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Diffstat (limited to 'Clustering.ipynb')
| -rw-r--r-- | Clustering.ipynb | 703 |
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diff --git a/Clustering.ipynb b/Clustering.ipynb new file mode 100644 index 0000000..4bb7721 --- /dev/null +++ b/Clustering.ipynb @@ -0,0 +1,703 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 248, + "id": "initial_id", + "metadata": { + "collapsed": true, + "ExecuteTime": { + "end_time": "2023-11-06T00:18:48.861074Z", + "start_time": "2023-11-06T00:18:48.853531Z" + } + }, + "outputs": [], + "source": [ + "import folium\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from scipy.cluster.hierarchy import dendrogram, linkage\n", + "from scipy.cluster.hierarchy import fcluster\n", + "from sklearn.metrics import silhouette_score\n", + "from sklearn.cluster import KMeans\n", + "import utils" + ] + }, + { + "cell_type": "code", + "execution_count": 249, + "outputs": [], + "source": [ + "# Load the data\n", + "ListA = pd.read_csv('List A.csv')\n", + "ListB = pd.read_csv('List B.csv')\n", + "ListC = pd.read_csv('List C.csv')\n", + "ListD = pd.read_csv('List D.csv')" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-06T00:18:49.113478Z", + "start_time": "2023-11-06T00:18:49.101708Z" + } + }, + "id": "bb6f57eef695cf76" + }, + { + "cell_type": "code", + "execution_count": 250, + "outputs": [ + { + "data": { + "text/plain": " name gps \\\n0 521 Commercial Street #525 42.3688272,-71.0553792 \n1 Acorn St 42.3576234,-71.0688746 \n2 Arlington's Great Meadows 42.4299758,-71.2038948 \n3 Arthur Fiedler Statue 42.3565057,-71.0754527 \n4 BU Beach 42.3511927,-71.1060828 \n.. ... ... \n33 The Quiet Few 42.3670906,-71.0359889 \n34 The Tall Ship Boston 42.3649544,-71.0414523 \n35 Toasted Flats 42.3711266,-71.0371343 \n36 Vega Market 42.3891835,-71.033703 \n37 Winthrop High School 42.3803348,-70.9799864 \n\n googleUrl \\\n0 https://maps.google.com/maps?q=+%4042.3688272,... \n1 https://maps.google.com/maps?q=+%4042.3576234,... \n2 https://maps.google.com/maps?q=+%4042.4299758,... \n3 https://maps.google.com/maps?q=+%4042.3565057,... \n4 https://maps.google.com/maps?q=+%4042.3511927,... \n.. ... \n33 https://maps.google.com/maps?q=+%4042.3670906,... \n34 https://maps.google.com/maps?q=+%4042.3649544,... \n35 https://maps.google.com/maps?q=+%4042.3711266,... \n36 https://maps.google.com/maps?q=+%4042.3891835,... \n37 https://maps.google.com/maps?q=+%4042.3803348,... \n\n originalUrl info types \\\n0 https://www.google.com/maps/place/521+Commerci... NaN NaN \n1 https://www.google.com/maps/place/Acorn+St/dat... NaN NaN \n2 https://www.google.com/maps/place/Arlington's+... NaN NaN \n3 https://www.google.com/maps/place/Arthur+Fiedl... NaN NaN \n4 https://www.google.com/maps/place/BU+Beach/dat... NaN NaN \n.. ... ... ... \n33 https://www.google.com/maps/place/The+Quiet+Fe... NaN NaN \n34 https://www.google.com/maps/place/The+Tall+Shi... NaN NaN \n35 https://www.google.com/maps/place/Toasted+Flat... NaN NaN \n36 https://www.google.com/maps/place/Vega+Market/... NaN NaN \n37 https://www.google.com/maps/place/Winthrop+Hig... NaN NaN \n\n address \\\n0 NaN \n1 NaN \n2 Minuteman Commuter Bikeway, Lexington, MA 0242... \n3 Charles River Esplanades, Boston, MA 02114, Un... \n4 270 Bay State Rd, Boston, MA 02215, United States \n.. ... \n33 331 Sumner St, East Boston, MA 02128, United S... \n34 1 E Pier Dr, Boston, MA 02128, United States \n35 53 Chelsea St, Boston, MA 02128, United States \n36 29 Maverick St, Chelsea, MA 02150, United States \n37 400 Main St, Winthrop, MA 02152, United States \n\n description type \\\n0 NaN NaN \n1 NaN NaN \n2 183-acres of wet meadows & uplands with trails... Nature preserve \n3 NaN Sculpture \n4 A sloping, grassy plaza on the university grou... Park \n.. ... ... \n33 NaN Bar \n34 Laid-back waterside tall ship with tables doli... Lounge \n35 Snug eatery whipping up health-conscious flatb... Sandwich shop \n36 NaN Grocery store \n37 NaN High school \n\n phone website \\\n0 NaN NaN \n1 NaN NaN \n2 +1 781-863-5385 http://www.foagm.org/ \n3 +1 617-332-2433 http://helmicksculpture.com/portfolio/arthur-f... \n4 NaN https://www.bu.edu/today/2009/icons-among-us-t... \n.. ... ... \n33 +1 617-561-1061 https://www.thequietfew.com/ \n34 +1 617-307-7714 https://www.tallshipboston.com/ \n35 +1 857-264-8531 https://toastedflats.com/ \n36 NaN NaN \n37 +1 617-846-5505 