1.0-initial-data-exploration.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Warnings and Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "\n",
    "def fxn():\n",
    "    warnings.warn(\"deprecated\", DeprecationWarning)\n",
    "\n",
    "with warnings.catch_warnings():\n",
    "    warnings.simplefilter(\"ignore\")\n",
    "    fxn()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tools\\_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
      "  import pandas.util.testing as tm\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pandas_profiling\n",
    "import matplotlib.pyplot as plt\n",
    "import sweetviz as sv\n",
    "import missingno\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing list of directors file\n",
    "\n",
    "Dataset link: [Kaggle](https://www.kaggle.com/carolzhangdc/imdb-5000-movie-dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('DataSets/Movie500/movie_metadata.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Getting a report using Pandas-Profiling**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "\n",
    "profile = df.profile_report(title='Movie Profiling Report')\n",
    "profile.to_file(output_file=\"moviesdirectors.html\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Report generation using sweetviz**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "# analyzing the dataset\n",
    "movies_report = sv.analyze(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# display the report\n",
    "movies_report.show_html('Movies_SweetViz.html')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Traditional approcah: exploring head values**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>color</th>\n",
       "      <th>director_name</th>\n",
       "      <th>num_critic_for_reviews</th>\n",
       "      <th>duration</th>\n",
       "      <th>director_facebook_likes</th>\n",
       "      <th>actor_3_facebook_likes</th>\n",
       "      <th>actor_2_name</th>\n",
       "      <th>actor_1_facebook_likes</th>\n",
       "      <th>gross</th>\n",
       "      <th>genres</th>\n",
       "      <th>...</th>\n",
       "      <th>num_user_for_reviews</th>\n",
       "      <th>language</th>\n",
       "      <th>country</th>\n",
       "      <th>content_rating</th>\n",
       "      <th>budget</th>\n",
       "      <th>title_year</th>\n",
       "      <th>actor_2_facebook_likes</th>\n",
       "      <th>imdb_score</th>\n",
       "      <th>aspect_ratio</th>\n",
       "      <th>movie_facebook_likes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Color</td>\n",
       "      <td>James Cameron</td>\n",
       "      <td>723.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>855.0</td>\n",
       "      <td>Joel David Moore</td>\n",
       "      <td>1000</td>\n",
       "      <td>760505847.0</td>\n",
       "      <td>Action|Adventure|Fantasy|Sci-Fi</td>\n",
       "      <td>...</td>\n",
       "      <td>3054.0</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>237000000.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>936.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>1.78</td>\n",
       "      <td>33000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Color</td>\n",
       "      <td>Gore Verbinski</td>\n",
       "      <td>302.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>563.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Orlando Bloom</td>\n",
       "      <td>40000</td>\n",
       "      <td>309404152.0</td>\n",
       "      <td>Action|Adventure|Fantasy</td>\n",
       "      <td>...</td>\n",
       "      <td>1238.0</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>300000000.0</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>2.35</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Color</td>\n",
       "      <td>Sam Mendes</td>\n",
       "      <td>602.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>Rory Kinnear</td>\n",
       "      <td>11000</td>\n",
       "      <td>200074175.0</td>\n",
       "      <td>Action|Adventure|Thriller</td>\n",
       "      <td>...</td>\n",
       "      <td>994.0</td>\n",
       "      <td>English</td>\n",
       "      <td>UK</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>245000000.0</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>393.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>2.35</td>\n",
       "      <td>85000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Color</td>\n",
       "      <td>Christopher Nolan</td>\n",
       "      <td>813.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>22000.0</td>\n",
       "      <td>23000.0</td>\n",
       "      <td>Christian Bale</td>\n",
       "      <td>27000</td>\n",
       "      <td>448130642.0</td>\n",
       "      <td>Action|Thriller</td>\n",
       "      <td>...</td>\n",
       "      <td>2701.0</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>250000000.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>23000.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>2.35</td>\n",
       "      <td>164000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Doug Walker</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>131.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rob Walker</td>\n",
