3.data_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import json\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get only json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "path_one = '/Users/denis.murataev/Jyputer_notebooks/comparative_analysis_of_cities/data_one'\n",
    "path_two = '/Users/denis.murataev/Jyputer_notebooks/comparative_analysis_of_cities/data_two'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# path to files\n",
    "def getsubs(dir):\n",
    "    dirs = []\n",
    "    files = []\n",
    "    for dirname, dirnames, filenames in os.walk(dir):\n",
    "        dirs.append(dirname)\n",
    "        for subdirname in dirnames:\n",
    "            dirs.append(os.path.join(dirname, subdirname))\n",
    "        for filename in filenames:\n",
    "            (mode, ino, dev, nlink, uid, gid, size, atime, mtime, ctime) = os.stat(dirname + \"/\" + filename)\n",
    "            files.append(os.path.join(dirname, filename))\n",
    "\n",
    "    return files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_data_one = getsubs(path_one)\n",
    "list_data_two = getsubs(path_two)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3264"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(list_data_one)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3229"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(list_data_two)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_json1 = list()\n",
    "data_json2 = list()\n",
    "def json_files(path_list, data_list):\n",
    "    for strok in path_list:\n",
    "        if strok.endswith('.json'):\n",
    "            data_list.append(strok)\n",
    "    return data_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_json1 = json_files(list_data_one, data_json1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_json2 = json_files(list_data_two, data_json2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1086"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data_json1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1074"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data_json2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## random list equation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = list()\n",
    "for i in range(len(data_json1)):\n",
    "    n = round(random.random()*len(data_json2))\n",
    "    a.append(n)\n",
    "#print(\"A =\", a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_list = list()\n",
    "data_json2eq = list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def equation(data_list, index_list, new_list):\n",
    "    for i in index_list:\n",
    "        new_list.append(data_list[i])\n",
    "    return new_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_json2eq = equation(data_json2, a, new_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1086"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data_json2eq)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create TXT files wthis parametrs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = path_one\n",
    "color1 = open(path + '/color.txt', 'w')\n",
    "color1.writelines('color' + '\\n')\n",
    "accentColor1 = open(path + '/accentColor.txt', 'w')\n",
    "accentColor1.writelines('accentColor' + '\\n')\n",
    "tags1 = open(path + '/a_tags.txt', 'w')\n",
    "tags1.writelines('tags' + '\\n')\n",
    "description1 = open(path + '/description.txt', 'w')\n",
    "description1.writelines('description' + '\\n')\n",
    "captions1 = open(path + '/captions.txt', 'w')\n",
    "captions1.writelines('captions' + '\\n')\n",
    "objects1 = open(path + '/objects.txt', 'w')\n",
    "objects1.writelines('objects' + '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = path_two\n",
    "color2 = open(path + '/color.txt', 'w')\n",
    "color2.writelines('color' + '\\n')\n",
    "accentColor2 = open(path + '/accentColor.txt', 'w')\n",
    "accentColor2.writelines('accentColor' + '\\n')\n",
    "tags2 = open(path + '/a_tags.txt', 'w')\n",
    "tags2.writelines('tags' + '\\n')\n",
    "description2 = open(path + '/description.txt', 'w')\n",
    "description2.writelines('description' + '\\n')\n",
    "captions2 = open(path + '/captions.txt', 'w')\n",
    "captions2.writelines('captions' + '\\n')\n",
    "objects2 = open(path + '/objects.txt', 'w')\n",
    "objects2.writelines('objects' + '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def data_collection(list_files, color, accentColor, tags, description, captions):\n",
    "    for pth in list_files:\n",
    "        with open(pth) as json_file:\n",
    "            data = json.load(json_file)\n",
    "        #color\n",
    "        for i in data['color']['dominantColors']:\n",
    "                color.writelines(i + '\\n')\n",
    "        #accentColor  \n",
    "        accentColor.writelines(data['color']['accentColor'] + '\\n')\n",
    "        #tags\n",
    "           \n",
    "        for i in data['tags']:\n",
    "            tags.writelines(i['name'] + '\\n')\n",
    "        #description\n",
    "            \n",
    "        for i in data['description']['tags']:\n",
    "            description.writelines(i + '\\n')\n",
    "        #captions\n",
    "\n",
    "        for i in data[\"description\"][\"captions\"]:\n",
