loading_datasets_example.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install pandas\n",
    "!pip install requests\n",
    "!pip install xarray\n",
    "!pip install geopandas\n",
    "!pip install pyaxis\n",
    "!pip install matplotlib\n",
    "!pip install owslib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import requests\n",
    "from matplotlib import pyplot\n",
    "import xarray as xr\n",
    "import geopandas as gpd\n",
    "import xarray as xr\n",
    "import pyaxis\n",
    "import owslib\n",
    "import tempfile"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "This notebook helps you go through the dataset listed under the **DATA** section of the challenge. Below you fill find some examples on how to load dataset depending on its format."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. TXT Example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first data from the list called [**Homogeneous data series since 1864, temp and precipitation, per month/year**](https://www.meteoswiss.admin.ch/home/climate/swiss-climate-in-detail/homogeneous-data-series-since-1864.html?region=Table) gives you an insight of precipitation or temperature through the years in different areas in Switzerland. \n",
    "\n",
    "You can access to a given dataset as such :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "url_prec_ge = \"https://www.meteoswiss.admin.ch/product/output/climate-data/homogenous-monthly-data-processing/data/homog_mo_GVE.txt\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "response = requests.get(url_prec_ge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"./data/GE.txt\", \"w\") as f:\n",
    "    f.write(response.text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"./data/GE.txt\", encoding=\"ISO-8859-1\", header = 18, delim_whitespace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Precipitation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1864</td>\n",
       "      <td>1</td>\n",
       "      <td>-4.2</td>\n",
       "      <td>13.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1864</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>17.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1864</td>\n",
       "      <td>3</td>\n",
       "      <td>5.3</td>\n",
       "      <td>32.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1864</td>\n",
       "      <td>4</td>\n",
       "      <td>8.3</td>\n",
       "      <td>35.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1864</td>\n",
       "      <td>5</td>\n",
       "      <td>13.5</td>\n",
       "      <td>68.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1899</th>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "      <td>10.3</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900</th>\n",
       "      <td>2022</td>\n",
       "      <td>5</td>\n",
       "      <td>17.6</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1901</th>\n",
       "      <td>2022</td>\n",
       "      <td>6</td>\n",
       "      <td>21.2</td>\n",
       "      <td>63.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1902</th>\n",
       "      <td>2022</td>\n",
       "      <td>7</td>\n",
       "      <td>23.6</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1903</th>\n",
       "      <td>2022</td>\n",
       "      <td>8</td>\n",
       "      <td>22.3</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1904 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Year  Month  Temperature  Precipitation\n",
       "0     1864      1         -4.2           13.6\n",
       "1     1864      2         -0.7           17.2\n",
       "2     1864      3          5.3           32.7\n",
       "3     1864      4          8.3           35.2\n",
       "4     1864      5         13.5           68.9\n",
       "...    ...    ...          ...            ...\n",
       "1899  2022      4         10.3           54.0\n",
       "1900  2022      5         17.6           12.0\n",
       "1901  2022      6         21.2           63.9\n",
       "1902  2022      7         23.6            5.0\n",
       "1903  2022      8         22.3           48.0\n",
       "\n",
       "[1904 rows x 4 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Year  Month  Temperature  Precipitation\n",
      "0  1864      1         -4.2           13.6\n",
      "1  1864      2         -0.7           17.2\n",
      "2  1864      3          5.3           32.7\n",
      "3  1864      4          8.3           35.2\n",
      "4  1864      5         13.5           68.9\n"
     ]
    }
   ],
   "source": [
    "series = df\n",
    "print(series.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "series['DATE'] = pd.to_datetime(series[['Year', 'Month']].assign(DAY=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Precipitation</th>\n",
