01_01-analyze_time_distribution.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d5d44e38-94d3-4bfe-9968-dcf7cc0ae035",
   "metadata": {},
   "source": [
    "# Analyzing the time distribution of ratings\n",
    "\n",
    "\n",
    "**Hypothesis** : Calibration tries to approximate the item genre distribution between hsitory and recommendation through a frequentist approach. However, this does not take into account how old is a movie rating: For example, a user that saw 100 movies through 4 years, watching 25 pear year where the first 25 are action movies, the second 25 are comedy movies, the third 25 are romance movies, and the final 25 are horror movies. Naturally, a user does not have a clear cut viewing history like this, but this example illustrates the point that a user interest changes throughout time. \n",
    "\n",
    "\n",
    "Calibrated recommendations can close the gap between the user preference and the recommendation list. However, the classical approach does not model scenarios where the user interest changes throughout time, something that sequential recommender models are capable of modelling through short term tendencies,whereas classical recommender systems - collaborative filtering, namely - are not. \n",
    "\n",
    "In this notebook, we investigate how the viewing history changes throughout time: are ratings uniformly distributed through time? Whats the average viewing history time delta?\n",
    "\n",
    "\n",
    "\n",
    "We'll be analyzing the median distance between the first and last movie for each user."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c3cd7abd-0294-40d8-929a-d3fd774e2811",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21164651-537b-4f7e-be82-34534d14683f",
   "metadata": {},
   "source": [
    "### Analyzing the rating distribution in Movielens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7530f21f-95c8-4248-813e-ff5052ccd44f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2b68022d-9e78-4e55-b0a7-1debf5f391a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "base = pd.read_csv(\"ml20m_filtered_300k_sample.csv\")[[\"userId\", \"movieId\", \"genres\", \"timestamp\", \"rating\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2a18334b-dd22-401a-a25d-caaa536e60ba",
   "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>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>genres</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>136670</td>\n",
       "      <td>1</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "      <td>862652804</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>83076</td>\n",
       "      <td>1</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "      <td>1234146518</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>42831</td>\n",
       "      <td>1</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "      <td>979348139</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>66181</td>\n",
       "      <td>1</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "      <td>939675916</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2758</td>\n",
       "      <td>1</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "      <td>833215908</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299995</th>\n",
       "      <td>84136</td>\n",
       "      <td>119145</td>\n",
       "      <td>Action|Adventure|Comedy|Crime</td>\n",
       "      <td>1424865002</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299996</th>\n",
       "      <td>24646</td>\n",
       "      <td>125916</td>\n",
       "      <td>Drama</td>\n",
       "      <td>1424274393</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299997</th>\n",
       "      <td>69470</td>\n",
       "      <td>127098</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>1422828040</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299998</th>\n",
       "      <td>136684</td>\n",
       "      <td>127098</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>1424133121</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299999</th>\n",
       "      <td>116204</td>\n",
       "      <td>129354</td>\n",
       "      <td>Comedy|Crime|Drama|Romance</td>\n",
       "      <td>1425718311</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>300000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        userId  movieId                                       genres  \\\n",
       "0       136670        1  Adventure|Animation|Children|Comedy|Fantasy   \n",
       "1        83076        1  Adventure|Animation|Children|Comedy|Fantasy   \n",
       "2        42831        1  Adventure|Animation|Children|Comedy|Fantasy   \n",
       "3        66181        1  Adventure|Animation|Children|Comedy|Fantasy   \n",
       "4         2758        1  Adventure|Animation|Children|Comedy|Fantasy   \n",
       "...        ...      ...                                          ...   \n",
       "299995   84136   119145                Action|Adventure|Comedy|Crime   \n",
       "299996   24646   125916                                        Drama   \n",
       "299997   69470   127098                                       Comedy   \n",
       "299998  136684   127098                                       Comedy   \n",
       "299999  116204   129354                   Comedy|Crime|Drama|Romance   \n",
       "\n",
       "         timestamp  rating  \n",
       "0        862652804     4.0  \n",
       "1       1234146518     4.5  \n",
       "2        979348139     4.0  \n",
       "3        939675916     5.0  \n",
       "4        833215908     4.0  \n",
       "...            ...     ...  \n",
       "299995  1424865002     4.5  \n",
       "299996  1424274393     4.0  \n",
       "299997  1422828040     4.5  \n",
       "299998  1424133121     4.5  \n",
       "299999  1425718311     4.0  \n",
       "\n",
       "[300000 rows x 5 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "be3d96d0-1fdc-47f2-81d2-450af81f351a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "91716"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(base.userId.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "eccd9bb3-4c57-4954-93e3-32a783d03c6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9071"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(base.movieId.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "142da2ca-ddae-4e14-a62a-4367941f1c0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "time_df = base[[\"userId\", \"timestamp\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fa91e02c-bac7-4941-bcb5-4db3ff225878",
   "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>userId</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>136670</td>\n",
       "      <td>862652804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>83076</td>\n",
       "      <td>1234146518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>42831</td>\n",
       "      <td>979348139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>66181</td>\n",
       "      <td>939675916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2758</td>\n",
       "      <td>833215908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299995</th>\n",
       "      <td>84136</td>\n",
       "      <td>1424865002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299996</th>\n",
       "      <td>24646</td>\n",
       "      <td>1424274393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299997</th>\n",
       "      <td>69470</td>\n",
       "      <td>1422828040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299998</th>\n",
       "      <td>136684</td>\n",
       "      <td>1424133121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299999</th>\n",
       "      <td>116204</td>\n",
