Notebooks/Facility.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e85eedb1",
   "metadata": {
    "cellId": "arzv3so3v6eu91k8xsm1o",
    "execution_id": "c620eaca-6cca-4248-b5e6-86e1f70b3c50"
   },
   "source": [
    "# Задача\n",
    "\n",
    "Сформировать сетку полигонов вокруг точек из таблицы `facility`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd2fe609",
   "metadata": {
    "cellId": "gj9sv530dctbzzguglam",
    "execution_id": "db326278-bc2a-4029-8a06-f8d7ea00f452"
   },
   "source": [
    "## Подключаем библиотеки\n",
    "\n",
    "* Единоразово устанавливаем h3pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0fd361c0",
   "metadata": {
    "cellId": "vn6jtcsqjho8z3wxmwuj2q"
   },
   "outputs": [],
   "source": [
    "# %pip install h3pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fd4b2de1",
   "metadata": {
    "cellId": "utaye6qr2rsmtife50rolf"
   },
   "outputs": [],
   "source": [
    "import h3pandas\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "from pyproj import CRS\n",
    "from shapely.geometry import Point\n",
    "from shapely.geometry import LineString\n",
    "from shapely.geometry.polygon import Polygon\n",
    "from shapely.geometry.multipolygon import MultiPolygon"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34ea42b1",
   "metadata": {
    "cellId": "gwxrkehttzppkdfzhc76h",
    "execution_id": "27b875a5-c774-43b2-a086-93d9381a905f"
   },
   "source": [
    "## Определяем функцию для вывода данных\n",
    "\n",
    "* Функция вывода центрального региона набора геоданных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "db1d9c06",
   "metadata": {
    "cellId": "1upwcsdwjnkjmwbfkc9e4b"
   },
   "outputs": [],
   "source": [
    "def get_center(df, qt = 0.1):\n",
    "    return df.loc[\n",
    "        (df['lng'] > df['lng'].quantile(qt)) &\n",
    "        (df['lng'] < df['lng'].quantile(1-qt)) &\n",
    "        (df['lat'] > df['lat'].quantile(qt)) &\n",
    "        (df['lat'] < df['lat'].quantile(1-qt))]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef783934",
   "metadata": {
    "cellId": "idlp5uaqgja3bvm83qm6s",
    "execution_id": "467c0d2a-e7c8-4395-8c9f-6300c7c623ce"
   },
   "source": [
    "## Грузим данные"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7fb8de08",
   "metadata": {
    "cellId": "k7184xzeoppwgmbyx6d7oo"
   },
   "outputs": [],
   "source": [
    "facility = pd.read_csv('data/facility.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dc3c99f0",
   "metadata": {
    "cellId": "c757s7b7h1q1s0ih279jqz"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5041, 10)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>facility_id</th>\n",
       "      <th>type2</th>\n",
       "      <th>name</th>\n",
       "      <th>coordinats</th>\n",
       "      <th>address</th>\n",
       "      <th>category</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>geometry</th>\n",
       "      <th>number_of_inhabitants</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5137</td>\n",
       "      <td>Дом</td>\n",
       "      <td>Многоквартирный дом</td>\n",
       "      <td>[64.455331, 40.62206]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64.455331</td>\n",
       "      <td>40.622060</td>\n",
       "      <td>0101000000B876A224241D50402B137EA99F4F4440</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>794</td>\n",
       "      <td>Дом</td>\n",
       "      <td>Многоквартирный дом</td>\n",
       "      <td>[64.455491, 40.622967]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64.455491</td>\n",
       "      <td>40.622967</td>\n",
       "      <td>0101000000D427B9C3261D50405EBEF561BD4F4440</td>\n",
       "      <td>32.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>795</td>\n",
       "      <td>Дом</td>\n",
       "      <td>Многоквартирный дом</td>\n",
       "      <td>[64.455494, 40.623901]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64.455494</td>\n",
       "      <td>40.623901</td>\n",
       "      <td>01010000008D614ED0261D50408978EBFCDB4F4440</td>\n",
       "      <td>15.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   facility_id  ... number_of_inhabitants\n",
       "0         5137  ...                  33.0\n",
       "1          794  ...                  32.6\n",
       "2          795  ...                  15.4\n",
       "\n",
       "[3 rows x 10 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(facility.shape)\n",
    "facility.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70458801",
   "metadata": {
    "cellId": "yrsodbq297nnwoy9lr4z",
    "execution_id": "89976b23-a7db-4d49-9854-a872b93bd10d"
   },
   "source": [
    "## Обрабатываем данные\n",
    "\n",
    "* Проверяем что нет нулей в object_id\n",
    "* Привеодим object_id к целочисленному значению\n",
    "* Переименовываем столбцы для h3pandas\n",
