n",
      "obtaine movies\n",
      "time: 3.0276697469998908\n",
      "calculate info\n",
      "time: 3.129685681999945\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__production__producer__2\n",
      "['crew__production__producer__2_avg_profit', 'crew__production__producer__2_avg_revenue', 'crew__production__producer__2_movies_before']\n",
      "time: 0.6569993309999518\n",
      "obtaine movies\n",
      "time: 12.26565067699994\n",
      "calculate info\n",
      "time: 8.555996365999817\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__editing__editor__1\n",
      "['crew__editing__editor__1_avg_profit', 'crew__editing__editor__1_avg_revenue', 'crew__editing__editor__1_movies_before']\n",
      "time: 0.6616781340001125\n",
      "obtaine movies\n",
      "time: 9.035130060000029\n",
      "calculate info\n",
      "time: 6.438944622000008\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__costume__costume_designer\n",
      "['crew__costume__costume_designer_avg_profit', 'crew__costume__costume_designer_avg_revenue', 'crew__costume__costume_designer_movies_before']\n",
      "time: 0.6813316620000478\n",
      "obtaine movies\n",
      "time: 2.6639321200000268\n",
      "calculate info\n",
      "time: 1.7980767940000533\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__costume__costume_supervisor\n",
      "['crew__costume__costume_supervisor_avg_profit', 'crew__costume__costume_supervisor_avg_revenue', 'crew__costume__costume_supervisor_movies_before']\n",
      "time: 0.6588941960001193\n",
      "obtaine movies\n",
      "time: 3.4156576349998886\n",
      "calculate info\n",
      "time: 2.768340857000112\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__costume__makeup_artist\n",
      "['crew__costume__makeup_artist_avg_profit', 'crew__costume__makeup_artist_avg_revenue', 'crew__costume__makeup_artist_movies_before']\n",
      "time: 0.6591548950000288\n",
      "obtaine movies\n",
      "time: 3.5536741460000485\n",
      "calculate info\n",
      "time: 2.358842866000032\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__crew__stunt_coordinator\n",
      "['crew__crew__stunt_coordinator_avg_profit', 'crew__crew__stunt_coordinator_avg_revenue', 'crew__crew__stunt_coordinator_movies_before']\n",
      "time: 0.6737299429998984\n",
      "obtaine movies\n",
      "time: 6.460588572000006\n",
      "calculate info\n",
      "time: 5.701209827999946\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__writing__screenplay__1\n",
      "['crew__writing__screenplay__1_avg_profit', 'crew__writing__screenplay__1_avg_revenue', 'crew__writing__screenplay__1_movies_before']\n",
      "time: 0.6753627099999449\n",
      "obtaine movies\n",
      "time: 6.143143046999967\n",
      "calculate info\n",
      "time: 4.977659823000067\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__art__art_direction\n",
      "['crew__art__art_direction_avg_profit', 'crew__art__art_direction_avg_revenue', 'crew__art__art_direction_movies_before']\n",
      "time: 0.6851998160000221\n",
      "obtaine movies\n",
      "time: 9.427505135000047\n",
      "calculate info\n",
      "time: 6.461858091999829\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__art__production_design\n",
      "['crew__art__production_design_avg_profit', 'crew__art__production_design_avg_revenue', 'crew__art__production_design_movies_before']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 0.6774820320001709\n",
      "obtaine movies\n",
      "time: 3.147450641999967\n",
      "calculate info\n",
      "time: 2.16235450399995\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__art__property_master\n",
      "['crew__art__property_master_avg_profit', 'crew__art__property_master_avg_revenue', 'crew__art__property_master_movies_before']\n",
      "time: 0.6782849279998118\n",
      "obtaine movies\n",
      "time: 7.0461755700000595\n",
      "calculate info\n",
      "time: 4.830708471999969\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__art__set_decoration\n",
      "['crew__art__set_decoration_avg_profit', 'crew__art__set_decoration_avg_revenue', 'crew__art__set_decoration_movies_before']\n",
      "time: 0.6725467409999055\n",
      "obtaine movies\n",
      "time: 2.8668473679999806\n",
      "calculate info\n",
      "time: 2.0953345089999402\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__visualeffects__visual_effects_supervisor\n",
      "['crew__visualeffects__visual_effects_supervisor_avg_profit', 'crew__visualeffects__visual_effects_supervisor_avg_revenue', 'crew__visualeffects__visual_effects_supervisor_movies_before']\n",
      "time: 0.6813727829999152\n",
      "obtaine movies\n",
      "time: 12.339098076000028\n",
      "calculate info\n",
      "time: 8.82159893800008\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__camera__director_of_photography\n",
      "['crew__camera__director_of_photography_avg_profit', 'crew__camera__director_of_photography_avg_revenue', 'crew__camera__director_of_photography_movies_before']\n",
      "time: 0.6783917489999567\n",
      "obtaine movies\n",
      "time: 2.588764918999914\n",
      "calculate info\n",
      "time: 1.5522668920000342\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__camera__steadicam_operator\n",
      "['crew__camera__steadicam_operator_avg_profit', 'crew__camera__steadicam_operator_avg_revenue', 'crew__camera__steadicam_operator_movies_before']\n",
      "time: 0.6813079730000027\n",
      "obtaine movies\n",
      "time: 4.760613347999879\n",
      "calculate info\n",
      "time: 3.2224897169999167\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__camera__still_photographer\n",
      "['crew__camera__still_photographer_avg_profit', 'crew__camera__still_photographer_avg_revenue', 'crew__camera__still_photographer_movies_before']\n",
      "time: 0.6882765299999392\n",
      "obtaine movies\n",
      "time: 2.3636536829999386\n",
      "calculate info\n",
      "time: 1.4926818639999055\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue']\n",
      "META__collection_name\n",
      "['collection_avg_profit', 'collection_avg_revenue']\n",
      "time: 0.6785545469999761\n",
      "overall time: 588.03831995\n",
      "------------------------------------------------------\n",
      "us\n",
      "-------------------------------------------------------\n",
      "obtaine movies\n",
      "time: 26.594569510999918\n",
      "calculate info\n",
      "time: 42.80015221100007\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue']\n",
      "META__production_company_1\n",
      "['production_company_1_avg_profit', 'production_company_1_avg_revenue']\n",
      "META__production_company_2\n",
      "['production_company_2_avg_profit', 'production_company_2_avg_revenue']\n",
      "META__production_company_3\n",
      "['production_company_3_avg_profit', 'production_company_3_avg_revenue']\n",
      "time: 1.4761096499998985\n",
      "obtaine movies\n",
      "time: 86.15839626999991\n",
      "calculate info\n",
      "time: 63.85845599300001\n",
      "get columns\n",
      "['avg_profit', 'experience', 'avg_revenue', 'movies_before']\n",
      "META__cast_1_name\n",
      "['cast_1_avg_profit', 'cast_1_experience', 'cast_1_avg_revenue', 'cast_1_movies_before']\n",
      "META__cast_2_name\n",
      "['cast_2_avg_profit', 'cast_2_experience', 'cast_2_avg_revenue', 'cast_2_movies_before']\n",
      "META__cast_3_name\n",
      "['cast_3_avg_profit', 'cast_3_experience', 'cast_3_avg_revenue', 'cast_3_movies_before']\n",
      "META__cast_4_name\n",
      "['cast_4_avg_profit', 'cast_4_experience', 'cast_4_avg_revenue', 'cast_4_movies_before']\n",
      "META__cast_5_name\n",
      "['cast_5_avg_profit', 'cast_5_experience', 'cast_5_avg_revenue', 'cast_5_movies_before']\n",
      "META__cast_6_name\n",
      "['cast_6_avg_profit', 'cast_6_experience', 'cast_6_avg_revenue', 'cast_6_movies_before']\n",
      "META__cast_7_name\n",
      "['cast_7_avg_profit', 'cast_7_experience', 'cast_7_avg_revenue', 'cast_7_movies_before']\n",
      "META__cast_8_name\n",
      "['cast_8_avg_profit', 'cast_8_experience', 'cast_8_avg_revenue', 'cast_8_movies_before']\n",
      "time: 4.005950310000117\n",
      "obtaine movies\n",
      "time: 4.316209720000188\n",
      "calculate info\n",
      "time: 2.2014340939999784\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__music_editor\n",
      "['crew__sound__music_editor_avg_profit', 'crew__sound__music_editor_avg_revenue', 'crew__sound__music_editor_movies_before']\n",
      "time: 0.4938384199999746\n",
      "obtaine movies\n",
      "time: 10.705401747999986\n",
      "calculate info\n",
      "time: 7.054718395000009\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__original_music_composer\n",
      "['crew__sound__original_music_composer_avg_profit', 'crew__sound__original_music_composer_avg_revenue', 'crew__sound__original_music_composer_movies_before']\n",
      "time: 0.4938609460000407\n",
      "obtaine movies\n",
      "time: 2.7405318940000143\n",
      "calculate info\n",
      "time: 1.497452457999998\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__sound_designer\n",
      "['crew__sound__sound_designer_avg_profit', 'crew__sound__sound_designer_avg_revenue', 'crew__sound__sound_designer_movies_before']\n",
      "time: 0.48785083500001747\n",
      "obtaine movies\n",
      "time: 3.1314765859999625\n",
      "calculate info\n",
      "time: 1.736734313999932\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__sound_effects_editor\n",
      "['crew__sound__sound_effects_editor_avg_profit', 'crew__sound__sound_effects_editor_avg_revenue', 'crew__sound__sound_effects_editor_movies_before']\n",
      "time: 0.4892344790000607\n",
      "obtaine movies\n",
      "time: 4.847470392000105\n",
      "calculate info\n",
      "time: 2.648603446000152\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__sound_re_recording_mixer\n",
      "['crew__sound__sound_re_recording_mixer_avg_profit', 'crew__sound__sound_re_recording_mixer_avg_revenue', 'crew__sound__sound_re_recording_mixer_movies_before']\n",
      "time: 0.4964423160001843\n",
      "obtaine movies\n",
      "time: 4.57841372300004\n",
      "calculate info\n",
      "time: 2.4419860380000955\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__supervising_sound_editor\n",
      "['crew__sound__supervising_sound_editor_avg_profit', 'crew__sound__supervising_sound_editor_avg_revenue', 'crew__sound__supervising_sound_editor_movies_before']\n",
      "time: 0.5028361839999889\n",
      "obtaine movies\n",
      "time: 11.857784432000017\n",
      "calculate info\n",
      "time: 7.4548950270000205\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__directing__director__1\n",
      "['crew__directing__director__1_avg_profit', 'crew__directing__director__1_avg_revenue', 'crew__directing__director__1_movies_before']\n",
      "time: 0.4991021810001257\n",
      "obtaine movies\n",
      "time: 5.744376299999885\n",
      "calculate info\n",
      "time: 3.1159061430000747\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__directing__script_supervisor\n",
      "['crew__directing__script_supervisor_avg_profit', 'crew__directing__script_supervisor_avg_revenue', 'crew__directing__script_supervisor_movies_before']\n",
      "time: 0.4972615010001391\n",
      "obtaine movies\n",
      "time: 10.802564246999964\n",
      "calculate info\n",
      "time: 7.4554685719999725\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__production__casting\n",
      "['crew__production__casting_avg_profit', 'crew__production__casting_avg_revenue', 'crew__production__casting_movies_before']\n",
      "time: 0.5018106219999936\n",
      "obtaine movies\n",
      "time: 6.335825893999981\n",
      "calculate info\n",
      "time: 4.92149452700005\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__production__executive_producer__1\n",
      "['crew__production__executive_producer__1_avg_profit', 'crew__production__executive_producer__1_avg_revenue', 'crew__production__executive_producer__1_movies_before']\n",
      "time: 0.4964407420000043\n",
      "obtaine movies\n",
      "time: 10.978272717999971\n",
      "calculate info\n",
      "time: 6.949678882000171\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__production__producer__1\n",
      "['crew__production__producer__1_avg_profit', 'crew__production__producer__1_avg_revenue', 'crew__production__producer__1_movies_before']\n",
      "time: 0.49655571999983295\n",
      "obtaine movies\n",
      "time: 3.0197083099999418\n",
      "calculate info\n",
      "time: 2.713073588000043\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__production__producer__2\n",
      "['crew__production__producer__2_avg_profit', 'crew__production__producer__2_avg_revenue', 'crew__production__producer__2_movies_before']\n",
      "time: 0.5008083370000804\n",
      "obtaine movies\n",
      "time: 12.669798698000022\n",
      "calculate info\n",
      "time: 6.9848981360000835\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__editing__editor__1\n",
      "['crew__editing__editor__1_avg_profit', 'crew__editing__editor__1_avg_revenue', 'crew__editing__editor__1_movies_before']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 0.5004339019999406\n",
      "obtaine movies\n",
      "time: 9.560884298000019\n",
      "calculate info\n",
      "time: 5.448489851000204\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__costume__costume_designer\n",
      "['crew__costume__costume_designer_avg_profit', 'crew__costume__costume_designer_avg_revenue', 'crew__costume__costume_designer_movies_before']\n",
      "time: 0.5096633269999984\n",
      "obtaine movies\n",
      "time: 2.8845739339999454\n",
      "calculate info\n",
      "time: 1.721160736999991\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__costume__costume_supervisor\n",
      "['crew__costume__costume_supervisor_avg_profit', 'crew__costume__costume_supervisor_avg_revenue', 'crew__costume__costume_supervisor_movies_before']\n",
      "time: 0.5010722970000643\n",
      "obtaine movies\n",
      "time: 3.724021386000004\n",
      "calculate info\n",
      "time: 2.4718669090000276\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__costume__makeup_artist\n",
      "['crew__costume__makeup_artist_avg_profit', 'crew__costume__makeup_artist_avg_revenue', 'crew__costume__makeup_artist_movies_before']\n",
      "time: 0.5003692740001497\n",
      "obtaine movies\n",
      "time: 3.6556326989998524\n",
      "calculate info\n",
      "time: 2.0470401960001254\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__crew__stunt_coordinator\n",
      "['crew__crew__stunt_coordinator_avg_profit', 'crew__crew__stunt_coordinator_avg_revenue', 'crew__crew__stunt_coordinator_movies_before']\n",
      "time: 0.5031784390000666\n",
      "obtaine movies\n",
      "time: 6.4275879669999085\n",
      "calculate info\n",
      "time: 4.611264355000003\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__writing__screenplay__1\n",
      "['crew__writing__screenplay__1_avg_profit', 'crew__writing__screenplay__1_avg_revenue', 'crew__writing__screenplay__1_movies_before']\n",
      "time: 0.5138218629999756\n",
      "obtaine movies\n",
      "time: 6.626944699999967\n",
      "calculate info\n",
      "time: 4.248445551000032\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__art__art_direction\n",
      "['crew__art__art_direction_avg_profit', 'crew__art__art_direction_avg_revenue', 'crew__art__art_direction_movies_before']\n",
      "time: 0.5159270689998721\n",
      "obtaine movies\n",
      "time: 10.149517928000023\n",
      "calculate info\n",
      "time: 5.816886031999957\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__art__production_design\n",
      "['crew__art__production_design_avg_profit', 'crew__art__production_design_avg_revenue', 'crew__art__production_design_movies_before']\n",
      "time: 0.5432437479998953\n",
      "obtaine movies\n",
      "time: 3.6709953179999957\n",
      "calculate info\n",
      "time: 2.054961091999985\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__art__property_master\n",
      "['crew__art__property_master_avg_profit', 'crew__art__property_master_avg_revenue', 'crew__art__property_master_movies_before']\n",
      "time: 0.5157616470000903\n",
      "obtaine movies\n",
      "time: 8.179023060999953\n",
      "calculate info\n",
      "time: 4.656186195999908\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__art__set_decoration\n",
      "['crew__art__set_decoration_avg_profit', 'crew__art__set_decoration_avg_revenue', 'crew__art__set_decoration_movies_before']\n",
      "time: 0.5290633899999193\n",
      "obtaine movies\n",
      "time: 3.233072921999792\n",
      "calculate info\n",
      "time: 1.9360815929999262\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__visualeffects__visual_effects_supervisor\n",
      "['crew__visualeffects__visual_effects_supervisor_avg_profit', 'crew__visualeffects__visual_effects_supervisor_avg_revenue', 'crew__visualeffects__visual_effects_supervisor_movies_before']\n",
      "time: 0.530429273999971\n",
      "obtaine movies\n",
      "time: 13.572944423999843\n",
      "calculate info\n",
      "time: 7.612224290000086\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__camera__director_of_photography\n",
      "['crew__camera__director_of_photography_avg_profit', 'crew__camera__director_of_photography_avg_revenue', 'crew__camera__director_of_photography_movies_before']\n",
      "time: 0.5405144950000249\n",
      "obtaine movies\n",
      "time: 2.9621737509999093\n",
      "calculate info\n",
      "time: 1.5058464650001042\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__camera__steadicam_operator\n",
      "['crew__camera__steadicam_operator_avg_profit', 'crew__camera__steadicam_operator_avg_revenue', 'crew__camera__steadicam_operator_movies_before']\n",
      "time: 0.5366554310000993\n",
      "obtaine movies\n",
      "time: 5.464717780000001\n",
      "calculate info\n",
      "time: 3.0340636460000496\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__camera__still_photographer\n",
      "['crew__camera__still_photographer_avg_profit', 'crew__camera__still_photographer_avg_revenue', 'crew__camera__still_photographer_movies_before']\n",
      "time: 0.5545712079999703\n",
      "obtaine movies\n",
      "time: 2.5722366679999595\n",
      "calculate info\n",
      "time: 1.4042283230000976\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue']\n",
      "META__collection_name\n",
      "['collection_avg_profit', 'collection_avg_revenue']\n",
      "time: 0.5481508149998717\n",
      "overall time: 523.8152183760001\n",
      "------------------------------------------------------\n",
      "gb\n",
      "-------------------------------------------------------\n",
      "obtaine movies\n",
      "time: 3.6874530600000526\n",
      "calculate info\n",
      "time: 3.2604406579998795\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue']\n",
      "META__production_company_1\n",
      "['production_company_1_avg_profit', 'production_company_1_avg_revenue']\n",
      "META__production_company_2\n",
      "['production_company_2_avg_profit', 'production_company_2_avg_revenue']\n",
      "META__production_company_3\n",
      "['production_company_3_avg_profit', 'production_company_3_avg_revenue']\n",
      "time: 0.2317124530000001\n",
      "obtaine movies\n",
      "time: 9.078320128999849\n",
      "calculate info\n",
      "time: 8.791379953999922\n",
      "get columns\n",
      "['avg_profit', 'experience', 'avg_revenue', 'movies_before']\n",
      "META__cast_1_name\n",
      "['cast_1_avg_profit', 'cast_1_experience', 'cast_1_avg_revenue', 'cast_1_movies_before']\n",
      "META__cast_2_name\n",
      "['cast_2_avg_profit', 'cast_2_experience', 'cast_2_avg_revenue', 'cast_2_movies_before']\n",
      "META__cast_3_name\n",
      "['cast_3_avg_profit', 'cast_3_experience', 'cast_3_avg_revenue', 'cast_3_movies_before']\n",
      "META__cast_4_name\n",
      "['cast_4_avg_profit', 'cast_4_experience', 'cast_4_avg_revenue', 'cast_4_movies_before']\n",
      "META__cast_5_name\n",
      "['cast_5_avg_profit', 'cast_5_experience', 'cast_5_avg_revenue', 'cast_5_movies_before']\n",
      "META__cast_6_name\n",
      "['cast_6_avg_profit', 'cast_6_experience', 'cast_6_avg_revenue', 'cast_6_movies_before']\n",
      "META__cast_7_name\n",
      "['cast_7_avg_profit', 'cast_7_experience', 'cast_7_avg_revenue', 'cast_7_movies_before']\n",
      "META__cast_8_name\n",
      "['cast_8_avg_profit', 'cast_8_experience', 'cast_8_avg_revenue', 'cast_8_movies_before']\n",
      "time: 0.6250423179999416\n",
      "obtaine movies\n",
      "time: 0.5094361569999819\n",
      "calculate info\n",
      "time: 0.3155459920001249\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__music_editor\n",
      "['crew__sound__music_editor_avg_profit', 'crew__sound__music_editor_avg_revenue', 'crew__sound__music_editor_movies_before']\n",
      "time: 0.07671644800007016\n",
      "obtaine movies\n",
      "time: 1.1935008689999904\n",
      "calculate info\n",
      "time: 0.7889109039999767\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__original_music_composer\n",
      "['crew__sound__original_music_composer_avg_profit', 'crew__sound__original_music_composer_avg_revenue', 'crew__sound__original_music_composer_movies_before']\n",
      "time: 0.0778358830000343\n",
      "obtaine movies\n",
      "time: 0.30958948500006045\n",
      "calculate info\n",
      "time: 0.24210254599984182\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__sound_designer\n",
      "['crew__sound__sound_designer_avg_profit', 'crew__sound__sound_designer_avg_revenue', 'crew__sound__sound_designer_movies_before']\n",
      "time: 0.0775684130001082\n",
      "obtaine movies\n",
      "time: 0.2840816069999619\n",
      "calculate info\n",
      "time: 0.24521380099986345\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__sound_effects_editor\n",
      "['crew__sound__sound_effects_editor_avg_profit', 'crew__sound__sound_effects_editor_avg_revenue', 'crew__sound__sound_effects_editor_movies_before']\n",
      "time: 0.07601533699994434\n",
      "obtaine movies\n",
      "time: 0.555874016999951\n",
      "calculate info\n",
      "time: 0.35546619399997326\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__sound_re_recording_mixer\n",
      "['crew__sound__sound_re_recording_mixer_avg_profit', 'crew__sound__sound_re_recording_mixer_avg_revenue', 'crew__sound__sound_re_recording_mixer_movies_before']\n",
      "time: 0.07637404700017214\n",
      "obtaine movies\n",
      "time: 0.5342748500002017\n",
      "calculate info\n",
      "time: 0.34497048799994445\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__supervising_sound_editor\n",
      "['crew__sound__supervising_sound_editor_avg_profit', 'crew__sound__supervising_sound_editor_avg_revenue', 'crew__sound__supervising_sound_editor_movies_before']\n",
      "time: 0.07652581299998928\n",
      "obtaine movies\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 1.2139902750000147\n",
      "calculate info\n",
      "time: 1.0431114590001016\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__directing__director__1\n",
      "['crew__directing__director__1_avg_profit', 'crew__directing__director__1_avg_revenue', 'crew__directing__director__1_movies_before']\n",
      "time: 0.07676021699990088\n",
      "obtaine movies\n",
      "time: 0.6961276120000548\n",
      "calculate info\n",
      "time: 0.4761828619998596\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__directing__script_supervisor\n",
      "['crew__directing__script_supervisor_avg_profit', 'crew__directing__script_supervisor_avg_revenue', 'crew__directing__script_supervisor_movies_before']\n",
      "time: 0.08008954000001722\n",
      "obtaine movies\n",
      "time: 1.5291948560000037\n",
      "calculate info\n",
      "time: 0.9028894409998429\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__production__casting\n",
      "['crew__production__casting_avg_profit', 'crew__production__casting_avg_revenue', 'crew__production__casting_movies_before']\n",
      "time: 0.07753322700000354\n",
      "obtaine movies\n",
      "time: 0.7332955680001305\n",
      "calculate info\n",
      "time: 0.6591745270000047\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__production__executive_producer__1\n",
      "['crew__production__executive_producer__1_avg_profit', 'crew__production__executive_producer__1_avg_revenue', 'crew__production__executive_producer__1_movies_before']\n",
      "time: 0.07764808299998549\n",
      "obtaine movies\n",
      "time: 1.2061717340000087\n",
      "calculate info\n",
      "time: 1.0403930719999153\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__production__producer__1\n",
      "['crew__production__producer__1_avg_profit', 'crew__production__producer__1_avg_revenue', 'crew__production__producer__1_movies_before']\n",
      "time: 0.07809857699999156\n",
      "obtaine movies\n",
      "time: 0.35622702899991054\n",
      "calculate info\n",
      "time: 0.45021020199988016\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__production__producer__2\n",
      "['crew__production__producer__2_avg_profit', 'crew__production__producer__2_avg_revenue', 'crew__production__producer__2_movies_before']\n",
      "time: 0.07655553100016732\n",
      "obtaine movies\n",
      "time: 1.4403071009999167\n",
      "calculate info\n",
      "time: 0.9965521069998431\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__editing__editor__1\n",
      "['crew__editing__editor__1_avg_profit', 'crew__editing__editor__1_avg_revenue', 'crew__editing__editor__1_movies_before']\n",
      "time: 0.078206924999904\n",
      "obtaine movies\n",
      "time: 1.257882747999929\n",
      "calculate info\n",
