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maptastik/geopandas-units-possible-solution.ipynb

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geopandas-units-possible-solution.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[Example from Geopandas documentation](http://geopandas.readthedocs.io/en/latest/gallery/create_geopandas_from_pandas.html)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>City</th>\n",
" <th>Country</th>\n",
" <th>Latitude</th>\n",
" <th>Longitude</th>\n",
" <th>Coordinates</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Buenos Aires</td>\n",
" <td>Argentina</td>\n",
" <td>-34.58</td>\n",
" <td>-58.66</td>\n",
" <td>POINT (-58.66 -34.58)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Brasilia</td>\n",
" <td>Brazil</td>\n",
" <td>-15.78</td>\n",
" <td>-47.91</td>\n",
" <td>POINT (-47.91 -15.78)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Santiago</td>\n",
" <td>Chile</td>\n",
" <td>-33.45</td>\n",
" <td>-70.66</td>\n",
" <td>POINT (-70.66 -33.45)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Bogota</td>\n",
" <td>Colombia</td>\n",
" <td>4.60</td>\n",
" <td>-74.08</td>\n",
" <td>POINT (-74.08 4.6)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Caracas</td>\n",
" <td>Venezuela</td>\n",
" <td>10.48</td>\n",
" <td>-66.86</td>\n",
" <td>POINT (-66.86 10.48)</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" City Country Latitude Longitude Coordinates\n",
"0 Buenos Aires Argentina -34.58 -58.66 POINT (-58.66 -34.58)\n",
"1 Brasilia Brazil -15.78 -47.91 POINT (-47.91 -15.78)\n",
"2 Santiago Chile -33.45 -70.66 POINT (-70.66 -33.45)\n",
"3 Bogota Colombia 4.60 -74.08 POINT (-74.08 4.6)\n",
"4 Caracas Venezuela 10.48 -66.86 POINT (-66.86 10.48)"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import geopandas as gpd\n",
"from shapely.geometry import Point\n",
"\n",
"df = pd.DataFrame(\n",
" {'City': ['Buenos Aires', 'Brasilia', 'Santiago', 'Bogota', 'Caracas'],\n",
" 'Country': ['Argentina', 'Brazil', 'Chile', 'Colombia', 'Venezuela'],\n",
" 'Latitude': [-34.58, -15.78, -33.45, 4.60, 10.48],\n",
" 'Longitude': [-58.66, -47.91, -70.66, -74.08, -66.86]})\n",
"\n",
"df['Coordinates'] = list(zip(df.Longitude, df.Latitude))\n",
"df['Coordinates'] = df['Coordinates'].apply(Point)\n",
"\n",
"gdf = gpd.GeoDataFrame(df, geometry='Coordinates')\n",
"gdf.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Set the CRS on the GeoDataFrame to the WGS84 (EPSG:4326). This is the native CRS for the coordinates you have supplied. Once that's set, then use `to_crs` to transform them into EPSG:3395."