https://www.winthrop.k12.ma.us/Domain/99 \n\n ratingsAverage ratingsTotal plusCode list \n0 NaN NaN NaN A \n1 NaN NaN NaN A \n2 4.6 171.0 CQHW+XC Lexington, Massachusetts, USA A \n3 4.6 14.0 9W4F+JR Boston, Massachusetts, USA A \n4 4.5 133.0 9V2V+FH Boston, Massachusetts, USA A \n.. ... ... ... ... \n33 4.7 257.0 9X87+RJ East Boston, Boston, MA, USA D \n34 4.2 549.0 9X75+XC Boston, Massachusetts, USA D \n35 4.7 372.0 9XC7+F4 Boston, Massachusetts, USA D \n36 4.8 5.0 9XQ8+MG Chelsea, Massachusetts, USA D \n37 NaN NaN 92JC+42 Winthrop, Massachusetts, USA D \n\n[169 rows x 15 columns]", + "text/html": "<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>name</th>\n <th>gps</th>\n <th>googleUrl</th>\n <th>originalUrl</th>\n <th>info</th>\n <th>types</th>\n <th>address</th>\n <th>description</th>\n <th>type</th>\n <th>phone</th>\n <th>website</th>\n <th>ratingsAverage</th>\n <th>ratingsTotal</th>\n <th>plusCode</th>\n <th>list</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>521 Commercial Street #525</td>\n <td>42.3688272,-71.0553792</td>\n <td>https://maps.google.com/maps?q=+%4042.3688272,...</td>\n <td>https://www.google.com/maps/place/521+Commerci...</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>A</td>\n </tr>\n <tr>\n <th>1</th>\n <td>Acorn St</td>\n <td>42.3576234,-71.0688746</td>\n <td>https://maps.google.com/maps?q=+%4042.3576234,...</td>\n <td>https://www.google.com/maps/place/Acorn+St/dat...</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>A</td>\n </tr>\n <tr>\n <th>2</th>\n <td>Arlington's Great Meadows</td>\n <td>42.4299758,-71.2038948</td>\n <td>https://maps.google.com/maps?q=+%4042.4299758,...</td>\n <td>https://www.google.com/maps/place/Arlington's+...</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>Minuteman Commuter Bikeway, Lexington, MA 0242...</td>\n <td>183-acres of wet meadows & uplands with trails...</td>\n <td>Nature preserve</td>\n <td>+1 781-863-5385</td>\n <td>http://www.foagm.org/</td>\n <td>4.6</td>\n <td>171.0</td>\n <td>CQHW+XC Lexington, Massachusetts, USA</td>\n <td>A</td>\n </tr>\n <tr>\n <th>3</th>\n <td>Arthur Fiedler Statue</td>\n <td>42.3565057,-71.0754527</td>\n <td>https://maps.google.com/maps?q=+%4042.3565057,...</td>\n <td>https://www.google.com/maps/place/Arthur+Fiedl...</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>Charles River Esplanades, Boston, MA 02114, Un...</td>\n <td>NaN</td>\n <td>Sculpture</td>\n <td>+1 617-332-2433</td>\n <td>http://helmicksculpture.com/portfolio/arthur-f...</td>\n <td>4.6</td>\n <td>14.0</td>\n <td>9W4F+JR Boston, Massachusetts, USA</td>\n <td>A</td>\n </tr>\n <tr>\n <th>4</th>\n <td>BU Beach</td>\n <td>42.3511927,-71.1060828</td>\n <td>https://maps.google.com/maps?q=+%4042.3511927,...</td>\n <td>https://www.google.com/maps/place/BU+Beach/dat...</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>270 Bay State Rd, Boston, MA 02215, United States</td>\n <td>A sloping, grassy plaza on the university grou...</td>\n <td>Park</td>\n <td>NaN</td>\n <td>https://www.bu.edu/today/2009/icons-among-us-t...</td>\n <td>4.5</td>\n <td>133.0</td>\n <td>9V2V+FH Boston, Massachusetts, USA</td>\n <td>A</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>33</th>\n <td>The Quiet Few</td>\n <td>42.3670906,-71.0359889</td>\n <td>https://maps.google.com/maps?q=+%4042.3670906,...</td>\n <td>https://www.google.com/maps/place/The+Quiet+Fe...</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>331 Sumner St, East Boston, MA 02128, United S...</td>\n <td>NaN</td>\n <td>Bar</td>\n <td>+1 617-561-1061</td>\n <td>https://www.thequietfew.com/</td>\n <td>4.7</td>\n <td>257.0</td>\n <td>9X87+RJ East Boston, Boston, MA, USA</td>\n <td>D</td>\n </tr>\n <tr>\n <th>34</th>\n <td>The Tall Ship Boston</td>\n <td>42.3649544,-71.0414523</td>\n <td>https://maps.google.com/maps?q=+%4042.3649544,...</td>\n <td>https://www.google.com/maps/place/The+Tall+Shi...</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>1 E Pier Dr, Boston, MA 02128, United States</td>\n <td>Laid-back waterside tall ship with tables doli...</td>\n <td>Lounge</td>\n <td>+1 617-307-7714</td>\n <td>https://www.tallshipboston.com/</td>\n <td>4.2</td>\n <td>549.0</td>\n <td>9X75+XC Boston, Massachusetts, USA</td>\n <td>D</td>\n </tr>\n <tr>\n <th>35</th>\n <td>Toasted Flats</td>\n <td>42.3711266,-71.0371343</td>\n <td>https://maps.google.com/maps?q=+%4042.3711266,...</td>\n <td>https://www.google.com/maps/place/Toasted+Flat...</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>53 Chelsea St, Boston, MA 02128, United States</td>\n <td>Snug eatery whipping up health-conscious flatb...</td>\n <td>Sandwich shop</td>\n <td>+1 857-264-8531</td>\n <td>https://toastedflats.com/</td>\n <td>4.7</td>\n <td>372.0</td>\n <td>9XC7+F4 Boston, Massachusetts, USA</td>\n <td>D</td>\n </tr>\n <tr>\n <th>36</th>\n <td>Vega Market</td>\n <td>42.3891835,-71.033703</td>\n <td>https://maps.google.com/maps?q=+%4042.3891835,...</td>\n <td>https://www.google.com/maps/place/Vega+Market/...</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>29 Maverick St, Chelsea, MA 02150, United States</td>\n <td>NaN</td>\n <td>Grocery store</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>4.8</td>\n <td>5.0</td>\n <td>9XQ8+MG Chelsea, Massachusetts, USA</td>\n <td>D</td>\n </tr>\n <tr>\n <th>37</th>\n <td>Winthrop High School</td>\n <td>42.3803348,-70.9799864</td>\n <td>https://maps.google.com/maps?q=+%4042.3803348,...</td>\n <td>https://www.google.com/maps/place/Winthrop+Hig...</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>400 Main St, Winthrop, MA 02152, United States</td>\n <td>NaN</td>\n <td>High school</td>\n <td>+1 617-846-5505</td>\n <td>https://www.winthrop.k12.ma.us/Domain/99</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>92JC+42 Winthrop, Massachusetts, USA</td>\n <td>D</td>\n </tr>\n </tbody>\n</table>\n<p>169 rows × 15 columns</p>\n</div>" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Combine the two lists and add a column to indicate the list\n", + "ListA['list'] = 'A'\n", + "ListB['list'] = 'B'\n", + "ListC['list'] = 'C'\n", + "ListD['list'] = 'D'\n", + "\n", + "TotalList = pd.concat([ListA, ListB, ListC, ListD])\n", + "display(TotalList)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-06T00:18:49.364284Z", + "start_time": "2023-11-06T00:18:49.351342Z" + } + }, + "id": "dc434958d5e4a3a8" + }, + { + "cell_type": "code", + "execution_count": 251, + "outputs": [], + "source": [ + "# Remove all columns but name and gps\n", + "TotalList = TotalList[['name', 'gps', 'list']]" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-06T00:18:49.785140Z", + "start_time": "2023-11-06T00:18:49.779600Z" + } + }, + "id": "2873c16423fe3119" + }, + { + "cell_type": "code", + "execution_count": 252, + "outputs": [], + "source": [ + "# Convert the gps column to a list of lists for k-means\n", + "TotalList['gps'] = TotalList['gps'].apply(lambda x: x.strip('[]').split(','))\n", + "TotalList['gps'] = TotalList['gps'].apply(lambda x: [float(i) for i in x])" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-06T00:18:50.022174Z", + "start_time": "2023-11-06T00:18:50.008422Z" + } + }, + "id": "29f9155ef8d75fda" + }, + { + "cell_type": "code", + "execution_count": 253, + "outputs": [ + { + "data": { + "text/plain": " name gps list\n0 521 Commercial Street #525 [42.3688272, -71.0553792] A\n1 Acorn St [42.3576234, -71.0688746] A\n2 Arlington's Great Meadows [42.4299758, -71.2038948] A\n3 Arthur Fiedler Statue [42.3565057, -71.0754527] A\n4 BU Beach [42.3511927, -71.1060828] A\n.. ... ... ...\n33 The Quiet Few [42.3670906, -71.0359889] D\n34 The Tall Ship Boston [42.3649544, -71.0414523] D\n35 Toasted Flats [42.3711266, -71.0371343] D\n36 Vega Market [42.3891835, -71.033703] D\n37 Winthrop High School [42.3803348, -70.9799864] D\n\n[169 rows x 3 columns]", + "text/html": "<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>name</th>\n <th>gps</th>\n <th>list</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>521 Commercial Street #525</td>\n <td>[42.3688272, -71.0553792]</td>\n <td>A</td>\n </tr>\n <tr>\n <th>1</th>\n <td>Acorn St</td>\n <td>[42.3576234, -71.0688746]</td>\n <td>A</td>\n </tr>\n <tr>\n <th>2</th>\n <td>Arlington's Great Meadows</td>\n <td>[42.4299758, -71.2038948]</td>\n <td>A</td>\n </tr>\n <tr>\n <th>3</th>\n <td>Arthur Fiedler Statue</td>\n <td>[42.3565057, -71.0754527]</td>\n <td>A</td>\n </tr>\n <tr>\n <th>4</th>\n <td>BU Beach</td>\n <td>[42.3511927, -71.1060828]</td>\n <td>A</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>33</th>\n <td>The Quiet Few</td>\n <td>[42.3670906, -71.0359889]</td>\n <td>D</td>\n </tr>\n <tr>\n <th>34</th>\n <td>The Tall Ship Boston</td>\n <td>[42.3649544, -71.0414523]</td>\n <td>D</td>\n </tr>\n <tr>\n <th>35</th>\n <td>Toasted Flats</td>\n <td>[42.3711266, -71.0371343]</td>\n <td>D</td>\n </tr>\n <tr>\n <th>36</th>\n <td>Vega Market</td>\n <td>[42.3891835, -71.033703]</td>\n <td>D</td>\n </tr>\n <tr>\n <th>37</th>\n <td>Winthrop High School</td>\n <td>[42.3803348, -70.9799864]</td>\n <td>D</td>\n </tr>\n </tbody>\n</table>\n<p>169 rows × 3 columns</p>\n</div>" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(TotalList)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-06T00:18:50.242086Z", + "start_time": "2023-11-06T00:18:50.237892Z" + } + }, + "id": "a03a7c5dacebddd0" + }, + { + "cell_type": "markdown", + "source": [ + "# Dendrogram" + ], + "metadata": { + "collapsed": false + }, + "id": "72e85d219be8c635" + }, + { + "cell_type": "code", + "execution_count": 254, + "outputs": [ + { + "data": { + "text/plain": "<Figure size 2500x1000 with 1 Axes>", + "image/png": 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+ }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create the linkage matrix\n", + "linkage_matrix = linkage(TotalList['gps'].values.tolist(), 'ward')\n", + "\n", + "# Plot the dendrogram\n", + "plt.figure(figsize=(25, 10))\n", + "plt.title('Hierarchical Clustering Dendrogram')\n", + "plt.xlabel('sample index')\n", + "plt.ylabel('distance')\n", + "dendrogram(linkage_matrix, leaf_rotation=90., leaf_font_size=8.)\n", + "plt.show()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-06T00:18:51.214742Z", + "start_time": "2023-11-06T00:18:50.736378Z" + } + }, + "id": "9e215df3a350e3cf" + }, + { + "cell_type": "code", + "execution_count": 255, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of clusters: 10\n", + "Silhouette score: 0.4218177237729999\n" + ] + } + ], + "source": [ + "# Set the threshold distance\n", + "threshold_distance = 0.15\n", + "\n", + "# Cut the dendrogram to get cluster labels\n", + "cluster_labels_hc = fcluster(linkage_matrix, t=threshold_distance, criterion='distance')\n", + "\n", + "# Now, you have the number of clusters determined by the dendrogram\n", + "num_clusters = len(np.unique(cluster_labels_hc))\n", + "print(\"Number of clusters:\", num_clusters)\n", + "\n", + "# Calculate the silhouette score to evaluate the clustering\n", + "silhouette_avg = silhouette_score(TotalList['gps'].values.tolist(), cluster_labels_hc)\n", + "print(\"Silhouette score:\", silhouette_avg)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-06T00:18:51.241741Z", + "start_time": "2023-11-06T00:18:51.217906Z" + } + }, + "id": "2f52d83746e670d" + }, + { + "cell_type": "markdown", + "source": [ + "# K-means" + ], + "metadata": { + "collapsed": false + }, + "id": "bc97c258908ac38a" + }, + { + "cell_type": "code", + "execution_count": 256, + "outputs": [], + "source": [ + "# Cluster the data using Gaussian Mixture Models\n", + "# Create two centroids, one in the North End and one in the Financial District\n", + "centroids = [[42.364506, -71.054733], [42.358894, -71.056742]]" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-06T00:18:54.543675Z", + "start_time": "2023-11-06T00:18:54.540819Z" + } + }, + "id": "45b59d81ae2de84e" + }, + { + "cell_type": "code", + "execution_count": 257, + "outputs": [ + { + "data": { + "text/plain": " name gps list weights\n0 521 Commercial Street #525 [42.3688272, -71.0553792] A 0.016268\n1 Acorn St [42.3576234, -71.0688746] A 0.007206\n2 Arlington's Great Meadows [42.4299758, -71.2038948] A 0.000607\n3 Arthur Fiedler Statue [42.3565057, -71.0754527] A 0.004854\n4 BU Beach [42.3511927, -71.1060828] A 0.001925\n.. ... ... ... ...\n33 The Quiet Few [42.3670906, -71.0359889] D 0.004835\n34 The Tall Ship Boston [42.3649544, -71.0414523] D 0.006736\n35 Toasted Flats [42.3711266, -71.0371343] D 0.004775\n36 Vega Market [42.3891835, -71.033703] D 0.002828\n37 Winthrop High School [42.3803348, -70.9799864] D 0.001269\n\n[169 rows x 4 columns]", + "text/html": "<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>name</th>\n <th>gps</th>\n <th>list</th>\n <th>weights</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>521 Commercial Street #525</td>\n <td>[42.3688272, -71.0553792]</td>\n <td>A</td>\n <td>0.016268</td>\n </tr>\n <tr>\n <th>1</th>\n <td>Acorn St</td>\n <td>[42.3576234, -71.0688746]</td>\n <td>A</td>\n <td>0.007206</td>\n </tr>\n <tr>\n <th>2</th>\n <td>Arlington's Great Meadows</td>\n <td>[42.4299758, -71.2038948]</td>\n <td>A</td>\n <td>0.000607</td>\n </tr>\n <tr>\n <th>3</th>\n <td>Arthur Fiedler Statue</td>\n <td>[42.3565057, -71.0754527]</td>\n <td>A</td>\n <td>0.004854</td>\n </tr>\n <tr>\n <th>4</th>\n <td>BU