      "time: 0.8325420529999974\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__costume__costume_designer\n",
      "['crew__costume__costume_designer_avg_profit', 'crew__costume__costume_designer_avg_revenue', 'crew__costume__costume_designer_movies_before']\n",
      "time: 0.078920466999989\n",
      "obtaine movies\n",
      "time: 0.33618356799979665\n",
      "calculate info\n",
      "time: 0.26387805900003514\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__costume__costume_supervisor\n",
      "['crew__costume__costume_supervisor_avg_profit', 'crew__costume__costume_supervisor_avg_revenue', 'crew__costume__costume_supervisor_movies_before']\n",
      "time: 0.07859583599997677\n",
      "obtaine movies\n",
      "time: 0.37663231800001995\n",
      "calculate info\n",
      "time: 0.38909142100010286\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__costume__makeup_artist\n",
      "['crew__costume__makeup_artist_avg_profit', 'crew__costume__makeup_artist_avg_revenue', 'crew__costume__makeup_artist_movies_before']\n",
      "time: 0.07863027899998087\n",
      "obtaine movies\n",
      "time: 0.40023909299998195\n",
      "calculate info\n",
      "time: 0.3161425360001431\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__crew__stunt_coordinator\n",
      "['crew__crew__stunt_coordinator_avg_profit', 'crew__crew__stunt_coordinator_avg_revenue', 'crew__crew__stunt_coordinator_movies_before']\n",
      "time: 0.07916970399992351\n",
      "obtaine movies\n",
      "time: 0.730306894000023\n",
      "calculate info\n",
      "time: 0.7325114239999948\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__writing__screenplay__1\n",
      "['crew__writing__screenplay__1_avg_profit', 'crew__writing__screenplay__1_avg_revenue', 'crew__writing__screenplay__1_movies_before']\n",
      "time: 0.08162213800005702\n",
      "obtaine movies\n",
      "time: 0.6155154309999489\n",
      "calculate info\n",
      "time: 0.681800591999945\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__art__art_direction\n",
      "['crew__art__art_direction_avg_profit', 'crew__art__art_direction_avg_revenue', 'crew__art__art_direction_movies_before']\n",
      "time: 0.08030495499997414\n",
      "obtaine movies\n",
      "time: 1.2867052539997985\n",
      "calculate info\n",
      "time: 0.9267966939999042\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__art__production_design\n",
      "['crew__art__production_design_avg_profit', 'crew__art__production_design_avg_revenue', 'crew__art__production_design_movies_before']\n",
      "time: 0.08303364099992905\n",
      "obtaine movies\n",
      "time: 0.40230814100004864\n",
      "calculate info\n",
      "time: 0.3234008360000189\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__art__property_master\n",
      "['crew__art__property_master_avg_profit', 'crew__art__property_master_avg_revenue', 'crew__art__property_master_movies_before']\n",
      "time: 0.08161894700015182\n",
      "obtaine movies\n",
      "time: 0.7724120320001475\n",
      "calculate info\n",
      "time: 0.6037484450000647\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__art__set_decoration\n",
      "['crew__art__set_decoration_avg_profit', 'crew__art__set_decoration_avg_revenue', 'crew__art__set_decoration_movies_before']\n",
      "time: 0.08173078200002237\n",
      "obtaine movies\n",
      "time: 0.38286356899993734\n",
      "calculate info\n",
      "time: 0.32252178899989303\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__visualeffects__visual_effects_supervisor\n",
      "['crew__visualeffects__visual_effects_supervisor_avg_profit', 'crew__visualeffects__visual_effects_supervisor_avg_revenue', 'crew__visualeffects__visual_effects_supervisor_movies_before']\n",
      "time: 0.0791883899998993\n",
      "obtaine movies\n",
      "time: 1.5483813660000578\n",
      "calculate info\n",
      "time: 1.0508511110001564\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__camera__director_of_photography\n",
      "['crew__camera__director_of_photography_avg_profit', 'crew__camera__director_of_photography_avg_revenue', 'crew__camera__director_of_photography_movies_before']\n",
      "time: 0.08085457699985454\n",
      "obtaine movies\n",
      "time: 0.41692050700021355\n",
      "calculate info\n",
      "time: 0.24909731600018858\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__camera__steadicam_operator\n",
      "['crew__camera__steadicam_operator_avg_profit', 'crew__camera__steadicam_operator_avg_revenue', 'crew__camera__steadicam_operator_movies_before']\n",
      "time: 0.08115199000008033\n",
      "obtaine movies\n",
      "time: 0.7277827519999391\n",
      "calculate info\n",
      "time: 0.4768969780000134\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__camera__still_photographer\n",
      "['crew__camera__still_photographer_avg_profit', 'crew__camera__still_photographer_avg_revenue', 'crew__camera__still_photographer_movies_before']\n",
      "time: 0.07953721299986682\n",
      "obtaine movies\n",
      "time: 0.3252379379998729\n",
      "calculate info\n",
      "time: 0.20511412800010476\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue']\n",
      "META__collection_name\n",
      "['collection_avg_profit', 'collection_avg_revenue']\n",
      "time: 0.08095327800015184\n",
      "overall time: 63.974247358000184\n",
      "------------------------------------------------------\n",
      "years2000-2018\n",
      "-------------------------------------------------------\n",
      "obtaine movies\n",
      "time: 21.8374802379999\n",
      "calculate info\n",
      "time: 22.84423945399999\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue']\n",
      "META__production_company_1\n",
      "['production_company_1_avg_profit', 'production_company_1_avg_revenue']\n",
      "META__production_company_2\n",
      "['production_company_2_avg_profit', 'production_company_2_avg_revenue']\n",
      "META__production_company_3\n",
      "['production_company_3_avg_profit', 'production_company_3_avg_revenue']\n",
      "time: 1.282697522000035\n",
      "obtaine movies\n",
      "time: 67.99758262799992\n",
      "calculate info\n",
      "time: 52.386105845999964\n",
      "get columns\n",
      "['avg_profit', 'experience', 'avg_revenue', 'movies_before']\n",
      "META__cast_1_name\n",
      "['cast_1_avg_profit', 'cast_1_experience', 'cast_1_avg_revenue', 'cast_1_movies_before']\n",
      "META__cast_2_name\n",
      "['cast_2_avg_profit', 'cast_2_experience', 'cast_2_avg_revenue', 'cast_2_movies_before']\n",
      "META__cast_3_name\n",
      "['cast_3_avg_profit', 'cast_3_experience', 'cast_3_avg_revenue', 'cast_3_movies_before']\n",
      "META__cast_4_name\n",
      "['cast_4_avg_profit', 'cast_4_experience', 'cast_4_avg_revenue', 'cast_4_movies_before']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "META__cast_5_name\n",
      "['cast_5_avg_profit', 'cast_5_experience', 'cast_5_avg_revenue', 'cast_5_movies_before']\n",
      "META__cast_6_name\n",
      "['cast_6_avg_profit', 'cast_6_experience', 'cast_6_avg_revenue', 'cast_6_movies_before']\n",
      "META__cast_7_name\n",
      "['cast_7_avg_profit', 'cast_7_experience', 'cast_7_avg_revenue', 'cast_7_movies_before']\n",
      "META__cast_8_name\n",
      "['cast_8_avg_profit', 'cast_8_experience', 'cast_8_avg_revenue', 'cast_8_movies_before']\n",
      "time: 3.4139326910001273\n",
      "obtaine movies\n",
      "time: 2.8075697539998146\n",
      "calculate info\n",
      "time: 1.7302352849999352\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__music_editor\n",
      "['crew__sound__music_editor_avg_profit', 'crew__sound__music_editor_avg_revenue', 'crew__sound__music_editor_movies_before']\n",
      "time: 0.42719095000006746\n",
      "obtaine movies\n",
      "time: 6.14535689000013\n",
      "calculate info\n",
      "time: 4.628648590000012\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__original_music_composer\n",
      "['crew__sound__original_music_composer_avg_profit', 'crew__sound__original_music_composer_avg_revenue', 'crew__sound__original_music_composer_movies_before']\n",
      "time: 0.4306070379998346\n",
      "obtaine movies\n",
      "time: 2.1503416969999307\n",
      "calculate info\n",
      "time: 1.4570430450000913\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__sound_designer\n",
      "['crew__sound__sound_designer_avg_profit', 'crew__sound__sound_designer_avg_revenue', 'crew__sound__sound_designer_movies_before']\n",
      "time: 0.42850483400002304\n",
      "obtaine movies\n",
      "time: 2.243158590999883\n",
      "calculate info\n",
      "time: 1.5083658559999549\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__sound_effects_editor\n",
      "['crew__sound__sound_effects_editor_avg_profit', 'crew__sound__sound_effects_editor_avg_revenue', 'crew__sound__sound_effects_editor_movies_before']\n",
      "time: 0.4248447400000259\n",
      "obtaine movies\n",
      "time: 3.2339740570000686\n",
      "calculate info\n",
      "time: 2.122602503000053\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__sound_re_recording_mixer\n",
      "['crew__sound__sound_re_recording_mixer_avg_profit', 'crew__sound__sound_re_recording_mixer_avg_revenue', 'crew__sound__sound_re_recording_mixer_movies_before']\n",
      "time: 0.4291708830000971\n",
      "obtaine movies\n",
      "time: 2.9157302759999766\n",
      "calculate info\n",
      "time: 1.880391475999886\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__supervising_sound_editor\n",
      "['crew__sound__supervising_sound_editor_avg_profit', 'crew__sound__supervising_sound_editor_avg_revenue', 'crew__sound__supervising_sound_editor_movies_before']\n",
      "time: 0.42819349299998066\n",
      "obtaine movies\n",
      "time: 6.818200708999939\n",
      "calculate info\n",
      "time: 5.829921704000071\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__directing__director__1\n",
      "['crew__directing__director__1_avg_profit', 'crew__directing__director__1_avg_revenue', 'crew__directing__director__1_movies_before']\n",
      "time: 0.4314557519999198\n",
      "obtaine movies\n",
      "time: 3.6268755339999643\n",
      "calculate info\n",
      "time: 2.400530814000149\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__directing__script_supervisor\n",
      "['crew__directing__script_supervisor_avg_profit', 'crew__directing__script_supervisor_avg_revenue', 'crew__directing__script_supervisor_movies_before']\n",
      "time: 0.424278195999932\n",
      "obtaine movies\n",
      "time: 6.722000666999747\n",
      "calculate info\n",
      "time: 5.670276802999979\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__production__casting\n",
      "['crew__production__casting_avg_profit', 'crew__production__casting_avg_revenue', 'crew__production__casting_movies_before']\n",
      "time: 0.43152528600012374\n",
      "obtaine movies\n",
      "time: 3.9023280890000933\n",
      "calculate info\n",
      "time: 3.7817427889999635\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__production__executive_producer__1\n",
      "['crew__production__executive_producer__1_avg_profit', 'crew__production__executive_producer__1_avg_revenue', 'crew__production__executive_producer__1_movies_before']\n",
      "time: 0.4284818289997929\n",
      "obtaine movies\n",
      "time: 6.582026563999989\n",
      "calculate info\n",
      "time: 5.3809061689999\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__production__producer__1\n",
      "['crew__production__producer__1_avg_profit', 'crew__production__producer__1_avg_revenue', 'crew__production__producer__1_movies_before']\n",
      "time: 0.4318607929999416\n",
      "obtaine movies\n",
      "time: 2.33510646600007\n",
      "calculate info\n",
      "time: 2.5438614559998314\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__production__producer__2\n",
      "['crew__production__producer__2_avg_profit', 'crew__production__producer__2_avg_revenue', 'crew__production__producer__2_movies_before']\n",
      "time: 0.43203194599982453\n",
      "obtaine movies\n",
      "time: 7.422882994000247\n",
      "calculate info\n",
      "time: 5.30037890199992\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__editing__editor__1\n",
      "['crew__editing__editor__1_avg_profit', 'crew__editing__editor__1_avg_revenue', 'crew__editing__editor__1_movies_before']\n",
      "time: 0.43901770000002216\n",
      "obtaine movies\n",
      "time: 6.035746114000176\n",
      "calculate info\n",
      "time: 4.1378852170000755\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__costume__costume_designer\n",
      "['crew__costume__costume_designer_avg_profit', 'crew__costume__costume_designer_avg_revenue', 'crew__costume__costume_designer_movies_before']\n",
      "time: 0.4379421590001584\n",
      "obtaine movies\n",
      "time: 1.9577982189998693\n",
      "calculate info\n",
      "time: 1.4023469979997571\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__costume__costume_supervisor\n",
      "['crew__costume__costume_supervisor_avg_profit', 'crew__costume__costume_supervisor_avg_revenue', 'crew__costume__costume_supervisor_movies_before']\n",
      "time: 0.4442044170000372\n",
      "obtaine movies\n",
      "time: 1.7762604890003786\n",
      "calculate info\n",
      "time: 1.6147909020000952\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__costume__makeup_artist\n",
      "['crew__costume__makeup_artist_avg_profit', 'crew__costume__makeup_artist_avg_revenue', 'crew__costume__makeup_artist_movies_before']\n",
      "time: 0.424574298999687\n",
      "obtaine movies\n",
      "time: 2.4520073099997717\n",
      "calculate info\n",
      "time: 1.6573371829999815\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__crew__stunt_coordinator\n",
      "['crew__crew__stunt_coordinator_avg_profit', 'crew__crew__stunt_coordinator_avg_revenue', 'crew__crew__stunt_coordinator_movies_before']\n",
      "time: 0.4275539459999891\n",
      "obtaine movies\n",
      "time: 3.29998098499982\n",
      "calculate info\n",
      "time: 3.3106877800000802\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__writing__screenplay__1\n",
      "['crew__writing__screenplay__1_avg_profit', 'crew__writing__screenplay__1_avg_revenue', 'crew__writing__screenplay__1_movies_before']\n",
      "time: 0.440724820000014\n",
      "obtaine movies\n",
      "time: 3.776757029000237\n",
      "calculate info\n",
      "time: 3.0587676869999996\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__art__art_direction\n",
      "['crew__art__art_direction_avg_profit', 'crew__art__art_direction_avg_revenue', 'crew__art__art_direction_movies_before']\n",
      "time: 0.4297878900001706\n",
      "obtaine movies\n",
      "time: 6.259945223000159\n",
      "calculate info\n",
      "time: 4.548671665999791\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__art__production_design\n",
      "['crew__art__production_design_avg_profit', 'crew__art__production_design_avg_revenue', 'crew__art__production_design_movies_before']\n",
      "time: 0.455646938999962\n",
      "obtaine movies\n",
      "time: 2.32438554700002\n",
      "calculate info\n",
      "time: 1.564561596999738\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__art__property_master\n",
      "['crew__art__property_master_avg_profit', 'crew__art__property_master_avg_revenue', 'crew__art__property_master_movies_before']\n",
      "time: 0.45631070000035834\n",
      "obtaine movies\n",
      "time: 4.552172792999954\n",
      "calculate info\n",
      "time: 3.187329114000022\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__art__set_decoration\n",
      "['crew__art__set_decoration_avg_profit', 'crew__art__set_decoration_avg_revenue', 'crew__art__set_decoration_movies_before']\n",
      "time: 0.4541358129999935\n",
      "obtaine movies\n",
      "time: 2.3552676919998703\n",
      "calculate info\n",
      "time: 1.8437235749997853\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__visualeffects__visual_effects_supervisor\n",
      "['crew__visualeffects__visual_effects_supervisor_avg_profit', 'crew__visualeffects__visual_effects_supervisor_avg_revenue', 'crew__visualeffects__visual_effects_supervisor_movies_before']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 0.4482502589999058\n",
      "obtaine movies\n",
      "time: 7.869185187999847\n",
      "calculate info\n",
      "time: 5.446342755999922\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__camera__director_of_photography\n",
      "['crew__camera__director_of_photography_avg_profit', 'crew__camera__director_of_photography_avg_revenue', 'crew__camera__director_of_photography_movies_before']\n",
      "time: 0.46529749899991657\n",
      "obtaine movies\n",
      "time: 2.0097277059999215\n",
      "calculate info\n",
      "time: 1.2261116180002318\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__camera__steadicam_operator\n",
      "['crew__camera__steadicam_operator_avg_profit', 'crew__camera__steadicam_operator_avg_revenue', 'crew__camera__steadicam_operator_movies_before']\n",
      "time: 0.4464492950000931\n",
      "obtaine movies\n",
      "time: 3.531519505000233\n",
      "calculate info\n",
      "time: 2.308758011000009\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__camera__still_photographer\n",
      "['crew__camera__still_photographer_avg_profit', 'crew__camera__still_photographer_avg_revenue', 'crew__camera__still_photographer_movies_before']\n",
      "time: 0.461395000000266\n",
      "obtaine movies\n",
      "time: 1.421234399000241\n",
      "calculate info\n",
      "time: 0.9445777669998279\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue']\n",
      "META__collection_name\n",
      "['collection_avg_profit', 'collection_avg_revenue']\n",
      "time: 0.4454491860001326\n",
      "overall time: 372.72504418100016\n",
      "------------------------------------------------------\n",
      "years1970-1999\n",
      "-------------------------------------------------------\n",
      "obtaine movies\n",
      "time: 8.1268726039998\n",
      "calculate info\n",
      "time: 10.099428225000338\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue']\n",
      "META__production_company_1\n",
      "['production_company_1_avg_profit', 'production_company_1_avg_revenue']\n",
      "META__production_company_2\n",
      "['production_company_2_avg_profit', 'production_company_2_avg_revenue']\n",
      "META__production_company_3\n",
      "['production_company_3_avg_profit', 'production_company_3_avg_revenue']\n",
      "time: 0.5332354669999404\n",
      "obtaine movies\n",
      "time: 25.819674668999596\n",
      "calculate info\n",
      "time: 21.089340358999834\n",
      "get columns\n",
      "['avg_profit', 'experience', 'avg_revenue', 'movies_before']\n",
      "META__cast_1_name\n",
      "['cast_1_avg_profit', 'cast_1_experience', 'cast_1_avg_revenue', 'cast_1_movies_before']\n",
      "META__cast_2_name\n",
      "['cast_2_avg_profit', 'cast_2_experience', 'cast_2_avg_revenue', 'cast_2_movies_before']\n",
      "META__cast_3_name\n",
      "['cast_3_avg_profit', 'cast_3_experience', 'cast_3_avg_revenue', 'cast_3_movies_before']\n",
      "META__cast_4_name\n",
      "['cast_4_avg_profit', 'cast_4_experience', 'cast_4_avg_revenue', 'cast_4_movies_before']\n",
      "META__cast_5_name\n",
      "['cast_5_avg_profit', 'cast_5_experience', 'cast_5_avg_revenue', 'cast_5_movies_before']\n",
      "META__cast_6_name\n",
      "['cast_6_avg_profit', 'cast_6_experience', 'cast_6_avg_revenue', 'cast_6_movies_before']\n",
      "META__cast_7_name\n",
      "['cast_7_avg_profit', 'cast_7_experience', 'cast_7_avg_revenue', 'cast_7_movies_before']\n",
      "META__cast_8_name\n",
      "['cast_8_avg_profit', 'cast_8_experience', 'cast_8_avg_revenue', 'cast_8_movies_before']\n",
      "time: 1.4641240159999143\n",
      "obtaine movies\n",
      "time: 1.1274046670000644\n",
      "calculate info\n",
      "time: 0.5054228230001172\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__music_editor\n",
      "['crew__sound__music_editor_avg_profit', 'crew__sound__music_editor_avg_revenue', 'crew__sound__music_editor_movies_before']\n",
      "time: 0.17805725099970005\n",
      "obtaine movies\n",
      "time: 4.352357094000126\n",
      "calculate info\n",
      "time: 2.316827729999659\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__original_music_composer\n",
      "['crew__sound__original_music_composer_avg_profit', 'crew__sound__original_music_composer_avg_revenue', 'crew__sound__original_music_composer_movies_before']\n",
      "time: 0.1841414470000018\n",
      "obtaine movies\n",
      "time: 0.510431361999963\n",
      "calculate info\n",
      "time: 0.2588381579998895\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__sound_designer\n",
      "['crew__sound__sound_designer_avg_profit', 'crew__sound__sound_designer_avg_revenue', 'crew__sound__sound_designer_movies_before']\n",
      "time: 0.17382862500016927\n",
      "obtaine movies\n",
      "time: 0.537557753999863\n",
      "calculate info\n",
      "time: 0.3308928849996846\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__sound_effects_editor\n",
      "['crew__sound__sound_effects_editor_avg_profit', 'crew__sound__sound_effects_editor_avg_revenue', 'crew__sound__sound_effects_editor_movies_before']\n",
      "time: 0.17848839199996291\n",
      "obtaine movies\n",
      "time: 1.2631138060000922\n",
      "calculate info\n",
      "time: 0.5871266459998878\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__sound_re_recording_mixer\n",
      "['crew__sound__sound_re_recording_mixer_avg_profit', 'crew__sound__sound_re_recording_mixer_avg_revenue', 'crew__sound__sound_re_recording_mixer_movies_before']\n",
      "time: 0.17853257899969321\n",
      "obtaine movies\n",
      "time: 1.1829119059998447\n",
      "calculate info\n",
      "time: 0.5785472430002301\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__supervising_sound_editor\n",
      "['crew__sound__supervising_sound_editor_avg_profit', 'crew__sound__supervising_sound_editor_avg_revenue', 'crew__sound__supervising_sound_editor_movies_before']\n",
      "time: 0.17704072800006543\n",
      "obtaine movies\n",
      "time: 4.639791858999615\n",
      "calculate info\n",
      "time: 2.6232563100002153\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__directing__director__1\n",
      "['crew__directing__director__1_avg_profit', 'crew__directing__director__1_avg_revenue', 'crew__directing__director__1_movies_before']\n",
      "time: 0.17853785500028607\n",
      "obtaine movies\n",
      "time: 1.4889727670001776\n",
      "calculate info\n",
      "time: 0.7835394789999555\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__directing__script_supervisor\n",
      "['crew__directing__script_supervisor_avg_profit', 'crew__directing__script_supervisor_avg_revenue', 'crew__directing__script_supervisor_movies_before']\n",
      "time: 0.17731838600002447\n",
      "obtaine movies\n",
      "time: 3.7573776880003606\n",
      "calculate info\n",
      "time: 1.7837984329999017\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__production__casting\n",
      "['crew__production__casting_avg_profit', 'crew__production__casting_avg_revenue', 'crew__production__casting_movies_before']\n",
      "time: 0.18056579500034786\n",
      "obtaine movies\n",
      "time: 1.9879713279997304\n",
      "calculate info\n",
      "time: 1.358463636000124\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__production__executive_producer__1\n",
      "['crew__production__executive_producer__1_avg_profit', 'crew__production__executive_producer__1_avg_revenue', 'crew__production__executive_producer__1_movies_before']\n",
      "time: 0.18090416999984882\n",
      "obtaine movies\n",
      "time: 3.709785658999863\n",
      "calculate info\n",
      "time: 2.2966448290003427\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__production__producer__1\n",
      "['crew__production__producer__1_avg_profit', 'crew__production__producer__1_avg_revenue', 'crew__production__producer__1_movies_before']\n",
      "time: 0.1885653409999577\n",
      "obtaine movies\n",
      "time: 0.4364643530002468\n",
      "calculate info\n",
      "time: 0.4570449359998747\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__production__producer__2\n",
      "['crew__production__producer__2_avg_profit', 'crew__production__producer__2_avg_revenue', 'crew__production__producer__2_movies_before']\n",
      "time: 0.17962696199992934\n",
      "obtaine movies\n",
      "time: 4.797745487000157\n",
      "calculate info\n",
      "time: 2.408002735000082\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__editing__editor__1\n",
      "['crew__editing__editor__1_avg_profit', 'crew__editing__editor__1_avg_revenue', 'crew__editing__editor__1_movies_before']\n",
      "time: 0.180149953000182\n",
      "obtaine movies\n",
      "time: 3.097015136000209\n",
      "calculate info\n",
      "time: 1.5661437030003071\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__costume__costume_designer\n",
      "['crew__costume__costume_designer_avg_profit', 'crew__costume__costume_designer_avg_revenue', 'crew__costume__costume_designer_movies_before']\n",
      "time: 0.17945476600016264\n",
      "obtaine movies\n",
      "time: 0.6004717669998172\n",
      "calculate info\n",
      "time: 0.3362094750000324\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__costume__costume_supervisor\n",
      "['crew__costume__costume_supervisor_avg_profit', 'crew__costume__costume_supervisor_avg_revenue', 'crew__costume__costume_supervisor_movies_before']\n",
      "time: 0.1768456929999047\n",
      "obtaine movies\n",
      "time: 1.3873277610000514\n",
      "calculate info\n",
      "time: 0.8605597099999613\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__costume__makeup_artist\n",
