02-exploratory-analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Analysis\n",
    "\n",
    "In this notebook we explore data looking for some valuable insights that will help in model development."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from plotnine import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ratings Distribution\n",
    "\n",
    "Let's take a look at the ratings dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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</th>\n",
       "      <th>id</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>307</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>481</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1091</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1257</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1449</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user    id  rating\n",
       "0     1   307     3.5\n",
       "1     1   481     3.5\n",
       "2     1  1091     1.5\n",
       "3     1  1257     4.5\n",
       "4     1  1449     4.5"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings = pd.read_csv('data/movie_ratings.csv')\n",
    "ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Num. Users: 273643'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'Num. Users: {}'.format(ratings['user'].nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Num. Movies: 15967'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'Num. Movies: {}'.format(ratings['id'].nunique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we check the vote proportion in each rating level:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>rating</th>\n",
       "      <th>count</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.5</td>\n",
       "      <td>429197</td>\n",
       "      <td>1.559762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>876669</td>\n",
       "      <td>3.185938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.5</td>\n",
       "      <td>433139</td>\n",
       "      <td>1.574088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1832493</td>\n",
       "      <td>6.659536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.5</td>\n",
       "      <td>1348596</td>\n",
       "      <td>4.900986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3.0</td>\n",
       "      <td>5466620</td>\n",
       "      <td>19.866461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.5</td>\n",
       "      <td>3361124</td>\n",
       "      <td>12.214794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4.0</td>\n",
       "      <td>7356391</td>\n",
       "      <td>26.734152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.5</td>\n",
       "      <td>2359304</td>\n",
       "      <td>8.574040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5.0</td>\n",
       "      <td>4053296</td>\n",
       "      <td>14.730244</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rating    count    percent\n",
       "0     0.5   429197   1.559762\n",
       "1     1.0   876669   3.185938\n",
       "2     1.5   433139   1.574088\n",
       "3     2.0  1832493   6.659536\n",
       "4     2.5  1348596   4.900986\n",
       "5     3.0  5466620  19.866461\n",
       "6     3.5  3361124  12.214794\n",
       "7     4.0  7356391  26.734152\n",
       "8     4.5  2359304   8.574040\n",
       "9     5.0  4053296  14.730244"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rating_prop = ratings.groupby('rating').agg(\n",
    "    count=pd.NamedAgg(column='rating', aggfunc='count')\n",
    ").reset_index(drop=False)\n",
    "rating_prop['percent'] = 100 * rating_prop['count'] / rating_prop['count'].sum()\n",
    "display(rating_prop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = ggplot(rating_prop, aes(x='factor(rating)', y='percent', fill='percent')) +\\\n",
    "    geom_col(size=1, alpha=0.8) +\\\n",
    "    scale_fill_distiller(guide=None, type='div', palette='Spectral') +\\\n",
    "    scale_y_continuous(breaks=np.arange(0, 100, 5)) +\\\n",
    "    labs(x='Rating', y='Percentage(%)') +\\\n",
    "    theme_bw()\n",
    "g = g.draw(return_ggplot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just a small part of the votes fall below 3.0, the same for the movie average rating:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ratings.groupby('id').agg(rating=('rating', 'mean')).rating.hist(bins=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ratings as Likes and Dislikes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's covert rantings into likes (>= 3.5) and dislikes (< 3.5). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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</th>\n",
       "      <th>id</th>\n",
       "      <th>rating</th>\n",
       "      <th>like</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>307</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>481</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1091</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1257</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1449</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user    id  rating  like\n",
       "0     1   307     3.5   1.0\n",
       "1     1   481     3.5   1.0\n",
       "2     1  1091     1.5   0.0\n",
       "3     1  1257     4.5   1.0\n",
       "4     1  1449     4.5   1.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings['like'] = np.where(ratings['rating'] >= 3.5, 1.0, 0.0)\n",
    "ratings.head()"
   ]
  },
  {
   "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>like</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>17471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>50939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>14793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>12345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>9718</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    like  count\n",
       "id             \n",
       "1    0.0  17471\n",
       "1    1.0  50939\n",
       "2    0.0  14793\n",
       "2    1.0  12345\n",
       "3    0.0   9718"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_likes = ratings.groupby(['id','like']).agg(\n",
    "    count=pd.NamedAgg(column='like', aggfunc='count')\n",
    ").reset_index(drop=False).set_index('id')\n",
    "movie_likes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "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": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "avg_rating = ratings.groupby('id').agg(\n",
    "    num_likes=pd.NamedAgg(column='like', aggfunc='sum'),\n",
    "    rating=pd.NamedAgg(column='rating', aggfunc='mean')\n",
    ")\n",
    "\n",
    "avg_rating.num_likes.hist(bins=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='num_likes', ylabel='rating'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RFs+d3OvmkHLNFnY1239DMWVGtQUWCERkEbBdVdeJyCnFDvPZVlCjiMglwCUAU6dO7VU5huqVUbFmnj69Vhque/AFuivsK8mMWb/6gQ08fc1Cblg8u6CzEg429fz488f79ock00osUthXcv73nuMbWVlOSzUP5FecAAtuerxse24lTQ59aVIp1mT2yN9/rGc2eKaMXcm0b/oKv7L1Jp9TdkVvV7PVUQspMwZTkE1DC4DFIvIG8FNgoYj8OO+YdmBK1u/NwLb8F1LVZao6T1XnTZgwoVeF2LTNvykkbPYnteKmm4zMKKL5RxxS9JhYJMIbHft67iKypdS/w7wrWVnzRab5B+hZq7U/48/zX7svTSrFmp7GNh1MbNjbMmaafi68a03BfI5yFX3QTWFhMm5EcGsC17rA7ghU9VrgWgDvjuBKVb0w77CHgMtF5Ke4ncS7VLXKzUIDN2JooDREBdXSI4CKcciM7C9vbyLFxq27SgbT/d1Jpo8bXnFupoxyzRfFrtardQXcnyaV/KvH1W3vseCmx3vKet2imRWXsVzTTyXNFmG/mjX9N+DzCETkUgBVvRN4BHfoaBvu8NHPVfv9Zk0aXe2XHHTFxvY3xhy6U8VHGgE4jvCzi+fzRsc+Nm7dxQ+febPke31t5WbSJWbuKsIF31vjztUoEpiGxSIk00oya3+pyjvT+ZyfhTSz4E4m1UZ/2nPLBZRK+w527u0qqMhvXLmZ686cyY0Ply9jJQFpQct4ln12Hn4jhjJqYfSNGboGJBCo6hPAE97jO7O2K3BZkO9dL1+OWARi0Qj7uvxnmaZS6l2V+7fLZ1z5iQ8TizqcevShzJ0yhnuee5MSyVm913foTvsflEqrN0O6ePBRhRvOmlVRxQhwz5q3CoKdppUzbl9Ng5cl9bozZzJ78uh+Zc4sdqVdru8gN9tpypuweFAsEmH25NE8fc3CssGkXEAaykNDzdBR9zOLh2pncbbZk0ayeM5kbvn1ywX7hkUjJJJpFEiUyFkRdyJ86thJ3PqbV4hFIuzrSoKIl8dG3YldRa7oU9r3mXHRiPQsf/nhw0bw5KvvcfKM8cw7Yhxt7+4pmATV0ZngjlVtBa/jjj5SuryOjhsf3szT1yzsV6AvttZvuRXNCpK8pfznLVRylV4qINlEJzNQ6j4Q9LYTsRZt3LaHjdte8t3X5QWBUr7wp0dyznHNnHn7ajeBXaYCU+3pL+hOKdGI5DQrxRy3Egd6spYWW9nMT8yBR//+Y7QcNpKvPvhCz9DV2x5v46jDmnjl3b09x2bScruzjSMFM3XzA1W1hkjmV9blmmr89jc4gor03K1Ua5EXGxpqBkrdB4JSaxIPdY54DTJlIsHu/UmWP99eMg2CQk4QiDvCvRfPJxZ1aB7b6C3mUpgXyE9jNEIa5ZYl7mSptnf3FMxfyA4C4KblvuiE6b5NJe6ksvxkesEMkSzXVOO3XyLCw5efVHIxoXL87h5saKgZKHUfCF56pzANcL2odNDQPc/5TyIrJRIRzr9rDQ1Rh65UirRWPrfBrboOtpu3ekNAy1nd9h7HTh3r29EKcMX963vKkEqnebrtvaq3l5cbpVNsf7nZwUGUxZhqqftA8GZH52AXYUjKTCrrSvU+UVPmziPTnj13ypiKnvcvj77Us2TmdYtmMnvS6Jy2++w+2WS6sL28WmmEyw3HHMjhmjY01AyEul+Y5pAm++L0VtwRGpzyC+A0RIWLTnTzAeXnBIKD7dkth43kohNzZ4R/+LCmnN+diJBIHpzcdePKzQXt5XEnt5kve5JWqUlZfVFuctFATj4K80QnMzDq/o6g9c2dg12Emhd3pKcJ6PJTZ/DJ2YezaOnqnLanaAScSIS4466Le/mpLZw/fyqr297jZ7/fgvhkCzmQPJhy4UunHcXx08fxXucBTmqZ0NN30LrlA4bFHK79+Qs9S1FCYaeoX3t5IpmiKe7Y6Bpj+qnuA8HvXg9+mcqhrMERvvtXH2V0YyznCrySxU5yF68p7D/IrNBWOBa+gZbDRvb8dHQmynaKZreXa1pJpJRIRFi0dDWXndJio2uM6Ye6DwSjh8XYwtCfSxAUiQizJo3KqTA7OhNMG9fESp+RMOWGWmZrjEXZtG1X2av1SjtFF8+dzKTRw/jMXWuAg/0YS1e9Sn7+QhtdY0zl6j4QRMss7F0PmuIR9vrMOHb3OfzdKR/i1sdeKRhl1BCVggrXbybrHJ/O3o7OBFve38eBImsoQ2YRFqnoar2STtHMer/5o5fijsMlJx/JHU+02egaY/qg7gPBjr31fTdw9tyJPLrx3aL7U6pMG9fkO9T0M8dP7Rl+mVlmMrP2bqm29hWtW7kyr0IeFovQndKcZUHPndfMrEmjKh4LX2ombqYZqth6v+fPn8r586cO2dE1YV443Qy+ur9cLreM4FAWAX658R0okRTuc38yvei+e9dsoaMz0TPi5tL/WFcw6Sw/fbKbEG5DwVV5MpXGyVtR7b617QBVSZPsl9oZctf7Haqja6o94smY3qr7O4J9QawgXyPSFM9EmvGjZ94klVbcjEJ5z1fNacP3k3/13r5zP06kcISQE4ngiNCVKlw0vhpj4f1GDVW63m8tsxFPphbU/R3BvsQAr1xfY/Z1pUgk076VdzKtbC1ypZ2ZG5B/9d48tjGn+SdDVQuS0+UvoNKfq/W+rvdb66q10I4x/VH3dwS9XC+lbjl5CeUyEsmU79KL7h1E4fGZdYSz0z1EI/CNT88BCDQdQj3OsrV8QqYW1H0gaIgKXV0WDYo5qWUCN5/T4GYXjQh7E27TzoFk8WaKBS3j+d5ffZTd+7sZ1RjLGX5aSUXdn47ReluAxfIJmVpQ94EgXYedxREoMnI/95hoNJKTGnntm+9z9zMHE9BddOLUnkldC1rGs+ql7Vz/0KacVNP5Qz39hpf2ZtF4W2ilUD3e6Zihpe4Dwd7uwS5B9VXS/Z0G7v2b43vSSI8b0cDiuZO56ITpBYvBgFuBn3r0ofzvFRtzXid/+cb+dGxax2hx9XanY4aWuu8srkexCv7VhsUiPUFg07bdPPnKdjo6E7QcNpIl86b4drLmd8g2RIXLTmnp2d/fjk3rGDWmNgV2RyAiw4AngQbvfZar6vV5x5wCrABe9zb9XFW/FlSZ6kFMiq9HnE0VHt34Nt998rWeyWQxR/jmp+cUNMVkt9lnminuWfMWd6xqY9mTr3HHE209uYb62rHZ0Zlg1/5uulLWMWpMrQmyaSgBLFTVThGJAatF5FFVfTbvuKdUdVGA5agr3WW6PIbHHbpTaZKpNHf+12u5z00pVy3PbYrxa7Nf0DKef3uijUQynbO2wNPXLKyoYzO/Mzj7PVLpNDFHGBZ1rGPUmBoRWCBQN/VkZlWYmPdTfz23VZZZfrIvw17jjjuL94r7W4sGDCciPZ2/xdrsl312XtH8QOU6NvMDS2a1sez3aIjCHRccy6xJoy0IGFMDAu0jEBFHRFqB7cBjqrrG57ATRWS9iDwqIrOKvM4lIrJWRNbu2LEjyCIPupTC1z81m6Z47/5p3LH8cxnVGMOR4s9NpbWnKaZYmz1oySagYpPDsgNLZoGZG36xiWjeZLa44zC6MW5BwJgaEWggUNWUqs4FmoHjRWR23iHPA9NUdQ5wO/BgkddZpqrzVHXehAkTgixyTUgkUxWvR3zh/Kks/cxc1vzjx1Hgb+9ey75u/4ygMUe4ZcnBppjmsY2+bfazJo3uU34g38DiRAoSxVm/gDG1ZUCGj6rqByLyBHA6sDFr++6sx4+IyL+JyHhVDfVqMrO9ivjK+9eTTiul0gn95x+2cv+6LXx+wRF896nXyMsZR9yBvznpSE780LiCppjVbe+RyrryjzkH01LnNwEBrN/yQdFx7m5ncFdOriFws59ef9ZMbly5ueIJU5aJ05iBFeSooQlAtxcEGoGPAzflHXM48K6qqogcj3uH0hFUmYaCaEQ4/3vPcdyU0b4pl/NlJn99J69jOOPWc49l0ZxJBdszWUSzA0dE3JnBGZmx7eUmga1o3crVy92ZyWl1m6kaY9GcY0+fdXhFlbtNODNm4AV5RzAR+JGIOLgV/H2qulJELgVQ1TuBJcAXRCQJ7AfOU63DqcAVyGQHTaYV0sqzrxdfa9kRiDsR9udf/vfCPWveKkg5HXecggVjyk0C6+hMFKxNEHOEOy44Lif1RCUTpmzCmTGDI8hRQxuAY32235n1eCmwNKgyDBXnHDuZX21+h85E8dW+skWdCOkKB2Bdcf960qo5V9UdnQnuWPVqwbFdqcK2e7/lKLPTTmzatqtgbQL3d+3zmgO29rAxA8tmFg+yhmiEL5zyoYqagaK4Ofi/umgmtyyZQ0O0MLV0vkQyzdUPbKCj8+BKbe079xN3nIJjLz+1paDCLZ8ds1gZypctn2XiNGZwWCAYRNGIO4qn5bCRfGpuYTt+viTuKJwbH94MwO/+4TSu+LOjaIgeHN3zhT89kuHx3Eo+P42DX4XbEBXOnz+14D391gHI7uydNWkU0by/omjE3d5b5d7LGBOMuk86V6sEcLwKtKMzwUPrt1X0vEzncGam7/nzpzJnymhAeirfH/zujZzn5F9V9zb1calJZONGNHDruXO5avl6HImQ0jS3LJnT58rbMnEaM/AsEAwSxV1m0p3J+5GCtvFyYpEI96x5i397oq1ghE0llXxvK9xSnb3VrrwtE6cxA8sCwSBLp9KA+K4SVkpXKs0dq14lkdSCETaVVszVrHCt8jZm6LI+gkHWlYaX3t6d0zae3+YO7pDR7L6Ay09tKejwze4L6O8awcaY8LA7ggGUmSuQ7xu/foVnrl3I09cs5K6nXvOdHPa1T83OmZQFcMcTbTnH+A3/NMaYcuyOYAAVGyDalUpz7xp3CcnvrX69YP/weITZXnqIzFV+psM35hwcpplKp3m6LdTZOYwxfWCBYIDEIvg2+WQsXfUqm7btJuYUHpRMu0M+OzoTrN/yQc+cgAUt48lO7JlMw1XLc+cMGGNMORYIBsgtS44pSAiXQ93/pHwybFx/1kxWt73Hgpse58K71rDgpsd5qHWr78SwRPLg3YUxxlTCAsEAee29fQwrsdhwIqVMGt3Y02ncFHeIO8LXvb6B/Dz/Vz+wgaa4U5BGGty7C7srMMZUyjqLB8jJM8az7Cn/DKHgLja/tyvlm/551UvbCxZ3iUXc4y8/tYVvPvZKzj6/5HHGGFOM3REMgDNnH86Y4XEuO6WFhmiEpobCPD9AwQpgmeag6x/aVJCQLjNb+Pz5UwtyDll+HmNMb9gdQUDiDpx1zCR+sWEbv3nxXR7e+I7XNKRcenILhzTFufHh4ou1ZKdkztbU4JBKa87xtyyZk5PiwfLzGGN6wwJBQK7886O59bFXcFMDuR3AmUr9jifaWHn5SSz77EfI5AjKr7j9UjI3xR1uOGsWpx59aM7x7quLN1Gh91k/jTHhZoEgADFHiDsRNO0/c0DTyhm3r6bBKb4Kl1+G0JRqQRDI3DlkLzJji7kYY3rD+ggC0J1Svv7wZhJF1hhIpJSuZO4IoPxRPpWmZPZdMD4v7bQxxpRidwQB6S4yZyAacVcYy277L7YKVyXJ42wxF2NMfwV2RyAiw0TkORFZLyKbROQGn2NERG4TkTYR2SAixwVVnloQj0b46d+eULC9K5Vm1/5u37H/5ZLH2WIuxpj+CvKOIAEsVNVOEYkBq0XkUVV9NuuYTwIzvJ/5wHe8/9eFaARE3HH9KXVH+sw7YlzOegEHkilS6TSX3fN80f6CcjJ3Dpu27SJ7gRpjjKlEkIvXK9Dp/RrzfvIbzc8G7vaOfVZExojIRFV9O6hyDYThsQiJZBoRoSEaoSulXH/WzJ4KPrvi/tu715JIwZ5EEijd0dvRmSjaTLS67T2uyVuMprcBxRgTToF2FouIIyKtwHbgMVVdk3fIZGBL1u/t3rb817lERNaKyNodO3YEVt5qaIhGSKTSpNTtNO5MpOhKprlx5eacpp9xIxoY3RgvuaZAthWtWwtyDWVkzzko1QFtjDF+Ag0EqppS1blAM3C8iMzOO8Rv0HvBUBtVXaaq81R13oQJEwIoafUkkml80v/4VvCVdvSWq+ht5JAxpj8GZPioqn4APAGcnrerHZiS9XszUNkq7jUo5ggNjv+ErkwFn51KulpDRG3kkDGmPwLrIxCRCUC3qn4gIo3Ax4Gb8g57CLhcRH6K20m8a6j2D8Qc4ScXz+fC7z8HefMHGqIRLjulhV9ufKcgrUQ1hohmAkq5BeuNMcZPkKOGJgI/EhEH987jPlVdKSKXAqjqncAjwBlAG7AP+FyA5QlMzBG++ek5BSOCulIpTjv6UH7z4nb+7Yk29ntzB/IXmy+38HslFX2lC9YbY0y+IEcNbQCO9dl+Z9ZjBS4LqgxB+5f/MZvJYxuZNLqRvV0pOjoTORVyU9zhk7c9RXdKC+4SoPhEMj+VVPTlAooxxvixmcV9FI/AzEmjeaNjL5f8x7qC5p5xIxp48pXtbhAoorft+FbRG2OCUFEgEJEXKBzNswtYC/wfVe2odsFqnUSEprjTM5rHr7nHf1AUNEYjqGDt+MaYmlDpHcGjQAq41/v9PO//u4EfAmdVt1i17/JTZ7C3K1WQKjq7uWfWpFFEI+SsVewI/PtF83xTTxtjzGCoNBAsUNUFWb+/ICJPq+oCEbkwiILVsoZohPPnTwUoO5rn1nPn5iwac8uSOZx8VG3PhTDGhEulgWCEiMzPzAwWkeOBEd6+ZCAlq1EN0Qi3LDnYpHPzOceUXB3MRvMYY2pdpYHgYuD7IjICt+F7N3CxiDQB/xJU4WqRO9Ap63eg3Opg1slrjKllFQUCVf098MciMhoQb6Zwxn1BFKxWdaW0p0MYsNXBjDFDXqWjhhqAc4DpQFTEvfJV1a8FVrIaFhFh07ZdjG6Ml+wsNsaYoaDSXEMrcFNGJ4G9WT+htK8rxd/evZaNW3dZjh9jzJBXaR9Bs6rmJ4wLtURSufHhzVy3aCY3rtxsOX6MMUNWpYHgdyLyx6r6QqClGWJikQizJ41m5eUn0brlA+ZOGUPLYSMHu1jGGNMrlQaCk4C/FpHXcZegFNxUQccEVrIaEAGcSPGF6LvTaTZu3eWbUdQYY4aKSgPBJwMtRQ3758WzeX9vF0tXvQoKiZQyLOZ2rWSahYqnmDDGmNpXMhCIyChV3Q3sGaDy1JQ0cOPDm3n6moWcP39qT0bRvV0pmsc29iwYY6OGjDFDWbk7gnuBRcA63LlT2TOmFDgyoHLVDEeE9p37mTNljG/lbqOGjDFDXcnho6q6yPv/Eap6pPf/zE/dBwGA7lTxir3SpSaNMaaWVTqh7Leqelq5bfXoyk98uGTFbrmEjDFDXbk+gmHAcGC8iIzlYNPQKGBSwGWrCdnpI4qxXELGmKGs3Mzi/4nbP3C09//MzwrgjlJPFJEpIrJKRF4UkU0i8iWfY04RkV0i0ur9fLVvHyM4S1e9SkdnYrCLYYwxgSl5R6Cq3wa+LSJfVNXbe/naSeAKVX1eREYC60TkMVXdnHfcU5m+iFoUdxwbBWSMqWuVZh+9XURmAzOBYVnb7y7xnLeBt73He0TkRWAykB8IapqNAjLG1LuKks6JyPXA7d7PqcDNwOJK30REpgPHAmt8dp8oIutF5FERmVXk+ZeIyFoRWbtjx45K37bPohFsFJAxJjQqnVm8BJgD/EFVPycihwF3VfJEbzGbB4Ave5PTsj0PTFPVThE5A3gQmJH/Gqq6DFgGMG/ePM3fX23f+su5TDmkyUYBGWNCodI01AdUNQ0kRWQUsJ0KJpOJSAw3CNyjqj/P36+qu1W103v8CBATkfEVlz4A0Qic+KHxRSeQGWNMvSl7RyDuKjQbRGQM8F3cUUOdwHMVPO97wIuqemuRYw4H3lVV9dZBjgAdvfoEVfYXx022AGCMCZWygcCrpOd6y1PeKSK/BEap6oYyT10AfBZ4QURavW3/CEz1XvdO3CanL4hIEtgPnKf5iwIPsPvXbuWa0//IgoExJjQq7SN4VkQ+qqq/V9U3KnmCqq4mNzeR3zFLgaUVlmFAKLBp225OPmrCYBfFGGMGRKWB4FTgf4rIm7hLVNb1egS793cNdhGMMWbA2HoEPq64fwNpxRaYMcaEQqUTyt4MuiC1JJFM2wIzxpjQqHT4aF37sz86lOFxJ2dbZoEZY4ypdxYIgIVHH0o6b7CSpZYwxoSFBQLg/b1dXLdopi0wY4wJpUo7i+va0lVtKHDdmTOZPXm0pZYwxoSK3REA+7vTHOhO87WVmywIGGNCxwJBlkRSuXfNW4NdDGOMGVAWCPIsXdVmK5IZY0LFAkGeuGPDRo0x4WKBII8NGzXGhI2NGgIaokLccehOp23YqDEmdCwQAP981ixmTrJho8aYcLJAgDuhbM6UMYNdDGOMGRTWRwBMG9c02EUwxphBU/eBoJIPePgoaw4yxoRX3QeCdAXHvNGxL/ByGGNMrQosEIjIFBFZJSIvisgmEfmSzzEiIreJSJuIbBCR44IqTylzrX/AGBNiQXYWJ4ErVPV5ERkJrBORx1R1c9YxnwRmeD/zge94/6+aqEBSi++/6MSptBw2sppvaYwxQ0pgdwSq+raqPu893gO8COSv/Xg2cLe6ngXGiMjEapbDKfMJLzphejXfzhhjhpwB6SMQkenAscCavF2TgS1Zv7dTGCwQkUtEZK2IrN2xY0ev3juRKr3/V5ve6dXrGWNMvQk8EIjICOAB4Muqujt/t89TChpyVHWZqs5T1XkTJkyoavk2b9tV1dczxpihJtBAICIx3CBwj6r+3OeQdmBK1u/NwLZqliFW5hP+5qUdlm3UGBNqQY4aEuB7wIuqemuRwx4CLvJGD50A7FLVt6tZjniZ7nDLNmqMCbsgRw0tAD4LvCAird62fwSmAqjqncAjwBlAG7AP+Fy1C7G3q/R+yzZqjAm7wAKBqq7Gvw8g+xgFLguqDJW47syZlmjOGBNqdT+zeGS89EeccsjwASqJMcbUproPBFo4CKngCGOMCbO6DwSNsdKtX7MmjR6gkhhjTG2q+0AwfkR8sItgjDE1re4DgRMp2V9tQ0eNMaFX94EgUSrjHNjQUWNM6NV9IDhkeKzoviPGDbeho8aY0Kv7QNC2o7PovtmTRg1gSYwxpjbVfSDoThVvGmo5dMQAlsQYY2pT3QeCMSWahnbt7x7AkhhjTG2q+0BQqg9g1wELBMYYU/eBYOzw4vMIpoy19BLGGFP3gaArmS66z5qGjDEmBIFgwsjiTUNr33h/AEtijDG1qe4DwdjhxXMNvbBtj61OZowJvboPBI0lliiLYCkmjDGm7gNBqXkEYCkmjDGm7gPB5m27i+676MRplmLCGBN6QS5e/30R2S4iG4vsP0VEdolIq/fz1SDKEXWK71v4R4cG8ZbGGDOkBLl4/Q+BpcDdJY55SlUXBVgGJo4u3vRji9IYY0yAdwSq+iQw6OMzD2kqPqFs596uASyJMcbUpsHuIzhRRNaLyKMiMqvYQSJyiYisFZG1O3bs6NUbjCkxs7h1ywe9ei1jjKlHgxkIngemqeoc4HbgwWIHquoyVZ2nqvMmTJjQqzd5+4Piw0PHlkhIZ4wxYTFogUBVd6tqp/f4ESAmIuOr/T6lEsvt3GcpJowxZtACgYgcLiLiPT7eK0tHtd9n3rRDiu6bO2VMtd/OGGOGnMBGDYnIT4BTgPEi0g5cD8QAVPVOYAnwBRFJAvuB81S19OyvPphRYvGZsSU6ko0xJiwCCwSq