    "* Удаляем лишние столбцы"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fdbd3cd0",
   "metadata": {
    "cellId": "5jyuru5khvyaihzrx5qxs"
   },
   "outputs": [],
   "source": [
    "nulls = len(facility[facility['facility_id'].isnull()])\n",
    "assert nulls == 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7da27e50",
   "metadata": {
    "cellId": "10ol8q7q1aohnb84w6kas2"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>facility_id</th>\n",
       "      <th>type2</th>\n",
       "      <th>name</th>\n",
       "      <th>coordinats</th>\n",
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       "      <th>0</th>\n",
       "      <td>5137</td>\n",
       "      <td>Дом</td>\n",
       "      <td>Многоквартирный дом</td>\n",
       "      <td>[64.455331, 40.62206]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64.455331</td>\n",
       "      <td>40.622060</td>\n",
       "      <td>0101000000B876A224241D50402B137EA99F4F4440</td>\n",
       "      <td>33.0</td>\n",
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       "      <th>1</th>\n",
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       "      <td>Дом</td>\n",
       "      <td>Многоквартирный дом</td>\n",
       "      <td>[64.455491, 40.622967]</td>\n",
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       "      <td>64.455491</td>\n",
       "      <td>40.622967</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>795</td>\n",
       "      <td>Дом</td>\n",
       "      <td>Многоквартирный дом</td>\n",
       "      <td>[64.455494, 40.623901]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64.455494</td>\n",
       "      <td>40.623901</td>\n",
       "      <td>01010000008D614ED0261D50408978EBFCDB4F4440</td>\n",
       "      <td>15.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   facility_id  ... number_of_inhabitants\n",
       "0         5137  ...                  33.0\n",
       "1          794  ...                  32.6\n",
       "2          795  ...                  15.4\n",
       "\n",
       "[3 rows x 10 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "facility = facility.astype({'facility_id': 'int32'})\n",
    "facility.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7b40054f",
   "metadata": {
    "cellId": "inoi2hwf11mq2jbq07wz9d"
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   "outputs": [
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       "      <th></th>\n",
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       "      <th>type2</th>\n",
       "      <th>name</th>\n",
       "      <th>coordinats</th>\n",
       "      <th>address</th>\n",
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       "      <th>lng</th>\n",
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       "      <td>Дом</td>\n",
       "      <td>Многоквартирный дом</td>\n",
       "      <td>[64.455491, 40.622967]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64.455491</td>\n",
       "      <td>40.622967</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>795</td>\n",
       "      <td>Дом</td>\n",
       "      <td>Многоквартирный дом</td>\n",
       "      <td>[64.455494, 40.623901]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64.455494</td>\n",
       "      <td>40.623901</td>\n",
       "      <td>01010000008D614ED0261D50408978EBFCDB4F4440</td>\n",
       "      <td>15.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   facility_id  ... number_of_inhabitants\n",
       "0         5137  ...                  33.0\n",
       "1          794  ...                  32.6\n",
       "2          795  ...                  15.4\n",
       "\n",
       "[3 rows x 10 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "facility = facility.rename({'y': 'lng', 'x': 'lat'}, axis=1)\n",
    "facility.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ae175ef6",
   "metadata": {
    "cellId": "uj36oph4jeisp6fmin1ap"
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   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>facility_id</th>\n",
       "      <th>type2</th>\n",
       "      <th>name</th>\n",
       "      <th>address</th>\n",
       "      <th>category</th>\n",
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       "      <th>lng</th>\n",
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       "      <td>5137</td>\n",
       "      <td>Дом</td>\n",
       "      <td>Многоквартирный дом</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64.455331</td>\n",
       "      <td>40.622060</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>794</td>\n",
       "      <td>Дом</td>\n",
       "      <td>Многоквартирный дом</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>40.622967</td>\n",
       "      <td>32.6</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>795</td>\n",
       "      <td>Дом</td>\n",