       "      <th>DATE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1864</td>\n",
       "      <td>1</td>\n",
       "      <td>-4.2</td>\n",
       "      <td>13.6</td>\n",
       "      <td>1864-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1864</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>17.2</td>\n",
       "      <td>1864-02-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1864</td>\n",
       "      <td>3</td>\n",
       "      <td>5.3</td>\n",
       "      <td>32.7</td>\n",
       "      <td>1864-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1864</td>\n",
       "      <td>4</td>\n",
       "      <td>8.3</td>\n",
       "      <td>35.2</td>\n",
       "      <td>1864-04-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1864</td>\n",
       "      <td>5</td>\n",
       "      <td>13.5</td>\n",
       "      <td>68.9</td>\n",
       "      <td>1864-05-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Month  Temperature  Precipitation       DATE\n",
       "0  1864      1         -4.2           13.6 1864-01-01\n",
       "1  1864      2         -0.7           17.2 1864-02-01\n",
       "2  1864      3          5.3           32.7 1864-03-01\n",
       "3  1864      4          8.3           35.2 1864-04-01\n",
       "4  1864      5         13.5           68.9 1864-05-01"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Year'>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Average temp through the years in Geneva\n",
    "series[:-8].groupby('Year', as_index=False).agg({\"Temperature\": \"mean\"}).plot(x='Year', y='Temperature')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Year'>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Temp though the years for July in Geneva\n",
    "series.loc[series[\"Month\"]==7].plot(x='Year', y='Temperature')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Year'>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Temp though the years for January in Geneva\n",
    "series.loc[series[\"Month\"]==1].plot(x='Year', y='Temperature')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Year'>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "series[:-8].groupby('Year', as_index=False).agg({\"Precipitation\": \"mean\"}).plot(x='Year', y='Precipitation')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. CSV Example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the link called [Klimamessnetz Tageswerte seit Messbeginn](https://opendata.swiss/de/dataset/klimamessnetz-tageswerte), you can download a zip file containing multiple csv. \n",
    "\n",
    "Check text file in [this](https://data.geo.admin.ch/ch.meteoschweiz.klima/nbcn-tageswerte/data.zip) zip to get an explenation of the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### List of urls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "url_list = \"https://data.geo.admin.ch/ch.meteoschweiz.klima/nbcn-tageswerte/liste-download-nbcn-d.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_urls = pd.read_csv(url_list, encoding=\"ISO-8859-1\", sep = \";\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for index, row in df_urls[:30].iterrows():\n",
    "    print(row[\"URL Current year\"]) # Also URL Previous years (verified data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3 .NC Example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data taken from this [url](https://opendata.swiss/de/dataset/klimamessnetz-tageswerte). Access to the text file within the zip folder to get the different urls to download a specific dataset.\n",
    "\n",
    "In this example, we used this [link](https://data.geo.admin.ch/ch.meteoschweiz.klimanormwerte-niederschlag_1961_1990/netcdf/2056/RnormY6190_ch01r.swiss.lv95_000001010000_000001010000.nc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = xr.open_dataset('./data/RnormY6190_ch01r.swiss.lv95_000001010000_000001010000.nc', decode_times=False)\n",
    "df = ds.to_dataframe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"./data/nc.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>swiss_lv95_coordinates</th>\n",
       "      <th>climatology_bounds</th>\n",
       "      <th>RnormY6190</th>\n",
       "      <th>lon</th>\n",
       "      <th>lat</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ncb</th>\n",
       "      <th>time</th>\n",
       "      <th>E</th>\n",
       "      <th>N</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 rowspan=\"5\" valign=\"top\">1.0</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">0.0</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">2474500.0</th>\n",
