wassname/geopandas
1
0
Fork 0
mirror of https://github.com/wassname/geopandas.git synced 2026-09-24 13:30:40 +08:00
Code Issues Packages Projects Releases Wiki Activity
Files
c823893cdebccd6a412152b412ec04ed48ff0d2d
geopandas/examples/spatial_joins.ipynb
T

1127 lines
71 KiB
Plaintext
Raw Blame History

{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Spatial Joins\n",
"\n",
"A *spatial join* uses [binary predicates](http://shapely.readthedocs.io/en/latest/manual.html#binary-predicates) \n",
"such as `intersects` and `crosses` to combine two `GeoDataFrames` based on the spatial relationship \n",
"between their geometries.\n",
"\n",
"A common use case might be a spatial join between a point layer and a polygon layer where you want to retain the point geometries and grab the attributes of the intersecting polygons.\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"ExecuteTime": {
"end_time": "2017-12-15T21:26:04.391570Z",
"start_time": "2017-12-15T21:26:04.361570Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"<img src=\"https://web.natur.cuni.cz/~langhamr/lectures/vtfg1/mapinfo_1/about_gis/Image23.gif\"/>"
],
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.core.display import Image \n",
"Image(url='https://web.natur.cuni.cz/~langhamr/lectures/vtfg1/mapinfo_1/about_gis/Image23.gif') "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Types of spatial joins\n",
"\n",
"We currently support the following methods of spatial joins. We refer to the *left_df* and *right_df* which are the correspond to the two dataframes passed in as args.\n",
"\n",
"### Left outer join\n",
"\n",
"In a LEFT OUTER JOIN (`how='left'`), we keep *all* rows from the left and duplicate them if necessary to represent multiple hits between the two dataframes. We retain attributes of the right if they intersect and lose right rows that don't intersect. A left outer join implies that we are interested in retaining the geometries of the left. \n",
"\n",
"This is equivalent to the PostGIS query:\n",
"```\n",
"SELECT pts.geom, pts.id as ptid, polys.id as polyid \n",
"FROM pts\n",
"LEFT OUTER JOIN polys\n",
"ON ST_Intersects(pts.geom, polys.geom);\n",
"\n",
" geom | ptid | polyid \n",
"--------------------------------------------+------+--------\n",
" 010100000040A9FBF2D88AD03F349CD47D796CE9BF | 4 | 10\n",
" 010100000048EABE3CB622D8BFA8FBF2D88AA0E9BF | 3 | 10\n",
" 010100000048EABE3CB622D8BFA8FBF2D88AA0E9BF | 3 | 20\n",
" 0101000000F0D88AA0E1A4EEBF7052F7E5B115E9BF | 2 | 20\n",
" 0101000000818693BA2F8FF7BF4ADD97C75604E9BF | 1 | \n",
"(5 rows)\n",
"```\n",
"\n",
"### Right outer join\n",
"\n",
"In a RIGHT OUTER JOIN (`how='right'`), we keep *all* rows from the right and duplicate them if necessary to represent multiple hits between the two dataframes. We retain attributes of the left if they intersect and lose left rows that don't intersect. A right outer join implies that we are interested in retaining the geometries of the right. \n",
"\n",
"This is equivalent to the PostGIS query:\n",
"```\n",
"SELECT polys.geom, pts.id as ptid, polys.id as polyid \n",
"FROM pts\n",
"RIGHT OUTER JOIN polys\n",
"ON ST_Intersects(pts.geom, polys.geom);\n",
"\n",
" geom | ptid | polyid \n",
"----------+------+--------\n",
" 01...9BF | 4 | 10\n",
" 01...9BF | 3 | 10\n",
" 02...7BF | 3 | 20\n",
" 02...7BF | 2 | 20\n",
" 00...5BF | | 30\n",
"(5 rows)\n",
"```\n",
"\n",
"### Inner join\n",
"\n",
"In an INNER JOIN (`how='inner'`), we keep rows from the right and left only where their binary predicate is `True`. We duplicate them if necessary to represent multiple hits between the two dataframes. We retain attributes of the right and left only if they intersect and lose all rows that do not. An inner join implies that we are interested in retaining the geometries of the left. \n",
"\n",
"This is equivalent to the PostGIS query:\n",
"```\n",
"SELECT pts.geom, pts.id as ptid, polys.id as polyid \n",
"FROM pts\n",
"INNER JOIN polys\n",
"ON ST_Intersects(pts.geom, polys.geom);\n",
"\n",
" geom | ptid | polyid \n",
"--------------------------------------------+------+--------\n",
" 010100000040A9FBF2D88AD03F349CD47D796CE9BF | 4 | 10\n",
" 010100000048EABE3CB622D8BFA8FBF2D88AA0E9BF | 3 | 10\n",
" 010100000048EABE3CB622D8BFA8FBF2D88AA0E9BF | 3 | 20\n",
" 0101000000F0D88AA0E1A4EEBF7052F7E5B115E9BF | 2 | 20\n",
"(4 rows) \n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Spatial Joins between two GeoDataFrames\n",
"\n",
"Let's take a look at how we'd implement these using `GeoPandas`. First, load up the NYC test data into `GeoDataFrames`:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"ExecuteTime": {
"end_time": "2017-12-15T21:26:07.191542Z",
"start_time": "2017-12-15T21:26:04.391570Z"
}
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"from shapely.geometry import Point\n",
"from geopandas import datasets, GeoDataFrame, read_file\n",
"from geopandas.tools import overlay\n",
"\n",
"# NYC Boros\n",
"zippath = datasets.get_path('nybb')\n",
"polydf = read_file(zippath)\n",
"\n",
"# Generate some points\n",
"b = [int(x) for x in polydf.total_bounds]\n",
"N = 8\n",
"pointdf = GeoDataFrame([\n",
" {'geometry': Point(x, y), 'value1': x + y, 'value2': x - y}\n",
" for x, y in zip(range(b[0], b[2], int((b[2] - b[0]) / N)),\n",
" range(b[1], b[3], int((b[3] - b[1]) / N)))])\n",
"\n",
"# Make sure they're using the same projection reference\n",
"pointdf.crs = polydf.crs"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"ExecuteTime": {
"end_time": "2017-12-15T21:26:07.211542Z",
"start_time": "2017-12-15T21:26:07.191542Z"
}
},
"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>geometry</th>\n",
" <th>value1</th>\n",
" <th>value2</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>POINT (913175 120121)</td>\n",
" <td>1033296</td>\n",
" <td>793054</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>POINT (932450 139211)</td>\n",
" <td>1071661</td>\n",
" <td>793239</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>POINT (951725 158301)</td>\n",
" <td>1110026</td>\n",
" <td>793424</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>POINT (971000 177391)</td>\n",
" <td>1148391</td>\n",
" <td>793609</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>POINT (990275 196481)</td>\n",
" <td>1186756</td>\n",
" <td>793794</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>POINT (1009550 215571)</td>\n",
" <td>1225121</td>\n",
" <td>793979</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>POINT (1028825 234661)</td>\n",
" <td>1263486</td>\n",
" <td>794164</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>POINT (1048100 253751)</td>\n",
" <td>1301851</td>\n",
" <td>794349</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>POINT (1067375 272841)</td>\n",
" <td>1340216</td>\n",
" <td>794534</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" geometry value1 value2\n",
"0 POINT (913175 120121) 1033296 793054\n",
"1 POINT (932450 139211) 1071661 793239\n",
"2 POINT (951725 158301) 1110026 793424\n",
"3 POINT (971000 177391) 1148391 793609\n",
"4 POINT (990275 196481) 1186756 793794\n",
