data_analytics_12.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>keyword</th>\n",
       "      <th>shows</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>вк</td>\n",
       "      <td>64292779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>одноклассники</td>\n",
       "      <td>63810309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>порно</td>\n",
       "      <td>41747114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ютуб</td>\n",
       "      <td>39995567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>вконтакте</td>\n",
       "      <td>21014195</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "         keyword     shows\n",
       "0             вк  64292779\n",
       "1  одноклассники  63810309\n",
       "2          порно  41747114\n",
       "3           ютуб  39995567\n",
       "4      вконтакте  21014195"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('keywords.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "geo_data = {\n",
    "\n",
    "    'Центр': ['москва', 'тула', 'ярославль'],\n",
    "\n",
    "    'Северо-Запад': ['петербург', 'псков', 'мурманск'],\n",
    "\n",
    "    'Дальний Восток': ['владивосток', 'сахалин', 'хабаровск']\n",
    "\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Центр ['москва', 'тула', 'ярославль']\n",
      "Северо-Запад ['петербург', 'псков', 'мурманск']\n",
      "Дальний Восток ['владивосток', 'сахалин', 'хабаровск']\n"
     ]
    }
   ],
   "source": [
    "for region, city_list in geo_data.items():\n",
    "    print(region, city_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def geo_classification(row):\n",
    "    for region, city_list in geo_data.items():\n",
    "        for city in city_list:\n",
    "            if city in row['keyword']:\n",
    "                return region\n",
    "    return 'undefined'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['city_group'] = data.apply(geo_classification, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 20.1 ms\n"
     ]
    },
    {
     "data": {
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       "    }\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>keyword</th>\n",
       "      <th>shows</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city_group</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>undefined</th>\n",
       "      <td>99253</td>\n",
       "      <td>99253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Дальний Восток</th>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Северо-Запад</th>\n",
       "      <td>266</td>\n",
       "      <td>266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Центр</th>\n",
       "      <td>387</td>\n",
       "      <td>387</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                keyword  shows\n",
       "city_group                    \n",
       "undefined         99253  99253\n",
       "Дальний Восток       94     94\n",
       "Северо-Запад        266    266\n",
       "Центр               387    387"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "data.groupby('city_group').count().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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