      "['crew__costume__makeup_artist_avg_profit', 'crew__costume__makeup_artist_avg_revenue', 'crew__costume__makeup_artist_movies_before']\n",
      "time: 0.17866604699975142\n",
      "obtaine movies\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 1.1128194739999344\n",
      "calculate info\n",
      "time: 0.5825986489999195\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__crew__stunt_coordinator\n",
      "['crew__crew__stunt_coordinator_avg_profit', 'crew__crew__stunt_coordinator_avg_revenue', 'crew__crew__stunt_coordinator_movies_before']\n",
      "time: 0.18025418300021556\n",
      "obtaine movies\n",
      "time: 2.557151489000262\n",
      "calculate info\n",
      "time: 1.806729502000053\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__writing__screenplay__1\n",
      "['crew__writing__screenplay__1_avg_profit', 'crew__writing__screenplay__1_avg_revenue', 'crew__writing__screenplay__1_movies_before']\n",
      "time: 0.19041255999991336\n",
      "obtaine movies\n",
      "time: 2.0048520459999963\n",
      "calculate info\n",
      "time: 1.3341923110001517\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__art__art_direction\n",
      "['crew__art__art_direction_avg_profit', 'crew__art__art_direction_avg_revenue', 'crew__art__art_direction_movies_before']\n",
      "time: 0.1826217530001486\n",
      "obtaine movies\n",
      "time: 3.428689347999807\n",
      "calculate info\n",
      "time: 1.7300881709998066\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__art__production_design\n",
      "['crew__art__production_design_avg_profit', 'crew__art__production_design_avg_revenue', 'crew__art__production_design_movies_before']\n",
      "time: 0.1819806299999982\n",
      "obtaine movies\n",
      "time: 0.9576273519996903\n",
      "calculate info\n",
      "time: 0.5442431890000989\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__art__property_master\n",
      "['crew__art__property_master_avg_profit', 'crew__art__property_master_avg_revenue', 'crew__art__property_master_movies_before']\n",
      "time: 0.1801658980002685\n",
      "obtaine movies\n",
      "time: 2.5692177939999965\n",
      "calculate info\n",
      "time: 1.3492966479998358\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__art__set_decoration\n",
      "['crew__art__set_decoration_avg_profit', 'crew__art__set_decoration_avg_revenue', 'crew__art__set_decoration_movies_before']\n",
      "time: 0.18049515499978952\n",
      "obtaine movies\n",
      "time: 0.4918440289998216\n",
      "calculate info\n",
      "time: 0.26066855899989605\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__visualeffects__visual_effects_supervisor\n",
      "['crew__visualeffects__visual_effects_supervisor_avg_profit', 'crew__visualeffects__visual_effects_supervisor_avg_revenue', 'crew__visualeffects__visual_effects_supervisor_movies_before']\n",
      "time: 0.18267668999988018\n",
      "obtaine movies\n",
      "time: 5.181397081999876\n",
      "calculate info\n",
      "time: 2.4942798060001223\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__camera__director_of_photography\n",
      "['crew__camera__director_of_photography_avg_profit', 'crew__camera__director_of_photography_avg_revenue', 'crew__camera__director_of_photography_movies_before']\n",
      "time: 0.18230133299994122\n",
      "obtaine movies\n",
      "time: 0.6338003410000965\n",
      "calculate info\n",
      "time: 0.2814063669998177\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__camera__steadicam_operator\n",
      "['crew__camera__steadicam_operator_avg_profit', 'crew__camera__steadicam_operator_avg_revenue', 'crew__camera__steadicam_operator_movies_before']\n",
      "time: 0.17969448399981047\n",
      "obtaine movies\n",
      "time: 1.4006495580001683\n",
      "calculate info\n",
      "time: 0.6843262369998229\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__camera__still_photographer\n",
      "['crew__camera__still_photographer_avg_profit', 'crew__camera__still_photographer_avg_revenue', 'crew__camera__still_photographer_movies_before']\n",
      "time: 0.18048465699985172\n",
      "obtaine movies\n",
      "time: 0.9379263699997864\n",
      "calculate info\n",
      "time: 0.49714467700005116\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue']\n",
      "META__collection_name\n",
      "['collection_avg_profit', 'collection_avg_revenue']\n",
      "time: 0.18030959399993662\n",
      "overall time: 160.50534993100018\n",
      "------------------------------------------------------\n",
      "profitability_positive\n",
      "-------------------------------------------------------\n",
      "obtaine movies\n",
      "time: 22.418654191000314\n",
      "calculate info\n",
      "time: 31.69533975100012\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue']\n",
      "META__production_company_1\n",
      "['production_company_1_avg_profit', 'production_company_1_avg_revenue']\n",
      "META__production_company_2\n",
      "['production_company_2_avg_profit', 'production_company_2_avg_revenue']\n",
      "META__production_company_3\n",
      "['production_company_3_avg_profit', 'production_company_3_avg_revenue']\n",
      "time: 1.317129286999716\n",
      "obtaine movies\n",
      "time: 69.91189743399991\n",
      "calculate info\n",
      "time: 54.395086236000225\n",
      "get columns\n",
      "['avg_profit', 'experience', 'avg_revenue', 'movies_before']\n",
      "META__cast_1_name\n",
      "['cast_1_avg_profit', 'cast_1_experience', 'cast_1_avg_revenue', 'cast_1_movies_before']\n",
      "META__cast_2_name\n",
      "['cast_2_avg_profit', 'cast_2_experience', 'cast_2_avg_revenue', 'cast_2_movies_before']\n",
      "META__cast_3_name\n",
      "['cast_3_avg_profit', 'cast_3_experience', 'cast_3_avg_revenue', 'cast_3_movies_before']\n",
      "META__cast_4_name\n",
      "['cast_4_avg_profit', 'cast_4_experience', 'cast_4_avg_revenue', 'cast_4_movies_before']\n",
      "META__cast_5_name\n",
      "['cast_5_avg_profit', 'cast_5_experience', 'cast_5_avg_revenue', 'cast_5_movies_before']\n",
      "META__cast_6_name\n",
      "['cast_6_avg_profit', 'cast_6_experience', 'cast_6_avg_revenue', 'cast_6_movies_before']\n",
      "META__cast_7_name\n",
      "['cast_7_avg_profit', 'cast_7_experience', 'cast_7_avg_revenue', 'cast_7_movies_before']\n",
      "META__cast_8_name\n",
      "['cast_8_avg_profit', 'cast_8_experience', 'cast_8_avg_revenue', 'cast_8_movies_before']\n",
      "time: 3.5197368499998447\n",
      "obtaine movies\n",
      "time: 2.8728252410001005\n",
      "calculate info\n",
      "time: 1.7968672040001366\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__music_editor\n",
      "['crew__sound__music_editor_avg_profit', 'crew__sound__music_editor_avg_revenue', 'crew__sound__music_editor_movies_before']\n",
      "time: 0.4489544189996195\n",
      "obtaine movies\n",
      "time: 7.2998109840000325\n",
      "calculate info\n",
      "time: 5.643045979999897\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__original_music_composer\n",
      "['crew__sound__original_music_composer_avg_profit', 'crew__sound__original_music_composer_avg_revenue', 'crew__sound__original_music_composer_movies_before']\n",
      "time: 0.4440222369998992\n",
      "obtaine movies\n",
      "time: 2.0713156179999714\n",
      "calculate info\n",
      "time: 1.4226402689996576\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__sound_designer\n",
      "['crew__sound__sound_designer_avg_profit', 'crew__sound__sound_designer_avg_revenue', 'crew__sound__sound_designer_movies_before']\n",
      "time: 0.44669906199987963\n",
      "obtaine movies\n",
      "time: 2.3258665820003444\n",
      "calculate info\n",
      "time: 1.521612917999846\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__sound_effects_editor\n",
      "['crew__sound__sound_effects_editor_avg_profit', 'crew__sound__sound_effects_editor_avg_revenue', 'crew__sound__sound_effects_editor_movies_before']\n",
      "time: 0.4565086600000541\n",
      "obtaine movies\n",
      "time: 3.3651400630001262\n",
      "calculate info\n",
      "time: 2.113279057999989\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__sound_re_recording_mixer\n",
      "['crew__sound__sound_re_recording_mixer_avg_profit', 'crew__sound__sound_re_recording_mixer_avg_revenue', 'crew__sound__sound_re_recording_mixer_movies_before']\n",
      "time: 0.44359314799976346\n",
      "obtaine movies\n",
      "time: 3.0485721149998426\n",
      "calculate info\n",
      "time: 1.9549913169998945\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__supervising_sound_editor\n",
      "['crew__sound__supervising_sound_editor_avg_profit', 'crew__sound__supervising_sound_editor_avg_revenue', 'crew__sound__supervising_sound_editor_movies_before']\n",
      "time: 0.4452217370003382\n",
      "obtaine movies\n",
      "time: 7.880658629999743\n",
      "calculate info\n",
      "time: 6.431342047000271\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__directing__director__1\n",
      "['crew__directing__director__1_avg_profit', 'crew__directing__director__1_avg_revenue', 'crew__directing__director__1_movies_before']\n",
      "time: 0.46484956800031796\n",
      "obtaine movies\n",
      "time: 3.6489334729999428\n",
      "calculate info\n",
      "time: 2.501241356999799\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__directing__script_supervisor\n",
      "['crew__directing__script_supervisor_avg_profit', 'crew__directing__script_supervisor_avg_revenue', 'crew__directing__script_supervisor_movies_before']\n",
      "time: 0.4780091419997916\n",
      "obtaine movies\n",
      "time: 6.903015595999932\n",
      "calculate info\n",
      "time: 5.468632503000208\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__production__casting\n",
      "['crew__production__casting_avg_profit', 'crew__production__casting_avg_revenue', 'crew__production__casting_movies_before']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 0.48824727100009113\n",
      "obtaine movies\n",
      "time: 4.7274761219996435\n",
      "calculate info\n",
      "time: 4.328388934000031\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__production__executive_producer__1\n",
      "['crew__production__executive_producer__1_avg_profit', 'crew__production__executive_producer__1_avg_revenue', 'crew__production__executive_producer__1_movies_before']\n",
      "time: 0.5206055540002126\n",
      "obtaine movies\n",
      "time: 8.896504395000193\n",
      "calculate info\n",
      "time: 7.149076228000013\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__production__producer__1\n",
      "['crew__production__producer__1_avg_profit', 'crew__production__producer__1_avg_revenue', 'crew__production__producer__1_movies_before']\n",
      "time: 0.496609762999924\n",
      "obtaine movies\n",
      "time: 2.36489691900033\n",
      "calculate info\n",
      "time: 2.4230386730000646\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__production__producer__2\n",
      "['crew__production__producer__2_avg_profit', 'crew__production__producer__2_avg_revenue', 'crew__production__producer__2_movies_before']\n",
      "time: 0.5239090820000456\n",
      "obtaine movies\n",
      "time: 10.431953008000164\n",
      "calculate info\n",
      "time: 6.797550184000102\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__editing__editor__1\n",
      "['crew__editing__editor__1_avg_profit', 'crew__editing__editor__1_avg_revenue', 'crew__editing__editor__1_movies_before']\n",
      "time: 0.518152948000079\n",
      "obtaine movies\n",
      "time: 6.976517589000196\n",
      "calculate info\n",
      "time: 4.729751659000158\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__costume__costume_designer\n",
      "['crew__costume__costume_designer_avg_profit', 'crew__costume__costume_designer_avg_revenue', 'crew__costume__costume_designer_movies_before']\n",
      "time: 0.4802794100000938\n",
      "obtaine movies\n",
      "time: 2.0751254580000023\n",
      "calculate info\n",
      "time: 1.442358072999923\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__costume__costume_supervisor\n",
      "['crew__costume__costume_supervisor_avg_profit', 'crew__costume__costume_supervisor_avg_revenue', 'crew__costume__costume_supervisor_movies_before']\n",
      "time: 0.4748961559998861\n",
      "obtaine movies\n",
      "time: 2.7296530710000297\n",
      "calculate info\n",
      "time: 2.1908559459998287\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__costume__makeup_artist\n",
      "['crew__costume__makeup_artist_avg_profit', 'crew__costume__makeup_artist_avg_revenue', 'crew__costume__makeup_artist_movies_before']\n",
      "time: 0.474329434000083\n",
      "obtaine movies\n",
      "time: 2.782812873000239\n",
      "calculate info\n",
      "time: 1.8323005010001907\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__crew__stunt_coordinator\n",
      "['crew__crew__stunt_coordinator_avg_profit', 'crew__crew__stunt_coordinator_avg_revenue', 'crew__crew__stunt_coordinator_movies_before']\n",
      "time: 0.4667839839999033\n",
      "obtaine movies\n",
      "time: 4.626789005000319\n",
      "calculate info\n",
      "time: 4.34717869699989\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__writing__screenplay__1\n",
      "['crew__writing__screenplay__1_avg_profit', 'crew__writing__screenplay__1_avg_revenue', 'crew__writing__screenplay__1_movies_before']\n",
      "time: 0.517700732999856\n",
      "obtaine movies\n",
      "time: 4.335878073999993\n",
      "calculate info\n",
      "time: 3.749501857999803\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__art__art_direction\n",
      "['crew__art__art_direction_avg_profit', 'crew__art__art_direction_avg_revenue', 'crew__art__art_direction_movies_before']\n",
      "time: 0.561654290000206\n",
      "obtaine movies\n",
      "time: 7.346398055000009\n",
      "calculate info\n",
      "time: 5.106324361000134\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__art__production_design\n",
      "['crew__art__production_design_avg_profit', 'crew__art__production_design_avg_revenue', 'crew__art__production_design_movies_before']\n",
      "time: 0.5824763939999684\n",
      "obtaine movies\n",
      "time: 2.6632448840000507\n",
      "calculate info\n",
      "time: 1.846669846000168\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__art__property_master\n",
      "['crew__art__property_master_avg_profit', 'crew__art__property_master_avg_revenue', 'crew__art__property_master_movies_before']\n",
      "time: 0.5080134709996855\n",
      "obtaine movies\n",
      "time: 5.808564132000356\n",
      "calculate info\n",
      "time: 4.141018322000036\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__art__set_decoration\n",
      "['crew__art__set_decoration_avg_profit', 'crew__art__set_decoration_avg_revenue', 'crew__art__set_decoration_movies_before']\n",
      "time: 0.5550736709997182\n",
      "obtaine movies\n",
      "time: 2.8469914390002486\n",
      "calculate info\n",
      "time: 2.5422111640000367\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__visualeffects__visual_effects_supervisor\n",
      "['crew__visualeffects__visual_effects_supervisor_avg_profit', 'crew__visualeffects__visual_effects_supervisor_avg_revenue', 'crew__visualeffects__visual_effects_supervisor_movies_before']\n",
      "time: 0.554673700999956\n",
      "obtaine movies\n",
      "time: 10.139517448999868\n",
      "calculate info\n",
      "time: 7.621137035000174\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__camera__director_of_photography\n",
      "['crew__camera__director_of_photography_avg_profit', 'crew__camera__director_of_photography_avg_revenue', 'crew__camera__director_of_photography_movies_before']\n",
      "time: 0.6202790709999135\n",
      "obtaine movies\n",
      "time: 2.445340825999665\n",
      "calculate info\n",
      "time: 1.4947287280001547\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__camera__steadicam_operator\n",
      "['crew__camera__steadicam_operator_avg_profit', 'crew__camera__steadicam_operator_avg_revenue', 'crew__camera__steadicam_operator_movies_before']\n",
      "time: 0.5107481669997469\n",
      "obtaine movies\n",
      "time: 3.9210516469997856\n",
      "calculate info\n",
      "time: 2.4581794000000627\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__camera__still_photographer\n",
      "['crew__camera__still_photographer_avg_profit', 'crew__camera__still_photographer_avg_revenue', 'crew__camera__still_photographer_movies_before']\n",
      "time: 0.4775959479998164\n",
      "obtaine movies\n",
      "time: 2.2325597869999\n",
      "calculate info\n",
      "time: 1.4770034269999996\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue']\n",
      "META__collection_name\n",
      "['collection_avg_profit', 'collection_avg_revenue']\n",
      "time: 0.4895966130002307\n",
      "overall time: 422.56383460899997\n",
      "------------------------------------------------------\n",
      "profitability_negative\n",
      "-------------------------------------------------------\n",
      "obtaine movies\n",
      "time: 7.932874216000073\n",
      "calculate info\n",
      "time: 7.655919860000267\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue']\n",
      "META__production_company_1\n",
      "['production_company_1_avg_profit', 'production_company_1_avg_revenue']\n",
      "META__production_company_2\n",
      "['production_company_2_avg_profit', 'production_company_2_avg_revenue']\n",
      "META__production_company_3\n",
      "['production_company_3_avg_profit', 'production_company_3_avg_revenue']\n",
      "time: 0.6123083830002543\n",
      "obtaine movies\n",
      "time: 27.841263217999767\n",
      "calculate info\n",
      "time: 25.91615942999988\n",
      "get columns\n",
      "['avg_profit', 'experience', 'avg_revenue', 'movies_before']\n",
      "META__cast_1_name\n",
      "['cast_1_avg_profit', 'cast_1_experience', 'cast_1_avg_revenue', 'cast_1_movies_before']\n",
      "META__cast_2_name\n",
      "['cast_2_avg_profit', 'cast_2_experience', 'cast_2_avg_revenue', 'cast_2_movies_before']\n",
      "META__cast_3_name\n",
      "['cast_3_avg_profit', 'cast_3_experience', 'cast_3_avg_revenue', 'cast_3_movies_before']\n",
      "META__cast_4_name\n",
      "['cast_4_avg_profit', 'cast_4_experience', 'cast_4_avg_revenue', 'cast_4_movies_before']\n",
      "META__cast_5_name\n",
      "['cast_5_avg_profit', 'cast_5_experience', 'cast_5_avg_revenue', 'cast_5_movies_before']\n",
      "META__cast_6_name\n",
      "['cast_6_avg_profit', 'cast_6_experience', 'cast_6_avg_revenue', 'cast_6_movies_before']\n",
      "META__cast_7_name\n",
      "['cast_7_avg_profit', 'cast_7_experience', 'cast_7_avg_revenue', 'cast_7_movies_before']\n",
      "META__cast_8_name\n",
      "['cast_8_avg_profit', 'cast_8_experience', 'cast_8_avg_revenue', 'cast_8_movies_before']\n",
      "time: 1.7559219830000075\n",
      "obtaine movies\n",
      "time: 0.7223376450001524\n",
      "calculate info\n",
      "time: 0.46440701600022294\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__music_editor\n",
      "['crew__sound__music_editor_avg_profit', 'crew__sound__music_editor_avg_revenue', 'crew__sound__music_editor_movies_before']\n",
      "time: 0.20070197999984885\n",
      "obtaine movies\n",
      "time: 2.655526964999808\n",
      "calculate info\n",
      "time: 1.6890637240003343\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__original_music_composer\n",
      "['crew__sound__original_music_composer_avg_profit', 'crew__sound__original_music_composer_avg_revenue', 'crew__sound__original_music_composer_movies_before']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 0.20446014699973603\n",
      "obtaine movies\n",
      "time: 0.5550610300001608\n",
      "calculate info\n",
      "time: 0.3909557609999865\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__sound_designer\n",
      "['crew__sound__sound_designer_avg_profit', 'crew__sound__sound_designer_avg_revenue', 'crew__sound__sound_designer_movies_before']\n",
      "time: 0.20363807700005054\n",
      "obtaine movies\n",
      "time: 0.4851073750000978\n",
      "calculate info\n",
      "time: 0.42065293900031975\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__sound_effects_editor\n",
      "['crew__sound__sound_effects_editor_avg_profit', 'crew__sound__sound_effects_editor_avg_revenue', 'crew__sound__sound_effects_editor_movies_before']\n",
      "time: 0.20845094099968264\n",
      "obtaine movies\n",
      "time: 1.0655528180000147\n",
      "calculate info\n",
      "time: 0.6495851139998194\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__sound_re_recording_mixer\n",
      "['crew__sound__sound_re_recording_mixer_avg_profit', 'crew__sound__sound_re_recording_mixer_avg_revenue', 'crew__sound__sound_re_recording_mixer_movies_before']\n",
      "time: 0.20578829299984136\n",
      "obtaine movies\n",
      "time: 0.7820229170001767\n",
      "calculate info\n",
      "time: 0.5529359680003836\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__supervising_sound_editor\n",
      "['crew__sound__supervising_sound_editor_avg_profit', 'crew__sound__supervising_sound_editor_avg_revenue', 'crew__sound__supervising_sound_editor_movies_before']\n",
      "time: 0.20357181100007438\n",
      "obtaine movies\n",
      "time: 2.7853324549996614\n",
      "calculate info\n",
      "time: 2.8090658229998553\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__directing__director__1\n",
      "['crew__directing__director__1_avg_profit', 'crew__directing__director__1_avg_revenue', 'crew__directing__director__1_movies_before']\n",
      "time: 0.21492134799973428\n",
      "obtaine movies\n",
      "time: 1.265766597000038\n",
      "calculate info\n",
      "time: 0.8752256540001326\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__directing__script_supervisor\n",
      "['crew__directing__script_supervisor_avg_profit', 'crew__directing__script_supervisor_avg_revenue', 'crew__directing__script_supervisor_movies_before']\n",
      "time: 0.21622355099998458\n",
      "obtaine movies\n",
      "time: 3.212345669000115\n",
      "calculate info\n",
      "time: 1.9440647579999677\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__production__casting\n",
      "['crew__production__casting_avg_profit', 'crew__production__casting_avg_revenue', 'crew__production__casting_movies_before']\n",
      "time: 0.21043021300010878\n",
      "obtaine movies\n",
      "time: 1.36037387999977\n",
      "calculate info\n",
      "time: 1.4594050969999444\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__production__executive_producer__1\n",
      "['crew__production__executive_producer__1_avg_profit', 'crew__production__executive_producer__1_avg_revenue', 'crew__production__executive_producer__1_movies_before']\n",
      "time: 0.2095450990000245\n",
      "obtaine movies\n",
      "time: 2.5909620839997842\n",
      "calculate info\n",
      "time: 2.329961090999859\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__production__producer__1\n",
      "['crew__production__producer__1_avg_profit', 'crew__production__producer__1_avg_revenue', 'crew__production__producer__1_movies_before']\n",
      "time: 0.20894565300022805\n",
      "obtaine movies\n",
      "time: 0.6433552470002724\n",
      "calculate info\n",
      "time: 0.9959154070002114\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__production__producer__2\n",
      "['crew__production__producer__2_avg_profit', 'crew__production__producer__2_avg_revenue', 'crew__production__producer__2_movies_before']\n",
      "time: 0.2087312869998641\n",
      "obtaine movies\n",
      "time: 3.4701309280003443\n",
      "calculate info\n",
      "time: 2.5566654199997174\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__editing__editor__1\n",
      "['crew__editing__editor__1_avg_profit', 'crew__editing__editor__1_avg_revenue', 'crew__editing__editor__1_movies_before']\n",
      "time: 0.2572521960000813\n",
      "obtaine movies\n",
      "time: 3.0212410540002566\n",
      "calculate info\n",
      "time: 1.9164319790002082\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__costume__costume_designer\n",
      "['crew__costume__costume_designer_avg_profit', 'crew__costume__costume_designer_avg_revenue', 'crew__costume__costume_designer_movies_before']\n",
      "time: 0.2249174620001213\n",
      "obtaine movies\n",
      "time: 0.5187690620000467\n",
      "calculate info\n",
      "time: 0.4541565859999537\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__costume__costume_supervisor\n",
      "['crew__costume__costume_supervisor_avg_profit', 'crew__costume__costume_supervisor_avg_revenue', 'crew__costume__costume_supervisor_movies_before']\n",
      "time: 0.2665850139997019\n",
      "obtaine movies\n",
      "time: 0.72885299499967\n",
      "calculate info\n",
      "time: 0.8108366450001085\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__costume__makeup_artist\n",
      "['crew__costume__makeup_artist_avg_profit', 'crew__costume__makeup_artist_avg_revenue', 'crew__costume__makeup_artist_movies_before']\n",
      "time: 0.27225124999995387\n",
      "obtaine movies\n",
      "time: 0.7564639659999557\n",
      "calculate info\n",
      "time: 0.5615661309998359\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__crew__stunt_coordinator\n",
      "['crew__crew__stunt_coordinator_avg_profit', 'crew__crew__stunt_coordinator_avg_revenue', 'crew__crew__stunt_coordinator_movies_before']\n",
      "time: 0.2206683949998478\n",
      "obtaine movies\n",
      "time: 1.3691336359997877\n",
      "calculate info\n",
      "time: 1.7023707220000688\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__writing__screenplay__1\n",