       "      <td>Многоквартирный дом</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64.455494</td>\n",
       "      <td>40.623901</td>\n",
       "      <td>15.4</td>\n",
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      ],
      "text/plain": [
       "   facility_id type2  ...        lng number_of_inhabitants\n",
       "0         5137   Дом  ...  40.622060                  33.0\n",
       "1          794   Дом  ...  40.622967                  32.6\n",
       "2          795   Дом  ...  40.623901                  15.4\n",
       "\n",
       "[3 rows x 8 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "facility = facility.drop(columns=['coordinats', 'geometry'])\n",
    "facility.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64593891",
   "metadata": {
    "cellId": "b2s5zja1lb7y1mvm2mmj5",
    "execution_id": "e3d9ef8e-b11b-4238-ac01-5b85c4e6ebed"
   },
   "source": [
    "## Проверяем, что у нас в центре города"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9ba1d9d1",
   "metadata": {
    "cellId": "q04bnm8v3tnxt1gaq1exin"
   },
   "outputs": [
    {
     "data": {
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     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "get_center(facility, 0.08).plot.scatter(x='lng', y='lat', figsize=(7,7), alpha=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd1afddc",
   "metadata": {
    "cellId": "jezkl1h4r0fhdm3sywrpde",
    "execution_id": "ae51f26b-a2c3-4d52-a273-0e023e6bda3d"
   },
   "source": [
    "## Приводим данные к формату h3pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ef8858d5",
   "metadata": {
    "cellId": "dtf3footz38fs6qlogkdy"
   },
   "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>facility_id</th>\n",
       "      <th>type2</th>\n",
       "      <th>name</th>\n",
       "      <th>address</th>\n",
       "      <th>category</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>number_of_inhabitants</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h3_09</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>8911503346fffff</th>\n",
       "      <td>5137</td>\n",
       "      <td>Дом</td>\n",
       "      <td>Многоквартирный дом</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64.455331</td>\n",
       "      <td>40.622060</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8911503346fffff</th>\n",
       "      <td>794</td>\n",
       "      <td>Дом</td>\n",
       "      <td>Многоквартирный дом</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64.455491</td>\n",
       "      <td>40.622967</td>\n",
       "      <td>32.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891150330d3ffff</th>\n",
       "      <td>795</td>\n",
       "      <td>Дом</td>\n",
       "      <td>Многоквартирный дом</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64.455494</td>\n",
       "      <td>40.623901</td>\n",
       "      <td>15.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891150330d7ffff</th>\n",
       "      <td>796</td>\n",
       "      <td>Дом</td>\n",
       "      <td>Многоквартирный дом</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64.455545</td>\n",
       "      <td>40.624683</td>\n",
       "      <td>22.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891150330d3ffff</th>\n",
       "      <td>797</td>\n",
       "      <td>Дом</td>\n",
       "      <td>Многоквартирный дом</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64.455172</td>\n",
       "      <td>40.625401</td>\n",
       "      <td>23.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 facility_id type2  ...        lng number_of_inhabitants\n",
       "h3_09                               ...                                 \n",
       "8911503346fffff         5137   Дом  ...  40.622060                  33.0\n",
       "8911503346fffff          794   Дом  ...  40.622967                  32.6\n",
       "891150330d3ffff          795   Дом  ...  40.623901                  15.4\n",
       "891150330d7ffff          796   Дом  ...  40.624683                  22.5\n",
       "891150330d3ffff          797   Дом  ...  40.625401                  23.5\n",
       "\n",
       "[5 rows x 8 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "facility3 = facility.h3.geo_to_h3(9)\n",
    "facility3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "04ffd93c",
   "metadata": {
    "cellId": "943dcpycj896huiw9ckcv7"
   },
   "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>facility_id</th>\n",
       "      <th>type2</th>\n",
       "      <th>name</th>\n",
       "      <th>address</th>\n",
       "      <th>category</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>number_of_inhabitants</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h3_09</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>8911503346fffff</th>\n",
       "      <td>5137</td>\n",