Beach</td>\n <td>[42.3511927, -71.1060828]</td>\n <td>A</td>\n <td>0.001925</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>33</th>\n <td>The Quiet Few</td>\n <td>[42.3670906, -71.0359889]</td>\n <td>D</td>\n <td>0.004835</td>\n </tr>\n <tr>\n <th>34</th>\n <td>The Tall Ship Boston</td>\n <td>[42.3649544, -71.0414523]</td>\n <td>D</td>\n <td>0.006736</td>\n </tr>\n <tr>\n <th>35</th>\n <td>Toasted Flats</td>\n <td>[42.3711266, -71.0371343]</td>\n <td>D</td>\n <td>0.004775</td>\n </tr>\n <tr>\n <th>36</th>\n <td>Vega Market</td>\n <td>[42.3891835, -71.033703]</td>\n <td>D</td>\n <td>0.002828</td>\n </tr>\n <tr>\n <th>37</th>\n <td>Winthrop High School</td>\n <td>[42.3803348, -70.9799864]</td>\n <td>D</td>\n <td>0.001269</td>\n </tr>\n </tbody>\n</table>\n<p>169 rows × 4 columns</p>\n</div>" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create a weights column that increases as the location gets closer to the centroids\n", + "\n", + "# Compute the distance from each point to each centroid\n", + "TotalList['weights'] = TotalList['gps'].apply(lambda x: [np.linalg.norm(np.array(x) - np.array(centroids[0])), np.linalg.norm(np.array(x) - np.array(centroids[1]))])\n", + "\n", + "# Invert the weights so that the locations closest to the centroids have the highest weights\n", + "TotalList['weights'] = TotalList['weights'].apply(lambda x: [1/i for i in x])\n", + "\n", + "# Sum the weights\n", + "TotalList['weights'] = TotalList['weights'].apply(lambda x: sum(x))\n", + "\n", + "# Normalize the weights\n", + "TotalList['weights'] = TotalList['weights'].apply(lambda x: x/sum(TotalList['weights']))\n", + "\n", + "display(TotalList)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-06T00:18:54.970160Z", + "start_time": "2023-11-06T00:18:54.956075Z" + } + }, + "id": "2f2975484d00129c" + }, + { + "cell_type": "code", + "execution_count": 258, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/garrinshieh/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " super()._check_params_vs_input(X, default_n_init=10)\n", + "/Users/garrinshieh/anaconda3/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: RuntimeWarning: Explicit initial center position passed: performing only one init in KMeans instead of n_init=10.\n", + " super()._check_params_vs_input(X, default_n_init=10)\n" + ] + } + ], + "source": [ + "kmeans = KMeans(n_clusters=2, init=centroids).fit(TotalList['gps'].values.tolist())" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-06T00:18:56.312805Z", + "start_time": "2023-11-06T00:18:56.299246Z" + } + }, + "id": "db1ef4b14a1da5f5" + }, + { + "cell_type": "code", + "execution_count": 259, + "outputs": [], + "source": [ + "# Add the cluster labels to the dataframe\n", + "TotalList['cluster'] = kmeans.labels_" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-06T00:18:58.475575Z", + "start_time": "2023-11-06T00:18:58.468594Z" + } + }, + "id": "99891fae96a2fff7" + }, + { + "cell_type": "code", + "execution_count": 260, + "outputs": [ + { + "data": { + "text/plain": " name gps list weights \\\n0 521 Commercial Street #525 [42.3688272, -71.0553792] A 0.016268 \n1 Acorn St [42.3576234, -71.0688746] A 0.007206 \n2 Arlington's Great Meadows [42.4299758, -71.2038948] A 0.000607 \n3 Arthur Fiedler Statue [42.3565057, -71.0754527] A 0.004854 \n4 BU Beach [42.3511927, -71.1060828] A 0.001925 \n.. ... ... ... ... \n33 The Quiet Few [42.3670906, -71.0359889] D 0.004835 \n34 The Tall Ship Boston [42.3649544, -71.0414523] D 0.006736 \n35 Toasted Flats [42.3711266, -71.0371343] D 0.004775 \n36 Vega Market [42.3891835, -71.033703] D 0.002828 \n37 Winthrop High School [42.3803348, -70.9799864] D 0.001269 \n\n cluster \n0 0 \n1 0 \n2 1 \n3 0 \n4 1 \n.. ... \n33 0 \n34 0 \n35 0 \n36 0 \n37 0 \n\n[169 rows x 5 columns]", + "text/html": "<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>name</th>\n <th>gps</th>\n <th>list</th>\n <th>weights</th>\n <th>cluster</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>521 Commercial Street #525</td>\n <td>[42.3688272, -71.0553792]</td>\n <td>A</td>\n <td>0.016268</td>\n <td>0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>Acorn St</td>\n <td>[42.3576234, -71.0688746]</td>\n <td>A</td>\n <td>0.007206</td>\n <td>0</td>\n </tr>\n <tr>\n <th>2</th>\n <td>Arlington's Great Meadows</td>\n <td>[42.4299758, -71.2038948]</td>\n <td>A</td>\n <td>0.000607</td>\n <td>1</td>\n </tr>\n <tr>\n <th>3</th>\n <td>Arthur Fiedler