diff options
| -rw-r--r-- | Clustering.ipynb | 215 | ||||
| -rw-r--r-- | List E.csv | 3 | ||||
| -rw-r--r-- | ZestySalesman.ipynb | 167 | ||||
| -rw-r--r-- | utils.py | 37 |
4 files changed, 242 insertions, 180 deletions
diff --git a/Clustering.ipynb b/Clustering.ipynb index 4bb7721..e658dcf 100644 --- a/Clustering.ipynb +++ b/Clustering.ipynb @@ -2,13 +2,13 @@ "cells": [ { "cell_type": "code", - "execution_count": 248, + "execution_count": 78, "id": "initial_id", "metadata": { "collapsed": true, "ExecuteTime": { - "end_time": "2023-11-06T00:18:48.861074Z", - "start_time": "2023-11-06T00:18:48.853531Z" + "end_time": "2023-11-06T01:14:10.536728Z", + "start_time": "2023-11-06T01:14:10.525881Z" } }, "outputs": [], @@ -26,7 +26,7 @@ }, { "cell_type": "code", - "execution_count": 249, + "execution_count": 79, "outputs": [], "source": [ "# Load the data\n", @@ -38,20 +38,20 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-11-06T00:18:49.113478Z", - "start_time": "2023-11-06T00:18:49.101708Z" + "end_time": "2023-11-06T01:14:10.821794Z", + "start_time": "2023-11-06T01:14:10.808507Z" } }, "id": "bb6f57eef695cf76" }, { "cell_type": "code", - "execution_count": 250, + "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.. ... ... \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>" + "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 & 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 & 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 & 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" @@ -64,21 +64,21 @@ "ListC['list'] = 'C'\n", "ListD['list'] = 'D'\n", "\n", - "TotalList = pd.concat([ListA, ListB, ListC, ListD])\n", + "TotalList = pd.concat([ListA, ListB, ListC])\n", "display(TotalList)" ], "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-11-06T00:18:49.364284Z", - "start_time": "2023-11-06T00:18:49.351342Z" + "end_time": "2023-11-06T01:14:11.326041Z", + "start_time": "2023-11-06T01:14:11.322857Z" } }, "id": "dc434958d5e4a3a8" }, { "cell_type": "code", - "execution_count": 251, + "execution_count": 81, "outputs": [], "source": [ "# Remove all columns but name and gps\n", @@ -87,15 +87,15 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-11-06T00:18:49.785140Z", - "start_time": "2023-11-06T00:18:49.779600Z" + "end_time": "2023-11-06T01:14:12.014736Z", + "start_time": "2023-11-06T01:14:12.007694Z" } }, "id": "2873c16423fe3119" }, { "cell_type": "code", - "execution_count": 252, + "execution_count": 82, "outputs": [], "source": [ "# Convert the gps column to a list of lists for k-means\n", @@ -105,20 +105,20 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-11-06T00:18:50.022174Z", - "start_time": "2023-11-06T00:18:50.008422Z" + "end_time": "2023-11-06T01:14:12.457221Z", + "start_time": "2023-11-06T01:14:12.448967Z" } }, "id": "29f9155ef8d75fda" }, { "cell_type": "code", - "execution_count": 253, + "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.. ... ... ...\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>" + "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" @@ -130,8 +130,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-11-06T00:18:50.242086Z", - "start_time": "2023-11-06T00:18:50.237892Z" + "end_time": "2023-11-06T01:14:13.043659Z", + "start_time": "2023-11-06T01:14:13.030154Z" } }, "id": "a03a7c5dacebddd0" @@ -148,12 +148,12 @@ }, { "cell_type": "code", - "execution_count": 254, + "execution_count": 84, "outputs": [ { "data": { "text/plain": "<Figure size 2500x1000 with 1 Axes>", - "image/png": 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}, "metadata": {}, "output_type": "display_data" @@ -174,22 +174,22 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-11-06T00:18:51.214742Z", - "start_time": "2023-11-06T00:18:50.736378Z" + "end_time": "2023-11-06T01:14:14.540031Z", + "start_time": "2023-11-06T01:14:14.088884Z" } }, "id": "9e215df3a350e3cf" }, { "cell_type": "code", - "execution_count": 255, + "execution_count": 85, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Number of clusters: 10\n", - "Silhouette score: 0.4218177237729999\n" + "Number