       "      <td>1425718311</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>300000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        userId   timestamp\n",
       "0       136670   862652804\n",
       "1        83076  1234146518\n",
       "2        42831   979348139\n",
       "3        66181   939675916\n",
       "4         2758   833215908\n",
       "...        ...         ...\n",
       "299995   84136  1424865002\n",
       "299996   24646  1424274393\n",
       "299997   69470  1422828040\n",
       "299998  136684  1424133121\n",
       "299999  116204  1425718311\n",
       "\n",
       "[300000 rows x 2 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "405e2b87-a4bd-4175-b94a-0862b3293468",
   "metadata": {},
   "outputs": [],
   "source": [
    "most_recent_df = time_df.groupby(\"userId\")[\"timestamp\"].max().reset_index()\n",
    "most_recent_df.columns = ['userId', 'most_recent_timestamp']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "67f967db-55f8-4433-9473-6a97d3024e97",
   "metadata": {
    "scrolled": true
   },
   "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>userId</th>\n",
       "      <th>most_recent_timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1112486138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>944918310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>851617728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>1011209096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>833982790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91711</th>\n",
       "      <td>138486</td>\n",
       "      <td>1207075178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91712</th>\n",
       "      <td>138487</td>\n",
       "      <td>965499756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91713</th>\n",
       "      <td>138490</td>\n",
       "      <td>975543932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91714</th>\n",
       "      <td>138491</td>\n",
       "      <td>1236130647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91715</th>\n",
       "      <td>138493</td>\n",
       "      <td>1258390758</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>91716 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       userId  most_recent_timestamp\n",
       "0           1             1112486138\n",
       "1           3              944918310\n",
       "2           5              851617728\n",
       "3           7             1011209096\n",
       "4           8              833982790\n",
       "...       ...                    ...\n",
       "91711  138486             1207075178\n",
       "91712  138487              965499756\n",
       "91713  138490              975543932\n",
       "91714  138491             1236130647\n",
       "91715  138493             1258390758\n",
       "\n",
       "[91716 rows x 2 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "most_recent_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "710368a3-fe61-43db-8d15-94ad20c16c05",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = time_df.merge(most_recent_df, on=\"userId\")\n",
    "df['delta'] = df['most_recent_timestamp'] - df['timestamp']\n",
    "df['delta_hours'] = df['delta'] / 3600\n",
    "df['delta_days'] = df['delta'] / 86400"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "33417a04-9e95-4f29-a71c-a3a0bbc326d7",
   "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>userId</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>most_recent_timestamp</th>\n",
       "      <th>delta</th>\n",
       "      <th>delta_hours</th>\n",
       "      <th>delta_days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>136670</td>\n",
       "      <td>862652804</td>\n",
       "      <td>862653089</td>\n",
       "      <td>285</td>\n",
       "      <td>0.079167</td>\n",
       "      <td>0.003299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>83076</td>\n",
       "      <td>1234146518</td>\n",
       "      <td>1234150066</td>\n",
       "      <td>3548</td>\n",
       "      <td>0.985556</td>\n",
       "      <td>0.041065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>42831</td>\n",
       "      <td>979348139</td>\n",
       "      <td>981161293</td>\n",
       "      <td>1813154</td>\n",
       "      <td>503.653889</td>\n",
       "      <td>20.985579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>66181</td>\n",
       "      <td>939675916</td>\n",
       "      <td>1231662789</td>\n",
       "      <td>291986873</td>\n",
       "      <td>81107.464722</td>\n",
       "      <td>3379.477697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2758</td>\n",
       "      <td>833215908</td>\n",
       "      <td>833215908</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299995</th>\n",
       "      <td>84136</td>\n",
       "      <td>1424865002</td>\n",
       "      <td>1424865002</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299996</th>\n",
       "      <td>24646</td>\n",
       "      <td>1424274393</td>\n",
       "      <td>1424274393</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299997</th>\n",
       "      <td>69470</td>\n",
       "      <td>1422828040</td>\n",
       "      <td>1422828040</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299998</th>\n",
       "      <td>136684</td>\n",
       "      <td>1424133121</td>\n",
       "      <td>1426972494</td>\n",
       "      <td>2839373</td>\n",
       "      <td>788.714722</td>\n",
       "      <td>32.863113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299999</th>\n",
       "      <td>116204</td>\n",
       "      <td>1425718311</td>\n",
       "      <td>1425718311</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>300000 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        userId   timestamp  most_recent_timestamp      delta   delta_hours  \\\n",
       "0       136670   862652804              862653089        285      0.079167   \n",
       "1        83076  1234146518             1234150066       3548      0.985556   \n",
       "2        42831   979348139              981161293    1813154    503.653889   \n",
       "3        66181   939675916             1231662789  291986873  81107.464722   \n",
       "4         2758   833215908              833215908          0      0.000000   \n",
       "...        ...         ...                    ...        ...           ...   \n",
       "299995   84136  1424865002             1424865002          0      0.000000   \n",
       "299996   24646  1424274393             1424274393          0      0.000000   \n",
       "299997   69470  1422828040             1422828040          0      0.000000   \n",
       "299998  136684  1424133121             1426972494    2839373    788.714722   \n",
       "299999  116204  1425718311             1425718311          0      0.000000   \n",
       "\n",
       "         delta_days  \n",
       "0          0.003299  \n",
       "1          0.041065  \n",
       "2         20.985579  \n",
       "3       3379.477697  \n",
       "4          0.000000  \n",
       "...             ...  \n",
       "299995     0.000000  \n",
       "299996     0.000000  \n",
       "299997     0.000000  \n",
       "299998    32.863113  \n",
       "299999     0.000000  \n",
       "\n",
       "[300000 rows x 6 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "46b171bb-3d45-4566-964c-4c4595475af5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting seaborn\n",
      "  Using cached seaborn-0.13.2-py3-none-any.whl (294 kB)\n",
      "Collecting matplotlib!=3.6.1,>=3.4\n",
      "  Using cached matplotlib-3.9.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (8.3 MB)\n",