Statue</td>\n <td>[42.3565057, -71.0754527]</td>\n <td>A</td>\n <td>0.004854</td>\n <td>0</td>\n </tr>\n <tr>\n <th>4</th>\n <td>BU Beach</td>\n <td>[42.3511927, -71.1060828]</td>\n <td>A</td>\n <td>0.001925</td>\n <td>1</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>33</th>\n <td>The Quiet Few</td>\n <td>[42.3670906, -71.0359889]</td>\n <td>D</td>\n <td>0.004835</td>\n <td>0</td>\n </tr>\n <tr>\n <th>34</th>\n <td>The Tall Ship Boston</td>\n <td>[42.3649544, -71.0414523]</td>\n <td>D</td>\n <td>0.006736</td>\n <td>0</td>\n </tr>\n <tr>\n <th>35</th>\n <td>Toasted Flats</td>\n <td>[42.3711266, -71.0371343]</td>\n <td>D</td>\n <td>0.004775</td>\n <td>0</td>\n </tr>\n <tr>\n <th>36</th>\n <td>Vega Market</td>\n <td>[42.3891835, -71.033703]</td>\n <td>D</td>\n <td>0.002828</td>\n <td>0</td>\n </tr>\n <tr>\n <th>37</th>\n <td>Winthrop High School</td>\n <td>[42.3803348, -70.9799864]</td>\n <td>D</td>\n <td>0.001269</td>\n <td>0</td>\n </tr>\n </tbody>\n</table>\n<p>169 rows × 5 columns</p>\n</div>" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Display the dataframe\n", + "display(TotalList)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-06T00:18:58.847605Z", + "start_time": "2023-11-06T00:18:58.843691Z" + } + }, + "id": "49fc751352022ad1" + }, + { + "cell_type": "markdown", + "source": [ + "# Map" + ], + "metadata": { + "collapsed": false + }, + "id": "8c46f8ae30caa721" + }, + { + "cell_type": "code", + "execution_count": 261, + "outputs": [], + "source": [ + "# Create a map in Boston\n", + "m = folium.Map(location=[42.3601, -71.0589], zoom_start=12)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-06T00:19:00.620249Z", + "start_time": "2023-11-06T00:19:00.612951Z" + } + }, + "id": "48d76bd40c44cc61" + }, + { + "cell_type": "code", + "execution_count": 262, + "outputs": [], + "source": [ + "# Plot the centroids on the map\n", + "for i in range(len(centroids)):\n", + " folium.Marker(centroids[i], popup='Centroid ' + str(i), icon=folium.Icon(color='black')).add_to(m)\n", + " \n", + "# Add the points to the map with different colors for each cluster\n", + "for i, row in TotalList.iterrows():\n", + " if row['cluster'] == 0:\n", + " folium.Marker([row['gps'][0], row['gps'][1]], popup=row['name'], icon=folium.Icon(color='red')).add_to(m)\n", + " elif row['cluster'] == 1:\n", + " folium.Marker([row['gps'][0], row['gps'][1]], popup=row['name'], icon=folium.Icon(color='blue')).add_to(m)\n", + " elif row['cluster'] == 2:\n", + " folium.Marker([row['gps'][0], row['gps'][1]], popup=row['name'], icon=folium.Icon(color='green')).add_to(m)\n", + " elif row['cluster'] == 3:\n", + " folium.Marker([row['gps'][0], row['gps'][1]], popup=row['name'], icon=folium.Icon(color='purple')).add_to(m)\n", + " elif row['cluster'] == 4:\n", + " folium.Marker([row['gps'][0], row['gps'][1]], popup=row['name'], icon=folium.Icon(color='orange')).add_to(m)\n", + " elif row['cluster'] == 5:\n", + " folium.Marker([row['gps'][0], row['gps'][1]], popup=row['name'], icon=folium.Icon(color='darkred')).add_to(m)\n", + " elif row['cluster'] == 6:\n", + " folium.Marker([row['gps'][0], row['gps'][1]], popup=row['name'], icon=folium.Icon(color='lightred')).add_to(m)\n", + " elif row['cluster'] == 7:\n", + " folium.Marker([row['gps'][0], row['gps'][1]], popup=row['name'], icon=folium.Icon(color='beige')).add_to(m)\n", + " elif row['cluster'] == 8:\n", + " folium.Marker([row['gps'][0], row['gps'][1]], popup=row['name'], icon=folium.Icon(color='darkblue')).add_to(m)\n", + " elif row['cluster'] == 9:\n", + " folium.Marker([row['gps'][0], row['gps'][1]], popup=row['name'], icon=folium.Icon(color='lightblue')).add_to(m)\n", + " elif row['cluster'] == 10:\n", + " folium.Marker([row['gps'][0], row['gps'][1]], popup=row['name'], icon=folium.Icon(color='cadet')).add_to(m)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-06T00:19:01.125434Z", + "start_time": 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{}\n ).addTo(map_1186debcedb6af6d79daead43924d6e0);\n \n \n var icon_3ee15c8340c3381981973d4688747504 = L.AwesomeMarkers.icon(\n {"extraClasses": "fa-rotate-0", "icon": "info-sign", "iconColor": "white", "markerColor": "red", "prefix": "glyphicon"}\n );\n marker_6f400089fdc83a0b383b70145c957153.setIcon(icon_3ee15c8340c3381981973d4688747504);\n \n \n var popup_1d8f6711eac85ed50e6ed3b2e2bd5296 = L.popup({"maxWidth": "100%"});\n\n \n \n var html_ce289e6f4821f7ad6a6e0a5337a7d858 = $(`<div