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Creating and working with multiple geometry columns in a single GeoDataFrame
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Creating a GeoDataFrame with multiple geometries\n",
"\n",
"I've run into an issue where I want to be able to have a GeoDataFrame with multiple GeoSeries. In this example, I'd like to have Raleigh park polygon geometries as one GeoSeries and then create a second GeoSeries with a buffer of some distance."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"\n",
"import os\n",
"import geopandas as gpd\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"parks_gdf = gpd.read_file('https://opendata.arcgis.com/datasets/43b5d6bf9d6e400599498d052545d331_0.geojson')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Because I usually work with Raleigh data in a local CRS, I'm going to reproject to EPSG:2264. While I'm at it, I'll get get rid of extraneous columns."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"def data_preprocessing(gdf, cols, epsg_code):\n",
" gdf = gdf[cols]\n",
" new_crs = {'init': 'epsg:{}'.format(epsg_code)}\n",
" gdf = gdf.to_crs(new_crs)\n",
" return gdf"
]
},
{
"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>PARKID</th>\n",
" <th>NAME</th>\n",
" <th>DEVELOPED</th>\n",
" <th>geometry</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>34</td>\n",
" <td>Windemere Beaver Dam</td>\n",
" <td>Developed</td>\n",
" <td>(POLYGON ((2097510.461880576 750949.1384854497...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>35</td>\n",
" <td>Walnut Creek North</td>\n",
" <td>Developed</td>\n",
" <td>(POLYGON ((2126075.468296753 733175.2605656629...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>Thornton Road Property</td>\n",
" <td>Undeveloped</td>\n",
" <td>POLYGON ((2137024.335360867 783502.8727651152,...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2</td>\n",
" <td>Mary Belle Pate</td>\n",
" <td>Undeveloped</td>\n",
" <td>POLYGON ((2095040.46173832 728970.3213452235, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>3</td>\n",
" <td>Eliza Pool</td>\n",
" <td>Developed</td>\n",
" <td>(POLYGON ((2106009.598623645 731174.2719958539...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PARKID NAME DEVELOPED \\\n",
"0 34 Windemere Beaver Dam Developed \n",
"1 35 Walnut Creek North Developed \n",
"2 1 Thornton Road Property Undeveloped \n",
"3 2 Mary Belle Pate Undeveloped \n",
"4 3 Eliza Pool Developed \n",
"\n",
" geometry \n",
"0 (POLYGON ((2097510.461880576 750949.1384854497... \n",
"1 (POLYGON ((2126075.468296753 733175.2605656629... \n",
"2 POLYGON ((2137024.335360867 783502.8727651152,... \n",
