datasetanalysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from py2neo import *\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "graph = Graph(\"bolt://localhost:7687\", auth=(\"neo4j\", \"dbms\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__DATASET ANALYSIS__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Number of movies per user__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>COUNT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>31813</th>\n",
       "      <td>71e1599b-ab75-44c1-9667-1ca41fae6c06</td>\n",
       "      <td>32202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28693</th>\n",
       "      <td>220ac93a-1d6d-484f-9030-cfbda6ce1212</td>\n",
       "      <td>9178</td>\n",
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       "      <th>9007</th>\n",
       "      <td>a5ef5a66-8586-4ba1-90b7-72510d060630</td>\n",
       "      <td>8913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45339</th>\n",
       "      <td>55e46524-926d-4343-8513-6b452794837c</td>\n",
       "      <td>7919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50246</th>\n",
       "      <td>79e268ec-800c-470e-8d55-f8cf7502ae6b</td>\n",
       "      <td>7488</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                                         ID  COUNT\n",
       "31813  71e1599b-ab75-44c1-9667-1ca41fae6c06  32202\n",
       "28693  220ac93a-1d6d-484f-9030-cfbda6ce1212   9178\n",
       "9007   a5ef5a66-8586-4ba1-90b7-72510d060630   8913\n",
       "45339  55e46524-926d-4343-8513-6b452794837c   7919\n",
       "50246  79e268ec-800c-470e-8d55-f8cf7502ae6b   7488"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_per_user_count = pd.DataFrame(\n",
    "    graph.run(\n",
    "        (\n",
    "            \"MATCH(u:User)-[h:RATES]->(m:Movie)\"\n",
    "            \"RETURN u.id as ID, count(*) AS COUNT\"\n",
    "        )\n",
    "    ).data()\n",
    ")\n",
    "\n",
    "movies_per_user_count.sort_values(by=\"COUNT\", ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see below, the number of movies per user is quite stable and limited (an everage of __153.8__ and a standard deviation of __268__).\n",
    "\n",
    "There is an outlier (a user with 32.202 reviews), but for all the others the number is below one thousand: overall, queries asking movies reviewed by a single user __shouldn't__ be too computationally expensive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>COUNT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>162541.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>153.807925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>268.047589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>36.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>71.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>162.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>32202.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               COUNT\n",
       "count  162541.000000\n",
       "mean      153.807925\n",
       "std       268.047589\n",
       "min        20.000000\n",
       "25%        36.000000\n",
       "50%        71.000000\n",
       "75%       162.000000\n",
       "max     32202.000000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_per_user_count.sort_values(by=\"COUNT\", ascending=False).describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Number of users per movie__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>TITLE</th>\n",
       "      <th>COUNT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>351</th>\n",
       "      <td>a251e83e-49f0-4a70-ba0c-0cc77ce23a8a</td>\n",
       "      <td>Forrest Gump</td>\n",
       "      <td>81491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314</th>\n",
       "      <td>b30eb0f4-790d-4ba0-8e7b-796f2b1d7534</td>\n",
       "      <td>Shawshank Redemption, The</td>\n",
       "      <td>81482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>9501228f-c2f2-402b-9b6c-402ff7fd1f58</td>\n",
       "      <td>Pulp Fiction</td>\n",
       "      <td>79672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>585</th>\n",
       "      <td>b71a26ee-41f5-4151-828e-fb54028c9201</td>\n",
       "      <td>Silence of the Lambs, The</td>\n",
       "      <td>74127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2480</th>\n",
       "      <td>333ea22d-cfd4-48a1-964e-45631cdc3cdd</td>\n",
       "      <td>Matrix, The</td>\n",
       "      <td>72674</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "                                        ID                      TITLE  COUNT\n",
       "351   a251e83e-49f0-4a70-ba0c-0cc77ce23a8a               Forrest Gump  81491\n",
       "314   b30eb0f4-790d-4ba0-8e7b-796f2b1d7534  Shawshank Redemption, The  81482\n",
       "292   9501228f-c2f2-402b-9b6c-402ff7fd1f58               Pulp Fiction  79672\n",
       "585   b71a26ee-41f5-4151-828e-fb54028c9201  Silence of the Lambs, The  74127\n",
       "2480  333ea22d-cfd4-48a1-964e-45631cdc3cdd                Matrix, The  72674"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_per_movie_count = pd.DataFrame(\n",
    "    graph.run(\n",
    "        (\n",
    "            \"MATCH(u:User)-[h:RATES]->(m:Movie)\"\n",
