path: root/Clustering.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2023-11-06T01:14:10.536728Z",
     "start_time": "2023-11-06T01:14:10.525881Z"
    }
   },
   "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": 79,
   "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": "2023-11-06T01:14:10.821794Z",
     "start_time": "2023-11-06T01:14:10.808507Z"
    }
   },
   "id": "bb6f57eef695cf76"
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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..                         ...                     ...   \n28                The Clam Box  42.2763168,-71.0092883   \n29               The Partisans  42.3478375,-71.0404428   \n30          Union Oyster House    42.361288,-71.056908   \n31            Victoria's Diner  42.3270498,-71.0667744   \n32             Wollaston Beach  42.2806539,-71.0119933   \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..                                                ...   \n28  https://maps.google.com/maps?q=+%4042.2763168,...   \n29  https://maps.google.com/maps?q=+%4042.3478375,...   \n30  https://maps.google.com/maps?q=+%4042.361288,-...   \n31  https://maps.google.com/maps?q=+%4042.3270498,...   \n32  https://maps.google.com/maps?q=+%4042.2806539,...   \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..                                                ...   ...    ...   \n28  https://www.google.com/maps/place/The+Clam+Box...   NaN    NaN   \n29  https://www.google.com/maps/place/The+Partisan...   NaN    NaN   \n30  https://www.google.com/maps/place/Union+Oyster...   NaN    NaN   \n31  https://www.google.com/maps/place/Victoria's+D...   NaN    NaN   \n32  https://www.google.com/maps/place/Wollaston+Be...   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..                                                ...   \n28  789 Quincy Shore Dr, Quincy, MA 02170, United ...   \n29                    Boston, MA 02210, United States   \n30       41 Union St, Boston, MA 02108, United States   \n31  1024 Massachusetts Ave, Boston, MA 02118, Unit...   \n32                          Quincy, MA, 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..                                                ...                 ...   \n28  Classic beachfront joint with a rustic vibe di...  Seafood restaurant   \n29                                                NaN           Sculpture   \n30  Historic eatery serving chowder & other New En...  Seafood restaurant   \n31  Long-standing classic diner for breakfast & sa...               Diner   \n32  Historic 2.3-mi.-long beach with a paved prome...               Beach   \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..              ...                                                ...   \n28  +1 617-302-3474                      http://www.clamboxquincy.com/   \n29              NaN  https://www.bostonseaport.xyz/venue/the-partis...   \n30  +1 617-227-2750  http://www.unionoysterhouse.com/?y_source=1_Mj...   \n31  +1 617-442-5965                     http://www.victoriasdiner.com/   \n32              NaN                                                NaN   \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..             ...           ...                                    ...  ...  \n28             4.3        2145.0     7XGR+G7 Quincy, Massachusetts, USA    C  \n29             4.8           6.0     8XX5+4R Boston, Massachusetts, USA    C  \n30             4.3        8497.0     9W6V+G6 Boston, Massachusetts, USA    C  \n31             4.1        1797.0     8WGM+R7 Boston, Massachusetts, USA    C  \n32             4.4         171.0                                    NaN    C  \n\n[131 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 &amp; 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>28</th>\n      <td>The Clam Box</td>\n      <td>42.2763168,-71.0092883</td>\n      <td>https://maps.google.com/maps?q=+%4042.2763168,...</td>\n      <td>https://www.google.com/maps/place/The+Clam+Box...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>789 Quincy Shore Dr, Quincy, MA 02170, United ...</td>\n      <td>Classic beachfront joint with a rustic vibe di...