"3 POLYGON ((2095040.46173832 728970.3213452235, ... \n",
"4 (POLYGON ((2106009.598623645 731174.2719958539... "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"parks_gdf = data_preprocessing(parks_gdf, ['PARKID', 'NAME', 'DEVELOPED', 'geometry'], 2264)\n",
"parks_gdf.head()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x200d3d1bba8>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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TNOTTOdLtDGHSRkSxbE7HIpArn/qCFRsKAnqNVreblNhIluSlccGM4JMEW7WzmK//+XO2H6sa8muL84UJWmuiHDZKapraNSbW7i31Oxg5ymHn3buWBF3MaRtnTk7n6ZsWMHXU0IenifOFCf/aU4pba+46ZxIOm2JPcQ1bCqq448UtvLC+wC9J54z4aEZEWdvlfHNr4YCSBF8+N4ulU0f2mkBqMAm+TnoY8+n+csYkx5Cb5r0QS3F1Ew674vn1BWQlxVDb1Ep8dARTMhOYkpmA1pqP9pWxu6iGGaa+ntaag+X1HCqrZ3LmCLJTBl/4RWs94LwzWmt++/4+Nk8+wS+XdQsd7pPUEVEDumYgEOcLIkpqmkiM8b57d6i8nkv/+1Pqmp0A3HfhlG7dQ6UUmYnRnZIauTVct3wdpbXN/NvJOTx0+czAfIAeKKtt5oxH13DxzFE8OsBsZEopHrh4atCtbhfnCyLGpcex83g1KSMiyfLii5Y6IpLvnjGelzcd40hFA7m9SJdNyez8vGO3Kf71gzM5WtlAXKRjUGNAYyLtvP390/xKJQhw3vTMAFk0dEhgdZBRXN1EclwEETYbdS1Gi9bfF7exxcV/vfMVi8alcukc3+f8nC73oCwNGu48tmovZ0xO7zMMz5/A6vC7o0FOZmI0UQ47G4+cYPFDH3L/azv6rRMTaefBy2cOyPFgcNbkBQNbjp7gdx/sG7TzS7czSJmQHseT188XCeZBJCkmkrK6wZNZC8+ftBAgdUTUgBzP2YOOfDjwwvoCfvbmTv7kg+bEfRdOYcwgDuJIyxdmhNuK+DaW5KXhcruJ8mE+b3RSDA9fMWvQbBLnCzPCVXI6OyWWGxbn+lTHblOD+mM1YKEUc9/3TTGUXUqp33jUGZZCKcLw5YX1BSx66EM+2hc+SY/7bfm01nuBOQBKKTtQCLyulDoLQ2Nhlta6WSmVYR4zDSP133SMvJ0fKKUmmekDn8RIMbgO+CeG7sK7wC3ACa31RKXUtcAjwDVKqRTg58ACjEzVm5RSb5m6DcIQUlHXTKTD5lMM5wNv7GBmViLXnJTT77FTR8UzNyeJ0/P6T2tR1dDCnuJaHDaFzaZw2BSzxgRfSgl/hFJuAx7WWjcDaK1LzWNEKGWYUlbbzKf7y32ud7yqgaWPf8TxKu8lpmubWnlvZ7HXq+Xn5iTz5PXzveoW3/S3L7l2+TqufOoLvv7nz1n2p8+CciDJH6GUScASpdSDGElzf6C1/hIRShl2PL/+CJV1Lfx57QEevsK3UDGtNbWNLnJT4xjhw3q9+OgIPrz3TGICHLDc1Oqirkva/Mkj44NyLtLru+khlNKWrt2BoZm3CDgJeEkpNR6LhFIwM2bn5PTfxQk3rlmQTYvLzaWzR+Ow+zaA0OrSREYo3rj9VJ+v60scqrdER9h59KrZrNxwlHd2FFHX7CQzsX/d+eGILy1fV6GUY8BrZhdyg1LKDaThn1DKsR6EUs7sUmdtV8O01suB5WCEl/nwmUKKkpomRiZ0/yI67DYcdhu5ab4Pbkc6bAy3AdJ5OcnMy0nmF5dNZ9WuYj7eH5yDNL601V2FUt7AEEhBKTUJiATKMURPrjVHMMfRIZRSBNQqpRaZz3M3Am+a52oTSgEPoRRgFXCeUirZFEs5zywLO7Ye7X+l9Xs7iwEjlnMga9t6Izc1sKknAkVMpJ2vzc3i8avnWG3KwPBGvhaIBSqARI+ySOAfwE5gM3C2x76fYshB78WUfjbLF5jHHwD+SEdgdzSGzFg+hnT0eI86N5vl+cA3+7M1FGWhvzxUocff/46+79Xt3fa5XG79yb4yrbXWbrd7qE3rRkFFvS6tsV6ueajAD1loWdUQJJz92FoKKhvY/+CF7SOClfUtrNhQQGJMBN84OWdYTKDf89JWpmTGc+vpEwb9Wk99dIB/OznH7+VI/iCrGsKAx6+ZwzM3L8Qz24PT7abF6eb6RWO9crzGFlf79qHyetYdqOhUFgjuu2AKY5JjB11xttnp4tFVeznntx8FnWhoGxJeFiTM6UGXICM+mrvPneT1OWIiO4b9x6XF0dTq4rv/2MQjV8xqHzEsr2tG0ZFeoanV5VN+k4yEaC6aOYrCqkavFvy2sf1YFS9+eRSXS+N0G92ys6Zk9JpztLqhFZdbU9fspLHFRX2zK+hGPcX5wpipoxL44fmT2VNcQ0psBK9uKWRPUQ3nTBvJkjxjxcRAEwv54ni7i2q44ekNVDd2bsHS4qN6db7n1h0B