+pky+5fiDi8N1MZtu4ru27RtNycf1bs+B2OMqTeDPWoocA0lZpTttjTUxhhT/4FABrsAxhhT4+o+EBwosTDNqMYgJ1YbY8zQUPeB4KQW/xGp0YhYigljjCEEgaDlsJFcdOLUnG0R4NZz51jmUWOMIQSBAOAj0w7ByeosiESs58AYYzLqPhB0dCa4evl6stenSaaVK+5fb8tUGmMMIQgE7Tv3Iz5jh7pTyqYScwyMMSYs6j4QNI9tpCtVbOSQNREZY0zdBwIAiiSumDR62MCWwxhjalDdB4L2nfsZFiucXRxzhL1dqUEokTHG1Ja6DwTNYxtJaWHTUESE5rHFl7E0xpiwqPtAMG5EA385b0rONici3LLkGJtHYIwxhCAQdHQmuG9de862aAQWFJlxbIwxYVP3gaB95340nddbrO52Y4wxIQgETXGHRCo3ECRSSlO8eHpqY4wJk7oPBHu7UgyL5X7MYbGIjRgyxhhP3QeCYiODbMSQMca4AgsEIvJ9EdkuIhuL7BcRuU1E2kRkg4gcF0Q5xo1o4OZzjqEhGmF43KEhGuHmc2zEkDHGZAR5R/BD4PQS+z8JzPB+LgG+E1RBNPNfPfibMcYYV2CBQFWfBN4vccjZwN3qehYYIyITq12Ojs4E1zywgURS2dedIpFUrn5gg2UeNcYYz2D2EUwGtmT93u5tq6r2nfuJRXI/ZiwSseGjxhjjGcxA4Jf607fdRkQuEZG1IrJ2x44dvXqT5rGNdKdzU0x0p9PWWWyMMZ7BDATtQHbuh2Zgm9+BqrpMVeep6rwJEyb06k0yncXDYhFGNkQZFrPOYmOMyRYdxPd+CLhcRH4KzAd2qerbQbzR4rmTWdAynvad+2ke22hBwBhjsgQWCETkJ8ApwHgRaQeuB2IAqnon8AhwBtAG7AM+F1RZjDHGFBdYIFDVz5TZr8BlQb1/thWtW7nmgQ3EIhG602luPucYFs+ter+0McYMSXU/szgzfPRAd5o9iSQHutM2fNQYY7LUfSAoNkzUho8aY4yr7gNBU9zhQHfu8NED3WnLPmqMMZ66DwR7u1I0OLlTFhpsvWJjjOlR94GgeWwjEskNBBKx9YqNMSaj7gOBTSgzxpjSBnNC2YCxCWXGGFNcKAIBuHcGFgCMMaZQ3TcNGWOMKc0CgTHGhJwFAmOMCTkLBMYYE3IWCIwxJuTETQI6dIjIDuDNPj59PPBeFYtTD+yc5LLzUcjOSa6hej6mqarvyl5DLhD0h4isVdV5g12OWmLnJJedj0J2TnLV4/mwpiFjjAk5CwTGGBNyYQsEywa7ADXIzkkuOx+F7JzkqrvzEao+AmOMMYXCdkdgjDEmjwUCY4wJudAEAhE5XUReFpE2EfmHwS5PNYnIFBFZJSIvisgmEfmSt/0QEXlMRF71/j826znXeufiZRH586ztHxGRF7x9t4mIeNsbRORn3vY1IjJ9wD9oL4mIIyJ/EJGV3u9hPx9jRGS5iLzk/a2cGOZzIiL/y/u+bBSRn4jIsNCeD1Wt+x/AAf4bOBKIA+uBmYNdrip+vonAcd7jkcArwEzgZuAfvO3/ANzkPZ7pnYMG4Ajv3DjevueAEwEBHgU+6W3/O+BO7/F5wM8G+3NXcF6+AtwLrPR+D/v5+BFwsfc4DowJ6zkBJgOvA43e7/cBfx3a8zHYBRigf/QTgV9l/X4tcO1glyvAz7sC+DPgZWCit20i8LLf5wd+5Z2jicBLWds/A/x79jHe4yjuzEoZ7M9a4hw0A78FFmYFgjCfj1FexSd520N5TrxAsAU4xCvrSuATYT0fYWkayvyjZ7R72+qOd/t5LLAGOExV3wbw/n+od1ix8zHZe5y/Pec5qpoEdgHjAvkQ1fEt4GognbUtzOfjSGAH8AOvuewuEWkipOdEVbcC3wDeAt4Gdqnqrwnp+QhLIBCfbXU3blZERgAPAF9W1d2lDvXZpiW2l3pOzRGRRcB2VV1X6VN8ttXN+fBEgeOA76jqscBe3KaPYur6nHht/2fjNvNMAppE5MJST/HZVjfnIyyBoB2YkvV7M7BtkMoSCBGJ4QaBe1T1597md0Vkord/IrDd217sfLR7j/O35zxHRKLAaOD96n+SqlgALBaRN4CfAgtF5MeE93yAW952VV3j/b4cNzCE9Zx8HHhdVXeoajfwc+BPCOn5CEsg+D0wQ0SOEJE4bsfNQ4NcpqrxRil8D3hRVW/N2vUQ8Ffe47/C7TvIbD/PG9VwBDADeM67Fd4jIid4r3lR3nMyr7UEeFy9xs9ao6rXqmqzqk7H/bd+XFUvJKTnA0BV3wG2iMiHvU2nAZsJ7zl5CzhBRIZ7n+M04EXCej4Gu5NioH6AM3BH0/w38E+DXZ4qf7aTcG85NwCt3s8ZuO2RvwVe9f5/SNZz/sk7Fy/jjXLwts8DNnr7lnJw9vkw4H6gDXeUxJGD/bkrPDencLCzONTnA5gLrPX+Th4Exob5nAA3AC95n+U/cEcEhfJ8WIoJY4wJubA0DRljjCnCAoExxoScBQJjjAk5CwTGGBNyFgiMMSbkLBAYY0zIWSAwJiAi8kMRWeI9vktEZnqPOwe3ZMbkig52AYwJA1W9eLDLYEwxdkdgQkVEpnuLsnzXW5Tk1yLSKCJPiMg875jxXp4iROSvReRBEfmFiLwuIpeLyFe8DJ7PisghFb5vz+tnbRsvIs+IyJkiMkFEHhCR33s/C7xj/lREWr2fP4jIyCqfEmMsEJhQmgHcoaqzgA+Ac8ocPxs4Hzge+DqwT90Mns/g5pbpNRE5DHgY+KqqPgx8G/h/qvpRrzx3eYdeCVymqnOBjwH7+/J+xpRiTUMmjF5X1Vbv8TpgepnjV6nqHtzkYruAX3jbXwCO6cP7x3Dz2Fymqv/lbfs4MNNb5RBglHf1/zRwq4jcA/xcVdsLXs2YfrI7AhNGiazHKdwLoiQHvw/DShyfzvo9Td8uppK4AejPs7ZFcFezmuv9TFbVPar6r8DFQCPwrIgc3Yf3M6YkCwTGuN4APuI9XhLweynweeBoEcksDvNr4PLMASIy1/v/h1T1BVW9CTdzqAUCU3UWCIxxfQP4goj8Dhgf9Jupagp3rYRTReTvgL8H5onIBhHZDFzqHfplEdkoIutx+wceDbpsJnwsDbUxxoSc3REYY0zI2aghY/pJRO7AXSc527dV9QeDUR5jesuahowxJuSsacgYY0LOAoExxoScBQJjjAk5CwTGGBNy/x/29FTcbgHoZAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "avg_rating.plot.scatter(x='num_likes', y='rating')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we obseerve a rating bias for movies with a massive number of likes. They are likely to have average rating above 3.5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = ggplot(avg_rating, aes(x='num_likes', y='rating')) +\\\n",
    "    geom_bin2d(binwidth=[2500, 0.05], alpha=0.8, size=0.1, color='#ffffff') +\\\n",
    "    scale_fill_distiller(type='div', palette='Spectral') +\\\n",
    "    scale_y_continuous(breaks=np.arange(0, 5, 0.25)) +\\\n",
    "    guides(fill = guide_colourbar(barwidth=5, barheight=25)) +\\\n",
    "    labs(x='Num. Likes', y='Avg. Rating') +\\\n",
    "    theme_bw()\n",
    "g = g.draw(return_ggplot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Most Liked and Most Hated Movies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's just a sanity test. For me the results make sense, but the final conclusions are personal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "movies = pd.read_csv('data/movie_info.csv')\n",
    "movies.set_index('id', inplace=True) \n",
    "movies = movies.join(movie_likes)"
   ]
  },
  {
   "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>original_title</th>\n",
       "      <th>title</th>\n",
       "      <th>overview</th>\n",
       "      <th>tagline</th>\n",
       "      <th>like</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "      <td>Framed in the 1940s for the double murder of h...</td>\n",
       "      <td>Fear can hold you prisoner. Hope can set you f...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>88846.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>Forrest Gump</td>\n",
       "      <td>Forrest Gump</td>\n",
       "      <td>A man with a low IQ has accomplished great thi...</td>\n",
       "      <td>The world will never be the same, once you've ...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>77143.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>Pulp Fiction</td>\n",
       "      <td>Pulp Fiction</td>\n",
       "      <td>A burger-loving hit man, his philosophical par...</td>\n",
       "      <td>Just because you are a character doesn't mean ...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>76334.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>593</th>\n",
       "      <td>The Silence of the Lambs</td>\n",
       "      <td>The Silence of the Lambs</td>\n",
       "      <td>FBI trainee, Clarice Starling ventures into a ...</td>\n",
       "      <td>To enter the mind of a killer she must challen...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>74607.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2571</th>\n",
       "      <td>The Matrix</td>\n",
       "      <td>The Matrix</td>\n",
       "      <td>Set in the 22nd century, The Matrix tells the ...</td>\n",
       "      <td>Welcome to the Real World.</td>\n",
       "      <td>1.0</td>\n",
       "      <td>71266.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>Star Wars</td>\n",
       "      <td>Star Wars</td>\n",
       "      <td>Princess Leia is captured and held hostage by ...</td>\n",
       "      <td>A long time ago in a galaxy far, far away...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>66945.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>527</th>\n",
       "      <td>Schindler's List</td>\n",
       "      <td>Schindler's List</td>\n",
       "      <td>The true story of how businessman Oskar Schind...</td>\n",
       "      <td>Whoever saves one life, saves the world entire.</td>\n",
       "      <td>1.0</td>\n",
       "      <td>62012.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2959</th>\n",
       "      <td>Fight Club</td>\n",
       "      <td>Fight Club</td>\n",
       "      <td>A ticking-time-bomb insomniac and a slippery s...</td>\n",
       "      <td>Mischief. Mayhem. Soap.</td>\n",
       "      <td>1.0</td>\n",
       "      <td>57479.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>The Usual Suspects</td>\n",
       "      <td>The Usual Suspects</td>\n",
       "      <td>Held in an L.A. interrogation room, Verbal Kin...</td>\n",
       "      <td>Five Criminals. One Line Up. No Coincidence.</td>\n",
       "      <td>1.0</td>\n",
       "      <td>55297.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1196</th>\n",
       "      <td>The Empire Strikes Back</td>\n",
       "      <td>The Empire Strikes Back</td>\n",
       "      <td>The epic saga continues as Luke Skywalker, in ...</td>\n",
       "      <td>The Adventure Continues...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>54653.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                original_title                     title  \\\n",
       "id                                                         \n",
       "318   The Shawshank Redemption  The Shawshank Redemption   \n",
       "356               Forrest Gump              Forrest Gump   \n",
       "296               Pulp Fiction              Pulp Fiction   \n",