</td>\n      <td>Seafood restaurant</td>\n      <td>+1 617-302-3474</td>\n      <td>http://www.clamboxquincy.com/</td>\n      <td>4.3</td>\n      <td>2145.0</td>\n      <td>7XGR+G7 Quincy, Massachusetts, USA</td>\n      <td>C</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>The Partisans</td>\n      <td>42.3478375,-71.0404428</td>\n      <td>https://maps.google.com/maps?q=+%4042.3478375,...</td>\n      <td>https://www.google.com/maps/place/The+Partisan...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Boston, MA 02210, United States</td>\n      <td>NaN</td>\n      <td>Sculpture</td>\n      <td>NaN</td>\n      <td>https://www.bostonseaport.xyz/venue/the-partis...</td>\n      <td>4.8</td>\n      <td>6.0</td>\n      <td>8XX5+4R Boston, Massachusetts, USA</td>\n      <td>C</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>Union Oyster House</td>\n      <td>42.361288,-71.056908</td>\n      <td>https://maps.google.com/maps?q=+%4042.361288,-...</td>\n      <td>https://www.google.com/maps/place/Union+Oyster...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>41 Union St, Boston, MA 02108, United States</td>\n      <td>Historic eatery serving chowder &amp; other New En...</td>\n      <td>Seafood restaurant</td>\n      <td>+1 617-227-2750</td>\n      <td>http://www.unionoysterhouse.com/?y_source=1_Mj...</td>\n      <td>4.3</td>\n      <td>8497.0</td>\n      <td>9W6V+G6 Boston, Massachusetts, USA</td>\n      <td>C</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>Victoria's Diner</td>\n      <td>42.3270498,-71.0667744</td>\n      <td>https://maps.google.com/maps?q=+%4042.3270498,...</td>\n      <td>https://www.google.com/maps/place/Victoria's+D...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1024 Massachusetts Ave, Boston, MA 02118, Unit...</td>\n      <td>Long-standing classic diner for breakfast &amp; sa...</td>\n      <td>Diner</td>\n      <td>+1 617-442-5965</td>\n      <td>http://www.victoriasdiner.com/</td>\n      <td>4.1</td>\n      <td>1797.0</td>\n      <td>8WGM+R7 Boston, Massachusetts, USA</td>\n      <td>C</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>Wollaston Beach</td>\n      <td>42.2806539,-71.0119933</td>\n      <td>https://maps.google.com/maps?q=+%4042.2806539,...</td>\n      <td>https://www.google.com/maps/place/Wollaston+Be...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Quincy, MA, United States</td>\n      <td>Historic 2.3-mi.-long beach with a paved prome...</td>\n      <td>Beach</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>4.4</td>\n      <td>171.0</td>\n      <td>NaN</td>\n      <td>C</td>\n    </tr>\n  </tbody>\n</table>\n<p>131 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])\n",
    "display(TotalList)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-06T01:14:11.326041Z",
     "start_time": "2023-11-06T01:14:11.322857Z"
    }
   },
   "id": "dc434958d5e4a3a8"
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "outputs": [],
   "source": [
    "# Remove all columns but name and gps\n",
    "TotalList = TotalList[['name', 'gps', 'list']]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-06T01:14:12.014736Z",
     "start_time": "2023-11-06T01:14:12.007694Z"
    }
   },
   "id": "2873c16423fe3119"
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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": "2023-11-06T01:14:12.457221Z",
     "start_time": "2023-11-06T01:14:12.448967Z"
    }
   },
   "id": "29f9155ef8d75fda"
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "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..                         ...                        ...  ...