-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy path.ipynb
432 lines (432 loc) · 27.5 KB
/
.ipynb
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
{
"cells": [
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"%matplotlib inline\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(564516, 4)"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = pd.read_csv('uber_rides_data.csv')\n",
"data.shape"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"data": {
"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>Date/Time</th>\n",
" <th>Lat</th>\n",
" <th>Lon</th>\n",
" <th>Base</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>4/1/2014 0:11:00</td>\n",
" <td>40.7690</td>\n",
" <td>-73.9549</td>\n",
" <td>B02512</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4/1/2014 0:17:00</td>\n",
" <td>40.7267</td>\n",
" <td>-74.0345</td>\n",
" <td>B02512</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4/1/2014 0:21:00</td>\n",
" <td>40.7316</td>\n",
" <td>-73.9873</td>\n",
" <td>B02512</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4/1/2014 0:28:00</td>\n",
" <td>40.7588</td>\n",
" <td>-73.9776</td>\n",
" <td>B02512</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4/1/2014 0:33:00</td>\n",
" <td>40.7594</td>\n",
" <td>-73.9722</td>\n",
" <td>B02512</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date/Time Lat Lon Base\n",
"0 4/1/2014 0:11:00 40.7690 -73.9549 B02512\n",
"1 4/1/2014 0:17:00 40.7267 -74.0345 B02512\n",
"2 4/1/2014 0:21:00 40.7316 -73.9873 B02512\n",
"3 4/1/2014 0:28:00 40.7588 -73.9776 B02512\n",
"4 4/1/2014 0:33:00 40.7594 -73.9722 B02512"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(564516, 2)"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"clus = data[['Lat','Lon']]\n",
"clus.shape"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.cluster import KMeans"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
" n_clusters=6, n_init=10, n_jobs=None, precompute_distances='auto',\n",
" random_state=1234, tol=0.0001, verbose=0)"
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"kmeans = KMeans(n_clusters=6, max_iter=300, random_state=1234)\n",
"kmeans.fit(clus)"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 40.76555831, -73.9728178 ],\n",
" [ 40.7005414 , -74.20167303],\n",
" [ 40.68860278, -73.96555041],\n",
" [ 40.79808351, -73.86879949],\n",
" [ 40.65952565, -73.7740721 ],\n",
" [ 40.73113043, -73.99860971]])"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"centroids = kmeans.cluster_centers_\n",
"centroids"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([4])"
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"new_location = [[40.6556, -73.5631]]\n",
"kmeans.predict(new_location)"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [
{
"data": {
"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>Latitude</th>\n",
" <th>Longitude</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>40.765558</td>\n",
" <td>-73.972818</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>40.700541</td>\n",
" <td>-74.201673</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>40.688603</td>\n",
" <td>-73.965550</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>40.798084</td>\n",
" <td>-73.868799</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>40.659526</td>\n",
" <td>-73.774072</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>40.731130</td>\n",
" <td>-73.998610</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Latitude Longitude\n",
"0 40.765558 -73.972818\n",
"1 40.700541 -74.201673\n",
"2 40.688603 -73.965550\n",
"3 40.798084 -73.868799\n",
"4 40.659526 -73.774072\n",
"5 40.731130 -73.998610"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"clocation=pd.DataFrame(centroids, columns=['Latitude','Longitude'])\n",
"clocation"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x26400512da0>"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(clocation['Latitude'],clocation['Longitude'],marker='x', color='R',s=200)"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [],
"source": [
"import folium"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[40.76555831002299, -73.972817799411], [40.70054140415895, -74.20167302861849], [40.68860278398665, -73.96555040867389], [40.798083507525085, -73.868799493102], [40.65952565241532, -73.77407209883398], [40.73113042965979, -73.9986097081231]]\n"
]
}
],
"source": [
"centroid = clocation.values.tolist()\n",
"print(centroid)\n",
"map = folium.Map(location=[40.79658011772687, -73.87341741832425])\n",
"for point in range(0,len(centroid)):\n",
" folium.Marker(centroid[point], popup = centroid[point]).add_to(map)"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><iframe src=\"about:blank\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" data-html=<!DOCTYPE html>
<head>    
    <meta http-equiv="content-type" content="text/html; charset=UTF-8" />
    
        <script>
            L_NO_TOUCH = false;
            L_DISABLE_3D = false;
        </script>
    
