main.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: scikit-surprise in /anaconda3/lib/python3.6/site-packages (1.0.6)\n",
      "Requirement already satisfied: six>=1.10.0 in /anaconda3/lib/python3.6/site-packages (from scikit-surprise) (1.12.0)\n",
      "Requirement already satisfied: scipy>=1.0.0 in /anaconda3/lib/python3.6/site-packages (from scikit-surprise) (1.3.0)\n",
      "Requirement already satisfied: joblib>=0.11 in /anaconda3/lib/python3.6/site-packages (from scikit-surprise) (0.13.2)\n",
      "Requirement already satisfied: numpy>=1.11.2 in /anaconda3/lib/python3.6/site-packages (from scikit-surprise) (1.16.4)\n"
     ]
    }
   ],
   "source": [
    "!pip install scikit-surprise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import ast\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from time import time\n",
    "from surprise import Reader, Dataset, SVD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "#read and clean json\n",
    "def json_parser(filename):\n",
    "    return ast.literal_eval(open(filename,'r').read().replace(\"\\n\",','))\n",
    "\n",
    "def save_json(fname,data):\n",
    "    '''\n",
    "    fname - file name (str)\n",
    "    data - the json data to be stored\n",
    "    '''\n",
    "    with open(fname, 'w') as outfile:\n",
    "        outfile.write(json.dumps(data))\n",
    "\n",
    "        \n",
    "def get_user_dataframe(data):\n",
    "    df = pd.DataFrame(data)\n",
    "    df['playtimes'] = df['items'].apply(lambda games: [game['playtime_forever'] for game in games])\n",
    "    df.drop(columns = ['items'],inplace=True)\n",
    "    return df\n",
    "\n",
    "\n",
    "def get_play_times(data):\n",
    "    r = []\n",
    "    for user in data:\n",
    "        gt = []\n",
    "        for game in user['items']:\n",
    "            gt.append(game['playtime_forever'])\n",
    "        r.append(gt)\n",
    "    return r\n",
    "\n",
    "def get_player_play_times_transformed(data,p):\n",
    "    '''\n",
    "    p - player number (index of data)\n",
    "    '''\n",
    "    return np.log(np.array(i['playtime_forever'] for i in data[p]['items'])+1)\n",
    "\n",
    "def get_all_da_games(data):\n",
    "    dict_ = {}\n",
    "    for user in data:\n",
    "        for game in user['items']:\n",
    "            dict_[game['item_id']] = game['item_name']\n",
    "    return dict_\n",
    "\n",
    "def get_all_da_times(data):\n",
    "    r = []\n",
    "    for user in data:\n",
    "        for game in user['items']:\n",
    "            r.append(\n",
    "                {'user':user['user_id'],\n",
    "                 'game':game['item_id'],\n",
    "                 'time_f':game['playtime_forever']}\n",
    "            )\n",
    "    return pd.DataFrame(r)\n",
    "\n",
    "def transform_playtime_data(data):\n",
    "    return data.groupby('user').time_f.transform(lambda x: np.log(x+1) / np.log(x.max()+1))\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Warning: The cell below takes 22 MINUTES!!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1319.8635227680206\n"
     ]
    }
   ],
   "source": [
    "start = time()\n",
    "d = json_parser('australian_users_items.json')\n",
    "print(time()-start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function loads in module json:\n",
      "\n",
      "loads(s, *, encoding=None, cls=None, object_hook=None, parse_float=None, parse_int=None, parse_constant=None, object_pairs_hook=None, **kw)\n",
      "    Deserialize ``s`` (a ``str``, ``bytes`` or ``bytearray`` instance\n",
      "    containing a JSON document) to a Python object.\n",
      "    \n",
      "    ``object_hook`` is an optional function that will be called with the\n",
      "    result of any object literal decode (a ``dict``). The return value of\n",
      "    ``object_hook`` will be used instead of the ``dict``. This feature\n",
      "    can be used to implement custom decoders (e.g. JSON-RPC class hinting).\n",
      "    \n",
      "    ``object_pairs_hook`` is an optional function that will be called with the\n",
      "    result of any object literal decoded with an ordered list of pairs.  The\n",
      "    return value of ``object_pairs_hook`` will be used instead of the ``dict``.\n",
      "    This feature can be used to implement custom decoders that rely on the\n",
      "    order that the key and value pairs are decoded (for example,\n",
      "    collections.OrderedDict will remember the order of insertion). If\n",
      "    ``object_hook`` is also defined, the ``object_pairs_hook`` takes priority.\n",
      "    \n",
      "    ``parse_float``, if specified, will be called with the string\n",
      "    of every JSON float to be decoded. By default this is equivalent to\n",
      "    float(num_str). This can be used to use another datatype or parser\n",
      "    for JSON floats (e.g. decimal.Decimal).\n",
      "    \n",
      "    ``parse_int``, if specified, will be called with the string\n",
      "    of every JSON int to be decoded. By default this is equivalent to\n",
      "    int(num_str). This can be used to use another datatype or parser\n",
      "    for JSON integers (e.g. float).\n",
      "    \n",
      "    ``parse_constant``, if specified, will be called with one of the\n",
      "    following strings: -Infinity, Infinity, NaN.\n",
      "    This can be used to raise an exception if invalid JSON numbers\n",
      "    are encountered.\n",
      "    \n",
      "    To use a custom ``JSONDecoder`` subclass, specify it with the ``cls``\n",
      "    kwarg; otherwise ``JSONDecoder`` is used.\n",
      "    \n",
      "    The ``encoding`` argument is ignored and deprecated.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(json.loads)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'user_id': '76561197970982479',\n",
       " 'items_count': 277,\n",
       " 'steam_id': '76561197970982479',\n",
       " 'user_url': 'http://steamcommunity.com/profiles/76561197970982479',\n",
       " 'items': [{'item_id': '10',\n",
       "   'item_name': 'Counter-Strike',\n",
       "   'playtime_forever': 6,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '20',\n",
       "   'item_name': 'Team Fortress Classic',\n",
       "   'playtime_forever': 0,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '30',\n",
       "   'item_name': 'Day of Defeat',\n",
       "   'playtime_forever': 7,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '40',\n",
       "   'item_name': 'Deathmatch Classic',\n",
       "   'playtime_forever': 0,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '50',\n",
       "   'item_name': 'Half-Life: Opposing Force',\n",
       "   'playtime_forever': 0,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '60',\n",
       "   'item_name': 'Ricochet',\n",
       "   'playtime_forever': 0,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '70',\n",
       "   'item_name': 'Half-Life',\n",
       "   'playtime_forever': 0,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '130',\n",
       "   'item_name': 'Half-Life: Blue Shift',\n",
       "   'playtime_forever': 0,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '300',\n",
       "   'item_name': 'Day of Defeat: Source',\n",
       "   'playtime_forever': 4733,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '240',\n",
       "   'item_name': 'Counter-Strike: Source',\n",
       "   'playtime_forever': 1853,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '3830',\n",
       "   'item_name': 'Psychonauts',\n",
       "   'playtime_forever': 333,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '2630',\n",
       "   'item_name': 'Call of Duty 2',\n",
       "   'playtime_forever': 75,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '3900',\n",
       "   'item_name': \"Sid Meier's Civilization IV\",\n",
       "   'playtime_forever': 338,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '34440',\n",
       "   'item_name': \"Sid Meier's Civilization IV\",\n",
