First Script With...Geopandas.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Welcome to a new geoprogramming video"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This time we will have our first script with... Geopandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Geopandas is one of the most used geospatial packages for python\n",
    "#It is based on the popular pandas, a package that allow to work with dataframes (kind of tables)\n",
    "# We are gonna create a choroplete map using geopandas \n",
    "# Let´s start !!!!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#The #this first step will be to load the package\n",
    "#If you are using anaconda enviorment, this package may cause some problems, so I would suggest you to follow \n",
    "#this  tutorial with colab (from Google)\n",
    "# still you can install the package on conda using this\n",
    "#conda create -n geo-env -c conda-forge geopandas\n",
    "# in our case, it is already installed soo, let´s load the package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geopandas as gpd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remember that the alias (as gdp) is not compulsory but helps to have faster coding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    " #to this example, we are gonna import a geojson file directly to gpd\n",
    "# https://datahub.io/core/geo-admin1-us, this is the webpage from where  it is comming \n",
    "gdf = gpd.read_file(\"https://datahub.io/core/geo-admin1-us/r/admin1-us.geojson\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>country</th>\n",
       "      <th>ISO3166-1-Alpha-3</th>\n",
       "      <th>state_code</th>\n",
       "      <th>id</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Minnesota</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>USA</td>\n",
       "      <td>MN</td>\n",
       "      <td>USA-3514</td>\n",
       "      <td>POLYGON ((-89.59940914585667 48.01027395282483...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Montana</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>USA</td>\n",
       "      <td>MT</td>\n",
       "      <td>USA-3515</td>\n",
       "      <td>POLYGON ((-111.1941892159301 44.56115631771988...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>North Dakota</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>USA</td>\n",
       "      <td>ND</td>\n",
       "      <td>USA-3516</td>\n",
       "      <td>POLYGON ((-96.60135901567638 46.3513571233911,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hawaii</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>USA</td>\n",
       "      <td>HI</td>\n",
       "      <td>USA-3517</td>\n",
       "      <td>(POLYGON ((-155.93665 19.05939, -155.90806 19....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Idaho</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>USA</td>\n",
       "      <td>ID</td>\n",
       "      <td>USA-3518</td>\n",
       "      <td>POLYGON ((-111.0497277494581 44.48816335710761...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "           name                   country ISO3166-1-Alpha-3 state_code  \\\n",
       "0     Minnesota  United States of America               USA         MN   \n",
       "1       Montana  United States of America               USA         MT   \n",
       "2  North Dakota  United States of America               USA         ND   \n",
       "3        Hawaii  United States of America               USA         HI   \n",
       "4         Idaho  United States of America               USA         ID   \n",
       "\n",
       "         id                                           geometry  \n",
       "0  USA-3514  POLYGON ((-89.59940914585667 48.01027395282483...  \n",
       "1  USA-3515  POLYGON ((-111.1941892159301 44.56115631771988...  \n",
       "2  USA-3516  POLYGON ((-96.60135901567638 46.3513571233911,...  \n",
       "3  USA-3517  (POLYGON ((-155.93665 19.05939, -155.90806 19....  \n",
       "4  USA-3518  POLYGON ((-111.0497277494581 44.48816335710761...  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let´s check what it contains with .head(number of rows to see)\n",
    "gdf.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    POLYGON ((-89.59940914585667 48.01027395282483...\n",
       "1    POLYGON ((-111.1941892159301 44.56115631771988...\n",
       "2    POLYGON ((-96.60135901567638 46.3513571233911,...\n",
       "3    (POLYGON ((-155.93665 19.05939, -155.90806 19....\n",
       "4    POLYGON ((-111.0497277494581 44.48816335710761...\n",
       "Name: geometry, dtype: geometry"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# a pandas file would be the same but without the geometry attribute\n",
    "# now, remember the sf video? the geometry is a wkt, in this case a poligon\n",
    "gdf.geometry.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "# so we can plot them individually\n",
    "gdf.geometry[0]"
   ]
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  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "         minx       miny        maxx       maxy\n",
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     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can also check the boundaries of each geometry \n",
    "gdf.bounds.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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   ],