of clusters: 7\n", + "Silhouette score: 0.42876627286716495\n" ] } ], @@ -211,8 +211,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-11-06T00:18:51.241741Z", - "start_time": "2023-11-06T00:18:51.217906Z" + "end_time": "2023-11-06T01:14:14.556841Z", + "start_time": "2023-11-06T01:14:14.545269Z" } }, "id": "2f52d83746e670d" @@ -229,7 +229,7 @@ }, { "cell_type": "code", - "execution_count": 256, + "execution_count": 86, "outputs": [], "source": [ "# Cluster the data using Gaussian Mixture Models\n", @@ -239,20 +239,20 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-11-06T00:18:54.543675Z", - "start_time": "2023-11-06T00:18:54.540819Z" + "end_time": "2023-11-06T01:14:15.329931Z", + "start_time": "2023-11-06T01:14:15.325838Z" } }, "id": "45b59d81ae2de84e" }, { "cell_type": "code", - "execution_count": 257, + "execution_count": 87, "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>" + "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" @@ -278,15 +278,15 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-11-06T00:18:54.970160Z", - "start_time": "2023-11-06T00:18:54.956075Z" + "end_time": "2023-11-06T01:14:15.942150Z", + "start_time": "2023-11-06T01:14:15.938980Z" } }, "id": "2f2975484d00129c" }, { "cell_type": "code", - "execution_count": 258, + "execution_count": 88, "outputs": [ { "name": "stderr", @@ -305,15 +305,15 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-11-06T00:18:56.312805Z", - "start_time": "2023-11-06T00:18:56.299246Z" + "end_time": "2023-11-06T01:14:16.878902Z", + "start_time": "2023-11-06T01:14:16.865126Z" } }, "id": "db1ef4b14a1da5f5" }, { "cell_type": "code", - "execution_count": 259, + "execution_count": 89, "outputs": [], "source": [ "# Add the cluster labels to the dataframe\n", @@ -322,20 +322,20 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-11-06T00:18:58.475575Z", - "start_time": "2023-11-06T00:18:58.468594Z" + "end_time": "2023-11-06T01:14:17.887765Z", + "start_time": "2023-11-06T01:14:17.880353Z" } }, "id": "99891fae96a2fff7" }, { "cell_type": "code", - "execution_count": 260, + "execution_count": 90, "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>" + "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" @@ -348,8 +348,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-11-06T00:18:58.847605Z", - "start_time": "2023-11-06T00:18:58.843691Z" + "end_time": "2023-11-06T01:14:19.060647Z", + "start_time": "2023-11-06T01:14:19.051699Z" } }, "id": "49fc751352022ad1" @@ -366,7 +366,7 @@ }, { "cell_type": "code", - "execution_count": 261, + "execution_count": 91, "outputs": [], "source": [ "# Create a map in Boston\n", @@ -375,15 +375,15 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-11-06T00:19:00.620249Z", - "start_time": "2023-11-06T00:19:00.612951Z" + "end_time": "2023-11-06T01:14:20.184965Z", + "start_time": "2023-11-06T01:14:20.177057Z" } }, "id": "48d76bd40c44cc61" }, { "cell_type": "code", - "execution_count": 262, + "execution_count": 92, "outputs": [], "source": [ "# Plot the centroids on the map\n", @@ -418,22 +418,22 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-11-06T00:19:01.125434Z", - "start_time": "2023-11-06T00:19:01.109369Z" + "end_time": "2023-11-06T01:14:20.573947Z", + "start_time": "2023-11-06T01:14:20.558985Z" } }, "id": "3c8a7d2b34d4f22d" }, { "cell_type": "code", - "execution_count": 263, + "execution_count": 93, "outputs": [ { "data": { - "text/plain": "<folium.folium.Map at 0x15e245d50>", - "text/html": "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe srcdoc=\"<!DOCTYPE html>\n<html>\n<head>\n \n <meta http-equiv="content-type" content="text/html; charset=UTF-8" />\n \n <script>\n L_NO_TOUCH = false;\n L_DISABLE_3D = false;\n </script>\n \n <style>html, body {width: 100%;height: 100%;margin: 0;padding: 0;}</style>\n <style>#map {position:absolute;top:0;bottom:0;right:0;left:0;}</style>\n <script src="https://cdn.jsdelivr.net/npm/leaflet@1.9.3/dist/leaflet.js"></script>\n <script src="https://code.jquery.com/jquery-1.12.4.min.js"></script>\n <script