get_post_by_hashtag.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Сбор постов Вконтакте по #хештегу"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Автор: Шмаков Сергей\n",
    "* http://freesmm.ru\n",
    "* https://vk.com/smmblog\n",
    "* https://vk.com/freesmm - сборник бесплатных скриптов для SMM\n",
    "* https://youtube.com/smmblog\n",
    "\n",
    "* **Вопросы по скрипту/доработки** : https://new.vk.com/topic-41212221_34161499\n",
    "\n",
    "> Хотите научиться самостоятельно делать такие скрипты? Проходите бесплатный курс по программированию адаптированные для SMM-щиков http://freesmm.tilda.ws/page241764.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Что умеет скрипт\n",
    "* Собирать основные данные в таблицу (без текста\\фото и прочего)\n",
    "* Экспресс-анализ данных\n",
    "\n",
    "Нужны доработки? обращайтесь - vk.me/smmblog"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Шаг 0 - подключить пакеты"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import time as t \n",
    "import csv\n",
    "import datetime as dt\n",
    "from datetime import datetime, date, time, timedelta\n",
    "import requests\n",
    "import itertools\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Шаг 1 - получить токен ВКонтакте\n",
    "\n",
    "Для получения токена перейдите по ссылке и скопируйте нужную часть в блок ниже\n",
    "https://oauth.vk.com/authorize?client_id=5453402&display=page&redirect_uri=http://localhost&scope=&response_type=token&v=5.53\n",
    "![alt](https://api.monosnap.com/rpc/file/download?id=H72wV79ObVw7rLBfJO8T4jOJ25szXW)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "token = 'вставить токен'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Шаг 2 - задать запрос"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "q = 'vinci' #без знака # только одно слово."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Шаг 3 - создать csv с колонками\n",
    "\n",
    "Создаём файл и записываем колонки таблицы (создаёт новую или перезаписывает **полностью** старую)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "with open(q+'.csv', 'w', newline='', encoding='UTF-8') as csvfile:\n",
    "                            datawriter = csv.writer(csvfile, delimiter=';',\n",
    "                                                quotechar='|', quoting=csv.QUOTE_MINIMAL)\n",
    "\n",
    "\n",
    "                            datawriter.writerow(['date']+['owner_id']+['post_id']+['likes']+\n",
    "                                                        ['reposts']+['comments']+\n",
    "\n",
    "                                                        ['coordinates']+  \n",
    "                                                        #не трогать, не работает.\n",
    "                                                        #['place_country']+['city']+['address']+['coordinates']+                                                            \n",
    "                                                        #['place_id']+['latitude']+['longitude']+\n",
    "                                                        #['created']+['updated']+['checkins']+                                                            \n",
    "\n",
    "                                                        ['post_type']+\n",
    "                                                        ['platform']+['platform_type'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "i = 0\n",
    "while True:\n",
    "\n",
    "    if i == 0:\n",
    "        b = datetime.now() #текущее время\n",
    "\n",
    "        end_time = 1475508828 #int(t.mktime(b.timetuple())) #подставить unix-время если требуется сбор с определённого момента\n",
    "        \n",
    "        print('Цербер начал собирать посты с:', end_time, dt.datetime.fromtimestamp( #функция преобразования\n",
    "                        int(end_time)\n",
    "                        ).strftime('%Y-%m-%d %H:%M:%S'))\n",
    "\n",
    "        i = 1\n",
    "\n",
    "    else:\n",
    "        end_time = r[0] #возвращает ВК крайнее время от которого нужно опускаться дальше\n",
    "\n",
    "\n",
    "\n",
    "    r = requests.post('https://api.vk.com/method/execute.Shmakov_search_post?q='+str(q)\n",
    "                      +'&end_time='+str(end_time)\n",
    "                      +'&access_token='+token).json()['response']\n",
    "    #print('крайнее время, должно совпадать',r[0])\n",
    "    \n",
    "\n",
    "    #print(len(r[1])) - сколько постов собрано за раз\n",
    "\n",
    "\n",
    "\n",
    "    #print(time_start)\n",
    "\n",
    "    for i in r[1]:\n",
    "\n",
    "\n",
    "        date = i['date'] #дата поста\n",
    "\n",
    "        owner_id = i['owner_id'] #создатель поста\n",
    "        post_id = i['id'] #id поста\n",
    "\n",
    "        likes = i['likes']['count'] #количество лайков\n",
    "        reposts = i['reposts']['count']  #количество репостов\n",
    "        comments = i['comments']['count'] #количество комментариев\n",
    "        geo = i.setdefault('geo', None) \n",
    "\n",
    "\n",