       "593   The Silence of the Lambs  The Silence of the Lambs   \n",
       "2571                The Matrix                The Matrix   \n",
       "260                  Star Wars                 Star Wars   \n",
       "527           Schindler's List          Schindler's List   \n",
       "2959                Fight Club                Fight Club   \n",
       "50          The Usual Suspects        The Usual Suspects   \n",
       "1196   The Empire Strikes Back   The Empire Strikes Back   \n",
       "\n",
       "                                               overview  \\\n",
       "id                                                        \n",
       "318   Framed in the 1940s for the double murder of h...   \n",
       "356   A man with a low IQ has accomplished great thi...   \n",
       "296   A burger-loving hit man, his philosophical par...   \n",
       "593   FBI trainee, Clarice Starling ventures into a ...   \n",
       "2571  Set in the 22nd century, The Matrix tells the ...   \n",
       "260   Princess Leia is captured and held hostage by ...   \n",
       "527   The true story of how businessman Oskar Schind...   \n",
       "2959  A ticking-time-bomb insomniac and a slippery s...   \n",
       "50    Held in an L.A. interrogation room, Verbal Kin...   \n",
       "1196  The epic saga continues as Luke Skywalker, in ...   \n",
       "\n",
       "                                                tagline  like    count  \n",
       "id                                                                      \n",
       "318   Fear can hold you prisoner. Hope can set you f...   1.0  88846.0  \n",
       "356   The world will never be the same, once you've ...   1.0  77143.0  \n",
       "296   Just because you are a character doesn't mean ...   1.0  76334.0  \n",
       "593   To enter the mind of a killer she must challen...   1.0  74607.0  \n",
       "2571                         Welcome to the Real World.   1.0  71266.0  \n",
       "260        A long time ago in a galaxy far, far away...   1.0  66945.0  \n",
       "527     Whoever saves one life, saves the world entire.   1.0  62012.0  \n",
       "2959                            Mischief. Mayhem. Soap.   1.0  57479.0  \n",
       "50         Five Criminals. One Line Up. No Coincidence.   1.0  55297.0  \n",
       "1196                         The Adventure Continues...   1.0  54653.0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.sort_values(['count'], ascending=False, inplace=True)\n",
    "movies.loc[movies['like']==1].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>original_title</th>\n",
       "      <th>title</th>\n",
       "      <th>overview</th>\n",
       "      <th>tagline</th>\n",
       "      <th>like</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>Ace Ventura: Pet Detective</td>\n",
       "      <td>Ace Ventura: Pet Detective</td>\n",
       "      <td>He's Ace Ventura: Pet Detective. Jim Carrey is...</td>\n",
       "      <td>He's the best there is! (Actually, he's the on...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29083.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>Batman</td>\n",
       "      <td>Batman</td>\n",
       "      <td>The Dark Knight of Gotham City begins his war ...</td>\n",
       "      <td>Have you ever danced with the devil in the pal...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28837.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>Batman Forever</td>\n",
       "      <td>Batman Forever</td>\n",
       "      <td>The Dark Knight of Gotham City confronts a das...</td>\n",
       "      <td>Courage now, truth always...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28704.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>780</th>\n",
       "      <td>Independence Day</td>\n",
       "      <td>Independence Day</td>\n",
       "      <td>On July 2, a giant alien mothership enters orb...</td>\n",
       "      <td>Earth. Take a good look. It might be your last.</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27731.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>480</th>\n",
       "      <td>Jurassic Park</td>\n",
       "      <td>Jurassic Park</td>\n",
       "      <td>A wealthy entrepreneur secretly creates a them...</td>\n",
       "      <td>An adventure 65 million years in the making.</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27542.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>Dumb and Dumber</td>\n",
       "      <td>Dumb and Dumber</td>\n",
       "      <td>Lloyd and Harry are two men whose stupidity is...</td>\n",
       "      <td>What the one doesn't have, the other is missing.</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23697.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>380</th>\n",
       "      <td>True Lies</td>\n",
       "      <td>True Lies</td>\n",
       "      <td>Harry Tasker is a secret agent for the United ...</td>\n",
       "      <td>When he said I do, he never said what he did.</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23032.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>736</th>\n",
       "      <td>Twister</td>\n",
       "      <td>Twister</td>\n",
       "      <td>TV weatherman Bill Harding is trying to get hi...</td>\n",
       "      <td>The Dark Side of Nature.</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22670.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>367</th>\n",
       "      <td>The Mask</td>\n",
       "      <td>The Mask</td>\n",
       "      <td>When timid bank clerk Stanley Ipkiss discovers...</td>\n",
       "      <td>From zero to hero.</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22388.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>Waterworld</td>\n",
       "      <td>Waterworld</td>\n",
       "      <td>In a futuristic world where the polar ice caps...</td>\n",
       "      <td>Beyond the horizon lies the secret to a new be...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22342.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 original_title                       title  \\\n",
       "id                                                            \n",
       "344  Ace Ventura: Pet Detective  Ace Ventura: Pet Detective   \n",
       "592                      Batman                      Batman   \n",
       "153              Batman Forever              Batman Forever   \n",
       "780            Independence Day            Independence Day   \n",
       "480               Jurassic Park               Jurassic Park   \n",
       "231             Dumb and Dumber             Dumb and Dumber   \n",
       "380                   True Lies                   True Lies   \n",
       "736                     Twister                     Twister   \n",
       "367                    The Mask                    The Mask   \n",
       "208                  Waterworld                  Waterworld   \n",
       "\n",
       "                                              overview  \\\n",
       "id                                                       \n",
       "344  He's Ace Ventura: Pet Detective. Jim Carrey is...   \n",
       "592  The Dark Knight of Gotham City begins his war ...   \n",
       "153  The Dark Knight of Gotham City confronts a das...   \n",
       "780  On July 2, a giant alien mothership enters orb...   \n",
       "480  A wealthy entrepreneur secretly creates a them...   \n",
       "231  Lloyd and Harry are two men whose stupidity is...   \n",
       "380  Harry Tasker is a secret agent for the United ...   \n",
       "736  TV weatherman Bill Harding is trying to get hi...   \n",
       "367  When timid bank clerk Stanley Ipkiss discovers...   \n",
       "208  In a futuristic world where the polar ice caps...   \n",
       "\n",
       "                                               tagline  like    count  \n",
       "id                                                                     \n",
       "344  He's the best there is! (Actually, he's the on...   0.0  29083.0  \n",
       "592  Have you ever danced with the devil in the pal...   0.0  28837.0  \n",
       "153                       Courage now, truth always...   0.0  28704.0  \n",
       "780    Earth. Take a good look. It might be your last.   0.0  27731.0  \n",