      "['crew__writing__screenplay__1_avg_profit', 'crew__writing__screenplay__1_avg_revenue', 'crew__writing__screenplay__1_movies_before']\n",
      "time: 0.21954310699993584\n",
      "obtaine movies\n",
      "time: 1.461601686999984\n",
      "calculate info\n",
      "time: 1.4361853370000972\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__art__art_direction\n",
      "['crew__art__art_direction_avg_profit', 'crew__art__art_direction_avg_revenue', 'crew__art__art_direction_movies_before']\n",
      "time: 0.232820189999984\n",
      "obtaine movies\n",
      "time: 2.818934260000333\n",
      "calculate info\n",
      "time: 1.9917614240002877\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__art__production_design\n",
      "['crew__art__production_design_avg_profit', 'crew__art__production_design_avg_revenue', 'crew__art__production_design_movies_before']\n",
      "time: 0.225044522999724\n",
      "obtaine movies\n",
      "time: 0.6302505339999698\n",
      "calculate info\n",
      "time: 0.547220403999745\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__art__property_master\n",
      "['crew__art__property_master_avg_profit', 'crew__art__property_master_avg_revenue', 'crew__art__property_master_movies_before']\n",
      "time: 0.23075381900025604\n",
      "obtaine movies\n",
      "time: 1.7158060340002521\n",
      "calculate info\n",
      "time: 1.413012826999875\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__art__set_decoration\n",
      "['crew__art__set_decoration_avg_profit', 'crew__art__set_decoration_avg_revenue', 'crew__art__set_decoration_movies_before']\n",
      "time: 0.2299706859998878\n",
      "obtaine movies\n",
      "time: 0.700854020999941\n",
      "calculate info\n",
      "time: 0.5243653140000788\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__visualeffects__visual_effects_supervisor\n",
      "['crew__visualeffects__visual_effects_supervisor_avg_profit', 'crew__visualeffects__visual_effects_supervisor_avg_revenue', 'crew__visualeffects__visual_effects_supervisor_movies_before']\n",
      "time: 0.2273842329996114\n",
      "obtaine movies\n",
      "time: 3.903464331000123\n",
      "calculate info\n",
      "time: 2.6123200900001393\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__camera__director_of_photography\n",
      "['crew__camera__director_of_photography_avg_profit', 'crew__camera__director_of_photography_avg_revenue', 'crew__camera__director_of_photography_movies_before']\n",
      "time: 0.28324949800025934\n",
      "obtaine movies\n",
      "time: 0.5604750030001924\n",
      "calculate info\n",
      "time: 0.39786066199985726\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__camera__steadicam_operator\n",
      "['crew__camera__steadicam_operator_avg_profit', 'crew__camera__steadicam_operator_avg_revenue', 'crew__camera__steadicam_operator_movies_before']\n",
      "time: 0.23358392699992692\n",
      "obtaine movies\n",
      "time: 1.1418515260002096\n",
      "calculate info\n",
      "time: 0.7499851990000934\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__camera__still_photographer\n",
      "['crew__camera__still_photographer_avg_profit', 'crew__camera__still_photographer_avg_revenue', 'crew__camera__still_photographer_movies_before']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 0.2546311500000229\n",
      "obtaine movies\n",
      "time: 0.23109235000038097\n",
      "calculate info\n",
      "time: 0.1072271279999768\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue']\n",
      "META__collection_name\n",
      "['collection_avg_profit', 'collection_avg_revenue']\n",
      "time: 0.280299690999982\n",
      "overall time: 153.57499712499975\n",
      "------------------------------------------------------\n",
      "budget_start_1percent\n",
      "-------------------------------------------------------\n",
      "obtaine movies\n",
      "time: 34.97370658\n",
      "calculate info\n",
      "time: 56.02719218399989\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue']\n",
      "META__production_company_1\n",
      "['production_company_1_avg_profit', 'production_company_1_avg_revenue']\n",
      "META__production_company_2\n",
      "['production_company_2_avg_profit', 'production_company_2_avg_revenue']\n",
      "META__production_company_3\n",
      "['production_company_3_avg_profit', 'production_company_3_avg_revenue']\n",
      "time: 2.325042250000024\n",
      "obtaine movies\n",
      "time: 119.48113457199997\n",
      "calculate info\n",
      "time: 83.56611134000013\n",
      "get columns\n",
      "['avg_profit', 'experience', 'avg_revenue', 'movies_before']\n",
      "META__cast_1_name\n",
      "['cast_1_avg_profit', 'cast_1_experience', 'cast_1_avg_revenue', 'cast_1_movies_before']\n",
      "META__cast_2_name\n",
      "['cast_2_avg_profit', 'cast_2_experience', 'cast_2_avg_revenue', 'cast_2_movies_before']\n",
      "META__cast_3_name\n",
      "['cast_3_avg_profit', 'cast_3_experience', 'cast_3_avg_revenue', 'cast_3_movies_before']\n",
      "META__cast_4_name\n",
      "['cast_4_avg_profit', 'cast_4_experience', 'cast_4_avg_revenue', 'cast_4_movies_before']\n",
      "META__cast_5_name\n",
      "['cast_5_avg_profit', 'cast_5_experience', 'cast_5_avg_revenue', 'cast_5_movies_before']\n",
      "META__cast_6_name\n",
      "['cast_6_avg_profit', 'cast_6_experience', 'cast_6_avg_revenue', 'cast_6_movies_before']\n",
      "META__cast_7_name\n",
      "['cast_7_avg_profit', 'cast_7_experience', 'cast_7_avg_revenue', 'cast_7_movies_before']\n",
      "META__cast_8_name\n",
      "['cast_8_avg_profit', 'cast_8_experience', 'cast_8_avg_revenue', 'cast_8_movies_before']\n",
      "time: 5.420679783999731\n",
      "obtaine movies\n",
      "time: 4.026508491000186\n",
      "calculate info\n",
      "time: 2.404912570000306\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__music_editor\n",
      "['crew__sound__music_editor_avg_profit', 'crew__sound__music_editor_avg_revenue', 'crew__sound__music_editor_movies_before']\n",
      "time: 0.674144540999805\n",
      "obtaine movies\n",
      "time: 10.74753408000015\n",
      "calculate info\n",
      "time: 8.777053574999627\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__original_music_composer\n",
      "['crew__sound__original_music_composer_avg_profit', 'crew__sound__original_music_composer_avg_revenue', 'crew__sound__original_music_composer_movies_before']\n",
      "time: 0.678330533999997\n",
      "obtaine movies\n",
      "time: 2.958122830999855\n",
      "calculate info\n",
      "time: 1.9693171420003637\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__sound_designer\n",
      "['crew__sound__sound_designer_avg_profit', 'crew__sound__sound_designer_avg_revenue', 'crew__sound__sound_designer_movies_before']\n",
      "time: 0.6794860559998597\n",
      "obtaine movies\n",
      "time: 3.0604409279999345\n",
      "calculate info\n",
      "time: 2.0325333800001317\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__sound_effects_editor\n",
      "['crew__sound__sound_effects_editor_avg_profit', 'crew__sound__sound_effects_editor_avg_revenue', 'crew__sound__sound_effects_editor_movies_before']\n",
      "time: 0.6824359480001476\n",
      "obtaine movies\n",
      "time: 4.789068849000159\n",
      "calculate info\n",
      "time: 3.0767255850000765\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__sound_re_recording_mixer\n",
      "['crew__sound__sound_re_recording_mixer_avg_profit', 'crew__sound__sound_re_recording_mixer_avg_revenue', 'crew__sound__sound_re_recording_mixer_movies_before']\n",
      "time: 0.6800920589998896\n",
      "obtaine movies\n",
      "time: 4.336442798999997\n",
      "calculate info\n",
      "time: 2.789859464000074\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__supervising_sound_editor\n",
      "['crew__sound__supervising_sound_editor_avg_profit', 'crew__sound__supervising_sound_editor_avg_revenue', 'crew__sound__supervising_sound_editor_movies_before']\n",
      "time: 0.6604980950000936\n",
      "obtaine movies\n",
      "time: 11.84968316100003\n",
      "calculate info\n",
      "time: 9.325478938000288\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__directing__director__1\n",
      "['crew__directing__director__1_avg_profit', 'crew__directing__director__1_avg_revenue', 'crew__directing__director__1_movies_before']\n",
      "time: 0.6806874470003095\n",
      "obtaine movies\n",
      "time: 5.20313027300017\n",
      "calculate info\n",
      "time: 3.5035274120000395\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__directing__script_supervisor\n",
      "['crew__directing__script_supervisor_avg_profit', 'crew__directing__script_supervisor_avg_revenue', 'crew__directing__script_supervisor_movies_before']\n",
      "time: 0.6812520109997422\n",
      "obtaine movies\n",
      "time: 10.612169724000069\n",
      "calculate info\n",
      "time: 10.91053478799995\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__production__casting\n",
      "['crew__production__casting_avg_profit', 'crew__production__casting_avg_revenue', 'crew__production__casting_movies_before']\n",
      "time: 0.7263955749999695\n",
      "obtaine movies\n",
      "time: 6.747091893999823\n",
      "calculate info\n",
      "time: 6.60558746300012\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__production__executive_producer__1\n",
      "['crew__production__executive_producer__1_avg_profit', 'crew__production__executive_producer__1_avg_revenue', 'crew__production__executive_producer__1_movies_before']\n",
      "time: 0.7243505319997894\n",
      "obtaine movies\n",
      "time: 12.503572862000055\n",
      "calculate info\n",
      "time: 10.058826339999996\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__production__producer__1\n",
      "['crew__production__producer__1_avg_profit', 'crew__production__producer__1_avg_revenue', 'crew__production__producer__1_movies_before']\n",
      "time: 0.6762063830001352\n",
      "obtaine movies\n",
      "time: 3.094561445999716\n",
      "calculate info\n",
      "time: 3.3246494089999032\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__production__producer__2\n",
      "['crew__production__producer__2_avg_profit', 'crew__production__producer__2_avg_revenue', 'crew__production__producer__2_movies_before']\n",
      "time: 0.6984286739998424\n",
      "obtaine movies\n",
      "time: 13.064794135999819\n",
      "calculate info\n",
      "time: 9.053413845999785\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__editing__editor__1\n",
      "['crew__editing__editor__1_avg_profit', 'crew__editing__editor__1_avg_revenue', 'crew__editing__editor__1_movies_before']\n",
      "time: 0.6903978450000068\n",
      "obtaine movies\n",
      "time: 10.371859736999795\n",
      "calculate info\n",
      "time: 7.565147163000347\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__costume__costume_designer\n",
      "['crew__costume__costume_designer_avg_profit', 'crew__costume__costume_designer_avg_revenue', 'crew__costume__costume_designer_movies_before']\n",
      "time: 0.8246351249999861\n",
      "obtaine movies\n",
      "time: 3.514089279000018\n",
      "calculate info\n",
      "time: 2.0727131260000533\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__costume__costume_supervisor\n",
      "['crew__costume__costume_supervisor_avg_profit', 'crew__costume__costume_supervisor_avg_revenue', 'crew__costume__costume_supervisor_movies_before']\n",
      "time: 0.7369124259998898\n",
      "obtaine movies\n",
      "time: 3.94102460699969\n",
      "calculate info\n",
      "time: 3.1242143849999593\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__costume__makeup_artist\n",
      "['crew__costume__makeup_artist_avg_profit', 'crew__costume__makeup_artist_avg_revenue', 'crew__costume__makeup_artist_movies_before']\n",
      "time: 0.8529653499999768\n",
      "obtaine movies\n",
      "time: 4.115838895000252\n",
      "calculate info\n",
      "time: 2.9438140699999167\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__crew__stunt_coordinator\n",
      "['crew__crew__stunt_coordinator_avg_profit', 'crew__crew__stunt_coordinator_avg_revenue', 'crew__crew__stunt_coordinator_movies_before']\n",
      "time: 0.7823050750002949\n",
      "obtaine movies\n",
      "time: 7.678099389000181\n",
      "calculate info\n",
      "time: 6.171190058999855\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__writing__screenplay__1\n",
      "['crew__writing__screenplay__1_avg_profit', 'crew__writing__screenplay__1_avg_revenue', 'crew__writing__screenplay__1_movies_before']\n",
      "time: 0.677578670999992\n",
      "obtaine movies\n",
      "time: 6.408297699000286\n",
      "calculate info\n",
      "time: 5.144080360999851\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__art__art_direction\n",
      "['crew__art__art_direction_avg_profit', 'crew__art__art_direction_avg_revenue', 'crew__art__art_direction_movies_before']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 0.7186285000002499\n",
      "obtaine movies\n",
      "time: 9.939138705999994\n",
      "calculate info\n",
      "time: 6.966343662999861\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__art__production_design\n",
      "['crew__art__production_design_avg_profit', 'crew__art__production_design_avg_revenue', 'crew__art__production_design_movies_before']\n",
      "time: 0.7279780449998725\n",
      "obtaine movies\n",
      "time: 3.2995587410000553\n",
      "calculate info\n",
      "time: 2.224464619999708\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__art__property_master\n",
      "['crew__art__property_master_avg_profit', 'crew__art__property_master_avg_revenue', 'crew__art__property_master_movies_before']\n",
      "time: 0.694418663000306\n",
      "obtaine movies\n",
      "time: 7.24382433899973\n",
      "calculate info\n",
      "time: 4.931159555000249\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__art__set_decoration\n",
      "['crew__art__set_decoration_avg_profit', 'crew__art__set_decoration_avg_revenue', 'crew__art__set_decoration_movies_before']\n",
      "time: 0.6808084500003133\n",
      "obtaine movies\n",
      "time: 2.966593095999997\n",
      "calculate info\n",
      "time: 2.162606183999742\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__visualeffects__visual_effects_supervisor\n",
      "['crew__visualeffects__visual_effects_supervisor_avg_profit', 'crew__visualeffects__visual_effects_supervisor_avg_revenue', 'crew__visualeffects__visual_effects_supervisor_movies_before']\n",
      "time: 0.691883383000004\n",
      "obtaine movies\n",
      "time: 12.756791780000185\n",
      "calculate info\n",
      "time: 8.906125916999827\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__camera__director_of_photography\n",
      "['crew__camera__director_of_photography_avg_profit', 'crew__camera__director_of_photography_avg_revenue', 'crew__camera__director_of_photography_movies_before']\n",
      "time: 0.6760578499997791\n",
      "obtaine movies\n",
      "time: 2.626770612999735\n",
      "calculate info\n",
      "time: 1.5696792789999563\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__camera__steadicam_operator\n",
      "['crew__camera__steadicam_operator_avg_profit', 'crew__camera__steadicam_operator_avg_revenue', 'crew__camera__steadicam_operator_movies_before']\n",
      "time: 0.6645747850002408\n",
      "obtaine movies\n",
      "time: 4.901303704999918\n",
      "calculate info\n",
      "time: 3.2577809659997\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__camera__still_photographer\n",
      "['crew__camera__still_photographer_avg_profit', 'crew__camera__still_photographer_avg_revenue', 'crew__camera__still_photographer_movies_before']\n",
      "time: 0.6739334510002664\n",
      "obtaine movies\n",
      "time: 2.5126238590000867\n",
      "calculate info\n",
      "time: 1.521133302999715\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue']\n",
      "META__collection_name\n",
      "['collection_avg_profit', 'collection_avg_revenue']\n",
      "time: 0.6776319300001887\n",
      "overall time: 634.7583382170001\n",
      "------------------------------------------------------\n",
      "budget_start_5percent\n",
      "-------------------------------------------------------\n",
      "obtaine movies\n",
      "time: 31.815508017999946\n",
      "calculate info\n",
      "time: 47.537906163000116\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue']\n",
      "META__production_company_1\n",
      "['production_company_1_avg_profit', 'production_company_1_avg_revenue']\n",
      "META__production_company_2\n",
      "['production_company_2_avg_profit', 'production_company_2_avg_revenue']\n",
      "META__production_company_3\n",
      "['production_company_3_avg_profit', 'production_company_3_avg_revenue']\n",
      "time: 1.8474123290002353\n",
      "obtaine movies\n",
      "time: 104.77891492499975\n",
      "calculate info\n",
      "time: 78.17357035299983\n",
      "get columns\n",
      "['avg_profit', 'experience', 'avg_revenue', 'movies_before']\n",
      "META__cast_1_name\n",
      "['cast_1_avg_profit', 'cast_1_experience', 'cast_1_avg_revenue', 'cast_1_movies_before']\n",
      "META__cast_2_name\n",
      "['cast_2_avg_profit', 'cast_2_experience', 'cast_2_avg_revenue', 'cast_2_movies_before']\n",
      "META__cast_3_name\n",
      "['cast_3_avg_profit', 'cast_3_experience', 'cast_3_avg_revenue', 'cast_3_movies_before']\n",
      "META__cast_4_name\n",
      "['cast_4_avg_profit', 'cast_4_experience', 'cast_4_avg_revenue', 'cast_4_movies_before']\n",
      "META__cast_5_name\n",
      "['cast_5_avg_profit', 'cast_5_experience', 'cast_5_avg_revenue', 'cast_5_movies_before']\n",
      "META__cast_6_name\n",
      "['cast_6_avg_profit', 'cast_6_experience', 'cast_6_avg_revenue', 'cast_6_movies_before']\n",
      "META__cast_7_name\n",
      "['cast_7_avg_profit', 'cast_7_experience', 'cast_7_avg_revenue', 'cast_7_movies_before']\n",
      "META__cast_8_name\n",
      "['cast_8_avg_profit', 'cast_8_experience', 'cast_8_avg_revenue', 'cast_8_movies_before']\n",
      "time: 4.9434383910002\n",
      "obtaine movies\n",
      "time: 3.810699835000378\n",
      "calculate info\n",
      "time: 2.3274402249999184\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__music_editor\n",
      "['crew__sound__music_editor_avg_profit', 'crew__sound__music_editor_avg_revenue', 'crew__sound__music_editor_movies_before']\n",
      "time: 0.6105913450001026\n",
      "obtaine movies\n",
      "time: 10.137560035000206\n",
      "calculate info\n",
      "time: 8.174506155000017\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__original_music_composer\n",
      "['crew__sound__original_music_composer_avg_profit', 'crew__sound__original_music_composer_avg_revenue', 'crew__sound__original_music_composer_movies_before']\n",
      "time: 0.6248480480003309\n",
      "obtaine movies\n",
      "time: 2.7530779559997427\n",
      "calculate info\n",
      "time: 1.8316195309998875\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__sound_designer\n",
      "['crew__sound__sound_designer_avg_profit', 'crew__sound__sound_designer_avg_revenue', 'crew__sound__sound_designer_movies_before']\n",
      "time: 0.6085629909998715\n",
      "obtaine movies\n",
      "time: 2.881163377000121\n",
      "calculate info\n",
      "time: 1.8783558270001777\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__sound_effects_editor\n",
      "['crew__sound__sound_effects_editor_avg_profit', 'crew__sound__sound_effects_editor_avg_revenue', 'crew__sound__sound_effects_editor_movies_before']\n",
      "time: 0.6155134020000332\n",
      "obtaine movies\n",
      "time: 4.3831559229997765\n",
      "calculate info\n",
      "time: 2.9159093299999768\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__sound_re_recording_mixer\n",
      "['crew__sound__sound_re_recording_mixer_avg_profit', 'crew__sound__sound_re_recording_mixer_avg_revenue', 'crew__sound__sound_re_recording_mixer_movies_before']\n",
      "time: 0.6205402339996908\n",
      "obtaine movies\n",
      "time: 3.9945150730000023\n",
      "calculate info\n",
      "time: 2.5674159999998665\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__supervising_sound_editor\n",
      "['crew__sound__supervising_sound_editor_avg_profit', 'crew__sound__supervising_sound_editor_avg_revenue', 'crew__sound__supervising_sound_editor_movies_before']\n",
      "time: 0.6142730709998432\n",
      "obtaine movies\n",
      "time: 11.508940148999955\n",
      "calculate info\n",
      "time: 8.885008048000145\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__directing__director__1\n",
      "['crew__directing__director__1_avg_profit', 'crew__directing__director__1_avg_revenue', 'crew__directing__director__1_movies_before']\n",
      "time: 0.6269247409995842\n",
      "obtaine movies\n",
      "time: 5.018314733999887\n",
      "calculate info\n",
      "time: 3.360859143000198\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__directing__script_supervisor\n",
      "['crew__directing__script_supervisor_avg_profit', 'crew__directing__script_supervisor_avg_revenue', 'crew__directing__script_supervisor_movies_before']\n",
      "time: 0.62748735499963\n",
      "obtaine movies\n",
      "time: 9.865440183999908\n",
      "calculate info\n",
      "time: 8.420465553000213\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__production__casting\n",
      "['crew__production__casting_avg_profit', 'crew__production__casting_avg_revenue', 'crew__production__casting_movies_before']\n",
      "time: 0.6366733049999311\n",
      "obtaine movies\n",
      "time: 5.942587696999908\n",
      "calculate info\n",
      "time: 5.503310318999866\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__production__executive_producer__1\n",
      "['crew__production__executive_producer__1_avg_profit', 'crew__production__executive_producer__1_avg_revenue', 'crew__production__executive_producer__1_movies_before']\n",
      "time: 0.6213715629996841\n",
      "obtaine movies\n",
      "time: 10.54667667900003\n",
      "calculate info\n",
      "time: 8.394842554999741\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__production__producer__1\n",
      "['crew__production__producer__1_avg_profit', 'crew__production__producer__1_avg_revenue', 'crew__production__producer__1_movies_before']\n",
      "time: 0.6285525530001905\n",
      "obtaine movies\n",
      "time: 2.9808915290000186\n",
      "calculate info\n",
      "time: 3.063238199999887\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__production__producer__2\n",
      "['crew__production__producer__2_avg_profit', 'crew__production__producer__2_avg_revenue', 'crew__production__producer__2_movies_before']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 0.6226980520000325\n",
      "obtaine movies\n",
      "time: 11.935108361999937\n",
      "calculate info\n",
      "time: 8.359756413000014\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__editing__editor__1\n",
      "['crew__editing__editor__1_avg_profit', 'crew__editing__editor__1_avg_revenue', 'crew__editing__editor__1_movies_before']\n",
      "time: 0.643493258000035\n",
      "obtaine movies\n",
      "time: 9.049647236000055\n",
      "calculate info\n",
      "time: 6.342315496000083\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__costume__costume_designer\n",
      "['crew__costume__costume_designer_avg_profit', 'crew__costume__costume_designer_avg_revenue', 'crew__costume__costume_designer_movies_before']\n",
      "time: 0.6317821860002368\n",
      "obtaine movies\n",
      "time: 2.634005696999793\n",
      "calculate info\n",
      "time: 1.7870872419998705\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__costume__costume_supervisor\n",
      "['crew__costume__costume_supervisor_avg_profit', 'crew__costume__costume_supervisor_avg_revenue', 'crew__costume__costume_supervisor_movies_before']\n",
      "time: 0.6238150950002819\n",
      "obtaine movies\n",
      "time: 3.3379513370000495\n",
      "calculate info\n",
      "time: 2.701501948999976\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__costume__makeup_artist\n",
      "['crew__costume__makeup_artist_avg_profit', 'crew__costume__makeup_artist_avg_revenue', 'crew__costume__makeup_artist_movies_before']\n",
      "time: 0.6276258100001542\n",
      "obtaine movies\n",
      "time: 3.465489092000098\n",
      "calculate info\n",
      "time: 2.3266070360000413\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__crew__stunt_coordinator\n",
      "['crew__crew__stunt_coordinator_avg_profit', 'crew__crew__stunt_coordinator_avg_revenue', 'crew__crew__stunt_coordinator_movies_before']\n",
      "time: 0.6315970599998764\n",
      "obtaine movies\n",
      "time: 6.24524878200009\n",
      "calculate info\n",
      "time: 5.501290954000069\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__writing__screenplay__1\n",
      "['crew__writing__screenplay__1_avg_profit', 'crew__writing__screenplay__1_avg_revenue', 'crew__writing__screenplay__1_movies_before']\n",
      "time: 0.6361918839997998\n",
      "obtaine movies\n",
      "time: 6.0362118369998825\n",
      "calculate info\n",
      "time: 4.879455548999886\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__art__art_direction\n",
      "['crew__art__art_direction_avg_profit', 'crew__art__art_direction_avg_revenue', 'crew__art__art_direction_movies_before']\n",
      "time: 0.6557248300000538\n",
      "obtaine movies\n",
      "time: 9.34596247699983\n",
      "calculate info\n",
      "time: 6.370303805999811\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__art__production_design\n",
      "['crew__art__production_design_avg_profit', 'crew__art__production_design_avg_revenue', 'crew__art__production_design_movies_before']\n",
      "time: 0.6350107120001667\n",
      "obtaine movies\n",
      "time: 3.1005786739997347\n",