\n28                The Clam Box  [42.2763168, -71.0092883]    C\n29               The Partisans  [42.3478375, -71.0404428]    C\n30          Union Oyster House    [42.361288, -71.056908]    C\n31            Victoria's Diner  [42.3270498, -71.0667744]    C\n32             Wollaston Beach  [42.2806539, -71.0119933]    C\n\n[131 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>28</th>\n      <td>The Clam Box</td>\n      <td>[42.2763168, -71.0092883]</td>\n      <td>C</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>The Partisans</td>\n      <td>[42.3478375, -71.0404428]</td>\n      <td>C</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>Union Oyster House</td>\n      <td>[42.361288, -71.056908]</td>\n      <td>C</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>Victoria's Diner</td>\n      <td>[42.3270498, -71.0667744]</td>\n      <td>C</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>Wollaston Beach</td>\n      <td>[42.2806539, -71.0119933]</td>\n      <td>C</td>\n    </tr>\n  </tbody>\n</table>\n<p>131 rows × 3 columns</p>\n</div>"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(TotalList)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-06T01:14:13.043659Z",
     "start_time": "2023-11-06T01:14:13.030154Z"
    }
   },
   "id": "a03a7c5dacebddd0"
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Dendrogram"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "72e85d219be8c635"
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "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": "2023-11-06T01:14:14.540031Z",
     "start_time": "2023-11-06T01:14:14.088884Z"
    }
   },
   "id": "9e215df3a350e3cf"
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of clusters: 7\n",
      "Silhouette score: 0.42876627286716495\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": "2023-11-06T01:14:14.556841Z",
     "start_time": "2023-11-06T01:14:14.545269Z"
    }
   },
   "id": "2f52d83746e670d"
  },
  {
   "cell_type": "markdown",
   "source": [
    "# K-means"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "bc97c258908ac38a"
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "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": "2023-11-06T01:14:15.329931Z",
     "start_time": "2023-11-06T01:14:15.325838Z"
    }
   },
   "id": "45b59d81ae2de84e"
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "outputs": [
    {
     "data": {
      "text/plain": "                          name                        gps list   weights\n0   521 Commercial Street #525  [42.3688272, -71.0553792]    A  0.018132\n1                     Acorn St  [42.3576234, -71.0688746]    A  0.008032\n2    Arlington's Great Meadows  [42.4299758, -71.2038948]    A  0.000676\n3        Arthur Fiedler Statue  [42.3565057, -71.0754527]    A  0.005410\n4                     BU Beach  [42.3511927, -71.1060828]    A  0.002145\n..                         ...                        ...  ...       ...\n28                The Clam Box  [42.2763168, -71.0092883]    C  0.001136\n29               The Partisans  [42.3478375, -71.0404428]    C  0.005315\n30          Union Oyster House    [42.361288, -71.056908]    C  0.037200\n31            Victoria's Diner  [42.3270498, -71.0667744]    C  0.003055\n32             Wollaston Beach  [42.2806539, -71.0119933]    C  0.001198\n\n[131 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>0.018132</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>0.008032</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>0.000676</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>0.005410</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>0.002145</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>28</th>\n      <td>The Clam Box</td>\n      <td>[42.2763168, -71.0092883]</td>\n      <td>C</td>\n      <td>0.001136</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>The Partisans</td>\n      <td>[42.3478375, -71.0404428]</td>\n      <td>C</td>\n      <td>0.005315</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>Union Oyster House</td>\n      <td>[42.361288, -71.056908]</td>\n      <td>C</td>\n      <td>0.037200</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>Victoria's Diner</td>\n      <td>[42.3270498, -71.0667744]</td>\n      <td>C</td>\n      <td>0.003055</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>Wollaston Beach</td>\n      <td>[42.2806539, -71.0119933]</td>\n      <td>C</td>\n      <td>0.001198</td>\n    </tr>\n  </tbody>\n</table>\n<p>131 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": "2023-11-06T01:14:15.942150Z",
     "start_time": "2023-11-06T01:14:15.938980Z"