    "        if geo == None:\n",
    "            coordinates = None\n",
    "        else:\n",
    "            '''\n",
    "            place_id = i['geo']['place'].setdefault('id',None)\n",
    "            latitude = geo['place'].setdefault('latitude',None) #широта места\n",
    "            longitude = geo['place'].setdefault('longitude',None) #долгота места\n",
    "            created= geo['place'].setdefault('created',None) # дата создания места\n",
    "            updated= geo['place'].setdefault('updated',None) # дата обновления информации о месте\n",
    "            checkins= geo['place'].setdefault('checkins',None) #количество чекинов в этом месте\n",
    "            place_name = i['geo'].setdefault('title',None)\n",
    "            country = i['geo'].setdefault('country',None)\n",
    "            city = i['geo'].setdefault('city',None)\n",
    "            address = i['geo'].setdefault('address',None)\n",
    "\n",
    "            '''\n",
    "            coordinates= geo.setdefault('coordinates',None) #координаты пользователя         \n",
    "\n",
    "\n",
    "        post_type = i['post_type']\n",
    "\n",
    "        platform = i['post_source'].setdefault('platform','direct')\n",
    "        platform_type = i['post_source'].setdefault('type',None)\n",
    "        platform_url = i['post_source'].setdefault('url',None)\n",
    "\n",
    "\n",
    "\n",
    "        with open(str(q)+'.csv', 'a', newline='', encoding='UTF-8') as csvfile:\n",
    "                                    datawriter = csv.writer(csvfile, delimiter=';',\n",
    "                                                        quotechar='|', quoting=csv.QUOTE_MINIMAL)\n",
    "\n",
    "\n",
    "                                    datawriter.writerow([date]+[owner_id]+[post_id]+[likes]+\n",
    "                                                        [reposts]+[comments]+\n",
    "\n",
    "                                                        #[geo]+\n",
    "                                                        [coordinates]+ \n",
    "\n",
    "                                                        #не трогать - не работает\n",
    "                                                        #[country]+[city]+[address]+                                                           \n",
    "                                                        #[place_id]+[latitude]+[longitude]+\n",
    "                                                        #[created]+[updated]+[checkins]+\n",
    "\n",
    "\n",
    "                                                        [post_type]+\n",
    "                                                        [platform]+[platform_type])\n",
    "    print('Цербер собрал посты до:',r[0],dt.datetime.fromtimestamp( #функция преобразования\n",
    "                        int(r[0])\n",
    "                        ).strftime('%Y-%m-%d %H:%M:%S'))\n",
    "    t.sleep(30) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(str(q)+'.csv', delimiter=';', encoding='UTF-8',error_bad_lines=False, parse_dates=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>owner_id</th>\n",
       "      <th>post_id</th>\n",
       "      <th>likes</th>\n",
       "      <th>reposts</th>\n",
       "      <th>comments</th>\n",
       "      <th>coordinates</th>\n",
       "      <th>post_type</th>\n",
       "      <th>platform</th>\n",
       "      <th>platform_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1477743102</td>\n",
       "      <td>-131803463</td>\n",
       "      <td>738</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>reply</td>\n",
       "      <td>direct</td>\n",
       "      <td>mvk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1477742854</td>\n",
       "      <td>343870044</td>\n",
       "      <td>1004</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>post</td>\n",
       "      <td>direct</td>\n",
       "      <td>vk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1477742834</td>\n",
       "      <td>94359618</td>\n",
       "      <td>13196</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>post</td>\n",
       "      <td>instagram</td>\n",
       "      <td>api</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1477742572</td>\n",
       "      <td>150538526</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>post</td>\n",
       "      <td>direct</td>\n",
       "      <td>vk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1477742312</td>\n",
       "      <td>150538526</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>post</td>\n",
       "      <td>direct</td>\n",
       "      <td>vk</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date   owner_id  post_id  likes  reposts  comments coordinates  \\\n",
       "0  1477743102 -131803463      738      0        0         0         NaN   \n",
       "1  1477742854  343870044     1004      0        0         0         NaN   \n",
       "2  1477742834   94359618    13196      0        0         0         NaN   \n",
       "3  1477742572  150538526       19      0        0         0         NaN   \n",