4KwpGWQkRAd0gGmokG5nmDMjK5EzJ2fwSX45Dc1Orjs5Z8gzeU0dlcCaH5xJRnwU80y9veTYCGaNSey1ztULslEKoh329uODDRlwESitaSKjy/zghkOVZKfEDIn2ncutqW5spb7ZSUZCFI0tLpJiI/utV93QSkKMw9KBJhlwEQaM1prKhpb2902tLtbsLWXVrmJ+4kWKikBgtylS4iLJToklymH3yvEAEmMjhsUI70CRZ74wp7qxlckj4wFoanVytLKB0/PSSR8Rxf5SUR0aTKTlC2J2Flbz3BeHu5W73NrrKYSk2Mj21mNPcR2Rdjt2m2LaqAS+Nse/GPa3th3nhqfXs7Ow2q/zhCrS8gUxM7IS27NMe/LOjiIumtF3HsuGZiexXVK8Tx4Z3z4d4Zmns7yumeLqBmZkJftk3wXTM5kzJmlQAqxDAXG+EENrzaysxH6X2HR1POiYByyqbiQ5NrJ91DNtRBRpA0irHumwkZMaG5Rr7YYC6XaGGPXNTuKi/Jsq2FNcS6TpvKUeqkYDJRjX2g0FcldCjKrGVlLj/BP/OGtyRnu3MyMhOqDa7f7S6nKz8XClX6pKw4Xhc1eFgFBU3ei1rkKbgEqwkF9ay/SfrRoUkU8rEOcLEZqdLlZ/VUKEvfcuZ4uzc/exrsnJkcr69vdF1Y39XqepNbCB2L4wJjmWC2ZkhswAjjhfiBDlsHNSbjJTRnZe+Pr3zw6x/OMDNLW6uq1Iz0yMJjWuY0I72YvJbStEJNuIctj44fmTuW5hDgvHpfD9FVu6xYMGE+J8QY7Wur01SoqNxKmN1sltrgyYkDGCdQcrWbWr50xiI6I6WpE2x6r2iHjpiYKKeu5YsZm/fHIwgJ+kf45WNtLsdHPfhVN4e9txaoLY8UCmGkICz9aoTZ65qdWF1rAkL530+KhuGnx90erSuNy6PVvzifoWks0W0uXWXPe/6ymuaSI7JbZbXZdbU1HX3C1WNBDkpHZc797zJgf8/EONOF+Q0VWoUilFTVMrNqU66aJHR9jbW8TeHK+irrl93Z7LbbSgLq1JiYuksqGlfW4v1mPqwm5TfHbf2TS2uDqtD/TcX1DZMCjOF2pItzOI+GhfGc9+cbhbeUJ0RCfHA9hVWM2WghNUNbTQ0NJ9VPNPa/I56cEPWH+wAoAVGwo4VF5PTWMrtc1OvjreERLmsNmo6tIV7cnx2ljQh5ik0IG0fEHE6XlpXqVTB4iKsFFX7SIhOqLTQEtlfQspcZF878wJXDJrFGNT41i7t5Sk2IhOoWqnT8po395ScIL/9+Yu3r1zScA+i+CnUIq5/wdKKa2USvMoE6GUQUAp5fUSmokZ8Zw7bSQtLnenOvtLamk1y8aaGu1pI6I4ow+tvwW5Kbz9H/0nzQ10PphQp1/n01rv1VrP0VrPAeYDDcDrAEqpbOBcoH3Gs4tQygXAn02BFegQSskzX226C+1CKcATGEIpeAilnAwsBH5u5u8UvCSyywhnYkwEdU1O6j0m2GdkJXYTQOmaAKm3ELH1ByvYcKgSAJt5SChEnwwF/gilgOEoP6JzCncRShkgXSfBA4HNpnh89T6OnWgAIDctjuPVjcT1EFjdtR50d8KujEmJZa6Z+iHKTOkQrgKcvuKr87ULpSilLgMKtdbbuhzTm7hJFl4KpQBhKZSycuPR/g/ykSPl9fxrdwkbDxuqakrB9NG950bpis2m2sPQyk19cq01LU43TpebrKSYYRX7GUx4fdc8hFJeVkrFYmSl/llPh/ZQNuhCKUqpjUqpjWVlwZm3H+CGRWMDer41e0q5/YXNVDe1UtdsTEi3tU6+MCLKwecHyttHVJVSRDpsfa5WqKxv6XMpkTehbKGOLz9ZnkIpE4BxwDal1GEMAZPNSqlM/BNKoQehlJ7O1Qmt9XKt9QKt9YL09N4HDoY7yz8+ENAv5ZK8NOaNTSbaYWdGVt/ikW9sKaSy3phOcLs1lfUtPPXRgfb9p0xI8ym0rG2usLflSCU1wZfqL9AMSChFa71Da52htc7VWudiOMk8rXUxIpTSJ0cq6nvdd8qEtF4XrWqtu8219Rfk7LDb+NWyGTxxzRwmpMf1eM42XG5NVX0zLU43r28pJC7KzsJc/8a2MuJ7Xo60ueAED7wxNMmZhjXeCDrQg1BKl/2HgTSP9yKU0gsn6pv73F94okEXVTV2K6+oa9YT7n9HH69q8PmaT6zeq896bI3+LL+sTzGVg6W1vb6vrDPsfnJtvt5ZWKW11vqLA+UDsqe2qVW/t7NIl9R0/5z90dDs1E+tzdeHy+t8rjsYIEIpHQR73s5NR04wKyuBiB6ezRpbXNhtxvOWt/xzRxF3vrjFELl02Lj33El85wxDxKTZ6Wp/BtxZWM2J+mZOy0tvnxc8VF7HuLTe5cEOldczLq17i+oNWmuanW6furJr9pTicmu2H6vm8nmj+7RtqJC8nSFAvpmmb/7Y5B4dD4yQLl8cD4yUEK0u4wd2ycQ0fv3uHn7y+g5anO5Ogy8zshL54mAltU3GyGaL0822o9V9ipCMS4vjs/zyAYmiKKV8Xp5U1djCnS9u4Q//2s+KDYEfGR5qxPksZu3eUv6x7gg3/9231rrr819vnDdtZPv2/hLDwV9YX8DiX3/IL97a1b5Pa828nCQSzIWqJTVN/PjV7Tzz+WGg8xykZ2/p1IlpXq+c95fL547hZ5dOAwj65UQgzmcpLU43P3ltBw+8sZOCygYOltV5XdfbrM7TRycwJTMem4Jaj6iWjIRovrVkXPt7pRRzsjsGWLJTYvnxBVNYOtVw3ghTy72gop6qBuu++MvmZDF5ZDyLJ6RaZkOgkMBqC6lqaOF4dRMAl8/NIjUuiq1Hq5iTndRpPZ0/KKV48PIZbCmo4vHV+wAjn+YT18whJtLOyi8LiI10cO60kTSYsZnNThcKxU2n5Lbb0PYcmJPa+RnPVymwumZntxUYvhAdYefN/ziVUBiqEOezEM8ERq0uNyjN6MRoDpTV8Yu3dnHBjEz+baH/irPzx6Ywf2wKT310kJtPHce9501qP+c/dxTzVVENl84e3b5Y1ZeJ+PLaZhKiHd1iQz35LL+crUerKKxqZNnsUYxMiMGlNXGRjgFp6lmZyiKQiPNZSNvgBhiqQHeu2EpUhI2dhTUUVjWy/lAl504bSUZ8YBamvvSdRYxP7zxC+OhVs3D7EVI620O0s23ecd3BCk6dmNY+x7e3uJaVXx7lqvljuGb5+vbjX/nu4qATtAwk8sxnITkpseSYqRimZyWydl8Zq3aVUFhlRLm0ON2UBjASpKvjgRE6lhQbQX5p9+fNVpebd7YXceuzG9sHa/oiOsJOaU0zT396iN++v699YObm08bx8Y/O4vK5HWG5SbERzOxDf8+TYEtx6C3S8llIclwkGfFRxEbaOWlsEmv2lHban5cxokcthkASG+mgprGFw+X1TMzo7Jx3rdzKpsMniIuyU1LTTJ6pZtQXOamxPHfLyT3uG5MSy/i0OI5UNnDn0jyvu7cbDlWQER896PdiqBHns5hXbjuFw+X17CqqJtJuo8UjFnLWmL7jMQNFQkwk53hMSbTx80unkRgT0a+TrP6qhNnZiV51j9/8j1P5qqjGp6702VO62xYKiPMNA3LT4shNi2P6qER+8fYuPs+vICbSzh1LJ1pqlzcO0tjiwmFXXjvTofJ6Hn53D9nJMeSmxlHZ0MLYlDi+ffp4f80NOsT5hhG5aXH8/ZsLKahoIMKhfJJkPlhW1+Mz3WDT1OrirMkZ/R8IPLZqL39ckw/AloIqpmbGU1jVyBPXzBlME4ctMuAyDMlJjfVZCz0lrvuk+0DCvnwluYfr9kRVQwt1zU5meQyy7C6u5WeXTOMKVc1wAAAKMklEQVRML5031BDnCxE8I15qmlr5xVu7mPqz97jgdx/zwVclAz7vvpLa9vSC/tr3w/Mnc6is85KqZ9cdCdu0E9LtDEESoiP4+aXTuHpBNm9sLWTJJO/SDfbE7z/Yz3u7inng4ql889Rx/Vfog3UHK/j1FTPZcKiS4uomluSl8cn+cr/OGcyI84UoSimmjU5g2mjv0sS3ON09rphwuTVjkmNYkmdkCPhwd0l7vCcYwQELx3mXJPfUicZq+EtmjW4vu2Fxrld1QxFxvjDD7dZsOFzJovGdA5N7W6r0++vmdJpqmJdjBF8fLq/niic/Z0lemtfOFyphYYFCnC/MqG12dgpr64+uc3xtAyxjU2N54duLmJzZ/8S70DMy4BJmJMZEcG4PE+q+opTy2fE2Hq70+7qhhDifMGRER9gHJTFwsCLdTmHICLXYTH8ZsFCKUupRpdQepdR2pdTrSqkkjzoilCIEhLZAgcr6Fj7aV8bWo1UWWxQ4/BFKWQ3M0FrPAvYB94MIpQiBo6iqkQt+9zHLPz7A058e5LZ/bOL9XcVWmxUwfO12egqlHPEoX4eR7BY8hFKAQ0qpNqGUw5hCKQBKqTahlHfNOr8w678C/LGrUIpZp00oZYWPdgtByKikGB65chZ2m+JEfQsXzhgVUl1XX52vXSilCzcDK83tLAxnbKNN3KQVL4VSlFJhKZQidGduTuh2dAYklNKl/KeAE3i+raiH6iKUIghdGKhQCmAMhgCXAN/QHckcRShFELxgQEIpAEqpC4AfA5dprRs8jhOhFCEguN2azw+U89dPD1ltyqDg1TOfqcd3LvAdj+I/AlHAanPGYJ3W+rta611KqZeArzC6o7drrdvkdG4D/g7EYAy0vGuWPw08Zw7OVGI8W6K1rlRK/SfwpXncr9oGX4TQ5Wt/+gy31jhdmq+KarDbFNeclN2vmm6wIUIpwrDjxr9uIMph4+N9ZTSbETF3LM3jnnMnWWxZd0QoRQgpnr15If974wKeun5+e9m7O4pCLjQttNpxIaRYPCGVF759Mq0uTavTjZ+Ju4cd4nzCsCU6ws4pEwa+Cn+4I93OIWJPcQ3LPz7Q/4FC2CDON0T87dPDPPLeXnYWVlttijBMEOcbIuKjHbjcml++vYv6ENUeEHxDnG+I+Pq8McRE2Pny8AnOfGwtL208yvZjobM8RvAdcb4hYtroBJ68fh4AZbXN/OiV7fzg5W0crTSCg/YU11hpnmABMto5hJyel84ls0bxf9uLANhXUsfvPtiHy60pqGzg+W8tIiZSMnyFC9LyDSE2m+Lxq+dwjkfey7pmJ29sPc7mgioW/NdqvjrecwtYXtfMPi808oTgQVq+ISbSYeMvNy3gSEU96w5WcObkDEYmRPPsF0eob3Fx5VOf8/AVs7hsdkdi2SMV9Tzwxk52F9Wy8YFzLLReCCTifBYxNjWOsalxAPxq2Qx2FFazpaCKhhYX97+6nZ2F1cwek8TFs0Zxz0vb2HTkBDZlpN+bNSap1yS3QvAggdXDhD3FNVz8h09xdVEWGp8eR2lNc7s08hmT0jg9L51bloSfnt1wxJ/Aamn5LGTj4Ur+853dFJ5ooLK+hSiHHXuE6qRBfrCLqs/4tDgumjVqqE0VBgFxPguZm5PMnUsnkj4imt1FNZyWl8be4lruWrmV6sbWHuuMTY3zWbtPGJ6I81mI3aba9cZnmqKRTa0ulk7J4LUthWQmRDN/bDJr9pby/bPzmD46gTk5Q6PTLgw+4nzDiLe3HWfTkRN8tK+Mi2eO4vFrZhPlsLOnuIbJI+NRobamJswR5xtGXDp7NJfOHs03Ts7heHVTu0LQlEzvNPaE4EKcbxiSNzKevJEivRXqyGSRIFiEP0IpKUqp1aaAyWpPDQURShGE/vFHKOU+4EOtdR7woflehFIEwUt87XZ6CqUsA54xy5/BED0BD6EUrfUhoE0oZRSmUIqZEPfZLnXazvUKsLSrUIrW+gSGMlKbwwpCUOOr83kKpYw0s1Bj/s0wy3sTN8nCS6EUwCehFNFqEIIRv4VSejq0h7JBFUoRrQYhGPFHKKXE7Epi/i01y4dcKEUQgpEBC6XQWdzkJjqLnohQiiD0gz9CKQ8DLymlbgEKgKsARChFELwj5NbzKaXK6CxZbRVpQLnVRngg9vTNQO0Zq7Ue0EBDyDnfcEEptXGgiywHA7Gnb6ywR8LLBMEixPkEwSLE+QaP5VYb0AWxp2+G3B555hMEi5CWTxCsQmsd1i+MCJo1wG5gF3CnWZ6CEci93/ybbJZ/A9jq8XIDc8x9a4G9HvsyzPIoYCVGkPl6INfj+jeZ19hvbrfZk4+xgqTMrHudaZ8bWNDlM9xvHr8XON+jfD6ww9z3Bzp6OoNmD8Z88CbzupuAsz32Dfn9AXKBRo9rPhXI++NRPs48dr9ZN7Lf757VX36rX8AoYJ65HQ/sA6YBvwHuM8vvAx7poe5M4GCXL9eCHo77Xts/HSOAYKWHgx80/yab21OAecBLwL+b9qwEfglM7noN09Zt5hdmHHAAsJv7NgCLMWJk3wUuHAJ75gKjze0ZQKHF9ycX2NnL/z4Q96ftR/kl4Fpz+yngNnE+353xTYxf773AKA8H3dvDsQ8BD3rx5VoFLDa3HRiTuQrj1/p/PI77H7NMmcc4THvuAFb1dA2MVu/+rtcybd7jUd5+rcG0p8vnVkAFEGXh/cmlB+cbrPtjli9us6evlzzzeWCuoJ+L0X3obcmUJ9fQOd4V4G/miv//17ZSH9+XTKUCVRiB5HMxur3dllJ1PXeXcwRyCZcv9nhyBbBFa93sUTbU9wdgnFJqi1LqI6XUEo9rBvT+mMd2PVeviPOZKKVGAK8Cd2mt+xXLU0qdDDRorXd6FH9Daz0TWGK+bmg7vIdT9LVkSpmvV4G7gDp6WEo1wHMPtj3GBZSajpGRwDMe2Ir7UwTkaK3nAvcALyilEvo490Dt6e1cvSLOByilIjD+kc9rrV8zi3tbMtWG58JiALTWhebfWuAFjNQX4PuSqSqzfIVpT19LqYZiCZcv9qCUGoORauRGrfWBtnIr7o82MipUmNubMJ6JJwX4/pQDSeaxXc/VK2HvfGbX52lgt9b6cY9dvS2ZQillw1jF8aJHmUMplWZuRwCXADt7OJc3S6aeBg7T0S3qdP0uDMUSLq/tUUolAe9gPId+ZvX9UUqlt+UQUkqNN+/PwUDeH3PfGvPYPu3pxFAPaAy3F3AaRhdhOx3D0Rdh9OM/xBg6/hBI8ahzJrCuy3niMIbWt2MMef+ejlHHaIwMAPkYI2zjPerdbJbnA9/0sGcPUA80A59gOPsx830JHg/0wE8xftH3Yo7YmeULML7gB4A/0jGUPmj2AA+Yx3lOx2RYdX8wnjt3YYwIbwYuDeT98Sgfbx6bb9aN6u+7JxEugmARYd/tFASrEOcTBIsQ5xMEixDnEwSLEOcTBIsQ5xMEixDnEwSLEOcTBIv4/xyAkVPyMH1aAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"parks_gdf.plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The thing with the `geometry` column is that it seems to be a special column name geopandas looks out for. You can set another column as the geometry for your GeoDataFrame, but after that, `geometry` will hold those geometry values. You will have effectively lost the original geometries in the `geometry` column. However, if we don't have any column named `geometry`, geopandas is a lot more flexible. You just need to reference the geometry column by it's name. As such, I created a new column, `geom` and set it equal to `geometry`. I set the GeoDataFrame's geometry to `geom` and then dropped the `geometry` column."