    }
   },
   "id": "2f2975484d00129c"
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "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": "2023-11-06T01:14:16.878902Z",
     "start_time": "2023-11-06T01:14:16.865126Z"
    }
   },
   "id": "db1ef4b14a1da5f5"
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "outputs": [],
   "source": [
    "# Add the cluster labels to the dataframe\n",
    "TotalList['cluster'] = kmeans.labels_"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-06T01:14:17.887765Z",
     "start_time": "2023-11-06T01:14:17.880353Z"
    }
   },
   "id": "99891fae96a2fff7"
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "outputs": [
    {
     "data": {
      "text/plain": "                          name                        gps list   weights  \\\n0   521 Commercial Street #525  [42.3688272, -71.0553792]    A  0.018132   \n1                     Acorn St  [42.3576234, -71.0688746]    A  0.008032   \n2    Arlington's Great Meadows  [42.4299758, -71.2038948]    A  0.000676   \n3        Arthur Fiedler Statue  [42.3565057, -71.0754527]    A  0.005410   \n4                     BU Beach  [42.3511927, -71.1060828]    A  0.002145   \n..                         ...                        ...  ...       ...   \n28                The Clam Box  [42.2763168, -71.0092883]    C  0.001136   \n29               The Partisans  [42.3478375, -71.0404428]    C  0.005315   \n30          Union Oyster House    [42.361288, -71.056908]    C  0.037200   \n31            Victoria's Diner  [42.3270498, -71.0667744]    C  0.003055   \n32             Wollaston Beach  [42.2806539, -71.0119933]    C  0.001198   \n\n    cluster  \n0         1  \n1         1  \n2         0  \n3         1  \n4         0  \n..      ...  \n28        1  \n29        1  \n30        1  \n31        1  \n32        1  \n\n[131 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>0.018132</td>\n      <td>1</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>0.008032</td>\n      <td>1</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>0.000676</td>\n      <td>0</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>0.005410</td>\n      <td>1</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>0.002145</td>\n      <td>0</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>28</th>\n      <td>The Clam Box</td>\n      <td>[42.2763168, -71.0092883]</td>\n      <td>C</td>\n      <td>0.001136</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>The Partisans</td>\n      <td>[42.3478375, -71.0404428]</td>\n      <td>C</td>\n      <td>0.005315</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>Union Oyster House</td>\n      <td>[42.361288, -71.056908]</td>\n      <td>C</td>\n      <td>0.037200</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>Victoria's Diner</td>\n      <td>[42.3270498, -71.0667744]</td>\n      <td>C</td>\n      <td>0.003055</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>Wollaston Beach</td>\n      <td>[42.2806539, -71.0119933]</td>\n      <td>C</td>\n      <td>0.001198</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n<p>131 rows × 5 columns</p>\n</div>"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display the dataframe\n",
    "display(TotalList)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-06T01:14:19.060647Z",
     "start_time": "2023-11-06T01:14:19.051699Z"
    }
   },
   "id": "49fc751352022ad1"
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Map"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "8c46f8ae30caa721"
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "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": "2023-11-06T01:14:20.184965Z",
     "start_time": "2023-11-06T01:14:20.177057Z"
    }
   },
   "id": "48d76bd40c44cc61"
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "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": "2023-11-06T01:14:20.573947Z",
     "start_time": "2023-11-06T01:14:20.558985Z"
    }
   },
   "id": "3c8a7d2b34d4f22d"
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "outputs": [
    {
     "data": {
      "text/plain": "<folium.folium.Map at 0x14072dc10>",