       "   'playtime_forever': 0,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '3920',\n",
       "   'item_name': \"Sid Meier's Pirates!\",\n",
       "   'playtime_forever': 2,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '6400',\n",
       "   'item_name': 'Joint Task Force',\n",
       "   'playtime_forever': 286,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '6910',\n",
       "   'item_name': 'Deus Ex: Game of the Year Edition',\n",
       "   'playtime_forever': 2685,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '7670',\n",
       "   'item_name': 'BioShock',\n",
       "   'playtime_forever': 633,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '409710',\n",
       "   'item_name': 'BioShock Remastered',\n",
       "   'playtime_forever': 0,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '220',\n",
       "   'item_name': 'Half-Life 2',\n",
       "   'playtime_forever': 696,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '320',\n",
       "   'item_name': 'Half-Life 2: Deathmatch',\n",
       "   'playtime_forever': 0,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '340',\n",
       "   'item_name': 'Half-Life 2: Lost Coast',\n",
       "   'playtime_forever': 37,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '360',\n",
       "   'item_name': 'Half-Life Deathmatch: Source',\n",
       "   'playtime_forever': 0,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '380',\n",
       "   'item_name': 'Half-Life 2: Episode One',\n",
       "   'playtime_forever': 168,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '400',\n",
       "   'item_name': 'Portal',\n",
       "   'playtime_forever': 173,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '420',\n",
       "   'item_name': 'Half-Life 2: Episode Two',\n",
       "   'playtime_forever': 323,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '9340',\n",
       "   'item_name': 'Company of Heroes: Opposing Fronts',\n",
       "   'playtime_forever': 692,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '228200',\n",
       "   'item_name': 'Company of Heroes (New Steam Version)',\n",
       "   'playtime_forever': 0,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '11450',\n",
       "   'item_name': 'Overlord',\n",
       "   'playtime_forever': 0,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '7940',\n",
       "   'item_name': 'Call of Duty 4: Modern Warfare',\n",
       "   'playtime_forever': 1185,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '4700',\n",
       "   'item_name': 'Medieval II: Total War',\n",
       "   'playtime_forever': 477,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '12900',\n",
       "   'item_name': 'Audiosurf',\n",
       "   'playtime_forever': 115,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '13250',\n",
       "   'item_name': 'Unreal Gold',\n",
       "   'playtime_forever': 0,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '16100',\n",
       "   'item_name': 'Virtual Villagers: A New Home',\n",
       "   'playtime_forever': 0,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '15700',\n",
       "   'item_name': \"Oddworld: Abe's Oddysee\",\n",
       "   'playtime_forever': 28,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '15710',\n",
       "   'item_name': \"Oddworld: Abe's Exoddus\",\n",
       "   'playtime_forever': 0,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '17330',\n",
       "   'item_name': 'Crysis Warhead',\n",
       "   'playtime_forever': 31,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '17340',\n",
       "   'item_name': 'Crysis Wars',\n",
       "   'playtime_forever': 0,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '22000',\n",
       "   'item_name': 'World of Goo',\n",
       "   'playtime_forever': 195,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '500',\n",
       "   'item_name': 'Left 4 Dead',\n",
       "   'playtime_forever': 513,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '4560',\n",
       "   'item_name': 'Company of Heroes',\n",
       "   'playtime_forever': 1061,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '17460',\n",
       "   'item_name': 'Mass Effect',\n",
       "   'playtime_forever': 1613,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '10500',\n",
       "   'item_name': 'Empire: Total War',\n",
       "   'playtime_forever': 186,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '24740',\n",
       "   'item_name': 'Burnout Paradise: The Ultimate Box',\n",
       "   'playtime_forever': 0,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '22200',\n",
       "   'item_name': 'Zeno Clash',\n",
       "   'playtime_forever': 271,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '26800',\n",
       "   'item_name': 'Braid',\n",
       "   'playtime_forever': 445,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '1250',\n",
       "   'item_name': 'Killing Floor',\n",
       "   'playtime_forever': 10006,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '35420',\n",
       "   'item_name': 'Killing Floor Mod: Defence Alliance 2',\n",
       "   'playtime_forever': 0,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '3590',\n",
       "   'item_name': 'Plants vs. Zombies: Game of the Year',\n",
       "   'playtime_forever': 4413,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '8880',\n",
       "   'item_name': 'Freedom Force',\n",
       "   'playtime_forever': 0,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '8890',\n",
       "   'item_name': 'Freedom Force vs. the 3rd Reich',\n",
       "   'playtime_forever': 0,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '23120',\n",
       "   'item_name': 'Droplitz',\n",
       "   'playtime_forever': 53,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '35700',\n",
       "   'item_name': 'Trine',\n",
       "   'playtime_forever': 199,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '10140',\n",
       "   'item_name': '3D Ultra Minigolf Adventures Deluxe',\n",
       "   'playtime_forever': 25,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '35010',\n",
       "   'item_name': 'Batman: Arkham Asylum',\n",
       "   'playtime_forever': 570,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '35140',\n",
       "   'item_name': 'Batman: Arkham Asylum GOTY Edition',\n",
       "   'playtime_forever': 0,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '29180',\n",
       "   'item_name': 'Osmos',\n",
       "   'playtime_forever': 940,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '15520',\n",
       "   'item_name': 'AaAaAA!!! - A Reckless Disregard for Gravity',\n",
       "   'playtime_forever': 110,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '32370',\n",
       "   'item_name': 'STAR WARS™: Knights of the Old Republic™',\n",
       "   'playtime_forever': 5,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '37700',\n",
       "   'item_name': 'Darkest of Days',\n",
       "   'playtime_forever': 782,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '6020',\n",
       "   'item_name': 'STAR WARS™ Jedi Knight: Jedi Academy™',\n",
       "   'playtime_forever': 77,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '24860',\n",
       "   'item_name': 'Battlefield 2',\n",
       "   'playtime_forever': 437,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '39530',\n",
       "   'item_name': 'Painkiller: Black Edition',\n",
       "   'playtime_forever': 503,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '550',\n",
       "   'item_name': 'Left 4 Dead 2',\n",
       "   'playtime_forever': 1474,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '223530',\n",
       "   'item_name': 'Left 4 Dead 2 Beta',\n",
       "   'playtime_forever': 0,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '8980',\n",
       "   'item_name': 'Borderlands',\n",
       "   'playtime_forever': 3061,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '41500',\n",
       "   'item_name': 'Torchlight',\n",
       "   'playtime_forever': 536,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '20900',\n",