      "calculate info\n",
      "time: 2.1094760559999486\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__art__property_master\n",
      "['crew__art__property_master_avg_profit', 'crew__art__property_master_avg_revenue', 'crew__art__property_master_movies_before']\n",
      "time: 0.6296893429998818\n",
      "obtaine movies\n",
      "time: 6.894628735999959\n",
      "calculate info\n",
      "time: 4.831501466000191\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__art__set_decoration\n",
      "['crew__art__set_decoration_avg_profit', 'crew__art__set_decoration_avg_revenue', 'crew__art__set_decoration_movies_before']\n",
      "time: 0.6423710959998061\n",
      "obtaine movies\n",
      "time: 2.913691504000326\n",
      "calculate info\n",
      "time: 2.1008390220004003\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__visualeffects__visual_effects_supervisor\n",
      "['crew__visualeffects__visual_effects_supervisor_avg_profit', 'crew__visualeffects__visual_effects_supervisor_avg_revenue', 'crew__visualeffects__visual_effects_supervisor_movies_before']\n",
      "time: 0.6378989419999925\n",
      "obtaine movies\n",
      "time: 12.175394490000144\n",
      "calculate info\n",
      "time: 8.580745634000777\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__camera__director_of_photography\n",
      "['crew__camera__director_of_photography_avg_profit', 'crew__camera__director_of_photography_avg_revenue', 'crew__camera__director_of_photography_movies_before']\n",
      "time: 0.6472155870005736\n",
      "obtaine movies\n",
      "time: 2.6250349829997504\n",
      "calculate info\n",
      "time: 1.561067037000612\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__camera__steadicam_operator\n",
      "['crew__camera__steadicam_operator_avg_profit', 'crew__camera__steadicam_operator_avg_revenue', 'crew__camera__steadicam_operator_movies_before']\n",
      "time: 0.6335960540000087\n",
      "obtaine movies\n",
      "time: 4.770439693000299\n",
      "calculate info\n",
      "time: 3.2290588329997263\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__camera__still_photographer\n",
      "['crew__camera__still_photographer_avg_profit', 'crew__camera__still_photographer_avg_revenue', 'crew__camera__still_photographer_movies_before']\n",
      "time: 0.6493212909999784\n",
      "obtaine movies\n",
      "time: 2.348045092000575\n",
      "calculate info\n",
      "time: 1.4873730339995745\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue']\n",
      "META__collection_name\n",
      "['collection_avg_profit', 'collection_avg_revenue']\n",
      "time: 0.6472014080000008\n",
      "overall time: 572.1512771480002\n",
      "------------------------------------------------------\n",
      "profitability_1percentile\n",
      "-------------------------------------------------------\n",
      "obtaine movies\n",
      "time: 31.682005392000065\n",
      "calculate info\n",
      "time: 47.276054480999846\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue']\n",
      "META__production_company_1\n",
      "['production_company_1_avg_profit', 'production_company_1_avg_revenue']\n",
      "META__production_company_2\n",
      "['production_company_2_avg_profit', 'production_company_2_avg_revenue']\n",
      "META__production_company_3\n",
      "['production_company_3_avg_profit', 'production_company_3_avg_revenue']\n",
      "time: 1.9238576299994747\n",
      "obtaine movies\n",
      "time: 112.62540359800005\n",
      "calculate info\n",
      "time: 88.50243949600008\n",
      "get columns\n",
      "['avg_profit', 'experience', 'avg_revenue', 'movies_before']\n",
      "META__cast_1_name\n",
      "['cast_1_avg_profit', 'cast_1_experience', 'cast_1_avg_revenue', 'cast_1_movies_before']\n",
      "META__cast_2_name\n",
      "['cast_2_avg_profit', 'cast_2_experience', 'cast_2_avg_revenue', 'cast_2_movies_before']\n",
      "META__cast_3_name\n",
      "['cast_3_avg_profit', 'cast_3_experience', 'cast_3_avg_revenue', 'cast_3_movies_before']\n",
      "META__cast_4_name\n",
      "['cast_4_avg_profit', 'cast_4_experience', 'cast_4_avg_revenue', 'cast_4_movies_before']\n",
      "META__cast_5_name\n",
      "['cast_5_avg_profit', 'cast_5_experience', 'cast_5_avg_revenue', 'cast_5_movies_before']\n",
      "META__cast_6_name\n",
      "['cast_6_avg_profit', 'cast_6_experience', 'cast_6_avg_revenue', 'cast_6_movies_before']\n",
      "META__cast_7_name\n",
      "['cast_7_avg_profit', 'cast_7_experience', 'cast_7_avg_revenue', 'cast_7_movies_before']\n",
      "META__cast_8_name\n",
      "['cast_8_avg_profit', 'cast_8_experience', 'cast_8_avg_revenue', 'cast_8_movies_before']\n",
      "time: 5.193084624000221\n",
      "obtaine movies\n",
      "time: 3.8827430450000975\n",
      "calculate info\n",
      "time: 2.330170139999609\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__music_editor\n",
      "['crew__sound__music_editor_avg_profit', 'crew__sound__music_editor_avg_revenue', 'crew__sound__music_editor_movies_before']\n",
      "time: 0.6429838690000906\n",
      "obtaine movies\n",
      "time: 10.19397023700003\n",
      "calculate info\n",
      "time: 8.429651971999192\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__original_music_composer\n",
      "['crew__sound__original_music_composer_avg_profit', 'crew__sound__original_music_composer_avg_revenue', 'crew__sound__original_music_composer_movies_before']\n",
      "time: 0.6622702689992366\n",
      "obtaine movies\n",
      "time: 2.8070033990006777\n",
      "calculate info\n",
      "time: 1.8634669800003394\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__sound_designer\n",
      "['crew__sound__sound_designer_avg_profit', 'crew__sound__sound_designer_avg_revenue', 'crew__sound__sound_designer_movies_before']\n",
      "time: 0.6398196400004963\n",
      "obtaine movies\n",
      "time: 2.9552675529994303\n",
      "calculate info\n",
      "time: 1.9391872750002221\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__sound_effects_editor\n",
      "['crew__sound__sound_effects_editor_avg_profit', 'crew__sound__sound_effects_editor_avg_revenue', 'crew__sound__sound_effects_editor_movies_before']\n",
      "time: 0.6587165509999977\n",
      "obtaine movies\n",
      "time: 4.55709801499961\n",
      "calculate info\n",
      "time: 2.9822080229996573\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__sound_re_recording_mixer\n",
      "['crew__sound__sound_re_recording_mixer_avg_profit', 'crew__sound__sound_re_recording_mixer_avg_revenue', 'crew__sound__sound_re_recording_mixer_movies_before']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 0.6549243950003074\n",
      "obtaine movies\n",
      "time: 4.289156863999779\n",
      "calculate info\n",
      "time: 2.6846169000000373\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__supervising_sound_editor\n",
      "['crew__sound__supervising_sound_editor_avg_profit', 'crew__sound__supervising_sound_editor_avg_revenue', 'crew__sound__supervising_sound_editor_movies_before']\n",
      "time: 0.676545356999668\n",
      "obtaine movies\n",
      "time: 12.2271052469996\n",
      "calculate info\n",
      "time: 9.359373063000021\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__directing__director__1\n",
      "['crew__directing__director__1_avg_profit', 'crew__directing__director__1_avg_revenue', 'crew__directing__director__1_movies_before']\n",
      "time: 0.6648802070003512\n",
      "obtaine movies\n",
      "time: 5.198761270000432\n",
      "calculate info\n",
      "time: 3.3748415290001503\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__directing__script_supervisor\n",
      "['crew__directing__script_supervisor_avg_profit', 'crew__directing__script_supervisor_avg_revenue', 'crew__directing__script_supervisor_movies_before']\n",
      "time: 0.659005834000709\n",
      "obtaine movies\n",
      "time: 9.844635272000232\n",
      "calculate info\n",
      "time: 8.248022044000209\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__production__casting\n",
      "['crew__production__casting_avg_profit', 'crew__production__casting_avg_revenue', 'crew__production__casting_movies_before']\n",
      "time: 0.6486132380005074\n",
      "obtaine movies\n",
      "time: 5.973727546999726\n",
      "calculate info\n",
      "time: 5.60048400300002\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__production__executive_producer__1\n",
      "['crew__production__executive_producer__1_avg_profit', 'crew__production__executive_producer__1_avg_revenue', 'crew__production__executive_producer__1_movies_before']\n",
      "time: 0.6535873269995136\n",
      "obtaine movies\n",
      "time: 10.842077099000562\n",
      "calculate info\n",
      "time: 8.595782547999988\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__production__producer__1\n",
      "['crew__production__producer__1_avg_profit', 'crew__production__producer__1_avg_revenue', 'crew__production__producer__1_movies_before']\n",
      "time: 0.6482723889994304\n",
      "obtaine movies\n",
      "time: 3.015349371999946\n",
      "calculate info\n",
      "time: 3.098976135999692\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__production__producer__2\n",
      "['crew__production__producer__2_avg_profit', 'crew__production__producer__2_avg_revenue', 'crew__production__producer__2_movies_before']\n",
      "time: 0.6591070710001077\n",
      "obtaine movies\n",
      "time: 12.251639421999243\n",
      "calculate info\n",
      "time: 8.62006791300064\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__editing__editor__1\n",
      "['crew__editing__editor__1_avg_profit', 'crew__editing__editor__1_avg_revenue', 'crew__editing__editor__1_movies_before']\n",
      "time: 0.6720413700004428\n",
      "obtaine movies\n",
      "time: 9.190308244000335\n",
      "calculate info\n",
      "time: 6.411116592000326\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__costume__costume_designer\n",
      "['crew__costume__costume_designer_avg_profit', 'crew__costume__costume_designer_avg_revenue', 'crew__costume__costume_designer_movies_before']\n",
      "time: 0.6452411270001903\n",
      "obtaine movies\n",
      "time: 2.6199780819997613\n",
      "calculate info\n",
      "time: 1.8004642200003218\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__costume__costume_supervisor\n",
      "['crew__costume__costume_supervisor_avg_profit', 'crew__costume__costume_supervisor_avg_revenue', 'crew__costume__costume_supervisor_movies_before']\n",
      "time: 0.6601894850000463\n",
      "obtaine movies\n",
      "time: 3.453115656000591\n",
      "calculate info\n",
      "time: 2.7783767210003134\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__costume__makeup_artist\n",
      "['crew__costume__makeup_artist_avg_profit', 'crew__costume__makeup_artist_avg_revenue', 'crew__costume__makeup_artist_movies_before']\n",
      "time: 0.653792861000511\n",
      "obtaine movies\n",
      "time: 3.604830412999945\n",
      "calculate info\n",
      "time: 2.3477428139995027\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__crew__stunt_coordinator\n",
      "['crew__crew__stunt_coordinator_avg_profit', 'crew__crew__stunt_coordinator_avg_revenue', 'crew__crew__stunt_coordinator_movies_before']\n",
      "time: 0.6645060470000317\n",
      "obtaine movies\n",
      "time: 6.407421111999611\n",
      "calculate info\n",
      "time: 5.566421073000129\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__writing__screenplay__1\n",
      "['crew__writing__screenplay__1_avg_profit', 'crew__writing__screenplay__1_avg_revenue', 'crew__writing__screenplay__1_movies_before']\n",
      "time: 0.6640594199998304\n",
      "obtaine movies\n",
      "time: 6.1137920820001455\n",
      "calculate info\n",
      "time: 4.859162325999932\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__art__art_direction\n",
      "['crew__art__art_direction_avg_profit', 'crew__art__art_direction_avg_revenue', 'crew__art__art_direction_movies_before']\n",
      "time: 0.673309363000044\n",
      "obtaine movies\n",
      "time: 9.439002095000433\n",
      "calculate info\n",
      "time: 6.518673298000067\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__art__production_design\n",
      "['crew__art__production_design_avg_profit', 'crew__art__production_design_avg_revenue', 'crew__art__production_design_movies_before']\n",
      "time: 0.7022434119999161\n",
      "obtaine movies\n",
      "time: 3.287585392999972\n",
      "calculate info\n",
      "time: 2.2321207310005775\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__art__property_master\n",
      "['crew__art__property_master_avg_profit', 'crew__art__property_master_avg_revenue', 'crew__art__property_master_movies_before']\n",
      "time: 0.7084560570001486\n",
      "obtaine movies\n",
      "time: 7.468508166999527\n",
      "calculate info\n",
      "time: 5.198508297999979\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__art__set_decoration\n",
      "['crew__art__set_decoration_avg_profit', 'crew__art__set_decoration_avg_revenue', 'crew__art__set_decoration_movies_before']\n",
      "time: 0.7127658189992871\n",
      "obtaine movies\n",
      "time: 3.154801620999933\n",
      "calculate info\n",
      "time: 2.1561844349998864\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__visualeffects__visual_effects_supervisor\n",
      "['crew__visualeffects__visual_effects_supervisor_avg_profit', 'crew__visualeffects__visual_effects_supervisor_avg_revenue', 'crew__visualeffects__visual_effects_supervisor_movies_before']\n",
      "time: 0.6353639849994579\n",
      "obtaine movies\n",
      "time: 12.610975177999535\n",
      "calculate info\n",
      "time: 9.029582959000436\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__camera__director_of_photography\n",
      "['crew__camera__director_of_photography_avg_profit', 'crew__camera__director_of_photography_avg_revenue', 'crew__camera__director_of_photography_movies_before']\n",
      "time: 0.6892834660002336\n",
      "obtaine movies\n",
      "time: 2.5799623800003246\n",
      "calculate info\n",
      "time: 1.529154118000406\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__camera__steadicam_operator\n",
      "['crew__camera__steadicam_operator_avg_profit', 'crew__camera__steadicam_operator_avg_revenue', 'crew__camera__steadicam_operator_movies_before']\n",
      "time: 0.6513770259998637\n",
      "obtaine movies\n",
      "time: 4.838911070999529\n",
      "calculate info\n",
      "time: 3.28388927099968\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__camera__still_photographer\n",
      "['crew__camera__still_photographer_avg_profit', 'crew__camera__still_photographer_avg_revenue', 'crew__camera__still_photographer_movies_before']\n",
      "time: 0.686477790999561\n",
      "obtaine movies\n",
      "time: 2.390585785999974\n",
      "calculate info\n",
      "time: 1.5144145439999193\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue']\n",
      "META__collection_name\n",
      "['collection_avg_profit', 'collection_avg_revenue']\n",
      "time: 0.6725551580002502\n",
      "overall time: 598.8278680529993\n",
      "------------------------------------------------------\n",
      "profitability_5percentile\n",
      "-------------------------------------------------------\n",
      "obtaine movies\n",
      "time: 30.84179749699979\n",
      "calculate info\n",
      "time: 44.84885149999991\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue']\n",
      "META__production_company_1\n",
      "['production_company_1_avg_profit', 'production_company_1_avg_revenue']\n",
      "META__production_company_2\n",
      "['production_company_2_avg_profit', 'production_company_2_avg_revenue']\n",
      "META__production_company_3\n",
      "['production_company_3_avg_profit', 'production_company_3_avg_revenue']\n",
      "time: 1.8226979189994381\n",
      "obtaine movies\n",
      "time: 98.7159412889996\n",
      "calculate info\n",
      "time: 72.11419779500011\n",
      "get columns\n",
      "['avg_profit', 'experience', 'avg_revenue', 'movies_before']\n",
      "META__cast_1_name\n",
      "['cast_1_avg_profit', 'cast_1_experience', 'cast_1_avg_revenue', 'cast_1_movies_before']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "META__cast_2_name\n",
      "['cast_2_avg_profit', 'cast_2_experience', 'cast_2_avg_revenue', 'cast_2_movies_before']\n",
      "META__cast_3_name\n",
      "['cast_3_avg_profit', 'cast_3_experience', 'cast_3_avg_revenue', 'cast_3_movies_before']\n",
      "META__cast_4_name\n",
      "['cast_4_avg_profit', 'cast_4_experience', 'cast_4_avg_revenue', 'cast_4_movies_before']\n",
      "META__cast_5_name\n",
      "['cast_5_avg_profit', 'cast_5_experience', 'cast_5_avg_revenue', 'cast_5_movies_before']\n",
      "META__cast_6_name\n",
      "['cast_6_avg_profit', 'cast_6_experience', 'cast_6_avg_revenue', 'cast_6_movies_before']\n",
      "META__cast_7_name\n",
      "['cast_7_avg_profit', 'cast_7_experience', 'cast_7_avg_revenue', 'cast_7_movies_before']\n",
      "META__cast_8_name\n",
      "['cast_8_avg_profit', 'cast_8_experience', 'cast_8_avg_revenue', 'cast_8_movies_before']\n",
      "time: 4.88384267299989\n",
      "obtaine movies\n",
      "time: 3.7644772399999056\n",
      "calculate info\n",
      "time: 2.2402809479999632\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__music_editor\n",
      "['crew__sound__music_editor_avg_profit', 'crew__sound__music_editor_avg_revenue', 'crew__sound__music_editor_movies_before']\n",
      "time: 0.6220197440006814\n",
      "obtaine movies\n",
      "time: 9.8214193419999\n",
      "calculate info\n",
      "time: 7.876698619999843\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__original_music_composer\n",
      "['crew__sound__original_music_composer_avg_profit', 'crew__sound__original_music_composer_avg_revenue', 'crew__sound__original_music_composer_movies_before']\n",
      "time: 0.6220305489996463\n",
      "obtaine movies\n",
      "time: 2.7542849170004047\n",
      "calculate info\n",
      "time: 1.802209364999726\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__sound_designer\n",
      "['crew__sound__sound_designer_avg_profit', 'crew__sound__sound_designer_avg_revenue', 'crew__sound__sound_designer_movies_before']\n",
      "time: 0.6246418000000631\n",
      "obtaine movies\n",
      "time: 2.894463036000161\n",
      "calculate info\n",
      "time: 1.869712458000322\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__sound_effects_editor\n",
      "['crew__sound__sound_effects_editor_avg_profit', 'crew__sound__sound_effects_editor_avg_revenue', 'crew__sound__sound_effects_editor_movies_before']\n",
      "time: 0.6215509789999487\n",
      "obtaine movies\n",
      "time: 4.284163328999966\n",
      "calculate info\n",
      "time: 2.8584582409994255\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__sound_re_recording_mixer\n",
      "['crew__sound__sound_re_recording_mixer_avg_profit', 'crew__sound__sound_re_recording_mixer_avg_revenue', 'crew__sound__sound_re_recording_mixer_movies_before']\n",
      "time: 0.6233492749997822\n",
      "obtaine movies\n",
      "time: 4.006935055999747\n",
      "calculate info\n",
      "time: 2.525528131000101\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__supervising_sound_editor\n",
      "['crew__sound__supervising_sound_editor_avg_profit', 'crew__sound__supervising_sound_editor_avg_revenue', 'crew__sound__supervising_sound_editor_movies_before']\n",
      "time: 0.6213689979995252\n",
      "obtaine movies\n",
      "time: 10.675807521000024\n",
      "calculate info\n",
      "time: 8.276795898999808\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__directing__director__1\n",
      "['crew__directing__director__1_avg_profit', 'crew__directing__director__1_avg_revenue', 'crew__directing__director__1_movies_before']\n",
      "time: 0.6014408659993933\n",
      "obtaine movies\n",
      "time: 4.726427249000153\n",
      "calculate info\n",
      "time: 3.1074392499995156\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__directing__script_supervisor\n",
      "['crew__directing__script_supervisor_avg_profit', 'crew__directing__script_supervisor_avg_revenue', 'crew__directing__script_supervisor_movies_before']\n",
      "time: 0.5967259630006083\n",
      "obtaine movies\n",
      "time: 8.968804606999583\n",
      "calculate info\n",
      "time: 7.433149049999884\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__production__casting\n",
      "['crew__production__casting_avg_profit', 'crew__production__casting_avg_revenue', 'crew__production__casting_movies_before']\n",
      "time: 0.5851014709996889\n",
      "obtaine movies\n",
      "time: 5.41902415200002\n",
      "calculate info\n",
      "time: 5.074199845000294\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__production__executive_producer__1\n",
      "['crew__production__executive_producer__1_avg_profit', 'crew__production__executive_producer__1_avg_revenue', 'crew__production__executive_producer__1_movies_before']\n",
      "time: 0.5899293399997987\n",
      "obtaine movies\n",
      "time: 9.815575344000536\n",
      "calculate info\n",
      "time: 7.823832940000102\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__production__producer__1\n",
      "['crew__production__producer__1_avg_profit', 'crew__production__producer__1_avg_revenue', 'crew__production__producer__1_movies_before']\n",
      "time: 0.5945443400005388\n",
      "obtaine movies\n",
      "time: 2.6959956259997853\n",
      "calculate info\n",
      "time: 2.842641211999762\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__production__producer__2\n",
      "['crew__production__producer__2_avg_profit', 'crew__production__producer__2_avg_revenue', 'crew__production__producer__2_movies_before']\n",
      "time: 0.5889549110006556\n",
      "obtaine movies\n",
      "time: 11.020595953000338\n",
      "calculate info\n",
      "time: 7.773499099000219\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__editing__editor__1\n",
      "['crew__editing__editor__1_avg_profit', 'crew__editing__editor__1_avg_revenue', 'crew__editing__editor__1_movies_before']\n",
      "time: 0.5992971850000686\n",
      "obtaine movies\n",
      "time: 8.46146961300019\n",
      "calculate info\n",
      "time: 5.943445574999714\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__costume__costume_designer\n",
      "['crew__costume__costume_designer_avg_profit', 'crew__costume__costume_designer_avg_revenue', 'crew__costume__costume_designer_movies_before']\n",
      "time: 0.6043409220001195\n",
      "obtaine movies\n",
      "time: 2.460845620999862\n",
      "calculate info\n",
      "time: 1.7038991499994154\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__costume__costume_supervisor\n",
      "['crew__costume__costume_supervisor_avg_profit', 'crew__costume__costume_supervisor_avg_revenue', 'crew__costume__costume_supervisor_movies_before']\n",
      "time: 0.5974894789997052\n",
      "obtaine movies\n",
      "time: 3.0680533060003654\n",
      "calculate info\n",
      "time: 2.4895394360000864\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__costume__makeup_artist\n",
      "['crew__costume__makeup_artist_avg_profit', 'crew__costume__makeup_artist_avg_revenue', 'crew__costume__makeup_artist_movies_before']\n",
      "time: 0.5907614459993056\n",
      "obtaine movies\n",
      "time: 3.259641573999943\n",
      "calculate info\n",
      "time: 2.143384119000075\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__crew__stunt_coordinator\n",
      "['crew__crew__stunt_coordinator_avg_profit', 'crew__crew__stunt_coordinator_avg_revenue', 'crew__crew__stunt_coordinator_movies_before']\n",
      "time: 0.5889508430000205\n",
      "obtaine movies\n",
      "time: 5.7517313790003755\n",
      "calculate info\n",
      "time: 5.141451032000077\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__writing__screenplay__1\n",
      "['crew__writing__screenplay__1_avg_profit', 'crew__writing__screenplay__1_avg_revenue', 'crew__writing__screenplay__1_movies_before']\n",
      "time: 0.6018720239999311\n",
      "obtaine movies\n",
      "time: 5.53928083899973\n",
      "calculate info\n",
      "time: 4.4645663599994805\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__art__art_direction\n",
      "['crew__art__art_direction_avg_profit', 'crew__art__art_direction_avg_revenue', 'crew__art__art_direction_movies_before']\n",
      "time: 0.6021595390002403\n",
      "obtaine movies\n",
      "time: 8.619001781999941\n",
      "calculate info\n",
      "time: 5.897173446999659\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__art__production_design\n",
      "['crew__art__production_design_avg_profit', 'crew__art__production_design_avg_revenue', 'crew__art__production_design_movies_before']\n",
      "time: 0.5991541859993958\n",
      "obtaine movies\n",
      "time: 2.8878321840002172\n",
      "calculate info\n",
      "time: 1.9609249979994274\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__art__property_master\n",
      "['crew__art__property_master_avg_profit', 'crew__art__property_master_avg_revenue', 'crew__art__property_master_movies_before']\n",
      "time: 0.5973495909993289\n",
      "obtaine movies\n",
      "time: 6.463435273999494\n",
      "calculate info\n",
      "time: 4.5370675269996354\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__art__set_decoration\n",
      "['crew__art__set_decoration_avg_profit', 'crew__art__set_decoration_avg_revenue', 'crew__art__set_decoration_movies_before']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 0.6174307530000078\n",
      "obtaine movies\n",
      "time: 2.708086405999893\n",
      "calculate info\n",
      "time: 1.9905956909997258\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__visualeffects__visual_effects_supervisor\n",