id="html_ce289e6f4821f7ad6a6e0a5337a7d858" style="width: 100.0%; height: 100.0%;">Toasted Flats</div>`)[0];\n popup_1d8f6711eac85ed50e6ed3b2e2bd5296.setContent(html_ce289e6f4821f7ad6a6e0a5337a7d858);\n \n \n\n marker_6f400089fdc83a0b383b70145c957153.bindPopup(popup_1d8f6711eac85ed50e6ed3b2e2bd5296)\n ;\n\n \n \n \n var marker_6f608d2d2d2c4c23dba4c233b7a82552 = L.marker(\n [42.3891835, -71.033703],\n {}\n ).addTo(map_1186debcedb6af6d79daead43924d6e0);\n \n \n var icon_31686cef401bc714f3b684002acc6a3b = L.AwesomeMarkers.icon(\n {"extraClasses": "fa-rotate-0", "icon": "info-sign", "iconColor": "white", "markerColor": "red", "prefix": "glyphicon"}\n );\n marker_6f608d2d2d2c4c23dba4c233b7a82552.setIcon(icon_31686cef401bc714f3b684002acc6a3b);\n \n \n var popup_da8d96de65a7789bd47ea285c19634b9 = L.popup({"maxWidth": "100%"});\n\n \n \n var html_9521374d2200232e588f6c31e0035803 = $(`<div id="html_9521374d2200232e588f6c31e0035803" style="width: 100.0%; height: 100.0%;">Vega Market</div>`)[0];\n popup_da8d96de65a7789bd47ea285c19634b9.setContent(html_9521374d2200232e588f6c31e0035803);\n \n \n\n marker_6f608d2d2d2c4c23dba4c233b7a82552.bindPopup(popup_da8d96de65a7789bd47ea285c19634b9)\n ;\n\n \n \n \n var marker_17656aa393b5860ff36b1f5e40b7e81f = L.marker(\n [42.3803348, -70.9799864],\n {}\n ).addTo(map_1186debcedb6af6d79daead43924d6e0);\n \n \n var icon_e24ea54b4b2453c9f8aa6a291b32be62 = L.AwesomeMarkers.icon(\n {"extraClasses": "fa-rotate-0", "icon": "info-sign", "iconColor": "white", "markerColor": "red", "prefix": "glyphicon"}\n );\n marker_17656aa393b5860ff36b1f5e40b7e81f.setIcon(icon_e24ea54b4b2453c9f8aa6a291b32be62);\n \n \n var popup_80aa1c3498d3e1a9b21cc2c3b9cec7e5 = L.popup({"maxWidth": "100%"});\n\n \n \n var html_2fbe5813ce8989978d9f96e75ea14bde = $(`<div id="html_2fbe5813ce8989978d9f96e75ea14bde" style="width: 100.0%; height: 100.0%;">Winthrop High School</div>`)[0];\n popup_80aa1c3498d3e1a9b21cc2c3b9cec7e5.setContent(html_2fbe5813ce8989978d9f96e75ea14bde);\n \n \n\n marker_17656aa393b5860ff36b1f5e40b7e81f.bindPopup(popup_80aa1c3498d3e1a9b21cc2c3b9cec7e5)\n ;\n\n \n \n</script>\n</html>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>" + }, + "execution_count": 263, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the map\n", + "m" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-06T00:19:02.155307Z", + "start_time": "2023-11-06T00:19:02.145235Z" + } + }, + "id": "d6941d1f0a203ee7" + }, + { + "cell_type": "code", + "execution_count": 264, + "outputs": [ + { + "data": { + "text/plain": "0 106\n1 63\nName: cluster, dtype: int64" + }, + "execution_count": 264, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display the number of locations in each cluster\n", + "TotalList['cluster'].value_counts()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-06T00:19:05.516139Z", + "start_time": "2023-11-06T00:19:05.510902Z" + } + }, + "id": "479ba8f36cdafbf8" + }, + { + "cell_type": "code", + "execution_count": 244, + "outputs": [], + "source": [ + "# create a method to move n number of locations from the largest cluster to the smallest cluster, taking distance into account\n", + "def equalize_clusters(df, n):\n", + " # Get the number of locations in each cluster\n", + " cluster_counts = df['cluster'].value_counts()\n", + " \n", + " # Get the largest and smallest clusters\n", + " largest_cluster = cluster_counts.index[0]\n", + " smallest_cluster = cluster_counts.index[-1]\n", + " \n", + " # Get the locations in the largest cluster\n", + " largest_cluster_locations = df[df['cluster'] == largest_cluster]\n", + " \n", + " # Get the locations in the smallest cluster\n", + " smallest_cluster_locations = df[df['cluster'] == smallest_cluster]\n", + " \n", + " # Create a list of distances from each location in the largest cluster to each location in the smallest cluster\n", + " distances = []\n", + " for i, row in largest_cluster_locations.iterrows():\n", + " for j, row2 in smallest_cluster_locations.iterrows():\n", + " distances.append([i, j, np.linalg.norm(np.array(row['gps']) - np.array(row2['gps']))])\n", + " \n", + " # Sort the distances by distance\n", + " distances.sort(key=lambda x: x[2])\n", + " \n", + " # Move the n closest locations from the largest cluster to the smallest cluster\n", + " for i in range(n):\n", + " df.loc[distances[i][0], 'cluster'] = smallest_cluster\n", + " df.loc[distances[i][1], 'cluster'] = largest_cluster\n", + " \n", + " return df" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-05T23:51:40.761645Z", + "start_time": "2023-11-05T23:51:40.752184Z" + } + }, + "id": "4b79215a12bf36e2" + }, + { + "cell_type": "code", + "execution_count": 245, + "outputs": [ + { + "data": { + "text/plain": "0 97\n1 72\nName: cluster, dtype: int64" + }, + "execution_count": 245, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Equalize the clusters\n", + "TotalList = equalize_clusters(TotalList, 20)\n", + "\n", + "# Display the number of locations in each cluster\n", + "TotalList['cluster'].value_counts()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-05T23:51:41.510095Z", + "start_time": "2023-11-05T23:51:41.434643Z" + } + }, + "id": "176d5f92130c67b8" + }, + { + "cell_type": "code", + "execution_count": 267, + "outputs": [ + { + "data": { + "text/plain": "0 106\n1 63\nName: cluster, dtype: int64" + }, + "execution_count": 267, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "TotalList['cluster'].value_counts()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-06T00:20:43.543111Z", + "start_time": "2023-11-06T00:20:43.529364Z" + } + }, + "id": "1b8e5c1793fb2feb" + }, + { + "cell_type": "code", + "execution_count": 265, + "outputs": [ + { + "data": { + "text/plain": 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+ }, + "execution_count": 265, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "utils.list_to_string(TotalList['gps'].values.tolist())" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-06T00:19:09.317992Z", + "start_time": "2023-11-06T00:19:09.308541Z" + } + }, + "id": "2d83e5db093608d2" + }, + { + "cell_type": "code", + "execution_count": 268, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "106\n" + ] + }, + { + "data": { + "text/plain": 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+ }, + "execution_count": 268, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Return the list of locations in each cluster\n", + "print(len(TotalList[TotalList['cluster'] == 0]['gps'].values.tolist()))\n", + "utils.list_to_string(TotalList[TotalList['cluster'] == 0]['gps'].values.tolist())" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-06T00:25:20.703565Z", + "start_time": "2023-11-06T00:25:20.690483Z" + } + }, + "id": "89297f77828e8ed8" + }, + { + "cell_type": "code", + "execution_count": 269, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "63\n" + ] + }, + { + "data": { + "text/plain": "'-71.2038948,42.4299758;-71.1060828,42.3511927;-71.0969274,42.3446263;-71.130887,42.35304;-71.1459593,42.3501823;-71.1460435,42.3495825;-71.1217152,42.3426377;-71.1258765,42.331864;-71.1095021,42.3364675;-71.133103,42.3890049;-71.0851891,42.3500031;-71.1123834,42.3360385;-71.2273649,42.3145041;-71.0834061,42.341987;-71.0992038,42.3306454;-71.0990577,42.3381442;-71.0949218,42.3419564;-71.0942861,42.3413301;-71.0908104,42.329969;-71.0913583,42.3490205;-71.1000217,42.3323776;-71.1241295,42.3518397;-71.1618052,42.3245965;-71.1625829,42.340795;-71.167854,42.4107892;-71.155555,42.3317473;-71.1227278,42.3965778;-71.3598149,42.3140229;-71.1126695,42.3836229;-71.119149,42.3884;-71.0872846,42.2961434;-71.1427371,42.3433772;-71.1438455,42.3569102;-71.1313443,42.3525708;-71.1284677,42.3631904;-71.119301,42.388547;-71.097883,42.381008;-71.1107166,42.3741209;-71.1194344,42.3754427;-71.1013044,42.3627462;-71.1108423,42.3838224;-71.1026937,42.3820702;-71.1189467,42.373465;-71.1208817,42.3732344;-71.0968274,42.3799095;-71.094048,42.339381;-71.1854722,42.3621177;-71.1146697,42.3782386;-71.0935443,42.3817274;-71.0906355,42.3616095;-71.1161887,42.3766442;-71.0962734,42.3627993;-71.1155576,42.3784629;-71.0949101,42.3797674;-71.1087411,42.3640287;-71.09476,42.37736;-71.1014951,42.3614115;-71.1024769,42.3822934;-71.1011111,42.3636597;-71.0898829,42.3463992;-71.0983169,42.3319001;-71.0945712,42.3253252;-71.1122037,42.4008442;'" + }, + "execution_count": 269, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(len(TotalList[TotalList['cluster'] == 1]['gps'].values.tolist()))\n", + "utils.list_to_string(TotalList[TotalList['cluster'] == 1]['gps'].values.tolist())" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-06T00:25:30.097229Z", + "start_time": "2023-11-06T00:25:30.070409Z" + } + }, + "id": "6ff82e29a0366d9e" + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [], + "metadata": { + "collapsed": false + }, + "id": "7949bddd34b6731" + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} |