       "      <td>131</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>...</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>12.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   color      director_name  num_critic_for_reviews  duration  \\\n",
       "0  Color      James Cameron                   723.0     178.0   \n",
       "1  Color     Gore Verbinski                   302.0     169.0   \n",
       "2  Color         Sam Mendes                   602.0     148.0   \n",
       "3  Color  Christopher Nolan                   813.0     164.0   \n",
       "4    NaN        Doug Walker                     NaN       NaN   \n",
       "\n",
       "   director_facebook_likes  actor_3_facebook_likes      actor_2_name  \\\n",
       "0                      0.0                   855.0  Joel David Moore   \n",
       "1                    563.0                  1000.0     Orlando Bloom   \n",
       "2                      0.0                   161.0      Rory Kinnear   \n",
       "3                  22000.0                 23000.0    Christian Bale   \n",
       "4                    131.0                     NaN        Rob Walker   \n",
       "\n",
       "   actor_1_facebook_likes        gross                           genres  ...  \\\n",
       "0                    1000  760505847.0  Action|Adventure|Fantasy|Sci-Fi  ...   \n",
       "1                   40000  309404152.0         Action|Adventure|Fantasy  ...   \n",
       "2                   11000  200074175.0        Action|Adventure|Thriller  ...   \n",
       "3                   27000  448130642.0                  Action|Thriller  ...   \n",
       "4                     131          NaN                      Documentary  ...   \n",
       "\n",
       "  num_user_for_reviews language  country  content_rating       budget  \\\n",
       "0               3054.0  English      USA           PG-13  237000000.0   \n",
       "1               1238.0  English      USA           PG-13  300000000.0   \n",
       "2                994.0  English       UK           PG-13  245000000.0   \n",
       "3               2701.0  English      USA           PG-13  250000000.0   \n",
       "4                  NaN      NaN      NaN             NaN          NaN   \n",
       "\n",
       "   title_year actor_2_facebook_likes imdb_score  aspect_ratio  \\\n",
       "0      2009.0                  936.0        7.9          1.78   \n",
       "1      2007.0                 5000.0        7.1          2.35   \n",
       "2      2015.0                  393.0        6.8          2.35   \n",
       "3      2012.0                23000.0        8.5          2.35   \n",
       "4         NaN                   12.0        7.1           NaN   \n",
       "\n",
       "  movie_facebook_likes  \n",
       "0              33000.0  \n",
       "1                  0.0  \n",
       "2              85000.0  \n",
       "3             164000.0  \n",
       "4                  0.0  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>color</th>\n",
       "      <th>director_name</th>\n",
       "      <th>num_critic_for_reviews</th>\n",
       "      <th>duration</th>\n",
       "      <th>director_facebook_likes</th>\n",
       "      <th>actor_3_facebook_likes</th>\n",
       "      <th>actor_2_name</th>\n",
       "      <th>actor_1_facebook_likes</th>\n",
       "      <th>gross</th>\n",
       "      <th>genres</th>\n",
       "      <th>...</th>\n",
       "      <th>num_user_for_reviews</th>\n",
       "      <th>language</th>\n",
       "      <th>country</th>\n",
       "      <th>content_rating</th>\n",
       "      <th>budget</th>\n",
       "      <th>title_year</th>\n",
       "      <th>actor_2_facebook_likes</th>\n",
       "      <th>imdb_score</th>\n",
       "      <th>aspect_ratio</th>\n",
       "      <th>movie_facebook_likes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3469</th>\n",
       "      <td>Color</td>\n",
       "      <td>Jane Campion</td>\n",
       "      <td>83.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>319.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>Ian Mune</td>\n",
       "      <td>1000</td>\n",
       "      <td>40158000.0</td>\n",
       "      <td>Drama|Music|Romance</td>\n",
       "      <td>...</td>\n",
       "      <td>241.0</td>\n",
       "      <td>English</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>R</td>\n",
       "      <td>7000000.0</td>\n",
       "      <td>1993.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>7.6</td>\n",
       "      <td>1.85</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3470</th>\n",
       "      <td>Color</td>\n",
       "      <td>Steven Soderbergh</td>\n",
       "      <td>324.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>Alex Pettyfer</td>\n",
       "      <td>17000</td>\n",
       "      <td>113709992.0</td>\n",
       "      <td>Comedy|Drama</td>\n",
       "      <td>...</td>\n",
       "      <td>281.0</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>R</td>\n",
       "      <td>7000000.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.35</td>\n",
       "      <td>54000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3471</th>\n",
       "      <td>Color</td>\n",
