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introduction-to-geopandas.ipynb
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| { | |
| "nbformat": 4, | |
| "nbformat_minor": 0, | |
| "metadata": { | |
| "colab": { | |
| "name": "introduction-to-geopandas.ipynb", | |
| "provenance": [], | |
| "collapsed_sections": [], | |
| "authorship_tag": "ABX9TyPGwoDkR4HCxE31n+D9NlYy", | |
| "include_colab_link": true | |
| }, | |
| "kernelspec": { | |
| "name": "python3", | |
| "display_name": "Python 3" | |
| }, | |
| "language_info": { | |
| "name": "python" | |
| } | |
| }, | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "view-in-github", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "<a href=\"https://colab.research.google.com/gist/shuuji3/f00f05396005a5d6d75ddcecb71375be/introduction-to-geopandas.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "YJ79cTWbYf6D" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "%pip install pillow pysal geopandas rtree matplotlib jupyterlab" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "import pandas as pd\n", | |
| "import geopandas as gpd\n", | |
| "from shapely.geometry import Point\n", | |
| "%matplotlib inline" | |
| ], | |
| "metadata": { | |
| "id": "MLEaGXxpYkNX" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "!rm -rf cb_2021_us_state_500k/\n", | |
| "!wget https://www2.census.gov/geo/tiger/GENZ2021/shp/cb_2021_us_state_500k.zip\n", | |
| "!unzip cb_2021_us_state_500k.zip -d cb_2021_us_state_500k/\n", | |
| "!ls cb_2021_us_state_500k/" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "KFKAOL7yYuDL", | |
| "outputId": "e331c3ca-fa0b-4cfe-9f60-99b57c635003" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "--2022-08-26 13:49:02-- https://www2.census.gov/geo/tiger/GENZ2021/shp/cb_2021_us_state_500k.zip\n", | |
| "Resolving www2.census.gov (www2.census.gov)... 104.97.106.142, 2600:1408:e000:19e::208c, 2600:1408:e000:1a3::208c\n", | |
| "Connecting to www2.census.gov (www2.census.gov)|104.97.106.142|:443... connected.\n", | |
| "HTTP request sent, awaiting response... 200 OK\n", | |
| "Length: unspecified [application/zip]\n", | |
| "Saving to: ‘cb_2021_us_state_500k.zip’\n", | |
| "\n", | |
| "cb_2021_us_state_50 [ <=> ] 3.17M --.-KB/s in 0.09s \n", | |
| "\n", | |
| "2022-08-26 13:49:03 (36.5 MB/s) - ‘cb_2021_us_state_500k.zip’ saved [3321661]\n", | |
| "\n", | |
| "Archive: cb_2021_us_state_500k.zip\n", | |
| " inflating: cb_2021_us_state_500k/cb_2021_us_state_500k.shp.ea.iso.xml \n", | |
| " inflating: cb_2021_us_state_500k/cb_2021_us_state_500k.shp.iso.xml \n", | |
| " inflating: cb_2021_us_state_500k/cb_2021_us_state_500k.shp \n", | |
| " inflating: cb_2021_us_state_500k/cb_2021_us_state_500k.shx \n", | |
| " inflating: cb_2021_us_state_500k/cb_2021_us_state_500k.dbf \n", | |
| " inflating: cb_2021_us_state_500k/cb_2021_us_state_500k.prj \n", | |
| " extracting: cb_2021_us_state_500k/cb_2021_us_state_500k.cpg \n", | |
| "cb_2021_us_state_500k.cpg cb_2021_us_state_500k.shp.ea.iso.xml\n", | |
| "cb_2021_us_state_500k.dbf cb_2021_us_state_500k.shp.iso.xml\n", | |
| "cb_2021_us_state_500k.prj cb_2021_us_state_500k.shx\n", | |
| "cb_2021_us_state_500k.shp\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "states = gpd.read_file('cb_2021_us_state_500k/cb_2021_us_state_500k.shp')\n", | |
| "states.head()" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 302 | |
| }, | |
| "id": "OKkm29liZ2PJ", | |
| "outputId": "e1cb74d1-b5b0-47e7-db05-02c2e7f29fdb" | |
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| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| " STATEFP STATENS AFFGEOID GEOID STUSPS NAME LSAD \\\n", | |
| "0 56 01779807 0400000US56 56 WY Wyoming 00 \n", | |
| "1 02 01785533 0400000US02 02 AK Alaska 00 \n", | |
| "2 24 01714934 0400000US24 24 MD Maryland 00 \n", | |
| "3 60 01802701 0400000US60 60 AS American Samoa 00 \n", | |