       "      <th>1064500.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.826852</td>\n",
       "      <td>45.720608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1065500.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.826589</td>\n",
       "      <td>45.729603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1066500.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.826325</td>\n",
       "      <td>45.738594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1067500.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.826061</td>\n",
       "      <td>45.747589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1068500.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.825797</td>\n",
       "      <td>45.756580</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              swiss_lv95_coordinates  climatology_bounds  \\\n",
       "ncb time E         N                                                       \n",
       "1.0 0.0  2474500.0 1064500.0                     1.0                 0.0   \n",
       "                   1065500.0                     1.0                 0.0   \n",
       "                   1066500.0                     1.0                 0.0   \n",
       "                   1067500.0                     1.0                 0.0   \n",
       "                   1068500.0                     1.0                 0.0   \n",
       "\n",
       "                              RnormY6190       lon        lat  \n",
       "ncb time E         N                                           \n",
       "1.0 0.0  2474500.0 1064500.0         NaN  5.826852  45.720608  \n",
       "                   1065500.0         NaN  5.826589  45.729603  \n",
       "                   1066500.0         NaN  5.826325  45.738594  \n",
       "                   1067500.0         NaN  5.826061  45.747589  \n",
       "                   1068500.0         NaN  5.825797  45.756580  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "gdf = gpd.GeoDataFrame(\n",
    "    df, geometry=gpd.points_from_xy(df.lon, df.lat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gdf.plot(column =\"RnormY6190\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "gdf.to_file(\"./data/geojson.json\", driver=\"GeoJSON\") # allow to get an geojson file, I use it on kepler to \"play\" around more easily"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. PX Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyaxis import pyaxis\n",
    "\n",
    "#set file path (or URL)\n",
    "fp = r\"https://dam-api.bfs.admin.ch/hub/api/dam/assets/12947667/master\" \n",
    "#parse contents of *.px file\n",
    "px = pyaxis.parse(uri = fp , encoding = 'ISO-8859-2')\n",
    "\n",
    "#store data as pandas dataframe\n",
    "data_df = px['DATA']\n",
    "#store metadata as a dictionary (not necessary in this case, but might be helpful)\n",
    "meta_dict = px['METADATA']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Szenario-Variante</th>\n",
       "      <th>Staatsangehörigkeit (Kategorie)</th>\n",
       "      <th>Geschlecht</th>\n",
       "      <th>Altersklasse</th>\n",
       "      <th>Jahr</th>\n",
       "      <th>Beobachtungseinheit</th>\n",
       "      <th>DATA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Referenzszenario A-00-2020</td>\n",
       "      <td>Staatsangehörigkeit - Total</td>\n",
       "      <td>Geschlecht - Total</td>\n",
       "      <td>Altersklasse - Total</td>\n",
       "      <td>2019</td>\n",
       "      <td>Bevölkerungsstand am 1. Januar</td>\n",
       "      <td>8544527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Referenzszenario A-00-2020</td>\n",
       "      <td>Staatsangehörigkeit - Total</td>\n",
       "      <td>Geschlecht - Total</td>\n",
       "      <td>Altersklasse - Total</td>\n",
       "      <td>2019</td>\n",
       "      <td>Lebendgeburten</td>\n",
       "      <td>88682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Referenzszenario A-00-2020</td>\n",
       "      <td>Staatsangehörigkeit - Total</td>\n",
       "      <td>Geschlecht - Total</td>\n",
       "      <td>Altersklasse - Total</td>\n",
       "      <td>2019</td>\n",
       "      <td>Todesfälle</td>\n",
       "      <td>67363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Referenzszenario A-00-2020</td>\n",
       "      <td>Staatsangehörigkeit - Total</td>\n",
       "      <td>Geschlecht - Total</td>\n",
       "      <td>Altersklasse - Total</td>\n",
       "      <td>2019</td>\n",
       "      <td>Einwanderungen</td>\n",
       "      <td>175000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Referenzszenario A-00-2020</td>\n",
       "      <td>Staatsangehörigkeit - Total</td>\n",
