data_manipulation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Данные"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "DATA_PREFIX = 'data/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import os\n",
    "os.chdir('{}/{}'.format(os.getcwd(),\n",
    "                        DATA_PREFIX))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "\n",
    "def print_time_range(times):\n",
    "    print('Time from: {0}, to: {1}'.format(datetime.fromtimestamp(times.min()), datetime.fromtimestamp(times.max())))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Лайки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "train_likes_df = pd.read_csv('train_likes.csv')\n",
    "print(train_likes_df.shape)\n",
    "train_likes_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "print_time_range(train_likes_df.time)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Контент"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "with open('items.json') as f:\n",
    "    items_dicts = json.load(f)\n",
    "    \n",
    "print(sorted(items_dicts[0].keys()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "У каждой единицы контента есть четыре обязательные характеристики:\n",
    "- id --- идентификатор\n",
    "- duration --- коэффициент продолжительности\n",
    "- year --- коэффициент года производства\n",
    "- genre --- жанр (категориальная переменная)\n",
    "\n",
    "и множество опциональныйх переменных вида \"f_N\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Замечание\n",
    "Описание есть не для всего контента (покрыто порядка 2/3 лайков)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Расписание"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#число строк в расписании\n",
    "!wc -l schedule.csv\n",
    "\n",
    "# first 10 lines\n",
    "!head schedule.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "schedule_df = pd.read_csv('schedule.csv', \n",
    "                          nrows=1000,# Первые 1000 строк\n",
    "                         )\n",
    "print(schedule_df.shape)\n",
    "schedule_df.head(3)"
   ]
  }
 ],
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