| "4 05 00068085 0400000US05 05 AR Arkansas 00 \n", | |
| "\n", | |
| " ALAND AWATER \\\n", | |
| "0 251458712294 1867503716 \n", | |
| "1 1478942847588 245378425142 \n", | |
| "2 25151992308 6979074857 \n", | |
| "3 197759069 1307243751 \n", | |
| "4 134660767709 3121950081 \n", | |
| "\n", | |
| " geometry \n", | |
| "0 POLYGON ((-111.05456 45.00095, -111.04507 45.0... \n", | |
| "1 MULTIPOLYGON (((179.48246 51.98283, 179.48657 ... \n", | |
| "2 MULTIPOLYGON (((-76.05015 37.98691, -76.04998 ... \n", | |
| "3 MULTIPOLYGON (((-168.14582 -14.54791, -168.145... \n", | |
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| " <div class=\"colab-df-container\">\n", | |
| " <div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
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| " .dataframe tbody tr th {\n", | |
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| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>STATEFP</th>\n", | |
| " <th>STATENS</th>\n", | |
| " <th>AFFGEOID</th>\n", | |
| " <th>GEOID</th>\n", | |
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| " <th>1</th>\n", | |
| " <td>02</td>\n", | |
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| " <td>0400000US02</td>\n", | |
| " <td>02</td>\n", | |
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| " <td>Arkansas</td>\n", | |
| " <td>00</td>\n", | |
| " <td>134660767709</td>\n", | |
| " <td>3121950081</td>\n", | |
| " <td>POLYGON ((-94.61792 36.49941, -94.61765 36.499...</td>\n", | |
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| "metadata": {}, | |
| "execution_count": 11 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "states.plot()" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 160 | |
| }, | |
| "id": "mU6V4oL1aRyL", | |
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| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "<matplotlib.axes._subplots.AxesSubplot at 0x7f8da04523d0>" | |
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| }, | |
| "metadata": {}, | |
| "execution_count": 19 | |
| }, | |
| { | |
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| "data": { | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ], | |
| "image/png": 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\n" | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "states.iloc[10:20].plot()" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 246 | |
| }, | |
| "id": "pMyA8cq5bPw1", | |
| "outputId": "97280b2b-8d43-413a-c0ba-5e7e2c5681f6" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "<matplotlib.axes._subplots.AxesSubplot at 0x7f8da08448d0>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 30 | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ], | |
| "image/png": 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\n" | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "df = pd.read_csv('https://github.com/datasciencelabs/data/raw/master/powerplants.csv', index_col='Unnamed: 0')\n", | |
| "df" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 424 | |
| }, | |
| "id": "blOem6nQbrrr", | |
| "outputId": "06445856-9dfb-4471-e45a-eff3469209d3" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| " id1 lat1 lon1 val1\n", | |
| "1 3 31.0069 -88.0103 18567.239\n", | |
| "2 7 34.0128 -85.9708 4548.031\n", | |
| "3 8 33.6446 -87.2003 7601.531\n", | |
| "4 10 32.6017 -87.7811 33627.550\n", | |
| "5 26 33.2442 -86.4567 73140.882\n", | |
| "... ... ... ... ...\n", | |
| "1237 56908 46.1066 -112.8755 0.219\n", | |
| "1238 56914 33.3590 -117.1110 0.160\n", | |
| "1239 56948 32.9175 -111.5041 0.000\n", | |
| "1240 57001 32.7971 -116.9720 0.028\n", | |
| "1241 70454 34.3387 -82.8207 0.886\n", | |
| "\n", | |
| "[1241 rows x 4 columns]" | |
| ], | |
| "text/html": [ | |
| "\n", | |
| " <div id=\"df-bd0f26ed-e6cd-402f-a3a1-507479f122bd\">\n", | |
| " <div class=\"colab-df-container\">\n", | |
| " <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>id1</th>\n", | |
