data-exploration.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dataset\n",
    "# u.data - 100000 ratings by 943 users on 1682 items - user id | item id | rating | timestamp \n",
    "# u.item - Information about the items (movies) - movie id | movie title | release date | video release date |\n",
    "        # IMDb URL | unknown | Action | Adventure | Animation |Children's | Comedy | Crime | Documentary | Drama | Fantasy |\n",
    "        # Film-Noir | Horror | Musical | Mystery | Romance | Sci-Fi |Thriller | War | Western |\n",
    "# u.genre - A list of the genres.\n",
    "# u.user  - Demographic information about the users - user id | age | gender | occupation | zip code\n",
    "# u.occupation - A list of the occupations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>user_id</th>\n",
       "      <th>movie_id</th>\n",
       "      <th>ratings</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>196</td>\n",
       "      <td>242</td>\n",
       "      <td>3</td>\n",
       "      <td>881250949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>186</td>\n",
       "      <td>302</td>\n",
       "      <td>3</td>\n",
       "      <td>891717742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22</td>\n",
       "      <td>377</td>\n",
       "      <td>1</td>\n",
       "      <td>878887116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>244</td>\n",
       "      <td>51</td>\n",
       "      <td>2</td>\n",
       "      <td>880606923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>166</td>\n",
       "      <td>346</td>\n",
       "      <td>1</td>\n",
       "      <td>886397596</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  movie_id  ratings  timestamp\n",
       "0      196       242        3  881250949\n",
       "1      186       302        3  891717742\n",
       "2       22       377        1  878887116\n",
       "3      244        51        2  880606923\n",
       "4      166       346        1  886397596"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create panda data frame for u.data\n",
    "user_rating_data = pd.read_csv('u.data', delim_whitespace=True, header=None)\n",
    "user_rating_data.columns = [\"user_id\", \"movie_id\", \"ratings\", \"timestamp\"]\n",
    "user_rating_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x112209208>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"ratings\",data=user_rating_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>user_id</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>occupation</th>\n",
       "      <th>zip_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>M</td>\n",
       "      <td>technician</td>\n",
       "      <td>85711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>53</td>\n",
       "      <td>F</td>\n",
       "      <td>other</td>\n",
       "      <td>94043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>M</td>\n",
       "      <td>writer</td>\n",
       "      <td>32067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>24</td>\n",
       "      <td>M</td>\n",
       "      <td>technician</td>\n",
       "      <td>43537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>33</td>\n",
       "      <td>F</td>\n",
       "      <td>other</td>\n",
       "      <td>15213</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  age gender  occupation zip_code\n",
       "0        1   24      M  technician    85711\n",
       "1        2   53      F       other    94043\n",
       "2        3   23      M      writer    32067\n",
       "3        4   24      M  technician    43537\n",
       "4        5   33      F       other    15213"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_data=pd.read_csv('u.user',sep='|',header=None)\n",
    "user_data.columns=['user_id','age','gender','occupation','zip_code']\n",
    "user_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.size(np.unique(user_data['occupation']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10b90b2b0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1584x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dims = (22, 8)\n",
    "fig, ax = plt.subplots(figsize=dims)\n",
    "sns.countplot(x=\"occupation\",data=user_data,hue=\"gender\",ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>occupation</th>\n",
       "      <th>zip_code</th>\n",
       "      <th>movie_id</th>\n",
       "      <th>ratings</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>M</td>\n",
       "      <td>technician</td>\n",
       "      <td>85711</td>\n",
       "      <td>61</td>\n",
       "      <td>4</td>\n",
       "      <td>878542420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>M</td>\n",
       "      <td>technician</td>\n",
       "      <td>85711</td>\n",
       "      <td>189</td>\n",
       "      <td>3</td>\n",