src="https://cdn.jsdelivr.net/npm/bootstrap@5.2.2/dist/js/bootstrap.bundle.min.js"></script>\n <script src="https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js"></script>\n <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/leaflet@1.9.3/dist/leaflet.css"/>\n <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/bootstrap@5.2.2/dist/css/bootstrap.min.css"/>\n <link rel="stylesheet" href="https://netdna.bootstrapcdn.com/bootstrap/3.0.0/css/bootstrap.min.css"/>\n <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/@fortawesome/fontawesome-free@6.2.0/css/all.min.css"/>\n <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.css"/>\n <link rel="stylesheet" href="https://cdn.jsdelivr.net/gh/python-visualization/folium/folium/templates/leaflet.awesome.rotate.min.css"/>\n \n <meta name="viewport" content="width=device-width,\n initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n <style>\n #map_1186debcedb6af6d79daead43924d6e0 {\n position: relative;\n width: 100.0%;\n height: 100.0%;\n left: 0.0%;\n top: 0.0%;\n }\n .leaflet-container { font-size: 1rem; }\n </style>\n \n</head>\n<body>\n \n \n <div class="folium-map" id="map_1186debcedb6af6d79daead43924d6e0" ></div>\n \n</body>\n<script>\n \n \n var map_1186debcedb6af6d79daead43924d6e0 = L.map(\n "map_1186debcedb6af6d79daead43924d6e0",\n {\n center: [42.3601, -71.0589],\n crs: L.CRS.EPSG3857,\n zoom: 12,\n zoomControl: 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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 {"extraClasses": "fa-rotate-0", "icon": "info-sign", "iconColor": "white", "markerColor": "blue", "prefix": "glyphicon"}\n );\n marker_842c31844b8cc75c25de66dd0352cb74.setIcon(icon_2e0f3dd707e46b2bb6b3d91ee300a2cd);\n \n \n var popup_d7fbb85020c3750ccf0e66838c6b414f = L.popup({"maxWidth": "100%"});\n\n \n \n var html_cdb73792b6a7c506881f24d5c066f1da = $(`<div id="html_cdb73792b6a7c506881f24d5c066f1da" style="width: 100.0%; height: 100.0%;">Wollaston Beach</div>`)[0];\n popup_d7fbb85020c3750ccf0e66838c6b414f.setContent(html_cdb73792b6a7c506881f24d5c066f1da);\n \n \n\n marker_842c31844b8cc75c25de66dd0352cb74.bindPopup(popup_d7fbb85020c3750ccf0e66838c6b414f)\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, + "execution_count": 93, "metadata": {}, "output_type": "execute_result" } @@ -445,21 +445,21 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-11-06T00:19:02.155307Z", - "start_time": "2023-11-06T00:19:02.145235Z" + "end_time": "2023-11-06T01:14:23.580878Z", + "start_time": "2023-11-06T01:14:23.507152Z" } }, "id": "d6941d1f0a203ee7" }, { "cell_type": "code", - "execution_count": 264, + "execution_count": 94, "outputs": [ { "data": { - "text/plain": "0 106\n1 63\nName: cluster, dtype: int64" + "text/plain": "1 74\n0 57\nName: cluster, dtype: int64" }, - "execution_count": 264, + "execution_count": 94, "metadata": {}, "output_type": "execute_result" } @@ -471,15 +471,15 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-11-06T00:19:05.516139Z", - "start_time": "2023-11-06T00:19:05.510902Z" + "end_time": "2023-11-06T01:14:28.465028Z", + "start_time": "2023-11-06T01:14:28.461813Z" } }, "id": "479ba8f36cdafbf8" }, { "cell_type": "code", - "execution_count": 244, + "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", @@ -516,21 +516,21 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-11-05T23:51:40.761645Z", - "start_time": "2023-11-05T23:51:40.752184Z" + "end_time": "2023-11-06T01:08:43.493687Z", + "start_time": "2023-11-06T01:08:43.480182Z" } }, "id": "4b79215a12bf36e2" }, { "cell_type": "code", - "execution_count": 245, + "execution_count": 74, "outputs": [ { "data": { "text/plain": "0 97\n1 72\nName: cluster, dtype: int64" }, - "execution_count": 245, + "execution_count": 74, "metadata": {}, "output_type": "execute_result" } @@ -545,46 +545,21 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-11-05T23:51:41.510095Z", - "start_time": "2023-11-05T23:51:41.434643Z" + "end_time": "2023-11-06T01:08:43.649954Z", + "start_time": "2023-11-06T01:08:43.542655Z" } }, "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, + "execution_count": 75, "outputs": [ { "data": { "text/plain": 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}, - "execution_count": 265, + "execution_count": 75, "metadata": {}, "output_type": "execute_result" } @@ -595,28 +570,28 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-11-06T00:19:09.317992Z", - "start_time": "2023-11-06T00:19:09.308541Z" + "end_time": "2023-11-06T01:08:43.650401Z", + "start_time": "2023-11-06T01:08:43.622162Z" } }, "id": "2d83e5db093608d2" }, { "cell_type": "code", - "execution_count": 268, + "execution_count": 95, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "106\n" + "57\n" ] }, { "data": { - "text/plain": 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}, - "execution_count": 268, + "execution_count": 95, "metadata": {}, "output_type": "execute_result" } @@ -629,28 +604,28 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-11-06T00:25:20.703565Z", - "start_time": "2023-11-06T00:25:20.690483Z" + "end_time": "2023-11-06T01:14:35.829990Z", + "start_time": "2023-11-06T01:14:35.821619Z" } }, "id": "89297f77828e8ed8" }, { "cell_type": "code", - "execution_count": 269, + "execution_count": 96, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "63\n" + "74\n" ] }, { "data": { - "text/plain": 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}, - "execution_count": 269, + "execution_count": 96, "metadata": {}, "output_type": "execute_result" } @@ -662,19 +637,23 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-11-06T00:25:30.097229Z", - "start_time": "2023-11-06T00:25:30.070409Z" + "end_time": "2023-11-06T01:14:36.909798Z", + "start_time": "2023-11-06T01:14:36.904157Z" } }, "id": "6ff82e29a0366d9e" }, { "cell_type": "code", - "execution_count": null, + "execution_count": 77, "outputs": [], "source": [], "metadata": { - "collapsed": false + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-06T01:08:43.651470Z", + "start_time": "2023-11-06T01:08:43.640872Z" + } }, "id": "7949bddd34b6731" } diff --git a/List E.csv b/List E.csv deleted file mode 100644 index 4424e36..0000000 --- a/List E.csv +++ /dev/null @@ -1,3 +0,0 @@ -Title,Note,URL,Comment -Somerville Museum,,https://www.google.com/maps/place/Somerville+Museum/data=!4m2!3m1!1s0x89e377319800f8e5:0xa135b53119b8c58a, -Make Way for Ducklings,,https://www.google.com/maps/place/Make+Way+for+Ducklings/data=!4m2!3m1!1s0x89e3709e47f660b1:0x7a756ec808941b41, diff --git a/ZestySalesman.ipynb b/ZestySalesman.ipynb index fb03d13..7e74f46 100644 --- a/ZestySalesman.ipynb +++ b/ZestySalesman.ipynb @@ -2,13 +2,13 @@ "cells": [ { "cell_type": "code", - "execution_count": 62, + "execution_count": 10, "id": "initial_id", "metadata": { "collapsed": true, "ExecuteTime": { - "end_time": "2023-11-06T00:26:14.565659Z", - "start_time": "2023-11-06T00:26:14.551906Z" + "end_time": "2023-11-06T01:23:16.767323Z", + "start_time": "2023-11-06T01:23:16.761053Z" } }, "outputs": [], @@ -16,107 +16,104 @@ "import pandas as pd\n", "import numpy as np\n", "import requests\n", - "import folium" + "import folium\n", + "import utils" ] }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 11, "outputs": [], "source": [ "# Create a JSON request for the API\n", "# This is the data we want to get from the API\n", "northeastern_coordinate = \"-71.09033,42.33976;\"\n", - "coordinate_string = '-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'\n", - "coordinates = requests.get('http://acetyl.net:5000/trip/v1/bike/' + northeastern_coordinate + coordinate_string)\n", - "\n", - "coordinates = coordinates.json()" + "route_1 = '-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;-71.1126695,42.3836229;-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'\n", + "route_2 = '-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'" ], "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-11-06T00:26:15.268775Z", - "start_time": "2023-11-06T00:26:14.558083Z" + "end_time": "2023-11-06T01:23:16.774149Z", + "start_time": "2023-11-06T01:23:16.765417Z" } }, "id": "aa618161182b5b07" }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 