      "['crew__visualeffects__visual_effects_supervisor_avg_profit', 'crew__visualeffects__visual_effects_supervisor_avg_revenue', 'crew__visualeffects__visual_effects_supervisor_movies_before']\n",
      "time: 0.5983487969997441\n",
      "obtaine movies\n",
      "time: 11.285699336999642\n",
      "calculate info\n",
      "time: 7.997172546000002\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__camera__director_of_photography\n",
      "['crew__camera__director_of_photography_avg_profit', 'crew__camera__director_of_photography_avg_revenue', 'crew__camera__director_of_photography_movies_before']\n",
      "time: 0.6058952589992259\n",
      "obtaine movies\n",
      "time: 2.402734555999814\n",
      "calculate info\n",
      "time: 1.4590347039993503\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__camera__steadicam_operator\n",
      "['crew__camera__steadicam_operator_avg_profit', 'crew__camera__steadicam_operator_avg_revenue', 'crew__camera__steadicam_operator_movies_before']\n",
      "time: 0.6073093369996059\n",
      "obtaine movies\n",
      "time: 4.439199712000118\n",
      "calculate info\n",
      "time: 2.9670302000004085\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__camera__still_photographer\n",
      "['crew__camera__still_photographer_avg_profit', 'crew__camera__still_photographer_avg_revenue', 'crew__camera__still_photographer_movies_before']\n",
      "time: 0.601794535999943\n",
      "obtaine movies\n",
      "time: 2.026113179999811\n",
      "calculate info\n",
      "time: 1.29920845200013\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue']\n",
      "META__collection_name\n",
      "['collection_avg_profit', 'collection_avg_revenue']\n",
      "time: 0.5986724370004595\n",
      "overall time: 536.7623736390005\n",
      "------------------------------------------------------\n",
      "profitability_10percentile\n",
      "-------------------------------------------------------\n",
      "obtaine movies\n",
      "time: 25.581586178999714\n",
      "calculate info\n",
      "time: 36.749575535000076\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue']\n",
      "META__production_company_1\n",
      "['production_company_1_avg_profit', 'production_company_1_avg_revenue']\n",
      "META__production_company_2\n",
      "['production_company_2_avg_profit', 'production_company_2_avg_revenue']\n",
      "META__production_company_3\n",
      "['production_company_3_avg_profit', 'production_company_3_avg_revenue']\n",
      "time: 1.5393395439996311\n",
      "obtaine movies\n",
      "time: 83.2305066580002\n",
      "calculate info\n",
      "time: 63.57963457200003\n",
      "get columns\n",
      "['avg_profit', 'experience', 'avg_revenue', 'movies_before']\n",
      "META__cast_1_name\n",
      "['cast_1_avg_profit', 'cast_1_experience', 'cast_1_avg_revenue', 'cast_1_movies_before']\n",
      "META__cast_2_name\n",
      "['cast_2_avg_profit', 'cast_2_experience', 'cast_2_avg_revenue', 'cast_2_movies_before']\n",
      "META__cast_3_name\n",
      "['cast_3_avg_profit', 'cast_3_experience', 'cast_3_avg_revenue', 'cast_3_movies_before']\n",
      "META__cast_4_name\n",
      "['cast_4_avg_profit', 'cast_4_experience', 'cast_4_avg_revenue', 'cast_4_movies_before']\n",
      "META__cast_5_name\n",
      "['cast_5_avg_profit', 'cast_5_experience', 'cast_5_avg_revenue', 'cast_5_movies_before']\n",
      "META__cast_6_name\n",
      "['cast_6_avg_profit', 'cast_6_experience', 'cast_6_avg_revenue', 'cast_6_movies_before']\n",
      "META__cast_7_name\n",
      "['cast_7_avg_profit', 'cast_7_experience', 'cast_7_avg_revenue', 'cast_7_movies_before']\n",
      "META__cast_8_name\n",
      "['cast_8_avg_profit', 'cast_8_experience', 'cast_8_avg_revenue', 'cast_8_movies_before']\n",
      "time: 4.09383900299963\n",
      "obtaine movies\n",
      "time: 3.1326161279994267\n",
      "calculate info\n",
      "time: 1.920289703999515\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__music_editor\n",
      "['crew__sound__music_editor_avg_profit', 'crew__sound__music_editor_avg_revenue', 'crew__sound__music_editor_movies_before']\n",
      "time: 0.5153084160001526\n",
      "obtaine movies\n",
      "time: 8.340565904999494\n",
      "calculate info\n",
      "time: 6.718913994000104\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__original_music_composer\n",
      "['crew__sound__original_music_composer_avg_profit', 'crew__sound__original_music_composer_avg_revenue', 'crew__sound__original_music_composer_movies_before']\n",
      "time: 0.528701467999781\n",
      "obtaine movies\n",
      "time: 2.4028166569996756\n",
      "calculate info\n",
      "time: 1.5780022609997104\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__sound_designer\n",
      "['crew__sound__sound_designer_avg_profit', 'crew__sound__sound_designer_avg_revenue', 'crew__sound__sound_designer_movies_before']\n",
      "time: 0.5279139310005121\n",
      "obtaine movies\n",
      "time: 2.423889360999965\n",
      "calculate info\n",
      "time: 1.6396976670002914\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__sound_effects_editor\n",
      "['crew__sound__sound_effects_editor_avg_profit', 'crew__sound__sound_effects_editor_avg_revenue', 'crew__sound__sound_effects_editor_movies_before']\n",
      "time: 0.5204376880001291\n",
      "obtaine movies\n",
      "time: 3.681677683000089\n",
      "calculate info\n",
      "time: 2.427976388999923\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__sound_re_recording_mixer\n",
      "['crew__sound__sound_re_recording_mixer_avg_profit', 'crew__sound__sound_re_recording_mixer_avg_revenue', 'crew__sound__sound_re_recording_mixer_movies_before']\n",
      "time: 0.5227781140001753\n",
      "obtaine movies\n",
      "time: 3.418860842999493\n",
      "calculate info\n",
      "time: 2.167985177999981\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__supervising_sound_editor\n",
      "['crew__sound__supervising_sound_editor_avg_profit', 'crew__sound__supervising_sound_editor_avg_revenue', 'crew__sound__supervising_sound_editor_movies_before']\n",
      "time: 0.5134005850004542\n",
      "obtaine movies\n",
      "time: 9.225894965999942\n",
      "calculate info\n",
      "time: 7.294640809000157\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__directing__director__1\n",
      "['crew__directing__director__1_avg_profit', 'crew__directing__director__1_avg_revenue', 'crew__directing__director__1_movies_before']\n",
      "time: 0.5303480370002944\n",
      "obtaine movies\n",
      "time: 4.21866224599944\n",
      "calculate info\n",
      "time: 2.7493610310002623\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__directing__script_supervisor\n",
      "['crew__directing__script_supervisor_avg_profit', 'crew__directing__script_supervisor_avg_revenue', 'crew__directing__script_supervisor_movies_before']\n",
      "time: 0.5253791900004217\n",
      "obtaine movies\n",
      "time: 7.8925410269994245\n",
      "calculate info\n",
      "time: 6.578274532999785\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__production__casting\n",
      "['crew__production__casting_avg_profit', 'crew__production__casting_avg_revenue', 'crew__production__casting_movies_before']\n",
      "time: 0.5215904900005626\n",
      "obtaine movies\n",
      "time: 4.879039786000249\n",
      "calculate info\n",
      "time: 4.536115110000537\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__production__executive_producer__1\n",
      "['crew__production__executive_producer__1_avg_profit', 'crew__production__executive_producer__1_avg_revenue', 'crew__production__executive_producer__1_movies_before']\n",
      "time: 0.5237904629993864\n",
      "obtaine movies\n",
      "time: 8.621862828000303\n",
      "calculate info\n",
      "time: 6.968976367999858\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__production__producer__1\n",
      "['crew__production__producer__1_avg_profit', 'crew__production__producer__1_avg_revenue', 'crew__production__producer__1_movies_before']\n",
      "time: 0.5201972049999313\n",
      "obtaine movies\n",
      "time: 2.4001961880003364\n",
      "calculate info\n",
      "time: 2.562908161999985\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__production__producer__2\n",
      "['crew__production__producer__2_avg_profit', 'crew__production__producer__2_avg_revenue', 'crew__production__producer__2_movies_before']\n",
      "time: 0.5267170440001792\n",
      "obtaine movies\n",
      "time: 9.78590740600066\n",
      "calculate info\n",
      "time: 6.8477029329997094\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__editing__editor__1\n",
      "['crew__editing__editor__1_avg_profit', 'crew__editing__editor__1_avg_revenue', 'crew__editing__editor__1_movies_before']\n",
      "time: 0.5259279219999371\n",
      "obtaine movies\n",
      "time: 7.366333648999898\n",
      "calculate info\n",
      "time: 5.21139647200016\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__costume__costume_designer\n",
      "['crew__costume__costume_designer_avg_profit', 'crew__costume__costume_designer_avg_revenue', 'crew__costume__costume_designer_movies_before']\n",
      "time: 0.5296736950003833\n",
      "obtaine movies\n",
      "time: 2.2887744030003887\n",
      "calculate info\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 1.5665531560007366\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__costume__costume_supervisor\n",
      "['crew__costume__costume_supervisor_avg_profit', 'crew__costume__costume_supervisor_avg_revenue', 'crew__costume__costume_supervisor_movies_before']\n",
      "time: 0.5355925839994597\n",
      "obtaine movies\n",
      "time: 2.6770235730000422\n",
      "calculate info\n",
      "time: 2.2379842029995416\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__costume__makeup_artist\n",
      "['crew__costume__makeup_artist_avg_profit', 'crew__costume__makeup_artist_avg_revenue', 'crew__costume__makeup_artist_movies_before']\n",
      "time: 0.5352267199996277\n",
      "obtaine movies\n",
      "time: 2.9340338100000736\n",
      "calculate info\n",
      "time: 1.9690780189994257\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__crew__stunt_coordinator\n",
      "['crew__crew__stunt_coordinator_avg_profit', 'crew__crew__stunt_coordinator_avg_revenue', 'crew__crew__stunt_coordinator_movies_before']\n",
      "time: 0.5394730990001335\n",
      "obtaine movies\n",
      "time: 5.127882892000343\n",
      "calculate info\n",
      "time: 4.634688609000477\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__writing__screenplay__1\n",
      "['crew__writing__screenplay__1_avg_profit', 'crew__writing__screenplay__1_avg_revenue', 'crew__writing__screenplay__1_movies_before']\n",
      "time: 0.5389873839994834\n",
      "obtaine movies\n",
      "time: 5.006826088999333\n",
      "calculate info\n",
      "time: 3.978039641000578\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__art__art_direction\n",
      "['crew__art__art_direction_avg_profit', 'crew__art__art_direction_avg_revenue', 'crew__art__art_direction_movies_before']\n",
      "time: 0.5397522889998072\n",
      "obtaine movies\n",
      "time: 7.549748061999708\n",
      "calculate info\n",
      "time: 5.313389263999852\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__art__production_design\n",
      "['crew__art__production_design_avg_profit', 'crew__art__production_design_avg_revenue', 'crew__art__production_design_movies_before']\n",
      "time: 0.5466749349998281\n",
      "obtaine movies\n",
      "time: 2.6512415810002494\n",
      "calculate info\n",
      "time: 1.7919997079998211\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__art__property_master\n",
      "['crew__art__property_master_avg_profit', 'crew__art__property_master_avg_revenue', 'crew__art__property_master_movies_before']\n",
      "time: 0.5362241869997888\n",
      "obtaine movies\n",
      "time: 5.7882505010002205\n",
      "calculate info\n",
      "time: 3.9824646179995398\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__art__set_decoration\n",
      "['crew__art__set_decoration_avg_profit', 'crew__art__set_decoration_avg_revenue', 'crew__art__set_decoration_movies_before']\n",
      "time: 0.5311823599995478\n",
      "obtaine movies\n",
      "time: 2.3937088570000924\n",
      "calculate info\n",
      "time: 1.8028294169998844\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__visualeffects__visual_effects_supervisor\n",
      "['crew__visualeffects__visual_effects_supervisor_avg_profit', 'crew__visualeffects__visual_effects_supervisor_avg_revenue', 'crew__visualeffects__visual_effects_supervisor_movies_before']\n",
      "time: 0.5294103019996328\n",
      "obtaine movies\n",
      "time: 9.959030601999984\n",
      "calculate info\n",
      "time: 7.047786918999918\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__camera__director_of_photography\n",
      "['crew__camera__director_of_photography_avg_profit', 'crew__camera__director_of_photography_avg_revenue', 'crew__camera__director_of_photography_movies_before']\n",
      "time: 0.5347402130000773\n",
      "obtaine movies\n",
      "time: 2.137210546999995\n",
      "calculate info\n",
      "time: 1.314186716000222\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__camera__steadicam_operator\n",
      "['crew__camera__steadicam_operator_avg_profit', 'crew__camera__steadicam_operator_avg_revenue', 'crew__camera__steadicam_operator_movies_before']\n",
      "time: 0.5360893669994766\n",
      "obtaine movies\n",
      "time: 3.944788281000001\n",
      "calculate info\n",
      "time: 2.647378364999895\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__camera__still_photographer\n",
      "['crew__camera__still_photographer_avg_profit', 'crew__camera__still_photographer_avg_revenue', 'crew__camera__still_photographer_movies_before']\n",
      "time: 0.5301870820003387\n",
      "obtaine movies\n",
      "time: 1.7244556260002355\n",
      "calculate info\n",
      "time: 1.1533151630001157\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue']\n",
      "META__collection_name\n",
      "['collection_avg_profit', 'collection_avg_revenue']\n",
      "time: 0.536904465000589\n",
      "overall time: 464.6091154799997\n",
      "------------------------------------------------------\n",
      "profitability_1percentile_years2000-2018\n",
      "-------------------------------------------------------\n",
      "obtaine movies\n",
      "time: 20.597335477000343\n",
      "calculate info\n",
      "time: 21.44969055799993\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue']\n",
      "META__production_company_1\n",
      "['production_company_1_avg_profit', 'production_company_1_avg_revenue']\n",
      "META__production_company_2\n",
      "['production_company_2_avg_profit', 'production_company_2_avg_revenue']\n",
      "META__production_company_3\n",
      "['production_company_3_avg_profit', 'production_company_3_avg_revenue']\n",
      "time: 1.2354475140000432\n",
      "obtaine movies\n",
      "time: 63.65224795699942\n",
      "calculate info\n",
      "time: 49.50636731099985\n",
      "get columns\n",
      "['avg_profit', 'experience', 'avg_revenue', 'movies_before']\n",
      "META__cast_1_name\n",
      "['cast_1_avg_profit', 'cast_1_experience', 'cast_1_avg_revenue', 'cast_1_movies_before']\n",
      "META__cast_2_name\n",
      "['cast_2_avg_profit', 'cast_2_experience', 'cast_2_avg_revenue', 'cast_2_movies_before']\n",
      "META__cast_3_name\n",
      "['cast_3_avg_profit', 'cast_3_experience', 'cast_3_avg_revenue', 'cast_3_movies_before']\n",
      "META__cast_4_name\n",
      "['cast_4_avg_profit', 'cast_4_experience', 'cast_4_avg_revenue', 'cast_4_movies_before']\n",
      "META__cast_5_name\n",
      "['cast_5_avg_profit', 'cast_5_experience', 'cast_5_avg_revenue', 'cast_5_movies_before']\n",
      "META__cast_6_name\n",
      "['cast_6_avg_profit', 'cast_6_experience', 'cast_6_avg_revenue', 'cast_6_movies_before']\n",
      "META__cast_7_name\n",
      "['cast_7_avg_profit', 'cast_7_experience', 'cast_7_avg_revenue', 'cast_7_movies_before']\n",
      "META__cast_8_name\n",
      "['cast_8_avg_profit', 'cast_8_experience', 'cast_8_avg_revenue', 'cast_8_movies_before']\n",
      "time: 3.2932587989998865\n",
      "obtaine movies\n",
      "time: 2.7653230630003236\n",
      "calculate info\n",
      "time: 1.6818172679995769\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__music_editor\n",
      "['crew__sound__music_editor_avg_profit', 'crew__sound__music_editor_avg_revenue', 'crew__sound__music_editor_movies_before']\n",
      "time: 0.4092563610001889\n",
      "obtaine movies\n",
      "time: 6.084108107000247\n",
      "calculate info\n",
      "time: 4.6121407290002026\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__original_music_composer\n",
      "['crew__sound__original_music_composer_avg_profit', 'crew__sound__original_music_composer_avg_revenue', 'crew__sound__original_music_composer_movies_before']\n",
      "time: 0.4182326809996084\n",
      "obtaine movies\n",
      "time: 2.152492743000039\n",
      "calculate info\n",
      "time: 1.4511477070000183\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__sound_designer\n",
      "['crew__sound__sound_designer_avg_profit', 'crew__sound__sound_designer_avg_revenue', 'crew__sound__sound_designer_movies_before']\n",
      "time: 0.4152745359997425\n",
      "obtaine movies\n",
      "time: 2.215896220000104\n",
      "calculate info\n",
      "time: 1.4740173299996968\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__sound_effects_editor\n",
      "['crew__sound__sound_effects_editor_avg_profit', 'crew__sound__sound_effects_editor_avg_revenue', 'crew__sound__sound_effects_editor_movies_before']\n",
      "time: 0.41273447100047633\n",
      "obtaine movies\n",
      "time: 3.1543406740001956\n",
      "calculate info\n",
      "time: 2.055935387000318\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__sound_re_recording_mixer\n",
      "['crew__sound__sound_re_recording_mixer_avg_profit', 'crew__sound__sound_re_recording_mixer_avg_revenue', 'crew__sound__sound_re_recording_mixer_movies_before']\n",
      "time: 0.4116974509997817\n",
      "obtaine movies\n",
      "time: 2.9166667780000353\n",
      "calculate info\n",
      "time: 1.8400032899999132\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__supervising_sound_editor\n",
      "['crew__sound__supervising_sound_editor_avg_profit', 'crew__sound__supervising_sound_editor_avg_revenue', 'crew__sound__supervising_sound_editor_movies_before']\n",
      "time: 0.41457562899995537\n",
      "obtaine movies\n",
      "time: 6.626970428000277\n",
      "calculate info\n",
      "time: 5.631095036000261\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__directing__director__1\n",
      "['crew__directing__director__1_avg_profit', 'crew__directing__director__1_avg_revenue', 'crew__directing__director__1_movies_before']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 0.42424578800000745\n",
      "obtaine movies\n",
      "time: 3.494952580000245\n",
      "calculate info\n",
      "time: 2.364675585000441\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__directing__script_supervisor\n",
      "['crew__directing__script_supervisor_avg_profit', 'crew__directing__script_supervisor_avg_revenue', 'crew__directing__script_supervisor_movies_before']\n",
      "time: 0.4221559970001181\n",
      "obtaine movies\n",
      "time: 6.618364509999992\n",
      "calculate info\n",
      "time: 5.594956699999784\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__production__casting\n",
      "['crew__production__casting_avg_profit', 'crew__production__casting_avg_revenue', 'crew__production__casting_movies_before']\n",
      "time: 0.4222412159997475\n",
      "obtaine movies\n",
      "time: 3.9408069790006266\n",
      "calculate info\n",
      "time: 3.7466235099991536\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__production__executive_producer__1\n",
      "['crew__production__executive_producer__1_avg_profit', 'crew__production__executive_producer__1_avg_revenue', 'crew__production__executive_producer__1_movies_before']\n",
      "time: 0.42071829499946034\n",
      "obtaine movies\n",
      "time: 6.458412651000799\n",
      "calculate info\n",
      "time: 5.313449806999415\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__production__producer__1\n",
      "['crew__production__producer__1_avg_profit', 'crew__production__producer__1_avg_revenue', 'crew__production__producer__1_movies_before']\n",
      "time: 0.42188858300050924\n",
      "obtaine movies\n",
      "time: 2.322663597999963\n",
      "calculate info\n",
      "time: 2.4990651649995925\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__production__producer__2\n",
      "['crew__production__producer__2_avg_profit', 'crew__production__producer__2_avg_revenue', 'crew__production__producer__2_movies_before']\n",
      "time: 0.42201537900018593\n",
      "obtaine movies\n",
      "time: 7.302612190000218\n",
      "calculate info\n",
      "time: 5.088602261000233\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__editing__editor__1\n",
      "['crew__editing__editor__1_avg_profit', 'crew__editing__editor__1_avg_revenue', 'crew__editing__editor__1_movies_before']\n",
      "time: 0.41609062800034735\n",
      "obtaine movies\n",
      "time: 5.711417998999423\n",
      "calculate info\n",
      "time: 4.002649394999935\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__costume__costume_designer\n",
      "['crew__costume__costume_designer_avg_profit', 'crew__costume__costume_designer_avg_revenue', 'crew__costume__costume_designer_movies_before']\n",
      "time: 0.4213344520003375\n",
      "obtaine movies\n",
      "time: 1.8954817789999652\n",
      "calculate info\n",
      "time: 1.367146952999974\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__costume__costume_supervisor\n",
      "['crew__costume__costume_supervisor_avg_profit', 'crew__costume__costume_supervisor_avg_revenue', 'crew__costume__costume_supervisor_movies_before']\n",
      "time: 0.4138694379998924\n",
      "obtaine movies\n",
      "time: 1.749328036999941\n",
      "calculate info\n",
      "time: 1.60733460100073\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__costume__makeup_artist\n",
      "['crew__costume__makeup_artist_avg_profit', 'crew__costume__makeup_artist_avg_revenue', 'crew__costume__makeup_artist_movies_before']\n",
      "time: 0.42443866899975546\n",
      "obtaine movies\n",
      "time: 2.4367524609997417\n",
      "calculate info\n",
      "time: 1.6380889970005228\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__crew__stunt_coordinator\n",
      "['crew__crew__stunt_coordinator_avg_profit', 'crew__crew__stunt_coordinator_avg_revenue', 'crew__crew__stunt_coordinator_movies_before']\n",
      "time: 0.41768476700053725\n",
      "obtaine movies\n",
      "time: 3.261097938000603\n",
      "calculate info\n",
      "time: 3.237630351000007\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__writing__screenplay__1\n",
      "['crew__writing__screenplay__1_avg_profit', 'crew__writing__screenplay__1_avg_revenue', 'crew__writing__screenplay__1_movies_before']\n",
      "time: 0.43114995999985695\n",
      "obtaine movies\n",
      "time: 3.684783635999338\n",
      "calculate info\n",
      "time: 3.0374181030001637\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__art__art_direction\n",
      "['crew__art__art_direction_avg_profit', 'crew__art__art_direction_avg_revenue', 'crew__art__art_direction_movies_before']\n",
      "time: 0.42356227000072977\n",
      "obtaine movies\n",
      "time: 5.973436854000283\n",
      "calculate info\n",
      "time: 4.23521663000065\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__art__production_design\n",
      "['crew__art__production_design_avg_profit', 'crew__art__production_design_avg_revenue', 'crew__art__production_design_movies_before']\n",
      "time: 0.4338600099999894\n",
      "obtaine movies\n",
      "time: 2.1816460559994084\n",
      "calculate info\n",
      "time: 1.46121743599997\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__art__property_master\n",
      "['crew__art__property_master_avg_profit', 'crew__art__property_master_avg_revenue', 'crew__art__property_master_movies_before']\n",
      "time: 0.41774668699963513\n",
      "obtaine movies\n",
      "time: 4.172691652999674\n",
      "calculate info\n",
      "time: 2.925879760000498\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__art__set_decoration\n",
      "['crew__art__set_decoration_avg_profit', 'crew__art__set_decoration_avg_revenue', 'crew__art__set_decoration_movies_before']\n",
      "time: 0.42228411699943535\n",
      "obtaine movies\n",
      "time: 2.2019341929999428\n",
      "calculate info\n",
      "time: 1.7343355249995511\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__visualeffects__visual_effects_supervisor\n",
      "['crew__visualeffects__visual_effects_supervisor_avg_profit', 'crew__visualeffects__visual_effects_supervisor_avg_revenue', 'crew__visualeffects__visual_effects_supervisor_movies_before']\n",
      "time: 0.4289139549991887\n",
      "obtaine movies\n",
      "time: 7.2504589209993355\n",
      "calculate info\n",
      "time: 5.132844810000279\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__camera__director_of_photography\n",
      "['crew__camera__director_of_photography_avg_profit', 'crew__camera__director_of_photography_avg_revenue', 'crew__camera__director_of_photography_movies_before']\n",
      "time: 0.4286371190000864\n",
      "obtaine movies\n",
      "time: 1.9330212340000799\n",
      "calculate info\n",
      "time: 1.2156783130003532\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__camera__steadicam_operator\n",