    "            captions.writelines(i[\"text\"] + '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# if len(data_json1) == len(data_json2eq):\n",
    "#     print('ok')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_collection(data_json1, color1, accentColor1, tags1, description1, captions1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_collection(data_json2, color2, accentColor2, tags2, description2, captions2)\n",
    "# data_collection(data_json2eq, color2, accentColor2, tags2, description2, captions2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "color1.close()\n",
    "accentColor1.close()\n",
    "tags1.close()\n",
    "description1.close()\n",
    "captions1.close()\n",
    "\n",
    "color2.close()\n",
    "accentColor2.close()\n",
    "tags2.close()\n",
    "description2.close()\n",
    "captions2.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Captions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_captions1 = pd.read_csv('/Users/denis.murataev/Jyputer_notebooks/comparative_analysis_of_cities/data_one/captions.txt')\n",
    "df_captions2 = pd.read_csv('/Users/denis.murataev/Jyputer_notebooks/comparative_analysis_of_cities/data_two/captions.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_captions1['count'] = 1\n",
    "df_captions2['count'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1_count_u1 = pd.value_counts(df_captions1['captions'].values.ravel()) \n",
    "df1_count_u2 = pd.value_counts(df_captions2['captions'].values.ravel()) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "captions1 = df1_count_u1.to_frame(name=\"tula\") #tula\n",
    "captions2 = df1_count_u2.to_frame(name=\"tver\") #tver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>tula</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a sign on the side of a road</th>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a car parked on the side of a road</th>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a view of the side of a road</th>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    tula\n",
       "a sign on the side of a road         300\n",
       "a car parked on the side of a road    78\n",
       "a view of the side of a road          51"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "captions1.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>tver</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a sign on the side of a road</th>\n",
       "      <td>385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a car parked on the side of a road</th>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a close up of a road</th>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    tver\n",
       "a sign on the side of a road         385\n",
       "a car parked on the side of a road    69\n",
       "a close up of a road                  56"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "captions2.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = pd.concat([captions1, captions2], axis=1, sort=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "result.to_csv('Captions.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tags"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_a_tags1 = pd.read_csv('/Users/denis.murataev/Jyputer_notebooks/comparative_analysis_of_cities/data_one/a_tags.txt')\n",
    "df_a_tags2 = pd.read_csv('/Users/denis.murataev/Jyputer_notebooks/comparative_analysis_of_cities/data_two/a_tags.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_a_tags1['count'] = 1\n",
    "df_a_tags2['count'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tags_u1 = pd.value_counts(df_a_tags1['tags'].values.ravel()) \n",
    "df_tags_u2 = pd.value_counts(df_a_tags2['tags'].values.ravel()) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df_tags_u1.to_frame(name=\"tula\")\n",
    "df2 = df_tags_u2.to_frame(name=\"tver\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_t = pd.concat([df1, df2], axis=1, sort=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_t.to_csv('Tags.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Descriptions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_dskrpt1 = pd.read_csv('/Users/denis.murataev/Jyputer_notebooks/comparative_analysis_of_cities/data_one/description.txt')\n",
    "df_dskrpt2 = pd.read_csv('/Users/denis.murataev/Jyputer_notebooks/comparative_analysis_of_cities/data_two/description.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_dskr_u1 = pd.value_counts(df_dskrpt1['description'].values.ravel()) \n",
    "df_dskr_u2 = pd.value_counts(df_dskrpt2['description'].values.ravel()) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df_dskr_u1.to_frame(name=\"tula\")\n",
    "df2 = df_dskr_u2.to_frame(name=\"tver\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_d = pd.concat([df1, df2], axis=1, sort=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_d.to_csv('Descriptions.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Color"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_color1 = pd.read_csv('/Users/denis.murataev/Jyputer_notebooks/comparative_analysis_of_cities/data_one/color.txt')\n",
    "df_color2 = pd.read_csv('/Users/denis.murataev/Jyputer_notebooks/comparative_analysis_of_cities/data_two/color.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_clr_u1 = pd.value_counts(df_color1['color'].values.ravel()) \n",