       "4  1477742312  150538526       15      0        0         0         NaN   \n",
       "\n",
       "  post_type   platform platform_type  \n",
       "0     reply     direct           mvk  \n",
       "1      post     direct            vk  \n",
       "2      post  instagram           api  \n",
       "3      post     direct            vk  \n",
       "4      post     direct            vk  "
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "удалим дубликаты. возможны будут если перезаписывать файл"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df.drop_duplicates() #удалит дубликаты если перезаписывали файл"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "переведём unix-time в нормальное время"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['date'] = pd.to_datetime(df['date'],unit='s') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "сгруппируем данные по дням"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>owner_id</th>\n",
       "      <th>post_id</th>\n",
       "      <th>likes</th>\n",
       "      <th>reposts</th>\n",
       "      <th>comments</th>\n",
       "      <th>coordinates</th>\n",
       "      <th>post_type</th>\n",
       "      <th>platform</th>\n",
       "      <th>platform_type</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-08-18</th>\n",
       "      <td>250</td>\n",
       "      <td>250</td>\n",
       "      <td>250</td>\n",
       "      <td>250</td>\n",
       "      <td>250</td>\n",
       "      <td>18</td>\n",
       "      <td>250</td>\n",
       "      <td>250</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-19</th>\n",
       "      <td>427</td>\n",
       "      <td>427</td>\n",
       "      <td>427</td>\n",
       "      <td>427</td>\n",
       "      <td>427</td>\n",
       "      <td>22</td>\n",
       "      <td>427</td>\n",
       "      <td>427</td>\n",
       "      <td>427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-20</th>\n",
       "      <td>456</td>\n",
       "      <td>456</td>\n",
       "      <td>456</td>\n",
       "      <td>456</td>\n",
       "      <td>456</td>\n",
       "      <td>25</td>\n",
       "      <td>456</td>\n",
       "      <td>456</td>\n",
       "      <td>456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-21</th>\n",
       "      <td>511</td>\n",
       "      <td>511</td>\n",
       "      <td>511</td>\n",
       "      <td>511</td>\n",
       "      <td>511</td>\n",
       "      <td>30</td>\n",
       "      <td>511</td>\n",
       "      <td>511</td>\n",
       "      <td>511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-22</th>\n",
       "      <td>410</td>\n",
       "      <td>410</td>\n",
       "      <td>410</td>\n",
       "      <td>410</td>\n",
       "      <td>410</td>\n",
       "      <td>32</td>\n",
       "      <td>410</td>\n",
       "      <td>410</td>\n",
       "      <td>410</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            owner_id  post_id  likes  reposts  comments  coordinates  \\\n",
       "date                                                                   \n",
       "2016-08-18       250      250    250      250       250           18   \n",
       "2016-08-19       427      427    427      427       427           22   \n",
       "2016-08-20       456      456    456      456       456           25   \n",
       "2016-08-21       511      511    511      511       511           30   \n",
       "2016-08-22       410      410    410      410       410           32   \n",
       "\n",
       "            post_type  platform  platform_type  \n",
       "date                                            \n",
       "2016-08-18        250       250            250  \n",
       "2016-08-19        427       427            427  \n",
       "2016-08-20        456       456            456  \n",
       "2016-08-21        511       511            511  \n",
       "2016-08-22        410       410            410  "
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_by_day = df.set_index('date').groupby(pd.TimeGrouper('D')).count()\n",
    "group_by_day.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Нарисуем график количества публикация за каждый день"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x14eeeb33a58>"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6lmHhQjjmGHfuSpIkSZIkSZPZ3FUWMx3LANVoBpu7kiRJkiRJ0kQ2d5XFTMcy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\nYfZZPTW18G0aNIDq1cPfmmHJEli3LvjiLtjU3N9+gzlzDr+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K/8K98AJs2ADj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XD11d4TtzQpCMAbdhs18snEevb0tA55efD3394b+MUXPfDXqRM8/njptp1uq1d7YHbR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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14eec83c470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(num=1, figsize=(17, 6)) #размер отрисованного графика\n",
    "\n",
    "plt.title('Date')\n",
    "plt.xlabel('date') #подпись оси x\n",
    "plt.ylabel('count post per day') #подпись оси y\n",
    "plt.grid()\n",