       "   'item_name': 'The Witcher: Enhanced Edition',\n",
       "   'playtime_forever': 139,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '10180',\n",
       "   'item_name': 'Call of Duty: Modern Warfare 2',\n",
       "   'playtime_forever': 1886,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '10190',\n",
       "   'item_name': 'Call of Duty: Modern Warfare 2 - Multiplayer',\n",
       "   'playtime_forever': 1784,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '17450',\n",
       "   'item_name': 'Dragon Age: Origins',\n",
       "   'playtime_forever': 4431,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '3170',\n",
       "   'item_name': \"King's Bounty: Armored Princess\",\n",
       "   'playtime_forever': 1021,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '25900',\n",
       "   'item_name': \"King's Bounty: The Legend\",\n",
       "   'playtime_forever': 1389,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '31410',\n",
       "   'item_name': 'Zombie Driver',\n",
       "   'playtime_forever': 95,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '24980',\n",
       "   'item_name': 'Mass Effect 2',\n",
       "   'playtime_forever': 5001,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '8850',\n",
       "   'item_name': 'BioShock 2',\n",
       "   'playtime_forever': 1504,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '409720',\n",
       "   'item_name': 'BioShock 2 Remastered',\n",
       "   'playtime_forever': 0,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '46000',\n",
       "   'item_name': 'Bob Came in Pieces',\n",
       "   'playtime_forever': 11,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '33230',\n",
       "   'item_name': \"Assassin's Creed II\",\n",
       "   'playtime_forever': 1636,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '20820',\n",
       "   'item_name': 'Shatter',\n",
       "   'playtime_forever': 46,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '47700',\n",
       "   'item_name': 'Command and Conquer 4: Tiberian Twilight',\n",
       "   'playtime_forever': 85,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '24960',\n",
       "   'item_name': 'Battlefield: Bad Company 2',\n",
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       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '241600',\n",
       "   'item_name': 'Rogue Legacy',\n",
       "   'playtime_forever': 73,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '238930',\n",
       "   'item_name': '7 Grand Steps, Step 1: What Ancients Begat',\n",
       "   'playtime_forever': 159,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '217140',\n",
       "   'item_name': 'Rise of the Triad',\n",
       "   'playtime_forever': 199,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '39140',\n",
       "   'item_name': 'FINAL FANTASY VII',\n",
       "   'playtime_forever': 2896,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '225080',\n",
       "   'item_name': 'Brothers - A Tale of Two Sons',\n",
       "   'playtime_forever': 0,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '239030',\n",
       "   'item_name': 'Papers, Please',\n",
       "   'playtime_forever': 194,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '248820',\n",
       "   'item_name': 'Risk of Rain',\n",
       "   'playtime_forever': 272,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '239350',\n",
       "   'item_name': 'Spelunky',\n",
       "   'playtime_forever': 458,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '218620',\n",
       "   'item_name': 'PAYDAY 2',\n",
       "   'playtime_forever': 574,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '206420',\n",
       "   'item_name': 'Saints Row IV',\n",
       "   'playtime_forever': 0,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '236850',\n",
       "   'item_name': 'Europa Universalis IV',\n",
       "   'playtime_forever': 89,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '47790',\n",
       "   'item_name': 'Medal of Honor(TM) Single Player',\n",
       "   'playtime_forever': 230,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '47830',\n",
       "   'item_name': 'Medal of Honor(TM) Multiplayer',\n",
       "   'playtime_forever': 0,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '250320',\n",
       "   'item_name': 'The Wolf Among Us',\n",
       "   'playtime_forever': 500,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '250900',\n",
       "   'item_name': 'The Binding of Isaac: Rebirth',\n",
       "   'playtime_forever': 329,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '204450',\n",
       "   'item_name': 'Call of Juarez Gunslinger',\n",
       "   'playtime_forever': 241,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '233270',\n",
       "   'item_name': 'Far Cry® 3 Blood Dragon',\n",
       "   'playtime_forever': 0,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '251990',\n",
       "   'item_name': 'Long Live The Queen',\n",
       "   'playtime_forever': 135,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '253980',\n",
       "   'item_name': 'Enclave',\n",
       "   'playtime_forever': 0,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '236450',\n",
       "   'item_name': 'PAC-MAN Championship Edition DX+',\n",
       "   'playtime_forever': 154,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '242050',\n",
       "   'item_name': \"Assassin's Creed IV Black Flag\",\n",
       "   'playtime_forever': 1377,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '257350',\n",
       "   'item_name': \"Baldur's Gate II: Enhanced Edition\",\n",
       "   'playtime_forever': 0,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '213670',\n",
       "   'item_name': 'South Park™: The Stick of Truth™',\n",
       "   'playtime_forever': 121,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '238010',\n",
       "   'item_name': \"Deus Ex: Human Revolution - Director's Cut\",\n",
       "   'playtime_forever': 2176,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '245470',\n",
       "   'item_name': 'Democracy 3',\n",
       "   'playtime_forever': 246,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '221910',\n",
       "   'item_name': 'The Stanley Parable',\n",
       "   'playtime_forever': 157,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '260230',\n",
       "   'item_name': 'Valiant Hearts: The Great War™ / Soldats Inconnus : Mémoires de la Grande Guerre™',\n",
       "   'playtime_forever': 467,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '238430',\n",
       "   'item_name': 'Contagion',\n",
       "   'playtime_forever': 77,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '261030',\n",
       "   'item_name': 'The Walking Dead: Season Two',\n",
       "   'playtime_forever': 567,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '261570',\n",
       "   'item_name': 'Ori and the Blind Forest',\n",
       "   'playtime_forever': 39,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '263980',\n",
       "   'item_name': 'Out There Somewhere',\n",
       "   'playtime_forever': 0,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '39150',\n",
       "   'item_name': 'FINAL FANTASY VIII',\n",
       "   'playtime_forever': 15,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '230230',\n",
       "   'item_name': 'Divinity: Original Sin (Classic)',\n",
       "   'playtime_forever': 522,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '373420',\n",
       "   'item_name': 'Divinity: Original Sin Enhanced Edition',\n",
       "   'playtime_forever': 0,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '232790',\n",
       "   'item_name': 'Broken Age',\n",
       "   'playtime_forever': 216,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '237990',\n",
       "   'item_name': 'The Banner Saga',\n",
       "   'playtime_forever': 19,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '273620',\n",
       "   'item_name': 'Freespace 2',\n",