       "      <td>Jonathan Liebesman</td>\n",
       "      <td>140.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>474.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>Emma Caulfield</td>\n",
       "      <td>1000</td>\n",
       "      <td>32131483.0</td>\n",
       "      <td>Horror|Mystery|Thriller</td>\n",
       "      <td>...</td>\n",
       "      <td>369.0</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>11000000.0</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>970.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>2.35</td>\n",
       "      <td>2000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3472</th>\n",
       "      <td>Color</td>\n",
       "      <td>Guy Hamilton</td>\n",
       "      <td>136.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>189.0</td>\n",
       "      <td>Geoffrey Holder</td>\n",
       "      <td>581</td>\n",
       "      <td>35400000.0</td>\n",
       "      <td>Action|Adventure|Thriller</td>\n",
       "      <td>...</td>\n",
       "      <td>251.0</td>\n",
       "      <td>English</td>\n",
       "      <td>UK</td>\n",
       "      <td>PG</td>\n",
       "      <td>7000000.0</td>\n",
       "      <td>1973.0</td>\n",
       "      <td>547.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>1.66</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3473</th>\n",
       "      <td>Color</td>\n",
       "      <td>Jay Russell</td>\n",
       "      <td>79.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>430.0</td>\n",
       "      <td>Frankie Muniz</td>\n",
       "      <td>1000</td>\n",
       "      <td>34099640.0</td>\n",
       "      <td>Drama|Family|Sport</td>\n",
       "      <td>...</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      color       director_name  num_critic_for_reviews  duration  \\\n",
       "3469  Color        Jane Campion                    83.0     121.0   \n",
       "3470  Color   Steven Soderbergh                   324.0     110.0   \n",
       "3471  Color  Jonathan Liebesman                   140.0      96.0   \n",
       "3472  Color        Guy Hamilton                   136.0     121.0   \n",
       "3473  Color         Jay Russell                    79.0      95.0   \n",
       "\n",
       "      director_facebook_likes  actor_3_facebook_likes     actor_2_name  \\\n",
       "3469                    319.0                    11.0         Ian Mune   \n",
       "3470                      0.0                 11000.0    Alex Pettyfer   \n",
       "3471                    474.0                    82.0   Emma Caulfield   \n",
       "3472                     82.0                   189.0  Geoffrey Holder   \n",
       "3473                     13.0                   430.0    Frankie Muniz   \n",
       "\n",
       "      actor_1_facebook_likes        gross                     genres  ...  \\\n",
       "3469                    1000   40158000.0        Drama|Music|Romance  ...   \n",
       "3470                   17000  113709992.0               Comedy|Drama  ...   \n",
       "3471                    1000   32131483.0    Horror|Mystery|Thriller  ...   \n",
       "3472                     581   35400000.0  Action|Adventure|Thriller  ...   \n",
       "3473                    1000   34099640.0         Drama|Family|Sport  ...   \n",
       "\n",
       "     num_user_for_reviews language      country  content_rating      budget  \\\n",
       "3469                241.0  English  New Zealand               R   7000000.0   \n",
       "3470                281.0  English          USA               R   7000000.0   \n",
       "3471                369.0  English          USA           PG-13  11000000.0   \n",
       "3472                251.0  English           UK              PG   7000000.0   \n",
       "3473                  NaN      NaN          NaN             NaN         NaN   \n",
       "\n",
       "      title_year actor_2_facebook_likes imdb_score  aspect_ratio  \\\n",
       "3469      1993.0                   18.0        7.6          1.85   \n",
       "3470      2012.0                15000.0        6.1          2.35   \n",
       "3471      2003.0                  970.0        4.9          2.35   \n",
       "3472      1973.0                  547.0        6.8          1.66   \n",
       "3473         NaN                    NaN        NaN           NaN   \n",
       "\n",
       "     movie_facebook_likes  \n",
       "3469                  0.0  \n",
       "3470              54000.0  \n",
       "3471               2000.0  \n",
       "3472                  0.0  \n",
       "3473                  NaN  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Preview of data with null values:\n",
      "xxxxxxxxxxxxx\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>color</th>\n",
       "      <th>director_name</th>\n",
       "      <th>num_critic_for_reviews</th>\n",
       "      <th>duration</th>\n",
       "      <th>director_facebook_likes</th>\n",
       "      <th>actor_3_facebook_likes</th>\n",
       "      <th>actor_2_name</th>\n",
       "      <th>actor_1_facebook_likes</th>\n",
       "      <th>gross</th>\n",
       "      <th>genres</th>\n",
       "      <th>...</th>\n",
       "      <th>num_user_for_reviews</th>\n",