       "      <td>888732928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>M</td>\n",
       "      <td>technician</td>\n",
       "      <td>85711</td>\n",
       "      <td>33</td>\n",
       "      <td>4</td>\n",
       "      <td>878542699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>M</td>\n",
       "      <td>technician</td>\n",
       "      <td>85711</td>\n",
       "      <td>160</td>\n",
       "      <td>4</td>\n",
       "      <td>875072547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>M</td>\n",
       "      <td>technician</td>\n",
       "      <td>85711</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>887431883</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  age gender  occupation zip_code  movie_id  ratings  timestamp\n",
       "0        1   24      M  technician    85711        61        4  878542420\n",
       "1        1   24      M  technician    85711       189        3  888732928\n",
       "2        1   24      M  technician    85711        33        4  878542699\n",
       "3        1   24      M  technician    85711       160        4  875072547\n",
       "4        1   24      M  technician    85711        20        4  887431883"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user_movies=pd.merge(user_data,user_rating_data,on='user_id')\n",
    "data_user_movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1125d5fd0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x=\"age\",y=\"ratings\",data=data_user_movies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x10b2d08d0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g=sns.PairGrid(user_rating_data)\n",
    "g.map(plt.scatter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>movie_id</th>\n",
       "      <th>movie_title</th>\n",
       "      <th>release_date</th>\n",
       "      <th>video_release_date</th>\n",
       "      <th>imdb_url</th>\n",
       "      <th>unknown</th>\n",
       "      <th>Action</th>\n",
       "      <th>Adventure</th>\n",
       "      <th>Animation</th>\n",
       "      <th>Children</th>\n",
       "      <th>...</th>\n",
       "      <th>Fantasy</th>\n",
       "      <th>Film-Noir</th>\n",
       "      <th>Horror</th>\n",
       "      <th>Musical</th>\n",
       "      <th>Mystery</th>\n",
       "      <th>Romance</th>\n",
       "      <th>Sci-Fi</th>\n",
       "      <th>Thriller</th>\n",
       "      <th>War</th>\n",
       "      <th>Western</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Toy%20Story%2...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>GoldenEye (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?GoldenEye%20(...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Four Rooms (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Four%20Rooms%...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Get Shorty (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Get%20Shorty%...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Copycat (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Copycat%20(1995)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   movie_id        movie_title release_date  video_release_date  \\\n",
       "0         1   Toy Story (1995)  01-Jan-1995                 NaN   \n",
       "1         2   GoldenEye (1995)  01-Jan-1995                 NaN   \n",
       "2         3  Four Rooms (1995)  01-Jan-1995                 NaN   \n",
       "3         4  Get Shorty (1995)  01-Jan-1995                 NaN   \n",
       "4         5     Copycat (1995)  01-Jan-1995                 NaN   \n",
       "\n",
       "                                            imdb_url  unknown  Action  \\\n",
       "0  http://us.imdb.com/M/title-exact?Toy%20Story%2...        0       0   \n",
       "1  http://us.imdb.com/M/title-exact?GoldenEye%20(...        0       1   \n",
       "2  http://us.imdb.com/M/title-exact?Four%20Rooms%...        0       0   \n",
       "3  http://us.imdb.com/M/title-exact?Get%20Shorty%...        0       1   \n",
       "4  http://us.imdb.com/M/title-exact?Copycat%20(1995)        0       0   \n",
       "\n",
       "   Adventure  Animation  Children   ...     Fantasy  Film-Noir  Horror  \\\n",
       "0          0          1         1   ...           0          0       0   \n",
       "1          1          0         0   ...           0          0       0   \n",
       "2          0          0         0   ...           0          0       0   \n",
       "3          0          0         0   ...           0          0       0   \n",
       "4          0          0         0   ...           0          0       0   \n",
       "\n",
       "   Musical  Mystery  Romance  Sci-Fi  Thriller  War  Western  \n",
       "0        0        0        0       0         0    0        0  \n",
       "1        0        0        0       0         1    0        0  \n",