      "Requirement already satisfied: numpy!=1.24.0,>=1.20 in ./temporalCalibrations/lib/python3.10/site-packages (from seaborn) (2.1.2)\n",
      "Requirement already satisfied: pandas>=1.2 in ./temporalCalibrations/lib/python3.10/site-packages (from seaborn) (2.2.3)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in ./temporalCalibrations/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.9.0.post0)\n",
      "Collecting fonttools>=4.22.0\n",
      "  Downloading fonttools-4.55.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.6 MB)\n",
      "\u001b[2K     \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.6/4.6 MB\u001b[0m \u001b[31m9.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m0m eta \u001b[36m0:00:01\u001b[0m[36m0:00:01\u001b[0m\n",
      "\u001b[?25hCollecting kiwisolver>=1.3.1\n",
      "  Downloading kiwisolver-1.4.7-cp310-cp310-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (1.6 MB)\n",
      "\u001b[2K     \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m13.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0mm eta \u001b[36m0:00:01\u001b[0m[36m0:00:01\u001b[0m\n",
      "\u001b[?25hCollecting cycler>=0.10\n",
      "  Using cached cycler-0.12.1-py3-none-any.whl (8.3 kB)\n",
      "Collecting pyparsing>=2.3.1\n",
      "  Downloading pyparsing-3.2.0-py3-none-any.whl (106 kB)\n",
      "\u001b[2K     \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m106.9/106.9 KB\u001b[0m \u001b[31m13.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hCollecting pillow>=8\n",
      "  Downloading pillow-11.0.0-cp310-cp310-manylinux_2_28_x86_64.whl (4.4 MB)\n",
      "\u001b[2K     \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.4/4.4 MB\u001b[0m \u001b[31m12.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0mm eta \u001b[36m0:00:01\u001b[0m0:01\u001b[0m:01\u001b[0m\n",
      "\u001b[?25hRequirement already satisfied: packaging>=20.0 in ./temporalCalibrations/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (24.1)\n",
      "Collecting contourpy>=1.0.1\n",
      "  Downloading contourpy-1.3.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (324 kB)\n",
      "\u001b[2K     \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m325.0/325.0 KB\u001b[0m \u001b[31m13.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hRequirement already satisfied: tzdata>=2022.7 in ./temporalCalibrations/lib/python3.10/site-packages (from pandas>=1.2->seaborn) (2024.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in ./temporalCalibrations/lib/python3.10/site-packages (from pandas>=1.2->seaborn) (2024.2)\n",
      "Requirement already satisfied: six>=1.5 in ./temporalCalibrations/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.16.0)\n",
      "Installing collected packages: pyparsing, pillow, kiwisolver, fonttools, cycler, contourpy, matplotlib, seaborn\n",
      "Successfully installed contourpy-1.3.1 cycler-0.12.1 fonttools-4.55.0 kiwisolver-1.4.7 matplotlib-3.9.2 pillow-11.0.0 pyparsing-3.2.0 seaborn-0.13.2\n"
     ]
    }
   ],
   "source": [
    "!pip install seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "b0030f82-4b5a-40f2-8da0-c844800d84ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    300000.000000\n",
       "mean        316.969989\n",
       "std         800.532613\n",
       "min           0.000000\n",
       "25%           0.000000\n",
       "50%           0.019294\n",
       "75%         107.550119\n",
       "max        6249.315903\n",
       "Name: delta_days, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.delta_days.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "cdaed7a7-8bf7-440a-92bb-3b60d21766db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    300000.000000\n",
       "mean       7607.279726\n",
       "std       19212.782703\n",
       "min           0.000000\n",
       "25%           0.000000\n",
       "50%           0.463056\n",
       "75%        2581.202847\n",
       "max      149983.581667\n",
       "Name: delta_hours, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.delta_hours.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f4743f1-0e02-4012-b821-167b75a579ad",
   "metadata": {},
   "source": [
    "Notice that most users have probably watched a single movie, given that almost 50% of users have basically a 0 days difference between the first and last movie they saw.\n",
    "\n",
    "Let's confirm this by looking at the number of movies watched per user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "d938c3e4-a6bd-4eb2-a2d1-99e33034c38b",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_movies = df.groupby(\"userId\").size().reset_index().rename(columns={0: \"num_movies\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "14a676cd-26ac-4817-af59-28b8246d5071",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    91716.000000\n",
       "mean         3.270967\n",
       "std          3.681225\n",
       "min          1.000000\n",
       "25%          1.000000\n",
       "50%          2.000000\n",
       "75%          4.000000\n",
       "max        105.000000\n",
       "Name: num_movies, dtype: float64"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_movies[\"num_movies\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "92c83c44-b6b6-424f-9823-d0198ad4de0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.histplot(num_movies['num_movies'], bins=60, kde=True)\n",
    "plt.xlabel('num_movies')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Distribution of Watched movies')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "1bca9aa3-1848-4cfd-b94e-d45047c3a476",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.boxplot(num_movies['num_movies'])\n",
    "plt.xlabel('num_movies')\n",
    "plt.title('Distribution of Watched movies')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "314b98c0-ca0a-412a-942f-fd2f6ffd9787",
   "metadata": {},
   "source": [
    "Over 75% of users have seen at most 4 movies. This can skew our time delta plots, as we can see in the following graph (For rating delta in days and hours). Users that have "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "d0a2e3a4-5f2c-45b5-937d-a539cfa04a18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(20, 6))  # Create a 1x2 grid of subplots\n",
    "\n",
    "sns.histplot(df['delta_days'], bins=60, kde=True, ax=ax[0])  # Use ax[0] for the first plot\n",
    "ax[0].set_xlabel('Delta (Days)')\n",
    "ax[0].set_ylabel('Frequency')\n",
    "ax[0].set_title('Distribution of Rating Deltas in Days')\n",
    "\n",
    "sns.histplot(df['delta_hours'], bins=60, kde=True, ax=ax[1])  # Use ax[0] for the first plot\n",
    "ax[1].set_xlabel('Delta (Hours)')\n",
    "ax[1].set_ylabel('Frequency')\n",
    "ax[1].set_title('Distribution of Rating Deltas in Hours')\n",
    "\n",
    "\n",
    "plt.tight_layout()  # Adjust spacing between plots\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7bef74f-e3d7-4013-88e8-b17a822084da",
   "metadata": {},
   "source": [
    "Now lets filter filter out inactive users. We'll assume a user is active if it has seen more than 10 movies. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ffc2abd8-037c-4588-a061-f937f2d5e5c7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "9bf41a61-89ed-471b-8cbb-0d9cbe60baec",
   "metadata": {},
   "outputs": [],
   "source": [
    "active_users = [user.item() for user in num_movies[num_movies[\"num_movies\"] >= 10].userId.unique()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "79ba9360-0ea2-4cf0-8b14-56f735c399b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "active_users = df.userId.isin(val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "416f07d5-5cfd-4724-96e8-a716f3d0b679",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_active = df[active_users]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "ed08d096-a1d5-4eaf-a0e6-02387fd7c0cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(20, 6))  # Create a 1x2 grid of subplots\n",