       "      <th>language</th>\n",
       "      <th>country</th>\n",
       "      <th>content_rating</th>\n",
       "      <th>budget</th>\n",
       "      <th>title_year</th>\n",
       "      <th>actor_2_facebook_likes</th>\n",
       "      <th>imdb_score</th>\n",
       "      <th>aspect_ratio</th>\n",
       "      <th>movie_facebook_likes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Doug Walker</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>131.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rob Walker</td>\n",
       "      <td>131</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>...</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>12.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Color</td>\n",
       "      <td>Peter Sohn</td>\n",
       "      <td>298.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>Jack McGraw</td>\n",
       "      <td>275</td>\n",
       "      <td>123070338.0</td>\n",
       "      <td>Adventure|Animation|Comedy|Family|Fantasy</td>\n",
       "      <td>...</td>\n",
       "      <td>345.0</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>PG</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>2.35</td>\n",
       "      <td>20000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Color</td>\n",
       "      <td>Roland Joffé</td>\n",
       "      <td>10.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>596.0</td>\n",
       "      <td>283.0</td>\n",
       "      <td>Alice Englert</td>\n",
       "      <td>622</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Action|Adventure|Romance|Sci-Fi</td>\n",
       "      <td>...</td>\n",
       "      <td>15.0</td>\n",
       "      <td>English</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>525.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>677.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>Color</td>\n",
       "      <td>Hideaki Anno</td>\n",
       "      <td>1.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Shin'ya Tsukamoto</td>\n",
       "      <td>544</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Action|Adventure|Drama|Horror|Sci-Fi</td>\n",
       "      <td>...</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Japanese</td>\n",
       "      <td>Japan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>8.2</td>\n",
       "      <td>2.35</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>Color</td>\n",
       "      <td>Peter Jackson</td>\n",
       "      <td>645.0</td>\n",
       "      <td>182.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>773.0</td>\n",
       "      <td>Adam Brown</td>\n",
       "      <td>5000</td>\n",
       "      <td>303001229.0</td>\n",
       "      <td>Adventure|Fantasy</td>\n",
       "      <td>...</td>\n",
       "      <td>1367.0</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>180000000.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>972.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>2.35</td>\n",
       "      <td>166000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>Color</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>184.0</td>\n",
       "      <td>Philip Michael Thomas</td>\n",
       "      <td>982</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Action|Crime|Drama|Mystery|Thriller</td>\n",
       "      <td>...</td>\n",
       "      <td>74.0</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>TV-14</td>\n",
       "      <td>1500000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>321.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>1.33</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>Color</td>\n",
       "      <td>Matt Birch</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>Dave Legeno</td>\n",
       "      <td>10000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Action|Fantasy</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>English</td>\n",
       "      <td>UK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>570.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>Color</td>\n",
       "      <td>Hideaki Anno</td>\n",
       "      <td>1.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Shin'ya Tsukamoto</td>\n",
       "      <td>544</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Action|Adventure|Drama|Horror|Sci-Fi</td>\n",
       "      <td>...</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Japanese</td>\n",
       "      <td>Japan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>8.2</td>\n",
       "      <td>2.35</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>Color</td>\n",
       "      <td>Matt Birch</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Toby Jones</td>\n",
       "      <td>10000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fantasy</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>English</td>\n",
       "      <td>UK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>6.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>Color</td>\n",
       "      <td>Frédéric Forestier</td>\n",
       "      <td>33.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>Santiago Segura</td>\n",
       "      <td>936</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Adventure|Comedy|Family|Fantasy</td>\n",