    "df_clr_u2 = pd.value_counts(df_color2['color'].values.ravel()) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df_clr_u1.to_frame(name=\"tula\")\n",
    "df2 = df_clr_u2.to_frame(name=\"tver\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_c = pd.concat([df1, df2], axis=1, sort=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_c.to_csv('Color.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## AccentColor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_acolor1 = pd.read_csv('/Users/denis.murataev/Jyputer_notebooks/comparative_analysis_of_cities/data_one/accentColor.txt')\n",
    "df_acolor2 = pd.read_csv('/Users/denis.murataev/Jyputer_notebooks/comparative_analysis_of_cities/data_two/accentColor.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_aclr_u1 = pd.value_counts(df_acolor1['accentColor'].values.ravel()) \n",
    "df_aclr_u2 = pd.value_counts(df_acolor2['accentColor'].values.ravel()) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df_aclr_u1.to_frame(name=\"tula\")\n",
    "df2 = df_aclr_u2.to_frame(name=\"tver\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_ac = pd.concat([df1, df2], axis=1, sort=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_ac.to_csv('AccentColor.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Select categories (do)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dodododoodododododododododododododo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Vizualization result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfv = pd.read_csv('/Users/denis.murataev/Jyputer_notebooks/comparative_analysis_of_cities/result_analisys/TulaTverCategory.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "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>Tula</th>\n",
       "      <th>Tver</th>\n",
       "      <th>Category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>701</td>\n",
       "      <td>649</td>\n",
       "      <td>Благоустройство</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>639</td>\n",
       "      <td>587</td>\n",
       "      <td>Природа</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1018</td>\n",
       "      <td>1087</td>\n",
       "      <td>Транспортная инфраструктура</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>464</td>\n",
       "      <td>529</td>\n",
       "      <td>Трафик</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>56</td>\n",
       "      <td>77</td>\n",
       "      <td>Велосипед</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>205</td>\n",
       "      <td>152</td>\n",
       "      <td>Город - застройка</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>23</td>\n",
       "      <td>13</td>\n",
       "      <td>Грязь</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2135</td>\n",
       "      <td>2158</td>\n",
       "      <td>Прочие объекты и определения обнаруженные в го...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4546</td>\n",
       "      <td>4588</td>\n",
       "      <td>Всего обнаружено тегов</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Tula  Tver                                           Category\n",
       "0   701   649                                    Благоустройство\n",
       "1   639   587                                            Природа\n",
       "2  1018  1087                        Транспортная инфраструктура\n",
       "3   464   529                                             Трафик\n",
       "4    56    77                                          Велосипед\n",
       "5   205   152                                  Город - застройка\n",
       "6    23    13                                              Грязь\n",
       "7  2135  2158  Прочие объекты и определения обнаруженные в го...\n",
       "8  4546  4588                             Всего обнаружено тегов"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x117ff25d0>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAqMAAAD4CAYAAADYSe91AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nO3de5xVVf3/8debEUEcQUT0S5oOKakpijaSmhe8ZPXV0pQSogQrrUwzv6JlFx0v31+Wmn7T1NQUb5mKkWYXKS/gHQa5KiqmpIgXREMxSIXP74+9jm6HM3MOMMOeYd7Px2Mes/faa6/92fucgc9Za+19FBGYmZmZmRWhS9EBmJmZmVnn5WTUzMzMzArjZNTMzMzMCuNk1MzMzMwK42TUzMzMzAqzTtEBmJl1NBtvvHHU1dUVHYaZWYcxZcqUVyOib7ltTkbNzFZSXV0djY2NRYdhZtZhSPpnc9s8TG9mZmZmhXEyamZmZmaFcTJqZmZmZoVxMmpmZmZmhfENTGZmK2v+VGjoVXQUZmZrTsOiNmvaPaNmZmZmVhgno2ZmZmZWGCejZmVIWiZpmqTpkh6VtEfRMZVIGijpVkmPSJosqabomMzMzFaV54yalbckIgYBSPo08FNgn2JDAkmbAFcA34qIaUXHY2ZmtrrcM2pWWU/g9dKKpFMkzUy9pueksq0k/VXSFEn3Sdo2lY+R9GzqZZ0maQ9J3SVdndqYKmnfpgdU5lxJs1K9I9KmocBy4Ldp2/dS/TpJs3L7D5U0Ji1/LvWiTpX0d0mbpvIGSddJulvSHElHp/LrJB2Sa+sGSZ+X9GlJd0laV9IQSXek7V+SNE5SF0k1Ke7JkmZI+maq8179tD5aUkNaHiTp4VR/nKTeTa7FVrnrtyy3/KHWuO6SRklakF7PpyUNX/m3iJmZrSr3jJqVt56kaUB3oB+wH4CkzwKHAp+IiH9L2ijVv5yst3KOpE8Al5T2AU6OiLGlhiWdBBARA1PyNF7SRyNiae74hwGDgJ2AjYHJkiYCfcmS410BAY9ImkAuWS7jfmC3iAhJ3wBOAU5K23YEdgPWB6ZK+hNwJXAicJukXsAewMiIeFfSR8h6Zq9O5/IJ4HvApyJiuaRjgEURsaukbsADksZXuNbXAsdHxARJZwKnpzZJ1+kf6VogaXGpxzqt38VqXve0+aaIOE7SF4HhwI1Ng0zndgxATc++1C29usJpmZl1fHPPOajNj+Fk1Ky8/DD97sC1knYADgCujoh/A0TEa5JqyRK2WySV9u/WQtt7Ahel/Z9Q9n29HwVmNKlzY0QsA15OCWcpAf19RLyVYvs9sBdwO7BVSqABegET0vLmwE2S+gHrAs/mjnNbRCwBlki6BxgcEX+Q9CtlUwIOA26NiHdT/SuAHwKbpp/bgJNK8QAHAjtKGpqLYwDwNrBXLr6+wBUp2d0wIkqxXgPc0sK1e08rXneAIyTtDdQBh5fbOSIuJ/vQQbd+A6KaGM3MrDIno2YVRMRDkjYmS6AENE1EugD/yvfYVaDKVZqt8wawYTPb/pFLoIcCB6fyi4BfRMTtkoYADbl9mp5Laf06YAQwDPhabvt3geuBHcgSua8ADZJuTT27IuvlvPMDJ5Md976IODitjwZqmzmParXmdS/1jA4A7gC2Wc3YzMysSp4zalZBGtKtARYC44GvSeqRtm0UEW8Az6Yh3tJ8z51aaHIiWaJHGibeAniyTJ0j0hzMvsDewCTgEeALknpIWh/4AnBfhVPoBbyQlkc22XZImkvZBxgCTE7lY0hD5RHxWIr1Q8Ao4EzgfOCeiBgH3ACcmva7E/i2pK6l80txlhURi4DXJe2Vir7K+z26LWqj6/4m0Kea45uZWetwz6hZeaU5o5D1qI1MQ+Z/lTQIaJT0NvBnsmHrEcClkn4MdAV+B0xvpu1LgMskzQTeBUZFxH+a1BkH7J7aCOCUiHgJeEnSLUAj2Y1MV0TEVEl1LZxLA9lQ9gvAw0D/3LZJwJ/IErOzImI+QES8LGk28Idc3QuBH0XEktywOMAFwMOSriebb1oHPKqs0gKyObYtGZmuRw/gGeCoCvXzVvu6p3M5QtKeZMP8JzWzv5mZtQFFeOqTWWek7G72xRFxXpltPYCZwC6p99JyuvUbEP1GXlh0GGZmba61bmCSNCUi6sttc8+omX2ApAOAq8jmmToRLWPgZr1oXAN3mJqZdQZORs06qYhoaKb872TD9mZmZm3ONzCZmZmZWWGcjJqZmZlZYZyMmpmZmVlhnIyamZmZWWGcjJqZmZlZYZyMmpmZmVlhnIyamZmZWWGcjJqZmZlZYZyMmpmZmVlhnIyamZmZWWGcjJqZmZlZYfzd9GZmK2v+VGjoVXQUtrZpWFR0BGaFcM+omZmZmRWmzZJRScskTcv9PCfp4rY6XmuT9A1J90lqlHR60fFYMSQNlHSrpEckTZZUU3RMZmZma5O2HKZfEhGDSiuSRgH1bXi8ViPp68BuwMER4XGTTkrSJsAVwLciYlrR8ZiZma2NChmmlzRG0mWp5/EpSQen8u6SrpY0U9JUSfum8gZJo9NynaRZaXlUqbdV0jaS3pU0NK1/XNIESVMk3SmpX5k4tpR0l6QZ6fcWadMxwIeB+yU9LGlHSTWph/dpSXeUaatG0rmp92yGpG+m8iGl+pL2ST1svSTdkNp7TdKzaflb+XPKtV0v6d4yx2z2/JvUG5TOY4akcZJ6p/J7JT2Z671elot5Yqr7eHqtuqRtB0p6SNKjkm6RVJs7zqxUf5qkxbnylvaZm17vx3Ov6/qSrkrXcqqkQ5qeb1q/OH3IKbWzcVq+PtfWuuk8ZqXjzC1zfZReu1KdI9KmocBy4Ldp2/dS/ffeg2l9qKQxaflz6TWeKunvkjZN5Q2SrpN0t6Q5ko5O5deVzi+t3yDp85I+nd6T6zZ5D30pnU+Xat5zaX20pIbca16f27a43D6pbOPS9WruWE3q10lakl7/ZySdV6HONEnXpvL90zWbmV77brnX9WeSJqWfrVN5c3+7pffFc6X3Yel8JR2d4p+urLe7Ryq/TdKRafmbkm5oGreZmbWdIueM1gH7AAcBl0nqDnwHICIGAsOBa1L5ckAV2jsLeAJAUlfgImBoRHwcuAr43zL7XAxcGxE7AjcAv0zlmwAPpjh+mOosSz2932jm+F8HFkXErsCuwNGS+pc2ShoI/B/whYhYFBEjUnu3AydHxKCIuKzCOVZ1/mVcC3w/nedMID/tYEQ69iBgSa58MHASMBDYCjgsJXs/Bg6IiF2ARuB/cvvUAJ9p0iNezT77AP+dK/sRcHe6lvsC50pav4prULrOO+SKPg10jYgdUlvlHAYMAnYCDkjH6wf0BXqSvZ67kb2mO1cI4X5gt4jYGfgdcEpu245k7/fdgdMkfQi4Ejgqxd4L2AP4c0TcCYwl65ktndsngO8BX4mI5VR4z7Wyao/1j/T67w6Maqatf5TecxFxZPobHwMckf7m1gG+nav/RkQMJvt7vTCVNfe3C9l76scpjsZc+e8jYteI2AmYnc4Jsg+fp0nai+w9f3yli2FmZq2nyLvpb07/oc6R9AywLbAnWRJJRDwh6Z/AR4F5ZElCWZI+TpZYl/7j2YYsIfmbJMj+c3qxzK67kyUiANcBPy81mdaJiLsl9ZHUKzdkv5ekaUAAF0XEVcCBwI56v2eyFzAAeBv4EPAX4PyImF/FtTlC0p7AO8AZwEstVS5z/vltvYANI2JCKroGuKWKGCZFxDOpjRvJXpulwMeAB9J1XRd4KLdPLfBak3Z2q7DPeqndnrmyA4HPK/WGA92BUs9X6doAbMaK53w2WbJd+vCxDOihlud67gncGBHLgJclTSBLuESWwLyVrsPvgb3IPkBsld4DkL3Wpeu7OXBTSmbXBZ7NHee2iFgCLJF0DzA4Iv4g6VfKpgQcBtwaEe+m+leQfRjaNP3cBpxUioeW33N75eLrSy6pBW6QVPrgsV6u/APv63SeJc0dK39+5K5Lf2CFntFmbAM8GxFPpfVryD6YlhLPG3O/L0jLzf3tQvn3IcAOks4GNkx17gSIiJclnQbcQ/Zhsdy+SDqGLHGlpmdf6pZeXeXpmbVs7jkHFR2CWaGKTEajzHpzvZ83AZ9TNjTahaynNO9sst6276d1AY9FxO6rGNMbFeK9LyIOTr1+syX9Lh3z+NSj9R5JQ8gS7WHAzyVdHxELKsRxU0QcJ2kAcC9wSIX6Tc+/NTT3+vwtIoY3rZx6t9aLiMVNN1XYp0tE/Dslqvl9Do+IJ5vU/wTp2qT1pjfE7QEsBqbnysaTJS0LgBfKnSjNv+/eIEtcyin1AJIStINT+UXALyLi9vTaN+T2KXdNIUumRpC9R76W2/5d4HqyD1YfBb4CNEi6NSKW0vJ77r6IKE1/GU2WfJWMiIjGtC3/euXf108Af883W+5YZfwjIgalIfBGSWMi4vkK+1Qa9Yhmlpur05/sA2xTY4BDI2K6sukdQ3LbBgILyT44lj9AxOXA5QDd+g1oLg4zM1tJRQ7Tf1HZvLetgI8ATwITyf5TRtJHyXrDnoyItyLiC2mo9b+btLMP8GJEzM6VPQn0lbR7aqurpO3LxPAgWQJAOu79afmRXBxDgFcjolyC+m+y/wRryHpZvp2mCCDpo7mh5bsj4nbg/5EN1VfrNSp/YCh3/u9JvbmvpyFIgK/yfi9eSwZL6q9srugRZNfmYeCTuXl7PdLrBPAF4K9l2mlpn6F8sJe05E7geKUMtYqh8ZIG4LR8QeplXAKcTPPD9BPJelxrJPUF9gYmkb0PvpBiXj+d430VYujF+0nvyCbbDlE2L7oPWSI0OZWPIRt+JyIeA0hD+KOAM4HzgXsiYhzZkPSpab+W3nOr403gXbL3dcnKHus/ZL3Svas43hNAXek9worv0SNyv0vvl7J/u5K2BPrxwQ8kJRsAL6ZzGFEqlDQY+CywMzC6Dac6mJlZGUX2jD5J9h/OpmR3Ky+VdAnZ/NGZZP8ZjoqI/1RoZwDZPLz3RMTbqbfql2mYeh2yIb/Hmuz7XeAqSSeT9Zwdlcp/AoyRNAN4ixWTitJwZneyofc3JV1JNg/20ZRELQAObRLXtZJGSPrviPhzC+d0mKRBZL1ZJ6/s+Zcxkuy69gCeyZ1nSx4CziHrMZoIjIuI5alH6UalG0yAH0vqCfwGeC03NLyepDMj4rRm9tmebF7gqDLHPovs9ZqRruVc3u95bMkjEfEPSXWlAklfAnpGxG9Sj18548iGfaeTfbg4JSJeAl6SdAvZVIDlwBURMTXffhkNwC2SXiBLxPOJzSTgT2Qfss4qTdlIw8SzgT/k6l4I/CgiljTpNb4AeFjS9WTzTeto4T23kvaQdD+wfjrOm7lt1R6rNEzfjaxHfEalg6a//aPIrts6ZEl6fv50N0mPkH14LvWwN/e3O5lsesTUdN22Bs4l+yDyE7IPGP8kmzu9QXpPXgEcFRHzJZ2U2t0vItz7aWa2BqiIf2+V3Xl8R0SMXeMHt4pSb/Do0jBvlfWHRERDrqwWuDgiRrVBiB2OsrvZF0dEuTvMe5AlR7v4UWIfpOxu/vqIeLXK+vdGxJAmZWMjYoWnTKyObv0GRL+RF1auaFYFzxm1zkDSlIgo+4hPfx2otYbHgabJwlLg0gJi6VAkHUD2tIdfOBFtFWeWKbugTJmZmbUThfSMmpl1ZPX19dHYuMLDK8zMrBkt9Yz6u+nNzMzMrDBORs3MzMysME5GzczMzKwwTkbNzMzMrDBORs3MzMysME5GzczMzKwwTkbNzMzMrDBORs3MzMysME5GzczMzKwwTkbNzMzMrDBORs3MzMysME5GzczMzKwwTkbNzMzMrDBORs3MzMysMOsUHYCZWSWSlgEzc0WXRcRlRcVjZmatx8momXUESyJiUNFBmJlZ6/MwvZl1WJIWSzpf0qOS7pLUN5UPljRd0jRJL0hqSOUXpbpPSDo7lY2SdHGBp2Fm1qk5GTWzjmx94NGI2AWYAJyeyr8PnJV6Uy8oVY6I41Pd3YETJHWv9kCSjpHUKKlx2pznWu8MzMw6OSejZtaRLQduSsvXA3um5WXABuV2kPRH4AXgFxGxNBUfkXpRJ0s6uNx+EXF5RNRHRH1Nj16tdwZmZp2ck1EzW5tE+t0AjJb0NHDiBypEfA74MHCQpJ6p+KbUi/pl4NdrKFYzM8PJqJl1bF2AoWn5y8D9afklYDGwN7lhekkbpsV3gE2BPk3aew3f2Glmtkb5H10z68jeAraXNAVYRDbcLmAM8MOImJ+tvucWSZsAPYDfRMSzkvYBDpM0CKgFTl6jZ2Bm1skpIirXMjNrhyQtjojaNX3cbv0GxH9enLOmD2tm1mFJmhIR9eW2eZjezGwlDdzMNzCZmbUWJ6Nm1mEV0StqZmaty8momZmZmRXGyaiZmZmZFcbJqJmZmZkVxsmomZmZmRXGyaiZmZmZFcbJqJmZmZkVxsmomZmZmRXGyaiZmZmZFcbJqJmZmZkVxsmomZmZmRVmnaIDMDPrcOZPhYYqvp++YVHbx2Jm1sG5Z9TMzMzMCuNk1MzMzMwK42TUrJ2TtEzStNzPt4qOqb2TdK2kRknXFR2LmZm1zHNGzdq/JRExqOggOpKIOLLoGMzMrDruGTXroCQNlzRT0ixJP8uVL5Z0vqRHJd0lqW8qHyTpYUkzJI2T1Du3zx2Snk49r29L2nglY3lI0lRJj0k6PJV9TtIjqfzvkjZN5bWSrk6xz5B0uKQT07Gfk7QgLV8pqU7SE5KuSXXHSuqR2tk/tT1T0lWSuqXyuaX4JV0vaVZaHiXp4rQ8TNKdkrqmY9yXrtejkvZYndfFzMxWjiKi6BjMrAWSFkdEbZOyDwEPAx8HXgfGA7+MiD9ICuArEXGDpNOATSLiOEkzgOMjYoKkM4GeEfG91N5fgJ9GxERJc4H6iHh1FWI9EDgjInZPye6/IiIkfQPYLiJOSolzt9yxe0fE62l5VDr2cWm9DngW2DMiHpB0FfA4cDEwB9g/Ip6SdC3waERcWIof6AdcB6wTETuU2gbGAWcDB0bEmym5XR4RSyUNAG6MiPoy53YMcAxATc++H9/821ev7OVh7jkHrfQ+ZmZrA0lTyv3bCu4ZNeuodgXujYgFEfEucAOwd9q2HLgpLV8P7CmpF7BhRExI5dfk6gPUAq+tajCSNpH0GHAr8L+peHPgTkkzgZOB7VP5AcCvSvuWEtEWPB8RD+TPB9gGeDYinkrlTc8HsoTz9CZlA8mS0Z9HxJuprCtwRYrzFuBj5YKIiMsjoj4i6mt6VPFYJzMzq4qTUbOOSStRt5rhjy2B+c0eTPpwSzdQRcQrEbE9cCDw7VR8EXBxRAwEvgl0z8W+MkMyTesGlc9/D2AxML1J+XbAl4EzJJXiORF4GdiJrOd03ZWIzczMVpOTUbOO6RFgH0kbS6oBhgOlXs8uwNC0/GXg/ohYBLwuaa9U/tVSfUm7Ac9FRLM9oxHxfEQMSj+X5bdJ6l6arwksBXZIy72AF9LyyNwu44Hjcvv3pmVbSNo9LQ8H7geeAOokbd30fJIG4LQybd0cEXcAY3PbewEvRsTy1E5NhXjMzKwVVZWMpv/szKydiIgXgVOBe8h6/x6NiNvS5reA7SVNAfYDzkzlI4Fz09zRQcCZae7pX4GPlno+gQ8B565EOJsCj0iaTjaM/r1U3gDcIuk+ID//9Gygd7rxajqwb4X2ZwMjU9wbAZdGxFLgqNT+TLKpCfkk+ZGI+EcLbf4U+KykHYFLUvsPAx8lu35mZraGVHUDk6RnyXoSro6Ix9s8KjNbZeVueGqhbh3QEBGjmpSPjYih5fZZk1J8d0TEDhWqrlHd+g2IfiMvXOn9fAOTmXVWLd3AVO1zRncEhgFXSuoCXAX8LiLeaKUYzawYC4BLy5RfsKYD6UgGbtaLRieWZmatoqph+oh4MyKuiIg9gFPI7lB9MT37b+sKu5vZGlRtr2iq+1ZEPFKm/IFy9de0iJjb3npFzcysdVU9Z1TS5yWNA/4POB/4CPBH4M9tGJ+ZmZmZrcWqHaafQ3ajxLkR8WCufKykps/2MzMzMzOrSsVkNN1JPyYiziy3PSK+2+pRmZmZmVmnUHGYPiKWUfnRK2ZmZmZmK63aYfoHJV1M9hWD7z2DLyIebZOozMzMzKxTqDYZ3SP9zg/VB9kDtc3MzMzMVklVyWhEeJjezMzMzFpdtY926iXpF5Ia08/5knq1dXBmZmZmtnarKhkl+8alN4EvpZ83gKvbKigzMzMz6xyqnTO6VUQcnls/Q9K0tgjIzMzMzDqPapPRJZL2jIj7ASR9EljSdmGZmbVj86dCQ5OZSg2LionFzKyDqzYZ/TZwTZonKuA1YFRbBWVmZmZmnUO1d9NPA3aS1DOtv9GmUZmZmZlZp1BVMirpf5qsAywCpqRE1cw6GUnLgJlkoyXLgOMi4sFiozIzs46m2mH6+vTzx7R+EDAZ+JakWyLi520RnJm1a0siYhCApE8DPwX2KTYkMzPraKp9tFMfYJeIOCkiTiJLTPsCe+O5o2YGPYHXSyuSTpY0WdIMSWfkyuskLZE0TdJz6WuGkTRI0sOp/jhJvVP51pL+Lmm6pEclbSVpiKQ70vaNJC2SNDqt3yvpydT+tNR7WymmjSW9neo/XWrbzMzWjGp7RrcA3s6tvwNsGRFLJP2n9cMysw5gvfSIt+5AP9LXA0s6EBgADCYbwr9d0t4RMRGoAeZExCBJo8g+2AJcCxwfERMknQmcDnwPuAE4JyLGSepO9gH6w7kYTgX+2SSuERHRmGJZXGVM81JMQ4DR5U5W0jHAMQA1PftStzR71PLccw5auatmZmYfUG0y+lvgYUm3pfXPATdKWh94vE0iM7P2Lj9MvztwraQdgAPTz9RUr5YsEZwIrAcszTeSntKxYURMSEXXALdI2gDYLCLGAUTE0lS/tN9mwG7AuCpibSmmWrInhLQoIi4HLgfo1m9AVHFMMzOrQrV3058l6c/AnmS9Ct8q9TwAI9oqODPrGCLiIUkbk03fEfDTiPh1maofAuZX2awqbD8dOAvYo8q2moupPzCvypjMzKyVVTtnFLIejTci4kLgn5L6t1FMZtbBSNqWbLh7IXAn8DVJtWnbZpI2SVW/CDyQ3zciFgGvS9orFX0VmJAeITdP0qGpnW6SeqQ6WwF1ETG+yhArxeR5omZmBan20U6nk83t2obsO+m7AtcDn2y70MysnSvNGYWs53FkRCwDxkvaDngoDakvBr6SbjJaH/hVmbZGApelZPMZ4KhU/lXg12ke6TtkiSPAtrk6FUVEczENJZsHuo+k48iG7PtK+nxE3F5t+2ZmtuoUUXnqU/oPZ2fg0YjYOZXNiIgd2zg+M7M2I6kBuDci7s2VHQxsHBFjmtuvW78B0W/khYBvYDIzq4akKRFRX25btTcwvR0RISlSg+u3WnRmZsUZC7zSpOxRoFtLOw3crBeNTkLNzFpFtcnozZJ+DWwo6Wjga8CVbReWmVnbi4hZZcqqvcHKzMxaQbV3058n6VPAG2TzRk+LiL+1aWRmZmZmttar9gamn0XE94G/lSkzMzMzM1sl1T7a6VNlyj7bmoGYmZmZWefTYs+opG8DxwIfkTQjt2kDmjwr0MzMzMxsZVUapv8t8Bfgp8APcuVvRkTFr88zMzMzM2tJi8lo+maURcBwgPSNJd2BWkm1EfFc24doZmZmZmurquaMSvqcpDnAs8AEYC5Zj6mZmZmZ2Sqr9gams4HdgKcioj+wP54zamZmZmarqdpk9J2IWAh0kdQlIu4BBrVhXGZmZmbWCVT7DUz/klQLTARukPQK8G7bhWVmZmZmnUGlRzttDWwKHAIsAU4ERgBbAse3eXRmZu3R/KnQ0KvoKN7XsKjoCMzMVlmlYfoLyR7j9FZELI+IdyPiGuDPQEObR2dmZmZma7VKyWhdRMxoWhgRjUBdm0RkZmZmZp1GpTmj3VvYtl5rBmJmnYukPsBdafW/gGXAgrQ+OCLeXsV25wL1EfHqagdpZmZtrlIyOlnS0RFxRb5Q0teBKW0Xlpmt7dITOgYBSGoAFkfEeYUGZWZma1ylYfrvAUdJulfS+elnAvAN4IS2D8/MOhtJdZKekHSNpBmSxkrqkbadJmmypFmSLpekVH6ApKckjQXWBc6XNFvSd9L2IZLuSMsbSVokaXRav1dSvaQaSbdLOqqYMzcz65wqfR3oy8AekvYFdkjFf4qIu9s8MjPrzLYBvh4RD0i6CjgWOA+4OCLOBJB0HXAw8EfgV8BBwL+B2cA1aZ9pkm5u0vapwD/LHPPXwMMRcXW5gCQdAxwDUNOzL3VLy1ZbI+aec1BhxzYza21VPWc0PeT+njaOxcys5PmIKH3L2/XAd8mS0X0lnQL0ADYCHpN0P9A1IuYASHoNmBERb0maBuxEei6ypM3Ivk1uXJPjNQCDgQ83F1BEXA5cDtCt34BojZM0M7Pqv4HJzGxNaprshaTuwCXA0IgYCFxBdpOlKrSV3346cFaZ9v9D1jP6o1WO2MzMVomTUTNrj7aQtHtaHg7cz/tP93g1fSPcUICIeA1YJmnr1PO5EbCjpPWBnYHS4+m2Intc3fgyx/spWZL6eUnbt8kZmZlZWU5Gzaw9mg2MlDSDLLm8NCL+RdYbOhP4AzA5V/944C9kX9SxGDiS7IkfF6W57wDbAqc1d8D0KKnvAJdL8r+NZmZriCI89cnM2g9JdcAdEbFDharN7T+XNn7OaLd+A6LfyAvbqvmKfAOTmXU0kqZERH25bVXdwGRmZu8buFkvGp0Qmpm1CiejZtauRMRc3n+U3KrsX9dqwZiZWZvzvCgzMzMzK4yTUTMzMzMrjJNRMzMzMyuMk1EzMzMzK4yTUTMzMzMrjJNRMzMzMyuMk1EzMzMzK4yTUTMzMzMrjJNRMzMzMyuMk1EzMzMzK4y/DtTMbGXNnwoNvYqOomNoWFR0BGbWzrln1MzMzMwK42TUzMzMzArjYXprFyT1Ae5Kq/8FLAMWpPXBEfF2IYGtxSQ1AIsj4ryiYzEzs87Lyai1CxGxEBgETpLMzMw6Ew/TW7smqU7SE5KukTRD0lhJPdK20yRNlvyoTwQAABPeSURBVDRL0uWSlMrvlVSfa2NxbvkUSTMlTZd0Tq7+k5KmpZ9lqVySzk3tz5R0RCofImmipHGSHpd0maQukk5M+z8naUFavjKdw6xcDEMljUnLn5P0iKSpkv4uadNUvlGKa0aK7d4y12aUpIvT8jaS3pU0NK3PlbRxWt5Y0ty03EvSXZImA7sD+0uaIulPktbL7fszSZPSz9YVYq2VdHW6RjMkHV7hWqzwekraX9K43Ll9StLv86+fpP9K7ezUQvtnSToh187/Svpuc69ZqnOppEZJj0k6YyXfomZmtprcM2odwTbA1yPiAUlXAccC5wEXR8SZAJKuAw4G/thcI5I+CxwKfCIi/i1po9zmERHRmOqVktfDyHprdwI2BiZLmpi2DQY+BvwT+CtwWERcAFwgaRRQHxHHpfbqWji3+4HdIiIkfQM4BTgJGAHMiojjUmJdqZf4LOCJCnUAjgceiIjTJN1K1gP9WUm/AL4BXJTqvRERgyUdCVxIdm2bi/UnwKKIGJjOt3dEvN7CtSj3ep4P/EpS34hYABwFXF0KWlJP4A/AiRExHZjeQvu/B/4vJZvDyF6rgZR5zYCxwI8i4jVJNcBdknaMiBlNL5ykY4BjAGp69qVu6dVNq3R6c885qOgQzKwDcs+odQTPR8QDafl6YM+0vG/qqZsJ7Adsn9vnhtRbNg1YL5UdAFwdEf8GiIjXKhx3T+DGiFgWES8DE4Bd07ZJEfFMRCwDbszF1JytcvGcmyvfHLgzncPJuXNYBmxQoU0AJH2c7G+5scmme9Lx7smV7Qr8PS3PTD+Qzdf9RK7ejbnfu1eI9QDgV6UdUyLakhVez4gI4DrgK5I2TMf8S6rTBRgHvBwR96zQWk5EzAUWStoZOBCYmqaAQPOv2ZckPQpMTef0sWbavjwi6iOivqaHH+tkZtZanIxaRxBN1yV1By4BhqYeuSuA7rk6IyJiUEQMApakMpVpqyVamZgqtPWPXDwn58ovIuvhHQh8k/fP4TpgI0kvATdUaPtsst7JpvZNx9s3V9bSOeW3RZnl5mJd2eva3LW7GvgKMBy4JSLeTeXrkfV495S0XxXtXwmMIutdvaql40rqD4wG9o+IHYE/8cH3kZmZtTEno9YRbCGp1Ds3nGy4uJQwvCqpFhhaRTvjga/p/TmnG1WoPxE4QlKNpL7A3sCktG2wpP5pKPiIFNOq6AW8kJZH5soXA+8CR5IN2TdnH+DFiJhd5fEayXoyIRu6HpiW9wcm5+odkfv9UIVYxwPHlVYk9a4QQ7nXk4iYD8wHfgyMydV/KyIuJEuAf1ma29qCccBnyHqB78yVl3vNegJvAYvSHNjPVmjbzMxamZNR6whmAyMlzQA2Ai6NiH+R9YbOJJtLOLmF/QGIiL8CtwONafh6dIVdxgEzyOYn3g2cEhEvpW0PAecAs4BnU91V0QDcIuk+4NVc+cnAjIgYX2H/AamNal0E7J1uYFof6CtpCtnQ9BW5et0kPQKcAJxYIdazgd7KbvSazgd7YstZ4fXMbbuBbBj/8aY7RcRTwG+BFm8ySo8Buwe4OQ3Jl6zwmqX5p1OBx8h6UR/AzMzWKGVTtczap3RDyh0RsUPBobxH0hBgdEQcXHQsq0PNPEJL2Z339RHxarn9VvOYdbTweip7OsDUiPjNahyjC/Ao8MWImJPKhtCKr1m3fgOi38gLW6OptYpvYDKz5kiaEhH15bb5bnozaxdSD+1bZHfor2obHwPuIOv1nNNasTU1cLNeNDrxMjNrFe4ZNTNbSfX19dHY2PThBWZm1pyWekY9Z9TMzMzMCuNk1MzMzMwK42TUzMzMzArjZNTMzMzMCuNk1MzMzMwK42TUzMzMzArjZNTMzMzMCuNk1MzMzMwK42TUzMzMzArjZNTMzMzMCuNk1MzMzMwKs07RAZiZdTjzp0JDr6KjaF7DoqIjMDOrmntGzczMzKww7hk1szYnaRkwM1e0EXB7RBxXUEhmZtZOOBk1szVhSUQMKq1IGgXUFxeOmZm1F05GzaxQksYAS4HtgU2B/4mIO0oJa0QcJ2kb4DFgWESMlTQXuAnYNzXz5Yh4WtKWwFVAX2ABcFREPJeOczHweeA1YGtgSEQ0SjoaOAZYF3ga+GpE/HsNnLqZdTLvvPMO8+bNY+nSpUWH0ma6d+/O5ptvTteuXavex8mombUHdcA+wFbAPZK2brL9LOCJJmVvRMRgSUcCFwIHAxcD10bENZK+BvwSODTVrwF+HBHXSro3187vI+IKAElnA18HLmq1MzMzS+bNm8cGG2xAXV0dkooOp9VFBAsXLmTevHn079+/6v2cjJpZe3BzRCwH5kh6Bti2tEHSx8lutmxsss+Nud8XpOXdgcPS8nXAz3P1a8l6RZvaISWhG6Y6d5YLUNIxZD2o1PTsS93Sq6s7syL84E8fWJ17zkEFBWJmeUuXLl1rE1EASfTp04cFCxas1H6+m97M2oNoYf1s4CcV9mm6f7ny/sC8MnXGAMdFxEDgDKB72YYiLo+I+oior+nRjh/rZGbt2tqaiJasyvk5GTWz9uCLkrpI2gr4CPBkKt8HeDEiZpfZ54jc74fS8oPAsLQ8ArgfIM0l7QdML9POBsCLkrqmfczMbA3yML2ZtQdPAhPIbmD6VkQsTZ+uBwDNjTF3k/QI2Yfq4ansu8BVkk4m3cCUyieT3aA0NbW7NXAu2Q1QPwEeAf5J9vipDVr1zMzMmlHXZErN6qo0JWfhwoXsv//+ALz00kvU1NTQt29fACZNmsS66677fltz53LwwQcza9asVo2xHCejZtbmIqK2yfoYsuHxkgci4sSW6kTEqCbN/ioizmiyz1xgvzIhPB4RQ/IFksamfS4FLq10DmZmHV2fPn2YNm0aAA0NDdTW1jJ69OiCo/IwvZl1DmeWKbugTJmZWacyatQoxo4d+956bW3tCnXmzp3LXnvtxS677MIuu+zCgw8+2KoxuGfUzApVpsezmn3qVrL+3WXKHljZ45YM3KwXjb5D3cw6iU022YS//e1vdO/enTlz5jB8+HAaG5s+4GTVORk1MzMzs2a98847HHfccUybNo2amhqeeuqpVm3fyaiZmZlZJ7XOOuuwfPlyIHto/dtvv71CnQsuuIBNN92U6dOns3z5crp3L/sEvFXmOaNmZmZmnVRdXR1TpkwB4LbbbuOdd95Zoc6iRYvo168fXbp04brrrmPZsmWtGoN7Rs3MzMwK0B6+He3oo4/mkEMOYfDgwey///6sv/76K9Q59thjOfzww7nlllvYd999y9ZZHYpo7otLzMysnPr6+mjNyftm1jnMnj2b7bbbrugw2ly585Q0JSLqy9X3ML2ZmZmZFcbJqJmZmZkVxsmomZmZmRXGyaiZmZmZFcbJqJmZmZkVxsmomZmZmRXGzxk1M1tZ86dCQ6+io1g5DYuKjsDMmmrtf0cq/J0vXLiQ/fffH4CXXnqJmpoa+vbtC8CkSZNYd911WzeeKjkZNTMzM+sE+vTpw7Rp0wBoaGigtraW0aNHr1abEUFE0KXLqg+2e5jezMzMrBP7/ve/zyWXXPLeekNDA+effz4A5557Lrvuuis77rgjp59+OgBz585lu+2249hjj2WXXXbh+eefX63jOxk164AkLZM0Lffzv0XH1JYkdZf0R0mNkn5edDxmZmuTYcOGcdNNN723fvPNN/PFL36R8ePHM2fOHCZNmsS0adOYMmUKEydOBODJJ5/kyCOPZOrUqWy55ZardXwP05t1TEsiYlDRQawpEbEU+FzRcZiZrY123nlnXnnlFebPn8+CBQvo3bs3W2yxBb/85S8ZP348O++8MwCLFy9mzpw5bLHFFmy55ZbsttturXJ8J6NmaxlJQ4DbgGeB/wLOi4jzJJ1GltCtBzwIfDMiQtK9QD9gSWriUOAN4CrgI8C/gWOAWcCTwB4RsUBSF+ApYDegBrgs1Qf4NvAF4FMphmXAAuB24G7gTGAhsA0wETg2IpZLGg78EBDwp4j4fjqnxRFRm5bvAxZFxMGSaoGLgHoggDOALYCRwEbpXF8AGoGzgdnpHHoCv4+I0ZIE/Bz4bGrj7Ih4v4vAzKwTGDp0KGPHjuWll15i2LBhQDYf9NRTT+Wb3/zmB+rOnTuX9ddfv9WO7WTUrGNaT9K0tPw6cGpEPJzWa4AJEfF5SQ25fS6OiDMBJF0HHAz8MW0bERGNpYqSLgKmRsShkvYDro2IQZKuB0YAFwIHANMj4lVJN6VjfkFSDVAbEQ+mthqAxRFxXlofAgwGPgb8E/grcJikB4GfAR9P5zRe0qER8YdcXAcBvYDSLaM/IUtMB6btvSPideACSaOA+og4Lm2rA/6RzmNT4DFgNHAYMAjYCdgYmCxpYkS8mL/gko4hS8qp6dmXuqVXN/vitEs/+NN7i3PPOajAQMysPRo2bBhHH300r776KhMmTADg05/+ND/5yU8YMWIEtbW1vPDCC3Tt2rXVj+1k1Kxjem+YXtLuwFhJH46IIOsNXFpmn30lnQL0IOs1fIz3k9Gm9gQOB4iIuyX1kdSLrLf0NrJk9GtAKSPbDzgy1V/G+8licyZFxDMp/hvT8d4B7o2IBan8BmBv4A9pXcCPgP8HfCW1cwAwrNRoSkRbslVK4vsD5+XO9cYU98uSJgC7kvXiviciLgcuB+jWb0BUOI6ZWWXt6JFr22+/PW+++SabbbYZ/fr1A+DAAw9k9uzZ7L777gDU1tZy/fXXU1NT06rHdjJq1sFFxEOSupL16i0APgTMz9eR1B24hKyn8PnUW9m9hWZV/lDxvKSXU2/pJ8h6SVcp7DLr5Y6ZNxy4F3ipSZwrkxiWekZ7AI2SxlRxXDOztU5DQ8MKZTNnzlyh7IQTTuCEE05YoXzWrFmtFovvpjfr4CRtS/bBcmEaIj8MeKBJtVLi+WqaZzm0QrMTSYlmGlZ/NSLeSNuuBK4Hbk69iQB3kc0TRVKNpJ4V2h8sqX+ad3oEcD/wCLCPpI3TeQwHJqT6XYATyeZ25o0HjiutSOpd4bgl/yGbx9o7nesRKe6+ZL2xk6psx8zMVpOTUbOOab3SY52AW4CjImI5cB0wB7g1Xzki/gVcAcwkG/aeXKH9BqBe0gzgHLIbgkpuB2p5f4ge4ASyaQAzgSnA9hXafyi1O4vsRqtxaY7mqcA9wHTg0Yi4rXS+wNh0HnlnA70lzZI0Hdi3wnFLw/SzgHsiYgYwDpiRjnk3cEpEvNRCG2Zm1oqUTTEzM6uOpHrggojYaxX3HwKMjoiDWzWwNahbvwHRb+SFRYexynwDk1kxZs+ezbbbbks2BX7tFBE88cQTbLfddh8olzQlIurL7eM5o2ZWNUk/IBuOX9W5omuFgZv1otEJnZmtpO7du7Nw4UL69OmzViakEcHChQvp3r2lWxJW5J5RM7OVVF9fH42NjZUrmpnlvPPOO8ybN4+lS8s98GTt0L17dzbffPMVHgHlnlEzMzOzgnXt2pX+/fsXHUa74xuYzMzMzKwwTkbNzMzMrDBORs3MzMysML6BycxsJUl6E3iy6DjamY2BV4sOoh3ydVmRr0l5a/t12TIi+pbb4BuYzMxW3pPN3RXaWUlq9DVZka/LinxNyuvM18XD9GZmZmZWGCejZmZmZlYYJ6NmZivv8qIDaId8TcrzdVmRr0l5nfa6+AYmMzMzMyuMe0bNzMzMrDBORs3MzMysME5GzcyqJOkzkp6U9LSkHxQdT1uSdJWkVyTNypVtJOlvkuak371z205N1+VJSZ/OlX9c0sy07ZeStKbPpTVJ+rCkeyTNlvSYpBNSeae9NpK6S5okaXq6Jmek8k57TUok1UiaKumOtN7pr0k5TkbNzKogqQb4FfBZ4GPAcEkfKzaqNjUG+EyTsh8Ad0XEAOCutE66DsOA7dM+l6TrBXApcAwwIP00bbOjeRc4KSK2A3YDvpPOvzNfm/8A+0XETsAg4DOSdqNzX5OSE4DZuXVfkzKcjJqZVWcw8HREPBMRbwO/Aw4pOKY2ExETgdeaFB8CXJOWrwEOzZX/LiL+ExHPAk8DgyX1A3pGxEOR3S17bW6fDikiXoyIR9Pym2SJxmZ04msTmcVptWv6CTrxNQGQtDlwEHBlrrhTX5PmOBk1M6vOZsDzufV5qawz2TQiXoQsKQM2SeXNXZvN0nLT8rWCpDpgZ+AROvm1ScPR04BXgL9FRKe/JsCFwCnA8lxZZ78mZTkZNTOrTrl5Wn42Xqa5a7PWXjNJtcCtwPci4o2WqpYpW+uuTUQsi4hBwOZkPXo7tFB9rb8mkg4GXomIKdXuUqZsrbomLXEyamZWnXnAh3PrmwPzC4qlKC+nYUPS71dSeXPXZl5ablreoUnqSpaI3hARv0/FvjZARPwLuJdsXmNnviafBD4vaS7ZlJ79JF1P574mzXIyamZWncnAAEn9Ja1LdrPB7QXHtKbdDoxMyyOB23LlwyR1k9Sf7CaLSWkY8k1Ju6U7gI/M7dMhpfP4DTA7In6R29Rpr42kvpI2TMvrAQcAT9CJr0lEnBoRm0dEHdm/FXdHxFfoxNekJesUHYCZWUcQEe9KOg64E6gBroqIxwoOq81IuhEYAmwsaR5wOnAOcLOkrwPPAV8EiIjHJN0MPE52t/l3ImJZaurbZHfmrwf8Jf10ZJ8EvgrMTHMkAX5I5742/YBr0t3fXYCbI+IOSQ/Rea9Jczrz+6RZ/jpQMzMzMyuMh+nNzMzMrDBORs3MzMysME5GzczMzKwwTkbNzMzMrDBORs3MzMysME5GzczMzKwwTkbNzMzMrDD/Hza8zMb7z/7NAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfv.plot(kind = 'barh', x = 'Category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Благоустройство\n",