12, "outputs": [], "source": [ "# Create a dataframe from the JSON\n", - "df = pd.DataFrame(coordinates['waypoints'])" + "df1 = utils.create_json_df(northeastern_coordinate + route_1)\n", + "df2 = utils.create_json_df(northeastern_coordinate + route_2)" ], "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-11-06T00:26:15.278022Z", - "start_time": "2023-11-06T00:26:15.269158Z" + "end_time": "2023-11-06T01:23:18.694403Z", + "start_time": "2023-11-06T01:23:16.768656Z" } }, "id": "32c485788eedd94" }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 13, "outputs": [], "source": [ - "# Separate the location column into lon and lat columns\n", - "df['lat'] = df['location'].apply(lambda x: x[0])\n", - "df['lon'] = df['location'].apply(lambda x: x[1])\n", - "\n", - "df['waypoint_index'] = df['waypoint_index'].astype(int)\n", + "# Add columns for the route number\n", + "df1['route'] = 1\n", + "df2['route'] = 2\n", "\n", - "# Map out the waypoints in order of the waypoint index\n", - "df = df.sort_values(by=['waypoint_index'])" + "# Concatenate the two dataframes\n", + "df = pd.concat([df1, df2], ignore_index=True)" ], "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-11-06T00:26:15.284313Z", - "start_time": "2023-11-06T00:26:15.275568Z" + "end_time": "2023-11-06T01:23:18.698699Z", + "start_time": "2023-11-06T01:23:18.696008Z" } }, - "id": "535ab02638d20cb2" + "id": "49dba1f17ca8337e" }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 14, "outputs": [ { "data": { - "text/plain": " waypoint_index trips_index \\\n0 0 0 \n14 1 0 \n60 2 0 \n46 3 0 \n16 4 0 \n.. ... ... \n61 59 0 \n15 60 0 \n19 61 0 \n31 62 0 \n62 63 0 \n\n hint distance \\\n0 DoUhgBeFIYCcAAAAJgAAAAAAAAARAAAAm0CKQdkZiEAAAA... 0.236958 \n14 lhgDgIkYA4BkAAAAIgEAAFoBAAAaAAAAJyAzQWNrAEI8Ax... 7.134933 \n60 tg0igLoNIoAOAAAAAAAAADwAAADtAQAA8CrBQAAAAACcrM... 17.496916 \n46 k4chgBiIIYAKAAAAFwAAAPQDAAB_AgAAHn2aP-biHUBi6e... 36.240351 \n16 rLwhgLG8IYAEAAAABQAAADAAAABsAAAA1X8CQHGb7D9Kz6... 24.054372 \n.. ... ... \n61 g38hgI1_IYBOAAAAfwAAAAAAAAAAAAAAZ4ECQsbEUkIAAA... 12.789906 \n15 cX8hgJF_IYA1AAAAMAAAAGcAAABOAAAATyWxQQ77nUEHMC... 22.776295 \n19 5tYhgJHXIYAIAAAArQAAADwAAABCAQAAaRlbQD16mUGpAc... 17.374491 \n31 WhEngAASJ4BcAAAAdwAAAHoBAAAFAAAAI3gkQQlZRUJLNi... 13.907079 \n62 s9QhgLbUIYAwAAAAkAAAAAAAAAAAAAAA2XmpQNgrgEEAAA... 4.111715 \n\n name location lat lon \n0 Northeastern (Inbound) [-71.090331, 42.339762] -71.090331 42.339762 \n14 [-71.083465, 42.34194] -71.083465 42.341940 \n60 Public Alley 901 [-71.089677, 42.346361] -71.089677 42.346361 \n46 [-71.093834, 42.339096] -71.093834 42.339096 \n16 [-71.099284, 42.338007] -71.099284 42.338007 \n.. ... ... ... ... \n61 Tremont Street [-71.098267, 42.332009] -71.098267 42.332009 \n15 Alleghany Street [-71.099348, 42.33047] -71.099348 42.330470 \n19 Dudley Street [-71.090904, 42.329829] -71.090904 42.329829 \n31 Blue Hill Avenue [-71.087449, 42.296172] -71.087449 42.296172 \n62 [-71.09454, 42.325354] -71.094540 42.325354 \n\n[64 rows x 8 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>waypoint_index</th>\n <th>trips_index</th>\n <th>hint</th>\n <th>distance</th>\n <th>name</th>\n <th>location</th>\n <th>lat</th>\n <th>lon</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>0</td>\n <td>0</td>\n <td>DoUhgBeFIYCcAAAAJgAAAAAAAAARAAAAm0CKQdkZiEAAAA...</td>\n <td>0.236958</td>\n <td>Northeastern (Inbound)</td>\n <td>[-71.090331, 42.339762]</td>\n <td>-71.090331</td>\n <td>42.339762</td>\n </tr>\n <tr>\n <th>14</th>\n <td>1</td>\n <td>0</td>\n <td>lhgDgIkYA4BkAAAAIgEAAFoBAAAaAAAAJyAzQWNrAEI8Ax...</td>\n <td>7.134933</td>\n <td></td>\n <td>[-71.083465, 42.34194]</td>\n <td>-71.083465</td>\n <td>42.341940</td>\n </tr>\n <tr>\n <th>60</th>\n <td>2</td>\n <td>0</td>\n <td>tg0igLoNIoAOAAAAAAAAADwAAADtAQAA8CrBQAAAAACcrM...</td>\n <td>17.496916</td>\n <td>Public Alley 901</td>\n <td>[-71.089677, 42.346361]</td>\n <td>-71.089677</td>\n <td>42.346361</td>\n </tr>\n <tr>\n <th>46</th>\n <td>3</td>\n <td>0</td>\n <td>k4chgBiIIYAKAAAAFwAAAPQDAAB_AgAAHn2aP-biHUBi6e...