       "480       An adventure 65 million years in the making.   0.0  27542.0  \n",
       "231   What the one doesn't have, the other is missing.   0.0  23697.0  \n",
       "380      When he said I do, he never said what he did.   0.0  23032.0  \n",
       "736                           The Dark Side of Nature.   0.0  22670.0  \n",
       "367                                 From zero to hero.   0.0  22388.0  \n",
       "208  Beyond the horizon lies the secret to a new be...   0.0  22342.0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.loc[movies['like']==0].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Genre Distribution\n",
    "\n",
    "Now let's check movie genres. Which one is the most popular?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Drama              9053\n",
       "Comedy             6240\n",
       "Thriller           4756\n",
       "Action             3872\n",
       "Romance            3415\n",
       "Horror             3160\n",
       "Crime              2606\n",
       "Adventure          2116\n",
       "Science Fiction    1971\n",
       "Family             1466\n",
       "Mystery            1426\n",
       "Fantasy            1259\n",
       "Documentary        1156\n",
       "Music               839\n",
       "Animation           717\n",
       "Western             683\n",
       "War                 655\n",
       "History             608\n",
       "Foreign             321\n",
       "TV Movie            207\n",
       "Name: genre_name, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genres = pd.read_csv('data/movie_genre.csv')\n",
    "genres['genre_name'].value_counts()"
   ]
  },
  {
   "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>genre_name</th>\n",
       "      <th>count</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Action</td>\n",
       "      <td>3872</td>\n",
       "      <td>8.322228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adventure</td>\n",
       "      <td>2116</td>\n",
       "      <td>4.547995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Animation</td>\n",
       "      <td>717</td>\n",
       "      <td>1.541074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Comedy</td>\n",
       "      <td>6240</td>\n",
       "      <td>13.411856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Crime</td>\n",
       "      <td>2606</td>\n",
       "      <td>5.601169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Documentary</td>\n",
       "      <td>1156</td>\n",
       "      <td>2.484632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Drama</td>\n",
       "      <td>9053</td>\n",
       "      <td>19.457937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Family</td>\n",
       "      <td>1466</td>\n",
       "      <td>3.150926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Fantasy</td>\n",
       "      <td>1259</td>\n",
       "      <td>2.706014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Foreign</td>\n",
       "      <td>321</td>\n",
       "      <td>0.689937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>History</td>\n",
       "      <td>608</td>\n",
       "      <td>1.306796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Horror</td>\n",
       "      <td>3160</td>\n",
       "      <td>6.791901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Music</td>\n",
       "      <td>839</td>\n",
       "      <td>1.803293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Mystery</td>\n",
       "      <td>1426</td>\n",
       "      <td>3.064953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Romance</td>\n",
       "      <td>3415</td>\n",
       "      <td>7.339982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Science Fiction</td>\n",
       "      <td>1971</td>\n",
       "      <td>4.236341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>TV Movie</td>\n",
       "      <td>207</td>\n",
       "      <td>0.444913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Thriller</td>\n",
       "      <td>4756</td>\n",
       "      <td>10.222241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>War</td>\n",
       "      <td>655</td>\n",
       "      <td>1.407815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Western</td>\n",
       "      <td>683</td>\n",
       "      <td>1.467996</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         genre_name  count    percent\n",
       "0            Action   3872   8.322228\n",
       "1         Adventure   2116   4.547995\n",
       "2         Animation    717   1.541074\n",
       "3            Comedy   6240  13.411856\n",
       "4             Crime   2606   5.601169\n",
       "5       Documentary   1156   2.484632\n",
       "6             Drama   9053  19.457937\n",
       "7            Family   1466   3.150926\n",
       "8           Fantasy   1259   2.706014\n",
       "9           Foreign    321   0.689937\n",
       "10          History    608   1.306796\n",
       "11           Horror   3160   6.791901\n",
       "12            Music    839   1.803293\n",
       "13          Mystery   1426   3.064953\n",
       "14          Romance   3415   7.339982\n",
       "15  Science Fiction   1971   4.236341\n",
       "16         TV Movie    207   0.444913\n",
       "17         Thriller   4756  10.222241\n",
       "18              War    655   1.407815\n",
       "19          Western    683   1.467996"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "genre_prop = genres.groupby('genre_name').agg(\n",
    "    count=pd.NamedAgg(column='genre_name', aggfunc='count')\n",
    ").reset_index(drop=False)\n",
    "genre_prop['percent'] = 100 * genre_prop['count'] / genre_prop['count'].sum()\n",
    "display(genre_prop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = ggplot(genre_prop, aes(x='factor(genre_name)', y='percent', fill='percent')) +\\\n",
    "    geom_col(size=1, alpha=0.8) +\\\n",
    "    scale_fill_distiller(guide=None, type='div', palette='Spectral') +\\\n",
    "    scale_y_continuous(breaks=np.arange(0, 100, 2.5)) +\\\n",
    "    labs(x='Genre', y='Percentage(%)') +\\\n",
    "    coord_flip() +\\\n",
    "    theme_bw()\n",
    "g = g.draw(return_ggplot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Most Liked Western and Horror Movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>original_title</th>\n",
       "      <th>title</th>\n",
       "      <th>overview</th>\n",
       "      <th>tagline</th>\n",
       "      <th>like</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>Dances with Wolves</td>\n",
       "      <td>Dances with Wolves</td>\n",
       "      <td>Wounded Civil War soldier, John Dunbar tries t...</td>\n",
       "      <td>Inside everyone is a frontier waiting to be di...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>33822.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99114</th>\n",
       "      <td>Django Unchained</td>\n",
       "      <td>Django Unchained</td>\n",
       "      <td>With the help of a German bounty hunter, a fre...</td>\n",
       "      <td>Life, liberty and the pursuit of vengeance.</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17188.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1201</th>\n",
       "      <td>Il buono, il brutto, il cattivo</td>\n",
       "      <td>The Good, the Bad and the Ugly</td>\n",
       "      <td>While the Civil War rages between the Union an...</td>\n",
       "      <td>For three men the Civil War wasn't hell. It wa...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16676.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1304</th>\n",
       "      <td>Butch Cassidy and the Sundance Kid</td>\n",
       "      <td>Butch Cassidy and the Sundance Kid</td>\n",
       "      <td>In late 1890s Wyoming, Butch Cassidy is the af...</td>\n",
       "      <td>Not that it matters, but most of it is true.</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14481.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1266</th>\n",
       "      <td>Unforgiven</td>\n",
       "      <td>Unforgiven</td>\n",
       "      <td>William Munny is a retired, once-ruthless kill...</td>\n",
       "      <td>Some legends will never be forgotten. Some wro...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13387.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>553</th>\n",