    "\n",
    "sns.histplot(df_active['delta_days'], bins=60, kde=True, ax=ax[0])  # Use ax[0] for the first plot\n",
    "ax[0].set_xlabel('Delta (Days)')\n",
    "ax[0].set_ylabel('Frequency')\n",
    "ax[0].set_title('Distribution of Rating Deltas in Days')\n",
    "\n",
    "sns.boxplot(y=df_active['delta_days'], ax=ax[1])\n",
    "ax[1].set_xlabel('')\n",
    "ax[1].set_ylabel('Delta (Days)')\n",
    "ax[1].set_title('boxplot of Rating Deltas in Days')\n",
    "\n",
    "\n",
    "plt.suptitle(\"Distribution of time delta for active users\")\n",
    "plt.tight_layout()  # Adjust spacing between plots\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f00a7c19-89ea-4efc-ad8a-f4f9074d4142",
   "metadata": {},
   "source": [
    "Now this distribution looks more well behaved. The boxplot looks nicer as well"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "e5180e46-b508-4d53-ba33-46f9569ee4ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "Q1 = df_active.quantile(0.25)\n",
    "Q3 = df_active.quantile(0.75)\n",
    "IQR = Q3 - Q1\n",
    "upper_whisker = Q3 + 1.5 * IQR\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "e4408b56-ea06-4caa-9cdd-a22804c70c94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    63166.000000\n",
       "mean      1025.506969\n",
       "std       1299.955559\n",
       "min          0.000000\n",
       "25%          5.020900\n",
       "50%        466.509248\n",
       "75%       1599.199821\n",
       "max       6249.315903\n",
       "Name: delta_days, dtype: float64"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_active['delta_days'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "6e52cb08-366b-4e21-8544-716118913ee0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.932789593639017"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "upper_whisker.delta_days.item() / 365"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec52d8e5-d95b-413b-80aa-a2d990e9cd4a",
   "metadata": {},
   "source": [
    "Notice that 50% of our data is comprised of users that have a history with movies ranging from 1 year to 3 years. This can be an indicative that calibration alone is not enough and that we should explore alternatives that take into account how far away was the movie seen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "1ff5212b-5509-43af-b15a-e7be99862959",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.histplot(b[b['userId'] == 38160]['delta_days'], bins=60, kde=True)\n",
    "plt.xlabel('Delta (Days)')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Distribution of Rating Deltas in Days')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79952876-4909-49ee-bc8e-25219a673f57",
   "metadata": {},
   "source": [
    "TODO: scatterplot with genres for timedelta"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16980ce8-d746-4c97-a373-d8faeb2cd880",
   "metadata": {},
   "source": [
    "## Analyzing data from kuairec dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "51a25c46-0895-4c41-92f5-73d7fee22481",
   "metadata": {},
   "outputs": [],
   "source": [
    "categories = pd.read_csv(\"KuaiRec 2.0/data/item_categories.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e9199c0b-058e-4210-bd4f-7781ee6a1e89",
   "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>video_id</th>\n",
       "      <th>feat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>[8]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>[27, 9]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>[9]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>[26]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>[5]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10723</th>\n",
       "      <td>10723</td>\n",
       "      <td>[11]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10724</th>\n",
       "      <td>10724</td>\n",
       "      <td>[2]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10725</th>\n",
       "      <td>10725</td>\n",
       "      <td>[15]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10726</th>\n",
       "      <td>10726</td>\n",
       "      <td>[19]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10727</th>\n",
       "      <td>10727</td>\n",
       "      <td>[5]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10728 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       video_id     feat\n",
       "0             0      [8]\n",
       "1             1  [27, 9]\n",
       "2             2      [9]\n",
       "3             3     [26]\n",
       "4             4      [5]\n",
       "...         ...      ...\n",
       "10723     10723     [11]\n",
       "10724     10724      [2]\n",
       "10725     10725     [15]\n",
       "10726     10726     [19]\n",
       "10727     10727      [5]\n",
       "\n",
       "[10728 rows x 2 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0d35b907-4678-4852-8004-423bb660c48a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "9a785085-d2ba-408b-a61d-9e51f7ba4323",
   "metadata": {},
   "outputs": [],
   "source": [
    "base = pd.read_csv(\"datasets/kuairec_filtered.csv\")[[\"user_id\", \"video_id\", \"timestamp\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "354b83c2-9afc-4e22-a68d-d82a4b3ccf17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>video_id</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14</td>\n",
       "      <td>148</td>\n",
       "      <td>1.593898e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14</td>\n",
       "      <td>183</td>\n",
       "      <td>1.593898e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>3649</td>\n",
       "      <td>1.593898e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14</td>\n",
       "      <td>5262</td>\n",
       "      <td>1.593898e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14</td>\n",
       "      <td>8234</td>\n",
       "      <td>1.593899e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4494573</th>\n",
       "      <td>7162</td>\n",
       "      <td>9177</td>\n",
       "      <td>1.598962e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4494574</th>\n",
       "      <td>7162</td>\n",
       "      <td>4987</td>\n",
       "      <td>1.599029e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4494575</th>\n",
       "      <td>7162</td>\n",
       "      <td>7988</td>\n",
       "      <td>1.599094e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4494576</th>\n",
       "      <td>7162</td>\n",
       "      <td>6533</td>\n",
       "      <td>1.599231e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4494577</th>\n",
       "      <td>7162</td>\n",
       "      <td>6523</td>\n",
       "      <td>1.599237e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4494578 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         user_id  video_id     timestamp\n",
       "0             14       148  1.593898e+09\n",
       "1             14       183  1.593898e+09\n",
       "2             14      3649  1.593898e+09\n",
       "3             14      5262  1.593898e+09\n",
       "4             14      8234  1.593899e+09\n",
       "...          ...       ...           ...\n",
       "4494573     7162      9177  1.598962e+09\n",
       "4494574     7162      4987  1.599029e+09\n",
       "4494575     7162      7988  1.599094e+09\n",
       "4494576     7162      6533  1.599231e+09\n",
       "4494577     7162      6523  1.599237e+09\n",
       "\n",
       "[4494578 rows x 3 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "37deb256-d3d2-48c2-a9e4-828062aedce2",