</td>\n <td>36.240351</td>\n <td></td>\n <td>[-71.093834, 42.339096]</td>\n <td>-71.093834</td>\n <td>42.339096</td>\n </tr>\n <tr>\n <th>16</th>\n <td>4</td>\n <td>0</td>\n <td>rLwhgLG8IYAEAAAABQAAADAAAABsAAAA1X8CQHGb7D9Kz6...</td>\n <td>24.054372</td>\n <td></td>\n <td>[-71.099284, 42.338007]</td>\n <td>-71.099284</td>\n <td>42.338007</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 </tr>\n <tr>\n <th>61</th>\n <td>59</td>\n <td>0</td>\n <td>g38hgI1_IYBOAAAAfwAAAAAAAAAAAAAAZ4ECQsbEUkIAAA...</td>\n <td>12.789906</td>\n <td>Tremont Street</td>\n <td>[-71.098267, 42.332009]</td>\n <td>-71.098267</td>\n <td>42.332009</td>\n </tr>\n <tr>\n <th>15</th>\n <td>60</td>\n <td>0</td>\n <td>cX8hgJF_IYA1AAAAMAAAAGcAAABOAAAATyWxQQ77nUEHMC...</td>\n <td>22.776295</td>\n <td>Alleghany Street</td>\n <td>[-71.099348, 42.33047]</td>\n <td>-71.099348</td>\n <td>42.330470</td>\n </tr>\n <tr>\n <th>19</th>\n <td>61</td>\n <td>0</td>\n <td>5tYhgJHXIYAIAAAArQAAADwAAABCAQAAaRlbQD16mUGpAc...</td>\n <td>17.374491</td>\n <td>Dudley Street</td>\n <td>[-71.090904, 42.329829]</td>\n <td>-71.090904</td>\n <td>42.329829</td>\n </tr>\n <tr>\n <th>31</th>\n <td>62</td>\n <td>0</td>\n <td>WhEngAASJ4BcAAAAdwAAAHoBAAAFAAAAI3gkQQlZRUJLNi...</td>\n <td>13.907079</td>\n <td>Blue Hill Avenue</td>\n <td>[-71.087449, 42.296172]</td>\n <td>-71.087449</td>\n <td>42.296172</td>\n </tr>\n <tr>\n <th>62</th>\n <td>63</td>\n <td>0</td>\n <td>s9QhgLbUIYAwAAAAkAAAAAAAAAAAAAAA2XmpQNgrgEEAAA...</td>\n <td>4.111715</td>\n <td></td>\n <td>[-71.09454, 42.325354]</td>\n <td>-71.094540</td>\n <td>42.325354</td>\n </tr>\n </tbody>\n</table>\n<p>64 rows × 8 columns</p>\n</div>" + "text/plain": " waypoint_index trips_index \\\n0 0 0 \n17 1 0 \n22 2 0 \n73 3 0 \n11 4 0 \n.. ... ... \n3 70 0 \n12 71 0 \n9 72 0 \n8 73 0 \n14 74 0 \n\n hint distance \\\n0 DoUhgBeFIYCcAAAAJgAAAAAAAAARAAAAm0CKQdkZiEAAAA... 0.236958 \n17 5tYhgJHXIYAIAAAArQAAADwAAABCAQAAaRlbQD16mUGpAc... 17.374491 \n22 XAAigHIAIoBKAAAASwAAAFUAAABDAQAARGUEQURlBEG2ZR... 11.054154 \n73 CdQhgB0OA4AYAAAAHgAAADkAAAAAAAAALdMlQdSMQ0Fd0r... 10.970598 \n11 43YhgPN2IYA1AAAAJAAAAAAAAAA5AAAAEha0QWgpbEEAAA... 18.896385 \n.. ... ... \n3 jt4hgJLeIYA7AAAALQAAAAAAAAAAAAAA4gPGQasVlUEAAA... 4.709088 \n12 0OEhgPvhIYADAAAABgAAAA8AAAA0AAAA2lq-PipQFD-Y-N... 2.009578 \n9 m8shgJ7LIYAOAAAAXgEAAAAAAAAAAAAAOFW-QDE5G0IAAA... 1.716409 \n8 YQ0DgBTPIYDvAAAAdAAAAAAAAAAAAAAAsgLVQbMxTUEAAA... 4.830022 \n14 lhgDgIkYA4BkAAAAIgEAAFoBAAAaAAAAJyAzQWNrAEI8Ax... 7.134933 \n\n name location lat lon \\\n0 Northeastern (Inbound) [-71.090331, 42.339762] -71.090331 42.339762 \n17 Dudley Street [-71.090904, 42.329829] -71.090904 42.329829 \n22 [-71.071196, 42.34085] -71.071196 42.340850 \n73 [-71.066844, 42.327134] -71.066844 42.327134 \n11 Lucy Street [-71.06221, 42.324934] -71.062210 42.324934 \n.. ... ... ... ... \n3 [-71.075414, 42.356537] -71.075414 42.356537 \n12 [-71.085166, 42.349997] -71.085166 42.349997 \n9 Piedmont Street [-71.067854, 42.349993] -71.067854 42.349993 \n8 [-71.072038, 42.348915] -71.072038 42.348915 \n14 [-71.083465, 42.34194] -71.083465 42.341940 \n\n route \n0 2 \n17 2 \n22 2 \n73 2 \n11 2 \n.. ... \n3 2 \n12 2 \n9 2 \n8 2 \n14 2 \n\n[75 rows x 9 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>waypoint_index</th>\n <th>trips_index</th>\n <th>hint</th>\n <th>distance</th>\n <th>name</th>\n <th>location</th>\n <th>lat</th>\n <th>lon</th>\n <th>route</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>0</td>\n <td>0</td>\n <td>DoUhgBeFIYCcAAAAJgAAAAAAAAARAAAAm0CKQdkZiEAAAA...</td>\n <td>0.236958</td>\n <td>Northeastern (Inbound)</td>\n <td>[-71.090331, 42.339762]</td>\n <td>-71.090331</td>\n <td>42.339762</td>\n <td>2</td>\n </tr>\n <tr>\n <th>17</th>\n <td>1</td>\n <td>0</td>\n <td>5tYhgJHXIYAIAAAArQAAADwAAABCAQAAaRlbQD16mUGpAc...</td>\n <td>17.374491</td>\n <td>Dudley Street</td>\n <td>[-71.090904, 42.329829]</td>\n <td>-71.090904</td>\n <td>42.329829</td>\n <td>2</td>\n </tr>\n <tr>\n <th>22</th>\n <td>2</td>\n <td>0</td>\n <td>XAAigHIAIoBKAAAASwAAAFUAAABDAQAARGUEQURlBEG2ZR...