       "      <td>...</td>\n",
       "      <td>36.0</td>\n",
       "      <td>French</td>\n",
       "      <td>France</td>\n",
       "      <td>NaN</td>\n",
       "      <td>78000000.0</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>276.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.35</td>\n",
       "      <td>291.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     color       director_name  num_critic_for_reviews  duration  \\\n",
       "4      NaN         Doug Walker                     NaN       NaN   \n",
       "55   Color          Peter Sohn                   298.0      93.0   \n",
       "84   Color        Roland Joffé                    10.0     109.0   \n",
       "98   Color        Hideaki Anno                     1.0     120.0   \n",
       "99   Color       Peter Jackson                   645.0     182.0   \n",
       "177  Color                 NaN                    21.0      60.0   \n",
       "199  Color          Matt Birch                     1.0       NaN   \n",
       "204  Color        Hideaki Anno                     1.0     120.0   \n",
       "206  Color          Matt Birch                     4.0       NaN   \n",
       "242  Color  Frédéric Forestier                    33.0     116.0   \n",
       "\n",
       "     director_facebook_likes  actor_3_facebook_likes           actor_2_name  \\\n",
       "4                      131.0                     NaN             Rob Walker   \n",
       "55                     113.0                   113.0            Jack McGraw   \n",
       "84                     596.0                   283.0          Alice Englert   \n",
       "98                      28.0                    12.0      Shin'ya Tsukamoto   \n",
       "99                       0.0                   773.0             Adam Brown   \n",
       "177                      NaN                   184.0  Philip Michael Thomas   \n",
       "199                      0.0                   159.0            Dave Legeno   \n",
       "204                     28.0                    12.0      Shin'ya Tsukamoto   \n",
       "206                      0.0                  1000.0             Toby Jones   \n",
       "242                      0.0                   141.0        Santiago Segura   \n",
       "\n",
       "     actor_1_facebook_likes        gross  \\\n",
       "4                       131          NaN   \n",
       "55                      275  123070338.0   \n",
       "84                      622          NaN   \n",
       "98                      544          NaN   \n",
       "99                     5000  303001229.0   \n",
       "177                     982          NaN   \n",
       "199                   10000          NaN   \n",
       "204                     544          NaN   \n",
       "206                   10000          NaN   \n",
       "242                     936          NaN   \n",
       "\n",
       "                                        genres  ... num_user_for_reviews  \\\n",
       "4                                  Documentary  ...                  NaN   \n",
       "55   Adventure|Animation|Comedy|Family|Fantasy  ...                345.0   \n",
       "84             Action|Adventure|Romance|Sci-Fi  ...                 15.0   \n",
       "98        Action|Adventure|Drama|Horror|Sci-Fi  ...                 13.0   \n",
       "99                           Adventure|Fantasy  ...               1367.0   \n",
       "177        Action|Crime|Drama|Mystery|Thriller  ...                 74.0   \n",
       "199                             Action|Fantasy  ...                  2.0   \n",
       "204       Action|Adventure|Drama|Horror|Sci-Fi  ...                 13.0   \n",
       "206                                    Fantasy  ...                  2.0   \n",
       "242            Adventure|Comedy|Family|Fantasy  ...                 36.0   \n",
       "\n",
       "     language  country  content_rating       budget  title_year  \\\n",
       "4         NaN      NaN             NaN          NaN         NaN   \n",
       "55    English      USA              PG          NaN      2015.0   \n",
       "84    English  Belgium               R          NaN      2015.0   \n",
       "98   Japanese    Japan             NaN          NaN      2016.0   \n",
       "99    English      USA           PG-13  180000000.0      2012.0   \n",
       "177   English      USA           TV-14    1500000.0         NaN   \n",
       "199   English       UK             NaN          NaN      2011.0   \n",
       "204  Japanese    Japan             NaN          NaN      2016.0   \n",
       "206   English       UK             NaN          NaN      2010.0   \n",
       "242    French   France             NaN   78000000.0      2008.0   \n",
       "\n",
       "    actor_2_facebook_likes imdb_score  aspect_ratio movie_facebook_likes  \n",
       "4                     12.0        7.1           NaN                  0.0  \n",
       "55                   150.0        6.8          2.35              20000.0  \n",
       "84                   525.0        4.5           NaN                677.0  \n",
       "98                   106.0        8.2          2.35                  0.0  \n",
       "99                   972.0        7.9          2.35             166000.0  \n",