       "      <td>Tombstone</td>\n",
       "      <td>Tombstone</td>\n",
       "      <td>Legendary marshal Wyatt Earp, now a weary gunf...</td>\n",
       "      <td>Justice is coming.</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10269.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3671</th>\n",
       "      <td>Blazing Saddles</td>\n",
       "      <td>Blazing Saddles</td>\n",
       "      <td>A town – where everyone seems to be named John...</td>\n",
       "      <td>Never give a saga an even break!</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9751.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>368</th>\n",
       "      <td>Maverick</td>\n",
       "      <td>Maverick</td>\n",
       "      <td>Maverick is a gambler who would rather con som...</td>\n",
       "      <td>In their hands, a deck of cards was the only t...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9694.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2951</th>\n",
       "      <td>Per un pugno di dollari</td>\n",
       "      <td>A Fistful of Dollars</td>\n",
       "      <td>The Man With No Name enters the Mexican villag...</td>\n",
       "      <td>In his own way he is perhaps, the most dangero...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6666.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82459</th>\n",
       "      <td>True Grit</td>\n",
       "      <td>True Grit</td>\n",
       "      <td>Following the murder of her father by hired ha...</td>\n",
       "      <td>Punishment Comes One Way or Another.</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6113.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           original_title                               title  \\\n",
       "id                                                                              \n",
       "590                    Dances with Wolves                  Dances with Wolves   \n",
       "99114                    Django Unchained                    Django Unchained   \n",
       "1201      Il buono, il brutto, il cattivo      The Good, the Bad and the Ugly   \n",
       "1304   Butch Cassidy and the Sundance Kid  Butch Cassidy and the Sundance Kid   \n",
       "1266                           Unforgiven                          Unforgiven   \n",
       "553                             Tombstone                           Tombstone   \n",
       "3671                      Blazing Saddles                     Blazing Saddles   \n",
       "368                              Maverick                            Maverick   \n",
       "2951              Per un pugno di dollari                A Fistful of Dollars   \n",
       "82459                           True Grit                           True Grit   \n",
       "\n",
       "                                                overview  \\\n",
       "id                                                         \n",
       "590    Wounded Civil War soldier, John Dunbar tries t...   \n",
       "99114  With the help of a German bounty hunter, a fre...   \n",
       "1201   While the Civil War rages between the Union an...   \n",
       "1304   In late 1890s Wyoming, Butch Cassidy is the af...   \n",
       "1266   William Munny is a retired, once-ruthless kill...   \n",
       "553    Legendary marshal Wyatt Earp, now a weary gunf...   \n",
       "3671   A town – where everyone seems to be named John...   \n",
       "368    Maverick is a gambler who would rather con som...   \n",
       "2951   The Man With No Name enters the Mexican villag...   \n",
       "82459  Following the murder of her father by hired ha...   \n",
       "\n",
       "                                                 tagline  like    count  \n",
       "id                                                                       \n",
       "590    Inside everyone is a frontier waiting to be di...   1.0  33822.0  \n",
       "99114        Life, liberty and the pursuit of vengeance.   1.0  17188.0  \n",
       "1201   For three men the Civil War wasn't hell. It wa...   1.0  16676.0  \n",
       "1304        Not that it matters, but most of it is true.   1.0  14481.0  \n",
       "1266   Some legends will never be forgotten. Some wro...   1.0  13387.0  \n",
       "553                                   Justice is coming.   1.0  10269.0  \n",
       "3671                    Never give a saga an even break!   1.0   9751.0  \n",
       "368    In their hands, a deck of cards was the only t...   1.0   9694.0  \n",
       "2951   In his own way he is perhaps, the most dangero...   1.0   6666.0  \n",
       "82459               Punishment Comes One Way or Another.   1.0   6113.0  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "western_movies = genres.loc[genres['genre_name']=='Western', 'id']\n",
    "movies.loc[(movies['like']==1) & movies.index.isin(western_movies)].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>original_title</th>\n",
       "      <th>title</th>\n",
       "      <th>overview</th>\n",
       "      <th>tagline</th>\n",
       "      <th>like</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1214</th>\n",
       "      <td>Alien</td>\n",
       "      <td>Alien</td>\n",
       "      <td>During its return to the earth, commercial spa...</td>\n",
       "      <td>In space no one can hear you scream.</td>\n",
       "      <td>1.0</td>\n",
       "      <td>32325.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1200</th>\n",
       "      <td>Aliens</td>\n",
       "      <td>Aliens</td>\n",
       "      <td>When Ripley's lifepod is found by a salvage cr...</td>\n",
       "      <td>This Time It's War</td>\n",
       "      <td>1.0</td>\n",
       "      <td>27645.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1258</th>\n",
       "      <td>The Shining</td>\n",
       "      <td>The Shining</td>\n",
       "      <td>Jack Torrance accepts a caretaker job at the O...</td>\n",
       "      <td>A masterpiece of modern horror.</td>\n",
       "      <td>1.0</td>\n",
       "      <td>26290.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1219</th>\n",
       "      <td>Psycho</td>\n",
       "      <td>Psycho</td>\n",
       "      <td>When larcenous real estate clerk Marion Crane ...</td>\n",
       "      <td>The master of suspense moves his cameras into ...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19887.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1387</th>\n",
       "      <td>Jaws</td>\n",
       "      <td>Jaws</td>\n",
       "      <td>An insatiable great white shark terrorizes the...</td>\n",
       "      <td>Don't go in the water.</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18761.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>Interview with the Vampire</td>\n",
       "      <td>Interview with the Vampire</td>\n",
       "      <td>A vampire relates his epic life story of love,...</td>\n",
       "      <td>Drink from me and live forever</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17297.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8874</th>\n",
       "      <td>Shaun of the Dead</td>\n",
       "      <td>Shaun of the Dead</td>\n",
       "      <td>Shaun lives a supremely uneventful life, which...</td>\n",
       "      <td>A romantic comedy. With zombies.</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16134.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6502</th>\n",
       "      <td>28 Days Later</td>\n",
       "      <td>28 Days Later</td>\n",
       "      <td>Twenty-eight days after a killer virus was acc...</td>\n",
       "      <td>His fear began when he woke up alone. His terr...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13346.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>The Exorcist</td>\n",
       "      <td>The Exorcist</td>\n",
       "      <td>12-year-old Regan MacNeil begins to adapt an e...</td>\n",
       "      <td>Something almost beyond comprehension is happe...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11961.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71535</th>\n",
       "      <td>Zombieland</td>\n",
       "      <td>Zombieland</td>\n",
       "      <td>Columbus has made a habit of running from what...</td>\n",