      "Природа\n",
      "Транспортная инфраструктура\n",
      "Трафик\n",
      "Велосипед\n",
      "Город - застройка\n",
      "Грязь\n",
      "Прочие объекты и определения обнаруженные в городах\n",
      "Всего обнаружено тегов\n"
     ]
    }
   ],
   "source": [
    "for index, row in dfv.iterrows():\n",
    "    print(row['Category'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1224x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1224x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1224x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1224x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1224x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1224x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1224x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1224x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1224x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Clean code\n",
    "for index, row in dfv.iterrows():\n",
    "    ax = row.to_frame().T.plot(kind = 'barh', colormap = 'tab20',figsize = (17,5),fontsize = 22, grid = True, legend = 'reverse')#, title = str(row.to_frame().T['Category']))#, x = 'Category'\n",
    "    rects = ax.patches\n",
    "    fontdict = {'fontsize': 26}\n",
    "    plt.title(row['Category'], fontdict=fontdict)\n",
    "    for rect in rects:\n",
    "        # Get X and Y placement of label from rect.\n",
    "        x_value = rect.get_width()\n",
    "        y_value = rect.get_y() + rect.get_height() / 2\n",
    "\n",
    "        # Number of points between bar and label. Change to your liking.\n",
    "        space = -100\n",
    "        # Vertical alignment for positive values\n",
    "        ha = 'left'\n",
    "\n",
    "#         # If value of bar is negative: Place label left of bar\n",
    "#         if x_value < 0:\n",
    "#             # Invert space to place label to the left\n",
    "#             space *= -1\n",
    "#             # Horizontally align label at right\n",
    "#             ha = 'right'\n",
    "\n",
    "        # Use X value as label and format number with one decimal place\n",
    "        label = \"{:.1f}\".format(x_value)\n",
    "\n",
    "        # Create annotation\n",
    "        plt.annotate(\n",
    "            label,                      # Use `label` as label\n",
    "            (x_value, y_value),         # Place label at end of the bar\n",
    "            xytext=(space, 0),          # Horizontally shift label by `space`\n",
    "            textcoords=\"offset points\", # Interpret `xytext` as offset in points\n",
    "            va='center',                # Vertically center label\n",
    "            ha=ha,                      # Horizontally align label differently for\n",
    "            fontsize = 26)                      \n",
    "                                        # positive and negative values.\n",
    "    \n",
    "    plt.savefig(str(row.to_frame().T['Category']) + \"image.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Treemap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "import squarify"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "clr_tula = pd.read_csv('/Users/denis.murataev/Jyputer_notebooks/comparative_analysis_of_cities/result_analisys/viz/clr_tula.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "clr_tver = pd.read_csv('/Users/denis.murataev/Jyputer_notebooks/comparative_analysis_of_cities/result_analisys/viz/clr_tver.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "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>color</th>\n",
       "      <th>tula</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>#515930</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>#505930</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>#505938</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>#515934</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>#505934</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     color  tula\n",
       "0  #515930     6\n",
       "1  #505930     5\n",
       "2  #505938     5\n",
       "3  #515934     5\n",
       "4  #505934     5"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clr_tula.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "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>Colors</th>\n",
       "      <th>tver</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>#505930</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>#505938</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>#515934</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>#51573A</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>#50593D</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Colors  tver\n",
       "0  #505930     7\n",
       "1  #505938     7\n",
       "2  #515934     5\n",
       "3  #51573A     4\n",