       "      <td>Дом</td>\n",
       "      <td>Многоквартирный дом</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64.455331</td>\n",
       "      <td>40.622060</td>\n",
       "      <td>33.0</td>\n",
       "      <td>POLYGON ((40.61759 64.45587, 40.62060 64.45488...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8911503346fffff</th>\n",
       "      <td>794</td>\n",
       "      <td>Дом</td>\n",
       "      <td>Многоквартирный дом</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64.455491</td>\n",
       "      <td>40.622967</td>\n",
       "      <td>32.6</td>\n",
       "      <td>POLYGON ((40.61759 64.45587, 40.62060 64.45488...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891150330d3ffff</th>\n",
       "      <td>795</td>\n",
       "      <td>Дом</td>\n",
       "      <td>Многоквартирный дом</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64.455494</td>\n",
       "      <td>40.623901</td>\n",
       "      <td>15.4</td>\n",
       "      <td>POLYGON ((40.61995 64.45313, 40.62296 64.45214...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 facility_id  ...                                           geometry\n",
       "h3_09                         ...                                                   \n",
       "8911503346fffff         5137  ...  POLYGON ((40.61759 64.45587, 40.62060 64.45488...\n",
       "8911503346fffff          794  ...  POLYGON ((40.61759 64.45587, 40.62060 64.45488...\n",
       "891150330d3ffff          795  ...  POLYGON ((40.61995 64.45313, 40.62296 64.45214...\n",
       "\n",
       "[3 rows x 9 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geofacility3 = facility3.h3.h3_to_geo_boundary()\n",
    "geofacility3.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48997ead",
   "metadata": {
    "cellId": "hs4bohp9kwviccu47cel3",
    "execution_id": "9b1bbeb3-8f8a-4260-be4c-40cff520e73d"
   },
   "source": [
    "## Рисуем наши полигоны в центер города"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cbd3d4bb",
   "metadata": {
    "cellId": "qe7n9dm9tfts6gp5908g"
   },
   "outputs": [],
   "source": [
    "# Раскомментировать в случае надобности\n",
    "#geofacility3.explore(width='30%', height='60%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ec102e52",
   "metadata": {
    "cellId": "i6ankx8kfvjxv8qx66b5k"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f328870ca00>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "get_center(geofacility3).plot(figsize=(7,7))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d88be311",
   "metadata": {
    "cellId": "zqnjzjyenqotdmxmfcfc1c",
    "execution_id": "bf324822-8a3e-45da-bc59-00ab47971483"
   },
   "source": [
    "## Строим набор данных для точек"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e7a36118",
   "metadata": {
    "cellId": "ik4sapf885qeu02n0k6h1"
   },
   "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>facility_id</th>\n",
       "      <th>type2</th>\n",
       "      <th>name</th>\n",
       "      <th>address</th>\n",
       "      <th>category</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>number_of_inhabitants</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5137</td>\n",
       "      <td>Дом</td>\n",
       "      <td>Многоквартирный дом</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64.455331</td>\n",
       "      <td>40.622060</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>794</td>\n",
       "      <td>Дом</td>\n",
       "      <td>Многоквартирный дом</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64.455491</td>\n",
       "      <td>40.622967</td>\n",
       "      <td>32.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>795</td>\n",
       "      <td>Дом</td>\n",
       "      <td>Многоквартирный дом</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64.455494</td>\n",
       "      <td>40.623901</td>\n",
       "      <td>15.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   facility_id type2  ...        lng number_of_inhabitants\n",
       "0         5137   Дом  ...  40.622060                  33.0\n",
       "1          794   Дом  ...  40.622967                  32.6\n",
       "2          795   Дом  ...  40.623901                  15.4\n",
       "\n",
       "[3 rows x 8 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "facility.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9f09e0d4",
   "metadata": {
    "cellId": "mywhgiv7migujd36la6nq"
   },
   "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>facility_id</th>\n",
       "      <th>type2</th>\n",
       "      <th>name</th>\n",
       "      <th>address</th>\n",
       "      <th>category</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>number_of_inhabitants</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5137</td>\n",
       "      <td>Дом</td>\n",