       "   'playtime_forever': 848,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '222880',\n",
       "   'item_name': 'Insurgency',\n",
       "   'playtime_forever': 3031,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '225840',\n",
       "   'item_name': 'Sven Co-op',\n",
       "   'playtime_forever': 26,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '222900',\n",
       "   'item_name': 'Dead Island: Epidemic',\n",
       "   'playtime_forever': 0,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '280220',\n",
       "   'item_name': 'Creeper World 3: Arc Eternal',\n",
       "   'playtime_forever': 941,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '236430',\n",
       "   'item_name': 'DARK SOULS™ II',\n",
       "   'playtime_forever': 592,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '201810',\n",
       "   'item_name': 'Wolfenstein: The New Order',\n",
       "   'playtime_forever': 599,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '294860',\n",
       "   'item_name': 'Valkyria Chronicles™',\n",
       "   'playtime_forever': 1735,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '295690',\n",
       "   'item_name': 'PARTICLE MACE',\n",
       "   'playtime_forever': 0,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '238460',\n",
       "   'item_name': 'BattleBlock Theater',\n",
       "   'playtime_forever': 407,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '237930',\n",
       "   'item_name': 'Transistor',\n",
       "   'playtime_forever': 154,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '301480',\n",
       "   'item_name': 'GEARCRACK Arena',\n",
       "   'playtime_forever': 0,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '303390',\n",
       "   'item_name': 'Dead Bits',\n",
       "   'playtime_forever': 0,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '306660',\n",
       "   'item_name': 'Ultimate General: Gettysburg',\n",
       "   'playtime_forever': 1245,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '316790',\n",
       "   'item_name': 'Grim Fandango Remastered',\n",
       "   'playtime_forever': 42,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '318430',\n",
       "   'item_name': 'Squishy the Suicidal Pig',\n",
       "   'playtime_forever': 0,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '241930',\n",
       "   'item_name': 'Middle-earth: Shadow of Mordor',\n",
       "   'playtime_forever': 889,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '328080',\n",
       "   'item_name': 'Retro-Pixel Castles',\n",
       "   'playtime_forever': 5,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '282070',\n",
       "   'item_name': 'This War of Mine',\n",
       "   'playtime_forever': 3,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '333930',\n",
       "   'item_name': 'Dirty Bomb',\n",
       "   'playtime_forever': 2782,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '334120',\n",
       "   'item_name': 'Armikrog',\n",
       "   'playtime_forever': 109,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '257510',\n",
       "   'item_name': 'The Talos Principle',\n",
       "   'playtime_forever': 0,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '319630',\n",
       "   'item_name': 'Life Is Strange™',\n",
       "   'playtime_forever': 37,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '346010',\n",
       "   'item_name': 'Besiege',\n",
       "   'playtime_forever': 464,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '255710',\n",
       "   'item_name': 'Cities: Skylines',\n",
       "   'playtime_forever': 246,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '232090',\n",
       "   'item_name': 'Killing Floor 2',\n",
       "   'playtime_forever': 6494,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '232150',\n",
       "   'item_name': 'Killing Floor 2 - SDK',\n",
       "   'playtime_forever': 0,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '377160',\n",
       "   'item_name': 'Fallout 4',\n",
       "   'playtime_forever': 2001,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '340170',\n",
       "   'item_name': 'FINAL FANTASY TYPE-0 HD',\n",
       "   'playtime_forever': 170,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '391540',\n",
       "   'item_name': 'Undertale',\n",
       "   'playtime_forever': 395,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '396660',\n",
       "   'item_name': 'Tahira: Echoes of the Astral Empire',\n",
       "   'playtime_forever': 0,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '337000',\n",
       "   'item_name': 'Deus Ex: Mankind Divided™',\n",
       "   'playtime_forever': 1159,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '368230',\n",
       "   'item_name': 'Kingdom: Classic',\n",
       "   'playtime_forever': 83,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '496300',\n",
       "   'item_name': 'Kingdom: New Lands',\n",
       "   'playtime_forever': 0,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '231430',\n",
       "   'item_name': 'Company of Heroes 2',\n",
       "   'playtime_forever': 0,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '289130',\n",
       "   'item_name': 'Endless Legend',\n",
       "   'playtime_forever': 593,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '268500',\n",
       "   'item_name': 'XCOM 2',\n",
       "   'playtime_forever': 1495,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '461640',\n",
       "   'item_name': 'Sins Of The Demon RPG',\n",
       "   'playtime_forever': 0,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '379720',\n",
       "   'item_name': 'DOOM',\n",
       "   'playtime_forever': 793,\n",
       "   'playtime_2weeks': 0},\n",
       "  {'item_id': '273350',\n",
       "   'item_name': 'Evolve Stage 2',\n",
       "   'playtime_forever': 58,\n",
       "   'playtime_2weeks': 0}]}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "88310"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#players\n",
    "len(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "player_one_playtimes = [i['playtime_forever'] for i in d[0]['items']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([    0.,     0.,   101.,   594., 23532.])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.percentile(player_one_playtimes, np.array([0,25,50,75,100]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x465fb9b70>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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t5luXLXA6nKARET535iwO1nWw/YiVFM2HWUIIYbVt3ZRUtnL5afQuGmlWejzJsR72HrWeJifz/L463ihr5O8vKSQj8cSTCIajK5fnkhTr4ZFtNr+R+TBLCCFsuLrodHoXjeQSYdGMZErrO6za6AT6B718/7kDzM1M4LNnznI6nKCLi3Zz5fIZPLenlvaefqfDMSHEEkIIe+VgA3MzE5ibmRjU6y7KSaF/UCmtt8VzRvPo9ioON3Zxx2UL8bgj809k46o8evq9bA7BabuNcyLztz0C9A4Msq28ifPmB3/Z0NkZCcRFudln1UYf0dk7wI9ffJ+1BdO4eGHoLXoTLCvyU5k3PZHf76h2OhQTQgJaIMdMvpKKVnr6vZw7LyPo13a7hAXZSew/1s6gVyOiO2Ww5vt/YV8djZ19XLsqjUe2VQXlmqFIRNi4Ko9/e+4Ahxs6mRPkUqgJT1ZCCFFvlDXgdgnr5kybkOsvnpFMT7+X8sauCbl+OGrv7ueNsgaW5qaE9YjkQH1qZS4uGVrfwRiwhBCy3ihrYkV+KkkTtOzhvOlJRLnFup/6eeVgPYNe5dJFWU6HMimmJ8dyfmEmT5XU2LTYBggwIYjIBhE5KCJlInLHKPvXi0iJiAyIyMYR+24SkVLf4ya/7atEZLfvmj+RcJwxbIK0He9nd3Ur50xAddGwaI+Lwqwk9tW22/KaQMvxPoqPtLBq1jTSI6yb6clcuzqfY+09vFHW6HQoJgSMmRBExA3cC1wGLAJuEJFFIw6rBG4GHh5x7jTgO8A6YC3wHREZHvL5c+BvgPm+x4ZT/hQR5u3DTXiVCWk/8LcoJ5mOngGqW7on9H3CwSsH6kHgY0XBb8QPZRctnE5qfBRPWrWRIbASwlqgTFUPq2of8Chwlf8BqnpEVXcBIzu2fxx4QVWbVbUFeAHYICI5QLKqvqND4+d/B1x9uh8mUrxZ1kh8tJsV+akT+j4LspNxCVO+t1FTZy8llS2sKZhGany00+FMqhiPm8uX5vDCvjq6bMK7KS+QhJAL+He3qPZtC8SJzs31PR/zmiJyi4gUi0hxQ0NDgG8b3t4sa2Td7GlEeya2iScu2k1BegIH66Z2Qnj5QD0uES6YYqWDYVevyKW7f5AX9tU5HYpxWMg3Kqvq/aq6WlVXZ2ZG/h9sTWs3hxu7OHcCxh+Mpig7ibr23im7klpDRy87q1o5c046yRPUgB/qVs9KIzc1jj/stBlQp7pAEkIN4L9mYJ5vWyBOdG6N7/mpXDOivVk61Lg30e0Hw4qyh5bkPHhsao5afvVgPR63sL4w8r9snIjLJXxy+QxeL22ksbPX6XCMgwJJCNuB+SIyW0SigeuBTQFefwtwqYik+RqTLwW2qGot0C4iZ/p6F30e+OMpxB9x3jzUSEZiDIVZkzNQKDMxhmkJ0VMyITR19vJedSvrZqeTGDO1x2hevXIGg15l826bymIqG/OvQFUHROR2hm7ubuBXqrpXRO4GilV1k4isAZ4G0oBPisg/q+piVW0WkX9hKKkA3K2qzb7nXwF+A8QBz/keU5qqsq28mXVzpk3aur0iQlF2EtvLm+kb8E54u0Uoea20AZcI586fnNLYRArGSO3s5Fh+8Xo5Htep/Q58Zt3M047BOCugr0WquhnYPGLbXX7Pt/PhKiD/434F/GqU7cXAkvEEG+lqWrupbethzSQvxrIgK4m3DzVxuLGTBdnJk/reTmk93kdJRStrZqdN2baDkZbnpbBlXx3NXX1MS5hava3MkKnzdTAMbD8yVHhaM3tipqs4kdkZCUS7XVOq2ui10kYUZf0kNd6Hg2W+bs7vVbc6HIlxiiWEELKtvIWkGM+kf0v3uF3Mm57IgWMdU2JZxY6efoqPNHPGzLQpN+7gZNLio5mVHs97VZYQpipLCCGk+EgzqwrSHJl9tCg7ibbufuraI7+XyRtljQx6lfOncM+iE1mWl0p9Ry/H2nucDsU4wBJCiGjp6qO0vpM1BZNbXTSsKGu4+2lkD1Lr7htka3kzS/NSptScRYFaMiMZAXZbtdGUZAkhRHzQfuBQQkiOi2JGaiwH6iK7HWFreRN9A14rHZxAUmwUczIT2FXdNiWqD82HWUIIEcUVLUS7XSzLS3EshqKsZCqbjnM8Que06R/08mZZI4VZieSkxDkdTshalpdKU1cfR9us2miqsYQQIraVN7MsL4XYKLdjMSzITkKB9+s7HYthIu2oaKGrb5DzCyN3acxgWDxjaNLDXVZtNOVYQggBx/sG2FPTNundTUfKTYsjIdodke0Ig17l9dIGZk6LpyA98ldDOx3x0R7mT09it1UbTTmWEELAzqpWBrzKWofaD4a5RCjMSuL9us6IWzRnT00bLcf7WT8/c9JGgYezpXkptHb3U9V83OlQzCSyhBACtpe3IAJnTPII5dEUZSfR3T8YUTcCVeW10gYyk2JYkJPkdDhhYVFOMh6XsKvGllidSiwhhIDiimaKspJIiXN+CoX505NwCRyIoFHLpfWd1Lb1sH5+Ji4rHQQkNspNYVYSu2vaIq60aE7MEoLDBr3Ku5WtrC5wvnQAQ4vmzEpPiKhpLF4rbSA51sPyfOd6cIWjpXkpdPQMUNEUOaVFc3KWEBz2fl0Hnb0DrAqB6qJhRVlJHGvvoTUCFs2pbjnO4YYuzpmXccqzeE5VC7KTiHILu2ust9FUYX8hDttR0QLAqpnONij7+2DRnAgYpPZ6aSOxUS7HBvyFsxiPm6KsJPbUtFu10RRhCcFhJRUtZCTGkD8tdAZKTU+KIS0+igO14Z0Qmjp72VPTxrrZ6Y6O7whnS/NS6ewdoLyxy+lQzCSwhOCwHZUtrJqVGlJdIUWERTnJHGropHdg0OlwTtkbZY24XMJZc9OdDiVsFWX5qo2qrbfRVGAJwUENHb1UNB0PqfaDYQtnJDPgVUrrwnPUckdPPzsqWliZn2oL4JyGaI+LBdnJ7DnaxqDXqo0inSUEB5VU+toPQjAhzJqWQFyUm3214Tlq+e1DTQx6bQGcYFiWl8LxvkEON4bnlwMTuIASgohsEJGDIlImIneMsj9GRB7z7d8qIgW+7TeKyE6/h1dEVvj2veq75vC+KTfBTIlvQrvFM0KvO6TbJSzMSeLAsfaw+2bY0z/IO+VNLJ6RTEaSTXF9ugqzkoj2uKzaaAoYMyGIiBu4F7gMWATcICKLRhz2RaBFVecBPwK+D6CqD6nqClVdAXwOKFfVnX7n3Ti8X1Xrg/B5wsqOihaW5CaHbIPnopxkevq9HGkKrwbF7Uea6en3st6muA6KKLeLhdlJ7D0afl8OzPgEUkJYC5Sp6mFV7QMeBa4accxVwG99z58ALpKPtpLe4DvXAL0Dg+yqaQvJ6qJh86YPNSjuOxo+1UYDg17eKGtkbmYCeWk2iV2wLMtLpbt/kLIInQnXDAkkIeQCVX6vq33bRj1GVQeANmBk147rgEdGbPu1r7ron0ZJIBFt79F2+ga8IZ0Qoj0u5mUmsq+2PWxmvdxZ1UpHz4CVDoJs/vREYqNcNiV2hJuURmURWQccV9U9fptvVNWlwHm+x+dOcO4tIlIsIsUNDQ2TEO3kKPENSDtjZugmBIBFM5Jp6+6nNgwWS/Gq8pf3G5iRGsu8zESnw4koHreLxTkp7Kttp3/Q63Q4ZoIEkhBqgHy/13m+baMeIyIeIAVo8tt/PSNKB6pa4/u3A3iYoaqpj1DV+1V1taquzsyMnG99OypayJ8Wx/TkWKdDOami7KE1dsOht9Humjaauvq4oHB6SI3riBRL81LoHfCGbVdkM7ZAEsJ2YL6IzBaRaIZu7ptGHLMJuMn3fCPwsvrqGETEBXwav/YDEfGISIbveRTwCWAPU4SqUlzREvKlA4DEGA8FGQnsrgntxVK8qrx6sJ7pSTEsmpHsdDgRaW5mIvHRbnbZ3EYRa8yE4GsTuB3YAuwHHlfVvSJyt4hc6Tvsl0C6iJQBXwf8u6auB6pU9bDfthhgi4jsAnYyVML4r9P+NGGiuqWbho5eVodw+4G/ZXkpNHT0cqw9dKuNDtR2UNfeywVFNsX1RHG7hCUzUthfO9T+ZSKPJ5CDVHUzsHnEtrv8nvcA157g3FeBM0ds6wJWjTPWiPHBhHazwmPCtSUzUvjTe0d5r6otJBenV1VeOVjPtIRoluamOh1ORFuWl8K2I80cONbOsjz7WUcaG6nsgOKKZhJjPB/MKhrqEmKG1tjdVd0akrNevlbaSE1rNxcUZuJ2WelgIhVkJJAU42G3raQWkSwhOGBHRSsrZ6aG1c1rmW+N3coQWyxFVfnpS6WkxEWxYqZ9Y51oLhGW5KVw8FgHPf3hO/GhGZ0lhEnW0dPPwWPtYdGg7G9RTjJRbuG9EOuH/lppI8UVLZxfmGkL4EySFXmpDHiVvWE0YNEExv6CJtnOqla8SsgsmRmomCg3C7KT2V0TOrNeqio/fP4gualxYffzDGd5aXFMS4gOuS8H5vRZQphkOypacAmsyA+/6o3leakc7wud6Qte3F/Pruo2vnrxfCsdTCIRYXleCofqO+no6Xc6HBNE9lc0yXZUtFCUnUxSGM7RX5g1NH1BKHwz9HqHSgezMxL41MqRM6mYibY8LxUFa1yOMJYQJtGgV3m3spVVs8KvdABD0xcsy01lT00bx/sGHI1l855aDhzr4O8uno/Hbb/Gk216ciw5KbHsrHL+y4EJHvtLmkQHj3XQ2TvA6jAZfzCadXOmMeDVD8ZSOKF/0Mv/feF9CrMS+cSyGY7FMdUtz0uluqWbps5ep0MxQWIJYRLtCOEV0gKVkxLHrGnxbC1vdmxMwkPvVHC4oYtvfHxBWHXdjTTL8oYWdgqFKkQTHJYQJtGOI81MT4ohLy30RvuOx5lz0mnu6nOkcbn1eB/3vFTKOfPSuXjhlFtkL6SkxkdTkJ7AzqrQnufKBM4SwiQqrmhh1ay0sJ+Jc3FuMokxHt453DT2wUH245dKae/u539dsSjsf46RYGV+Ko2dvdS0djsdigkCSwiT5GhrN9Ut3awpCN/2g2Eel4s1BWkcPNZBc1ffpL3voYZOHni7guvWzGRhjs1oGgqW5KbgcQkllc61KZngsYQwSbYfaQZg7ezwTwgAa2enIwLbyievlPCvz+4nNsrN1y8pnLT3NCcXF+1mYU4y71W12QyoEcASwiTZWt5MUownYr7ZpsRFsTAnmW1Hmunum/g5bTbvruWlA/X8zwvnkZkUM+HvZwJ3xsw0uvsHeflAvdOhmNNkCWGSbCtvZnVBWkT1irlwwXR6+728VjqxS5u2dPVx1x/3sCQ3mS+eO3tC38uM37zpiSTFeHiypNrpUMxpsoQwCRo7eymr72Tt7HSnQwmqnJQ4luen8tahRuomcPGcf3lmH63H+/n3a5bbILQQ5HYJK/JTeeVAvY1JCHP21zUJiiOs/cDfxQuz8HrhJy+VTsj1XzlQz1Pv1vCVC+ba0pghbOXMNAa8yp/eO+p0KOY0WEKYBFvLm4mNcrE0N8XpUIJuWkI0a2ZP49HtVZQ3dgX12i1dffzj07uZPz2R2y6cF9Rrm+DKToll8YxkniypcToUcxosIUyCbeXNnDEzjWhPZP64P1aUSYzHxQ+2HAzaNQcGvdz+SAlNXX388NPLifG4g3ZtMzE2rspjd00b+2ttnYRwFdAdSkQ2iMhBESkTkTtG2R8jIo/59m8VkQLf9gIR6RaRnb7H//M7Z5WI7Pad8xOJ0FFG7T397Kttj8jqomFJsVHcsn4Oz+6u5Zldwaky+I8tB3mzrInvXr3E1u4NE/9jZS7RHhePbqt0OhRzisZMCCLiBu4FLgMWATeIyKIRh30RaFHVecCPgO/77Tukqit8jy/5bf858DfAfN9jw6l/jNC140gLqrA2AgakncxtH5vHGTNTuePJ3Rw5zaqjTe8d5b7XDvO5M2fx6dX5QYrQTLTU+GguX5LN0+/WTEpXZBN8gZQQ1gJlqnpYVfuAR4GrRhxzFfBb3/MngItO9o1fRHKAZFV9R4cmQfkdcPW4ow8DW8ub8biElWG2ZOZ4Rbld/PQzZ+BxC7c9XHLK6+2+UdrIN594j9Wz0vinT4z83mFC3fVrZ9LeM8Dm3bVOh2JOQSAJIReo8nsqujOWAAAShUlEQVRd7ds26jGqOgC0AcN9LGeLyLsi8hcROc/veP9Oy6NdEwARuUVEikWkuKFhYvu7T4Rt5U0sy0shLjry68BzU+P44bXL2Xu0nbuf2TfuCc/+vKeWv/rNdgrSE/j5Z1dFbJtLJFs3expzMhJ4dLtVG4Wjif6LqwVmqupK4OvAwyIyrr6Dqnq/qq5W1dWZmZkTEuRE6eodYFd1W8SNPziZixZmcev6OTy8tZKvP/5ewCWFx4ur+MpDJSzJTeaxW86y0chhSkS4fm0+24+0UFrX4XQ4ZpwCSQg1gH9Fbp5v26jHiIgHSAGaVLVXVZsAVHUHcAgo9B2fN8Y1w97W8iYGvMq58zKcDmVSfWvDAr5+SSFPv1vDp+97m9q2E8+EWdHUxZcf3ME3n9jFOfMyePCv15ESH37Li5r/ds0ZeUS5hUe3V419sAkpngCO2Q7MF5HZDN20rwc+M+KYTcBNwNvARuBlVVURyQSaVXVQROYw1Hh8WFWbRaRdRM4EtgKfB34anI8UOt4obSLG42J1QWS3H4zkcgl/e9F8FuYk87XHdrLhnte5bEk2lyzK4sw56dR39FLe2MkbpU08+E4FbpfwtYsL+dIFc6x7aQRIT4zh0sXZPFlSzTc+XkRslP2fhosxE4KqDojI7cAWwA38SlX3isjdQLGqbgJ+CTwgImVAM0NJA2A9cLeI9ANe4Euq2uzb9xXgN0Ac8JzvEVHeKGtgTcG0KfsHccmiLP5w29nc82Ipz+yq/cg3RhG4dlUef39pEVnJsQ5FaSbCjWtn8uyuWp7ZVcvGVXljn2BCQiAlBFR1M7B5xLa7/J73ANeOct6TwJMnuGYxsGQ8wYaT+vYe3q/r5FNnTO0/hnnTk/jZZ86gd2CQrYeb2VnVSk5KLHMyE5iTkUhaQrTTIZoJcNbcdIqykvj1m+Vcc0auLWYUJgJKCGb83ihrBJhy7QcnEuNxs74wk/WF4dUxwJwaEeHmcwq486ndbD/SEtEDMyOJ9eubIG+UNZIWH8WiCFn/wJjxunpFLilxUfzmrXKnQzEBsoQwAVSVN0obOXteBq4IWv/AmPGIi3Zz/dp8tuytszWXw4QlhAlQVt9JfUcv51l1kZniPn9WAarKA29XOB2KCYAlhAnweulQ+8E5lhDMFJebGsfHF2fzyLZKm98oDFhCmABvljVSkB5P/rR4p0MxxnFfOGc2bd39PF5sA9VCnSWEIOsb8PLO4SbOnW+lA2MA1hSksXpWGvf95RB9A16nwzEnYQkhyLaWN9HVN8j5hdOdDsWYkCAi3PaxeRxt6+GPOyNuhpqIYgkhyF7YV0dslMvGHxjj54KiTBblJPPzVw8x6B3fLLhm8tjAtCBSVV7cV8d58zOnxHTX/h7eatMdmxMbLiXc9nAJf95zjCuW5TgdkhmFlRCCaO/Rdo629XDJoiynQzEm5GxYks2cjATufaVs3GtlmMlhCSGInt9Xh0vgogXWfmDMSG6X8KUL5rKvtp2X9tc7HY4ZhSWEIHphXx2rZqWRnmiLuxgzmv+xMpeC9Hj+Y8tBa0sIQZYQgqSq+Tj7a9utusiYk4hyu/j7S4s4WNdhPY5CkCWEIHlxfx0AlyzKdjgSY0LbFUtzWJKbzA+ff5/eARu9HEosIQTJC/vqmDc9kdkZCU6HYkxIc7mEb358ATWt3Tz0jvVOCyWWEIKg9XgfW8ubrbrImACdNz+Ds+em87NXyujo6Xc6HONjCSEIntlVy6BXuWKp9a02JhAiwrc2LKC5q4+fvVLmdDjGJ6CEICIbROSgiJSJyB2j7I8Rkcd8+7eKSIFv+yUiskNEdvv+vdDvnFd919zpe4RtX80nS6opykpi8QxbDMeYQC3PT2Xjqjx++Xo5pXUdTodjCCAhiIgbuBe4DFgE3CAii0Yc9kWgRVXnAT8Cvu/b3gh8UlWXAjcBD4w470ZVXeF7hGXH5MMNnbxb2cqnbN1YY8btzssWkBDj4Z/+uMcGq4WAQEoIa4EyVT2sqn3Ao8BVI465Cvit7/kTwEUiIqr6rqoe9W3fC8SJSER10n/63RpcAlevzHU6FGPCTnpiDN/4eBHvHG5m03tHxz7BTKhAEkIu4D+RebVv26jHqOoA0AakjzjmGqBEVXv9tv3aV130TxKGX6+9XuWpkhrOnZ9JVnKs0+EYE5ZuWDuT5Xkp/Msz+2m3BmZHTUqjsogsZqga6Va/zTf6qpLO8z0+d4JzbxGRYhEpbmhomPhgx2FreTM1rd1cc4aVDow5VW6X8N2rl9LU1cu/Prvf6XCmtEASQg2Q7/c6z7dt1GNExAOkAE2+13nA08DnVfXQ8AmqWuP7twN4mKGqqY9Q1ftVdbWqrs7MzAzkM02ap0qqSYzxcKkNRjPmtCzNS+HW9XN5dHsVL+6rczqcKSuQhLAdmC8is0UkGrge2DTimE0MNRoDbAReVlUVkVTgWeAOVX1z+GAR8YhIhu95FPAJYM/pfZTJ1d03yObdtVy+NHvKTXVtzET42iXzWZiTzB1P7aKxs3fsE0zQjZkQfG0CtwNbgP3A46q6V0TuFpErfYf9EkgXkTLg68Bw19TbgXnAXSO6l8YAW0RkF7CToRLGfwXzg020J0qq6eob5NrV+WMfbIwZU4zHzT3XraC9e4A7ntxtvY4cIOH0Q1+9erUWFxc7HQaDXuXCH75Kanw0f/jK2Y53N7XFaUwkeaO0gc17jnHVihmsmz2yb8qJfWbdzAmMKryJyA5VXT3WcTZS+RQ8v/cYFU3HuXX9HMeTgTGR5ux5Gcyfnsgzu2qpbOpyOpwpxRLCOKkq9712mFnp8Xx8sTUmGxNsLhGuW5NPSlwUD22rpL3buqJOFksI47T9SAs7q1r563Nn43ZZ6cCYiRAf7eGz62bR2+/loa0VDAx6nQ5pSrCEME73v3aIaQnRbFxljcnGTKTslFg2rsqjqqWbp96twRtG7Z3hyhLCOBw81sGL++v53JmzrKupMZNgSW4Kly7KYmdVK8/sOmo9jyaYx+kAwoWqcvcze0mO9XDT2QVOh2PMlHF+YSbd/YO8XtpIrMfNpdZ2N2EsIQRoy9463ixr4p+vXMy0hGinwzFmyhARNizOpqffy6vvN+Bxu/hYUab18JsAlhAC0NM/yHef3UdRVhI3Wl9nYyadiHDVihkMDHp5cX8d3X0DXLY0B5clhaCyhBCA+187THVLNw//zTo8bmt2McYJLhGuWZVHXLSbNw810dk7wDWr8vC47G8yWCwhjKGq+Tj/+WoZVyzN4ey5GU6HY8yU5hLhiqU5JMV42LKvjo6eAa5fO5PEGLuVBYOl1pPoHRjk9odL8Lhc3Hn5AqfDMcYwVH10ftF0rl2VR2XzcX72cikVNqI5KCwhnMTdf9rHe9Vt/ODa5eSlxTsdjjHGz8qZaXzp/Ll43C7+6/XD3P/aIQa91i31dFhCOIEndlTz0NZKbj1/DhuWWDc3Y0LRjNQ4brtgHguyk/nXzQfY+P/eorSuw+mwwpYlhFHsrGrl20/v5qw56Xzj0iKnwzHGnERctJsb183knutWcKSxiyt+8gb3vPg+3X2DTocWdiwhjPDO4SY++4utTE+O4Sc3rLReRcaEARHh6pW5vPD187l0cRb3vFjKBT94hce2V1o10jjY3c7PS/vruOlX28hJieX3t55NZlKM0yEZY8YhIzGGn33mDH7/pbOYkRrHt57czcfveY3fF1fRN2AT5I3FEgLg9Sq/frOcWx/YQVF2Eo/dehbZKbFOh2WMOUVrCqbx1JfP5j9vPAOPS/jGE7s4799f5uevHqKhw5bnPJEp33n3SGMX33xyF9vKm7mgKJOf3rCSpNgop8MyxpwmEeHypTlctiSb10obuf+1Q3z/zwf4wfMH+VjRdDauyuOCokxio2yiymFTNiE0dPTyu7eP8F+vHybK7eLfNy7j2lV5Nj+KMRFGRDi/MJPzCzMpq+/g9zuqeaqkhhf31xEX5eb8wkwuXZzFufMymJ48tWsGAkoIIrIB+DHgBn6hqv82Yn8M8DtgFdAEXKeqR3z77gS+CAwCf6uqWwK55kTwepU9R9t4ZFslT5bU0D/oZcPibL7zycVWRWTMFDBvehJ3XraQb1xaxFuHmnh+3zGe31vHn/ce8+1P5Kw56azIT2V5fgpzMhJxTaGFsMZMCCLiBu4FLgGqge0isklV9/kd9kWgRVXnicj1wPeB60RkEXA9sBiYAbwoIoW+c8a6ZtC8erCeLXuP8dL+euo7eonxuNi4Ko8vnjubuZmJE/GWxpgQ5nG7WF+YyfrCTO6+cgl7j7bz1qFG3j7cxFMl1TzwTgUAiTEe5mclMn96IvOnJzEzPZ78tHjyp8VFZNVyICWEtUCZqh4GEJFHgasA/5v3VcD/9j1/AviZDNW9XAU8qqq9QLmIlPmuRwDXDJqHtlby9qEmzi/M5MIF07lwwXTSbAprYwzgcglL81JYmpfCrefPZdCrHGro5L2qVvbUtPF+XScvH2jg8eLqD52XEO1menIs05NiSE+MJjU+mrT4KJJjo0iM9ZAY4yEh2kNctJvYKDcxHpfv4SbKI3hcLqLcgtvl9xDBJYIIjlRfB5IQcoEqv9fVwLoTHaOqAyLSBqT7tr8z4txc3/Oxrhk037t6Canx0UR7rFOVMebk3C6hMCuJwqwkrl3930vlth7vo7L5ONUt3VQ1H6euvZf6jh7q23t5v66T1uN9tBzvD9q4BxEQ+CBBPPfV9cybPrE1GiHfqCwitwC3+F52ishBB8PJABodfP8TCdW4IHRjC9W4IHRjC9W4ADJuDM3YgvYzm/+vp3X6rEAOCiQh1AD+K8rn+baNdky1iHiAFIYal0927ljXBEBV7wfuDyDOCScixaq62uk4RgrVuCB0YwvVuCB0YwvVuCB0YwvVuE4kkDqU7cB8EZktItEMNRJvGnHMJuAm3/ONwMs6tBr2JuB6EYkRkdnAfGBbgNc0xhgzicYsIfjaBG4HtjDURfRXqrpXRO4GilV1E/BL4AFfo3EzQzd4fMc9zlBj8QBwm6oOAox2zeB/PGOMMYEKqA1BVTcDm0dsu8vveQ9w7QnO/R7wvUCuGQZCoupqFKEaF4RubKEaF4RubKEaF4RubKEa16hkqGbHGGPMVGf9MI0xxgCWEMZNRP5DRA6IyC4ReVpEUh2OZ4OIHBSRMhG5w8lYholIvoi8IiL7RGSviHzV6ZhGEhG3iLwrIs84HcswEUkVkSd8v1/7ReQsp2MaJiJf8/1f7hGRR0TEsbleRORXIlIvInv8tk0TkRdEpNT3b1qIxBVS94uxWEIYvxeAJaq6DHgfuNOpQPymFbkMWATc4JsuxGkDwN+r6iLgTOC2EInL31eB/U4HMcKPgT+r6gJgOSESn4jkAn8LrFbVJQx1BLnewZB+A2wYse0O4CVVnQ+85Hs92X7DR+MKmftFICwhjJOqPq+qA76X7zA0hsIpH0wroqp9wPAUII5S1VpVLfE972DoxpZ78rMmj4jkAVcAv3A6lmEikgKsZ6jHHqrap6qtzkb1IR4gzjfOKB446lQgqvoaQ70Z/V0F/Nb3/LfA1ZMaFKPHFWL3izFZQjg9fwU85+D7jzatSMjceAFEpABYCWx1NpIPuQf4JhBKS2jNBhqAX/uqsn4hIglOBwWgqjXAD4BKoBZoU9XnnY3qI7JUtdb3/BiQ5WQwJ+D0/WJMlhBGISIv+upKRz6u8jvm2wxVjTzkXKShTUQSgSeBv1PVdqfjARCRTwD1qrrD6VhG8ABnAD9X1ZVAF85Ue3yErz7+KoaS1gwgQUQ+62xUJ+YbFBtS3SfD5X4R8nMZOUFVLz7ZfhG5GfgEcJE62283kGlFHCEiUQwlg4dU9Smn4/FzDnCliFwOxALJIvKgqjp9g6sGqlV1uCT1BCGSEICLgXJVbQAQkaeAs4EHHY3qw+pEJEdVa0UkB6h3OqBhIXS/GJOVEMbJt7DPN4ErVfW4w+GE5BQgvqnPfwnsV9X/63Q8/lT1TlXNU9UChn5eL4dAMkBVjwFVIlLk23QREzQd/CmoBM4UkXjf/+1FhEiDtx//6XNuAv7oYCwfCLH7xZhsYNo4+abniGFo8j6Ad1T1Sw7GczlDdeLDU4B8ZFT4ZBORc4HXgd38dz39P/pGp4cMEbkA+AdV/YTTsQCIyAqGGrqjgcPAF1S1xdmohojIPwPXMVTt8S7w1751TpyI5RHgAoZmEq0DvgP8AXgcmAlUAJ9W1ZENz07EdSchdL8YiyUEY4wxgFUZGWOM8bGEYIwxBrCEYIwxxscSgjHGGMASgjHGGB9LCMYYYwBLCMYYY3wsIRhjjAHg/wNzBwfLVfwfigAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(np.log(np.array(player_one_playtimes)+1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1141387b8>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(np.log(np.array(get_player_play_times(d,1000))+1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#games per user\n",
    "sns.distplot([len(i['items']) for i in d if len(i['items']) < 200])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "58.353629260559394"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean([i['items_count'] for i in d])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21.53643274307251\n"
     ]
    }
   ],
   "source": [
    "start = time()\n",
    "games = get_all_da_games(d)\n",
    "print(time() - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "g = pd.DataFrame(games,index=[0]).T.reset_index()\n",
    "g.columns = ['id','name']\n",
    "g.sort_values('id').reset_index(drop=True,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10978, 2)"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "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>id</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>Counter-Strike</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>Team Fortress Classic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30</td>\n",
       "      <td>Day of Defeat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>Deathmatch Classic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50</td>\n",
       "      <td>Half-Life: Opposing Force</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id                       name\n",
       "0  10             Counter-Strike\n",
       "1  20      Team Fortress Classic\n",
       "2  30              Day of Defeat\n",
       "3  40         Deathmatch Classic\n",
       "4  50  Half-Life: Opposing Force"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "#save CSV\n",
    "g.to_csv('all_games.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### All playtimes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24.368303060531616\n"
     ]
    }
   ],
   "source": [
    "start = time()\n",
    "p = get_all_da_times(d)\n",
    "print(time()-start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "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>game</th>\n",
       "      <th>time_f</th>\n",
       "      <th>user</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>76561197970982479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>76561197970982479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30</td>\n",
       "      <td>7</td>\n",
       "      <td>76561197970982479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>76561197970982479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>76561197970982479</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  game  time_f               user\n",
       "0   10       6  76561197970982479\n",
       "1   20       0  76561197970982479\n",
       "2   30       7  76561197970982479\n",
       "3   40       0  76561197970982479\n",
       "4   50       0  76561197970982479"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12b50a4a8>"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(np.log(p.time_f+1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "148.11099696159363\n"
     ]
    }
   ],
   "source": [
    "start = time()\n",
    "p['rating'] = transform_playtime_data(p)\n",
    "print(time()-start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "p = p[[\n",
    "    'user',\n",
    "    'game',\n",
    "    'time_f',\n",
    "    'rating'\n",
    "]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user</th>\n",
       "      <th>game</th>\n",
       "      <th>time_f</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>76561197970982479</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>0.193312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>76561197970982479</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>76561197970982479</td>\n",
       "      <td>30</td>\n",
       "      <td>7</td>\n",
       "      <td>0.206577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>76561197970982479</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>76561197970982479</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>76561197970982479</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>76561197970982479</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>76561197970982479</td>\n",
       "      <td>130</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>76561197970982479</td>\n",
       "      <td>300</td>\n",
       "      <td>4733</td>\n",
       "      <td>0.840691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>76561197970982479</td>\n",
       "      <td>240</td>\n",
       "      <td>1853</td>\n",
       "      <td>0.747564</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                user game  time_f    rating\n",
       "0  76561197970982479   10       6  0.193312\n",
       "1  76561197970982479   20       0  0.000000\n",
       "2  76561197970982479   30       7  0.206577\n",
       "3  76561197970982479   40       0  0.000000\n",
       "4  76561197970982479   50       0  0.000000\n",
       "5  76561197970982479   60       0  0.000000\n",
       "6  76561197970982479   70       0  0.000000\n",