"5 POINT (1009550 215571) 1225121 793979\n",
"6 POINT (1028825 234661) 1263486 794164\n",
"7 POINT (1048100 253751) 1301851 794349\n",
"8 POINT (1067375 272841) 1340216 794534"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pointdf"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"ExecuteTime": {
"end_time": "2017-12-15T21:26:07.921534Z",
"start_time": "2017-12-15T21:26:07.211542Z"
}
},
"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>BoroCode</th>\n",
" <th>BoroName</th>\n",
" <th>Shape_Leng</th>\n",
" <th>Shape_Area</th>\n",
" <th>geometry</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>5</td>\n",
" <td>Staten Island</td>\n",
" <td>330470.010332</td>\n",
" <td>1.623820e+09</td>\n",
" <td>(POLYGON ((970217.0223999023 145643.3322143555...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4</td>\n",
" <td>Queens</td>\n",
" <td>896344.047763</td>\n",
" <td>3.045213e+09</td>\n",
" <td>(POLYGON ((1029606.076599121 156073.8142089844...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>Brooklyn</td>\n",
" <td>741080.523166</td>\n",
" <td>1.937479e+09</td>\n",
" <td>(POLYGON ((1021176.479003906 151374.7969970703...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>Manhattan</td>\n",
" <td>359299.096471</td>\n",
" <td>6.364715e+08</td>\n",
" <td>(POLYGON ((981219.0557861328 188655.3157958984...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2</td>\n",
" <td>Bronx</td>\n",
" <td>464392.991824</td>\n",
" <td>1.186925e+09</td>\n",
" <td>(POLYGON ((1012821.805786133 229228.2645874023...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" BoroCode BoroName Shape_Leng Shape_Area \\\n",
"0 5 Staten Island 330470.010332 1.623820e+09 \n",
"1 4 Queens 896344.047763 3.045213e+09 \n",
"2 3 Brooklyn 741080.523166 1.937479e+09 \n",
"3 1 Manhattan 359299.096471 6.364715e+08 \n",
"4 2 Bronx 464392.991824 1.186925e+09 \n",
"\n",
" geometry \n",
"0 (POLYGON ((970217.0223999023 145643.3322143555... \n",
"1 (POLYGON ((1029606.076599121 156073.8142089844... \n",
"2 (POLYGON ((1021176.479003906 151374.7969970703... \n",
"3 (POLYGON ((981219.0557861328 188655.3157958984... \n",
"4 (POLYGON ((1012821.805786133 229228.2645874023... "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"polydf"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"ExecuteTime": {
"end_time": "2017-12-15T21:26:08.271531Z",
"start_time": "2017-12-15T21:26:07.921534Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x4f1b0b8>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAS0AAAD8CAYAAAAi9vLQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAFCxJREFUeJzt3X2QXXV9x/H3twQjtmgSwrNkEhSYQSpPEUEGH7AVpFat\nrQrDaKyMjGAZoRYKUp9rB4i2o9OpaAuDdVCJFVLbakMApQ/TgAnPT5EHFRMeEogIIwF5+PaP87vs\nybLLbu7uvXd/u+/XzJ09+7vn3PO759793nt+5+z5RGYiSbX4rUF3QJK2hkVLUlUsWpKqYtGSVBWL\nlqSqWLQkVWXMohURe0TEDyPitoi4NSI+WtoPiIhVEXFDRKyOiENay5wVEXdFxNqIOKrVfnBE3Fzu\n+3JERGmfHRGXlPZrImJha5klEXFnuS2ZzCcvqUKZ+YI3YFfgoDK9PfATYF/gcuCtpf0Y4Edlel/g\nRmA2sAi4G9im3HctcCgQwA9ay58MnF+mjwUuKdPzgHvKz7lleu5Yffbmzdv0vY35TSsz78/M68r0\nY8DtwO5AAi8ts70MuK9MvwP4dmY+mZk/Be4CDomIXYGXZuaqzEzgn4F3tpb5epn+F+DN5VvYUcDK\nzNyUmb8EVgJHj9VnSdPXrK2Zuey2HQhcA5wKrIiIL9DsZr6uzLY7sKq12LrS9lSZHt7eWeYXAJn5\ndET8Ctih3T7CMiOaP39+Lly4cGuelqQurVmz5qHM3LGf6xx30YqI3wG+C5yamY9GxF8Dp2XmdyPi\nPcAFwO/1qJ9j9e1E4ESABQsWsHr16kF0Q5pxIuLn/V7nuI4eRsS2NAXr4sy8tDQvATrT3wE6A/Hr\ngT1ai7+8tK0v08Pbt1gmImbR7G4+/AKPtYXM/FpmLs7MxTvu2NeiL6nPxnP0MGi+Rd2emX/buus+\n4A1l+kjgzjL9PeDYckRwEbAXcG1m3g88GhGHlsd8P/CvrWU6Rwb/BLiqjHutAN4SEXMjYi7wltIm\naYYaz+7h4cD7gJsj4obS9nHgQ8CXyjejJyi7Z5l5a0QsA24DngY+kpnPlOVOBi4CtqM5eviD0n4B\n8I2IuAvYRHMEkczcFBGfA35c5vtsZm7q8rlKmgai+UIzfSxevDgd05L6IyLWZObifq7TM+IlVWWr\nTnmQNL0tv349S1es5b5HNrPbnO04/ah9eOeBL3iWUd9ZtCQBTcE669Kb2fxUMwS9/pHNnHXpzQBT\nqnC5eygJgKUr1j5XsDo2P/UMS1esHVCPRmbRkgTAfY9s3qr2QbFoSQJgtznbbVX7oFi0JAFw+lH7\nsN2222zRtt2223D6UfsMqEcjcyBeEjA02O7RQ0nVeOeBu0+5IjWcu4eSqmLRklQVi5akqli0JFXF\noiWpKhYtSVWxaEmqikVLUlW6Tpgu950SEXeU9vNa7SZMS+qJ8ZwR/zTwscy8LiK2B9ZExEpgZ5qQ\n1f0z88mI2AkgIvalucb7q4DdgCsiYu9ynfiv0Fxb/hrg+zTBqz8ATgB+mZmvjIhjgXOB90bEPOBT\nwGKacNg1EfG9EtwqaQaaSML0ScA5mflkuW9DWcSEaUk9s1VjWsMSpvcGjii7c1dHxGvKbKOlQu/O\nOBOmga4TpiVNbxNJmJ4FzAMOBV4DLIuIPXvTzTH7tkXCtKTpayIJ0+uAS7NxLfAsMB8TpiX10EQS\nppcDbyrz7A28CHgIE6Yl9dBEEqYvBC6MiFuA3wBLSqExYVpSz5gwLalrg0iY9sqlUiVqCFLtB4uW\nVIFaglT7wf89lCpQS5BqP1i0pArUEqTaDxYtqQK1BKn2g0VLqkAtQar94EC8VIFaglT7waIlVaKG\nINV+cPdQUlUsWpKqYtGSVBWLlqSqWLQkVcWiJakqFi1JVbFoSaqKRUtSVSaUMF3u/1hEZETMb7WZ\nMC2pJ8bzTauTML0vTVzYR0qKNBGxB03YxL2dmYclTB8N/ENEdP7Ts5MwvVe5dYJXn0uYBv6OJmGa\nVsL0a4FDgE+VgAtJM9REEqahKTBn0ETWd5gwLalnuk6Yjoh3AOsz88Zhs5kwLalnukqYptll/DjN\nruHAmTAtzRzdJky/AlgE3BgRP6NJfr4uInbBhGlJPdRVwnRm3pyZO2XmwsxcSLPbdlBmPoAJ05J6\nqOuE6cz8/kgzZ6YJ05J6xoRpaRLM1CBVE6alChmk2l/+G480QQap9pdFS5ogg1T7y6IlTZBBqv1l\n0ZImyCDV/nIgXpogg1T7y6IlTQKDVPvH3UNJVbFoSaqKRUtSVSxakqpi0ZJUFYuWpKpYtCRVxaIl\nqSoWLUlVsWhJqkrXCdMRsTQi7oiImyLisoiY01rGhGlJPTGRhOmVwH6Z+WrgJ8BZYMK0pN7qOmE6\nMy8vwaoAqxiKBzNhWlLPdJ0wPeyuDzKUrGPCtKSeGXfRaidMZ+ajrfazaXYhL5787o27bydGxOqI\nWL1x48ZBdUNSH3SbMN1p/wDwNuD4HMoiM2FaUs90lTBd2o8GzgDenpmPtxYxYVpTyvLr13P4OVex\n6Mz/4PBzrmL59c/73FNFuk6YBr4MzAZWljMXVmXmh02Y1lRiJuH0Y8K0prXDz7mK9SNEee0+Zzv+\n98wjB9Cj6WUQCdOeEa9pzUzC6ceipWnNTMLpx6Klac1MwunHCDFNa2YSTj8WLU17ZhJOL+4eSqqK\nRUtSVSxakqpi0ZJUFYuWpKpYtCRVxaIlqSoWLUlVsWhJqopFS1JVLFqSqmLRklSViSRMz4uIlSX5\neWU7RNWEaUm9MpGE6TOBKzNzL+DK8rsJ05J6quuEabZMhf46W6ZFmzAtqScmkjC9c4kFA3gA2LlM\nmzAtqWcmnDANUL45DSzWx4RpaeaYSML0g2WXj/JzQ2k3YVrjZpCqtlbXCdNsmQq9hC3Tok2Y1pg6\nQarrH9lMMhSkauHSC5lIwvQ5wLKIOAH4OfAeABOmNV5LV6x9Lvm5Y/NTz7B0xVqv6a5RjVm0MvN/\ngBjl7jePsszngc+P0L4a2G+E9ieAd4/yWBcCF47VT9XHIFV1wzPiNTAGqaobFi0NjEGq6oa5hxoY\ng1TVDYuWBsogVW0tdw8lVcWiJakqFi1JVbFoSaqKRUtSVSxakqpi0ZJUFYuWpKpYtCRVxaIlqSoW\nLUlVsWhJqopFS1JVxnON+AsjYkNE3NJqOyAiVkXEDSUF55DWfaZLS+qZ8XzTuojnB6SeB3wmMw8A\nPll+N11aUs+NJ2H6v2jCJrZoBl5apl8G3FemTZeW1FPdXgTwVGBFRHyBpvC9rrTvDqxqzddJhH6K\ncaZLR8RWp0tHxInAiQALFizo8ilJqkG3A/EnAadl5h7AaTQRYANjWGtvGKSqqajborUE6CRNf4dm\nzAkGkC6t3jBIVVNVt0XrPuANZfpI4M4ybbr0NPFCQarSII05phUR3wLeCMyPiHU0R/Q+BHypfDN6\ngjKeZLr09GGQqqaq8SRMHzfKXQePMr/p0tPAbnO2Y/0IBcogVQ2aZ8RrRAapaqoy91AjMkhVU5VF\nS6MySFVTkbuHkqpi0ZJUFYuWpKpYtCRVxaIlqSoWLUlVsWhJqopFS1JVLFqSqmLRklQVi5akqli0\nJFXFoiWpKhYtSVXpKmG6tJ8SEXdExK0RcV6r3YRpST3TVcJ0RLyJJmR1/8x8FfCF0m7CtKSe6jZh\n+iTgnMx8ssyzobSbMN0nZhJqpup2TGtv4IiyO3d1RLymtI+WCr0740yYBrpKmI6I1RGxeuPGjV0+\npXqYSaiZrNuiNQuYBxwKnA4s64xRDcJMS5g2k1AzWbdFax1waTauBZ4F5mPCdF+YSaiZrNuitRx4\nE0BE7A28CHgIE6b7YrTsQTMJNROM55SHbwH/B+wTEesi4gSaANU9y2kQ3waWlG9dtwKdhOn/5PkJ\n0/9EMzh/N1smTO9QEqb/HDgTmoRpoJMw/WNMmH6OmYSayaL5UjN9LF68OFevXj3obvTc8uvXm0mo\ngYuINZm5uJ/rNPewUmYSaqby33gkVcWiJakqFi1JVbFoSaqKRUtSVSxakqpi0ZJUFYuWpKpYtCRV\nxaIlqSoWLUlVsWhJqopFS1JVLFqSqmLRklQVi5akqnSdMF3u+1hEZETMb7WZMC2pZ7pKmAaIiD1o\nwibubbWZMI1BqlIvdZswDU2BOQNoX2R+xidMG6Qq9VZXY1oR8Q5gfWbeOOyuGZ8wbZCq1FtbXbQi\n4iXAx4FPTn53ujOVEqYNUpV6q5tvWq8AFgE3RsTPaJKfr4uIXTBh2iBVqce2umhl5s2ZuVNmLszM\nhTS7bQdl5gOYMG2QqtRjY+YeloTpNwLzI2Id8KnMvGCkeTPz1ojoJEw/zfMTpi8CtqNJl24nTH+j\nJExvojn6SGZuiohOwjRUkjDdySI0SFXqDROmJXVtEAnTnhEvqSoWLUlVsWhJqopFS1JVLFqSqmLR\nklQVi5akqli0JFXFoiWpKhYtSVWxaEmqikVLUlUsWpKqYtGSVBWLlqSqWLQkVaWrsNaIWBoRd0TE\nTRFxWUTMad1nWKuknuk2rHUlsF9mvhr4CXAW1BHWapCqVLeuwloz8/KSUQiwiqGknSkd1mqQqlS/\nyRjT+iBDIRUDCWsdL4NUpfpNqGhFxNk0qTsXT053uu7HuBKmDVKV6td10YqIDwBvA47PoUifgYS1\njjdh2iBVqX5dFa2IOBo4A3h7Zj7eumtKh7UapCrVr6uwVpqjhbOBleXMhVWZ+eGpHtZqkKpUP8Na\nJXXNsFZJGoNFS1JVLFqSqmLRklQVi5akqky7o4cRsRH4eR9WNR94qA/rcf1Ttw+DXv9U6MM+mbl9\nP1c45nlatcnM0U+Jn0QRsbrfh3pd/9Tqw6DXPxX6EBF9P7/I3UNJVbFoSaqKRat7X3P9AzfoPgx6\n/TD4PvR9/dNuIF7S9OY3LUl1ycwZdQM+CtwC3AqcWtqWAncANwGXAXNK+0JgM3BDuZ3fepyDgZtp\nLin9ZYa+tc4GLint1wALW8ssAe4ENtJcibXdh0/TXC+ss65jWsudVR5vLXDUJPRhI/Bk6UNn/Ze0\n1v0z4IbJ3AbAhcCGss47y+1kmsto31l+zu3hc/4VzZVH1rXaDyjtvwEeAHYq7b8PrCnrWQMc2Vrm\nR6VPne2xUw/WPynbfIT33a+AR4FbSvsiYDXwOPAYcEXnNQCOb63/BuBZ4IAJboPO676k1b6ozHtX\nWfZFY/4ND7qI9Llg7UdTsF5Cc7rHFcAraa7VNavMcy5wbuvNc8soj3UtcCgQNJfZeWtpP7nzJqO5\nzM4lZXoecA/wOppL9/yU5hybTh8+DfzFCOvZF7ixvCEWAXcD20ygD78Abgd2K/35EfDKYev8IvDJ\nydwGwOtpLnH0m9KPucAjwGfKfGe2tvtkP+d7gD8A3lDW3/nDvAP4ZpleBawo0wcCu7XeM+uHFa3F\nI2yLyVz/pGzzYeufBxxD86FxW7lvGc317M4Ezqf5wD53hHX+LnD3JGyDzut+T2sbLAOOLdPnAyeN\n+Xc86ELSzxvwbuCC1u+fAM4YNs8fARe/0JsH2BW4o/X7ccBXy/QK4LAyPYvmxL/ozNPpQ5k+rtMH\nRi9aZwFntX5fARw2gT6s7GyD0odl7W1Q5vsFsFcPtsEpwKbWMo903qTl8db26Dl/tfVcNpW2oPnm\n8/Jy39uAX4/wPKMsM3uMP9hJW/8kb/Pn5in3XVxe3yjzrC2Pexjww85rMGy9fwN8vvV719ug9b47\nrtWHzheGwyiF+4VuM21M6xbgiIjYISJeQvPJs8ewedpBHQCLIuKGiLg6Io4obRMJ6rgFOILmktIL\nh/XhlJIleWErLm2yw0Ju62wD4EGaeLb2NjgCeDAz7+zBNtgFeKq1zGzgt8v0A8DOPXrO7cd6qrTt\nQLNr1Xm8G4EX83x/DFyXmU+22r5etscnOvmdPVj/ZL/vOh6gKSg70Hxo7JzNlYXXATsy9Bq0vRf4\n1rC2iWyDTr93AB7JoWSvcYXXTLsz4l9IZt4eEecClwO/ptkffy6eZ4SgjvuBBZn5cEQcDCyPiFdN\nUh8+SzOutKL04SvA54AsP79IU0An20aaXeDLad4099HaBjSfgO036KRvg5FkZkZETvbjTkR5nufS\nDB90HJ+Z6yNie+C7wPtoIvEmU1+2+Si2eA0i4rXA45l5S6u5H9tgVDPtmxaZeUFmHpyZrwd+SRM2\nO2JQRzb5jQ+X6TU0Yyt7M8Ggjsy8APh34OxOHzLzwcx8JjOfBf6R5hvQFo83bF1d96GzDWgK5obW\nNpgFvItmDKqzvSZzGzwAbNta5kmaDw9KNuaGXj3n1jLblraHgYyIzuPtDzzRmam0Xwa8PzPvbm2P\n9eXnY8A3GeF1muj6e/W+K3ah+WB+GJgDPFi2/ctpPtA2sKVjGfYtaxK2QaffDwNzyrzDn8/oxtp/\nnG43ho50LKAZCJ1DEwJ7G7DjsHl3ZGgAeM+yQeeV34cPiB5T2j/CloORy8r0PJrB97k0gR8/pRng\n7PRh19Z6T6MJvYUmrbs9KH0Pow9Kj7cPe5V+3EtTsDpHS48Gru7hNtifMhDNyAPx5/XwOc8FXl3W\n3+n/WrYcCL+8TM8p63/XsG0xC5hfprelCRf+cA/W36v33VzKgZhy33eAf2NoIH555zUo9/9WWfee\nk7gN5pbpea0+tAfiTx7zb3jQRWQAReu/aQrUjcCbS9td5cXc4hAzzXjGraXtOuAPW4+zmGZ86m7g\n7xk69Pzi8kLcVd5g7Rf8g6V9c3kztPvwDZpD2TfRHNFpF7Gzy3rWUo4WTbAPm2n+eO7trL/cd1Hn\nDdhqm5RtQPNpfT/Np/zTNONpfwZcSXMY/IrOG7lHz/mx1rrXAScABzF0ysGDwC5l/r9iaPjgucP6\nNONva8prdCvwJYaKy2Suv1fvu8doPiieKn34y/L4nVMerhz2GryRJrSm/X6YyDa4q9z+tNW+Z5n3\nrrLs7LH+hj0jXlJVZtyYlqS6WbQkVcWiJakqFi1JVbFoSaqKRUtSVSxakqpi0ZJUlf8H8UztvyKh\nSMoAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x4efcc88>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pointdf.plot()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"ExecuteTime": {
"end_time": "2017-12-15T21:26:10.561508Z",
"start_time": "2017-12-15T21:26:08.271531Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x12991208>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAS0AAAD8CAYAAAAi9vLQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXd4m9XZh+8jybK8955JHGdvZ0BYAUqAMAshtMxSSgst\nhZZCgdJCKaOlLauFr6WQMsqGACEkQAiBJCRx9nScON57b1vWOt8fkhXvKQ/Z574uX5GOzvvqKIl+\nPuN5np+QUqJQKBTugmakB6BQKBT9QYmWQqFwK5RoKRQKt0KJlkKhcCuUaCkUCrdCiZZCoXArehUt\nIUScEGKzECJNCHFUCHGXo32uEGKnEOKAEGKPEGJRm2seEEKcFEIcF0Isb9O+QAhx2PHa80II4Wj3\nFEK862hPFUIktrnmJiFEhuPnJld+eIVC4YZIKXv8AaKA+Y7HfsAJYDrwJXCRo/1i4BvH4+nAQcAT\nmABkAlrHa7uAJYAANrS5/g7gX47H1wLvOh4HA1mOP4Mcj4N6G7P6UT/qZ+z+9DrTklIWSyn3OR7X\nA8eAGEAC/o5uAUCR4/HlwDtSyhYpZTZwElgkhIgC/KWUO6WUEngduKLNNa85Hn8AnOeYhS0HNkop\nq6SU1cBG4MLexqxQKMYuuv50dizb5gGpwN3AF0KIv2FfZp7u6BYD7GxzWYGjzex43LG99Zp8ACml\nRQhRC4S0be/imi4JDQ2ViYmJ/flYCoVigOzdu7dCShk2nO/ZZ9ESQvgCHwJ3SynrhBCPAb+SUn4o\nhLgGeAU4f4jG2dvYbgNuA4iPj2fPnj0jMQyFYtwhhMgd7vfs0+mhEMIDu2C9KaVc42i+CWh9/D7Q\nuhFfCMS1uTzW0VboeNyxvd01Qggd9uVmZQ/3aoeU8iUpZYqUMiUsbFhFX6FQDDN9OT0U2GdRx6SU\nT7d5qQg42/H4XCDD8XgtcK3jRHACMBnYJaUsBuqEEEsc97wR+KTNNa0ng1cDXzv2vb4ALhBCBAkh\ngoALHG0KhWKc0pfl4VLgBuCwEOKAo+1B4CfAc46ZkRHH8kxKeVQI8R6QBliAn0sprY7r7gBeBbyw\nnx5ucLS/ArwhhDgJVGE/QURKWSWE+BOw29HvUSll1QA/q0KhGAMI+4Rm7JCSkiLVnpZCMTwIIfZK\nKVOG8z1VRLxCoXArlGgpFAq3QomWQqFwK5RoKRQKt0KJlmJc8PLWLD7eX4jJYhvpoSgGSb/SeBQK\nd+UfX5+kttnMkxuOcU1KHJfPjSYp3G+kh6UYAGqmpRjz1DaZqW02A1Ba18I/vj7J+U9v4fJ/buOd\nXXk0tFj6fc9mk5XsikZXD1XRB5RoKcY8+/Kru2w/WFDL/WsOs/Cxr/j1ewdIzaqkr3GLb6bmYjRb\ne++ocDlqeagY82w/WdHj681mK2v2FbJmXyFTIvxYmRLLFfNiCPX17LJ/UU0znx0u5sujpXh6aLj7\n/MksSAgeiqErukCJlmJMYzRb+eJoaZ/7Hy+t57HPjvHnDemcOzWcqxfEsmxqOB7aU4uSDUdK2J9X\n43y+J6eae5dPYcXsKCL8DS4dv6IzSrQUY5pDBbXkVTX1+zqLTfJlWilfppUS6qtn1cI4rpofi01K\nXtue065vs9nKo+vSCPT24PvzY7u+ocJlKNFSjGnSS+oGfY+KBhMvbM7khc2ZxAZ5UVDd3GW/13fk\nKtEaBtRGvGJMs+VEuUvv151gAVQ3mVz6XoquUaKlGLOYLDZSs4avklFxrZHGAYRPKPqHEi3FmCU1\nu5L6YRQRk8XGr949wObjZVhtY6vk02hC7WkpxixbM3oOdRgKWjfvowIMrFoYxzUpcQgBAkFkgDpZ\ndAVKtBRjlq/Ty0bsvYtrjTz7VQbPbcogOdyPE2X1nDslnMvmRnPu1HD8DB4jNjZ3Z8AO047X7hRC\npDvan2rTrhymFSNKflUTJ8saRnoYSGmP/ZISNqWXcdc7B1jwp6+45dXdfLy/kNom80gP0e3oy0zL\nAtwjpdwnhPAD9gohNgIR2E1W50gpW4QQ4QBCiOnYa7zPAKKBr4QQyY468f+HvbZ8KrAeu/HqBuDH\nQLWUMkkIcS3wF2CVECIYeBhIwW4Ou1cIsdZh3KpQdMunh4p67zRCmKw2vk4v4+v0MrQawVmTQ1kx\nO5rvTY8gwEvNwHpjMA7TtwN/llK2OF5rnYsrh2nFiCKl5OP9nZzmRiVWm2Tz8XJ+8/5BFj72FZuO\n9T16f7zSr9PDDg7TycCZjuXct0KIhY5u3blCx9BHh2lgwA7TCsWx4npOlI780rC/hPt7smRiCF+l\nlZJZ3qBqf3VDn0Wro8M09qVlMLAEuBd4r3WPargRQtwmhNgjhNhTXu7aYEKF+1HV2MKUiFO1sjy0\ngsUTgtHrRneEz8LEYB79NI1H16Xx8tasET1IGM0MxmG6AFgj7ewCbEAoymFaMUJYrDaeWH+Ml7Zm\n89TVs4kL9iLcz5OYQC9Ss6uYGe0/0kPskeuXxJNf3UReVRNv78onrahWxXt1wWAcpj8Gljn6JAN6\noALlMK0YId7elcdLW7LYcqKckjojf7xsBr88L4mVC+KID/bGbB29y627z5/MvLggZ5pQYog3P1qa\n2K6+V71RnTTC4BymVwOrhRBHABNwk0NolMO0YtixWG383zeZzud/+Tyd4hojzWYrQsBfvj+b13fm\njNwAuyEhxBudRnD53BiufyXVWZFCr9PwxPp0/nTFTHRae1+tRlBaZxz35W96FS0p5Tagu72q67u5\n5nHg8S7a9wAzu2g3Aiu7uddq7AKpUHTLrpwqimqNzudZ5adKIUsJZquN9OL6kRhat5w/LYKqxhb+\n+cP5PLz2KNszKwEweGgI9fWkstHUro6XViOoajQp0RrpASgUrqCoxtjta1qNINDbA8so2x86XlrH\n09fMpaKhhY1pp0IdPvjZ6cyMCaDFYkWrOTVf8NRpmRblT7PJipdeOxJDHhUo0VKMCdYfLu72tSvn\nxbDp2Og7icuvaua21/cwIzrA2ZYc4cvMGPtzT11nYTKarVj7WMd+rDK6z4AVij6wN7e62/CAGdH+\nXDo7ijWjJNjUz6AjNsjL+by6ycy2NjXsA731zsfNps7GGQYPLb6e43uuoURL4dZIKfnjp0e7fM1D\nK7jz3CTufHv/MI+qe3574VS+uPssFiUGE+yjZ1KYT7vXD+TVOE85rVI6HX9MFhs1qsggoERL4eZ8\nfqSEQwW1ndrnxAVy45IENhwpoc44egrzPfTxEfbn1fDU1bN58OJpnDYppN3rb/5kMR5aDcdL6tmc\nXobBw75E1Os0eOt1PPjR4S5nYOMJJVoKt6XFYuWvXxzv8rVJYT6E+nnyyYHRlzidml1JYqgPV86L\nYXIbl2s/Tx3JEb7ctHoX8cHeXDonut11Oo3grVR7LNp4RomWwm35cG8hWd24PF80M5KnN54Y5hH1\njRpHORqtRuDlmElNjfRj62+XYTRZ2ZFVSUF1E5UNLRxpM4tsTZJ7eVsWWeXul1vpKsb3jp7CbWk2\nWXlpS2aXr62YFcnqbTmYraPrlE2rEXzy86XtKpg2O/asFiYGE+itp8lkITbIi08OFPGb5VMIaWMY\n22iy4uWhZfXNC5kY5jvs4x8tqJmWwu04UljLZf/cRk5lZz9DrUZwVnIYO7IqR2BkPWPQaQj382zn\nXP2DRfFMCPUhp9I+Y/TQarh2YRwvfHOSRz9Na3e9TmMv2dw6OxuvKNFSuB2fHS4mo5uqpCsXxLBm\n3+gIb+hIo8nK2oPt99j0Og1XzY/h8StmAXbRKqxuRkr4+EBhu/paBg8tiycEE+7vyXhGiZbC7fAz\ndL2r4anTkBTuR2r26E1P9dRpOp3+/eLcycSHeAP2EI7vHOk8VY0m9ufVYGsTye/v5THqlr3DjRIt\nhduR1M1+zqqFcfz3u5zhHUw/MHho+MfXJ0kr7hyi0YoQgjvOmeR8fsW8GNpK1L3Lpzhjt8YrSrQU\nbsfRoq6t7ieH+1JY070D9EhjNNu4/ZxJzrSdk2UNrD1YhKVDyZyzk8OICbRHzZfVG7HYTr3uodUw\naRxvwoMSLYWbYbbaeDM1t1P7lAg/0ktGVxWHrtifV+MMGA3x0RPo5eE8QWwlxNeTlSn2eplv7szr\nMgdxPKNES+FW7MispKKhczrLqoVxrDvUfdL0cLAgIajXPvPiA52Pg3z0nJUc1qUH4t3nJzM/PtAp\ncIpTqDgthVuxJ6fzJruPXovFaqO2eWQre5osNjy0oseN8uQ2teu7o7Va6TOr5o778IauUKKlcCt2\ndnEyuHhiCHtyR94Ks7bZTKivJ8W13df2qu5D0nN9iwWrVZIQ4tNr3/HIoBymHa/fI4SQQojQNm3K\nYVrhco4U1rKrC9Hy1Gkoq28ZgRGdQqcRTIvy61GwwL6n1Rv+Bg+CfPS99huv9GVPq9Vhejp2u7Cf\nO1ykEULEYTebyGvt3MFh+kLgRSFE6xy31WF6suOn1XjV6TANPIPdYZo2DtOLgUXAww6DC8U45O9f\ndp0cXdlgIniEv+RLJoaw+Xjv9nXv7cmnrL5nYVP0zGAcpsEuMPdBu1AS5TCtcCktFiuv78jpVhSK\napsJ9xvZKPGKhhY8tb3PAeqNFv63I5fKhpGdGbozA3aYFkJcDhRKKQ926KYcphUu5e9fnuAPn3Rd\n6A/sQjDSM630knoWTgjuU98Vs6M5XlrPtyeUsfBA6PNGfFuHaexLxgexLw1HHCHEbcBtAPHx8SM8\nGoUrabFY2XCk51CGhhYLgd6dwwaGG58+lkG+6539pJfUc3ZyGGcnK3Ph/jJQh+lJwATgoBAiB7vz\n8z4hRCTKYVrhQmw2ej32t9oknqPA8t7uSdj7MrU1CHZf3sifeLojA3KYllIellKGSykTpZSJ2Jdt\n86WUJSiHaYUL+c/WLE6U9l7wbjS4g503Nbxf46g3Wth0rLRTGo+iZwbsMC2lXN9VZymlcphWuIQD\n+TU8vymjT31HWrQmhvkwOzaQ8n6EXuh1Gn7y+h5CfD158OKpXDkvtveLFIN2mG7tk9jhuXKYVgwK\nm01y/4eH+myw6urV4YRQH5bPiGR3ThV7uwhcFcK+bG1ylJm5cUkCiaHe/XqPYG89kyN82ZpRQXGt\nESklQvT4VVOgIuIVo5R1h4v7lQDdYnHtVCu7opF/b8nksStmUt1kIqu8fS16Lw8t06P80WoEu3Oq\nuHh2FAfzuy45E+KjZ9nUcM6ZEkaIjyfpJXXsz6th7cEifrQ0kcUTgrn97ElKsPqIEi3FqMNqkzzd\nTSBpV2g1AtsQuC5LCS9uzuSJ78/i718eJ7OsgUbHzKrJZGVPbjUPXzqd86dFEO5nIMT3VFkcjYB7\nLpjC1Eg/TpsUgrf+1FfttEkh/GgpxAR58dymDN7/2WlKsPqBEi3FqGPtwcIu67/3hH831UwHS2FN\nMx5awdpfnIHVJsmuaOTqf213OupMj/Jn8US7d+H8+CAWJASxN7eaFbOj+fmypB7vfd/yKUyJ8OPr\nY2Xc895B7liWxKWzo5SA9cLInxMrFG1oMll46vO+z7LAPjOraDAxOzagz9dohL2UTEpCEAFePcd4\nrT9sjxPTagRhfp7twiv+/Hl6u3LIV8yzxz73pbqoEIIr5sVw53mT+cMl03lpSyZX/d92Ptxb0Ou1\n4xk101KMKv6zJbvXpOOueH5TBs9cM4fffXykV0fpG09L4PK50cyPt6exNputvLg5k39uPtll/yvm\nnkrCCPDy4OUbF/LpoSKsNsmkMF9sUqJxnFXdsCSBM5NCya3q30zx9KRQPr5jKY98epRREL0xqlGi\npRg1VDS0dOtl2BsWm2R7ZiVPXjmLL4+VkppVRUldZ/G7/6Kp/OzsSZworafFYsPgocVbr+OeC5KR\nSEpqW5ge7c+zX52g3mjh0jnRpCS2T8+ZFRvArB5mdYmhPiSG9r+sjE6r4TGHK4+ie5RoKUYN//z6\npHOjeyB8l1nJitnRfHKgiEWJQZ1E6+zkMH561kQAXtqSxeVzozlzsj2DQgjBvcunOvsunxFBeX2L\ns1b7YJBS8r+duQR66ztZ3Sv6jxItxaigoLqpy9rv/cFitbE1w56EvCunmkWJQTS0WEkrriM2yIun\nrp7t3OT+/YrpBDjyFXMrG/nmeDkrZkc5jVRjg7yJDepf3JXRbEWrEXi0qfZQWmfk8yMl9mWfhJ1Z\nlVy9IJZZMQHo+lAVQtEZJVqKUcEzGzMG7ecX5m8gLviU0OzKqSbcT89bty4mKdyXcP9TdvTFdc2c\nLK9nQUIwP35tDyfLGnh7Vx4v35TSL7EqqzeyLaOCpUmh/GVDOosmBHPtInvSfl5lE5e9sM150gjw\nZmoeb6bm4aPXMi8+iOhAAzYJM6L9WZkSh2+HpOu8yiYeX59GbJA3D62Ypk4WUaKlGAUcL6lnzf7B\nnZhF+ht4+NLpvLY9p117Wb0JCe0EC2BqpD8ANU0mTjrcqtNL6jnzqc0kBHvzi3Mnc/WC7tNqqhpN\nzhSjV7fn4K23R8cX1DQ7RSsu2At9N7OpRpOVbScrnM8/2AuPfXaMgw9f0E64NBr44mipY8x+rEyJ\n63Sv8YYSLcWI8+cNxxhMbOiMaH9ev2URGiE6uTcvSAhiycSQblNkOqYJSQk5lU385v2DZJTVc/d5\nyXjpO1eZ0ArBxwcKnbOo1nSegqompJTUNVt45btsyvtR7M9Tp8G7TUWL1riwVv71bSZXzosZ98tK\nJVqKEWVXdlWfyhR3h0bAs6vmcrKsgR++nIp3G4FZNCGYF6+bT1WjiTvf3kdNk5nzp0WwICGIpUmh\n6HUaQh3Jyk+sT+90739/m4XJYuPhS2d0em3DkeJ2y75W/L08SM2uorTO2Odk71aaTFa+TCtl/eFi\nciobqWkyk9cmdCKzvJGdWVWcMTm0h7uMfZRoKUYMKSVPfd5ZLPrDWQ435ltf34PVJql3xGh567X8\n9erZ+Oh1XP7CNmd5m9Z8xnOmhPHidfPx1uv40dIJrDtUzOHC2k4zvsYWCxarrd3spqTWyJ7cavRa\nDaYOZWXSS+q59qWdA7b++tn/9nbZrtdqSI70pcUy8NPVsYISLcWIsf5wyaCsvwK8PHhoxXRe2ZZN\nboe0n99eOJWEEB8e/TSty3pc3xwv58n16fzpipl4aDWs/cUZGM1WDubXsCu7Ck8PDb6eOppMFprM\nVvwdotVisXL5C9soret52dfRNXqwmKw2UhKCOW9ahEvv646M78WxYsSw2WS3Eeh9YWKYDxt/dRZJ\n4b4E+uiJbLPRHuKj57I50by0JZPXd+R0utZDK5gW5U9KYhBGs9WZhmPw0LJ4Ygg/PXsSKxfEMTMm\ngL98fpyaxlPLQE+dlvuWT3WGRgwnr+/I6bHaaYmjvM1YR820FCPCF0dLOFZcN+Drz0wKJdzfQGZ5\nAzcsSeCGJQl8e6KcrSfKufO8JD7cW9jlPhXAnNhAKhtN3P3uAZLCfHnjx4uJDLCLXrPJyjfHy/jd\nx0fwN+j428o5xIe0D4G4akEs8+IDiQ704s6397MxrXTAn6M/2CTc8b99rL/rzE5GHrVNZpY/u4XT\nJobwt2vmdAqdGEuomZZi2DGarbzaITShv2zNqEBK2W6GdXZyGA9ePI01+wp5dF1at9fuya0mu6IR\nKSGjrMG52b32YBHTH/6c29/cR1WjiZzKJv6yIZ0tXbjmTAzzBewnfMNJSZ2Rv35xvNOM6v29+dQ2\nm/n8aAlX/9928vuZ++hODNhhWgjxVyFEuhDikBDiIyFEYJtrlMO0ols+O1RMahdO0f0hq6KRkjoj\nPp46HlhziNOf3MT2zApue2Mvf/y0e8Hqim0Z5UgpCfHRd9qIL6o1cuPqXfzq3QPUNrc/LTR4aFnU\nR9swV/J1emmnA4Dv2sR8pZfUc8k/trHp2PDMAIebwThMbwRmSilnAyeAB0A5TCu6R0qJ0Wzt1im6\nP4T46An00pNWVMfbu/IpqjXyw/+k8tUAvqgvbc3CapNoNd1Hm3+0v5Dzn/6WdYeK2s1yzJbhN6Uo\nrWvhk/1F7do6WpHVNpv58Wt7eO6rjHalc8YCA3aYllJ+6TBWBdjJKXsw5TCt6JLM8gYe/+wYRQMo\nPdORJ78/i9yqRrQaQdAgPQ+tNslZT23m2pd29tivvL6FX7y1n5v+u5t8RxCpKz7LQPjL5+ntloBB\n3ZjVPvPVCX7y+h7qjZ1jytyVATtMd3jpFk456yiHaUUnmkwWGlqsrNk3+AJ3vzo/GZuE+z44xJRI\nP/59Q8qg7me29k98qhtN6HUahBBMj/Ib1HsPlMpGEze8kkp1owmA1duyu+27Kb2My/75HUcKu65h\n7270WbTaOkxLKevatP8O+xLyTdcPr89ju00IsUcIsae8XFmNj0ZOljWw4UjxoErPAMyJDeCalFj+\ntC6NRy6dwZupudz1zn4XjbJvLJ4QTITjAKBtgvZwk1PZxK/fs7v6RQX0XEKntUz0pweLeuznDgzU\nYbq1/WbgEuA6eWqhrxymFe2oM5qx2CQ7MysHdR+dRvD4lbN4bP0xfr4sif/tzOV3Hx0ZUKXTgRAT\n6MVbP1nMAxdPc7YtmRjCillRRAUY+MGieHrYFhsSNh8v5/qXU9Fqe39jo9nGnW/v56nP04f91NOV\nDMhh2tF+IXAfcJmUsu35qnKYVrQjs6yBPTlVHCwY3PLkznMnU1DdTGVDCwFeOtbs7/T7a0gprGnu\nlG9o8NDywnXzefG6+Xx1rHRETGO3naxgy4lywv36FvD64jeZ3LR6l9vuc/VlptXqMH2uEOKA4+di\n4J+AH7DR0fYvsDtMA60O05/T2WH6Zeyb85m0d5gOcThM/xq433GvKqDVYXo3ymHa7TBZbORVNXUb\n6NlX5sYFsnJBDL//5Ah/vGwGT24Y3P0Gyp/WpZFZfiotSErJ7pwqbv7v7n65S7uaeqMFrUYwoY9l\nnredrGDlv3ZQWNPce+dRhhhrYf8pKSlyz549Iz0MBfZTuZNlDfzsf3vblVjpL9EBBj7++VL+uC6N\npZNCKaxp4oXNA6sl7wo8tIKLZ0Vx0cxIDhfW8vLWbFpGIPShK7z1WsxWW58LKob66nnhh/OdNmj9\nRQixV0o5uJOQfjJ2Y/0VI45GwJr9BYMSLA+t4KUbU9h8vAyrVbJoQhAXPXfEhaPsP2ar5JMDRXxy\nYPRtajeZrPjotZitfTvwqGgwcd3LqTzx/Vlc4yYFBlUaj2LIsNgkG48OLir7Z2dPItTXk5e3ZvP7\nS6bx8Nqjgy7LPNbp7wmtxSa574NDPLL2qFuUvlGipRgSpJSsPVBE1iBmWYsSg/nluZO5f80h/n7N\nHHZmVfHdycGdQCq659XtOaz6907KurBeG00o0VIMGRuOFA/4Wm+9lr+unM03J8q5an4sYX6ePLL2\nqAtHp+iKA/k1vL0rv/eOI4ja01IMCSdKG9g+iLis+5ZPISHEB5uEuCAvfv/JUepbenaOVriGin7U\ntR8J1ExLMSQU1Ta3q9feH6ZF+XPDaYlYrDYmhPrwz80n+fZ4GQYP9d91OCgd5ctDNdNSDAkhPnqg\n/+HhWo3gL1fNQqsRSGkPm/jsUPGIJSaPR+pGedCp+tWlGBI0AzQVXTEritmx9tJsQgjeTM0lo6xz\njXewC1xs0OBt6xXtqWwwjfQQekSJlsLlSCnJLG+gtrn///kXdiiqt/5Q95v5UyP9Rv1Sxh0pGeWz\nWrU8VAwJB/JrBhRPNalNGkpFQwsVjSbuOi+J8gYTBp2WioYWNqeXMSHMh4YWi4rZGgISQr27Nbcd\nDSjRUrgce50p/35f52/Q0VaCQn09efL7s7j+5VRnmoyvp447z02irM7I27tH99G8u3L3ecmjVrBA\nLQ8VQ8RAEnFnxQawpE0OnMVq49fvHWiX19fQYuE/W7N5IzWP6xYnuGSsilNMi/LnvGnhIz2MHlGi\npXA5VY0mZ0XN/lBSa2xXp72m2URxTef9lYqGFkwWG/vzqztZaSkGx13nJY3qWRYo0VIMAVtOlHNo\nAKV9syoa27nKhPoa2s28OrInp5qrF8TiM8B4MEV7pkb6ccH0yJEeRq+oPS2Fy7Ha5ICOzaWEd3bn\nszQp1NkW6tvzTOrlrVksmxJObLAXGuDNXfmYRkmZGHdBI2ByuB8PXDwNzXCXXh0ASrQULsVmkySG\neuNnGNh/rY7OOlqNBg+t6PaU0Cbtxg1gL8d821kTePGbrAG993hiWpQ/l82JZmlSCMkRfhg83Ge2\nqkRL4VI0GsGChOAB7WkBTA73dT42ma3syq4kLtibrPLeq0VYbBI/gwf3XTiFL46WcjC/ZkBjGKvo\ntRpWLYzjhtMSSI4YGRchVzAYh+lgIcRGh/PzxrYmqsphenxTVmcccNrNVEeohJSSdYeLya9u7pNg\ntbL+cAnl9S1KsLrgHz+cx5+umOnWggWDc5i+H9gkpZwMbHI8Vw7TCnZkDay6gxD2zWCA4lojj3/W\nP3t7gMOFtXi70VJnONmfNzaEfMAO07R3hX6N9m7RymF6nPL4Z2k8s/HEgK6dFumPn8G+p/X0l8ep\nbOxf4m5UgIHFE4JpMlkdCduKtuzNHRueMINxmI5w2IIBlAARjsfKYXocsz+vhpzKpt47dsG5U+1B\njY0tFj47XNKva330WubFB7Int5rcqiaiAw0DGsNY5khhnVv7HbYyaIdpAMfMacT+NpTD9OjB38uj\n907dcMmcKAC2Z1bSbO5frfJAbz35Vc1YbZKs8gaKughKHe80m62c7KZihjsxGIfpUseSD8efZY52\n5TA9TmlosbAre2BLkPOnRTA10r4JnzqAPbEQX73TaTqnsolJYX3z/xtvpBUPzjB3NNBryEN3DtOc\ncoX+s+PPtm7RbwkhngaiOeUwbRVC1AkhlmBfXt4I/KPDvXbQxmFaCPEF8ESbzfcLgAcG/GkVQ0p6\ncR0NPZREnhcfyJzYQGbGBGC22qhqNBEb5MU5U8IJcMzQKhtaODyAaPpDBbUkhnhT4ZhINJtVgGlX\ntIyBv5e+xGm1OkwfFkIccLQ9iF2s3hNC/BjIBa4Bu8O0EKLVYdpCZ4fpVwEv7O7SbR2m33A4TFdh\nP31ESlmjwAKUAAAgAElEQVQlhGh1mAblMD2q6am2+L9vWMDyGT2niORXNXEgv4bQPtq7d6TtXtrh\nwlpmRPuTX91EXbOqLd+K2ToOREtKuY3u6+ae1801jwOPd9G+B5jZRbsRWNnNvVYDq3sbp2LkabHY\n0Ah7lHorsUFe3LAkwZmak1Faj03Cntwq9uZWU1bfQn2zmUcum0FCiA9v7MxlYh+t3XvjaFEd4X6e\nhId7crJs4FZmYwnjOJlpKRR94rI50by+I5e9udXO5788L4mEEB88tBrWHyrizxuOkVfdeZP8ifXH\n+O+PFnHxzEhe3pbdY+pOfyirb3GLfLrhwtjPA47RiBItRZ85WVZPk8nqrOHeESEEz66ay7NfZbBs\nahhzYgOJC/YG7HtVBwtquxQsgN051dz9zgE8dRo0wjWC1UpJrZGpkX6kl9S77J7uSoPJ/ZfKSrQU\nfaK2yczvPjrCwsTgbkULIC7Ym7+tnN2pJtPaA0WsO1TU43tsPl42ZHFEUkK4nydl9aPb02+oMZrc\nf6al6mkp+oS/l44fLU3knguSe+3bUbCsNolBr6Wwl9ipoQx8PF5aj9FsZXZMwJC9hztgGgM19dVM\nS9EnhBBcODNqQNd+uLeAV7/Lce2ABkCd0YKhHwUDp0f54a3XodGIAcefjTbGQq0xNdNSuASrTbLq\n3zuY/6eNPPdVhjNeq7S2mSNFtRwvHR37SQfya0hJ6DnnXqeBRYlBpBXXsye3ekwsqVoxWtz/syjR\nUvTKB3sLeOrzdGw9LN+e3nic1OwqqhpNPPPVCbSOJeK+vBq8PLR4jxJLe5PFRmUPtb70WkFyhD+7\ncqqdbT3Fn7kb4yW4VDHOuWp+DMdL67sNHVh3qIgXNmcC9sTllSlxeOm1ZJc3sD2zgjd25g3ncHsl\np7IRX08tDS2dZx3JEX4cKWqXWktxnREvD82YiLKvH+WW931BiZaiV4QQzrzAjtQ0mbj3/UNoBFy3\nOIGfnTOJmEAvKhtaeHVHLt+eGH0J7FLCjOgAUrOr0GnAYoPoQAPhfgYOdFE8UEpICPEZEyETNU1K\ntBTjnAAvDx6+dDoTw3yZHx+ITqshp6KRXdlVvLY9Z6SH1y378qqZEe1Hi0Vi0GnIqWyiqKb7Inm+\nnmPjq2K2uf9scWz8SyhGDCEE1y6Kdz7ffrKCr9PLKKjuv1nrcGK2So6XNGDpY5hFWX0LUyL8Rs2B\nwkAZC/W0lGgpXMaGw8Xc/uY+tBrhFl+OvgoWQF5VE4sSg4dwNMODWYU8KBSnaD1lcwfBGgj786vR\nunka4/Ro9w+uVaKlcBnLZ9pLz+jGaIKy2SqduZTuygXTI3rvNMpRoqVwGR4aDT9cHM/EMVw1NMR3\nYLW+RgvJke5tHwZKtBQuJKeykbvPn8z1SxKcTtFhjoJ+8W4+Q2mlqrHFrfe2Ojp4uyNKtBQuwx5Q\n2siqhXHcfs4kFk0IZlZMABH+nlw1P7b3G7gB2RVNVDUNzD17NBA2wKqwo4m+OEyvFkKUCSGOtGmb\nK4TYKYQ44HDBWdTmNeUuPU6JDvSiuNaIp05LoJee1Tcv5O8r5/DQium8tye/9xu4CXlVTd2W8h3t\neOrc38i2LzOtV+lskPoU8Ecp5VzgD47nyl16nONv8MDPoOO93fksmRiClJLjpfXMjQskKdx3pIfn\nMsxWm9v6KuZVDcyTcjTRF4fpLdjNJto1A615HQFAa3U35S49zjlvWgQXzIhACHhkbRpfpZVy0+pd\nHBmAw85oZV5cYJe1wXQawdQeNrpnxvjjbxjZ0EjvfpTmGa0M9G/wbuALIcTfsAvf6Y72GGBnm36t\njtBm+uguLYTot7u0EOI24DaA+Pj4rroohonNx8uoajBxvLSeAC8PXt6WPdJDcikBXh6IHtaGc2ID\nO+Uohvt5cv2SBHZkVnL2lHD0Wg3rDxf325DWFXx2qJhbzpgw7O/rSgYqWrcDv5JSfiiEuAa7Bdj5\nrhtW/5BSvgS8BJCSkjI2IxvdgHqjmaKaZp5cn44A6nvwQHRHpkf5ER3gxVfpZV2+brFJZ5T9hFAf\nyuqMNJqsnDctgtd35I6KEjdr9he4vWgN9PTwJqDVafp97HtOMALu0orRQ3l9C4fya2k2W8ecYAHE\nBHkzNarnOCerIyFZr9UQFehlvy7QMCoEC+y2ahY39z4cqGgVAWc7Hp8LZDgerwWudZwITuCUu3Qx\nUCeEWOLYr7qR9o7UrSeDTndp4AvgAiFEkGMD/gJHm2KUIaVka0Y5H+0v5Iu0ErdP44n0N+Cp6/zV\n2JhWyqGCWqIDut+EF0Kg0wh8DTryHZveNgnaUZIlICVkV7i3B2RfQh7exm5XP0UIUeBwlP4J8Hch\nxEHgCRz7SVLKo0Cru/TndHaXfhn75nwm7d2lQxzu0r8G7nfcqwpodZfejXKXHrWkFddxorSBf3x9\nckzUayqpMzIprOvTzi0ZFdx1/mRCfT3x9dR1OhXNKm8gKdyXmiYT0Y6ZVmZ5A9N6maENB3qthmtS\nYonoQXTdAWGf1IwdUlJS5J49e0Z6GKMSm00iRGe3nMFy59v7+eJICSY3XXaE+npS2dhC269CfLA3\nTSYLFQ2dA0lXLYzjj5fNoKyuhStf/K5d+WZPnYYLZkTy6cEi/nvzQv60Lo3FE4MJ8tbz4jeZw/Fx\n2uHnqeP60xKYGxdISkKQy9OQhBB7pZQpLr1pL6jSNOOAFouVd3bl88xXJ4jwMxDg7cHKBbFcvSB2\nUALW0GLhWHEdCcHebitYAL6eWr4/fyIvbclytuVVNXHaxBAqGio79Z8dG4DBQ8uH+wo61Ztvsdi4\nZHYUF86I5JwpYSxNOgujxYqnTsP2zMouK6O6gmlR/tQ1mymsOVXH7ILpETx19WwCvfVD8p4jhRKt\nIaa22QwSvD215FY2kRjijU7r+uyptKI6vjtZgcUm0QiYExdIcW0zeZXNvJma6zQpbV2+7cquIqey\nkZ+ePQl/w8Dy0ZpNVnz0Ol745qTLPsdIkFPZxFfHSvE36KgznjpAKKkzcvWCWD7YW9Cuf0W9XaiW\nJoXyr28zabHYCPXVE+FvIC7Im5hAL2Y6/BX1OoHesT/26o8W8smBIp7flNGjucZAOFZcxyWzo5gQ\n6sO2kxUA/HBx/JgTLFCiNWRIKXl5azabj5exN7ea1Tcv5LHPjnHF3GhuO2siYF+mvbw1i5omM79Z\nPmVA79FisfGvbzN5YfPJflvJv7A5k/yqZp5ZNbdfG8UF1U08uT6dr9PLMHhoGAs7DFnljSybEsYV\n82K4650DABRWN/P6LYvYm1tNdkUjXh5aFk0IJsoRDb9oQjBb7luGv8EDrz4EbQZ667np9ER+uDie\nl7Zk8dKWLPsvNRex7lAxK2bZhSu7ohGDh/sHknaF2tMaIp7ZeILnNmV0ag/y9iAhxIfEEG+uSYlD\no4EQH08mR3TeqG02WTmQX8OXaSXUNVs4b1o450+z10P6cF8BeVVNfH2sbFAlgB+/ciaXz43pVw30\n3310mDdTR5fDjqu4dmEcQsDbu+xxzZfOiWZqpB+rt2Xz2i2LnDMoV1BaZ+S5TRm8sysPVx64/nBx\nPG+l5vH53Wd2a0jiKtSe1hjhP1uyuhQsgOomM9VNNRzIr2FjWin3XDCFq1MCaLFY0Ws1FNY0sz2z\nkoKqJt7dk09p3an4ng/3FRDk7YHZKp1mqINlXlxQvwSryWQhq9y9j8x74p3d+Ty0YhpTI/1IL6nn\n04NF/OTMpayYFUViaNd1wopqmp0nha0cKaxlepQ/Go1gb24VCxI6l7OJ8DfwxJWzuOm0RB76+DC7\n23gtDgYPjSDUV09xjXHIRWskUKLlYj49WMTj64/1qW+jycqj69J45qsTTAz1IT7Ehy0nyqkzmrtd\nclW7OKSgtN5IstW3z/tsVY0mdmR13pweSzz22THeunUxr+3IIdLfgEaIbgWroLqJFc9v45OfL23X\n54O9BZTVG3nu2nnsza1mTmxgt3/HUyL9eP9np7M7p4oH1xwmo6xhwGOPCjCwIDGYO8+bzPt7Clg2\nNXzA9xqtKNFyIbVNZp76Ir3f19UbLRwsqOVgwfAnFccEevXrYGDNvrGflCCE3T7+3zf0vurZdKyM\nMyaHEtkh9um8aeHc8MouUrM2Ud9iYcnEEGbHBvZ4r4WJwXz5q7O45/2DfHKgqN9BupH+Bu6/aCqX\nzYnGbLURF+yFlNLlIS4jjRItF1HdaOKm/+4iv2p0W2d1pD9fDCklHx9wL9HSCPq1X+TloeWvK2dz\n7tTua6nvzKrkv99ls3xGJPlVTTx48bROm96nTwrF4KFxnhIezK/pVbSsNolNSv529RwumR3FA2sO\nt9se6I4JoT7ceuYErpof6xyHh1bDJbOje73WHVGVS13E/32byaERmCkNliZT3/fGjhTWud1+1h8u\nmc6Pz5jQ59PRF66b1+uX/aN9hVQ2mAjy1vPgxdOI6bCfBZBb2YjRfCp2rbXyw8/f2sfe3M57VzaH\nsnpoNWg0gnOnRvD1PecwJ7b7jf+oAAOPXTGTjb86i+sWJ4zZ08KOKNFyEVtGof17X8ivaiavsm+F\n4fbnu2ajeDh55NM0MssbeP7aecyN636mExfsxS1LJ7QTmlaklJxss8+0fGYEf7x8BksmhvD4+mMU\n1XSeXadmt884++xwMfVGM1nljazZ1z7uy76E65yf6OOpw9/LHkOn77CEv+OcSWz+zTlcvyRhSOL+\nRjPj69MOIfVG96xqYJOS+JC+mU6U1nUufOcOfHO8nLvf3c+SiSE8tGIa4R3qpMcEevHh7afzh0un\nc5HDBq2VeqOZu989wGX/3EZreNAXR0pZ8fw2pv3hc/bmVjtPDndlVzkrKCyfEcmkNq5ENU1mvv/i\ndlYuiGHDkRKMjlpaUkr+l5rHrEe+dASqnqqx9e7uPLZmVPDIpdM58sflfM9h/xXqq+eX500eNzOr\njqg9LRcgpUTjpvLfn5OqKhdHcQ8nZqvkX99mEu7nyXPXzmNLRjmvbM3GZLVx7/IphPvZN9Lbblof\nLqjlF2/vI7eyiZtOS6CgupnYIC+uWxLPsqnheOo0JIR409BiYUdmpVNUAI4W1XZysK43Wgj01mOy\n2DheUk+Ev4E73tzLvrwa9FoN2eWN5Fc1kxTuy+bjZTz40RGSI3yds6nnrp3LdS+nMi8uaNwKFijR\nchkNbjrT+t+OXH50eiLh/r1n/nvr3f+/S1l9C/e8d4Bnr53H0kmhRPh3Hdi7Ob2Mn/1vLy0OG/mi\nWiOxQV4IIZgZHYCPp46C6mZe35HLw5dOZ07cqb2nnIpGUrMqqesQ7Z4U7svMmAASQ72JDfLi86Ml\n7Muz5yIuSAjiiStncv+aw2g1go8PFGK1Se6/aKpz+WfQablhSQIe42w52JHx/eldhBCiy//47kB9\ni4VbXtvdpyJ1wT5jI4+tqNbINf/ewZ/WpXX575ZV3sBPXt/jFCyw53a2zsKK64y8lZqHn0HHXedN\nRgjhnKkBFFY38enBok4xddtOVvDhvgKWT4/EZLUxOdz+3n4GHZfOieZIUR3v7y3gnd35GM02fn/J\nNJZNORVnJbGXbv5oX4HbF/IbDEq0XMRIGxYMhiOFdfz4tT1U97L8S3ZTYW6lo6lDdxb3e3Or2y3t\n4oK9uOv8yc7nMYFe/P6S6cyPDyKoCyGvbDJ3uaHvoRXct3wql8+NYX9eDQsTg5gY5oOvp46rFsTw\nYYcN+hazrd1yVSPgo/1FfH28nPJRUgl1JFCi5SJclVYzUhzMr+HOt/f3+Bt8shvbgGk1gr9cNRuf\nNsJl7uazLk0K5cbTEvjJmRN4/2enseXeZVyTEtdl347UG828vj0Hq5R4aNufBpqtktI6I/Eh3syP\nD0IIwfIZkYT7GzhZ1sCHHapJHC+p4+1dec5wiPL6FoSwi2hlF3W+xgtKtFzA4YJadma5f1HVbScr\nekyETgjxZkGCe1lPLp8RQbifJ1ab5HcfHeZX30umNbJgZ1Ylj36a1sneLDrQi0cvn8nvVkxnYWJw\nrxHlJouN93bnY7HaMJpt/Ov6+ay78wxSHzyfh1ZMY2bMqfy/h9cepc5odkbQz4sL5M5lSby/p4BG\nk/3kUK/TcPGsSLIrmnjis2POWVWLxYZOI5gU5su7u8eO+W1/GZDDtKP9TiFEuhDiqBDiqTbt485h\nuqjWvaLge+LV7TlUdrP0EELw7Kq5brUUjgvydoYX1BktvLQliwcumgbYReCzw0U8sf5Yt7OurpBS\nYjRbOVpUy+dHirHYbDzy6VFueGUXFz67hSaTjcgAL4J99Nx65kQevHgas2IC8NFr8fXU4d3m5O+C\nGZGcPz2C5Ag/pkXZxe3cKeF8fqSEyRF+rL/rTCIchyTFtUYO5NfwzfFyPthbwGPr0th8vMw5ptSs\nSn793gF++fb+fn0ed2NADtNCiGXYTVbnSClnAH9ztI9Lh+mSWveMX+qK7IrGdhU8OxIX7M0PFruH\nt2SgtwdWKdsV9iurbyGvqskZaFpa18Lpk0K6PZF7csMxOpZvEkLw5w3p/PbDQ4T5GahsMNFkspKa\nXckFMyIJ9tGz7lARZXVGvkor5fRJoaxaGEeIrydfp5d1CiI1W23Migng2VVzeOTS6SybGs7qmxfy\n92vmEBfszd7cKu56Zz/NZitr7jid702PoNls5eVt2dzz3kFSHtvI3Ec3suqlnazZV8jag0V8vN+9\n0q36w0Adpm8H/iylbHH0aTWCG5cO015jLGbmP1uzeoySv2/5VOb0EF0+Wvj+vFjnLAtORZyX1Bmd\nMWdhfp78+IyJPd7n2xPlNJvaG6teNT+W/9yYwoRQH/759Um0GsG8+CDuXT6Fl7Zk8ou39vPennw+\nP1rifJ+8qiaCvD3aLTctVhuPfZbGY5+l8eBHR7h56QTOnRrOOY5Tw9zKRm7+724+OVBERmk93nod\nJ9rUT6tqNFHRYOpUTPDV7Tlj9oRxoPP8ZOBMIcTjgBH4jZRyN+PUYfrM5FD0Og0my9j4T2KTsOL5\nrWz77bkEeHcuxazVCF69eSH3fnCILSfKR219+Nd35PDElbNYd6gYm02y7pdnsuVEOaG+nkgkZquN\nS2ZH46HVsDWjnIzSBuYnBLVL97l2YTzhfp6dKpPOcuQE1jWZ8DVo+cGiOGICvQn20XN2cjgRAYZ2\novnnDfbqHw9cPI0mk8UZ82ay2lh3sAhvTx2XzYlGSklYm4j9e9476My2iA3yoslkoby+95PDo0V1\n/GHtUR69bMaYS/MZqGjpgGBgCbAQeE8I0fOvqyFkpB2mowK8eOyKmdz3waHhfusho77FQm5VI7O9\nu55RBfnoefmmFCxWG3lVTZwobeDtXXnsz6tutxwbLrw8tDx6+QzubfNvYLFJnthwjL+tnIPZasPX\nU8fiicFklDawI6sSf4MHGaUnOFhQw3cn7TXCHloxrZ1oJQR7U91kwmy1Eeitp6KhhdA2jjb+3np+\nvmwyWg0EeNnDHxYkBrEg0b6T4aXXYrbaqGo0oRF2U4zFT2zioRXTWLUwHm+9jj0Pfc9ZP611FvZ1\neikL4oOd9eVDfPScMyUcm5Q0dZj1dcdbqXkkh/ty81L3dpTuyEBFqwBY41jq7RJC2IBQBucwXdCF\nw/Q5Ha75ZoDjHXKumh/LX7843qffgu5CX8qi6LQaJob5MjHMlwtnRiKlZF9eNc9+lcHWjIphGKWd\nZrOVI4W1/OmKmfz183SeWTWXdYeK+Wh/IT99Yy9+Bh16raZXQ4m04rp2z4vrjPgZdPgbPNiWUcH1\nr6TiZ9ARG+TNw5dOZ8nEkF6Dbnc7chIXTQgmws9AQog3nx4sZtVC+6pACEHritFmk3y4r4BjxfUc\nyK9le2YlCxODWDY1nA1Hip2b8n0lp4/J8O7EQOeNHwPLAIQQyYAeqGAcO0xrNYJlU8JGehguIybQ\nq5MRaV8QQrAgIZjXb1nEQyumMZzGyq/tyOU/W7J46ydLaGix0Gyy8uSVs9AIe95fXxxwtnUQWo3A\nHooOBHh5oNdpqDfardMeWXu00yZ9KzabxGSxsTm9jF05VbRY7BHwGo3gqavmsHyGPU/RYrXx/KYM\nfv7mPn7w0k7O+utm3tiZy8wYf57flEG4nydv3rqEo0V1fLy/CItVkhzR93+X/pTSdhd6/UQOh+lz\ngFAhRAH2E73VwGpHGIQJuMkhNEeFEK0O0xY6O0y/Cnhhd5du6zD9hsNhugr76SNSyiohRKvDNLiB\nw7S7B5i24m/Q8doti5jQTYnhviCE4NYzJxIf7M0db+7rlDw8VORVNfHLt/dz7/IpfH28jPzqJm5Y\nksBrO3L7dH1ZfQu5lY0khNg/e1SAl3PTflZsAH+8bAb//S6bqAAvfv295Hab6marjaKaZsL9DFz/\nSirpxXW0WGxYbBKtRnD7OZMAmB7tz/Roe3jDmn2FPL3xRLsxfG96BK9sy3aO58Jnt5DlsLL/th8l\nkCaF+eCpG1v7WaDceFxGVnkD5z397Ziw0/rvzQtdWlv8H5sy+HuHL6armRrph04rOFJoX94tmRjM\nzacl8psPDnHjaQn8e0tWn6u0rr45pcfKpV2RXdHIb94/yL68ap68chY1zWYaWyyE+xvw0WuxSbh6\nQWy7az7aX8DX6eXkVzW1M3FNCveltM44oHJHGgErZkeTkhCERtj3Hoeygqly43FTGlos/OKt/WNC\nsP62co7LzRBuXppIVkUj6w4V9dubsa8UVDdz65kTnKK1M6uKvMomfnzGBErrjIT46J2Gtb2xO6e6\nk2jVG8349WBqG+HvyZXzYiisbub+NYd7FH4pJe/vLeDj/YVoNaJdWAbQruBgT/h56vAz6CiuMyKl\nfSn4zm1LXGpzNhpRojVIzFYbj61L67SB647ceFpCp9mAK/AzePCLc5M4mF/jXOa4moYWC0eL6vD1\n1BHm50l2RSNFtUZ2ZlWy+uaFHC2q67NobTpWym8vnOp8vjunihtf2cWFMyN5ZtXcLq/x1uu4fkkC\nob567v3gEMdK6roUrdYZWWvJZZ1G9Lp0jgn0IiUxiNggLyL9DcQEeREb5E1SmC8ajaDeaOZEaQNJ\nYb5dhqiMNZRoDZJms5V3xkAeWKC3B7/+XvKQ3V+v1XDVglg+3FcwZHXm58QGkBjizX+2ZjvbtBpB\ni8VGdZMJg4cGb70Os8XGxDCfbt2PCqqbMVtteGg1NJks3PLf3TSbrXy0v5CfnDnRuR/VFRfOjOKC\n6e2rn5qtNqSE9/fm8/uPj7Qz2uhKsPwMOqZH+XPm5FAunBnJpDDfHvMf/QwebpcTOhiUaA0Sg879\no+H9DDre++lpBHoPXb2sgupm1h4oGlJjjE8PFnPNwjgumhlJi8VGYogPs2L92ZlVyZXzYliQEMSM\n6AD8vXRICfP/tLHLmKcmk12grkmJI7Oskfo2Byw+nr3/e2vaHJk+vymD13fkYLFJarrxrPQz6Dht\nYghLJoawMDGYGdH+7e6haI8SrUFS3eTeJUKEgOevnTfktbK+OlbK8dJ6NAIunBnJ+sMlA7pPmJ8n\nfp46Z4G+OqMZk8VGi8VGSZ2Rj/YXMCc2kPL6Fj4/Usx/t2cTF+TNlvuWdbrX+dMiWHuwqMv3SSuy\nL/dbwz60GsGtZ0xwnir2hMli45vjZazZV8iXaSWdLMxmxviTFOZLcqQfZ00OIznCzxlEqugdJVqD\nZF8XdlDuxOVzojlniOLLdmVXkVFWz8zoAC5yLHPmxAWwwSFYAV4enXLmemLlglh8PHXMiglgyaQQ\novwNmG02jCYbdUYzQT56Z1yS0Wxlc3oZWRWNlNe3kFXewMSw9vFNp00K6VK0dBrBrWfao8i99Foe\nWjGN6dH+nD4ptF2/7ZkVfHm01L7XFGCgrtnC2oOFHCqo7TSDiwv24uJZUVw1P9btiymONEq0Bsmm\n9LLeO41STpsYwlNXz+lyvySjtJ7yhpZOX9S+YLNJ7nxnP58dKu70fhabjbOnhKHXafj4QGG/ROv9\nDkXy2jIlwo8vfnWW87nBQ8tFs6J6vF9eVedo8XA/T35zwRRig05VNb31zPYZanVGM4+sPdqr27Y9\n4Dic65bEc/bkMLXkcxFKtAZJzhCdhg0106P8+fs1c9B180VqMlk7ee31lYyyhk6CBbAjq5IdWfYc\nv/46P/fGyfKGXsMSOnJNShxrDxRx4cxIksJ9mRzuy5y4wB6NI3ZmVXLPewcp7MLrsJXoAAM/PzeJ\nS+dE49+P8Sj6hhKtQWA0W9nfJijQXZge5c+rP1rYowPPYErPTAj16bXqxUAFS6cRTIn04+zkMIpr\njRg8NPgbPLDYJC0WG/1ZeE0I9eG7+8/tU98Wi5WnvzzBS1uzuo3Hi/Q38KOlidx0euK4tvgaapRo\nDYKTZQ19jrIeLSSF+/LOT5cM6QxAr9Pw1q2L2Z5ZyTfHy5w2WX3BU6dhcoQvUyP9iQ6wxyRF+BuI\nDDCg02iIDfIadkHYl1fN/R8e4kRp10Gfk8J8uP2cJC6fGz3u7b2GAyVag+BoUddxPqOVmEAv3rx1\n8bAsWVISg0lJDOaX503myQ3HeGVrtjMmyd+gY1K4LwnB3kQEGIgJ9CLU15PYIC+SI/zw1Gl6rcs+\nHDS2WPjbl8d5dXtOl7OraVH+3HHOJC6eFdWpGqli6FCiNQg2ppWO9BD6jJ+njmevndvv0iau4Ffn\nJ3P/hVMxOYIs24qSyWIb0eN+s9VGk8lKgJcHZXVGhBAUVDeRX93MU5+nU1Ddee9qyUS7GA/kkEIx\neJRoDZDaZjNbhrFe1GCYGOrDY1fOZGFicL+vbWixDLq8SetyzrOLQNyRFCybTXLdy6nszqki3M+T\n8voWNKLrtJpQX09OnxTCtQvjOD1JidVIokRrgHxxtMQtyisLAX+/Zg7z4geW5lFU0+zWcUU2m2Tr\nyQryqpq4fnG8c4YnpWT1d9nsyrZXO2oteGjrsA6cHuXPT86awMWzoroUXcXwo0RrgGS0MRcYrYT6\n6nklFiMAAA7bSURBVHngomkDFixwT1dpk8VGvdFMWnEd/9mazRZHDaqP9hVwxbwYSmqNHCmqc7Z3\nRAi4dHY0Pz17ItOj/EfF/priFEq0Bkhf63SPJPcun8JVQ1C1YbQipeR/qXk8ti7NmebTln15NT2e\nZEb6G7gmJZbvz48lcRAFEBVDixKtAWCzSXZkVo70MHrkoRXTuHxul+ZFYxKL1cYfP03jjZ19q1Da\nip+njnOmhrMqJY7FE4NVyIIb0Jdyy6uBS4AyKeXMDq/dg92oNUxKWeFoewC7AasV+KWU8gtH+wJO\nlVteD9wlpZRCCE/sPogLsBtarJJS5jiuuQl4yPF2j0kpW/0Rh4XGFgtrDxbhZ9A5N7G99Foe/uTo\nkNWFcgVz4gL58RkTxsWyJreykYfXHiWtj/WyzkgKdeZaTon0Y05coIpadzP6MtN6FfgndmFxIoSI\nw242kdemra3DdDTwlRAi2VEnvtVhOhW7aF2IvU6802FaCHEtdofpVW0cplOwWwvsFUKsdRi3DjlG\ns5XvPf0tRW3coyeG+VBaa6RxlC4N44O9CfLR8+5tS8aFYEkpue31vRzvw/7iuVPD+fX3ksd8Vc/x\nQK+iJaXcIoRI7OKlZ4D7OOWqA20cpoFsh1nFIiFEDg6HaQAhRKvD9AbHNY84rv8A+GdHh2nHNa0O\n02/37yP2n5e3ZvHB3oJ2ggUMaS2oweJv0PHmrYuJDDCMmyVOVaOpV8E6c3Iov7lgils4Yiv6xoD2\ntIQQlwOFUsqDHX6ju73DdGFNM2/tyhvVAtUVd52fTFywd+8dxxA9pfOcPimEX5ybxGkTQ8bFrHM8\n0W/REkJ4Aw9iXxqOClzhMJ1b2YjRbOPnb+1zG8HSCFiaFMrUSD+uXzI4sR4rLJsSxt3nJ6uZ1Rhm\nIDOtScAEoHWWFQvsE0Iswk0dpgtrmrnyxe1Of7vRxNRIP24/ZxILE4P55EARf/k8HbDHYN1yxgTu\nOCdphEc4crStxXX6pBB+sSxJRauPA/otWlLKw4DTZsSxX5UipawQQqwF3hJCPI19I77VYdoqhKgT\nQizBvhF/I/APxy1aHaZ30MZhWgjxBfCEw10a7DO7BwbyIXvjP1uyRqVgBXp78Pdr5jgDHG8/ZxLH\nS+rYdrKS9b88o8fSMuOBmiYzp00M4Q+XTmdaVPdmE4qxxYAcpqWUr3TVV0rplg7TtyydwJupuUPm\nyTdQHr9iFtMi20dkT4n056JZUeNesMB+mvv2bUtGehiKYaYvp4c/6OX1xA7PHwce76LfHmBmF+1G\nYGU3914NrO5tjIMlPsSbqZH+HC4cXaVmLDZbuxK9x0vqOVZc57RXH++oQnvjk/FxNt4HRptvXFK4\nb7s65SW1Rm57Yw8/PXtiD1cpFGMflcbj4FffS2ZjWmmPtb+HA61GcNNpifxoaSJ+Bh0vbD6Jp07D\nmn2FLJ4QzIxoFRypGN8o0XIQ4OXBvPjAERUtvU7DY5fP5JqF9gPYOqOZFzafpMlkJSrAwIMXTxux\nsSkUowW1PGxDdKDXiL23n6eOD392ulOwAPwNHiyfEYmfQceL180fUgdohcJdUDOtNnxzfGQ8DOfE\nBfLklbOYHt352P53K6Zx9/mT++RsrFCMB5RoOahtNpNR1rXbylBxwfQIVqbEcUZSKF76rk/CQn09\nCfX1HNZxKRSjGSVaDuqazWiEwNqdqZ0L8dRpeOrq2Vw2J1rlxSkU/USJloO4YG8CvTyoHMLI+Lhg\nL56/dh4hPp7Eh4yv5GaFwlWojfg2PHftPJf71505OZR7l09hYWIQH92xlHnxQUqwFIpBMO5nWjab\nRAh4blMGG9NKXeoY/dgVM7l+SQIAd5wzSS0FFQoXMO5F60BBDe/uyufdPfm9d+4Ht54xwSlYgBIs\nhcJFjGvRyiit56bVu6g3WgZ9LyFwWqefOTlUBYIqFEPEuBatV7fnDEqwVsyKYvnMSObHB7Izq4p7\nPzhIdIB9s13j4r0xhUJhZ9yKltFsJTV74JVurpofy99WznYu+65e4E24nychvnqCfFTkukIxVIxb\n0frPlixODjCYNMLfkwcvntppn+qs5DBXDE2hUPTAuBWtgZ4RzosP5I0fL8bXc9z+1SkUI0qvcVpC\niNVCiDIhxJE2bX8VQqQLIQ4JIT4SQgS2ee0BIcRJIcRxIcTyNu0LhBCHHa8977AJQwjhKYR419Ge\n2tauTAhxkxAiw/Fzk6s+NMCkMN9+9Q/z8/z/9s4+xqqjCuC/aReWUqj7wcKCLWVJoU2ppZYXLaaL\nH00/QGy0pgohbRX+adFGTbSUYA3aaLKa/qHRSNvQaAw1rJrWj3/4aBT9h+huA3WhbFkWW6Dsglsp\npCBVe/xjzuybvbxlyXv3vbd39/ySm503d+6cc8/MPffembtzaJ03jafuX2gOyzCqSLHBWncA6zXk\nVxt+7fZ1WQrWeveCGTRfNYm+0/++YN+0KRNZfvMsPpe7htmNk3nz1DnmTZ9iny0YxihgxCctEfkz\nfu32OG+7iIRpt93kI+0MBmsVkcNACNY6Ew3WKiKCd4Cfjo4J4e5/DdyRDNaqjioEa02Fmssv4yer\nbmViIrDpkvlN7PrGx9l47wJunHUVU2prmD9jqjkswxglpPGesxrYqumqBGstlkXX1rNu6Q20/+0I\n65Zez/uumMgNzVO50l7/DGPUUtLV6ZzbgI+6syUddYrWo+gI02tub2HN7S3lUMswjDJQ9D9MO+e+\nACwHVukrH5QWrJUCwVoL1XUBIvKMiOREJNfUZJ8dGMZYpiin5Zy7B3gMuFdEzka7fges0BnBFvLB\nWo8Dp51zt+l41YPAb6NjwszgYLBWYBtwl3OuXgO23qV5hmGMY4oK1oqfLawFdugA9W4ReTirwVoN\nw8gOTiqwUmclyeVy0tHRUW01DGNc4JzrFJFcJWXaIoCGYWQKc1qGYWQKc1qGYWQKc1qGYWQKc1qG\nYWSKMTd76Jw7CbxeAVHTgH9WQI7JH706VFv+aNDhehGZWkmBY+6f7ESkIp/EO+c6Kj3Va/JHlw7V\nlj8adHDOVfz7Ins9NAwjU5jTMgwjU5jTKp5nTH7VqbYO1ZYP1deh4vLH3EC8YRhjG3vSMgwjW4jI\nuNqArwBdwD7gq5r3A+AA8ArwAlCn+XOAc8Ae3TZF9SwC/o5fUvpH5J9aa/Erufbg18OfEx3zEHAQ\nOIlfiTXWYSN+vbAga1l03Hqtrxu4OwUdTgLnVYcgf2sk+x/AnjRtADwHnFCZB3Vbi19G+6D+rS/j\nOb+NX3nkaJR/i+a/C/QB0zX/TqBT5XQCn4iO+ZPqFOwxvQzyU7F5gX73NnAa6NL8FqADOAucAXaG\nNgBWRfL3AO8Bt5Rog9DuD0X5LVq2R4+dOOI1XG0nUmGHdRPeYU3Gf+6xE7gOv1ZXjZZpA9qiztM1\nTF1/BW4DHH6ZnaWavzZ0MvwyO1s13QD0Ah/BL91zGP+NTdBhI/D1AnJuBPZqh2gBDgGXl6DDEeBV\nfOCRXu2A1yVkPgV8K00bAEvwSxy9q3rUA6eAb2u5xyO7p33OvcAngY+q/HBhHgCe1/RuYJumPwjM\nivrMsYTTyhWwRZryU7F5Qn4DsAx/09iv+9rx69k9DmzC37DbCsj8AHAoBRuEdu+NbNAOrND0JuCR\nEa/jajuSSm7A/cDm6PcTwGOJMp8Btlys8wAzgQPR75XA05reBizWdA3+wz8XygQdNL0y6MDwTms9\nPvIRcf0l6LAj2EB1aI9toOWOAPPKYINHgbeiY06FTqr1dZfpnJ+OzuUtzXP4J5+rdd9y4J0C5+n0\nmNoRLtjU5Kds88Eyum+Ltq/TMt1a72Lgj6ENEnK/B3w3+l20DaJ+tzLSITwwLEYd98W28Tam1QW0\nOucanXOT8XeeaxJlVpNfoBCgxTm3xzm3yznXqnnv5xIDdeAfyeNAHV1AK35J6TkJHR7VWJLP6Wqt\nQ+pLyCpWh/3BBkA/8KGEDVqBfhE5WAYbNOODnARqgSs13QfMKNM5x3X9R/Ma8a9Wob69wCQu5LPA\nyyJyPsr7udrjiRC/swzy0+53gT68Q2nE3zRmiF9Z+CjQRL4NYj4P/DKRV4oNgt6NwCnJR/a6pOA1\nY+6L+IshIq9qnMbtwDv49/GwsmqhQB3HgdkiMuCcWwS86JxbkJIO38GPK21THX4KPImP8fgk/hVt\ndSmyhuEk/hV4O77TvElkA/wdMO6gqdugECIizjlJu95S0PNsww8fBFaJyDHn3FTgN8ADDI0JmgYV\nsfkwDGkD59yHgbMi0hVlV8IGwzLenrQQkc0iskhElgD/Al6DwoE6xMdvHNB0J35sZT4lBuoQkc3A\nH4ANQQcR6ReR/4nIe8Cz+CegIfUlZBWtQ7AB3mGeiGxQA9xHPiRc2jboAyZEx5zH3zzQ2JgnynXO\n0TETNG8AEOdcqG8hMBi5V/NfAB4UkUORPY7p3zPA8xRop1Lll6vfKc34G/MAUAf0q+2vxt/QTjCU\nFSSeslKwQdB7AKjTssnzGZ6R3h/H2kZ+pmM2fiC0Dh8Edj/QlCjbRH4AeK4atEF/JwdEl2n+lxg6\nGNmu6Qb84Hs9PuDHYfwAZ9BhZiT3a/igt+CjdceD0r0MPyh9qTrMUz3ewDusMFt6D7CrjDZYiA5E\nU3gg/vtlPOd64GaVH/TvZuhA+HZN16n8+xK2qAGmaXoCPrjww2WQX65+V49OxOi+XwG/Jz8Q/2Jo\nA91/mcqem6IN6jXdEOkQD8SvHfEarrYTqYLT+gveQe0F7tC8Hm3MIVPM+PGMfZr3MvCpqJ4cfnzq\nEPBj8lPPk7QherSDxQ2+WvPPaWeIdfgFfir7FfyMTuzENqicbnS2qEQdzuEvnjeCfN33s9ABo7xU\nbIC/Wx/H3+X/ix9P+zLwEn4afGfoyGU65zOR7KPAGuBW8p8c9APNWv6b5IcPBqf18eNvndpG+4Af\nkncuacovV787g79RhODJ67T+8MnDS4k2+Bg+aE3cH0qxQY9uX4zy52rZHj22dqRr2L6INwwjU4y7\nMS3DMLKNOS3DMDKFOS3DMDKFOS3DMDKFOS3DMDKFOS3DMDKFOS3DMDKFOS3DMDLF/wGms8Jolysl\nyQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x129caf98>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"polydf.plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Joins"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"ExecuteTime": {
"end_time": "2017-12-15T21:26:12.951484Z",
"start_time": "2017-12-15T21:26:10.561508Z"
}
},
"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>geometry</th>\n",
" <th>value1</th>\n",
" <th>value2</th>\n",
" <th>index_right</th>\n",
" <th>BoroCode</th>\n",
" <th>BoroName</th>\n",
" <th>Shape_Leng</th>\n",
" <th>Shape_Area</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>POINT (913175 120121)</td>\n",
" <td>1033296</td>\n",
" <td>793054</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>POINT (932450 139211)</td>\n",
" <td>1071661</td>\n",
" <td>793239</td>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>Staten Island</td>\n",
" <td>330470.010332</td>\n",
" <td>1.623820e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>POINT (951725 158301)</td>\n",
" <td>1110026</td>\n",
" <td>793424</td>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>Staten Island</td>\n",
" <td>330470.010332</td>\n",
" <td>1.623820e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>POINT (971000 177391)</td>\n",
" <td>1148391</td>\n",
" <td>793609</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>POINT (990275 196481)</td>\n",
" <td>1186756</td>\n",
" <td>793794</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>POINT (1009550 215571)</td>\n",
" <td>1225121</td>\n",
" <td>793979</td>\n",
" <td>1.0</td>\n",
" <td>4.0</td>\n",
" <td>Queens</td>\n",
" <td>896344.047763</td>\n",
" <td>3.045213e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>POINT (1028825 234661)</td>\n",
" <td>1263486</td>\n",
" <td>794164</td>\n",
" <td>4.0</td>\n",
" <td>2.0</td>\n",
" <td>Bronx</td>\n",
" <td>464392.991824</td>\n",
" <td>1.186925e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>POINT (1048100 253751)</td>\n",
" <td>1301851</td>\n",
" <td>794349</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>POINT (1067375 272841)</td>\n",
" <td>1340216</td>\n",
" <td>794534</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" geometry value1 value2 index_right BoroCode \\\n",
"0 POINT (913175 120121) 1033296 793054 NaN NaN \n",
"1 POINT (932450 139211) 1071661 793239 0.0 5.0 \n",
"2 POINT (951725 158301) 1110026 793424 0.0 5.0 \n",
"3 POINT (971000 177391) 1148391 793609 NaN NaN \n",
"4 POINT (990275 196481) 1186756 793794 NaN NaN \n",
"5 POINT (1009550 215571) 1225121 793979 1.0 4.0 \n",
"6 POINT (1028825 234661) 1263486 794164 4.0 2.0 \n",
"7 POINT (1048100 253751) 1301851 794349 NaN NaN \n",
"8 POINT (1067375 272841) 1340216 794534 NaN NaN \n",
"\n",
" BoroName Shape_Leng Shape_Area \n",
"0 NaN NaN NaN \n",
"1 Staten Island 330470.010332 1.623820e+09 \n",
"2 Staten Island 330470.010332 1.623820e+09 \n",
"3 NaN NaN NaN \n",
"4 NaN NaN NaN \n",
"5 Queens 896344.047763 3.045213e+09 \n",
"6 Bronx 464392.991824 1.186925e+09 \n",
"7 NaN NaN NaN \n",
"8 NaN NaN NaN "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from geopandas.tools import sjoin\n",
"join_left_df = sjoin(pointdf, polydf, how=\"left\")\n",
"join_left_df\n",
"# Note the NaNs where the point did not intersect a boro"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"ExecuteTime": {
"end_time": "2017-12-15T21:26:13.871475Z",
"start_time": "2017-12-15T21:26:12.951484Z"
}
},
"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>index_left</th>\n",
" <th>value1</th>\n",
" <th>value2</th>\n",
" <th>BoroCode</th>\n",
" <th>BoroName</th>\n",
" <th>Shape_Leng</th>\n",
" <th>Shape_Area</th>\n",
" <th>geometry</th>\n",
" </tr>\n",
" <tr>\n",
" <th>index_right</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></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>0</th>\n",
" <td>1.0</td>\n",
" <td>1071661.0</td>\n",
" <td>793239.0</td>\n",
" <td>5</td>\n",
" <td>Staten Island</td>\n",
" <td>330470.010332</td>\n",
" <td>1.623820e+09</td>\n",
" <td>(POLYGON ((970217.0223999023 145643.3322143555...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2.0</td>\n",
" <td>1110026.0</td>\n",
" <td>793424.0</td>\n",
" <td>5</td>\n",
" <td>Staten Island</td>\n",
" <td>330470.010332</td>\n",
" <td>1.623820e+09</td>\n",
" <td>(POLYGON ((970217.0223999023 145643.3322143555...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>5.0</td>\n",
" <td>1225121.0</td>\n",
" <td>793979.0</td>\n",
" <td>4</td>\n",
" <td>Queens</td>\n",
" <td>896344.047763</td>\n",
" <td>3.045213e+09</td>\n",
" <td>(POLYGON ((1029606.076599121 156073.8142089844...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>6.0</td>\n",
" <td>1263486.0</td>\n",
" <td>794164.0</td>\n",
" <td>2</td>\n",
" <td>Bronx</td>\n",
" <td>464392.991824</td>\n",