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style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;The Partisans&lt;/div&gt;`)[0];\n                popup_517d171af81d5a8b940206a8f4047f49.setContent(html_e8808a8f85b4d1154be2384ddf1b8ff6);\n            \n        \n\n        marker_b395989c7e72be79e460e76ec66b6f55.bindPopup(popup_517d171af81d5a8b940206a8f4047f49)\n        ;\n\n        \n    \n    \n            var marker_67bfc171d1110042f7bfa1e0963767be = L.marker(\n                [42.361288, -71.056908],\n                {}\n            ).addTo(map_51f4900d02ea9cf32faa0a55f07ff42c);\n        \n    \n            var icon_daefce6268f0ad64aa2fcbaabfa9d3b6 = L.AwesomeMarkers.icon(\n                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;info-sign&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;blue&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n            );\n            marker_67bfc171d1110042f7bfa1e0963767be.setIcon(icon_daefce6268f0ad64aa2fcbaabfa9d3b6);\n        \n    \n        var popup_7d7d7a07a9919dcac61907f9abe79710 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n\n        \n            \n                var html_37ed077b2952493d4d2ee73d010bc41a = $(`&lt;div id=&quot;html_37ed077b2952493d4d2ee73d010bc41a&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Union Oyster House&lt;/div&gt;`)[0];\n                popup_7d7d7a07a9919dcac61907f9abe79710.setContent(html_37ed077b2952493d4d2ee73d010bc41a);\n            \n        \n\n        marker_67bfc171d1110042f7bfa1e0963767be.bindPopup(popup_7d7d7a07a9919dcac61907f9abe79710)\n        ;\n\n        \n    \n    \n            var marker_82fe8db81aee3ef5c352ff37b4573700 = L.marker(\n                [42.3270498, -71.0667744],\n                {}\n            ).addTo(map_51f4900d02ea9cf32faa0a55f07ff42c);\n        \n    \n            var icon_351e33946fd7a1b5dc39a9200feb3b8c = L.AwesomeMarkers.icon(\n                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;info-sign&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;blue&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n            );\n            marker_82fe8db81aee3ef5c352ff37b4573700.setIcon(icon_351e33946fd7a1b5dc39a9200feb3b8c);\n        \n    \n        var popup_45265f89f15044b99c92a5dd081accdc = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n\n        \n            \n                var html_2c5c562de175e30c853a5c0509273f9a = $(`&lt;div id=&quot;html_2c5c562de175e30c853a5c0509273f9a&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Victoria&#x27;s Diner&lt;/div&gt;`)[0];\n                popup_45265f89f15044b99c92a5dd081accdc.setContent(html_2c5c562de175e30c853a5c0509273f9a);\n            \n        \n\n        marker_82fe8db81aee3ef5c352ff37b4573700.bindPopup(popup_45265f89f15044b99c92a5dd081accdc)\n        ;\n\n        \n    \n    \n            var marker_842c31844b8cc75c25de66dd0352cb74 = L.marker(\n                [42.2806539, -71.0119933],\n                {}\n            ).addTo(map_51f4900d02ea9cf32faa0a55f07ff42c);\n        \n    \n            var icon_2e0f3dd707e46b2bb6b3d91ee300a2cd = L.AwesomeMarkers.icon(\n                {&quot;extraClasses&quot;: &quot;fa-rotate-0&quot;, &quot;icon&quot;: &quot;info-sign&quot;, &quot;iconColor&quot;: &quot;white&quot;, &quot;markerColor&quot;: &quot;blue&quot;, &quot;prefix&quot;: &quot;glyphicon&quot;}\n            );\n            marker_842c31844b8cc75c25de66dd0352cb74.setIcon(icon_2e0f3dd707e46b2bb6b3d91ee300a2cd);\n        \n    \n        var popup_d7fbb85020c3750ccf0e66838c6b414f = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n\n        \n            \n                var html_cdb73792b6a7c506881f24d5c066f1da = $(`&lt;div id=&quot;html_cdb73792b6a7c506881f24d5c066f1da&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Wollaston Beach&lt;/div&gt;`)[0];\n                popup_d7fbb85020c3750ccf0e66838c6b414f.setContent(html_cdb73792b6a7c506881f24d5c066f1da);\n            \n        \n\n        marker_842c31844b8cc75c25de66dd0352cb74.bindPopup(popup_d7fbb85020c3750ccf0e66838c6b414f)\n        ;\n\n        \n    \n&lt;/script&gt;\n&lt;/html&gt;\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the map\n",