   "source": [
    "# and even find its centroid\n",
    "gdf.centroid.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# but let´s plot the whole geometry\n",
    "# not working right? so to plot it we need to assing at least one ax (we will talk more about this in future videos)\n",
    "ax = gdf.plot()\n",
    "\n",
    "# here we go, we have our map\n",
    "# now we need to increase the size to see it in a better way"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x255e708a278>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gdf.plot(figsize=(10, 10))\n",
    "# and, of course, we need to add a projection here, we are geo folks, we need good projection!!!!!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x255e7168240>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gdf = gdf.to_crs(epsg=2163)\n",
    "gdf.plot(figsize=(10, 10))\n",
    "\n",
    "# now it looks much better "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>name</th>\n",
       "      <th>country</th>\n",
       "      <th>ISO3166-1-Alpha-3</th>\n",
       "      <th>state_code</th>\n",
       "      <th>id</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Minnesota</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>USA</td>\n",
       "      <td>MN</td>\n",
       "      <td>USA-3514</td>\n",
       "      <td>POLYGON ((771214.9506389854 384973.0993258929,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Montana</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>USA</td>\n",
       "      <td>MT</td>\n",
       "      <td>USA-3515</td>\n",
       "      <td>POLYGON ((-883371.462586803 12299.90338338311,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>North Dakota</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>USA</td>\n",
       "      <td>ND</td>\n",
       "      <td>USA-3516</td>\n",
       "      <td>POLYGON ((260768.5370219305 155763.1776856319,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hawaii</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>USA</td>\n",
       "      <td>HI</td>\n",
       "      <td>USA-3517</td>\n",
       "      <td>(POLYGON ((-5568212.874012694 -1020057.3226881...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Idaho</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>USA</td>\n",
       "      <td>ID</td>\n",
       "      <td>USA-3518</td>\n",
       "      <td>POLYGON ((-873158.7315897648 2673.916408162856...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           name                   country ISO3166-1-Alpha-3 state_code  \\\n",
       "0     Minnesota  United States of America               USA         MN   \n",
       "1       Montana  United States of America               USA         MT   \n",
       "2  North Dakota  United States of America               USA         ND   \n",
       "3        Hawaii  United States of America               USA         HI   \n",
       "4         Idaho  United States of America               USA         ID   \n",
       "\n",
       "         id                                           geometry  \n",
       "0  USA-3514  POLYGON ((771214.9506389854 384973.0993258929,...  \n",
       "1  USA-3515  POLYGON ((-883371.462586803 12299.90338338311,...  \n",
       "2  USA-3516  POLYGON ((260768.5370219305 155763.1776856319,...  \n",
       "3  USA-3517  (POLYGON ((-5568212.874012694 -1020057.3226881...  \n",
       "4  USA-3518  POLYGON ((-873158.7315897648 2673.916408162856...  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let´s add some color, it would be great to have a numerical attribute to create our choroplete map\n",
    "# but if you remember, there is no such a column, but we can create it using the geometry, so let´s calculate the area\n",
    "gdf.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "gdf['area'] = gdf['geometry'].area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\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>name</th>\n",
       "      <th>country</th>\n",
       "      <th>ISO3166-1-Alpha-3</th>\n",
       "      <th>state_code</th>\n",
       "      <th>id</th>\n",
       "      <th>geometry</th>\n",
       "      <th>area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Minnesota</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>USA</td>\n",
       "      <td>MN</td>\n",
       "      <td>USA-3514</td>\n",
       "      <td>POLYGON ((771214.9506389854 384973.0993258929,...</td>\n",
       "      <td>2.257249e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Montana</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>USA</td>\n",
       "      <td>MT</td>\n",
       "      <td>USA-3515</td>\n",
       "      <td>POLYGON ((-883371.462586803 12299.90338338311,...</td>\n",
       "      <td>3.760453e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>North Dakota</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>USA</td>\n",
       "      <td>ND</td>\n",
       "      <td>USA-3516</td>\n",
       "      <td>POLYGON ((260768.5370219305 155763.1776856319,...</td>\n",
       "      <td>1.825232e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hawaii</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>USA</td>\n",
       "      <td>HI</td>\n",
       "      <td>USA-3517</td>\n",
       "      <td>(POLYGON ((-5568212.874012694 -1020057.3226881...</td>\n",