    "            \"RETURN m.id as ID, m.title as TITLE, count(*) AS COUNT\"\n",
    "        )\n",
    "    ).data()\n",
    ")\n",
    "\n",
    "user_per_movie_count.sort_values(by=\"COUNT\", ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On the contrary, the number of users per movie is much more __unstable__, touching much highest __values__ (for particularly __famous movies__, for example _Forrest Gump_ and _Pulp fiction_).\n",
    "We expect, therefore, bigger latency to get users that saw certain movies and their reviews."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>COUNT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>59047.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>423.393128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2477.885797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>36.000000</td>\n",
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       "      <th>max</th>\n",
       "      <td>81491.000000</td>\n",
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      ],
      "text/plain": [
       "              COUNT\n",
       "count  59047.000000\n",
       "mean     423.393128\n",
       "std     2477.885797\n",
       "min        1.000000\n",
       "25%        2.000000\n",
       "50%        6.000000\n",
       "75%       36.000000\n",
       "max    81491.000000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_per_movie_count.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Number of movies per genre__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The number of genres per movie is naturally limited by the genre cardinality, while the distribution of movies per genre can be visualized in the graph below.\n",
    "We expect to have bigger latency for the most associated genres, i.e. __Drama (7)__ and __Comedy (4)__."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'number of movies')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "genre_count = pd.DataFrame(\n",
    "    graph.run(\n",
    "        (\n",
    "            \"MATCH(g:Genre)<-[h:HAS_GENRE]-(m:Movie)\"\n",
    "            \"RETURN g.id as ID, g.name as GENRE, count(*) AS COUNT\"\n",
    "        )\n",
    "    ).data()\n",
    ")\n",
    "\n",
    "genre_count.sort_values(by=[\"COUNT\"], ascending=False).plot.bar()\n",
    "plt.xlabel(\"genres\")\n",
    "plt.ylabel(\"number of movies\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>COUNT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>19.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5644.473684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6186.998791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>195.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2302.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3595.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6668.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>25606.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              COUNT\n",
       "count     19.000000\n",
       "mean    5644.473684\n",
       "std     6186.998791\n",
       "min      195.000000\n",
       "25%     2302.500000\n",
       "50%     3595.000000\n",
       "75%     6668.500000\n",
       "max    25606.000000"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genre_count.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>ID</th>\n",
       "      <th>COUNT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5324</th>\n",
       "      <td>fe0aebaa-a0a9-49a9-9e83-95b39528b306</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9355</th>\n",
       "      <td>64ab2429-f337-4fc7-bc1d-74bf25bc87b2</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9549</th>\n",
       "      <td>043fd9dc-e446-420d-b344-0aec4c759ca2</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9180</th>\n",
       "      <td>9c8bf105-fdc8-434f-904e-2f3f2440a372</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6717</th>\n",
       "      <td>ca4e797b-4574-40a0-afa7-5542b15c63f8</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37188</th>\n",
       "      <td>e38254b0-359d-4678-b5a8-202b998725f6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18579</th>\n",
       "      <td>2a5aa0af-70e8-4c4b-8910-ef2b359136cb</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37186</th>\n",
       "      <td>db2ef135-d534-41ce-8d96-4eaf95a7c9ed</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37185</th>\n",
       "      <td>7d835dfc-009c-4a68-9032-06cdaee910a3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57360</th>\n",
       "      <td>051d75a0-0410-476d-a746-3f3286727f80</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>57361 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         ID  COUNT\n",
       "5324   fe0aebaa-a0a9-49a9-9e83-95b39528b306     10\n",
       "9355   64ab2429-f337-4fc7-bc1d-74bf25bc87b2      8\n",
       "9549   043fd9dc-e446-420d-b344-0aec4c759ca2      8\n",
       "9180   9c8bf105-fdc8-434f-904e-2f3f2440a372      7\n",
       "6717   ca4e797b-4574-40a0-afa7-5542b15c63f8      7\n",
       "...                                     ...    ...\n",
       "37188  e38254b0-359d-4678-b5a8-202b998725f6      1\n",
       "18579  2a5aa0af-70e8-4c4b-8910-ef2b359136cb      1\n",