       "2        0        0        0       0         1    0        0  \n",
       "3        0        0        0       0         0    0        0  \n",
       "4        0        0        0       0         1    0        0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating panda dataframe for u.item\n",
    "movie_data=pd.read_csv('u.item',sep='|',header=None,encoding='latin-1')\n",
    "#movie_data=movie_data.iloc[:,0:6]\n",
    "movie_data.columns=['movie_id','movie_title','release_date','video_release_date','imdb_url','unknown','Action','Adventure','Animation','Children','Comedy','Crime','Documentary','Drama','Fantasy','Film-Noir','Horror','Musical','Mystery','Romance','Sci-Fi','Thriller','War','Western']\n",
    "movie_data.dropna(subset=[\"release_date\"],inplace= True)\n",
    "movie_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to bucket movie years\n",
    "def movie_bucket(year):\n",
    "    year=int(year);\n",
    "    if (year<1970):\n",
    "      return 1960;\n",
    "    elif (year>1970 and year<1980):\n",
    "      return 1980;\n",
    "    elif (year>1980 and year<1990):\n",
    "      return 1990;\n",
    "    elif (year>1990 and year<2000):\n",
    "      return 2000;\n",
    "    elif (year>2000 and year<2010):\n",
    "      return 2010;\n",
    "    elif (year>2010):\n",
    "      return 2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>movie_id</th>\n",
       "      <th>movie_title</th>\n",
       "      <th>release_date</th>\n",
       "      <th>video_release_date</th>\n",
       "      <th>imdb_url</th>\n",
       "      <th>unknown</th>\n",
       "      <th>Action</th>\n",
       "      <th>Adventure</th>\n",
       "      <th>Animation</th>\n",
       "      <th>Children</th>\n",
       "      <th>...</th>\n",
       "      <th>Horror</th>\n",
       "      <th>Musical</th>\n",
       "      <th>Mystery</th>\n",
       "      <th>Romance</th>\n",
       "      <th>Sci-Fi</th>\n",
       "      <th>Thriller</th>\n",
       "      <th>War</th>\n",
       "      <th>Western</th>\n",
       "      <th>movie_year</th>\n",
       "      <th>movie_decade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Toy%20Story%2...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</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>1995</td>\n",
       "      <td>2000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>GoldenEye (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?GoldenEye%20(...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>1995</td>\n",
       "      <td>2000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Four Rooms (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Four%20Rooms%...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>1995</td>\n",
       "      <td>2000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Get Shorty (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Get%20Shorty%...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>1995</td>\n",
       "      <td>2000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Copycat (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Copycat%20(1995)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>1995</td>\n",
       "      <td>2000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   movie_id        movie_title release_date  video_release_date  \\\n",
       "0         1   Toy Story (1995)  01-Jan-1995                 NaN   \n",
       "1         2   GoldenEye (1995)  01-Jan-1995                 NaN   \n",
       "2         3  Four Rooms (1995)  01-Jan-1995                 NaN   \n",
       "3         4  Get Shorty (1995)  01-Jan-1995                 NaN   \n",
       "4         5     Copycat (1995)  01-Jan-1995                 NaN   \n",
       "\n",
       "                                            imdb_url  unknown  Action  \\\n",
       "0  http://us.imdb.com/M/title-exact?Toy%20Story%2...        0       0   \n",
       "1  http://us.imdb.com/M/title-exact?GoldenEye%20(...        0       1   \n",
       "2  http://us.imdb.com/M/title-exact?Four%20Rooms%...        0       0   \n",
       "3  http://us.imdb.com/M/title-exact?Get%20Shorty%...        0       1   \n",
       "4  http://us.imdb.com/M/title-exact?Copycat%20(1995)        0       0   \n",
       "\n",
       "   Adventure  Animation  Children      ...       Horror  Musical  Mystery  \\\n",
       "0          0          1         1      ...            0        0        0   \n",
       "1          1          0         0      ...            0        0        0   \n",
       "2          0          0         0      ...            0        0        0   \n",
       "3          0          0         0      ...            0        0        0   \n",
       "4          0          0         0      ...            0        0        0   \n",
       "\n",
       "   Romance  Sci-Fi  Thriller  War  Western  movie_year  movie_decade  \n",