    "plt.plot(group_by_day['owner_id'],label=str(q)) #что рисуем и в какой форме: точка и др.\n",
    "\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>owner_id</th>\n",
       "      <th>post_id</th>\n",
       "      <th>likes</th>\n",
       "      <th>reposts</th>\n",
       "      <th>comments</th>\n",
       "      <th>coordinates</th>\n",
       "      <th>post_type</th>\n",
       "      <th>platform_type</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>platform</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>android</th>\n",
       "      <td>2676</td>\n",
       "      <td>2676</td>\n",
       "      <td>2676</td>\n",
       "      <td>2676</td>\n",
       "      <td>2676</td>\n",
       "      <td>2676</td>\n",
       "      <td>30</td>\n",
       "      <td>2676</td>\n",
       "      <td>2676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chronicle</th>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>direct</th>\n",
       "      <td>3917</td>\n",
       "      <td>3917</td>\n",
       "      <td>3917</td>\n",
       "      <td>3917</td>\n",
       "      <td>3917</td>\n",
       "      <td>3917</td>\n",
       "      <td>9</td>\n",
       "      <td>3917</td>\n",
       "      <td>3917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>instagram</th>\n",
       "      <td>3807</td>\n",
       "      <td>3807</td>\n",
       "      <td>3807</td>\n",
       "      <td>3807</td>\n",
       "      <td>3807</td>\n",
       "      <td>3807</td>\n",
       "      <td>1252</td>\n",
       "      <td>3807</td>\n",
       "      <td>3807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ipad</th>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>iphone</th>\n",
       "      <td>499</td>\n",
       "      <td>499</td>\n",
       "      <td>499</td>\n",
       "      <td>499</td>\n",
       "      <td>499</td>\n",
       "      <td>499</td>\n",
       "      <td>11</td>\n",
       "      <td>499</td>\n",
       "      <td>499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prisma</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vinci</th>\n",
       "      <td>5602</td>\n",
       "      <td>5602</td>\n",
       "      <td>5602</td>\n",
       "      <td>5602</td>\n",
       "      <td>5602</td>\n",
       "      <td>5602</td>\n",
       "      <td>19</td>\n",
       "      <td>5602</td>\n",
       "      <td>5602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wphone</th>\n",
       "      <td>4427</td>\n",
       "      <td>4427</td>\n",
       "      <td>4427</td>\n",
       "      <td>4427</td>\n",
       "      <td>4427</td>\n",
       "      <td>4427</td>\n",
       "      <td>25</td>\n",
       "      <td>4427</td>\n",
       "      <td>4427</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           date  owner_id  post_id  likes  reposts  comments  coordinates  \\\n",
       "platform                                                                    \n",
       "android    2676      2676     2676   2676     2676      2676           30   \n",
       "chronicle    11        11       11     11       11        11            0   \n",
       "direct     3917      3917     3917   3917     3917      3917            9   \n",
       "instagram  3807      3807     3807   3807     3807      3807         1252   \n",
       "ipad         60        60       60     60       60        60            0   \n",
       "iphone      499       499      499    499      499       499           11   \n",
       "prisma        1         1        1      1        1         1            0   \n",
       "vinci      5602      5602     5602   5602     5602      5602           19   \n",
       "wphone     4427      4427     4427   4427     4427      4427           25   \n",
       "\n",
       "           post_type  platform_type  \n",
       "platform                             \n",
       "android         2676           2676  \n",
       "chronicle         11             11  \n",
       "direct          3917           3917  \n",
       "instagram       3807           3807  \n",
       "ipad              60             60  \n",
       "iphone           499            499  \n",
       "prisma             1              1  \n",
       "vinci           5602           5602  \n",
       "wphone          4427           4427  "
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "platform = df.groupby(df['platform']).count()\n",
    "platform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "platform_sort = platform.sort_values(['platform_type'],ascending=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14eec89cbe0>"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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vSfYc9blVdfRUZpG0YK6InSWSrAtsADwAXFRV13UcST2S5GyaRv7H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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14eec89cc88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(num=1, figsize=(17, 6)) #размер отрисованного графика\n",
    "plt.grid()\n",
    "platform_sort['platform_type'].plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}