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Build US Airman Database and Map
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "code", | |
| "execution_count": 1, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "import pandas as pd\n", | |
| "import numpy as np\n", | |
| "import requests\n", | |
| "import sqlite3\n", | |
| "import folium\n", | |
| "import folium.plugins as folium_plugins\n", | |
| "from time import sleep\n", | |
| "from pyTamuGeocode import TamuGeocode\n", | |
| "from ast import literal_eval\n", | |
| "from decimal import Decimal\n", | |
| "from sqlalchemy import create_engine" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 2, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "tamu_api_key = 'api-key-here'\n", | |
| "cache_db_file = 'geocode_cache.db'\n", | |
| "tg = TamuGeocode(cache_db_file, tamu_api_key)\n", | |
| "\n", | |
| "e = create_engine('sqlite:///usa_airman_201902.db')\n", | |
| "q = 'SELECT * FROM airman WHERE state = \\'MI\\';'\n", | |
| "df = pd.read_sql(q, e)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 3, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "def geo_series(r, gcf):\n", | |
| " \"\"\"\n", | |
| " Geocode addresses from a dataframe series\n", | |
| " r is the series row\n", | |
| " gcf is the function actually doing the geocoding - it should have a signature taking address, city, state, zip\n", | |
| " \"\"\"\n", | |
| " adata = (r['street_1'], r['city'], r['state'], r['zip_code'])\n", | |
| " \n", | |
| " latlonstr = str(gcf(*adata)) # Stringify the damn tuple because dataframes pukes otherwise\n", | |
| "\n", | |
| " return latlonstr" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 4, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "df['latlon'] = df.apply(geo_series, axis=1, gcf=tg.geocode)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 5, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "# df.head()\n", | |
| "ddf = df[['unique_id', 'latlon']]\n", | |
| "\n", | |
| "ddf = ddf.drop_duplicates()\n", | |
| "\n", | |
| "# ddf.head()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 6, | |
| "metadata": { | |
| "scrolled": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "Map = folium.Map\n", | |
| "Marker = folium.Marker\n", | |
| "FMC = folium_plugins.FastMarkerCluster\n", | |
| "MC = folium_plugins.MarkerCluster" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 7, | |
| "metadata": { | |
| "scrolled": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "m = Map(\n", | |
| " location=(42.70541,-84.66623), # LW\n", | |
| " zoom_start = 12\n", | |
| ")\n", | |
| "\n", | |
| "# mc = MC()\n", | |
| "# for x in list(zip(ddf.unique_id, ddf.latlon)):\n", | |
| "# Marker(list(literal_eval(x[1])), popup=x[0]).add_to(mc)\n", | |
| "# mc.add_to(m)\n", | |
| "\n", | |
| "mc = FMC([literal_eval(x) for x in ddf.latlon])\n", | |
| "mc.add_to(m)\n", | |
| "\n", | |
| "m.save('mich-cert-map.html')\n", | |
| "\n", | |
| "# display(m)" | |
| ] | |
| } | |
| ], | |
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| "kernelspec": { | |
| "display_name": "Python 3", | |
| "language": "python", | |
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