]
},
{
"cell_type": "code",
"execution_count": 6,
"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>PARKID</th>\n",
" <th>NAME</th>\n",
" <th>DEVELOPED</th>\n",
" <th>geom</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>34</td>\n",
" <td>Windemere Beaver Dam</td>\n",
" <td>Developed</td>\n",
" <td>(POLYGON ((2097510.461880576 750949.1384854497...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>35</td>\n",
" <td>Walnut Creek North</td>\n",
" <td>Developed</td>\n",
" <td>(POLYGON ((2126075.468296753 733175.2605656629...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>Thornton Road Property</td>\n",
" <td>Undeveloped</td>\n",
" <td>POLYGON ((2137024.335360867 783502.8727651152,...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2</td>\n",
" <td>Mary Belle Pate</td>\n",
" <td>Undeveloped</td>\n",
" <td>POLYGON ((2095040.46173832 728970.3213452235, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>3</td>\n",
" <td>Eliza Pool</td>\n",
" <td>Developed</td>\n",
" <td>(POLYGON ((2106009.598623645 731174.2719958539...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PARKID NAME DEVELOPED \\\n",
"0 34 Windemere Beaver Dam Developed \n",
"1 35 Walnut Creek North Developed \n",
"2 1 Thornton Road Property Undeveloped \n",
"3 2 Mary Belle Pate Undeveloped \n",
"4 3 Eliza Pool Developed \n",
"\n",
" geom \n",
"0 (POLYGON ((2097510.461880576 750949.1384854497... \n",
"1 (POLYGON ((2126075.468296753 733175.2605656629... \n",
"2 POLYGON ((2137024.335360867 783502.8727651152,... \n",
"3 POLYGON ((2095040.46173832 728970.3213452235, ... \n",
"4 (POLYGON ((2106009.598623645 731174.2719958539... "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"parks_gdf['geom'] = parks_gdf['geometry']\n",
"parks_gdf = parks_gdf.set_geometry('geom')\n",
"parks_gdf = parks_gdf.drop('geometry', axis = 1)\n",
"parks_gdf.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can use a lambda function and `.apply()` to iterate through each row to buffer each feature. The result is added to a new column called `buffer`. "
]
},
{
"cell_type": "code",
"execution_count": 7,
"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>PARKID</th>\n",
" <th>NAME</th>\n",
" <th>DEVELOPED</th>\n",
" <th>geom</th>\n",
" <th>buffer</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>34</td>\n",
" <td>Windemere Beaver Dam</td>\n",
" <td>Developed</td>\n",
" <td>(POLYGON ((2097510.461880576 750949.1384854497...</td>\n",
" <td>POLYGON ((2095096.494260158 746927.2344195718,...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>35</td>\n",
" <td>Walnut Creek North</td>\n",
" <td>Developed</td>\n",