</td>\n <td>11.054154</td>\n <td></td>\n <td>[-71.071196, 42.34085]</td>\n <td>-71.071196</td>\n <td>42.340850</td>\n <td>2</td>\n </tr>\n <tr>\n <th>73</th>\n <td>3</td>\n <td>0</td>\n <td>CdQhgB0OA4AYAAAAHgAAADkAAAAAAAAALdMlQdSMQ0Fd0r...</td>\n <td>10.970598</td>\n <td></td>\n <td>[-71.066844, 42.327134]</td>\n <td>-71.066844</td>\n <td>42.327134</td>\n <td>2</td>\n </tr>\n <tr>\n <th>11</th>\n <td>4</td>\n <td>0</td>\n <td>43YhgPN2IYA1AAAAJAAAAAAAAAA5AAAAEha0QWgpbEEAAA...</td>\n <td>18.896385</td>\n <td>Lucy Street</td>\n <td>[-71.06221, 42.324934]</td>\n <td>-71.062210</td>\n <td>42.324934</td>\n <td>2</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 </tr>\n <tr>\n <th>3</th>\n <td>70</td>\n <td>0</td>\n <td>jt4hgJLeIYA7AAAALQAAAAAAAAAAAAAA4gPGQasVlUEAAA...</td>\n <td>4.709088</td>\n <td></td>\n <td>[-71.075414, 42.356537]</td>\n <td>-71.075414</td>\n <td>42.356537</td>\n <td>2</td>\n </tr>\n <tr>\n <th>12</th>\n <td>71</td>\n <td>0</td>\n <td>0OEhgPvhIYADAAAABgAAAA8AAAA0AAAA2lq-PipQFD-Y-N...</td>\n <td>2.009578</td>\n <td></td>\n <td>[-71.085166, 42.349997]</td>\n <td>-71.085166</td>\n <td>42.349997</td>\n <td>2</td>\n </tr>\n <tr>\n <th>9</th>\n <td>72</td>\n <td>0</td>\n <td>m8shgJ7LIYAOAAAAXgEAAAAAAAAAAAAAOFW-QDE5G0IAAA...</td>\n <td>1.716409</td>\n <td>Piedmont Street</td>\n <td>[-71.067854, 42.349993]</td>\n <td>-71.067854</td>\n <td>42.349993</td>\n <td>2</td>\n </tr>\n <tr>\n <th>8</th>\n <td>73</td>\n <td>0</td>\n <td>YQ0DgBTPIYDvAAAAdAAAAAAAAAAAAAAAsgLVQbMxTUEAAA...</td>\n <td>4.830022</td>\n <td></td>\n <td>[-71.072038, 42.348915]</td>\n <td>-71.072038</td>\n <td>42.348915</td>\n <td>2</td>\n </tr>\n <tr>\n <th>14</th>\n <td>74</td>\n <td>0</td>\n <td>lhgDgIkYA4BkAAAAIgEAAFoBAAAaAAAAJyAzQWNrAEI8Ax...</td>\n <td>7.134933</td>\n <td></td>\n <td>[-71.083465, 42.34194]</td>\n <td>-71.083465</td>\n <td>42.341940</td>\n <td>2</td>\n </tr>\n </tbody>\n</table>\n<p>75 rows × 9 columns</p>\n</div>" }, - "execution_count": 66, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "df" + "display(df2)" ], "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-11-06T00:26:15.297169Z", - "start_time": "2023-11-06T00:26:15.287354Z" + "end_time": "2023-11-06T01:23:18.708601Z", + "start_time": "2023-11-06T01:23:18.705324Z" } }, - "id": "49dba1f17ca8337e" + "id": "f231d9a35358988c" }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 15, "outputs": [ { "data": { - "text/plain": "<folium.folium.Map at 0x12d416a90>", - 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"dashOffset": null, "fill": false, "fillColor": "blue", "fillOpacity": 0.2, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 3, "stroke": true, "weight": 3}\n ).addTo(map_aeeac5a1da4c82f72a6ebc2ad6ef0a4f);\n \n \n var circle_marker_ce317e52ff1e620443a1b4e5aca44ed1 = L.circleMarker(\n [42.34194, -71.083465],\n {"bubblingMouseEvents": true, "color": "blue", "dashArray": null, "dashOffset": null, "fill": false, "fillColor": "blue", "fillOpacity": 0.2, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 3, "stroke": true, "weight": 3}\n ).addTo(map_aeeac5a1da4c82f72a6ebc2ad6ef0a4f);\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": 67, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -125,12 +122,14 @@ "# Create a map\n", "m = folium.Map(location=[df['lon'].mean(), df['lat'].mean()], zoom_start=11)\n", "\n", - "# Add a marker for each waypoint and connect with a line as the salesman would travel\n", - "for i in range(len(df)):\n", - " folium.Marker([df.iloc[i]['lon'], df.iloc[i]['lat']], popup=df.iloc[i]['name']).add_to(m)\n", - " if i < len(df) - 1:\n", - " folium.PolyLine([[df.iloc[i]['lon'], df.iloc[i]['lat']], [df.iloc[i + 1]['lon'], df.iloc[i + 1]['lat']]],\n", - " color=\"red\", weight=2.5, opacity=1).add_to(m)\n", + "# Add the points and lines for the two routes with different colors\n", + "colors = ['red', 'blue']\n", + "\n", + "for route in df['route'].unique():\n", + " df_route = df[df['route'] == route]\n", + " folium.PolyLine(df_route[['lon', 'lat']].values.tolist(), color=colors[route-1]).add_to(m)\n", + " for i in range(len(df_route)):\n", + " folium.CircleMarker(df_route[['lon', 