       "177                  321.0        7.5          1.33                  0.0  \n",
       "199                  570.0        7.5           NaN                 40.0  \n",
       "204                  106.0        8.2          2.35                  0.0  \n",
       "206                 2000.0        6.4           NaN                 25.0  \n",
       "242                  276.0        5.1          2.35                291.0  \n",
       "\n",
       "[10 rows x 28 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# generate preview of entries with null values\n",
    "if len(df[df.isnull().any(axis=1)] != 0):\n",
    "    print(\"\\nPreview of data with null values:\\nxxxxxxxxxxxxx\")\n",
    "    display(df[df.isnull().any(axis=1)].head(10))\n",
    "    missingno.matrix(df)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**traditional approach**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "color                          8\n",
       "director_name                 65\n",
       "num_critic_for_reviews        11\n",
       "duration                       4\n",
       "director_facebook_likes       65\n",
       "actor_3_facebook_likes         6\n",
       "actor_2_name                   4\n",
       "actor_1_facebook_likes         0\n",
       "gross                        297\n",
       "genres                         0\n",
       "actor_1_name                   0\n",
       "movie_title                    0\n",
       "num_voted_users                0\n",
       "cast_total_facebook_likes      0\n",
       "actor_3_name                   6\n",
       "facenumber_in_poster           5\n",
       "plot_keywords                 44\n",
       "movie_imdb_link                0\n",
       "num_user_for_reviews           9\n",
       "language                       5\n",
       "country                        5\n",
       "content_rating                84\n",
       "budget                       227\n",
       "title_year                    70\n",
       "actor_2_facebook_likes         5\n",
       "imdb_score                     1\n",
       "aspect_ratio                  78\n",
       "movie_facebook_likes           1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extracting required columns\n",
    "\n",
    "*The goal of my project is to create a recommendation system which also displays the top 10 actors/cast in the movie. Therefore, i'm going to shed the other columns which are not going to contribute towards my goal*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>director_name</th>\n",
       "      <th>actor_1_name</th>\n",
       "      <th>actor_2_name</th>\n",
       "      <th>actor_3_name</th>\n",
       "      <th>genres</th>\n",
       "      <th>movie_title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>James Cameron</td>\n",
       "      <td>CCH Pounder</td>\n",
       "      <td>Joel David Moore</td>\n",
       "      <td>Wes Studi</td>\n",
       "      <td>Action|Adventure|Fantasy|Sci-Fi</td>\n",
       "      <td>Avatar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Gore Verbinski</td>\n",
       "      <td>Johnny Depp</td>\n",
       "      <td>Orlando Bloom</td>\n",
       "      <td>Jack Davenport</td>\n",
       "      <td>Action|Adventure|Fantasy</td>\n",
       "      <td>Pirates of the Caribbean: At World's End</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sam Mendes</td>\n",
       "      <td>Christoph Waltz</td>\n",
       "      <td>Rory Kinnear</td>\n",
       "      <td>Stephanie Sigman</td>\n",
       "      <td>Action|Adventure|Thriller</td>\n",
       "      <td>Spectre</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Christopher Nolan</td>\n",
       "      <td>Tom Hardy</td>\n",
       "      <td>Christian Bale</td>\n",
       "      <td>Joseph Gordon-Levitt</td>\n",
       "      <td>Action|Thriller</td>\n",
       "      <td>The Dark Knight Rises</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Doug Walker</td>\n",
       "      <td>Doug Walker</td>\n",
       "      <td>Rob Walker</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>Star Wars: Episode VII - The Force Awakens    ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Andrew Stanton</td>\n",
       "      <td>Daryl Sabara</td>\n",
       "      <td>Samantha Morton</td>\n",
       "      <td>Polly Walker</td>\n",
       "      <td>Action|Adventure|Sci-Fi</td>\n",
       "      <td>John Carter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Sam Raimi</td>\n",
       "      <td>J.K. Simmons</td>\n",
       "      <td>James Franco</td>\n",
       "      <td>Kirsten Dunst</td>\n",
       "      <td>Action|Adventure|Romance</td>\n",
       "      <td>Spider-Man 3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Nathan Greno</td>\n",
       "      <td>Brad Garrett</td>\n",
       "      <td>Donna Murphy</td>\n",
       "      <td>M.C. Gainey</td>\n",
       "      <td>Adventure|Animation|Comedy|Family|Fantasy|Musi...</td>\n",
       "      <td>Tangled</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Joss Whedon</td>\n",
       "      <td>Chris Hemsworth</td>\n",
       "      <td>Robert Downey Jr.</td>\n",
       "      <td>Scarlett Johansson</td>\n",
       "      <td>Action|Adventure|Sci-Fi</td>\n",
