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4.3 Creating a GeoDataFrame from coordinates.ipynb
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"<a href=\"https://colab.research.google.com/gist/mehak-sachdeva/356a86598d7658cd14f3f9d39e513fa8/4-3-creating-a-geodataframe-from-coordinates.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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"source": [
"# 4.3 Creating a GeoDataFrame from coordinates\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"<br></br>\n",
"In this notebook, you will learn how to create a GeoDataFrame from a list of coordinates in python using `geopandas` library.\n",
"\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
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"source": [
"<p>First of all, lets find some interesting places on ASU campus. Here is a list of mine. Each place is linked with a pair of coordinates: latitude and longitude.&nbsp;</p>\n",
"<table style=\"border-collapse: collapse; width: 75.51282051282051%; height: 211px;\" border=\"1\">\n",
"<tbody>\n",
"<tr style=\"height: 24px;\">\n",
"<td style=\"width: 33.333333333333336%; height: 24px;\">Cities</td>\n",
"<td style=\"width: 33.333333333333336%; height: 24px;\">Latitude</td>\n",
"<td style=\"width: 33.333333333333336%; height: 24px;\">Longitude</td>\n",
"</tr>\n",
"<tr style=\"height: 24px;\">\n",
"<td style=\"width: 33.333333333333336%; height: 24px;\">New York</td>\n",
"<td style=\"width: 33.333333333333336%; height: 24px;\"><span>40.7128</span></td>\n",
"<td style=\"width: 33.333333333333336%; height: 24px;\"><span>-74.0060</span></td>\n",
"</tr>\n",
"<tr style=\"height: 24px;\">\n",
"<td style=\"width: 33.333333333333336%; height: 24px;\">Dallas</td>\n",
"<td style=\"width: 33.333333333333336%; height: 24px;\"><span>32.7767</span></td>\n",
"<td style=\"width: 33.333333333333336%; height: 24px;\"><span>-96.7970</span></td>\n",
"</tr>\n",
"<tr style=\"height: 24px;\">\n",
"<td style=\"width: 33.333333333333336%; height: 24px;\">Seattle</td>\n",
"<td style=\"width: 33.333333333333336%; height: 24px;\"><span>47.6062</span></td>\n",
"<td style=\"width: 33.333333333333336%; height: 24px;\"><span>-122.3321</span></td>\n",
"</tr>\n",
"<tr style=\"height: 24px;\">\n",
"<td style=\"width: 33.333333333333336%; height: 24px;\">Phoenix</td>\n",
"<td style=\"width: 33.333333333333336%; height: 24px;\"><span>33.4484</span></td>\n",
"<td style=\"width: 33.333333333333336%; height: 24px;\"><span>-112.0740</span></td>\n",
"</tr>\n",
"</tbody>\n",
"</table>"
]
},
{
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"source": [
"<br></br>\n",
"Then let's try to make a DataFrame from the list of places we have."
]
},
{
"cell_type": "code",
"metadata": {
"id": "FYBcWQIMsOff"
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"source": [
"cities = [['New York', 40.7128, -74.0060], \n",
" ['Dallas', 32.7767, -96.7970],\n",
" ['Seattle', 47.6062, -122.3321],\n",
" ['Phoenix', 33.4484, -112.0740]] "
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "ZUqQ-5y4sOgC",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 85
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"outputId": "aa06f774-5dcc-4b5b-f422-f970b1277bf5"
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"source": [
"cities"
],
"execution_count": null,
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{
"output_type": "execute_result",
"data": {
"text/plain": [
"[['New York', 40.7128, -74.006],\n",
" ['Dallas', 32.7767, -96.797],\n",
" ['Seattle', 47.6062, -122.3321],\n",
" ['Phoenix', 33.4484, -112.074]]"
]
},
"metadata": {
"tags": []
},
"execution_count": 7
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "U9st_v9ysOgN"
},
"source": [
"import pandas as pd\n",
"cities_df = pd.DataFrame(cities, columns = ['Cities', 'Lat','Long']) "
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "2CfuKZstsOgT",
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"base_uri": "https://localhost:8080/",
"height": 173
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"outputId": "90f99e09-d4df-47c8-eb23-32acb4b30165"
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"source": [
"cities_df"
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{
"output_type": "execute_result",
"data": {
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"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .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>Cities</th>\n",
" <th>Lat</th>\n",
" <th>Long</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>New York</td>\n",
" <td>40.7128</td>\n",
" <td>-74.0060</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Dallas</td>\n",
" <td>32.7767</td>\n",
" <td>-96.7970</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Seattle</td>\n",
" <td>47.6062</td>\n",
" <td>-122.3321</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Phoenix</td>\n",
" <td>33.4484</td>\n",
" <td>-112.0740</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
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"text/plain": [
" Cities Lat Long\n",
"0 New York 40.7128 -74.0060\n",
"1 Dallas 32.7767 -96.7970\n",
"2 Seattle 47.6062 -122.3321\n",
"3 Phoenix 33.4484 -112.0740"
]
},
"metadata": {
"tags": []
},
"execution_count": 9
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "VSxCFr4ksOgb"
},
"source": [
"After that, we can use the method provided in geopandas `geopandas.points_from_xy(longitude, latitude)` to convert an ordinary `DataFrame` to `GeoDataFrame`."