   "metadata": {},
   "outputs": [],
   "source": [
    "time_df = base[[\"user_id\", \"timestamp\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9b39a78c-eba7-40c6-815f-11fb9047d6f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "most_recent_df = time_df.groupby(\"user_id\")[\"timestamp\"].max().reset_index()\n",
    "most_recent_df.columns = ['user_id', 'most_recent_timestamp']\n",
    "df = time_df.merge(most_recent_df, on=\"user_id\")\n",
    "df['delta'] = df['most_recent_timestamp'] - df['timestamp']\n",
    "df['delta_days'] = df['delta'] / 86400"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "16bbd33f-5065-4f9d-8f3d-3ceb234abcd6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>most_recent_timestamp</th>\n",
       "      <th>delta</th>\n",
       "      <th>delta_days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14</td>\n",
       "      <td>1.593898e+09</td>\n",
       "      <td>1.599277e+09</td>\n",
       "      <td>5379214.515</td>\n",
       "      <td>62.259427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14</td>\n",
       "      <td>1.593898e+09</td>\n",
       "      <td>1.599277e+09</td>\n",
       "      <td>5379202.836</td>\n",
       "      <td>62.259292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>1.593898e+09</td>\n",
       "      <td>1.599277e+09</td>\n",
       "      <td>5379133.414</td>\n",
       "      <td>62.258489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14</td>\n",
       "      <td>1.593898e+09</td>\n",
       "      <td>1.599277e+09</td>\n",
       "      <td>5379039.608</td>\n",
       "      <td>62.257403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14</td>\n",
       "      <td>1.593899e+09</td>\n",
       "      <td>1.599277e+09</td>\n",
       "      <td>5378739.434</td>\n",
       "      <td>62.253929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4494573</th>\n",
       "      <td>7162</td>\n",
       "      <td>1.598962e+09</td>\n",
       "      <td>1.599237e+09</td>\n",
       "      <td>275133.170</td>\n",
       "      <td>3.184412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4494574</th>\n",
       "      <td>7162</td>\n",
       "      <td>1.599029e+09</td>\n",
       "      <td>1.599237e+09</td>\n",
       "      <td>208037.812</td>\n",
       "      <td>2.407845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4494575</th>\n",
       "      <td>7162</td>\n",
       "      <td>1.599094e+09</td>\n",
       "      <td>1.599237e+09</td>\n",
       "      <td>143227.680</td>\n",
       "      <td>1.657728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4494576</th>\n",
       "      <td>7162</td>\n",
       "      <td>1.599231e+09</td>\n",
       "      <td>1.599237e+09</td>\n",
       "      <td>5737.133</td>\n",
       "      <td>0.066402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4494577</th>\n",
       "      <td>7162</td>\n",
       "      <td>1.599237e+09</td>\n",
       "      <td>1.599237e+09</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4494578 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         user_id     timestamp  most_recent_timestamp        delta  delta_days\n",
       "0             14  1.593898e+09           1.599277e+09  5379214.515   62.259427\n",
       "1             14  1.593898e+09           1.599277e+09  5379202.836   62.259292\n",
       "2             14  1.593898e+09           1.599277e+09  5379133.414   62.258489\n",
       "3             14  1.593898e+09           1.599277e+09  5379039.608   62.257403\n",
       "4             14  1.593899e+09           1.599277e+09  5378739.434   62.253929\n",
       "...          ...           ...                    ...          ...         ...\n",
       "4494573     7162  1.598962e+09           1.599237e+09   275133.170    3.184412\n",
       "4494574     7162  1.599029e+09           1.599237e+09   208037.812    2.407845\n",
       "4494575     7162  1.599094e+09           1.599237e+09   143227.680    1.657728\n",
       "4494576     7162  1.599231e+09           1.599237e+09     5737.133    0.066402\n",
       "4494577     7162  1.599237e+09           1.599237e+09        0.000    0.000000\n",
       "\n",
       "[4494578 rows x 5 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7da9e99a-b65d-439b-8c56-d36dd371d75a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    4.494578e+06\n",
       "mean     3.476125e+01\n",
       "std      1.453281e+01\n",
       "min      0.000000e+00\n",
       "25%      2.448302e+01\n",
       "50%      3.499996e+01\n",
       "75%      4.675184e+01\n",
       "max      6.299424e+01\n",
       "Name: delta_days, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.delta_days.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "400b7064-86f9-4680-93f4-b1070e6d6af5",
   "metadata": {},
   "source": [
    "Apparently users are more active in this dataset: the max distance between videos watched is 60 days and the median is 30 days."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b6ede848-4551-4fb0-9fc0-346d4624e2d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(20, 6))  # Create a 1x2 grid of subplots\n",
    "\n",
    "sns.histplot(df['delta_days'], bins=60, kde=True, ax=ax[0])  # Use ax[0] for the first plot\n",
    "ax[0].set_xlabel('Delta (Days)')\n",
    "ax[0].set_ylabel('Frequency')\n",
    "ax[0].set_title('Distribution of watched videos Delta in Days')\n",
    "\n",
    "sns.boxplot(y=df['delta_days'], ax=ax[1])\n",
    "ax[1].set_xlabel('')\n",
    "ax[1].set_ylabel('Delta (Days)')\n",
    "ax[1].set_title('boxplot of watched videos Delta in Days')\n",
    "\n",
    "\n",
    "plt.suptitle(\"Distribution of time delta\")\n",
    "plt.tight_layout()  # Adjust spacing between plots\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e115cfd-2f9f-4a78-920f-a479b99c0ec1",
   "metadata": {},
   "source": [
    "Because users are very active in this platforms, it might be interesting to look at it in hours maybe, This tells that users are very active in this platform. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d0d2cff4-e114-4820-8889-8071c569a87c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_videos = df.groupby(\"user_id\").size().reset_index().rename(columns={0: \"num_videos\"})\n",
    "\n",
    "num_videos[\"num_videos\"].describe()\n",
    "\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.histplot(num_videos['num_videos'], bins=60, kde=True)\n",
    "plt.xlabel('num_videos')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Distribution of Watched movies')\n",
    "plt.show()\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.boxplot(num_videos['num_videos'])\n",
    "plt.xlabel('num_videos')\n",
    "plt.title('Distribution of Watched movies')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a268b17-3daf-472d-8701-fd7af54d30a6",
   "metadata": {},
   "source": [
    "Because this platform is based on a tiktok-like platform, the potential effect of temporal calibration might be more well observed here than on the movielens dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1c16ff57-8075-46cd-a618-c48ad531b8be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "random_user_id = df['user_id'].sample(n=1).iloc[0]\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.histplot(df[df['user_id'] == random_user_id]['delta_days'], bins=60, kde=True)\n",
    "plt.xlabel('Delta (Days)')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title(f'Distribution of watched videos in Days (User ID: {random_user_id})')\n",
    "\n",
    "plt.gca().invert_xaxis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f4d17c3c-96a5-40d6-a2b7-d48b31ca1d82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>most_recent_timestamp</th>\n",