       "      <td>Многоквартирный дом</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64.455331</td>\n",
       "      <td>40.622060</td>\n",
       "      <td>33.0</td>\n",
       "      <td>POINT (40.62206 64.45533)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>794</td>\n",
       "      <td>Дом</td>\n",
       "      <td>Многоквартирный дом</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64.455491</td>\n",
       "      <td>40.622967</td>\n",
       "      <td>32.6</td>\n",
       "      <td>POINT (40.62297 64.45549)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>795</td>\n",
       "      <td>Дом</td>\n",
       "      <td>Многоквартирный дом</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64.455494</td>\n",
       "      <td>40.623901</td>\n",
       "      <td>15.4</td>\n",
       "      <td>POINT (40.62390 64.45549)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   facility_id type2  ... number_of_inhabitants                   geometry\n",
       "0         5137   Дом  ...                  33.0  POINT (40.62206 64.45533)\n",
       "1          794   Дом  ...                  32.6  POINT (40.62297 64.45549)\n",
       "2          795   Дом  ...                  15.4  POINT (40.62390 64.45549)\n",
       "\n",
       "[3 rows x 9 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geometry = [Point(xy) for xy in zip(facility.lng, facility.lat)]\n",
    "geofacility = gpd.GeoDataFrame(facility, crs=geofacility3.crs, geometry=geometry)\n",
    "geofacility.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ea9475b",
   "metadata": {
    "cellId": "8wqpbu2letcyllseabphog",
    "execution_id": "dc4a1dc0-8521-458f-83ff-7f27b90543ee"
   },
   "source": [
    "## Отображаем точки и полигоны"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "14768d62",
   "metadata": {
    "cellId": "z85983hppmh0sl88kidf7z"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f327970b3a0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = get_center(geofacility).plot(\n",
    "    figsize=(10,10), facecolor=\"teal\", markersize=2)\n",
    "get_center(geofacility3).plot(\n",
    "    ax=ax, figsize=(10,10), facecolor=\"none\", edgecolor=\"black\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e2a0b63",
   "metadata": {
    "cellId": "pcyjokrmnlc4ynpwa4foxn",
    "execution_id": "a659fe22-8b24-4e32-afad-93ab4b638328"
   },
   "source": [
    "## Готовим набор к экспорту"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b15832f0",
   "metadata": {
    "cellId": "c4f03h93jrwq4mevbyodu"
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>facility_id</th>\n",
       "      <th>type2</th>\n",
       "      <th>name</th>\n",
       "      <th>address</th>\n",
       "      <th>category</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>number_of_inhabitants</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h3_09</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>5137</td>\n",
       "      <td>Дом</td>\n",
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       "      <td>64.455331</td>\n",
       "      <td>40.622060</td>\n",
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       "      <td>POLYGON ((40.61759 64.45587, 40.62060 64.45488...</td>\n",
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       "      <td>794</td>\n",
       "      <td>Дом</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64.455491</td>\n",
       "      <td>40.622967</td>\n",
       "      <td>32.6</td>\n",
       "      <td>POLYGON ((40.61759 64.45587, 40.62060 64.45488...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64.455494</td>\n",
       "      <td>40.623901</td>\n",
       "      <td>15.4</td>\n",
       "      <td>POLYGON ((40.61995 64.45313, 40.62296 64.45214...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 facility_id  ...                                           geometry\n",
       "h3_09                         ...                                                   \n",
       "8911503346fffff         5137  ...  POLYGON ((40.61759 64.45587, 40.62060 64.45488...\n",
       "8911503346fffff          794  ...  POLYGON ((40.61759 64.45587, 40.62060 64.45488...\n",
       "891150330d3ffff          795  ...  POLYGON ((40.61995 64.45313, 40.62296 64.45214...\n",
       "\n",
       "[3 rows x 9 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geofacility3.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f7db9ddd",
   "metadata": {
    "cellId": "h8nko5y7f086d6hxzompqk"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "   facility_id type2                                           geometry\n",
       "0         5137   Дом  POLYGON ((40.61759 64.45587, 40.62060 64.45488...\n",
       "1          794   Дом  POLYGON ((40.61759 64.45587, 40.62060 64.45488...\n",
       "2          795   Дом  POLYGON ((40.61995 64.45313, 40.62296 64.45214..."