      "['crew__camera__steadicam_operator_avg_profit', 'crew__camera__steadicam_operator_avg_revenue', 'crew__camera__steadicam_operator_movies_before']\n",
      "time: 0.43415218500013\n",
      "obtaine movies\n",
      "time: 3.299570180000046\n",
      "calculate info\n",
      "time: 2.1770144080001046\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__camera__still_photographer\n",
      "['crew__camera__still_photographer_avg_profit', 'crew__camera__still_photographer_avg_revenue', 'crew__camera__still_photographer_movies_before']\n",
      "time: 0.42814945400004945\n",
      "obtaine movies\n",
      "time: 1.3123065969994059\n",
      "calculate info\n",
      "time: 0.8872675050006364\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue']\n",
      "META__collection_name\n",
      "['collection_avg_profit', 'collection_avg_revenue']\n",
      "time: 0.4281084020003618\n",
      "overall time: 356.1765088510001\n",
      "------------------------------------------------------\n",
      "profitability_1percentile_years2000-2018_budget_start_1percent\n",
      "-------------------------------------------------------\n",
      "obtaine movies\n",
      "time: 20.62016837400006\n",
      "calculate info\n",
      "time: 21.583690535999267\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue']\n",
      "META__production_company_1\n",
      "['production_company_1_avg_profit', 'production_company_1_avg_revenue']\n",
      "META__production_company_2\n",
      "['production_company_2_avg_profit', 'production_company_2_avg_revenue']\n",
      "META__production_company_3\n",
      "['production_company_3_avg_profit', 'production_company_3_avg_revenue']\n",
      "time: 1.228188443000363\n",
      "obtaine movies\n",
      "time: 63.50226292199932\n",
      "calculate info\n",
      "time: 48.88947990100041\n",
      "get columns\n",
      "['avg_profit', 'experience', 'avg_revenue', 'movies_before']\n",
      "META__cast_1_name\n",
      "['cast_1_avg_profit', 'cast_1_experience', 'cast_1_avg_revenue', 'cast_1_movies_before']\n",
      "META__cast_2_name\n",
      "['cast_2_avg_profit', 'cast_2_experience', 'cast_2_avg_revenue', 'cast_2_movies_before']\n",
      "META__cast_3_name\n",
      "['cast_3_avg_profit', 'cast_3_experience', 'cast_3_avg_revenue', 'cast_3_movies_before']\n",
      "META__cast_4_name\n",
      "['cast_4_avg_profit', 'cast_4_experience', 'cast_4_avg_revenue', 'cast_4_movies_before']\n",
      "META__cast_5_name\n",
      "['cast_5_avg_profit', 'cast_5_experience', 'cast_5_avg_revenue', 'cast_5_movies_before']\n",
      "META__cast_6_name\n",
      "['cast_6_avg_profit', 'cast_6_experience', 'cast_6_avg_revenue', 'cast_6_movies_before']\n",
      "META__cast_7_name\n",
      "['cast_7_avg_profit', 'cast_7_experience', 'cast_7_avg_revenue', 'cast_7_movies_before']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "META__cast_8_name\n",
      "['cast_8_avg_profit', 'cast_8_experience', 'cast_8_avg_revenue', 'cast_8_movies_before']\n",
      "time: 3.1905782440007897\n",
      "obtaine movies\n",
      "time: 2.711724394000157\n",
      "calculate info\n",
      "time: 1.6742312000005768\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__music_editor\n",
      "['crew__sound__music_editor_avg_profit', 'crew__sound__music_editor_avg_revenue', 'crew__sound__music_editor_movies_before']\n",
      "time: 0.4148681110000325\n",
      "obtaine movies\n",
      "time: 6.05908152900065\n",
      "calculate info\n",
      "time: 4.595228652000515\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__original_music_composer\n",
      "['crew__sound__original_music_composer_avg_profit', 'crew__sound__original_music_composer_avg_revenue', 'crew__sound__original_music_composer_movies_before']\n",
      "time: 0.4157394550002209\n",
      "obtaine movies\n",
      "time: 2.145218697999553\n",
      "calculate info\n",
      "time: 1.4467854229997101\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__sound_designer\n",
      "['crew__sound__sound_designer_avg_profit', 'crew__sound__sound_designer_avg_revenue', 'crew__sound__sound_designer_movies_before']\n",
      "time: 0.40997176699966076\n",
      "obtaine movies\n",
      "time: 2.284066556000653\n",
      "calculate info\n",
      "time: 1.4751722210003209\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__sound_effects_editor\n",
      "['crew__sound__sound_effects_editor_avg_profit', 'crew__sound__sound_effects_editor_avg_revenue', 'crew__sound__sound_effects_editor_movies_before']\n",
      "time: 0.4057292919997053\n",
      "obtaine movies\n",
      "time: 3.1523669910002354\n",
      "calculate info\n",
      "time: 2.073141788000612\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__sound_re_recording_mixer\n",
      "['crew__sound__sound_re_recording_mixer_avg_profit', 'crew__sound__sound_re_recording_mixer_avg_revenue', 'crew__sound__sound_re_recording_mixer_movies_before']\n",
      "time: 0.4090218349992938\n",
      "obtaine movies\n",
      "time: 2.869863552999959\n",
      "calculate info\n",
      "time: 1.8420443049999449\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__supervising_sound_editor\n",
      "['crew__sound__supervising_sound_editor_avg_profit', 'crew__sound__supervising_sound_editor_avg_revenue', 'crew__sound__supervising_sound_editor_movies_before']\n",
      "time: 0.41056172200023866\n",
      "obtaine movies\n",
      "time: 6.595368702000087\n",
      "calculate info\n",
      "time: 5.631166416000269\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__directing__director__1\n",
      "['crew__directing__director__1_avg_profit', 'crew__directing__director__1_avg_revenue', 'crew__directing__director__1_movies_before']\n",
      "time: 0.41759409600035724\n",
      "obtaine movies\n",
      "time: 3.459800677000203\n",
      "calculate info\n",
      "time: 2.3366770780003208\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__directing__script_supervisor\n",
      "['crew__directing__script_supervisor_avg_profit', 'crew__directing__script_supervisor_avg_revenue', 'crew__directing__script_supervisor_movies_before']\n",
      "time: 0.4090913019999789\n",
      "obtaine movies\n",
      "time: 6.674792720999903\n",
      "calculate info\n",
      "time: 5.605617742000504\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__production__casting\n",
      "['crew__production__casting_avg_profit', 'crew__production__casting_avg_revenue', 'crew__production__casting_movies_before']\n",
      "time: 0.4232127559998844\n",
      "obtaine movies\n",
      "time: 3.877505958000256\n",
      "calculate info\n",
      "time: 3.7258257209996373\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__production__executive_producer__1\n",
      "['crew__production__executive_producer__1_avg_profit', 'crew__production__executive_producer__1_avg_revenue', 'crew__production__executive_producer__1_movies_before']\n",
      "time: 0.41778559099930135\n",
      "obtaine movies\n",
      "time: 6.54284632100007\n",
      "calculate info\n",
      "time: 5.341717887000414\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__production__producer__1\n",
      "['crew__production__producer__1_avg_profit', 'crew__production__producer__1_avg_revenue', 'crew__production__producer__1_movies_before']\n",
      "time: 0.4254238550001901\n",
      "obtaine movies\n",
      "time: 2.2683095869997487\n",
      "calculate info\n",
      "time: 2.503051529000004\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__production__producer__2\n",
      "['crew__production__producer__2_avg_profit', 'crew__production__producer__2_avg_revenue', 'crew__production__producer__2_movies_before']\n",
      "time: 0.4182291509996503\n",
      "obtaine movies\n",
      "time: 7.305852729000435\n",
      "calculate info\n",
      "time: 5.078382320999481\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__editing__editor__1\n",
      "['crew__editing__editor__1_avg_profit', 'crew__editing__editor__1_avg_revenue', 'crew__editing__editor__1_movies_before']\n",
      "time: 0.4149124550003762\n",
      "obtaine movies\n",
      "time: 5.705061991000548\n",
      "calculate info\n",
      "time: 3.982587608000358\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__costume__costume_designer\n",
      "['crew__costume__costume_designer_avg_profit', 'crew__costume__costume_designer_avg_revenue', 'crew__costume__costume_designer_movies_before']\n",
      "time: 0.41506956099965464\n",
      "obtaine movies\n",
      "time: 1.968917610999597\n",
      "calculate info\n",
      "time: 1.3868888059996607\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__costume__costume_supervisor\n",
      "['crew__costume__costume_supervisor_avg_profit', 'crew__costume__costume_supervisor_avg_revenue', 'crew__costume__costume_supervisor_movies_before']\n",
      "time: 0.41211088499949255\n",
      "obtaine movies\n",
      "time: 1.7338049579993822\n",
      "calculate info\n",
      "time: 1.5930055260005247\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__costume__makeup_artist\n",
      "['crew__costume__makeup_artist_avg_profit', 'crew__costume__makeup_artist_avg_revenue', 'crew__costume__makeup_artist_movies_before']\n",
      "time: 0.4215393000004042\n",
      "obtaine movies\n",
      "time: 2.366575809999631\n",
      "calculate info\n",
      "time: 1.6271725390006395\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__crew__stunt_coordinator\n",
      "['crew__crew__stunt_coordinator_avg_profit', 'crew__crew__stunt_coordinator_avg_revenue', 'crew__crew__stunt_coordinator_movies_before']\n",
      "time: 0.4128151409995553\n",
      "obtaine movies\n",
      "time: 3.3071937980002986\n",
      "calculate info\n",
      "time: 3.2232263430005332\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__writing__screenplay__1\n",
      "['crew__writing__screenplay__1_avg_profit', 'crew__writing__screenplay__1_avg_revenue', 'crew__writing__screenplay__1_movies_before']\n",
      "time: 0.4249199600008069\n",
      "obtaine movies\n",
      "time: 3.5948113589993227\n",
      "calculate info\n",
      "time: 3.014759484000024\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__art__art_direction\n",
      "['crew__art__art_direction_avg_profit', 'crew__art__art_direction_avg_revenue', 'crew__art__art_direction_movies_before']\n",
      "time: 0.41795363999972324\n",
      "obtaine movies\n",
      "time: 6.01398169299955\n",
      "calculate info\n",
      "time: 4.208631294999577\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__art__production_design\n",
      "['crew__art__production_design_avg_profit', 'crew__art__production_design_avg_revenue', 'crew__art__production_design_movies_before']\n",
      "time: 0.4225409900000159\n",
      "obtaine movies\n",
      "time: 2.103947499000242\n",
      "calculate info\n",
      "time: 1.472561670999312\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__art__property_master\n",
      "['crew__art__property_master_avg_profit', 'crew__art__property_master_avg_revenue', 'crew__art__property_master_movies_before']\n",
      "time: 0.4236571299998104\n",
      "obtaine movies\n",
      "time: 4.2362339159999465\n",
      "calculate info\n",
      "time: 2.9414785449998817\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__art__set_decoration\n",
      "['crew__art__set_decoration_avg_profit', 'crew__art__set_decoration_avg_revenue', 'crew__art__set_decoration_movies_before']\n",
      "time: 0.4178755770008138\n",
      "obtaine movies\n",
      "time: 2.2754450649999853\n",
      "calculate info\n",
      "time: 1.7396654089998265\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__visualeffects__visual_effects_supervisor\n",
      "['crew__visualeffects__visual_effects_supervisor_avg_profit', 'crew__visualeffects__visual_effects_supervisor_avg_revenue', 'crew__visualeffects__visual_effects_supervisor_movies_before']\n",
      "time: 0.42201891300010175\n",
      "obtaine movies\n",
      "time: 7.236031847000049\n",
      "calculate info\n",
      "time: 5.124951454999973\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__camera__director_of_photography\n",
      "['crew__camera__director_of_photography_avg_profit', 'crew__camera__director_of_photography_avg_revenue', 'crew__camera__director_of_photography_movies_before']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 0.43334584399963205\n",
      "obtaine movies\n",
      "time: 1.9223838469997645\n",
      "calculate info\n",
      "time: 1.2152322090005327\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__camera__steadicam_operator\n",
      "['crew__camera__steadicam_operator_avg_profit', 'crew__camera__steadicam_operator_avg_revenue', 'crew__camera__steadicam_operator_movies_before']\n",
      "time: 0.4218123969994849\n",
      "obtaine movies\n",
      "time: 3.27607886000078\n",
      "calculate info\n",
      "time: 2.15976480000063\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__camera__still_photographer\n",
      "['crew__camera__still_photographer_avg_profit', 'crew__camera__still_photographer_avg_revenue', 'crew__camera__still_photographer_movies_before']\n",
      "time: 0.43207372300003044\n",
      "obtaine movies\n",
      "time: 1.3738515389995882\n",
      "calculate info\n",
      "time: 0.8865554410003824\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue']\n",
      "META__collection_name\n",
      "['collection_avg_profit', 'collection_avg_revenue']\n",
      "time: 0.4268673309998121\n",
      "overall time: 355.1935167820002\n",
      "------------------------------------------------------\n",
      "profitability_1percentile_years2000-2018_budget_start_5percent\n",
      "-------------------------------------------------------\n",
      "obtaine movies\n",
      "time: 21.123557808999976\n",
      "calculate info\n",
      "time: 22.146091337000144\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue']\n",
      "META__production_company_1\n",
      "['production_company_1_avg_profit', 'production_company_1_avg_revenue']\n",
      "META__production_company_2\n",
      "['production_company_2_avg_profit', 'production_company_2_avg_revenue']\n",
      "META__production_company_3\n",
      "['production_company_3_avg_profit', 'production_company_3_avg_revenue']\n",
      "time: 1.2335795490007513\n",
      "obtaine movies\n",
      "time: 64.26255072100048\n",
      "calculate info\n",
      "time: 49.63218676999986\n",
      "get columns\n",
      "['avg_profit', 'experience', 'avg_revenue', 'movies_before']\n",
      "META__cast_1_name\n",
      "['cast_1_avg_profit', 'cast_1_experience', 'cast_1_avg_revenue', 'cast_1_movies_before']\n",
      "META__cast_2_name\n",
      "['cast_2_avg_profit', 'cast_2_experience', 'cast_2_avg_revenue', 'cast_2_movies_before']\n",
      "META__cast_3_name\n",
      "['cast_3_avg_profit', 'cast_3_experience', 'cast_3_avg_revenue', 'cast_3_movies_before']\n",
      "META__cast_4_name\n",
      "['cast_4_avg_profit', 'cast_4_experience', 'cast_4_avg_revenue', 'cast_4_movies_before']\n",
      "META__cast_5_name\n",
      "['cast_5_avg_profit', 'cast_5_experience', 'cast_5_avg_revenue', 'cast_5_movies_before']\n",
      "META__cast_6_name\n",
      "['cast_6_avg_profit', 'cast_6_experience', 'cast_6_avg_revenue', 'cast_6_movies_before']\n",
      "META__cast_7_name\n",
      "['cast_7_avg_profit', 'cast_7_experience', 'cast_7_avg_revenue', 'cast_7_movies_before']\n",
      "META__cast_8_name\n",
      "['cast_8_avg_profit', 'cast_8_experience', 'cast_8_avg_revenue', 'cast_8_movies_before']\n",
      "time: 3.2980273359999046\n",
      "obtaine movies\n",
      "time: 2.910945310999523\n",
      "calculate info\n",
      "time: 1.7714248989996122\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__music_editor\n",
      "['crew__sound__music_editor_avg_profit', 'crew__sound__music_editor_avg_revenue', 'crew__sound__music_editor_movies_before']\n",
      "time: 0.4213508329994511\n",
      "obtaine movies\n",
      "time: 6.208394207999845\n",
      "calculate info\n",
      "time: 4.818647170999611\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__original_music_composer\n",
      "['crew__sound__original_music_composer_avg_profit', 'crew__sound__original_music_composer_avg_revenue', 'crew__sound__original_music_composer_movies_before']\n",
      "time: 0.420666498999708\n",
      "obtaine movies\n",
      "time: 2.266082082000139\n",
      "calculate info\n",
      "time: 1.5255644480002957\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__sound_designer\n",
      "['crew__sound__sound_designer_avg_profit', 'crew__sound__sound_designer_avg_revenue', 'crew__sound__sound_designer_movies_before']\n",
      "time: 0.40381248899939237\n",
      "obtaine movies\n",
      "time: 2.3063030189996425\n",
      "calculate info\n",
      "time: 1.5607203669997034\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__sound_effects_editor\n",
      "['crew__sound__sound_effects_editor_avg_profit', 'crew__sound__sound_effects_editor_avg_revenue', 'crew__sound__sound_effects_editor_movies_before']\n",
      "time: 0.4143877529995734\n",
      "obtaine movies\n",
      "time: 3.2970888110003216\n",
      "calculate info\n",
      "time: 2.151584114999423\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__sound_re_recording_mixer\n",
      "['crew__sound__sound_re_recording_mixer_avg_profit', 'crew__sound__sound_re_recording_mixer_avg_revenue', 'crew__sound__sound_re_recording_mixer_movies_before']\n",
      "time: 0.4147136270003102\n",
      "obtaine movies\n",
      "time: 2.9537634680000338\n",
      "calculate info\n",
      "time: 1.9275017909994858\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__supervising_sound_editor\n",
      "['crew__sound__supervising_sound_editor_avg_profit', 'crew__sound__supervising_sound_editor_avg_revenue', 'crew__sound__supervising_sound_editor_movies_before']\n",
      "time: 0.41654817100061337\n",
      "obtaine movies\n",
      "time: 6.763630633000503\n",
      "calculate info\n",
      "time: 5.686523723999926\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__directing__director__1\n",
      "['crew__directing__director__1_avg_profit', 'crew__directing__director__1_avg_revenue', 'crew__directing__director__1_movies_before']\n",
      "time: 0.4192201200003183\n",
      "obtaine movies\n",
      "time: 3.68345813400083\n",
      "calculate info\n",
      "time: 2.4367224170000554\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__directing__script_supervisor\n",
      "['crew__directing__script_supervisor_avg_profit', 'crew__directing__script_supervisor_avg_revenue', 'crew__directing__script_supervisor_movies_before']\n",
      "time: 0.4258436630007054\n",
      "obtaine movies\n",
      "time: 6.898245739999766\n",
      "calculate info\n",
      "time: 5.670202216000689\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__production__casting\n",
      "['crew__production__casting_avg_profit', 'crew__production__casting_avg_revenue', 'crew__production__casting_movies_before']\n",
      "time: 0.4165144810003767\n",
      "obtaine movies\n",
      "time: 3.9442427630001475\n",
      "calculate info\n",
      "time: 3.769333737999659\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__production__executive_producer__1\n",
      "['crew__production__executive_producer__1_avg_profit', 'crew__production__executive_producer__1_avg_revenue', 'crew__production__executive_producer__1_movies_before']\n",
      "time: 0.4126343709995126\n",
      "obtaine movies\n",
      "time: 6.7434853400000065\n",
      "calculate info\n",
      "time: 5.438211054000021\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__production__producer__1\n",
      "['crew__production__producer__1_avg_profit', 'crew__production__producer__1_avg_revenue', 'crew__production__producer__1_movies_before']\n",
      "time: 0.41650841800037597\n",
      "obtaine movies\n",
      "time: 2.2846876210005576\n",
      "calculate info\n",
      "time: 2.4485247829998116\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__production__producer__2\n",
      "['crew__production__producer__2_avg_profit', 'crew__production__producer__2_avg_revenue', 'crew__production__producer__2_movies_before']\n",
      "time: 0.39566643699981796\n",
      "obtaine movies\n",
      "time: 7.1886155150004925\n",
      "calculate info\n",
      "time: 5.036077162999391\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__editing__editor__1\n",
      "['crew__editing__editor__1_avg_profit', 'crew__editing__editor__1_avg_revenue', 'crew__editing__editor__1_movies_before']\n",
      "time: 0.4037419520000185\n",
      "obtaine movies\n",
      "time: 5.748824825000156\n",
      "calculate info\n",
      "time: 3.9963671049999903\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__costume__costume_designer\n",
      "['crew__costume__costume_designer_avg_profit', 'crew__costume__costume_designer_avg_revenue', 'crew__costume__costume_designer_movies_before']\n",
      "time: 0.41245099699972343\n",
      "obtaine movies\n",
      "time: 1.9375725330000932\n",
      "calculate info\n",
      "time: 1.3780121919999146\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__costume__costume_supervisor\n",
      "['crew__costume__costume_supervisor_avg_profit', 'crew__costume__costume_supervisor_avg_revenue', 'crew__costume__costume_supervisor_movies_before']\n",
      "time: 0.3993198799998936\n",
      "obtaine movies\n",
      "time: 1.723933787999158\n",
      "calculate info\n",
      "time: 1.587468051999167\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__costume__makeup_artist\n",
      "['crew__costume__makeup_artist_avg_profit', 'crew__costume__makeup_artist_avg_revenue', 'crew__costume__makeup_artist_movies_before']\n",
      "time: 0.3950758049995784\n",
      "obtaine movies\n",
      "time: 2.377929213000243\n",
      "calculate info\n",
      "time: 1.6199314730001788\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__crew__stunt_coordinator\n",
      "['crew__crew__stunt_coordinator_avg_profit', 'crew__crew__stunt_coordinator_avg_revenue', 'crew__crew__stunt_coordinator_movies_before']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 0.4002911829993536\n",
      "obtaine movies\n",
      "time: 3.277320881000378\n",
      "calculate info\n",
      "time: 3.1994917310003075\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__writing__screenplay__1\n",
      "['crew__writing__screenplay__1_avg_profit', 'crew__writing__screenplay__1_avg_revenue', 'crew__writing__screenplay__1_movies_before']\n",
      "time: 0.4037151000002268\n",
      "obtaine movies\n",
      "time: 3.6731306559995573\n",
      "calculate info\n",
      "time: 3.093828003999988\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__art__art_direction\n",
      "['crew__art__art_direction_avg_profit', 'crew__art__art_direction_avg_revenue', 'crew__art__art_direction_movies_before']\n",
      "time: 0.41124124599991774\n",
      "obtaine movies\n",
      "time: 6.430156292999527\n",
      "calculate info\n",
      "time: 4.39658052499999\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__art__production_design\n",
      "['crew__art__production_design_avg_profit', 'crew__art__production_design_avg_revenue', 'crew__art__production_design_movies_before']\n",
      "time: 0.4142729140003212\n",
      "obtaine movies\n",
      "time: 2.1166454349995547\n",
      "calculate info\n",
      "time: 1.483447960000376\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__art__property_master\n",
      "['crew__art__property_master_avg_profit', 'crew__art__property_master_avg_revenue', 'crew__art__property_master_movies_before']\n",
      "time: 0.4127600860001621\n",
      "obtaine movies\n",
      "time: 4.333128753999517\n",
      "calculate info\n",
      "time: 2.9789063560001523\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__art__set_decoration\n",
      "['crew__art__set_decoration_avg_profit', 'crew__art__set_decoration_avg_revenue', 'crew__art__set_decoration_movies_before']\n",
      "time: 0.4094676750000872\n",
      "obtaine movies\n",
      "time: 2.2463125779995607\n",
      "calculate info\n",
      "time: 1.8058068130003448\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__visualeffects__visual_effects_supervisor\n",
      "['crew__visualeffects__visual_effects_supervisor_avg_profit', 'crew__visualeffects__visual_effects_supervisor_avg_revenue', 'crew__visualeffects__visual_effects_supervisor_movies_before']\n",
      "time: 0.41904640200027643\n",
      "obtaine movies\n",
      "time: 7.2918208889996095\n",
      "calculate info\n",
      "time: 5.1388909079996665\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__camera__director_of_photography\n",
      "['crew__camera__director_of_photography_avg_profit', 'crew__camera__director_of_photography_avg_revenue', 'crew__camera__director_of_photography_movies_before']\n",
      "time: 0.4129781580004419\n",
      "obtaine movies\n",
      "time: 2.0425706819996776\n",
      "calculate info\n",
      "time: 1.2780081719993177\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__camera__steadicam_operator\n",
      "['crew__camera__steadicam_operator_avg_profit', 'crew__camera__steadicam_operator_avg_revenue', 'crew__camera__steadicam_operator_movies_before']\n",
      "time: 0.4249929119996523\n",
      "obtaine movies\n",
      "time: 3.417526254999757\n",
      "calculate info\n",
      "time: 2.2652508789997228\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__camera__still_photographer\n",
      "['crew__camera__still_photographer_avg_profit', 'crew__camera__still_photographer_avg_revenue', 'crew__camera__still_photographer_movies_before']\n",
      "time: 0.42743029600023874\n",
      "obtaine movies\n",
      "time: 1.3472229970002445\n",
      "calculate info\n",
      "time: 0.9117274479995103\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue']\n",