       "37186  db2ef135-d534-41ce-8d96-4eaf95a7c9ed      1\n",
       "37185  7d835dfc-009c-4a68-9032-06cdaee910a3      1\n",
       "57360  051d75a0-0410-476d-a746-3f3286727f80      1\n",
       "\n",
       "[57361 rows x 2 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genre_per_movie_count = pd.DataFrame(\n",
    "    graph.run(\n",
    "        (\n",
    "            \"MATCH(g:Genre)<-[h:HAS_GENRE]-(m:Movie)\"\n",
    "            \"RETURN m.id as ID, count(*) AS COUNT\"\n",
    "        )\n",
    "    ).data()\n",
    ")\n",
    "\n",
    "genre_per_movie_count.sort_values(by=[\"COUNT\"], ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>COUNT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>57361.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.869650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.973913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              COUNT\n",
       "count  57361.000000\n",
       "mean       1.869650\n",
       "std        0.973913\n",
       "min        1.000000\n",
       "25%        1.000000\n",
       "50%        2.000000\n",
       "75%        2.000000\n",
       "max       10.000000"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genre_per_movie_count.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the original dataset, every movie is associated to each one of the  __1128 categories__ and every category is associated to every movie, with a certain score. For computational and semantic reasons (we are only interested in relevant categories), I inserted in the database only associations with __score $\\geq$ 0.4__."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Number of movies per category__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CATEGORY</th>\n",
       "      <th>COUNT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>741</th>\n",
       "      <td>original</td>\n",
       "      <td>13816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>catastrophe</td>\n",
       "      <td>13136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>645</th>\n",
       "      <td>mentor</td>\n",
       "      <td>11850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>467</th>\n",
       "      <td>great ending</td>\n",
       "      <td>9240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>866</th>\n",
       "      <td>runaway</td>\n",
       "      <td>7961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1029</th>\n",
       "      <td>tolkien</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>hannibal lecter</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>figure skating</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>697</th>\n",
       "      <td>neil gaiman</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>easily confused with other movie(s) (title)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1128 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         CATEGORY  COUNT\n",
       "741                                      original  13816\n",
       "187                                   catastrophe  13136\n",
       "645                                        mentor  11850\n",
       "467                                  great ending   9240\n",
       "866                                       runaway   7961\n",
       "...                                           ...    ...\n",
       "1029                                      tolkien      8\n",
       "488                               hannibal lecter      7\n",
       "391                                figure skating      7\n",
       "697                                   neil gaiman      4\n",
       "338   easily confused with other movie(s) (title)      1\n",
       "\n",
       "[1128 rows x 2 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_per_category_count = pd.DataFrame(\n",
    "    graph.run(\n",
    "        (\n",
    "            \"MATCH(m:Movie)-[h:HAS_CATEGORY]->(c:Category)\"\n",
    "            \"RETURN c.name as CATEGORY, count(*) AS COUNT\"\n",
    "        )\n",
    "    ).data()\n",
    ")\n",
    "\n",
    "movies_per_category_count.sort_values(by=[\"COUNT\"], ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>COUNT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1128.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>853.917553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1296.324007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>140.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>375.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1028.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>13816.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              COUNT\n",
       "count   1128.000000\n",
       "mean     853.917553\n",
       "std     1296.324007\n",
       "min        1.000000\n",
       "25%      140.750000\n",
       "50%      375.000000\n",
       "75%     1028.750000\n",
       "max    13816.000000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_per_category_count.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Number of categories per movie__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Even if filtering categories brings to a remarkable reduction of movies associations per category, we can se that (as happened for genres) the number of categories per movie is much less than the number of movies per category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>TITLE</th>\n",