    "m"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-06T01:14:23.580878Z",
     "start_time": "2023-11-06T01:14:23.507152Z"
    }
   },
   "id": "d6941d1f0a203ee7"
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "outputs": [
    {
     "data": {
      "text/plain": "1    74\n0    57\nName: cluster, dtype: int64"
     },
     "execution_count": 94,
     "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": "2023-11-06T01:14:28.465028Z",
     "start_time": "2023-11-06T01:14:28.461813Z"
    }
   },
   "id": "479ba8f36cdafbf8"
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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": "2023-11-06T01:08:43.493687Z",
     "start_time": "2023-11-06T01:08:43.480182Z"
    }
   },
   "id": "4b79215a12bf36e2"
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "outputs": [
    {
     "data": {
      "text/plain": "0    97\n1    72\nName: cluster, dtype: int64"
     },
     "execution_count": 74,
     "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": "2023-11-06T01:08:43.649954Z",
     "start_time": "2023-11-06T01:08:43.542655Z"
    }
   },
   "id": "176d5f92130c67b8"
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "outputs": [
    {
     "data": {
      "text/plain": "'-71.0553792,42.3688272;-71.0688746,42.3576234;-71.2038948,42.4299758;-71.0754527,42.3565057;-71.1060828,42.3511927;-71.0969274,42.3446263;-71.130887,42.35304;-71.0620802,42.3579151;-71.1459593,42.3501823;-71.0586014,42.357357;-71.0572023,42.3587627;-71.0556268,42.36521;-71.1460435,42.3495825;-71.1217152,42.3426377;-71.0720926,42.3489004;-71.067859,42.3500079;-71.0632036,42.3556154;-71.1258765,42.331864;-71.1095021,42.3364675;-71.133103,42.3890049;-71.0620134,42.3248471;-71.0851891,42.3500031;-71.1123834,42.3360385;-71.066414,42.354296;-71.2273649,42.3145041;-71.0834061,42.341987;-71.0992038,42.3306454;-71.0990577,42.3381442;-71.0569649,42.3604952;-71.0949218,42.3419564;-71.0942861,42.3413301;-71.0498714,42.3256817;-71.0908104,42.329969;-71.0616035,42.3537983;-71.0359433,42.3485465;-71.0913583,42.3490205;-71.1000217,42.3323776;-71.1241295,42.3518397;-71.1618052,42.3245965;-71.0638101,42.3587772;-71.1625829,42.340795;-71.167854,42.4107892;-71.155555,42.3317473;-71.1227278,42.3965778;-71.3598149,42.3140229;-71.1126695,42.3836229;-71.0555003,42.3640137;-71.119149,42.3884;-71.0712561,42.3407613;-71.0561781,42.3668968;-71.0664019,42.3554589;-71.059228,42.359349;-71.0668408,42.3524116;-71.0872846,42.2961434;-71.062146,42.366198;-71.1427371,42.3433772;-71.1438455,42.3569102;-71.0651214,42.3553972;-71.0596124,42.3509517;-71.0359354,42.3478381;-71.1313443,42.3525708;-71.1284677,42.3631904;-71.061757,42.3691906;-71.119301,42.388547;-71.097883,42.381008;-71.1107166,42.3741209;-71.0609962,42.3803747;-71.0516339,42.3609921;-71.1194344,42.3754427;-71.0809932,42.3675275;-71.0545357,42.3597994;-71.1013044,42.3627462;-71.1108423,42.3838224;-71.1026937,42.3820702;-71.1189467,42.373465;-71.1208817,42.3732344;-71.0342146,42.316274;-71.0756902,42.3695046;-71.0678704,42.3701829;-71.0968274,42.3799095;-71.0656594,42.3718401;-71.094048,42.339381;-71.1854722,42.3621177;-71.1146697,42.3782386;-71.0935443,42.3817274;-71.0611749,42.3551807;-71.0906355,42.3616095;-71.1161887,42.3766442;-71.0962734,42.3627993;-71.1155576,42.3784629;-71.0949101,42.3797674;-71.1087411,42.3640287;-71.0554239,42.3739796;-71.09476,42.37736;-71.1014951,42.3614115;-71.1024769,42.3822934;-71.1011111,42.3636597;-71.0631664,42.3741694;-71.056823,42.361531;-71.0632852,42.2857047;-71.0637877,42.2845163;-71.0496839,42.3519736;-71.0454645,42.3162356;-71.0336324,42.3441918;-71.0487437,42.3508756;-71.0512911,42.3521821;-71.0013637,42.2075316;-71.0607764,42.3763541;-71.0374911,42.316031;-71.0125206,42.3378699;-71.0672898,42.3523158;-71.02832,42.2576602;-71.0502126,42.3516479;-71.0331956,42.3639107;-71.0432778,42.3528151;-71.0035279,42.2392354;-71.0470633,42.3537343;-71.0352443,42.3291218;-71.0898829,42.3463992;-71.0240951,42.2743442;-71.0234949,42.3358743;-70.985881,42.420226;-71.0005483,42.2454086;-71.0096371,42.3367603;-71.0447796,42.3509709