       "      <th>delta</th>\n",
       "      <th>delta_days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14</td>\n",
       "      <td>1.593898e+09</td>\n",
       "      <td>1.599277e+09</td>\n",
       "      <td>5379214.515</td>\n",
       "      <td>62.259427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14</td>\n",
       "      <td>1.593898e+09</td>\n",
       "      <td>1.599277e+09</td>\n",
       "      <td>5379202.836</td>\n",
       "      <td>62.259292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>1.593898e+09</td>\n",
       "      <td>1.599277e+09</td>\n",
       "      <td>5379133.414</td>\n",
       "      <td>62.258489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14</td>\n",
       "      <td>1.593898e+09</td>\n",
       "      <td>1.599277e+09</td>\n",
       "      <td>5379039.608</td>\n",
       "      <td>62.257403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14</td>\n",
       "      <td>1.593899e+09</td>\n",
       "      <td>1.599277e+09</td>\n",
       "      <td>5378739.434</td>\n",
       "      <td>62.253929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4494573</th>\n",
       "      <td>7162</td>\n",
       "      <td>1.598962e+09</td>\n",
       "      <td>1.599237e+09</td>\n",
       "      <td>275133.170</td>\n",
       "      <td>3.184412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4494574</th>\n",
       "      <td>7162</td>\n",
       "      <td>1.599029e+09</td>\n",
       "      <td>1.599237e+09</td>\n",
       "      <td>208037.812</td>\n",
       "      <td>2.407845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4494575</th>\n",
       "      <td>7162</td>\n",
       "      <td>1.599094e+09</td>\n",
       "      <td>1.599237e+09</td>\n",
       "      <td>143227.680</td>\n",
       "      <td>1.657728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4494576</th>\n",
       "      <td>7162</td>\n",
       "      <td>1.599231e+09</td>\n",
       "      <td>1.599237e+09</td>\n",
       "      <td>5737.133</td>\n",
       "      <td>0.066402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4494577</th>\n",
       "      <td>7162</td>\n",
       "      <td>1.599237e+09</td>\n",
       "      <td>1.599237e+09</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4494578 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         user_id     timestamp  most_recent_timestamp        delta  delta_days\n",
       "0             14  1.593898e+09           1.599277e+09  5379214.515   62.259427\n",
       "1             14  1.593898e+09           1.599277e+09  5379202.836   62.259292\n",
       "2             14  1.593898e+09           1.599277e+09  5379133.414   62.258489\n",
       "3             14  1.593898e+09           1.599277e+09  5379039.608   62.257403\n",
       "4             14  1.593899e+09           1.599277e+09  5378739.434   62.253929\n",
       "...          ...           ...                    ...          ...         ...\n",
       "4494573     7162  1.598962e+09           1.599237e+09   275133.170    3.184412\n",
       "4494574     7162  1.599029e+09           1.599237e+09   208037.812    2.407845\n",
       "4494575     7162  1.599094e+09           1.599237e+09   143227.680    1.657728\n",
       "4494576     7162  1.599231e+09           1.599237e+09     5737.133    0.066402\n",
       "4494577     7162  1.599237e+09           1.599237e+09        0.000    0.000000\n",
       "\n",
       "[4494578 rows x 5 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "45332839-346d-4ed9-8805-3745eb25b8f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>video_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>feat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14</td>\n",
       "      <td>148</td>\n",
       "      <td>1.593898e+09</td>\n",
       "      <td>[11, 28, 19]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14</td>\n",
       "      <td>183</td>\n",
       "      <td>1.593898e+09</td>\n",
       "      <td>[28]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>3649</td>\n",
       "      <td>1.593898e+09</td>\n",
       "      <td>[9]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14</td>\n",
       "      <td>5262</td>\n",
       "      <td>1.593898e+09</td>\n",
       "      <td>[25]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14</td>\n",
       "      <td>8234</td>\n",
       "      <td>1.593899e+09</td>\n",
       "      <td>[6]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4494573</th>\n",
       "      <td>7162</td>\n",
       "      <td>9177</td>\n",
       "      <td>1.598962e+09</td>\n",
       "      <td>[16]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4494574</th>\n",
       "      <td>7162</td>\n",
       "      <td>4987</td>\n",
       "      <td>1.599029e+09</td>\n",
       "      <td>[28]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4494575</th>\n",
       "      <td>7162</td>\n",
       "      <td>7988</td>\n",
       "      <td>1.599094e+09</td>\n",
       "      <td>[16]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4494576</th>\n",
       "      <td>7162</td>\n",
       "      <td>6533</td>\n",
       "      <td>1.599231e+09</td>\n",
       "      <td>[28]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4494577</th>\n",
       "      <td>7162</td>\n",
       "      <td>6523</td>\n",
       "      <td>1.599237e+09</td>\n",
       "      <td>[25, 28]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4494578 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         user_id  video_id     timestamp          feat\n",
       "0             14       148  1.593898e+09  [11, 28, 19]\n",
       "1             14       183  1.593898e+09          [28]\n",
       "2             14      3649  1.593898e+09           [9]\n",
       "3             14      5262  1.593898e+09          [25]\n",
       "4             14      8234  1.593899e+09           [6]\n",
       "...          ...       ...           ...           ...\n",
       "4494573     7162      9177  1.598962e+09          [16]\n",
       "4494574     7162      4987  1.599029e+09          [28]\n",
       "4494575     7162      7988  1.599094e+09          [16]\n",
       "4494576     7162      6533  1.599231e+09          [28]\n",
       "4494577     7162      6523  1.599237e+09      [25, 28]\n",
       "\n",
       "[4494578 rows x 4 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with_genres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "066de453-2722-4eb1-b7e1-595810f1bb6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>video_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>feat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14</td>\n",
       "      <td>148</td>\n",
       "      <td>1.593898e+09</td>\n",
       "      <td>[11, 28, 19]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14</td>\n",
       "      <td>183</td>\n",
       "      <td>1.593898e+09</td>\n",
       "      <td>[28]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>3649</td>\n",
       "      <td>1.593898e+09</td>\n",
       "      <td>[9]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14</td>\n",
       "      <td>5262</td>\n",
       "      <td>1.593898e+09</td>\n",
       "      <td>[25]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14</td>\n",
       "      <td>8234</td>\n",
       "      <td>1.593899e+09</td>\n",
       "      <td>[6]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4494573</th>\n",
       "      <td>7162</td>\n",
       "      <td>9177</td>\n",
       "      <td>1.598962e+09</td>\n",
       "      <td>[16]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4494574</th>\n",
       "      <td>7162</td>\n",
       "      <td>4987</td>\n",
       "      <td>1.599029e+09</td>\n",