       "0        0       0         0    0        0        1995        2000.0  \n",
       "1        0       0         1    0        0        1995        2000.0  \n",
       "2        0       0         1    0        0        1995        2000.0  \n",
       "3        0       0         0    0        0        1995        2000.0  \n",
       "4        0       0         1    0        0        1995        2000.0  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_data['movie_year']=movie_data['release_date'].str.split('-').str.get(2);\n",
    "movie_data['movie_decade']=movie_data['movie_year'].apply(movie_bucket);\n",
    "movie_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a20ec3470>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1584x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dims = (22, 8)\n",
    "fig, ax = plt.subplots(figsize=dims)\n",
    "sns.countplot(x=\"movie_year\",data=movie_data,ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a31a82f98>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"movie_decade\",data=movie_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>user_id</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>occupation</th>\n",
       "      <th>zip_code</th>\n",
       "      <th>movie_id</th>\n",
       "      <th>ratings</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>movie_title</th>\n",
       "      <th>release_date</th>\n",
       "      <th>...</th>\n",
       "      <th>Horror</th>\n",
       "      <th>Musical</th>\n",
       "      <th>Mystery</th>\n",
       "      <th>Romance</th>\n",
       "      <th>Sci-Fi</th>\n",
       "      <th>Thriller</th>\n",
       "      <th>War</th>\n",
       "      <th>Western</th>\n",
       "      <th>movie_year</th>\n",
       "      <th>movie_decade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>M</td>\n",
       "      <td>technician</td>\n",
       "      <td>85711</td>\n",
       "      <td>61</td>\n",
       "      <td>4</td>\n",
       "      <td>878542420</td>\n",
       "      <td>Three Colors: White (1994)</td>\n",
       "      <td>01-Jan-1994</td>\n",
       "      <td>...</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>1994</td>\n",
       "      <td>2000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13</td>\n",
       "      <td>47</td>\n",
       "      <td>M</td>\n",
       "      <td>educator</td>\n",
       "      <td>29206</td>\n",
       "      <td>61</td>\n",
       "      <td>4</td>\n",
       "      <td>882140552</td>\n",
       "      <td>Three Colors: White (1994)</td>\n",
       "      <td>01-Jan-1994</td>\n",
       "      <td>...</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>1994</td>\n",
       "      <td>2000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>35</td>\n",
       "      <td>F</td>\n",
       "      <td>other</td>\n",
       "      <td>37212</td>\n",
       "      <td>61</td>\n",
       "      <td>4</td>\n",
       "      <td>880130803</td>\n",
       "      <td>Three Colors: White (1994)</td>\n",
       "      <td>01-Jan-1994</td>\n",
       "      <td>...</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>1994</td>\n",
       "      <td>2000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>58</td>\n",
       "      <td>27</td>\n",
       "      <td>M</td>\n",
       "      <td>programmer</td>\n",
       "      <td>52246</td>\n",
       "      <td>61</td>\n",
       "      <td>5</td>\n",
       "      <td>884305271</td>\n",
       "      <td>Three Colors: White (1994)</td>\n",
       "      <td>01-Jan-1994</td>\n",
       "      <td>...</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>1994</td>\n",
       "      <td>2000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>59</td>\n",
       "      <td>49</td>\n",
       "      <td>M</td>\n",
       "      <td>educator</td>\n",
       "      <td>08403</td>\n",
       "      <td>61</td>\n",
       "      <td>4</td>\n",
       "      <td>888204597</td>\n",
       "      <td>Three Colors: White (1994)</td>\n",
       "      <td>01-Jan-1994</td>\n",
       "      <td>...</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>1994</td>\n",
       "      <td>2000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  age gender  occupation zip_code  movie_id  ratings  timestamp  \\\n",
       "0        1   24      M  technician    85711        61        4  878542420   \n",
       "1       13   47      M    educator    29206        61        4  882140552   \n",
       "2       18   35      F       other    37212        61        4  880130803   \n",
       "3       58   27      M  programmer    52246        61        5  884305271   \n",
       "4       59   49      M    educator    08403        61        4  888204597   \n",
       "\n",
       "                  movie_title release_date      ...       Horror Musical  \\\n",