       "7  76561197970982479  130       0  0.000000\n",
       "8  76561197970982479  300    4733  0.840691\n",
       "9  76561197970982479  240    1853  0.747564"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "p.to_csv('user_ratings.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5153209, 4)"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 5 million rows\n",
    "p.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5148800, 4)"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.dropna().shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "p.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12b4dd470>"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#classic hagia sophia distribution\n",
    "sns.distplot(p[p.rating!=0].rating)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3285246, 4)"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p[p.rating!=0].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 3.3 million ratings\n",
    "- 10.8 thousand items\n",
    "- 8.8 thousand users"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Surprise Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "reader = Reader()\n",
    "data = Dataset.load_from_df(p[p.rating!=0][['user', 'game', 'rating']], reader)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cell below takes 8 minutes to run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "490.28220891952515\n"
     ]
    }
   ],
   "source": [
    "start = time()\n",
    "algo = SVD()\n",
    "trainset = data.build_full_trainset()\n",
    "algo.fit(trainset)\n",
    "print(time()-start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on cython_function_or_method in module surprise.prediction_algorithms.matrix_factorization:\n",
      "\n",
      "fit(self, trainset)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(algo.fit)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center>\n",
    "$ \\hat{r}_{ui} = \\mu + b_u + b_i + q_i^Tp_u $\n",
    "</center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on class SVD in module surprise.prediction_algorithms.matrix_factorization:\n",
      "\n",
      "class SVD(surprise.prediction_algorithms.algo_base.AlgoBase)\n",
      " |  The famous *SVD* algorithm, as popularized by `Simon Funk\n",
      " |  <http://sifter.org/~simon/journal/20061211.html>`_ during the Netflix\n",
      " |  Prize. When baselines are not used, this is equivalent to Probabilistic\n",
      " |  Matrix Factorization :cite:`salakhutdinov2008a` (see :ref:`note\n",
      " |  <unbiased_note>` below).\n",
      " |  \n",
      " |  The prediction :math:`\\hat{r}_{ui}` is set as:\n",
      " |  \n",
      " |  .. math::\n",
      " |      \\hat{r}_{ui} = \\mu + b_u + b_i + q_i^Tp_u\n",
      " |  \n",
      " |  If user :math:`u` is unknown, then the bias :math:`b_u` and the factors\n",
      " |  :math:`p_u` are assumed to be zero. The same applies for item :math:`i`\n",
      " |  with :math:`b_i` and :math:`q_i`.\n",
      " |  \n",
      " |  For details, see equation (5) from :cite:`Koren:2009`. See also\n",
      " |  :cite:`Ricci:2010`, section 5.3.1.\n",
      " |  \n",
      " |  To estimate all the unknown, we minimize the following regularized squared\n",
      " |  error:\n",
      " |  \n",
      " |  .. math::\n",
      " |      \\sum_{r_{ui} \\in R_{train}} \\left(r_{ui} - \\hat{r}_{ui} \\right)^2 +\n",
      " |      \\lambda\\left(b_i^2 + b_u^2 + ||q_i||^2 + ||p_u||^2\\right)\n",
      " |  \n",
      " |  \n",
      " |  The minimization is performed by a very straightforward stochastic gradient\n",
      " |  descent:\n",
      " |  \n",
      " |  .. math::\n",
      " |      b_u &\\leftarrow b_u &+ \\gamma (e_{ui} - \\lambda b_u)\\\\\n",
      " |      b_i &\\leftarrow b_i &+ \\gamma (e_{ui} - \\lambda b_i)\\\\\n",
      " |      p_u &\\leftarrow p_u &+ \\gamma (e_{ui} \\cdot q_i - \\lambda p_u)\\\\\n",
      " |      q_i &\\leftarrow q_i &+ \\gamma (e_{ui} \\cdot p_u - \\lambda q_i)\n",
      " |  \n",
      " |  where :math:`e_{ui} = r_{ui} - \\hat{r}_{ui}`. These steps are performed\n",
      " |  over all the ratings of the trainset and repeated ``n_epochs`` times.\n",
      " |  Baselines are initialized to ``0``. User and item factors are randomly\n",
      " |  initialized according to a normal distribution, which can be tuned using\n",
      " |  the ``init_mean`` and ``init_std_dev`` parameters.\n",
      " |  \n",
      " |  You also have control over the learning rate :math:`\\gamma` and the\n",
      " |  regularization term :math:`\\lambda`. Both can be different for each\n",
      " |  kind of parameter (see below). By default, learning rates are set to\n",
      " |  ``0.005`` and regularization terms are set to ``0.02``.\n",
      " |  \n",
      " |  .. _unbiased_note:\n",
      " |  \n",
      " |  .. note::\n",
      " |      You can choose to use an unbiased version of this algorithm, simply\n",
      " |      predicting:\n",
      " |  \n",
      " |      .. math::\n",
      " |          \\hat{r}_{ui} = q_i^Tp_u\n",
      " |  \n",
      " |      This is equivalent to Probabilistic Matrix Factorization\n",
      " |      (:cite:`salakhutdinov2008a`, section 2) and can be achieved by setting\n",
      " |      the ``biased`` parameter to ``False``.\n",
      " |  \n",
      " |  \n",
      " |  Args:\n",
      " |      n_factors: The number of factors. Default is ``100``.\n",
      " |      n_epochs: The number of iteration of the SGD procedure. Default is\n",
      " |          ``20``.\n",
      " |      biased(bool): Whether to use baselines (or biases). See :ref:`note\n",
      " |          <unbiased_note>` above.  Default is ``True``.\n",
      " |      init_mean: The mean of the normal distribution for factor vectors\n",
      " |          initialization. Default is ``0``.\n",
      " |      init_std_dev: The standard deviation of the normal distribution for\n",
      " |          factor vectors initialization. Default is ``0.1``.\n",
      " |      lr_all: The learning rate for all parameters. Default is ``0.005``.\n",
      " |      reg_all: The regularization term for all parameters. Default is\n",
      " |          ``0.02``.\n",
      " |      lr_bu: The learning rate for :math:`b_u`. Takes precedence over\n",
      " |          ``lr_all`` if set. Default is ``None``.\n",
      " |      lr_bi: The learning rate for :math:`b_i`. Takes precedence over\n",
      " |          ``lr_all`` if set. Default is ``None``.\n",
      " |      lr_pu: The learning rate for :math:`p_u`. Takes precedence over\n",
      " |          ``lr_all`` if set. Default is ``None``.\n",
      " |      lr_qi: The learning rate for :math:`q_i`. Takes precedence over\n",
      " |          ``lr_all`` if set. Default is ``None``.\n",
      " |      reg_bu: The regularization term for :math:`b_u`. Takes precedence\n",
      " |          over ``reg_all`` if set. Default is ``None``.\n",
      " |      reg_bi: The regularization term for :math:`b_i`. Takes precedence\n",
      " |          over ``reg_all`` if set. Default is ``None``.\n",
      " |      reg_pu: The regularization term for :math:`p_u`. Takes precedence\n",
      " |          over ``reg_all`` if set. Default is ``None``.\n",
      " |      reg_qi: The regularization term for :math:`q_i`. Takes precedence\n",
      " |          over ``reg_all`` if set. Default is ``None``.\n",
      " |      random_state(int, RandomState instance from numpy, or ``None``):\n",
      " |          Determines the RNG that will be used for initialization. If\n",
      " |          int, ``random_state`` will be used as a seed for a new RNG. This is\n",
      " |          useful to get the same initialization over multiple calls to\n",
      " |          ``fit()``.  If RandomState instance, this same instance is used as\n",
      " |          RNG. If ``None``, the current RNG from numpy is used.  Default is\n",
      " |          ``None``.\n",
      " |      verbose: If ``True``, prints the current epoch. Default is ``False``.\n",
      " |  \n",
      " |  Attributes:\n",