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geometry = geofacility3['geometry']\n",
    "data = geofacility3[['facility_id', 'type2']]\n",
    "gdf = gpd.GeoDataFrame(data=data, geometry=geometry, crs=geofacility3.crs)\n",
    "gdf = gdf.reset_index()\n",
    "gdf = gdf.drop(columns=['h3_09'])\n",
    "gdf.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "10a19ebf",
   "metadata": {
    "cellId": "en0tl34ba8ddisjbe50mgg"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5041, 3)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02395360",
   "metadata": {
    "cellId": "a5ly4zp30c7t165vlvkwui",
    "execution_id": "604b6586-2317-49ad-8df5-ed58341d798a"
   },
   "source": [
    "## Экспортируем набор данных с полигонами"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "91828785",
   "metadata": {
    "cellId": "hvqrqttyk4emxahbfctign"
   },
   "outputs": [],
   "source": [
    "gdf.to_file('data/facility_polygons.geojson', driver='GeoJSON')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a160c05b",
   "metadata": {
    "cellId": "zkqtido0c8lvndy29dhav9",
    "execution_id": "04f052cf-db02-4446-b28d-d7fba80ee8ac"
   },
   "source": [
    "## Экспортируем данные для DataLens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3a45bd17",
   "metadata": {
    "cellId": "iq9jbf5oi51as3p19rq0i"
   },
   "outputs": [],
   "source": [
    "all_res = []\n",
    "\n",
    "def convert(df):\n",
    "    for _, row in df.iterrows():\n",
    "        polygon = row.geometry\n",
    "        x, y = polygon.exterior.xy\n",
    "        pairs = zip(x, y)\n",
    "        coords = []\n",
    "        for pair in pairs:\n",
    "            coords.append([pair[1], pair[0]])\n",
    "        obj = {}\n",
    "        obj['facility_id'] = row.facility_id\n",
    "        obj['type2'] = row.type2\n",
    "        obj['geometry'] = [coords]\n",
    "        all_res.append(obj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2aae77d1",
   "metadata": {
    "cellId": "3vu03c5g77tm11pm8j2g0b"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>facility_id</th>\n",
       "      <th>type2</th>\n",
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       "  <tbody>\n",
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       "      <th>2</th>\n",
       "      <td>795</td>\n",
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      "text/plain": [
       "   facility_id type2                                           geometry\n",
       "0         5137   Дом  [[[64.45587444575847, 40.617592620485716], [64...\n",
       "1          794   Дом  [[[64.45587444575847, 40.617592620485716], [64...\n",
       "2          795   Дом  [[[64.45313033286712, 40.61995296608718], [64...."
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "convert(gdf)\n",
    "\n",
    "gdf_df = pd.DataFrame(all_res)\n",
    "gdf_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "bf222848",
   "metadata": {
    "cellId": "xduz7t7ogo97jl6km1havg"
   },
   "outputs": [],
   "source": [
    "gdf_df.to_csv('data/facility_polygons_lens.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "104c9357",
   "metadata": {
    "cellId": "b3z61fgtgpsoxsalez6ow"
   },
   "outputs": [
    {
     "data": {
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       "      <td>Многоквартирный дом</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "   facility_id type2  ...        lng number_of_inhabitants\n",
       "0         5137   Дом  ...  40.622060                  33.0\n",
       "1          794   Дом  ...  40.622967                  32.6\n",
       "2          795   Дом  ...  40.623901                  15.4\n",
       "\n",
       "[3 rows x 8 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "facility.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "383c1caf",
   "metadata": {
    "cellId": "vutrvq74fgo2ahyt7fdv2i"
   },
   "outputs": [],
   "source": [
    "geometry = [[xy[1], xy[0]] for xy in zip(facility.lng, facility.lat)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "83caac53",
   "metadata": {
    "cellId": "4wyy9hzdc537uu5euowv5"
   },
   "outputs": [
    {
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      ],
      "text/plain": [
       "   facility_id type2  ... category                geometry\n",
       "0         5137   Дом  ...      NaN   [64.455331, 40.62206]\n",
       "1          794   Дом  ...      NaN  [64.455491, 40.622967]\n",
       "2          795   Дом  ...      NaN  [64.455494, 40.623901]\n",
       "\n",
       "[3 rows x 6 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "facility_dl = pd.DataFrame(facility[['facility_id', 'type2', 'name', 'address', 'category']])\n",
    "facility_dl['geometry'] = geometry\n",
    "facility_dl.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e1d0d5cf",
   "metadata": {
    "cellId": "okuzpn3js6u391mz4tgf"
   },
   "outputs": [],
   "source": [
    "facility_dl.to_csv('data/facility_lens.csv', quotechar='$', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bade1859",
   "metadata": {
    "cellId": "gkl8rjv2dkg315rgir0tca"
   },
   "outputs": [],
   "source": []
  }
 ],
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