7705598840-woodvolume.ipynb

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     "data": {
      "text/plain": [
       "'3.10.0'"
      ]
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     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "import plotly\n",
    "plotly.__version__"
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   "cell_type": "code",
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   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.graph_objs as go\n",
    "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n",
    "import numpy as np\n",
    "from plotly import tools # for sub figures\n",
    "import json\n",
    "import seaborn as sb\n",
    "import math\n",
    " \n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
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       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
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       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
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     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "init_notebook_mode(connected=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Объем заготовленной древесины (тысяча кубических метров)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2019-06-09 13:24:21--  https://data.gov.ru/opendata/7705598840-woodvolume/data-20190605T1013-structure-20171226T0931.csv\n",
      "Selvitet\"a\"an osoitetta data.gov.ru (data.gov.ru)... 46.61.230.118\n",
      "Yhdistet\"a\"an palvelimeen data.gov.ru (data.gov.ru)|46.61.230.118|:443... yhdistetty.\n",
      "HTTP-pyynt\"o l\"ahetetty, odotetaan vastausta... 200 OK\n",
      "Pituus: 4015 (3.9K) [text/plain]\n",
      "Tallennetaan kohteeseen <<data-20190605T1013-structure-20171226T0931.csv.4>>\n",
      "\n",
      "data-20190605T1013- 100%[===================>]   3.92K  --.-KB/s    in 0s      \n",
      "\n",
      "2019-06-09 13:24:21 (79.8 MB/s) - <<data-20190605T1013-structure-20171226T0931.csv.4>> tallennettu [4015/4015]\n",
      "\n",
      "7705598840-sanitationactivities.ipynb\n",
      "7705598840-woodvolume.ipynb\n",
      "7705598840-woodvolume_map.html\n",
      "7705598840-woodvolume_map.json\n",
      "README.MD\n",
      "data-20190605T1013-structure-20171226T0931.csv\n",
      "data-20190605T1013-structure-20171226T0931.csv.1\n",
      "data-20190605T1013-structure-20171226T0931.csv.2\n",
      "data-20190605T1013-structure-20171226T0931.csv.3\n",
      "data-20190605T1013-structure-20171226T0931.csv.4\n",
      "data-20190606T1257-structure-20190606T1257.csv\n",
      "get_color.js\n",
      "heatmap.json\n",
      "index.css\n",
      "index.html\n",
      "index.js\n",
      "\u001b[1m\u001b[31mru_regions.geojson\u001b[m\u001b[m\n",
      "spb.csv\n",
      "spb.geojson\n",
      "spb_Leaflet.html\n"
     ]
    }
   ],
   "source": [
    "!wget https://data.gov.ru/opendata/7705598840-woodvolume/data-20190605T1013-structure-20171226T0931.csv\n",
    "!ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "from colour import Color\n",
    "\n",
    "red = Color(\"seagreen\")\n",
    "colors = list(red.range_to(Color(\"lightsalmon\"), 21))\n",
    "\n",
    "def convert_to_rgb(minval, maxval, val):\n",
    "    if math.isnan(val):\n",
    "        return red.hex\n",
    "\n",
    "    t = (val - minval) / (maxval - minval) #[0..1]\n",
    "    c = colors[int(t * 20 )]\n",
    "    return c.hex\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('data-20190605T1013-structure-20171226T0931.csv', thousands=r',')\n",
    "df['The volume of harvested wood'] = df['The volume of harvested wood'].apply(lambda t: float(t.replace(\".\",\"\").replace(\",\",\".\").replace(\" \", \"\")))\n",
    "df['Subject'] = df['Subject'].apply(lambda t: t.replace(\"обл\",\"область\"))"
   ]
  },
  {
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   "execution_count": 148,
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     "text": [
      "                      Subject  The volume of harvested wood\n",
      "2        Белгородская область                       62.8000\n",
      "3            Брянская область                     1313.6497\n",
      "4        Владимирская область                     1795.5000\n",
      "5         Воронежская область                      342.7560\n",
      "6          Ивановская область                     1194.5210\n",
      "7           Калужская область                     1281.1000\n",
      "8         Костромская область                     4841.3010\n",
      "9             Курская область                      179.4920\n",
      "10           Липецкая область                      134.2630\n",
