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feat: introduce geolocation examples
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‎install.sh‎

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#!/bin/sh
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pip install -r requirements.txt

‎notebooks/ex09_geolocation_moving_obj.csv‎

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cab_26,43.602508,39.715685,14:47:44
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cab_112,43.582243,39.752077,14:47:55
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cab_26,43.607480,39.721521,14:49:11
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cab_112,43.579258,39.758944,14:49:51
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cab_112,43.574906,39.766325,14:51:53
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cab_26,43.612203,39.720491,14:52:48

‎notebooks/sn09_analyzing_location_data.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "d22b455e-8d88-49cd-82e6-55838d526cbc",
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"metadata": {},
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"outputs": [],
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"source": [
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"import geocoder\n",
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"\n",
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"geocode_res = geocoder.osm('1 Apple Park Way, Cupertino, California, United States')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"id": "1caf59dc-c1f8-4bc7-b478-39edf657d9b8",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[-122.010747, 37.3317424]\n"
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]
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}
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],
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"source": [
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"coordinates = geocode_res.geometry['coordinates']\n",
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"print(coordinates)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 42,
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"id": "ddedf165-a1cd-4fb8-9d42-175eb5a56dc5",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>cab</th>\n",
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" <th>lat</th>\n",
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" <th>long</th>\n",
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" <th>tm</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>cab_26</td>\n",
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" <td>43.602508</td>\n",
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" <td>39.715685</td>\n",
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" <td>14:47:44</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>cab_112</td>\n",
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" <td>43.582243</td>\n",
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" <td>39.752077</td>\n",
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" <td>14:47:55</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>cab_26</td>\n",
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" <td>43.607480</td>\n",
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" <td>39.721521</td>\n",
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" <td>14:49:11</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>cab_112</td>\n",
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" <td>43.579258</td>\n",
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" <td>39.758944</td>\n",
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" <td>14:49:51</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>cab_112</td>\n",
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" <td>43.574906</td>\n",
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" <td>39.766325</td>\n",
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" <td>14:51:53</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>5</th>\n",
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" <td>cab_26</td>\n",
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" <td>43.612203</td>\n",
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" <td>39.720491</td>\n",
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" <td>14:52:48</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" cab lat long tm\n",
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"0 cab_26 43.602508 39.715685 14:47:44\n",
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"1 cab_112 43.582243 39.752077 14:47:55\n",
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"2 cab_26 43.607480 39.721521 14:49:11\n",
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"3 cab_112 43.579258 39.758944 14:49:51\n",
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"4 cab_112 43.574906 39.766325 14:51:53\n",
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"5 cab_26 43.612203 39.720491 14:52:48"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"import pandas as pd\n",
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"\n",
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"df = pd.read_csv(\"ex09_geolocation_moving_obj.csv\", names=['cab', 'lat', 'long', 'tm'])\n",
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"display(df)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 43,
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"id": "9f5412aa-d334-45a5-af1f-b430f161413f",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>cab</th>\n",
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" <th>lat</th>\n",
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" <th>long</th>\n",
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" <th>tm</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>5</th>\n",
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" <td>cab_26</td>\n",
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" <td>43.612203</td>\n",
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" <td>39.720491</td>\n",
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" <td>14:52:48</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>cab_112</td>\n",
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" <td>43.574906</td>\n",
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" <td>39.766325</td>\n",
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" <td>14:51:53</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" cab lat long tm\n",
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"5 cab_26 43.612203 39.720491 14:52:48\n",
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"4 cab_112 43.574906 39.766325 14:51:53"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"latestrows = df.sort_values(['cab', 'tm'], ascending=False).drop_duplicates('cab')\n",
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"display(latestrows)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 44,
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"id": "dee21ad0-9ffb-42b5-bdc4-c7fcfa828427",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[['cab_26', 43.612203, 39.720491, '14:52:48'],\n",
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" ['cab_112', 43.574906, 39.766325, '14:51:53']]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"latestrows_numpy = latestrows.values\n",
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"latestrows = latestrows_numpy.tolist()\n",
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"display(latestrows)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 45,
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"id": "eaf3773d-9880-4026-86e0-0758a998fa2b",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"cab_26: 4636\n",
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"cab_112: 1015\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"[['cab_26', 43.612203, 39.720491, '14:52:48', 4636],\n",
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" ['cab_112', 43.574906, 39.766325, '14:51:53', 1015]]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"# calculate the distance between each cab and a pickup place\n",
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"from geopy.distance import distance\n",
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"\n",
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"pick_up = (43.578854, 39.754995)\n",
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"\n",
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"for i, row in enumerate(latestrows):\n",
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" lat = row[1]\n",
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" long = row[2]\n",
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" cab = (lat, long)\n",
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" dist_meters = distance(pick_up, cab).m\n",
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"\n",
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" print(row[0] + ':', round(dist_meters))\n",
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" latestrows[i].append(round(dist_meters))\n",
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"\n",
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"display(latestrows)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 46,
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"id": "e12cf848-4505-40c1-ae6e-a9a2c2c962e1",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"The closest cab is: cab_112 . Distance in meters: 1015\n"
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]
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}
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],
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"source": [
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"closest_cab = min(latestrows, key=lambda x: x[4])\n",
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"print('The closest cab is: ', closest_cab[0], '. Distance in meters: ', closest_cab[4])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "61ce9cf6-d5e7-47d3-b836-a2daf422ab23",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.17"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

‎requirements.txt‎

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Cartopy == 0.23.0
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geocoder == 1.38.1
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geopy == 2.4.1
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h5py == 3.10.0
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jupyter == 1.0.0
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keras == 3.3.3
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pillow == 10.3.0
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scikit-learn == 1.4.0
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seaborn == 0.13.2
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shapely == 2.0.6
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spacy == 3.7.4
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yfinance == 0.2.38

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