       "      <td>Avengers: Age of Ultron</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>David Yates</td>\n",
       "      <td>Alan Rickman</td>\n",
       "      <td>Daniel Radcliffe</td>\n",
       "      <td>Rupert Grint</td>\n",
       "      <td>Adventure|Family|Fantasy|Mystery</td>\n",
       "      <td>Harry Potter and the Half-Blood Prince</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       director_name     actor_1_name       actor_2_name  \\\n",
       "0      James Cameron      CCH Pounder   Joel David Moore   \n",
       "1     Gore Verbinski      Johnny Depp      Orlando Bloom   \n",
       "2         Sam Mendes  Christoph Waltz       Rory Kinnear   \n",
       "3  Christopher Nolan        Tom Hardy     Christian Bale   \n",
       "4        Doug Walker      Doug Walker         Rob Walker   \n",
       "5     Andrew Stanton     Daryl Sabara    Samantha Morton   \n",
       "6          Sam Raimi     J.K. Simmons       James Franco   \n",
       "7       Nathan Greno     Brad Garrett       Donna Murphy   \n",
       "8        Joss Whedon  Chris Hemsworth  Robert Downey Jr.   \n",
       "9        David Yates     Alan Rickman   Daniel Radcliffe   \n",
       "\n",
       "           actor_3_name                                             genres  \\\n",
       "0             Wes Studi                    Action|Adventure|Fantasy|Sci-Fi   \n",
       "1        Jack Davenport                           Action|Adventure|Fantasy   \n",
       "2      Stephanie Sigman                          Action|Adventure|Thriller   \n",
       "3  Joseph Gordon-Levitt                                    Action|Thriller   \n",
       "4                   NaN                                        Documentary   \n",
       "5          Polly Walker                            Action|Adventure|Sci-Fi   \n",
       "6         Kirsten Dunst                           Action|Adventure|Romance   \n",
       "7           M.C. Gainey  Adventure|Animation|Comedy|Family|Fantasy|Musi...   \n",
       "8    Scarlett Johansson                            Action|Adventure|Sci-Fi   \n",
       "9          Rupert Grint                   Adventure|Family|Fantasy|Mystery   \n",
       "\n",
       "                                         movie_title  \n",
       "0                                            Avatar   \n",
       "1          Pirates of the Caribbean: At World's End   \n",
       "2                                           Spectre   \n",
       "3                             The Dark Knight Rises   \n",
       "4  Star Wars: Episode VII - The Force Awakens    ...  \n",
       "5                                       John Carter   \n",
       "6                                      Spider-Man 3   \n",
       "7                                           Tangled   \n",
       "8                           Avengers: Age of Ultron   \n",
       "9            Harry Potter and the Half-Blood Prince   "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = df.loc[:, ['director_name', 'actor_1_name',\n",
    "                  'actor_2_name', 'actor_3_name', 'genres', 'movie_title']]\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['actor_1_name'] = data['actor_1_name'].replace(np.nan, 'unknown')\n",
    "data['actor_2_name'] = data['actor_2_name'].replace(np.nan, 'unknown')\n",
    "data['actor_3_name'] = data['actor_3_name'].replace(np.nan, 'unknown')\n",
    "data['director_name'] = data['director_name'].replace(np.nan, 'unknown')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['genres'] = data['genres'].replace('|', ' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['movie_title'] = data['movie_title'].str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"pirates of the caribbean: at world's end\\xa0\""
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['movie_title'][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['movie_title'] = data['movie_title'].apply(lambda x: x[:-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exporting as CSV and dependencies list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.to_csv('data.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'watermark'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-18-f0fbd6544567>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mget_ipython\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'load_ext'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'watermark'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;31m# python, ipython, packages, and machine characteristics\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'watermark'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'-v -m -p wget,pandas,numpy,geopy,altair,vega,vega_datasets,watermark '\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py\u001b[0m in \u001b[0;36mrun_line_magic\u001b[1;34m(self, magic_name, line, _stack_depth)\u001b[0m\n\u001b[0;32m   2324\u001b[0m                 \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'local_ns'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getframe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstack_depth\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf_locals\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2325\u001b[0m             \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuiltin_trap\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2326\u001b[1;33m                 \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2327\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2328\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<decorator-gen-65>\u001b[0m in \u001b[0;36mload_ext\u001b[1;34m(self, module_str)\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\IPython\\core\\magic.py\u001b[0m in \u001b[0;36m<lambda>\u001b[1;34m(f, *a, **k)\u001b[0m\n\u001b[0;32m    185\u001b[0m     \u001b[1;31m# but it's overkill for just that one bit of state.