       "0  Three Colors: White (1994)  01-Jan-1994      ...            0       0   \n",
       "1  Three Colors: White (1994)  01-Jan-1994      ...            0       0   \n",
       "2  Three Colors: White (1994)  01-Jan-1994      ...            0       0   \n",
       "3  Three Colors: White (1994)  01-Jan-1994      ...            0       0   \n",
       "4  Three Colors: White (1994)  01-Jan-1994      ...            0       0   \n",
       "\n",
       "   Mystery  Romance  Sci-Fi  Thriller  War  Western  movie_year  movie_decade  \n",
       "0        0        0       0         0    0        0        1994        2000.0  \n",
       "1        0        0       0         0    0        0        1994        2000.0  \n",
       "2        0        0       0         0    0        0        1994        2000.0  \n",
       "3        0        0       0         0    0        0        1994        2000.0  \n",
       "4        0        0       0         0    0        0        1994        2000.0  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user_movies_det=pd.merge(data_user_movies,movie_data,on='movie_id')\n",
    "data_user_movies_det.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>Action</th>\n",
       "      <th>Adventure</th>\n",
       "      <th>Animation</th>\n",
       "      <th>Children</th>\n",
       "      <th>Comedy</th>\n",
       "      <th>Crime</th>\n",
       "      <th>Documentary</th>\n",
       "      <th>Drama</th>\n",
       "      <th>Fantasy</th>\n",
       "      <th>Film-Noir</th>\n",
       "      <th>...</th>\n",
       "      <th>War</th>\n",
       "      <th>Western</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>movie_decade</th>\n",
       "      <th>movie_title</th>\n",
       "      <th>movie_year</th>\n",
       "      <th>occupation</th>\n",
       "      <th>ratings</th>\n",
       "      <th>unknown</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>M</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>Three Colors: White (1994)</td>\n",
       "      <td>1994</td>\n",
       "      <td>technician</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>47</td>\n",
       "      <td>M</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>Three Colors: White (1994)</td>\n",
       "      <td>1994</td>\n",
       "      <td>educator</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>F</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>Three Colors: White (1994)</td>\n",
       "      <td>1994</td>\n",
       "      <td>other</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>M</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>Three Colors: White (1994)</td>\n",
       "      <td>1994</td>\n",
       "      <td>programmer</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>M</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>Three Colors: White (1994)</td>\n",
       "      <td>1994</td>\n",
       "      <td>educator</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Action  Adventure  Animation  Children  Comedy  Crime  Documentary  Drama  \\\n",
       "0       0          0          0         0       0      0            0      1   \n",
       "1       0          0          0         0       0      0            0      1   \n",
       "2       0          0          0         0       0      0            0      1   \n",
       "3       0          0          0         0       0      0            0      1   \n",
       "4       0          0          0         0       0      0            0      1   \n",
       "\n",
       "   Fantasy  Film-Noir   ...    War  Western  age  gender  movie_decade  \\\n",
       "0        0          0   ...      0        0   24       M        2000.0   \n",
       "1        0          0   ...      0        0   47       M        2000.0   \n",
       "2        0          0   ...      0        0   35       F        2000.0   \n",
       "3        0          0   ...      0        0   27       M        2000.0   \n",
       "4        0          0   ...      0        0   49       M        2000.0   \n",
       "\n",
       "                  movie_title  movie_year  occupation  ratings unknown  \n",
       "0  Three Colors: White (1994)        1994  technician        4       0  \n",
       "1  Three Colors: White (1994)        1994    educator        4       0  \n",
       "2  Three Colors: White (1994)        1994       other        4       0  \n",
       "3  Three Colors: White (1994)        1994  programmer        5       0  \n",
       "4  Three Colors: White (1994)        1994    educator        4       0  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user_movies_det[data_user_movies_det.columns.difference(['user_id','movie_id','release_date','zip_code','video_release_date', 'timestamp','imdb_url'])].head()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}