      "META__collection_name\n",
      "['collection_avg_profit', 'collection_avg_revenue']\n",
      "time: 0.42489291099991533\n",
      "overall time: 361.4575508349999\n",
      "------------------------------------------------------\n",
      "revenue_1percentile\n",
      "-------------------------------------------------------\n",
      "obtaine movies\n",
      "time: 31.753673731999697\n",
      "calculate info\n",
      "time: 46.75927035299992\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue']\n",
      "META__production_company_1\n",
      "['production_company_1_avg_profit', 'production_company_1_avg_revenue']\n",
      "META__production_company_2\n",
      "['production_company_2_avg_profit', 'production_company_2_avg_revenue']\n",
      "META__production_company_3\n",
      "['production_company_3_avg_profit', 'production_company_3_avg_revenue']\n",
      "time: 1.9191769010003554\n",
      "obtaine movies\n",
      "time: 108.21045989499999\n",
      "calculate info\n",
      "time: 81.05116401500072\n",
      "get columns\n",
      "['avg_profit', 'experience', 'avg_revenue', 'movies_before']\n",
      "META__cast_1_name\n",
      "['cast_1_avg_profit', 'cast_1_experience', 'cast_1_avg_revenue', 'cast_1_movies_before']\n",
      "META__cast_2_name\n",
      "['cast_2_avg_profit', 'cast_2_experience', 'cast_2_avg_revenue', 'cast_2_movies_before']\n",
      "META__cast_3_name\n",
      "['cast_3_avg_profit', 'cast_3_experience', 'cast_3_avg_revenue', 'cast_3_movies_before']\n",
      "META__cast_4_name\n",
      "['cast_4_avg_profit', 'cast_4_experience', 'cast_4_avg_revenue', 'cast_4_movies_before']\n",
      "META__cast_5_name\n",
      "['cast_5_avg_profit', 'cast_5_experience', 'cast_5_avg_revenue', 'cast_5_movies_before']\n",
      "META__cast_6_name\n",
      "['cast_6_avg_profit', 'cast_6_experience', 'cast_6_avg_revenue', 'cast_6_movies_before']\n",
      "META__cast_7_name\n",
      "['cast_7_avg_profit', 'cast_7_experience', 'cast_7_avg_revenue', 'cast_7_movies_before']\n",
      "META__cast_8_name\n",
      "['cast_8_avg_profit', 'cast_8_experience', 'cast_8_avg_revenue', 'cast_8_movies_before']\n",
      "time: 5.191816601000028\n",
      "obtaine movies\n",
      "time: 3.7925265940002646\n",
      "calculate info\n",
      "time: 2.279087100000652\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__music_editor\n",
      "['crew__sound__music_editor_avg_profit', 'crew__sound__music_editor_avg_revenue', 'crew__sound__music_editor_movies_before']\n",
      "time: 0.6511811069995019\n",
      "obtaine movies\n",
      "time: 10.047900890000165\n",
      "calculate info\n",
      "time: 8.034955758999786\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__original_music_composer\n",
      "['crew__sound__original_music_composer_avg_profit', 'crew__sound__original_music_composer_avg_revenue', 'crew__sound__original_music_composer_movies_before']\n",
      "time: 0.6394134619995384\n",
      "obtaine movies\n",
      "time: 2.7021812370003317\n",
      "calculate info\n",
      "time: 1.8003824710003755\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__sound_designer\n",
      "['crew__sound__sound_designer_avg_profit', 'crew__sound__sound_designer_avg_revenue', 'crew__sound__sound_designer_movies_before']\n",
      "time: 0.6487222710002243\n",
      "obtaine movies\n",
      "time: 2.7829678680000143\n",
      "calculate info\n",
      "time: 1.8488917049999145\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__sound_effects_editor\n",
      "['crew__sound__sound_effects_editor_avg_profit', 'crew__sound__sound_effects_editor_avg_revenue', 'crew__sound__sound_effects_editor_movies_before']\n",
      "time: 0.6344320660000449\n",
      "obtaine movies\n",
      "time: 4.363692423999964\n",
      "calculate info\n",
      "time: 2.811737070000163\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__sound_re_recording_mixer\n",
      "['crew__sound__sound_re_recording_mixer_avg_profit', 'crew__sound__sound_re_recording_mixer_avg_revenue', 'crew__sound__sound_re_recording_mixer_movies_before']\n",
      "time: 0.6438258109992603\n",
      "obtaine movies\n",
      "time: 3.999037182000393\n",
      "calculate info\n",
      "time: 2.5808579889999237\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__supervising_sound_editor\n",
      "['crew__sound__supervising_sound_editor_avg_profit', 'crew__sound__supervising_sound_editor_avg_revenue', 'crew__sound__supervising_sound_editor_movies_before']\n",
      "time: 0.6536170520002997\n",
      "obtaine movies\n",
      "time: 11.891657926000335\n",
      "calculate info\n",
      "time: 9.22626959199988\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__directing__director__1\n",
      "['crew__directing__director__1_avg_profit', 'crew__directing__director__1_avg_revenue', 'crew__directing__director__1_movies_before']\n",
      "time: 0.6526832090003154\n",
      "obtaine movies\n",
      "time: 4.980919894000181\n",
      "calculate info\n",
      "time: 3.2966916129998936\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__directing__script_supervisor\n",
      "['crew__directing__script_supervisor_avg_profit', 'crew__directing__script_supervisor_avg_revenue', 'crew__directing__script_supervisor_movies_before']\n",
      "time: 0.6583826109999791\n",
      "obtaine movies\n",
      "time: 9.58154288699916\n",
      "calculate info\n",
      "time: 8.191538281999783\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__production__casting\n",
      "['crew__production__casting_avg_profit', 'crew__production__casting_avg_revenue', 'crew__production__casting_movies_before']\n",
      "time: 0.6470240819999162\n",
      "obtaine movies\n",
      "time: 5.833469991999664\n",
      "calculate info\n",
      "time: 5.542818394999813\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__production__executive_producer__1\n",
      "['crew__production__executive_producer__1_avg_profit', 'crew__production__executive_producer__1_avg_revenue', 'crew__production__executive_producer__1_movies_before']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 0.6639088739993895\n",
      "obtaine movies\n",
      "time: 10.943256979999205\n",
      "calculate info\n",
      "time: 8.877879609000047\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__production__producer__1\n",
      "['crew__production__producer__1_avg_profit', 'crew__production__producer__1_avg_revenue', 'crew__production__producer__1_movies_before']\n",
      "time: 0.6877513409999665\n",
      "obtaine movies\n",
      "time: 3.132661492999432\n",
      "calculate info\n",
      "time: 3.278778345000319\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__production__producer__2\n",
      "['crew__production__producer__2_avg_profit', 'crew__production__producer__2_avg_revenue', 'crew__production__producer__2_movies_before']\n",
      "time: 0.6781304080004702\n",
      "obtaine movies\n",
      "time: 12.488896402000137\n",
      "calculate info\n",
      "time: 8.564988083999197\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__editing__editor__1\n",
      "['crew__editing__editor__1_avg_profit', 'crew__editing__editor__1_avg_revenue', 'crew__editing__editor__1_movies_before']\n",
      "time: 0.6593565940002009\n",
      "obtaine movies\n",
      "time: 9.148763700000018\n",
      "calculate info\n",
      "time: 6.582764940999368\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__costume__costume_designer\n",
      "['crew__costume__costume_designer_avg_profit', 'crew__costume__costume_designer_avg_revenue', 'crew__costume__costume_designer_movies_before']\n",
      "time: 0.6788560139993933\n",
      "obtaine movies\n",
      "time: 2.6447918309995657\n",
      "calculate info\n",
      "time: 1.829205009999896\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__costume__costume_supervisor\n",
      "['crew__costume__costume_supervisor_avg_profit', 'crew__costume__costume_supervisor_avg_revenue', 'crew__costume__costume_supervisor_movies_before']\n",
      "time: 0.6784905140002593\n",
      "obtaine movies\n",
      "time: 3.506566236999788\n",
      "calculate info\n",
      "time: 2.8241478030004146\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__costume__makeup_artist\n",
      "['crew__costume__makeup_artist_avg_profit', 'crew__costume__makeup_artist_avg_revenue', 'crew__costume__makeup_artist_movies_before']\n",
      "time: 0.6671765559995038\n",
      "obtaine movies\n",
      "time: 3.515335979000156\n",
      "calculate info\n",
      "time: 2.3355654140004845\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__crew__stunt_coordinator\n",
      "['crew__crew__stunt_coordinator_avg_profit', 'crew__crew__stunt_coordinator_avg_revenue', 'crew__crew__stunt_coordinator_movies_before']\n",
      "time: 0.6627683469996555\n",
      "obtaine movies\n",
      "time: 6.332394451000255\n",
      "calculate info\n",
      "time: 5.720905770999707\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__writing__screenplay__1\n",
      "['crew__writing__screenplay__1_avg_profit', 'crew__writing__screenplay__1_avg_revenue', 'crew__writing__screenplay__1_movies_before']\n",
      "time: 0.6765284360008081\n",
      "obtaine movies\n",
      "time: 6.157665676999386\n",
      "calculate info\n",
      "time: 4.922045297000295\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__art__art_direction\n",
      "['crew__art__art_direction_avg_profit', 'crew__art__art_direction_avg_revenue', 'crew__art__art_direction_movies_before']\n",
      "time: 0.6868323170001531\n",
      "obtaine movies\n",
      "time: 9.593694170000163\n",
      "calculate info\n",
      "time: 6.478535261999241\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__art__production_design\n",
      "['crew__art__production_design_avg_profit', 'crew__art__production_design_avg_revenue', 'crew__art__production_design_movies_before']\n",
      "time: 0.6645161799997368\n",
      "obtaine movies\n",
      "time: 3.1925429010007065\n",
      "calculate info\n",
      "time: 2.0915878750001866\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__art__property_master\n",
      "['crew__art__property_master_avg_profit', 'crew__art__property_master_avg_revenue', 'crew__art__property_master_movies_before']\n",
      "time: 0.6723268779996943\n",
      "obtaine movies\n",
      "time: 6.9845872320001945\n",
      "calculate info\n",
      "time: 5.092836668999553\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__art__set_decoration\n",
      "['crew__art__set_decoration_avg_profit', 'crew__art__set_decoration_avg_revenue', 'crew__art__set_decoration_movies_before']\n",
      "time: 0.6856145620004099\n",
      "obtaine movies\n",
      "time: 2.8888649929995154\n",
      "calculate info\n",
      "time: 2.1694988720000765\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__visualeffects__visual_effects_supervisor\n",
      "['crew__visualeffects__visual_effects_supervisor_avg_profit', 'crew__visualeffects__visual_effects_supervisor_avg_revenue', 'crew__visualeffects__visual_effects_supervisor_movies_before']\n",
      "time: 0.7088438569999198\n",
      "obtaine movies\n",
      "time: 12.629810241999621\n",
      "calculate info\n",
      "time: 8.618143257000156\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__camera__director_of_photography\n",
      "['crew__camera__director_of_photography_avg_profit', 'crew__camera__director_of_photography_avg_revenue', 'crew__camera__director_of_photography_movies_before']\n",
      "time: 0.6621479430004911\n",
      "obtaine movies\n",
      "time: 2.543851493999682\n",
      "calculate info\n",
      "time: 1.51607984400016\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__camera__steadicam_operator\n",
      "['crew__camera__steadicam_operator_avg_profit', 'crew__camera__steadicam_operator_avg_revenue', 'crew__camera__steadicam_operator_movies_before']\n",
      "time: 0.6571458870002971\n",
      "obtaine movies\n",
      "time: 4.648613527000634\n",
      "calculate info\n",
      "time: 3.1956351119997635\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__camera__still_photographer\n",
      "['crew__camera__still_photographer_avg_profit', 'crew__camera__still_photographer_avg_revenue', 'crew__camera__still_photographer_movies_before']\n",
      "time: 0.6623125989999608\n",
      "obtaine movies\n",
      "time: 2.2614196089998586\n",
      "calculate info\n",
      "time: 1.390624048999598\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue']\n",
      "META__collection_name\n",
      "['collection_avg_profit', 'collection_avg_revenue']\n",
      "time: 0.6628258609998738\n",
      "overall time: 582.5776396689998\n",
      "------------------------------------------------------\n",
      "revenue_5percentile\n",
      "-------------------------------------------------------\n",
      "obtaine movies\n",
      "time: 29.889311788999294\n",
      "calculate info\n",
      "time: 42.257907051000075\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue']\n",
      "META__production_company_1\n",
      "['production_company_1_avg_profit', 'production_company_1_avg_revenue']\n",
      "META__production_company_2\n",
      "['production_company_2_avg_profit', 'production_company_2_avg_revenue']\n",
      "META__production_company_3\n",
      "['production_company_3_avg_profit', 'production_company_3_avg_revenue']\n",
      "time: 1.8027145400001245\n",
      "obtaine movies\n",
      "time: 99.1255275780004\n",
      "calculate info\n",
      "time: 72.36534527399999\n",
      "get columns\n",
      "['avg_profit', 'experience', 'avg_revenue', 'movies_before']\n",
      "META__cast_1_name\n",
      "['cast_1_avg_profit', 'cast_1_experience', 'cast_1_avg_revenue', 'cast_1_movies_before']\n",
      "META__cast_2_name\n",
      "['cast_2_avg_profit', 'cast_2_experience', 'cast_2_avg_revenue', 'cast_2_movies_before']\n",
      "META__cast_3_name\n",
      "['cast_3_avg_profit', 'cast_3_experience', 'cast_3_avg_revenue', 'cast_3_movies_before']\n",
      "META__cast_4_name\n",
      "['cast_4_avg_profit', 'cast_4_experience', 'cast_4_avg_revenue', 'cast_4_movies_before']\n",
      "META__cast_5_name\n",
      "['cast_5_avg_profit', 'cast_5_experience', 'cast_5_avg_revenue', 'cast_5_movies_before']\n",
      "META__cast_6_name\n",
      "['cast_6_avg_profit', 'cast_6_experience', 'cast_6_avg_revenue', 'cast_6_movies_before']\n",
      "META__cast_7_name\n",
      "['cast_7_avg_profit', 'cast_7_experience', 'cast_7_avg_revenue', 'cast_7_movies_before']\n",
      "META__cast_8_name\n",
      "['cast_8_avg_profit', 'cast_8_experience', 'cast_8_avg_revenue', 'cast_8_movies_before']\n",
      "time: 4.688573958000234\n",
      "obtaine movies\n",
      "time: 3.4118607530008376\n",
      "calculate info\n",
      "time: 1.9495835360003184\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__music_editor\n",
      "['crew__sound__music_editor_avg_profit', 'crew__sound__music_editor_avg_revenue', 'crew__sound__music_editor_movies_before']\n",
      "time: 0.5865103539999836\n",
      "obtaine movies\n",
      "time: 9.31427622999945\n",
      "calculate info\n",
      "time: 7.207882433999657\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__original_music_composer\n",
      "['crew__sound__original_music_composer_avg_profit', 'crew__sound__original_music_composer_avg_revenue', 'crew__sound__original_music_composer_movies_before']\n",
      "time: 0.5926385519996984\n",
      "obtaine movies\n",
      "time: 2.265737720999823\n",
      "calculate info\n",
      "time: 1.520871246999377\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__sound_designer\n",
      "['crew__sound__sound_designer_avg_profit', 'crew__sound__sound_designer_avg_revenue', 'crew__sound__sound_designer_movies_before']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 0.5906714040002043\n",
      "obtaine movies\n",
      "time: 2.4609885469999426\n",
      "calculate info\n",
      "time: 1.617129852999824\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__sound_effects_editor\n",
      "['crew__sound__sound_effects_editor_avg_profit', 'crew__sound__sound_effects_editor_avg_revenue', 'crew__sound__sound_effects_editor_movies_before']\n",
      "time: 0.5857584079994922\n",
      "obtaine movies\n",
      "time: 3.8308347459997094\n",
      "calculate info\n",
      "time: 2.4788722690000213\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__sound_re_recording_mixer\n",
      "['crew__sound__sound_re_recording_mixer_avg_profit', 'crew__sound__sound_re_recording_mixer_avg_revenue', 'crew__sound__sound_re_recording_mixer_movies_before']\n",
      "time: 0.5898780450006598\n",
      "obtaine movies\n",
      "time: 3.3765117419998205\n",
      "calculate info\n",
      "time: 2.152590736000093\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__sound__supervising_sound_editor\n",
      "['crew__sound__supervising_sound_editor_avg_profit', 'crew__sound__supervising_sound_editor_avg_revenue', 'crew__sound__supervising_sound_editor_movies_before']\n",
      "time: 0.5829637879996881\n",
      "obtaine movies\n",
      "time: 10.570070091999696\n",
      "calculate info\n",
      "time: 8.4706569330001\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__directing__director__1\n",
      "['crew__directing__director__1_avg_profit', 'crew__directing__director__1_avg_revenue', 'crew__directing__director__1_movies_before']\n",
      "time: 0.594057747000079\n",
      "obtaine movies\n",
      "time: 4.560463415999948\n",
      "calculate info\n",
      "time: 3.0031297140003517\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__directing__script_supervisor\n",
      "['crew__directing__script_supervisor_avg_profit', 'crew__directing__script_supervisor_avg_revenue', 'crew__directing__script_supervisor_movies_before']\n",
      "time: 0.6016450429997349\n",
      "obtaine movies\n",
      "time: 8.776835071000278\n",
      "calculate info\n",
      "time: 7.292686692000643\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__production__casting\n",
      "['crew__production__casting_avg_profit', 'crew__production__casting_avg_revenue', 'crew__production__casting_movies_before']\n",
      "time: 0.5999544229998719\n",
      "obtaine movies\n",
      "time: 5.2357533350004815\n",
      "calculate info\n",
      "time: 4.973229563000132\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__production__executive_producer__1\n",
      "['crew__production__executive_producer__1_avg_profit', 'crew__production__executive_producer__1_avg_revenue', 'crew__production__executive_producer__1_movies_before']\n",
      "time: 0.5995182410006237\n",
      "obtaine movies\n",
      "time: 9.849182816000393\n",
      "calculate info\n",
      "time: 8.274495016999936\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__production__producer__1\n",
      "['crew__production__producer__1_avg_profit', 'crew__production__producer__1_avg_revenue', 'crew__production__producer__1_movies_before']\n",
      "time: 0.6213268659994355\n",
      "obtaine movies\n",
      "time: 2.7401226669999232\n",
      "calculate info\n",
      "time: 2.933506858000328\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__production__producer__2\n",
      "['crew__production__producer__2_avg_profit', 'crew__production__producer__2_avg_revenue', 'crew__production__producer__2_movies_before']\n",
      "time: 0.6129623500000889\n",
      "obtaine movies\n",
      "time: 11.607107554000322\n",
      "calculate info\n",
      "time: 8.043518129999939\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__editing__editor__1\n",
      "['crew__editing__editor__1_avg_profit', 'crew__editing__editor__1_avg_revenue', 'crew__editing__editor__1_movies_before']\n",
      "time: 0.6183029009998791\n",
      "obtaine movies\n",
      "time: 8.77627787900019\n",
      "calculate info\n",
      "time: 6.07848889700017\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__costume__costume_designer\n",
      "['crew__costume__costume_designer_avg_profit', 'crew__costume__costume_designer_avg_revenue', 'crew__costume__costume_designer_movies_before']\n",
      "time: 0.6320098450005389\n",
      "obtaine movies\n",
      "time: 2.2728443799996967\n",
      "calculate info\n",
      "time: 1.5983142730001418\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__costume__costume_supervisor\n",
      "['crew__costume__costume_supervisor_avg_profit', 'crew__costume__costume_supervisor_avg_revenue', 'crew__costume__costume_supervisor_movies_before']\n",
      "time: 0.6176652490003107\n",
      "obtaine movies\n",
      "time: 3.100378307000028\n",
      "calculate info\n",
      "time: 2.5514162179997584\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__costume__makeup_artist\n",
      "['crew__costume__makeup_artist_avg_profit', 'crew__costume__makeup_artist_avg_revenue', 'crew__costume__makeup_artist_movies_before']\n",
      "time: 0.6168288690005284\n",
      "obtaine movies\n",
      "time: 3.279397076999885\n",
      "calculate info\n",
      "time: 2.132486950999919\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__crew__stunt_coordinator\n",
      "['crew__crew__stunt_coordinator_avg_profit', 'crew__crew__stunt_coordinator_avg_revenue', 'crew__crew__stunt_coordinator_movies_before']\n",
      "time: 0.6176856609999959\n",
      "obtaine movies\n",
      "time: 5.973639056000138\n",
      "calculate info\n",
      "time: 5.426552867999817\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__writing__screenplay__1\n",
      "['crew__writing__screenplay__1_avg_profit', 'crew__writing__screenplay__1_avg_revenue', 'crew__writing__screenplay__1_movies_before']\n",
      "time: 0.6383410829994318\n",
      "obtaine movies\n",
      "time: 5.527008928999749\n",
      "calculate info\n",
      "time: 4.634156768000139\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__art__art_direction\n",
      "['crew__art__art_direction_avg_profit', 'crew__art__art_direction_avg_revenue', 'crew__art__art_direction_movies_before']\n",
      "time: 0.6400111259999903\n",
      "obtaine movies\n",
      "time: 8.95437266800036\n",
      "calculate info\n",
      "time: 6.124144193000575\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__art__production_design\n",
      "['crew__art__production_design_avg_profit', 'crew__art__production_design_avg_revenue', 'crew__art__production_design_movies_before']\n",
      "time: 0.6507926159993076\n",
      "obtaine movies\n",
      "time: 2.735752499999762\n",
      "calculate info\n",
      "time: 1.9201488270000482\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__art__property_master\n",
      "['crew__art__property_master_avg_profit', 'crew__art__property_master_avg_revenue', 'crew__art__property_master_movies_before']\n",
      "time: 0.6261049539998567\n",
      "obtaine movies\n",
      "time: 6.505164890999367\n",
      "calculate info\n",
      "time: 4.560731239999768\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__art__set_decoration\n",
      "['crew__art__set_decoration_avg_profit', 'crew__art__set_decoration_avg_revenue', 'crew__art__set_decoration_movies_before']\n",
      "time: 0.6263445410004351\n",
      "obtaine movies\n",
      "time: 2.4122183070003302\n",
      "calculate info\n",
      "time: 1.7959092910004983\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__visualeffects__visual_effects_supervisor\n",
      "['crew__visualeffects__visual_effects_supervisor_avg_profit', 'crew__visualeffects__visual_effects_supervisor_avg_revenue', 'crew__visualeffects__visual_effects_supervisor_movies_before']\n",
      "time: 0.6191226809996806\n",
      "obtaine movies\n",
      "time: 11.720605075000094\n",
      "calculate info\n",
      "time: 8.22845543099993\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__camera__director_of_photography\n",
      "['crew__camera__director_of_photography_avg_profit', 'crew__camera__director_of_photography_avg_revenue', 'crew__camera__director_of_photography_movies_before']\n",
      "time: 0.6330275689997507\n",
      "obtaine movies\n",
      "time: 2.2358360569996876\n",
      "calculate info\n",
      "time: 1.372880009000255\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__camera__steadicam_operator\n",
      "['crew__camera__steadicam_operator_avg_profit', 'crew__camera__steadicam_operator_avg_revenue', 'crew__camera__steadicam_operator_movies_before']\n",
      "time: 0.6278800319996662\n",
      "obtaine movies\n",
      "time: 4.427446149000389\n",
      "calculate info\n",
      "time: 2.8759110219998547\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue', 'movies_before']\n",
      "META__crew__camera__still_photographer\n",
      "['crew__camera__still_photographer_avg_profit', 'crew__camera__still_photographer_avg_revenue', 'crew__camera__still_photographer_movies_before']\n",
      "time: 0.6377616110003146\n",
      "obtaine movies\n",
      "time: 1.8648970399999598\n",
      "calculate info\n",
      "time: 1.1839982760002385\n",
      "get columns\n",
      "['avg_profit', 'avg_revenue']\n",
      "META__collection_name\n",
      "['collection_avg_profit', 'collection_avg_revenue']\n",
      "time: 0.6302163129994369\n",
      "overall time: 530.6382846140004\n"
     ]
    }
   ],
   "source": [
    "for dataset in raw_datasets.keys():\n",
    "    print('------------------------------------------------------')\n",
    "    print(dataset)\n",
    "    print('-------------------------------------------------------')\n",
    "    raw_filename = f'datasets/dataset_{dataset}_raw.csv'\n",
    "    filename = f'datasets/dataset_{dataset}.csv'\n",
    "    raw_datasets[dataset].to_csv(raw_filename, index=False)\n",
    "    raw_dataset = pd.read_csv(raw_filename)\n",
    "    prepared = prepare_df(raw_dataset)\n",
    "    prepared.to_csv(filename, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5-final"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}