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       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2075</th>\n",
       "      <td>47440b33-743a-4f83-a6d7-bebe66ae1eab</td>\n",
       "      <td>Passchendaele</td>\n",
       "      <td>421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>02f3a948-5832-478b-b367-932799079cfe</td>\n",
       "      <td>Welcome to Dongmakgol</td>\n",
       "      <td>358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>430</th>\n",
       "      <td>8da091da-625b-43ca-bda9-80d6402ad866</td>\n",
       "      <td>Drunk Stoned Brilliant Dead: The Story of the ...</td>\n",
       "      <td>333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>889e05a1-d654-4b35-ba08-5a00dfe3ac33</td>\n",
       "      <td>Band Called Death, A</td>\n",
       "      <td>332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2236</th>\n",
       "      <td>c58aa124-c89c-4704-967f-ed3427059f6c</td>\n",
       "      <td>Death Rides a Horse (Da uomo a uomo)</td>\n",
       "      <td>328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>13721</th>\n",
       "      <td>0d1f1e1e-fa2d-4a06-adbd-b8c4f28c1e0f</td>\n",
       "      <td>Alarmist, The (a.k.a. Life During Wartime)</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13544</th>\n",
       "      <td>62f51648-6013-45dd-9983-14549577b2a5</td>\n",
       "      <td>Home Again</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13676</th>\n",
       "      <td>889f7451-75e5-48df-8449-2fada93d54ec</td>\n",
       "      <td>Happy Together</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13614</th>\n",
       "      <td>085ea15b-40b8-4f03-9cd1-10a54d7dd72b</td>\n",
       "      <td>House of Angels (Änglagård)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13815</th>\n",
       "      <td>a85dc446-48a5-4114-bbc5-c3c86a6df234</td>\n",
       "      <td>The Longest Week</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13816 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         ID  \\\n",
       "2075   47440b33-743a-4f83-a6d7-bebe66ae1eab   \n",
       "127    02f3a948-5832-478b-b367-932799079cfe   \n",
       "430    8da091da-625b-43ca-bda9-80d6402ad866   \n",
       "1980   889e05a1-d654-4b35-ba08-5a00dfe3ac33   \n",
       "2236   c58aa124-c89c-4704-967f-ed3427059f6c   \n",
       "...                                     ...   \n",
       "13721  0d1f1e1e-fa2d-4a06-adbd-b8c4f28c1e0f   \n",
       "13544  62f51648-6013-45dd-9983-14549577b2a5   \n",
       "13676  889f7451-75e5-48df-8449-2fada93d54ec   \n",
       "13614  085ea15b-40b8-4f03-9cd1-10a54d7dd72b   \n",
       "13815  a85dc446-48a5-4114-bbc5-c3c86a6df234   \n",
       "\n",
       "                                                   TITLE  COUNT  \n",
       "2075                                       Passchendaele    421  \n",
       "127                                Welcome to Dongmakgol    358  \n",
       "430    Drunk Stoned Brilliant Dead: The Story of the ...    333  \n",
       "1980                                Band Called Death, A    332  \n",
       "2236                Death Rides a Horse (Da uomo a uomo)    328  \n",
       "...                                                  ...    ...  \n",
       "13721         Alarmist, The (a.k.a. Life During Wartime)      7  \n",
       "13544                                         Home Again      6  \n",
       "13676                                     Happy Together      6  \n",
       "13614                        House of Angels (Änglagård)      4  \n",
       "13815                                   The Longest Week      3  \n",
       "\n",
       "[13816 rows x 3 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categories_per_movie_count = pd.DataFrame(\n",
    "    graph.run(\n",
    "        (\n",
    "            \"MATCH(m:Movie)-[h:HAS_CATEGORY]->(c:Category)\"\n",
    "            \"RETURN m.id as ID, m.title as TITLE, count(*) AS COUNT\"\n",
    "        )\n",
    "    ).data()\n",
    ")\n",
    "\n",
    "categories_per_movie_count.sort_values(by=[\"COUNT\"], ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>COUNT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>13816.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>69.717646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>38.721222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>42.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>60.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>89.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>421.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              COUNT\n",
       "count  13816.000000\n",
       "mean      69.717646\n",
       "std       38.721222\n",
       "min        3.000000\n",
       "25%       42.000000\n",
       "50%       60.000000\n",
       "75%       89.000000\n",
       "max      421.000000"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categories_per_movie_count.describe()"
   ]
  }
 ],
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