| " <th>lat1</th>\n", | |
| " <th>lon1</th>\n", | |
| " <th>val1</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>3</td>\n", | |
| " <td>31.0069</td>\n", | |
| " <td>-88.0103</td>\n", | |
| " <td>18567.239</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>7</td>\n", | |
| " <td>34.0128</td>\n", | |
| " <td>-85.9708</td>\n", | |
| " <td>4548.031</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>8</td>\n", | |
| " <td>33.6446</td>\n", | |
| " <td>-87.2003</td>\n", | |
| " <td>7601.531</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>10</td>\n", | |
| " <td>32.6017</td>\n", | |
| " <td>-87.7811</td>\n", | |
| " <td>33627.550</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>5</th>\n", | |
| " <td>26</td>\n", | |
| " <td>33.2442</td>\n", | |
| " <td>-86.4567</td>\n", | |
| " <td>73140.882</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>...</th>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " <td>...</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1237</th>\n", | |
| " <td>56908</td>\n", | |
| " <td>46.1066</td>\n", | |
| " <td>-112.8755</td>\n", | |
| " <td>0.219</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1238</th>\n", | |
| " <td>56914</td>\n", | |
| " <td>33.3590</td>\n", | |
| " <td>-117.1110</td>\n", | |
| " <td>0.160</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1239</th>\n", | |
| " <td>56948</td>\n", | |
| " <td>32.9175</td>\n", | |
| " <td>-111.5041</td>\n", | |
| " <td>0.000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1240</th>\n", | |
| " <td>57001</td>\n", | |
| " <td>32.7971</td>\n", | |
| " <td>-116.9720</td>\n", | |
| " <td>0.028</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1241</th>\n", | |
| " <td>70454</td>\n", | |
| " <td>34.3387</td>\n", | |
| " <td>-82.8207</td>\n", | |
| " <td>0.886</td>\n", | |
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| " </tbody>\n", | |
| "</table>\n", | |
| "<p>1241 rows × 4 columns</p>\n", | |
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| "Point(35,135)" | |
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| "points = df.apply(lambda x: Point(x.lon1, x.lat1), axis=1)\n", | |
| "points.head()" | |
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| "/usr/local/lib/python3.7/dist-packages/pandas/core/dtypes/cast.py:118: ShapelyDeprecationWarning: The array interface is deprecated and will no longer work in Shapely 2.0. Convert the '.coords' to a numpy array instead.\n", | |
| " arr = construct_1d_object_array_from_listlike(values)\n" | |
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| "dtype: object" | |
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| "powerplants = gpd.GeoDataFrame(df, geometry=points)\n", | |
| "powerplants.crs = {'init': 'epsg:4326'}\n", | |
| "powerplants.head()" | |
| ], | |
| "metadata": { | |
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| "/usr/local/lib/python3.7/dist-packages/pyproj/crs/crs.py:131: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n", | |
| " in_crs_string = _prepare_from_proj_string(in_crs_string)\n" | |
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| "metadata": {}, | |
| "execution_count": 59 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "powerplants.plot()" | |
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| "metadata": { | |
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| "height": 252 | |
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| "<matplotlib.axes._subplots.AxesSubplot at 0x7f8d9b63db50>" | |
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| "<Figure size 432x288 with 1 Axes>" | |
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\n" | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [], | |
| "metadata": { | |
| "id": "tdCixg9hiLql" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| } | |
| ] | |
| } |
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