       "      <td>This place is so dead</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10524.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   original_title                       title  \\\n",
       "id                                                              \n",
       "1214                        Alien                       Alien   \n",
       "1200                       Aliens                      Aliens   \n",
       "1258                  The Shining                 The Shining   \n",
       "1219                       Psycho                      Psycho   \n",
       "1387                         Jaws                        Jaws   \n",
       "253    Interview with the Vampire  Interview with the Vampire   \n",
       "8874            Shaun of the Dead           Shaun of the Dead   \n",
       "6502                28 Days Later               28 Days Later   \n",
       "1997                 The Exorcist                The Exorcist   \n",
       "71535                  Zombieland                  Zombieland   \n",
       "\n",
       "                                                overview  \\\n",
       "id                                                         \n",
       "1214   During its return to the earth, commercial spa...   \n",
       "1200   When Ripley's lifepod is found by a salvage cr...   \n",
       "1258   Jack Torrance accepts a caretaker job at the O...   \n",
       "1219   When larcenous real estate clerk Marion Crane ...   \n",
       "1387   An insatiable great white shark terrorizes the...   \n",
       "253    A vampire relates his epic life story of love,...   \n",
       "8874   Shaun lives a supremely uneventful life, which...   \n",
       "6502   Twenty-eight days after a killer virus was acc...   \n",
       "1997   12-year-old Regan MacNeil begins to adapt an e...   \n",
       "71535  Columbus has made a habit of running from what...   \n",
       "\n",
       "                                                 tagline  like    count  \n",
       "id                                                                       \n",
       "1214                In space no one can hear you scream.   1.0  32325.0  \n",
       "1200                                  This Time It's War   1.0  27645.0  \n",
       "1258                     A masterpiece of modern horror.   1.0  26290.0  \n",
       "1219   The master of suspense moves his cameras into ...   1.0  19887.0  \n",
       "1387                              Don't go in the water.   1.0  18761.0  \n",
       "253                       Drink from me and live forever   1.0  17297.0  \n",
       "8874                    A romantic comedy. With zombies.   1.0  16134.0  \n",
       "6502   His fear began when he woke up alone. His terr...   1.0  13346.0  \n",
       "1997   Something almost beyond comprehension is happe...   1.0  11961.0  \n",
       "71535                              This place is so dead   1.0  10524.0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "horror_movies = genres.loc[genres['genre_name']=='Horror', 'id']\n",
    "movies.loc[(movies['like']==1) & movies.index.isin(horror_movies)].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Most Liked and Most Hated Directors\n",
    "\n",
    "Sum up movie likes (+1) with dislikes (-1) aggregated by director:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Num. Directors: 2503'"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "directors = pd.read_csv('data/movie_producer.csv')\n",
    "\n",
    "'Num. Directors: {}'.format(directors.loc[directors['job']=='Director', 'person_id'].nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>person_name</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1730</th>\n",
       "      <td>Steven Spielberg</td>\n",
       "      <td>269758.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>Christopher Nolan</td>\n",
       "      <td>169616.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1455</th>\n",
       "      <td>Quentin Tarantino</td>\n",
       "      <td>164889.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1414</th>\n",
       "      <td>Peter Jackson</td>\n",
       "      <td>134236.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1192</th>\n",
       "      <td>Martin Scorsese</td>\n",
       "      <td>125040.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>David Fincher</td>\n",
       "      <td>123644.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1560</th>\n",
       "      <td>Robert Zemeckis</td>\n",
       "      <td>123116.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>896</th>\n",
       "      <td>Joel Coen</td>\n",
       "      <td>114318.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1513</th>\n",
       "      <td>Ridley Scott</td>\n",
       "      <td>110348.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1694</th>\n",
       "      <td>Stanley Kubrick</td>\n",
       "      <td>108636.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            person_name     count\n",
       "1730   Steven Spielberg  269758.0\n",
       "326   Christopher Nolan  169616.0\n",
       "1455  Quentin Tarantino  164889.0\n",
       "1414      Peter Jackson  134236.0\n",
       "1192    Martin Scorsese  125040.0\n",
       "407       David Fincher  123644.0\n",
       "1560    Robert Zemeckis  123116.0\n",
       "896           Joel Coen  114318.0\n",
       "1513       Ridley Scott  110348.0\n",
       "1694    Stanley Kubrick  108636.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "directors = directors.loc[directors['job']=='Director'].set_index('id').join(movie_likes)\n",
    "directors = directors.groupby(['person_name','like']).agg(\n",
    "    count=pd.NamedAgg(column='count', aggfunc='sum')\n",
    ").reset_index(drop=False)\n",
    "directors['like'] = np.where(directors['like']==1, 1, -1)\n",
    "directors['count'] = directors['count'] * directors['like']\n",
    "directors = directors.groupby(['person_name']).agg(\n",
    "    count=pd.NamedAgg(column='count', aggfunc='sum')\n",
    ").reset_index(drop=False)\n",
    "\n",
    "directors.sort_values('count', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>person_name</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1811</th>\n",
       "      <td>Tom Shadyac</td>\n",
       "      <td>-23201.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>898</th>\n",
       "      <td>Joel Schumacher</td>\n",
       "      <td>-18418.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>Frank Marshall</td>\n",
       "      <td>-16645.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1481</th>\n",
       "      <td>Renny Harlin</td>\n",
       "      <td>-16169.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>Brian Levant</td>\n",
       "      <td>-15459.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>330</th>\n",
       "      <td>Chuck Russell</td>\n",
       "      <td>-14196.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1712</th>\n",
       "      <td>Steve Barron</td>\n",
       "      <td>-12376.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1683</th>\n",
       "      <td>Simon Wincer</td>\n",
       "      <td>-12282.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1389</th>\n",
       "      <td>Paul W.S. Anderson</td>\n",
       "      <td>-12178.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1718</th>\n",
       "      <td>Steve Oedekerk</td>\n",
       "      <td>-12083.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             person_name    count\n",
       "1811         Tom Shadyac -23201.0\n",
       "898      Joel Schumacher -18418.0\n",
       "568       Frank Marshall -16645.0\n",
       "1481        Renny Harlin -16169.0\n",
       "227         Brian Levant -15459.0\n",
       "330        Chuck Russell -14196.0\n",
       "1712        Steve Barron -12376.0\n",
       "1683        Simon Wincer -12282.0\n",
       "1389  Paul W.S. Anderson -12178.0\n",
       "1718      Steve Oedekerk -12083.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "directors.sort_values('count').head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Most Liked and Most Hated Actors\n",
    "\n",
    "Sum up movies likes (+1) with dislikes (-1) aggregated by actor:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Num. Actors: 10465'"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actors = pd.read_csv('data/movie_actor.csv')\n",