       "      <td>1.697257e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Idaho</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>USA</td>\n",
       "      <td>ID</td>\n",
       "      <td>USA-3518</td>\n",
       "      <td>POLYGON ((-873158.7315897648 2673.916408162856...</td>\n",
       "      <td>2.151887e+11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           name                   country ISO3166-1-Alpha-3 state_code  \\\n",
       "0     Minnesota  United States of America               USA         MN   \n",
       "1       Montana  United States of America               USA         MT   \n",
       "2  North Dakota  United States of America               USA         ND   \n",
       "3        Hawaii  United States of America               USA         HI   \n",
       "4         Idaho  United States of America               USA         ID   \n",
       "\n",
       "         id                                           geometry          area  \n",
       "0  USA-3514  POLYGON ((771214.9506389854 384973.0993258929,...  2.257249e+11  \n",
       "1  USA-3515  POLYGON ((-883371.462586803 12299.90338338311,...  3.760453e+11  \n",
       "2  USA-3516  POLYGON ((260768.5370219305 155763.1776856319,...  1.825232e+11  \n",
       "3  USA-3517  (POLYGON ((-5568212.874012694 -1020057.3226881...  1.697257e+10  \n",
       "4  USA-3518  POLYGON ((-873158.7315897648 2673.916408162856...  2.151887e+11  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf.head(5)\n",
    "# we have the area in m2, to transform it to km2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "gdf['area'] = gdf['geometry'].area / 10**6\n",
    "#realize here that we are overwriting the area column instead of creating a new one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>name</th>\n",
       "      <th>country</th>\n",
       "      <th>ISO3166-1-Alpha-3</th>\n",
       "      <th>state_code</th>\n",
       "      <th>id</th>\n",
       "      <th>geometry</th>\n",
       "      <th>area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Minnesota</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>USA</td>\n",
       "      <td>MN</td>\n",
       "      <td>USA-3514</td>\n",
       "      <td>POLYGON ((771214.9506389854 384973.0993258929,...</td>\n",
       "      <td>225724.894588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Montana</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>USA</td>\n",
       "      <td>MT</td>\n",
       "      <td>USA-3515</td>\n",
       "      <td>POLYGON ((-883371.462586803 12299.90338338311,...</td>\n",
       "      <td>376045.290194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>North Dakota</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>USA</td>\n",
       "      <td>ND</td>\n",
       "      <td>USA-3516</td>\n",
       "      <td>POLYGON ((260768.5370219305 155763.1776856319,...</td>\n",
       "      <td>182523.214240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hawaii</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>USA</td>\n",
       "      <td>HI</td>\n",
       "      <td>USA-3517</td>\n",
       "      <td>(POLYGON ((-5568212.874012694 -1020057.3226881...</td>\n",
       "      <td>16972.567361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Idaho</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>USA</td>\n",
       "      <td>ID</td>\n",
       "      <td>USA-3518</td>\n",
       "      <td>POLYGON ((-873158.7315897648 2673.916408162856...</td>\n",
       "      <td>215188.724301</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           name                   country ISO3166-1-Alpha-3 state_code  \\\n",
       "0     Minnesota  United States of America               USA         MN   \n",
       "1       Montana  United States of America               USA         MT   \n",
       "2  North Dakota  United States of America               USA         ND   \n",
       "3        Hawaii  United States of America               USA         HI   \n",
       "4         Idaho  United States of America               USA         ID   \n",
       "\n",
       "         id                                           geometry           area  \n",
       "0  USA-3514  POLYGON ((771214.9506389854 384973.0993258929,...  225724.894588  \n",
       "1  USA-3515  POLYGON ((-883371.462586803 12299.90338338311,...  376045.290194  \n",
       "2  USA-3516  POLYGON ((260768.5370219305 155763.1776856319,...  182523.214240  \n",
       "3  USA-3517  (POLYGON ((-5568212.874012694 -1020057.3226881...   16972.567361  \n",
       "4  USA-3518  POLYGON ((-873158.7315897648 2673.916408162856...  215188.724301  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x255e7101b00>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let´s finish with the choroplete map, see that we use the column area to plot it and the 'YlGnBu' as color (+ legent true \n",
    "# to show the legend)\n",
    "gdf.plot(figsize=(10, 10), column='area', cmap ='YlGnBu', legend = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Unfortunately, geopandas package does not contains basemaps, but the good thing of python is that we can mix packages\n",
    "#and for example, use the folium package to provide the base map\n",
    "# but that will be another tutorial\n",
    "# thank you for wa"
   ]
  }
 ],
 "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": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}