'lat']].iloc[i].values.tolist(), radius=3, color=colors[route-1]).add_to(m)\n", "\n", "# Display the map\n", "m" @@ -138,50 +137,104 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-11-06T00:26:15.360267Z", - "start_time": "2023-11-06T00:26:15.298760Z" + "end_time": "2023-11-06T01:23:18.781393Z", + "start_time": "2023-11-06T01:23:18.709803Z" } }, "id": "80fd847da2833913" }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 16, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "The trip will take 10.198055555555555 hours\n" + "The trip will take 6.789722222222222 hours\n", + "The trip will take 9.955833333333333 hours\n" ] } ], "source": [ - "trip_hrs = int(coordinates['trips'][0]['duration'])/3600\n", - "print(\"The trip will take {} hours\".format(trip_hrs))" + "trip_hrs_1 = utils.get_trip_time(route_1)\n", + "print(\"The trip will take {} hours\".format(trip_hrs_1))\n", + "trip_hrs_2 = utils.get_trip_time(route_2)\n", + "print(\"The trip will take {} hours\".format(trip_hrs_2))" ], "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-11-06T00:26:15.360383Z", - "start_time": "2023-11-06T00:26:15.353591Z" + "end_time": "2023-11-06T01:23:20.448487Z", + "start_time": "2023-11-06T01:23:18.761079Z" } }, "id": "a3ec09dfb5cbb5b3" }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 17, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " waypoint_index trips_index \\\n", + "20 20 0 \n", + "\n", + " hint distance name \\\n", + "20 2M4pgNrOKYCCAQAADAAAAAAAAAAAAAAALKILQ27Ah0AAAA... 0.0 Echo Bridge \n", + "\n", + " location lat lon route \n", + "20 [-71.227365, 42.314504] -71.227365 42.314504 1 \n" + ] + } + ], + "source": [ + "# Find the westmost point in Route 1\n", + "df1 = df[df['route'] == 1]\n", + "west = df1[df1['lon'] == df1['lon'].min()]\n", + "print(west)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-06T01:23:20.455714Z", + "start_time": "2023-11-06T01:23:20.453647Z" + } + }, + "id": "be94c3708a1bd250" + }, + { + "cell_type": "code", + "execution_count": 18, + "outputs": [], + "source": [ + "# Remove the westmost point from Route 1\n", + "df = df.drop(west.index)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-06T01:23:20.460791Z", + "start_time": "2023-11-06T01:23:20.456599Z" + } + }, + "id": "21fef07e5b2a03a0" + }, + { + "cell_type": "code", + "execution_count": 18, "outputs": [], "source": [], "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-11-06T00:26:15.360410Z", - "start_time": "2023-11-06T00:26:15.355568Z" + "end_time": "2023-11-06T01:23:20.460900Z", + "start_time": "2023-11-06T01:23:20.458522Z" } }, - "id": "be94c3708a1bd250" + "id": "eafe5678c44e94fd" } ], "metadata": { @@ -1,5 +1,9 @@ -# make a function that turns a list of lists of coordinates into a string +import folium +import pandas as pd +import requests + +# make a function that turns a list of lists of coordinates into a string def list_to_string(list_of_lists): """ Takes a list of lists of coordinates and returns a string of the coordinates @@ -7,4 +11,33 @@ def list_to_string(list_of_lists): string = '' for i in list_of_lists: string += str(i[1]) + ',' + str(i[0]) + ';' - return string
\ No newline at end of file + return string + + +def create_json_df(coordinate_string): + coordinates = requests.get('http://acetyl.net:5000/trip/v1/bike/' + coordinate_string) + coordinates = coordinates.json() + + # Create a dataframe from the JSON + df = pd.DataFrame(coordinates['waypoints']) + + # Separate the location column into lon and lat columns + df['lat'] = df['location'].apply(lambda x: x[0]) + df['lon'] = df['location'].apply(lambda x: x[1]) + + df['waypoint_index'] = df['waypoint_index'].astype(int) + + # Map out the waypoints in order of the waypoint index + df = df.sort_values(by=['waypoint_index']) + + return df + + +def get_trip_time(coordinate_string): + """ + Takes a list of lists of coordinates and returns the time of the trip in hours + """ + coordinates = requests.get('http://acetyl.net:5000/trip/v1/bike/' + coordinate_string) + coordinates = coordinates.json() + + return int(coordinates['trips'][0]['duration']) / 3600 |