]
},
{
"cell_type": "code",
"metadata": {
"id": "MIHHXIPpssDg"
},
"source": [
"!pip install geopandas\n",
"import geopandas as gpd"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "Kad8YesFsOgc"
},
"source": [
"cities_gdf = gpd.GeoDataFrame(\n",
" cities_df, geometry=gpd.points_from_xy(cities_df.Long, cities_df.Lat))"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "qM_HAl-msOgg"
},
"source": [
"<br></br>\n",
"Plot it to see if it looks right."
]
},
{
"cell_type": "code",
"metadata": {
"id": "X7nLrDzTsOgh",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 171
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"outputId": "e1cd76ed-5933-4d1f-9144-3c9310114b13"
},
"source": [
"cities_gdf.plot()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f7506fd02b0>"
]
},
"metadata": {
"tags": []
},
"execution_count": 12
},
{
"output_type": "display_data",
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"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
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}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "z0HxS4OJsOgq"
},
"source": [
"<br></br>\n",
"For better to look at it, we need a basemap, let's read in the US map we have made in the Module 4.1."
]
},
{
"cell_type": "code",
"metadata": {
"id": "aQXiLqBwsOgs"
},
"source": [
"%matplotlib inline"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "IMRrNk_-sOgv"
},
"source": [
"contig = gpd.read_file(\"US_lower_48.shp\")"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "Qyjxil7CsOgz",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 213
},
"outputId": "c20d6aa2-3710-45d6-e115-41b4666561a6"
},
"source": [
"contig.plot()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f7504785160>"
]
},
"metadata": {
"tags": []
},
"execution_count": 15
},
{
"output_type": "display_data",
"data": {
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MwWUz4rKZGXB7GZ2Mr8BXQ7kFl9VEIKQxEYh/dnCqf3r0S5XdjNNi4PRAePsr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\nrkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "RD1mQ4AtsOg3"
},
"source": [
"<br></br>\n",
"<font size=\"3\">Overlay both together, it seems alright!</font>"
]
},
{
"cell_type": "code",
"metadata": {
"id": "9QqsnJHusOg5",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 309
},
"outputId": "ee0b7417-13da-4c0d-bcf2-9888331a5b92"
},
"source": [
"import matplotlib.pyplot as plt\n",
"fig, ax = plt.subplots(1, 1,figsize=(10,10))\n",
"contig.plot(ax=ax)\n",
"cities_gdf.plot(ax=ax,color=\"red\")"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f75043a5ef0>"
]
},
"metadata": {
"tags": []
},
"execution_count": 16
},
{
"output_type": "display_data",
"data": {
"image/png": 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ZkH09LjTbDFXbn5isdd5sBg2uzZY2jUkBHB9yo9Gqz2xDi80NFiakMsjn1y0C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\n",
"text/plain": [
"<Figure size 720x720 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "2LxT81xksOg8"
},
"source": [
""
],
"execution_count": null,
"outputs": []
}
]
}
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