       "      <td>Geschlecht - Total</td>\n",
       "      <td>Altersklasse - Total</td>\n",
       "      <td>2019</td>\n",
       "      <td>Auswanderungen</td>\n",
       "      <td>125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1497595</th>\n",
       "      <td>Variante A-07-2020 'stabile Auswanderungsziffern'</td>\n",
       "      <td>Ausland Nicht-EWR</td>\n",
       "      <td>Frau</td>\n",
       "      <td>115-120 Jahre</td>\n",
       "      <td>2070</td>\n",
       "      <td>Einwanderungen</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1497596</th>\n",
       "      <td>Variante A-07-2020 'stabile Auswanderungsziffern'</td>\n",
       "      <td>Ausland Nicht-EWR</td>\n",
       "      <td>Frau</td>\n",
       "      <td>115-120 Jahre</td>\n",
       "      <td>2070</td>\n",
       "      <td>Auswanderungen</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1497597</th>\n",
       "      <td>Variante A-07-2020 'stabile Auswanderungsziffern'</td>\n",
       "      <td>Ausland Nicht-EWR</td>\n",
       "      <td>Frau</td>\n",
       "      <td>115-120 Jahre</td>\n",
       "      <td>2070</td>\n",
       "      <td>Erwerb des Schweizer Bürgerrechts</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1497598</th>\n",
       "      <td>Variante A-07-2020 'stabile Auswanderungsziffern'</td>\n",
       "      <td>Ausland Nicht-EWR</td>\n",
       "      <td>Frau</td>\n",
       "      <td>115-120 Jahre</td>\n",
       "      <td>2070</td>\n",
       "      <td>Bevölkerungsstand am 31. Dezember</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1497599</th>\n",
       "      <td>Variante A-07-2020 'stabile Auswanderungsziffern'</td>\n",
       "      <td>Ausland Nicht-EWR</td>\n",
       "      <td>Frau</td>\n",
       "      <td>115-120 Jahre</td>\n",
       "      <td>2070</td>\n",
       "      <td>Lebendgeburten nach Alter und Staatsangehörigk...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1497600 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         Szenario-Variante  \\\n",
       "0                               Referenzszenario A-00-2020   \n",
       "1                               Referenzszenario A-00-2020   \n",
       "2                               Referenzszenario A-00-2020   \n",
       "3                               Referenzszenario A-00-2020   \n",
       "4                               Referenzszenario A-00-2020   \n",
       "...                                                    ...   \n",
       "1497595  Variante A-07-2020 'stabile Auswanderungsziffern'   \n",
       "1497596  Variante A-07-2020 'stabile Auswanderungsziffern'   \n",
       "1497597  Variante A-07-2020 'stabile Auswanderungsziffern'   \n",
       "1497598  Variante A-07-2020 'stabile Auswanderungsziffern'   \n",
       "1497599  Variante A-07-2020 'stabile Auswanderungsziffern'   \n",
       "\n",
       "        Staatsangehörigkeit (Kategorie)          Geschlecht  \\\n",
       "0           Staatsangehörigkeit - Total  Geschlecht - Total   \n",
       "1           Staatsangehörigkeit - Total  Geschlecht - Total   \n",
       "2           Staatsangehörigkeit - Total  Geschlecht - Total   \n",
       "3           Staatsangehörigkeit - Total  Geschlecht - Total   \n",
       "4           Staatsangehörigkeit - Total  Geschlecht - Total   \n",
       "...                                 ...                 ...   \n",
       "1497595               Ausland Nicht-EWR                Frau   \n",
       "1497596               Ausland Nicht-EWR                Frau   \n",
       "1497597               Ausland Nicht-EWR                Frau   \n",
       "1497598               Ausland Nicht-EWR                Frau   \n",
       "1497599               Ausland Nicht-EWR                Frau   \n",
       "\n",
       "                 Altersklasse  Jahr  \\\n",
       "0        Altersklasse - Total  2019   \n",
       "1        Altersklasse - Total  2019   \n",
       "2        Altersklasse - Total  2019   \n",
       "3        Altersklasse - Total  2019   \n",
       "4        Altersklasse - Total  2019   \n",
       "...                       ...   ...   \n",
       "1497595         115-120 Jahre  2070   \n",
       "1497596         115-120 Jahre  2070   \n",
       "1497597         115-120 Jahre  2070   \n",
       "1497598         115-120 Jahre  2070   \n",
       "1497599         115-120 Jahre  2070   \n",
       "\n",
       "                                       Beobachtungseinheit     DATA  \n",
       "0                           Bevölkerungsstand am 1. Januar  8544527  \n",
       "1                                           Lebendgeburten    88682  \n",
       "2                                               Todesfälle    67363  \n",