       "      <td>[28]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4494575</th>\n",
       "      <td>7162</td>\n",
       "      <td>7988</td>\n",
       "      <td>1.599094e+09</td>\n",
       "      <td>[16]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4494576</th>\n",
       "      <td>7162</td>\n",
       "      <td>6533</td>\n",
       "      <td>1.599231e+09</td>\n",
       "      <td>[28]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4494577</th>\n",
       "      <td>7162</td>\n",
       "      <td>6523</td>\n",
       "      <td>1.599237e+09</td>\n",
       "      <td>[25, 28]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4494578 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         user_id  video_id     timestamp          feat\n",
       "0             14       148  1.593898e+09  [11, 28, 19]\n",
       "1             14       183  1.593898e+09          [28]\n",
       "2             14      3649  1.593898e+09           [9]\n",
       "3             14      5262  1.593898e+09          [25]\n",
       "4             14      8234  1.593899e+09           [6]\n",
       "...          ...       ...           ...           ...\n",
       "4494573     7162      9177  1.598962e+09          [16]\n",
       "4494574     7162      4987  1.599029e+09          [28]\n",
       "4494575     7162      7988  1.599094e+09          [16]\n",
       "4494576     7162      6533  1.599231e+09          [28]\n",
       "4494577     7162      6523  1.599237e+09      [25, 28]\n",
       "\n",
       "[4494578 rows x 4 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with_genres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "382e4bd3-f1e5-44d7-97ff-1f49b86c491d",
   "metadata": {},
   "outputs": [],
   "source": [
    "most_recent_df = time_df.groupby(\"user_id\")[\"timestamp\"].max().reset_index()\n",
    "most_recent_df.columns = ['user_id', 'most_recent_timestamp']\n",
    "df = with_genres.merge(most_recent_df, on=\"user_id\")\n",
    "df['delta'] = df['most_recent_timestamp'] - df['timestamp']\n",
    "df['delta_days'] = df['delta'] / 86400"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "f6e808f2-5337-4ab4-80ce-d1b8b9aba251",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>video_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>feat</th>\n",
       "      <th>most_recent_timestamp</th>\n",
       "      <th>delta</th>\n",
       "      <th>delta_days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14</td>\n",
       "      <td>148</td>\n",
       "      <td>1.593898e+09</td>\n",
       "      <td>[11, 28, 19]</td>\n",
       "      <td>1.599277e+09</td>\n",
       "      <td>5379214.515</td>\n",
       "      <td>62.259427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14</td>\n",
       "      <td>183</td>\n",
       "      <td>1.593898e+09</td>\n",
       "      <td>[28]</td>\n",
       "      <td>1.599277e+09</td>\n",
       "      <td>5379202.836</td>\n",
       "      <td>62.259292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>3649</td>\n",
       "      <td>1.593898e+09</td>\n",
       "      <td>[9]</td>\n",
       "      <td>1.599277e+09</td>\n",
       "      <td>5379133.414</td>\n",
       "      <td>62.258489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14</td>\n",
       "      <td>5262</td>\n",
       "      <td>1.593898e+09</td>\n",
       "      <td>[25]</td>\n",
       "      <td>1.599277e+09</td>\n",
       "      <td>5379039.608</td>\n",
       "      <td>62.257403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14</td>\n",
       "      <td>8234</td>\n",
       "      <td>1.593899e+09</td>\n",
       "      <td>[6]</td>\n",
       "      <td>1.599277e+09</td>\n",
       "      <td>5378739.434</td>\n",
       "      <td>62.253929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4494573</th>\n",
       "      <td>7162</td>\n",
       "      <td>9177</td>\n",
       "      <td>1.598962e+09</td>\n",
       "      <td>[16]</td>\n",
       "      <td>1.599237e+09</td>\n",
       "      <td>275133.170</td>\n",
       "      <td>3.184412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4494574</th>\n",
       "      <td>7162</td>\n",
       "      <td>4987</td>\n",
       "      <td>1.599029e+09</td>\n",
       "      <td>[28]</td>\n",
       "      <td>1.599237e+09</td>\n",
       "      <td>208037.812</td>\n",
       "      <td>2.407845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4494575</th>\n",
       "      <td>7162</td>\n",
       "      <td>7988</td>\n",
       "      <td>1.599094e+09</td>\n",
       "      <td>[16]</td>\n",
       "      <td>1.599237e+09</td>\n",
       "      <td>143227.680</td>\n",
       "      <td>1.657728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4494576</th>\n",
       "      <td>7162</td>\n",
       "      <td>6533</td>\n",
       "      <td>1.599231e+09</td>\n",
       "      <td>[28]</td>\n",
       "      <td>1.599237e+09</td>\n",
       "      <td>5737.133</td>\n",
       "      <td>0.066402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4494577</th>\n",
       "      <td>7162</td>\n",
       "      <td>6523</td>\n",
       "      <td>1.599237e+09</td>\n",
       "      <td>[25, 28]</td>\n",
       "      <td>1.599237e+09</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4494578 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         user_id  video_id     timestamp          feat  most_recent_timestamp  \\\n",
       "0             14       148  1.593898e+09  [11, 28, 19]           1.599277e+09   \n",
       "1             14       183  1.593898e+09          [28]           1.599277e+09   \n",
       "2             14      3649  1.593898e+09           [9]           1.599277e+09   \n",
       "3             14      5262  1.593898e+09          [25]           1.599277e+09   \n",
       "4             14      8234  1.593899e+09           [6]           1.599277e+09   \n",
       "...          ...       ...           ...           ...                    ...   \n",
       "4494573     7162      9177  1.598962e+09          [16]           1.599237e+09   \n",
       "4494574     7162      4987  1.599029e+09          [28]           1.599237e+09   \n",
       "4494575     7162      7988  1.599094e+09          [16]           1.599237e+09   \n",
       "4494576     7162      6533  1.599231e+09          [28]           1.599237e+09   \n",
       "4494577     7162      6523  1.599237e+09      [25, 28]           1.599237e+09   \n",
       "\n",
       "               delta  delta_days  \n",
       "0        5379214.515   62.259427  \n",
       "1        5379202.836   62.259292  \n",
       "2        5379133.414   62.258489  \n",
       "3        5379039.608   62.257403  \n",
       "4        5378739.434   62.253929  \n",
       "...              ...         ...  \n",
       "4494573   275133.170    3.184412  \n",
       "4494574   208037.812    2.407845  \n",
       "4494575   143227.680    1.657728  \n",
       "4494576     5737.133    0.066402  \n",
       "4494577        0.000    0.000000  \n",
       "\n",
       "[4494578 rows x 7 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "8aaa9c2b-75e9-49c8-bec6-0c60e0f8291c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>video_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>feat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14</td>\n",
       "      <td>148</td>\n",
       "      <td>1.593898e+09</td>\n",
       "      <td>[11, 28, 19]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14</td>\n",
       "      <td>183</td>\n",
       "      <td>1.593898e+09</td>\n",
       "      <td>[28]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>3649</td>\n",
       "      <td>1.593898e+09</td>\n",