      "11         Московская область                     2106.7912\n",
      "12          Орловская область                       14.9000\n",
      "13          Рязанская область                     1257.0780\n",
      "14         Смоленская область                     2033.5350\n",
      "15         Тамбовская область                      520.1851\n",
      "16           Тверская область                     4507.3166\n",
      "17           Тульская область                      124.6635\n",
      "18        Ярославская область                     1421.7087\n",
      "20               Респ Карелия                     6710.3000\n",
      "21                  Респ Коми                     8694.0000\n",
      "22      Архангельская область                    12675.7150\n",
      "23        Вологодская область                    15627.4000\n",
      "24    Калининградская область                      278.7000\n",
      "25      Ленинградская область                     5461.1820\n",
      "26         Мурманская область                      119.4800\n",
      "27  Ненецкий автономный округ                        1.9011\n",
      "28       Новгородская область                     2981.7000\n",
      "29          Псковская область                     1636.0291\n",
      "30    Южный федеральный округ                      623.9653\n",
      "31                Респ Адыгея                      261.4620\n",
      "32              Респ Калмыкия                        0.2070\n",
      "..                        ...                           ...\n",
      "57          Самарская область                       77.9070\n",
      "58        Саратовская область                       93.6469\n",
      "59        Ульяновская область                     1049.7745\n",
      "61         Курганская область                     1271.3000\n",
      "62       Свердловская область                     6539.2098\n",
      "63          Тюменская область                     1571.5656\n",
      "64        Челябинская область                     1306.6710\n",
      "65        Ханты-Мансийский АО                     4208.4408\n",
      "66          Ямало-Ненецкий АО                      295.7000\n",
      "68                 Респ Алтай                      481.4940\n",
      "69               Респ Бурятия                     3085.8630\n",
      "70                  Респ Тыва                      131.5732\n",
      "71               Респ Хакасия                      366.4064\n",
      "72             Алтайский край                     2639.9920\n",
      "73         Забайкальский край                     2055.9934\n",
      "74          Красноярский край                    18597.5321\n",
      "75          Иркутская область                    35336.5126\n",
      "76        Кемеровская область                     1348.0600\n",
      "77      Новосибирская область                     1094.3077\n",
      "78             Омская область                     1555.4591\n",
      "79            Томская область                     5254.9000\n",
      "81        Респ. Саха (Якутия)                     2144.8100\n",
      "82            Камчатский край                      143.4796\n",
      "83            Приморский край                     4387.8762\n",
      "84           Хабаровский край                     7558.8000\n",
      "85           Амурская область                     1616.8430\n",
      "86        Магаданская область                       78.1000\n",
      "87        Сахалинская область                      269.4850\n",
      "88               Еврейская АО                      267.1830\n",
      "89               Чукотский АО                        4.4000\n",
      "\n",
      "[83 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "df = df.drop(df.index[[0, 1, 19, 45, 60, 67, 80]])\n",
    "df.dropna()\n",
    "\n",
    "data = [go.Bar(x=df.iloc[:,0], y=df.iloc[:,1])]\n",
    "iplot(data, filename='basic-bar')\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "a, b = float(min(df.iloc[:,1])), float(max(df.iloc[:,1]))\n",
    "df['name'] = df.iloc[:,0]\n",
    "df['value'] = df.iloc[:,1]\n",
    "df['value'] = df['value'].map(lambda t: convert_to_rgb(a, b, t) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "json_data = {}\n",
    "for index, row in df.iterrows():\n",
    "    json_data[row['name']] = row['value']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save data to display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('7705598840-woodvolume_map.json', 'w') as outfile:\n",
    "    json.dump(json_data, outfile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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