" <td>(POLYGON ((2126075.468296753 733175.2605656629...</td>\n",
" <td>POLYGON ((2122769.214909975 731302.8963692773,...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>Thornton Road Property</td>\n",
" <td>Undeveloped</td>\n",
" <td>POLYGON ((2137024.335360867 783502.8727651152,...</td>\n",
" <td>POLYGON ((2134929.693431573 785199.8217412524,...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2</td>\n",
" <td>Mary Belle Pate</td>\n",
" <td>Undeveloped</td>\n",
" <td>POLYGON ((2095040.46173832 728970.3213452235, ...</td>\n",
" <td>POLYGON ((2095823.978824902 728088.8998613623,...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>3</td>\n",
" <td>Eliza Pool</td>\n",
" <td>Developed</td>\n",
" <td>(POLYGON ((2106009.598623645 731174.2719958539...</td>\n",
" <td>POLYGON ((2104609.055921355 731558.4034951449,...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PARKID NAME DEVELOPED \\\n",
"0 34 Windemere Beaver Dam Developed \n",
"1 35 Walnut Creek North Developed \n",
"2 1 Thornton Road Property Undeveloped \n",
"3 2 Mary Belle Pate Undeveloped \n",
"4 3 Eliza Pool Developed \n",
"\n",
" geom \\\n",
"0 (POLYGON ((2097510.461880576 750949.1384854497... \n",
"1 (POLYGON ((2126075.468296753 733175.2605656629... \n",
"2 POLYGON ((2137024.335360867 783502.8727651152,... \n",
"3 POLYGON ((2095040.46173832 728970.3213452235, ... \n",
"4 (POLYGON ((2106009.598623645 731174.2719958539... \n",
"\n",
" buffer \n",
"0 POLYGON ((2095096.494260158 746927.2344195718,... \n",
"1 POLYGON ((2122769.214909975 731302.8963692773,... \n",
"2 POLYGON ((2134929.693431573 785199.8217412524,... \n",
"3 POLYGON ((2095823.978824902 728088.8998613623,... \n",
"4 POLYGON ((2104609.055921355 731558.4034951449,... "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"parks_gdf['buffer'] = parks_gdf.apply(lambda row: row['geom'].buffer(1000), axis = 1)\n",
"parks_gdf.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can use `.set_geometry()` to change the geometry column for the GeoDataFrame to the newly created `buffer` column. "
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x200d40aa0f0>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"parks_gdf = parks_gdf.set_geometry('buffer')\n",
"parks_gdf.plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"At this point, we have two columns with well-known text, each of which can be used as the geometry column for the GeoDataFrame. We can stop here, but what if we want to reproject our data? This isn't too difficult, but in order for both `geom` and `buffer` to have the same CRS, we'll have to work through setting the CRS separately for both columns. This is pretty straightforward for the currently active geometry column."