       "3                                           Einwanderungen   175000  \n",
       "4                                           Auswanderungen   125000  \n",
       "...                                                    ...      ...  \n",
       "1497595                                     Einwanderungen        0  \n",
       "1497596                                     Auswanderungen        0  \n",
       "1497597                  Erwerb des Schweizer Bürgerrechts        0  \n",
       "1497598                  Bevölkerungsstand am 31. Dezember        0  \n",
       "1497599  Lebendgeburten nach Alter und Staatsangehörigk...        0  \n",
       "\n",
       "[1497600 rows x 7 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5 WFS Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from owslib.wfs import WebFeatureService\n",
    "from requests import Request\n",
    "url = \"https://map.geo.sz.ch/mapserv_proxy\" # WFS URL as Example\n",
    "wfs = WebFeatureService(url=url, version = \"1.1.0\") # Depending on the provider, you can use version 2.0.0\n",
    "layer_list = list(wfs.contents) # Gives you a list of layers available for this URL\n",
    "layer = \"ms:ch.sz.a012.naturgefahrenkarte.synoptisch.aggregiert.gstufe_erheblich_mittel\" # Layer to adapt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "response = wfs.getfeature(typename=layer, maxfeatures=10) #Max Features set to 100 for example, you can remove it\n",
    "data = response.read()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "with tempfile.NamedTemporaryFile() as temp:\n",
    "    temp.write(data)\n",
    "    gdf = gpd.read_file(temp.name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>gml_id</th>\n",
       "      <th>label_hochwasser</th>\n",
       "      <th>label_rutschung</th>\n",
       "      <th>label_sturz</th>\n",
       "      <th>label_lawine</th>\n",
       "      <th>label_gleitschnee</th>\n",
       "      <th>label_doline</th>\n",
       "      <th>pzg_cd</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ch.sz.a012.naturgefahrenkarte.synoptisch.aggre...</td>\n",
       "      <td>keine Gefährdung</td>\n",
       "      <td>Gefahrenhinweis</td>\n",
       "      <td>keine Gefährdung</td>\n",
       "      <td>keine Gefährdung</td>\n",
       "      <td>keine Gefährdung</td>\n",
       "      <td>keine Gefährdung</td>\n",
       "      <td>50</td>\n",
       "      <td>POLYGON ((2705390.210 1212769.050, 2705390.250...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ch.sz.a012.naturgefahrenkarte.synoptisch.aggre...</td>\n",
       "      <td>Gefahrenhinweis</td>\n",
       "      <td>Gefahrenhinweis</td>\n",
       "      <td>keine Gefährdung</td>\n",
       "      <td>keine Gefährdung</td>\n",
       "      <td>keine Gefährdung</td>\n",
       "      <td>keine Gefährdung</td>\n",
       "      <td>550</td>\n",
       "      <td>POLYGON ((2700145.828 1210144.033, 2700146.953...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ch.sz.a012.naturgefahrenkarte.synoptisch.aggre...</td>\n",
       "      <td>keine Gefährdung</td>\n",
       "      <td>Gefahrenhinweis</td>\n",
       "      <td>Gefahrenhinweis</td>\n",
       "      <td>keine Gefährdung</td>\n",
       "      <td>keine Gefährdung</td>\n",
       "      <td>keine Gefährdung</td>\n",
       "      <td>5050</td>\n",
       "      <td>POLYGON ((2705177.353 1212297.518, 2705175.310...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ch.sz.a012.naturgefahrenkarte.synoptisch.aggre...</td>\n",
       "      <td>Gefahrenhinweis</td>\n",
       "      <td>Gefahrenhinweis</td>\n",
       "      <td>keine Gefährdung</td>\n",
       "      <td>keine Gefährdung</td>\n",
       "      <td>Gefahrenhinweis</td>\n",
       "      <td>keine Gefährdung</td>\n",
       "      <td>50550</td>\n",
       "      <td>POLYGON ((2701633.270 1208592.200, 2701634.770...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ch.sz.a012.naturgefahrenkarte.synoptisch.aggre...</td>\n",
       "      <td>keine Gefährdung</td>\n",
       "      <td>keine Gefährdung</td>\n",
       "      <td>Gefahrenhinweis</td>\n",
       "      <td>Gefahrenhinweis</td>\n",
       "      <td>Gefahrenhinweis</td>\n",
       "      <td>keine Gefährdung</td>\n",
       "      <td>555000</td>\n",
       "      <td>POLYGON ((2705049.635 1207076.152, 2705045.958...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              gml_id  label_hochwasser  \\\n",
       "0  ch.sz.a012.naturgefahrenkarte.synoptisch.aggre...  keine Gefährdung   \n",
       "1  ch.sz.a012.naturgefahrenkarte.synoptisch.aggre...   Gefahrenhinweis   \n",
       "2  ch.sz.a012.naturgefahrenkarte.synoptisch.aggre...  keine Gefährdung   \n",