       "4  #50593D     4"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clr_tver.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tula"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "sizes_tula = clr_tula['tula']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "color_tula = clr_tula['color']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1224x1224 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, figsize = (17,17))\n",
    "squarify.plot(sizes=sizes_tula, color=color_tula, alpha=1, )\n",
    "plt.axis('off')\n",
    "plt.savefig(\"treemap_color_tula.png\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "sizes_tver = clr_tver['tver']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "color_tver = clr_tver['Colors']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1224x1224 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, figsize = (17,17))\n",
    "squarify.plot(sizes=sizes_tver, color=color_tver, alpha=1, )\n",
    "plt.axis('off')\n",
    "plt.savefig(\"treemap_color_tver.png\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## In matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://habr.com/ru/post/468295/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'joypy'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-1dabd353a259>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mwarnings\u001b[0m\u001b[0;34m;\u001b[0m \u001b[0mwarnings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilterwarnings\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maction\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'once'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'matplotlib inline'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mjoypy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0mlarge\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m22\u001b[0m\u001b[0;34m;\u001b[0m \u001b[0mmed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m16\u001b[0m\u001b[0;34m;\u001b[0m \u001b[0msmall\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m12\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'joypy'"
     ]
    }
   ],
   "source": [
    "# !pip install brewer2mpl\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings; warnings.filterwarnings(action='once')\n",
    "%matplotlib inline\n",
    "import joypy\n",
    "\n",
    "large = 22; med = 16; small = 12\n",
    "params = {'axes.titlesize': large,\n",
    "          'legend.fontsize': med,\n",
    "          'figure.figsize': (16, 10),\n",
    "          'axes.labelsize': med,\n",
    "          'axes.titlesize': med,\n",
    "          'xtick.labelsize': med,\n",
    "          'ytick.labelsize': med,\n",
    "          'figure.titlesize': large}\n",
    "plt.rcParams.update(params)\n",
    "plt.style.use('seaborn-whitegrid')\n",
    "sns.set_style(\"white\")\n",
    "%matplotlib inline\n",
    "\n",
    "# Version\n",
    "print(mpl.__version__)  #> 3.0.0\n",
    "print(sns.__version__)  #> 0.9.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'plt' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-75-7f321fd5bf00>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# Draw Plot\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m13\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0;36m80\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0mgroup_col\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'Gender'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0morder_of_bars\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mStage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'plt' is not defined"
     ]
    }
   ],
   "source": [
    "# Read data\n",
    "df = pd.read_csv(\"https://raw.githubusercontent.com/selva86/datasets/master/email_campaign_funnel.csv\")\n",
    "\n",
    "# Draw Plot\n",
    "plt.figure(figsize=(13,10), dpi= 80)\n",
    "group_col = 'Gender'\n",
    "order_of_bars = df.Stage.unique()[::-1]\n",
    "colors = [plt.cm.Spectral(i/float(len(df[group_col].unique())-1)) for i in range(len(df[group_col].unique()))]\n",
    "\n",
    "for c, group in zip(colors, df[group_col].unique()):\n",
    "    sns.barplot(x='Users', y='Stage', data=df.loc[df[group_col]==group, :], order=order_of_bars, color=c, label=group)\n",
    "\n",
    "# Decorations    \n",
    "plt.xlabel(\"$Users$\")\n",
    "plt.ylabel(\"Stage of Purchase\")\n",
    "plt.yticks(fontsize=12)\n",
    "plt.title(\"Population Pyramid of the Marketing Funnel\", fontsize=22)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install joypy\n",
    "# Import Data\n",
    "mpg = pd.read_csv(\"https://github.com/selva86/datasets/raw/master/mpg_ggplot2.csv\")\n",
    "\n",
    "# Draw Plot\n",
    "plt.figure(figsize=(16,10), dpi= 80)\n",
    "fig, axes = joypy.joyplot(mpg, column=['hwy', 'cty'], by=\"class\", ylim='own', figsize=(14,10))\n",
    "\n",
    "# Decoration\n",
    "plt.title('Joy Plot of City and Highway Mileage by Class', fontsize=22)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfv.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Draw Plot\n",
    "plt.figure(figsize=(16,10), dpi= 80)\n",
    "fig, axes = joypy.joyplot(dfv, column=['Tula', 'Tver'], by=\"Category\", ylim='own', figsize=(14,10))\n",
    "\n",
    "# Decoration\n",
    "plt.title('Joy Plot of City and Highway Mileage by Class', fontsize=22)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}