]
},
{
"cell_type": "code",
"execution_count": 9,
"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>PARKID</th>\n",
" <th>NAME</th>\n",
" <th>DEVELOPED</th>\n",
" <th>geom</th>\n",
" <th>buffer</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>34</td>\n",
" <td>Windemere Beaver Dam</td>\n",
" <td>Developed</td>\n",
" <td>(POLYGON ((2097510.461880576 750949.1384854497...</td>\n",
" <td>POLYGON ((-78.67929748801919 35.80186012413554...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>35</td>\n",
" <td>Walnut Creek North</td>\n",
" <td>Developed</td>\n",
" <td>(POLYGON ((2126075.468296753 733175.2605656629...</td>\n",
" <td>POLYGON ((-78.58619486943442 35.75865440904293...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>Thornton Road Property</td>\n",
" <td>Undeveloped</td>\n",
" <td>POLYGON ((2137024.335360867 783502.8727651152,...</td>\n",
" <td>POLYGON ((-78.54437359535919 35.90657508816413...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2</td>\n",
" <td>Mary Belle Pate</td>\n",
" <td>Undeveloped</td>\n",
" <td>POLYGON ((2095040.46173832 728970.3213452235, ...</td>\n",
" <td>POLYGON ((-78.67705082128717 35.75009928558416...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>3</td>\n",
" <td>Eliza Pool</td>\n",
" <td>Developed</td>\n",
" <td>(POLYGON ((2106009.598623645 731174.2719958539...</td>\n",
" <td>POLYGON ((-78.6474017667436 35.75954895462665,...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PARKID NAME DEVELOPED \\\n",
"0 34 Windemere Beaver Dam Developed \n",
"1 35 Walnut Creek North Developed \n",
"2 1 Thornton Road Property Undeveloped \n",
"3 2 Mary Belle Pate Undeveloped \n",
"4 3 Eliza Pool Developed \n",
"\n",
" geom \\\n",
"0 (POLYGON ((2097510.461880576 750949.1384854497... \n",
"1 (POLYGON ((2126075.468296753 733175.2605656629... \n",
"2 POLYGON ((2137024.335360867 783502.8727651152,... \n",
"3 POLYGON ((2095040.46173832 728970.3213452235, ... \n",
"4 (POLYGON ((2106009.598623645 731174.2719958539... \n",
"\n",
" buffer \n",
"0 POLYGON ((-78.67929748801919 35.80186012413554... \n",
"1 POLYGON ((-78.58619486943442 35.75865440904293... \n",
"2 POLYGON ((-78.54437359535919 35.90657508816413... \n",
"3 POLYGON ((-78.67705082128717 35.75009928558416... \n",
"4 POLYGON ((-78.6474017667436 35.75954895462665,... "
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"parks_gdf = parks_gdf.to_crs({'init': 'epsg:4326'})\n",
"parks_gdf.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It's a little extra work to update the CRS on the inactive column, `geom`. First, we have to set it as the geometry column. Then, we need to specify the current CRS of the column. The we can use `.to_crs()` to reproject."
]
},
{
"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",
" .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>PARKID</th>\n",
" <th>NAME</th>\n",
" <th>DEVELOPED</th>\n",
" <th>geom</th>\n",
" <th>buffer</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>34</td>\n",
" <td>Windemere Beaver Dam</td>\n",
" <td>Developed</td>\n",
" <td>(POLYGON ((-78.6711117690899 35.81288764531879...</td>\n",
" <td>POLYGON ((-78.67929748801919 35.80186012413554...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>35</td>\n",
" <td>Walnut Creek North</td>\n",
" <td>Developed</td>\n",
" <td>(POLYGON ((-78.57502395011019 35.7637599669244...</td>\n",
" <td>POLYGON ((-78.58619486943442 35.75865440904293...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>Thornton Road Property</td>\n",
" <td>Undeveloped</td>\n",
" <td>POLYGON ((-78.5373272819207 35.90188669471672,...</td>\n",
" <td>POLYGON ((-78.54437359535919 35.90657508816413...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2</td>\n",
" <td>Mary Belle Pate</td>\n",
" <td>Undeveloped</td>\n",
" <td>POLYGON ((-78.6796818530931 35.75252779924669,...</td>\n",
" <td>POLYGON ((-78.67705082128717 35.75009928558416...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>3</td>\n",
" <td>Eliza Pool</td>\n",
" <td>Developed</td>\n",
" <td>(POLYGON ((-78.6426857589002 35.75847987138462...</td>\n",
" <td>POLYGON ((-78.6474017667436 35.75954895462665,...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PARKID NAME DEVELOPED \\\n",
"0 34 Windemere Beaver Dam Developed \n",
"1 35 Walnut Creek North Developed \n",
"2 1 Thornton Road Property Undeveloped \n",
"3 2 Mary Belle Pate Undeveloped \n",
"4 3 Eliza Pool Developed \n",
"\n",
" geom \\\n",
"0 (POLYGON ((-78.6711117690899 35.81288764531879... \n",
"1 (POLYGON ((-78.57502395011019 35.7637599669244... \n",
"2 POLYGON ((-78.5373272819207 35.90188669471672,... \n",
"3 POLYGON ((-78.6796818530931 35.75252779924669,... \n",
"4 (POLYGON ((-78.6426857589002 35.75847987138462... \n",
"\n",
" buffer \n",
"0 POLYGON ((-78.67929748801919 35.80186012413554... \n",
"1 POLYGON ((-78.58619486943442 35.75865440904293... \n",
"2 POLYGON ((-78.54437359535919 35.90657508816413... \n",
"3 POLYGON ((-78.67705082128717 35.75009928558416... \n",
"4 POLYGON ((-78.6474017667436 35.75954895462665,... "
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"parks_gdf = parks_gdf.set_geometry('geom')\n",
"parks_gdf.crs = {'init': 'epsg:2264'}\n",
"parks_gdf = parks_gdf.to_crs({'init': 'epsg:4326'})\n",
"parks_gdf.head()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x200d409a4a8>"
]
},
"execution_count": 11,
"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": [
"parks_gdf.plot()"
]
},
{
"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": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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