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'init': 'epsg:3395', 'no_defs': True}"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# not in the example - add mercato projection, set units to meters\n",
"gdf.crs = {'init': 'epsg:4326'}\n",
"gdf = gdf.to_crs(epsg = 3395)\n",
"gdf.crs"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>City</th>\n",
" <th>Country</th>\n",
" <th>Latitude</th>\n",
" <th>Longitude</th>\n",
" <th>Coordinates</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Buenos Aires</td>\n",
" <td>Argentina</td>\n",
" <td>-34.58</td>\n",
" <td>-58.66</td>\n",
" <td>POINT (-6530001.329933428 -4082699.516779151)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Brasilia</td>\n",
" <td>Brazil</td>\n",
" <td>-15.78</td>\n",
" <td>-47.91</td>\n",
" <td>POINT (-5333316.803905737 -1767646.178485063)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Santiago</td>\n",
" <td>Chile</td>\n",
" <td>-33.45</td>\n",
" <td>-70.66</td>\n",
" <td>POINT (-7865835.21945271 -3931636.078604394)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Bogota</td>\n",
" <td>Colombia</td>\n",
" <td>4.60</td>\n",
" <td>-74.08</td>\n",
" <td>POINT (-8246547.877965705 509196.2974916489)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Caracas</td>\n",
" <td>Venezuela</td>\n",
" <td>10.48</td>\n",
" <td>-66.86</td>\n",
" <td>POINT (-7442821.154438271 1165421.424891677)</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" City Country Latitude Longitude \\\n",
"0 Buenos Aires Argentina -34.58 -58.66 \n",
"1 Brasilia Brazil -15.78 -47.91 \n",
"2 Santiago Chile -33.45 -70.66 \n",
"3 Bogota Colombia 4.60 -74.08 \n",
"4 Caracas Venezuela 10.48 -66.86 \n",
"\n",
" Coordinates \n",
"0 POINT (-6530001.329933428 -4082699.516779151) \n",
"1 POINT (-5333316.803905737 -1767646.178485063) \n",
"2 POINT (-7865835.21945271 -3931636.078604394) \n",
"3 POINT (-8246547.877965705 509196.2974916489) \n",
"4 POINT (-7442821.154438271 1165421.424891677) "
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gdf.head()"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>City</th>\n",
" <th>Country</th>\n",
" <th>Latitude</th>\n",
" <th>Longitude</th>\n",
" <th>Coordinates</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Buenos Aires</td>\n",
" <td>Argentina</td>\n",
" <td>-34.58</td>\n",
" <td>-58.66</td>\n",
" <td>POLYGON ((-6529991.329933428 -4082699.51677915...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Brasilia</td>\n",
" <td>Brazil</td>\n",
" <td>-15.78</td>\n",
" <td>-47.91</td>\n",
" <td>POLYGON ((-5333306.803905737 -1767646.17848506...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Santiago</td>\n",
" <td>Chile</td>\n",
" <td>-33.45</td>\n",
" <td>-70.66</td>\n",
" <td>POLYGON ((-7865825.21945271 -3931636.078604394...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Bogota</td>\n",
" <td>Colombia</td>\n",
" <td>4.60</td>\n",
" <td>-74.08</td>\n",
" <td>POLYGON ((-8246537.877965705 509196.2974916489...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Caracas</td>\n",
" <td>Venezuela</td>\n",
" <td>10.48</td>\n",
" <td>-66.86</td>\n",
" <td>POLYGON ((-7442811.154438271 1165421.424891677...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" City Country Latitude Longitude \\\n",
"0 Buenos Aires Argentina -34.58 -58.66 \n",
"1 Brasilia Brazil -15.78 -47.91 \n",
"2 Santiago Chile -33.45 -70.66 \n",
"3 Bogota Colombia 4.60 -74.08 \n",
"4 Caracas Venezuela 10.48 -66.86 \n",
"\n",
" Coordinates \n",
"0 POLYGON ((-6529991.329933428 -4082699.51677915... \n",
"1 POLYGON ((-5333306.803905737 -1767646.17848506... \n",
"2 POLYGON ((-7865825.21945271 -3931636.078604394... \n",
"3 POLYGON ((-8246537.877965705 509196.2974916489... \n",
"4 POLYGON ((-7442811.154438271 1165421.424891677... "
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gdf['Coordinates'] = gdf.buffer(10)\n",
"gdf.head()"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 POLYGON ((-6529991.329933428 -4082699.51677915...\n",
"1 POLYGON ((-5333306.803905737 -1767646.17848506...\n",
"2 POLYGON ((-7865825.21945271 -3931636.078604394...\n",
"3 POLYGON ((-8246537.877965705 509196.2974916489...\n",
"4 POLYGON ((-7442811.154438271 1165421.424891677...\n",
"Name: Coordinates, dtype: object"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gdf.geometry"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1e517dbd4a8>"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x1e51798c9b0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"gdf.plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The buffers are there, but because the data are millions (billions?) of meters apart and the polygons only 20m wide they don't show up. However if you isolate a single location..."
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1e519148160>"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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SkZxzYk+vS4kpFhoxJClJuHxCP6aO6s3fF2zmifdLqKlv9LqshJPTOZUbzx7C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\nH6nQH2JHgNy1AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1e5192b2828>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"gdf2.loc[0:0].plot()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"...you see one of your 10m buffered polygons."
]
}
],
"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": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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