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    186\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mmagic_deco\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 187\u001b[1;33m         \u001b[0mcall\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    188\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    189\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\IPython\\core\\magics\\extension.py\u001b[0m in \u001b[0;36mload_ext\u001b[1;34m(self, module_str)\u001b[0m\n\u001b[0;32m     31\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mmodule_str\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     32\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mUsageError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Missing module name.'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 33\u001b[1;33m         \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshell\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextension_manager\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload_extension\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodule_str\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     34\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     35\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'already loaded'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\IPython\\core\\extensions.py\u001b[0m in \u001b[0;36mload_extension\u001b[1;34m(self, module_str)\u001b[0m\n\u001b[0;32m     78\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mmodule_str\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0msys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodules\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     79\u001b[0m                 \u001b[1;32mwith\u001b[0m \u001b[0mprepended_to_syspath\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mipython_extension_dir\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 80\u001b[1;33m                     \u001b[0mmod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mimport_module\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodule_str\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     81\u001b[0m                     \u001b[1;32mif\u001b[0m \u001b[0mmod\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__file__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstartswith\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mipython_extension_dir\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     82\u001b[0m                         print((\"Loading extensions from {dir} is deprecated. \"\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\importlib\\__init__.py\u001b[0m in \u001b[0;36mimport_module\u001b[1;34m(name, package)\u001b[0m\n\u001b[0;32m    125\u001b[0m                 \u001b[1;32mbreak\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    126\u001b[0m             \u001b[0mlevel\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 127\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0m_bootstrap\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_gcd_import\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpackage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    128\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    129\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\importlib\\_bootstrap.py\u001b[0m in \u001b[0;36m_gcd_import\u001b[1;34m(name, package, level)\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\importlib\\_bootstrap.py\u001b[0m in \u001b[0;36m_find_and_load\u001b[1;34m(name, import_)\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\importlib\\_bootstrap.py\u001b[0m in \u001b[0;36m_find_and_load_unlocked\u001b[1;34m(name, import_)\u001b[0m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'watermark'"
     ]
    }
   ],
   "source": [
    "# %load_ext watermark\n",
    "\n",
    "# # python, ipython, packages, and machine characteristics\n",
    "# %watermark -v -m -p wget,pandas,numpy,geopy,altair,vega,vega_datasets,watermark \n",
    "\n",
    "# # date\n",
    "# print (\" \")\n",
    "# %watermark -u -n -t -z "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from pip._internal.operations.freeze import freeze\n",
    "# for requirement in freeze(local_only=True):\n",
    "#     print(requirement)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "hide_input": false,
  "kernelspec": {
   "display_name": "Movies",
   "language": "python",
   "name": "movies"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
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