;-71.0983169,42.3319001;-71.0092883,42.2763168;-71.0404428,42.3478375;-71.056908,42.361288;-71.0667744,42.3270498;-71.0119933,42.2806539;-71.0618764,42.4074484;-71.0612182,42.3986053;-71.0392667,42.3855456;-71.0515875,42.4025721;-70.9903023,42.3917606;-71.055873,42.4206339;-71.0433886,42.4222989;-71.06088,42.3761612;-71.0412802,42.3936888;-71.0714924,42.3968978;-71.0282154,42.3778389;-71.0350852,42.3809511;-71.0331398,42.3734483;-70.9693867,42.3895122;-71.0945712,42.3253252;-71.0280157,42.398422;-71.0155516,42.4114215;-70.993656,42.4110462;-71.0355621,42.3976519;-71.0056995,42.390191;-71.0589219,42.403759;-71.037937,42.3698284;-71.0386285,42.3903823;-71.0316196,42.4122481;-71.0328839,42.3861321;-71.0270609,42.4213082;-71.0366491,42.391236;-71.0361399,42.3649623;-71.0116946,42.3827415;-70.9973058,42.4183123;-71.1122037,42.4008442;-70.997123,42.390501;-71.0506461,42.41826;-71.0359889,42.3670906;-71.0414523,42.3649544;-71.0371343,42.3711266;-71.033703,42.3891835;-70.9799864,42.3803348;'"
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "utils.list_to_string(TotalList['gps'].values.tolist())"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-06T01:08:43.650401Z",
     "start_time": "2023-11-06T01:08:43.622162Z"
    }
   },
   "id": "2d83e5db093608d2"
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "57\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;-71.1123834,42.3360385;-71.2273649,42.3145041;-71.0992038,42.3306454;-71.0990577,42.3381442;-71.0949218,42.3419564;-71.0942861,42.3413301;-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;-71.1427371,42.3433772;-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;'"
     },
     "execution_count": 95,
     "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": "2023-11-06T01:14:35.829990Z",
     "start_time": "2023-11-06T01:14:35.821619Z"
    }
   },
   "id": "89297f77828e8ed8"
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "74\n"
     ]
    },
    {
     "data": {
      "text/plain": "'-71.0553792,42.3688272;-71.0688746,42.3576234;-71.0754527,42.3565057;-71.0620802,42.3579151;-71.0586014,42.357357;-71.0572023,42.3587627;-71.0556268,42.36521;-71.0720926,42.3489004;-71.067859,42.3500079;-71.0632036,42.3556154;-71.0620134,42.3248471;-71.0851891,42.3500031;-71.066414,42.354296;-71.0834061,42.341987;-71.0569649,42.3604952;-71.0498714,42.3256817;-71.0908104,42.329969;-71.0616035,42.3537983;-71.0359433,42.3485465;-71.0638101,42.3587772;-71.0555003,42.3640137;-71.0712561,42.3407613;-71.0561781,42.3668968;-71.0664019,42.3554589;-71.059228,42.359349;-71.0668408,42.3524116;-71.0872846,42.2961434;-71.062146,42.366198;-71.0651214,42.3553972;-71.0596124,42.3509517;-71.0359354,42.3478381;-71.061757,42.3691906;-71.0609962,42.3803747;-71.0516339,42.3609921;-71.0809932,42.3675275;-71.0545357,42.3597994;-71.0342146,42.316274;-71.0756902,42.3695046;-71.0678704,42.3701829;-71.0656594,42.3718401;-71.0611749,42.3551807;-71.0554239,42.3739796;-71.0631664,42.3741694;-71.056823,42.361531;-71.0632852,42.2857047;-71.0637877,42.2845163;-71.0496839,42.3519736;-71.0454645,42.3162356;-71.0336324,42.3441918;-71.0487437,42.3508756;-71.0512911,42.3521821;-71.0013637,42.2075316;-71.0607764,42.3763541;-71.0374911,42.316031;-71.0125206,42.3378699;-71.0672898,42.3523158;-71.02832,42.2576602;-71.0502126,42.3516479;-71.0331956,42.3639107;-71.0432778,42.3528151;-71.0035279,42.2392354;-71.0470633,42.3537343;-71.0352443,42.3291218;-71.0240951,42.2743442;-71.0234949,42.3358743;-70.985881,42.420226;-71.0005483,42.2454086;-71.0096371,42.3367603;-71.0447796,42.3509709;-71.0092883,42.2763168;-71.0404428,42.3478375;-71.056908,42.361288;-71.0667744,42.3270498;-71.0119933,42.2806539;'"
     },
     "execution_count": 96,
     "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": "2023-11-06T01:14:36.909798Z",
     "start_time": "2023-11-06T01:14:36.904157Z"
    }
   },
   "id": "6ff82e29a0366d9e"
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-06T01:08:43.651470Z",
     "start_time": "2023-11-06T01:08:43.640872Z"
    }
   },
   "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,
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}