       "3  ch.sz.a012.naturgefahrenkarte.synoptisch.aggre...   Gefahrenhinweis   \n",
       "4  ch.sz.a012.naturgefahrenkarte.synoptisch.aggre...  keine Gefährdung   \n",
       "\n",
       "    label_rutschung       label_sturz      label_lawine label_gleitschnee  \\\n",
       "0   Gefahrenhinweis  keine Gefährdung  keine Gefährdung  keine Gefährdung   \n",
       "1   Gefahrenhinweis  keine Gefährdung  keine Gefährdung  keine Gefährdung   \n",
       "2   Gefahrenhinweis   Gefahrenhinweis  keine Gefährdung  keine Gefährdung   \n",
       "3   Gefahrenhinweis  keine Gefährdung  keine Gefährdung   Gefahrenhinweis   \n",
       "4  keine Gefährdung   Gefahrenhinweis   Gefahrenhinweis   Gefahrenhinweis   \n",
       "\n",
       "       label_doline  pzg_cd                                           geometry  \n",
       "0  keine Gefährdung      50  POLYGON ((2705390.210 1212769.050, 2705390.250...  \n",
       "1  keine Gefährdung     550  POLYGON ((2700145.828 1210144.033, 2700146.953...  \n",
       "2  keine Gefährdung    5050  POLYGON ((2705177.353 1212297.518, 2705175.310...  \n",
       "3  keine Gefährdung   50550  POLYGON ((2701633.270 1208592.200, 2701634.770...  \n",
       "4  keine Gefährdung  555000  POLYGON ((2705049.635 1207076.152, 2705045.958...  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6 API ARCGIS Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = \"https://api.geo.admin.ch/rest/services/api/MapServer/identify\"\n",
    "\n",
    "envelope = \"548945.5,147956,549402,148103.5\" # Swiss coordinates 21781\n",
    "layers = \"all:ch.are.belastung-personenverkehr-bahn_zukunft\" # Layer selected\n",
    "todo = {\"layers\":layers,\n",
    "        \"geometryType\":\"esriGeometryEnvelope\", \n",
    "        \"geometry\":envelope,\n",
    "        \"geometryFormat\":\"geojson\", \n",
    "        \"tolerance\":\"1\", \n",
    "        \"imageDisplay\":\"500,600,96\", \n",
    "        \"mapExtent\":envelope}\n",
    "response = requests.get(url, params = todo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "metadata": {},
   "outputs": [],
   "source": [
    "json_file = json.loads(response.content)\n",
    "df = pd.read_json(json.dumps(json_file[\"results\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transform column geom into a geoshape\n",
    "from shapely.geometry import shape\n",
    "def transform_to_shape(origin):\n",
    "    temp = [origin]\n",
    "    geom = [shape(i) for i in temp]\n",
    "    return geom[0]\n",
    "df['geom'] = df.apply(lambda row : transform_to_shape(row['geometry']), axis = 1)\n",
    "gdf = gpd.GeoDataFrame(df, geometry=df[\"geom\"])\n",
    "gdf = gdf.set_crs(\"21781\").to_crs(\"4326\")\n",
    "gdf[\"geometry\"].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.DataFrame(df['properties'].values.tolist())\n",
    "final_df = df[[\"id\", \"layerName\", \"geometry\"]].join(df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>layerName</th>\n",
       "      <th>geometry</th>\n",
       "      <th>nr</th>\n",
       "      <th>dwv_oev</th>\n",
       "      <th>dtv_oev</th>\n",
       "      <th>msp_oev</th>\n",
       "      <th>asp_oev</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7646</td>\n",
       "      <td>Personenverkehr Schiene 2050</td>\n",
       "      <td>LINESTRING (548128.800 148744.100, 548130.800 ...</td>\n",
       "      <td>980000896</td>\n",
       "      <td>1794</td>\n",
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       "      <td>980000896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7647</td>\n",
       "      <td>Personenverkehr Schiene 2050</td>\n",
       "      <td>LINESTRING (549310.000 147962.100, 549316.200 ...</td>\n",
       "      <td>980000896</td>\n",
       "      <td>2157</td>\n",
       "      <td>1951</td>\n",
       "      <td>170</td>\n",
       "      <td>220</td>\n",
       "      <td>980000896</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     id                     layerName  \\\n",
       "0  7646  Personenverkehr Schiene 2050   \n",
       "1  7647  Personenverkehr Schiene 2050   \n",
       "\n",
       "                                            geometry         nr  dwv_oev  \\\n",
       "0  LINESTRING (548128.800 148744.100, 548130.800 ...  980000896     1794   \n",
       "1  LINESTRING (549310.000 147962.100, 549316.200 ...  980000896     2157   \n",
       "\n",
       "   dtv_oev  msp_oev  asp_oev      label  \n",
       "0     1594      155      177  980000896  \n",
       "1     1951      170      220  980000896  "
      ]
     },
     "execution_count": 386,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df"
   ]
  }
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