       "      <td>[9]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14</td>\n",
       "      <td>5262</td>\n",
       "      <td>1.593898e+09</td>\n",
       "      <td>[25]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14</td>\n",
       "      <td>8234</td>\n",
       "      <td>1.593899e+09</td>\n",
       "      <td>[6]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4494573</th>\n",
       "      <td>7162</td>\n",
       "      <td>9177</td>\n",
       "      <td>1.598962e+09</td>\n",
       "      <td>[16]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4494574</th>\n",
       "      <td>7162</td>\n",
       "      <td>4987</td>\n",
       "      <td>1.599029e+09</td>\n",
       "      <td>[28]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4494575</th>\n",
       "      <td>7162</td>\n",
       "      <td>7988</td>\n",
       "      <td>1.599094e+09</td>\n",
       "      <td>[16]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4494576</th>\n",
       "      <td>7162</td>\n",
       "      <td>6533</td>\n",
       "      <td>1.599231e+09</td>\n",
       "      <td>[28]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4494577</th>\n",
       "      <td>7162</td>\n",
       "      <td>6523</td>\n",
       "      <td>1.599237e+09</td>\n",
       "      <td>[25, 28]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4494578 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         user_id  video_id     timestamp          feat\n",
       "0             14       148  1.593898e+09  [11, 28, 19]\n",
       "1             14       183  1.593898e+09          [28]\n",
       "2             14      3649  1.593898e+09           [9]\n",
       "3             14      5262  1.593898e+09          [25]\n",
       "4             14      8234  1.593899e+09           [6]\n",
       "...          ...       ...           ...           ...\n",
       "4494573     7162      9177  1.598962e+09          [16]\n",
       "4494574     7162      4987  1.599029e+09          [28]\n",
       "4494575     7162      7988  1.599094e+09          [16]\n",
       "4494576     7162      6533  1.599231e+09          [28]\n",
       "4494577     7162      6523  1.599237e+09      [25, 28]\n",
       "\n",
       "[4494578 rows x 4 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with_genres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "b946cbe8-474f-40e2-8e2d-744a071de261",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "9165a250-0dbc-4489-84f4-605980da7cf0",
   "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>userId</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>most_recent_timestamp</th>\n",
       "      <th>delta</th>\n",
       "      <th>delta_hours</th>\n",
       "      <th>delta_days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>136670</td>\n",
       "      <td>862652804</td>\n",
       "      <td>862653089</td>\n",
       "      <td>285</td>\n",
       "      <td>0.079167</td>\n",
       "      <td>0.003299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>83076</td>\n",
       "      <td>1234146518</td>\n",
       "      <td>1234150066</td>\n",
       "      <td>3548</td>\n",
       "      <td>0.985556</td>\n",
       "      <td>0.041065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>42831</td>\n",
       "      <td>979348139</td>\n",
       "      <td>981161293</td>\n",
       "      <td>1813154</td>\n",
       "      <td>503.653889</td>\n",
       "      <td>20.985579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>66181</td>\n",
       "      <td>939675916</td>\n",
       "      <td>1231662789</td>\n",
       "      <td>291986873</td>\n",
       "      <td>81107.464722</td>\n",
       "      <td>3379.477697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2758</td>\n",
       "      <td>833215908</td>\n",
       "      <td>833215908</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299995</th>\n",
       "      <td>84136</td>\n",
       "      <td>1424865002</td>\n",
       "      <td>1424865002</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299996</th>\n",
       "      <td>24646</td>\n",
       "      <td>1424274393</td>\n",
       "      <td>1424274393</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299997</th>\n",
       "      <td>69470</td>\n",
       "      <td>1422828040</td>\n",
       "      <td>1422828040</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299998</th>\n",
       "      <td>136684</td>\n",
       "      <td>1424133121</td>\n",
       "      <td>1426972494</td>\n",
       "      <td>2839373</td>\n",
       "      <td>788.714722</td>\n",
       "      <td>32.863113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299999</th>\n",
       "      <td>116204</td>\n",
       "      <td>1425718311</td>\n",
       "      <td>1425718311</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>300000 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        userId   timestamp  most_recent_timestamp      delta   delta_hours  \\\n",
       "0       136670   862652804              862653089        285      0.079167   \n",
       "1        83076  1234146518             1234150066       3548      0.985556   \n",
       "2        42831   979348139              981161293    1813154    503.653889   \n",
       "3        66181   939675916             1231662789  291986873  81107.464722   \n",
       "4         2758   833215908              833215908          0      0.000000   \n",
       "...        ...         ...                    ...        ...           ...   \n",
       "299995   84136  1424865002             1424865002          0      0.000000   \n",
       "299996   24646  1424274393             1424274393          0      0.000000   \n",
       "299997   69470  1422828040             1422828040          0      0.000000   \n",
       "299998  136684  1424133121             1426972494    2839373    788.714722   \n",
       "299999  116204  1425718311             1425718311          0      0.000000   \n",
       "\n",
       "         delta_days  \n",
       "0          0.003299  \n",
       "1          0.041065  \n",
       "2         20.985579  \n",
       "3       3379.477697  \n",
       "4          0.000000  \n",
       "...             ...  \n",
       "299995     0.000000  \n",
       "299996     0.000000  \n",
       "299997     0.000000  \n",
       "299998    32.863113  \n",
       "299999     0.000000  \n",
       "\n",
       "[300000 rows x 6 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "9cbb57d8-cdf1-4296-87be-03f3ada66832",
   "metadata": {},
   "outputs": [],
   "source": [
    "timedelta_distribution_by_user_df = filtered_df.groupby(\"userId\").agg({\"delta_hours\": \"median\", \"delta_days\": \"median\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "d4ea2978-40fb-43d6-be92-79e4b89081df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "sns.histplot(timedelta_distribution_by_user_df[\"delta_days\"], bins=60, kde=True)\n",
    "plt.xlabel('Delta (Days)')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Distribution of Rating Deltas in Days')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "dc47ccd9-3dcb-4f17-85d6-d9e337763f13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    3930.000000\n",
       "mean      990.639174\n",
       "std      1165.435615\n",
       "min         0.000220\n",
       "25%        14.961264\n",
       "50%       554.376024\n",
       "75%      1559.280667\n",
       "max      5604.342581\n",
       "Name: delta_days, dtype: float64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "timedelta_distribution_by_user_df[\"delta_days\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7babb9b3-e5a4-4a44-a9e4-bbbecfa4e743",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}