      " |      pu(numpy array of size (n_users, n_factors)): The user factors (only\n",
      " |          exists if ``fit()`` has been called)\n",
      " |      qi(numpy array of size (n_items, n_factors)): The item factors (only\n",
      " |          exists if ``fit()`` has been called)\n",
      " |      bu(numpy array of size (n_users)): The user biases (only\n",
      " |          exists if ``fit()`` has been called)\n",
      " |      bi(numpy array of size (n_items)): The item biases (only\n",
      " |          exists if ``fit()`` has been called)\n",
      " |  \n",
      " |  Method resolution order:\n",
      " |      SVD\n",
      " |      surprise.prediction_algorithms.algo_base.AlgoBase\n",
      " |      builtins.object\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __init__(self, n_factors=100, n_epochs=20, biased=True, init_mean=0, init_std_dev=0.1, lr_all=0.005, reg_all=0.02, lr_bu=None, lr_bi=None, lr_pu=None, lr_qi=None, reg_bu=None, reg_bi=None, reg_pu=None, reg_qi=None, random_state=None, verbose=False)\n",
      " |  \n",
      " |  estimate(self, u, i)\n",
      " |  \n",
      " |  fit(self, trainset)\n",
      " |  \n",
      " |  sgd(self, trainset)\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from surprise.prediction_algorithms.algo_base.AlgoBase:\n",
      " |  \n",
      " |  compute_baselines(self)\n",
      " |      Compute users and items baselines.\n",
      " |      \n",
      " |      The way baselines are computed depends on the ``bsl_options`` parameter\n",
      " |      passed at the creation of the algorithm (see\n",
      " |      :ref:`baseline_estimates_configuration`).\n",
      " |      \n",
      " |      This method is only relevant for algorithms using :func:`Pearson\n",
      " |      baseline similarty<surprise.similarities.pearson_baseline>` or the\n",
      " |      :class:`BaselineOnly\n",
      " |      <surprise.prediction_algorithms.baseline_only.BaselineOnly>` algorithm.\n",
      " |      \n",
      " |      Returns:\n",
      " |          A tuple ``(bu, bi)``, which are users and items baselines.\n",
      " |  \n",
      " |  compute_similarities(self)\n",
      " |      Build the similarity matrix.\n",
      " |      \n",
      " |      The way the similarity matrix is computed depends on the\n",
      " |      ``sim_options`` parameter passed at the creation of the algorithm (see\n",
      " |      :ref:`similarity_measures_configuration`).\n",
      " |      \n",
      " |      This method is only relevant for algorithms using a similarity measure,\n",
      " |      such as the :ref:`k-NN algorithms <pred_package_knn_inpired>`.\n",
      " |      \n",
      " |      Returns:\n",
      " |          The similarity matrix.\n",
      " |  \n",
      " |  default_prediction(self)\n",
      " |      Used when the ``PredictionImpossible`` exception is raised during a\n",
      " |      call to :meth:`predict()\n",
      " |      <surprise.prediction_algorithms.algo_base.AlgoBase.predict>`. By\n",
      " |      default, return the global mean of all ratings (can be overridden in\n",
      " |      child classes).\n",
      " |      \n",
      " |      Returns:\n",
      " |          (float): The mean of all ratings in the trainset.\n",
      " |  \n",
      " |  get_neighbors(self, iid, k)\n",
      " |      Return the ``k`` nearest neighbors of ``iid``, which is the inner id\n",
      " |      of a user or an item, depending on the ``user_based`` field of\n",
      " |      ``sim_options`` (see :ref:`similarity_measures_configuration`).\n",
      " |      \n",
      " |      As the similarities are computed on the basis of a similarity measure,\n",
      " |      this method is only relevant for algorithms using a similarity measure,\n",
      " |      such as the :ref:`k-NN algorithms <pred_package_knn_inpired>`.\n",
      " |      \n",
      " |      For a usage example, see the :ref:`FAQ <get_k_nearest_neighbors>`.\n",
      " |      \n",
      " |      Args:\n",
      " |          iid(int): The (inner) id of the user (or item) for which we want\n",
      " |              the nearest neighbors. See :ref:`this note<raw_inner_note>`.\n",
      " |      \n",
      " |          k(int): The number of neighbors to retrieve.\n",
      " |      \n",
      " |      Returns:\n",
      " |          The list of the ``k`` (inner) ids of the closest users (or items)\n",
      " |          to ``iid``.\n",
      " |  \n",
      " |  predict(self, uid, iid, r_ui=None, clip=True, verbose=False)\n",
      " |      Compute the rating prediction for given user and item.\n",
      " |      \n",
      " |      The ``predict`` method converts raw ids to inner ids and then calls the\n",
      " |      ``estimate`` method which is defined in every derived class. If the\n",
      " |      prediction is impossible (e.g. because the user and/or the item is\n",
      " |      unkown), the prediction is set according to :meth:`default_prediction()\n",
      " |      <surprise.prediction_algorithms.algo_base.AlgoBase.default_prediction>`.\n",
      " |      \n",
      " |      Args:\n",
      " |          uid: (Raw) id of the user. See :ref:`this note<raw_inner_note>`.\n",
      " |          iid: (Raw) id of the item. See :ref:`this note<raw_inner_note>`.\n",
      " |          r_ui(float): The true rating :math:`r_{ui}`. Optional, default is\n",
      " |              ``None``.\n",
      " |          clip(bool): Whether to clip the estimation into the rating scale.\n",
      " |              For example, if :math:`\\hat{r}_{ui}` is :math:`5.5` while the\n",
      " |              rating scale is :math:`[1, 5]`, then :math:`\\hat{r}_{ui}` is\n",
      " |              set to :math:`5`. Same goes if :math:`\\hat{r}_{ui} < 1`.\n",
      " |              Default is ``True``.\n",
      " |          verbose(bool): Whether to print details of the prediction.  Default\n",
      " |              is False.\n",
      " |      \n",
      " |      Returns:\n",
      " |          A :obj:`Prediction            <surprise.prediction_algorithms.predictions.Prediction>` object\n",
      " |          containing:\n",
      " |      \n",
      " |          - The (raw) user id ``uid``.\n",
      " |          - The (raw) item id ``iid``.\n",
      " |          - The true rating ``r_ui`` (:math:`\\hat{r}_{ui}`).\n",
      " |          - The estimated rating (:math:`\\hat{r}_{ui}`).\n",
      " |          - Some additional details about the prediction that might be useful\n",
      " |            for later analysis.\n",
      " |  \n",
      " |  test(self, testset, verbose=False)\n",
      " |      Test the algorithm on given testset, i.e. estimate all the ratings\n",
      " |      in the given testset.\n",
      " |      \n",
      " |      Args:\n",
      " |          testset: A test set, as returned by a :ref:`cross-validation\n",
      " |              itertor<use_cross_validation_iterators>` or by the\n",
      " |              :meth:`build_testset() <surprise.Trainset.build_testset>`\n",
      " |              method.\n",
      " |          verbose(bool): Whether to print details for each predictions.\n",
      " |              Default is False.\n",
      " |      \n",
      " |      Returns:\n",
      " |          A list of :class:`Prediction            <surprise.prediction_algorithms.predictions.Prediction>` objects\n",
      " |          that contains all the estimated ratings.\n",
      " |  \n",
      " |  train(self, trainset)\n",
      " |      Deprecated method: use :meth:`fit() <AlgoBase.fit>`\n",
      " |      instead.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors inherited from surprise.prediction_algorithms.algo_base.AlgoBase:\n",
      " |  \n",
      " |  __dict__\n",
      " |      dictionary for instance variables (if defined)\n",
      " |  \n",
      " |  __weakref__\n",
      " |      list of weak references to the object (if defined)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(SVD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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