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darribas/pt2xy2pt.ipynb

Created November 27, 2017 16:29
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Conversion on `geopandas` points to XYs to points
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pt2xy2pt.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"\n",
"import geopandas as gpd\n",
"import pysal as ps\n",
"import pandas as pd\n",
"import numpy as np\n",
"from shapely.geometry import Point"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Obtain a point dataset"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x10c0ba6a0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pts = gpd.read_file(ps.examples.get_path('columbus.shp')).centroid\n",
"pts.plot();"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Extract XY coords. into columns"
]
},
{
"cell_type": "code",
"execution_count": 3,
"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>X</th>\n",
" <th>Y</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>8.827218</td>\n",
" <td>14.369076</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>8.332658</td>\n",
" <td>14.031624</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>9.012265</td>\n",
" <td>13.819719</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>8.460801</td>\n",
" <td>13.716962</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>9.007982</td>\n",
" <td>13.296366</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" X Y\n",
"0 8.827218 14.369076\n",
"1 8.332658 14.031624\n",
"2 9.012265 13.819719\n",
"3 8.460801 13.716962\n",
"4 9.007982 13.296366"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"xys = pd.DataFrame(pts.apply(lambda pt: pd.Series({'X': pt.x, 'Y': pt.y})))\n",
"xys.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Pack XY coords into geometry"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1236d6198>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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unwUcGvTxyspvJ4sti/JBlqNBUgzt899oOgO1gCWtBT4MrIuIZzucdh9wtqSVkl4CXAV8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\nQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10c0ba4e0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"new_pts = gpd.GeoDataFrame({'geometry': \n",
" xys.apply(lambda r: Point(r['X'], r['Y']), axis=1)\n",
" }, crs=pts.crs)\n",
"new_pts.plot()"
]
}
],
"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": "3.5.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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