" <td>1.186925e+09</td>\n",
" <td>(POLYGON ((1012821.805786133 229228.2645874023...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>3</td>\n",
" <td>Brooklyn</td>\n",
" <td>741080.523166</td>\n",
" <td>1.937479e+09</td>\n",
" <td>(POLYGON ((1021176.479003906 151374.7969970703...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>Manhattan</td>\n",
" <td>359299.096471</td>\n",
" <td>6.364715e+08</td>\n",
" <td>(POLYGON ((981219.0557861328 188655.3157958984...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" index_left value1 value2 BoroCode BoroName \\\n",
"index_right \n",
"0 1.0 1071661.0 793239.0 5 Staten Island \n",
"0 2.0 1110026.0 793424.0 5 Staten Island \n",
"1 5.0 1225121.0 793979.0 4 Queens \n",
"4 6.0 1263486.0 794164.0 2 Bronx \n",
"2 NaN NaN NaN 3 Brooklyn \n",
"3 NaN NaN NaN 1 Manhattan \n",
"\n",
" Shape_Leng Shape_Area \\\n",
"index_right \n",
"0 330470.010332 1.623820e+09 \n",
"0 330470.010332 1.623820e+09 \n",
"1 896344.047763 3.045213e+09 \n",
"4 464392.991824 1.186925e+09 \n",
"2 741080.523166 1.937479e+09 \n",
"3 359299.096471 6.364715e+08 \n",
"\n",
" geometry \n",
"index_right \n",
"0 (POLYGON ((970217.0223999023 145643.3322143555... \n",
"0 (POLYGON ((970217.0223999023 145643.3322143555... \n",
"1 (POLYGON ((1029606.076599121 156073.8142089844... \n",
"4 (POLYGON ((1012821.805786133 229228.2645874023... \n",
"2 (POLYGON ((1021176.479003906 151374.7969970703... \n",
"3 (POLYGON ((981219.0557861328 188655.3157958984... "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"join_right_df = sjoin(pointdf, polydf, how=\"right\")\n",
"join_right_df\n",
"# Note Staten Island is repeated"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"ExecuteTime": {
"end_time": "2017-12-15T21:26:13.961474Z",
"start_time": "2017-12-15T21:26:13.881475Z"
}
},
"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>geometry</th>\n",
" <th>value1</th>\n",
" <th>value2</th>\n",
" <th>index_right</th>\n",
" <th>BoroCode</th>\n",
" <th>BoroName</th>\n",
" <th>Shape_Leng</th>\n",
" <th>Shape_Area</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>POINT (932450 139211)</td>\n",
" <td>1071661</td>\n",
" <td>793239</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>Staten Island</td>\n",
" <td>330470.010332</td>\n",
" <td>1.623820e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>POINT (951725 158301)</td>\n",
" <td>1110026</td>\n",
" <td>793424</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>Staten Island</td>\n",
" <td>330470.010332</td>\n",
" <td>1.623820e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>POINT (1009550 215571)</td>\n",
" <td>1225121</td>\n",
" <td>793979</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>Queens</td>\n",
" <td>896344.047763</td>\n",
" <td>3.045213e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>POINT (1028825 234661)</td>\n",
" <td>1263486</td>\n",
" <td>794164</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>Bronx</td>\n",
" <td>464392.991824</td>\n",
" <td>1.186925e+09</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" geometry value1 value2 index_right BoroCode \\\n",
"1 POINT (932450 139211) 1071661 793239 0 5 \n",
"2 POINT (951725 158301) 1110026 793424 0 5 \n",
"5 POINT (1009550 215571) 1225121 793979 1 4 \n",
"6 POINT (1028825 234661) 1263486 794164 4 2 \n",
"\n",
" BoroName Shape_Leng Shape_Area \n",
"1 Staten Island 330470.010332 1.623820e+09 \n",
"2 Staten Island 330470.010332 1.623820e+09 \n",
"5 Queens 896344.047763 3.045213e+09 \n",
"6 Bronx 464392.991824 1.186925e+09 "
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"join_inner_df = sjoin(pointdf, polydf, how=\"inner\")\n",
"join_inner_df\n",
"# Note the lack of NaNs; dropped anything that didn't intersect"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We're not limited to using the `intersection` binary predicate. Any of the `Shapely` geometry methods that return a Boolean can be used by specifying the `op` kwarg."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"ExecuteTime": {
"end_time": "2017-12-15T21:26:14.191472Z",
"start_time": "2017-12-15T21:26:13.961474Z"
}
},
"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>geometry</th>\n",
" <th>value1</th>\n",
" <th>value2</th>\n",
" <th>index_right</th>\n",
" <th>BoroCode</th>\n",
" <th>BoroName</th>\n",
" <th>Shape_Leng</th>\n",
" <th>Shape_Area</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>POINT (913175 120121)</td>\n",
" <td>1033296</td>\n",
" <td>793054</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>POINT (932450 139211)</td>\n",
" <td>1071661</td>\n",
" <td>793239</td>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>Staten Island</td>\n",
" <td>330470.010332</td>\n",
" <td>1.623820e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>POINT (951725 158301)</td>\n",
" <td>1110026</td>\n",
" <td>793424</td>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>Staten Island</td>\n",
" <td>330470.010332</td>\n",
" <td>1.623820e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>POINT (971000 177391)</td>\n",
" <td>1148391</td>\n",
" <td>793609</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>POINT (990275 196481)</td>\n",
" <td>1186756</td>\n",
" <td>793794</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>POINT (1009550 215571)</td>\n",
" <td>1225121</td>\n",
" <td>793979</td>\n",
" <td>1.0</td>\n",
" <td>4.0</td>\n",
" <td>Queens</td>\n",
" <td>896344.047763</td>\n",
" <td>3.045213e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>POINT (1028825 234661)</td>\n",
" <td>1263486</td>\n",
" <td>794164</td>\n",
" <td>4.0</td>\n",
" <td>2.0</td>\n",
" <td>Bronx</td>\n",
" <td>464392.991824</td>\n",
" <td>1.186925e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>POINT (1048100 253751)</td>\n",
" <td>1301851</td>\n",
" <td>794349</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>POINT (1067375 272841)</td>\n",
" <td>1340216</td>\n",
" <td>794534</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" geometry value1 value2 index_right BoroCode \\\n",
"0 POINT (913175 120121) 1033296 793054 NaN NaN \n",
"1 POINT (932450 139211) 1071661 793239 0.0 5.0 \n",
"2 POINT (951725 158301) 1110026 793424 0.0 5.0 \n",
"3 POINT (971000 177391) 1148391 793609 NaN NaN \n",
"4 POINT (990275 196481) 1186756 793794 NaN NaN \n",
"5 POINT (1009550 215571) 1225121 793979 1.0 4.0 \n",
"6 POINT (1028825 234661) 1263486 794164 4.0 2.0 \n",
"7 POINT (1048100 253751) 1301851 794349 NaN NaN \n",
"8 POINT (1067375 272841) 1340216 794534 NaN NaN \n",
"\n",
" BoroName Shape_Leng Shape_Area \n",
"0 NaN NaN NaN \n",
"1 Staten Island 330470.010332 1.623820e+09 \n",
"2 Staten Island 330470.010332 1.623820e+09 \n",
"3 NaN NaN NaN \n",
"4 NaN NaN NaN \n",
"5 Queens 896344.047763 3.045213e+09 \n",
"6 Bronx 464392.991824 1.186925e+09 \n",
"7 NaN NaN NaN \n",
"8 NaN NaN NaN "
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sjoin(pointdf, polydf, how=\"left\", op=\"within\")"
]
}
],
"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": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
Reference in New Issue View Git Blame Copy Permalink