    "\n",
    "'Num. Actors: {}'.format(actors['actor_id'].nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>actor_name</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7811</th>\n",
       "      <td>Philip Ettington</td>\n",
       "      <td>395555.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3715</th>\n",
       "      <td>Harrison Ford</td>\n",
       "      <td>313386.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9587</th>\n",
       "      <td>Tom Hanks</td>\n",
       "      <td>269594.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8748</th>\n",
       "      <td>Samuel L. Jackson</td>\n",
       "      <td>249131.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1123</th>\n",
       "      <td>Brad Pitt</td>\n",
       "      <td>245564.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5029</th>\n",
       "      <td>John Ratzenberger</td>\n",
       "      <td>237802.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7090</th>\n",
       "      <td>Morgan Freeman</td>\n",
       "      <td>228893.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6608</th>\n",
       "      <td>Matt Damon</td>\n",
       "      <td>226620.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3963</th>\n",
       "      <td>Hugo Weaving</td>\n",
       "      <td>217895.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4265</th>\n",
       "      <td>James Earl Jones</td>\n",
       "      <td>202880.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1255</th>\n",
       "      <td>Bruce Willis</td>\n",
       "      <td>189848.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5030</th>\n",
       "      <td>John Rhys-Davies</td>\n",
       "      <td>185237.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8309</th>\n",
       "      <td>Robert De Niro</td>\n",
       "      <td>183586.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5613</th>\n",
       "      <td>Kevin Spacey</td>\n",
       "      <td>179924.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3988</th>\n",
       "      <td>Ian Holm</td>\n",
       "      <td>179836.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9177</th>\n",
       "      <td>Steve Buscemi</td>\n",
       "      <td>173859.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1416</th>\n",
       "      <td>Cate Blanchett</td>\n",
       "      <td>169727.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>Alec Guinness</td>\n",
       "      <td>166560.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3272</th>\n",
       "      <td>Gary Oldman</td>\n",
       "      <td>163113.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6772</th>\n",
       "      <td>Michael Caine</td>\n",
       "      <td>162765.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             actor_name     count\n",
       "7811   Philip Ettington  395555.0\n",
       "3715      Harrison Ford  313386.0\n",
       "9587          Tom Hanks  269594.0\n",
       "8748  Samuel L. Jackson  249131.0\n",
       "1123          Brad Pitt  245564.0\n",
       "5029  John Ratzenberger  237802.0\n",
       "7090     Morgan Freeman  228893.0\n",
       "6608         Matt Damon  226620.0\n",
       "3963       Hugo Weaving  217895.0\n",
       "4265   James Earl Jones  202880.0\n",
       "1255       Bruce Willis  189848.0\n",
       "5030   John Rhys-Davies  185237.0\n",
       "8309     Robert De Niro  183586.0\n",
       "5613       Kevin Spacey  179924.0\n",
       "3988           Ian Holm  179836.0\n",
       "9177      Steve Buscemi  173859.0\n",
       "1416     Cate Blanchett  169727.0\n",
       "205       Alec Guinness  166560.0\n",
       "3272        Gary Oldman  163113.0\n",
       "6772      Michael Caine  162765.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actors = actors.set_index('id').join(movie_likes)\n",
    "actors = actors.groupby(['actor_name','like']).agg(\n",
    "    count=pd.NamedAgg(column='count', aggfunc='sum')\n",
    ").reset_index(drop=False)\n",
    "actors['like'] = np.where(actors['like']==1, 1, -1)\n",
    "actors['count'] = actors['count'] * actors['like']\n",
    "actors = actors.groupby(['actor_name']).agg(\n",
    "    count=pd.NamedAgg(column='count', aggfunc='sum')\n",
    ").reset_index(drop=False)\n",
    "\n",
    "actors.sort_values('count', ascending=False).head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>actor_name</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8280</th>\n",
       "      <td>Rob Schneider</td>\n",
       "      <td>-37207.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7511</th>\n",
       "      <td>Pat Hingle</td>\n",
       "      <td>-29946.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9292</th>\n",
       "      <td>Sylvester Stallone</td>\n",
       "      <td>-26230.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4697</th>\n",
       "      <td>Jim Carrey</td>\n",
       "      <td>-25171.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1204</th>\n",
       "      <td>Bridgette Wilson</td>\n",
       "      <td>-24518.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1879</th>\n",
       "      <td>Courteney Cox</td>\n",
       "      <td>-24107.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4500</th>\n",
       "      <td>Jean-Claude Van Damme</td>\n",
       "      <td>-23798.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7796</th>\n",
       "      <td>Phil Hartman</td>\n",
       "      <td>-23769.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1628</th>\n",
       "      <td>Chris O'Donnell</td>\n",
       "      <td>-22480.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9230</th>\n",
       "      <td>Stuart Pankin</td>\n",
       "      <td>-21718.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9748</th>\n",
       "      <td>Udo Kier</td>\n",
       "      <td>-21193.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6804</th>\n",
       "      <td>Michael Gough</td>\n",
       "      <td>-20518.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Adam Sandler</td>\n",
       "      <td>-20114.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9721</th>\n",
       "      <td>Troy Evans</td>\n",
       "      <td>-19226.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2574</th>\n",
       "      <td>Eddie Griffin</td>\n",
       "      <td>-18671.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6467</th>\n",
       "      <td>Mark L. Taylor</td>\n",
       "      <td>-18139.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2129</th>\n",
       "      <td>David Graf</td>\n",
       "      <td>-18051.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8857</th>\n",
       "      <td>Sean Whalen</td>\n",
       "      <td>-17906.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>Alexa PenaVega</td>\n",
       "      <td>-17756.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10027</th>\n",
       "      <td>Wesley Snipes</td>\n",
       "      <td>-17664.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  actor_name    count\n",
       "8280           Rob Schneider -37207.0\n",
       "7511              Pat Hingle -29946.0\n",
       "9292      Sylvester Stallone -26230.0\n",
       "4697              Jim Carrey -25171.0\n",
       "1204        Bridgette Wilson -24518.0\n",
       "1879           Courteney Cox -24107.0\n",
       "4500   Jean-Claude Van Damme -23798.0\n",
       "7796            Phil Hartman -23769.0\n",
       "1628         Chris O'Donnell -22480.0\n",
       "9230           Stuart Pankin -21718.0\n",
       "9748                Udo Kier -21193.0\n",
       "6804           Michael Gough -20518.0\n",
       "45              Adam Sandler -20114.0\n",
       "9721              Troy Evans -19226.0\n",
       "2574           Eddie Griffin -18671.0\n",
       "6467          Mark L. Taylor -18139.0\n",
       "2129              David Graf -18051.0\n",
       "8857             Sean Whalen -17906.0\n",
       "247           Alexa PenaVega -17756.0\n",
       "10027          Wesley Snipes -17664.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actors.sort_values('count').head(20)"
   ]
  }
 ],
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