    <script src="https://cdn.jsdelivr.net/npm/leaflet@1.5.1/dist/leaflet.js"></script>
    <script src="https://code.jquery.com/jquery-1.12.4.min.js"></script>
    <script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/js/bootstrap.min.js"></script>
    <script src="https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js"></script>
    <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/leaflet@1.5.1/dist/leaflet.css"/>
    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap.min.css"/>
    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap-theme.min.css"/>
    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css"/>
    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.css"/>
    <link rel="stylesheet" href="https://rawcdn.githack.com/python-visualization/folium/master/folium/templates/leaflet.awesome.rotate.css"/>
    <style>html, body {width: 100%;height: 100%;margin: 0;padding: 0;}</style>
    <style>#map {position:absolute;top:0;bottom:0;right:0;left:0;}</style>
    
            <meta name="viewport" content="width=device-width,
                initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />
            <style>
                #map_acfa1792315a4d019d5ac53d9e5a4a9d {
                    position: relative;
                    width: 100.0%;
                    height: 100.0%;
                    left: 0.0%;
                    top: 0.0%;
                }
            </style>
        
</head>
<body>    
    
            <div class="folium-map" id="map_acfa1792315a4d019d5ac53d9e5a4a9d" ></div>
        
</body>
<script>    
    
            var map_acfa1792315a4d019d5ac53d9e5a4a9d = L.map(
                "map_acfa1792315a4d019d5ac53d9e5a4a9d",
                {
                    center: [40.79658011772687, -73.87341741832425],
                    crs: L.CRS.EPSG3857,
                    zoom: 10,
                    zoomControl: true,
                    preferCanvas: false,
                }
            );

            

        
    
            var tile_layer_2c8eea7f92d54e8ca94b8d627d40d3cb = L.tileLayer(
                "https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png",
                {"attribution": "Data by \u0026copy; \u003ca href=\"http://openstreetmap.org\"\u003eOpenStreetMap\u003c/a\u003e, under \u003ca href=\"http://www.openstreetmap.org/copyright\"\u003eODbL\u003c/a\u003e.", "detectRetina": false, "maxNativeZoom": 18, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 1, "subdomains": "abc", "tms": false}
            ).addTo(map_acfa1792315a4d019d5ac53d9e5a4a9d);
        
    
            var marker_ffd743ae20f843069b5925384412774a = L.marker(
                [40.76555831002299, -73.972817799411],
                {}
            ).addTo(map_acfa1792315a4d019d5ac53d9e5a4a9d);
        
    
        var popup_d3b6e069f845468aaea60745baf1377a = L.popup({"maxWidth": "100%"});

        
            var html_ae674a627a554f9b95bd92a6a59ce4b6 = $(`<div id="html_ae674a627a554f9b95bd92a6a59ce4b6" style="width: 100.0%; height: 100.0%;">[40.76555831002299, -73.972817799411]</div>`)[0];
            popup_d3b6e069f845468aaea60745baf1377a.setContent(html_ae674a627a554f9b95bd92a6a59ce4b6);
        

        marker_ffd743ae20f843069b5925384412774a.bindPopup(popup_d3b6e069f845468aaea60745baf1377a)
        ;

        
    
    
            var marker_bd1375f58d2a48c5a38f4447903fa0c0 = L.marker(
                [40.70054140415895, -74.20167302861849],
                {}
            ).addTo(map_acfa1792315a4d019d5ac53d9e5a4a9d);
        
    
        var popup_fc071ac294c640029927f3e7da557330 = L.popup({"maxWidth": "100%"});

        
            var html_3b0435870fe7433886b0d01c73d0c231 = $(`<div id="html_3b0435870fe7433886b0d01c73d0c231" style="width: 100.0%; height: 100.0%;">[40.70054140415895, -74.20167302861849]</div>`)[0];
            popup_fc071ac294c640029927f3e7da557330.setContent(html_3b0435870fe7433886b0d01c73d0c231);
        

        marker_bd1375f58d2a48c5a38f4447903fa0c0.bindPopup(popup_fc071ac294c640029927f3e7da557330)
        ;

        
    
    
            var marker_d705cd216eec48b98d7244aeb43ab411 = L.marker(
                [40.68860278398665, -73.96555040867389],
                {}
            ).addTo(map_acfa1792315a4d019d5ac53d9e5a4a9d);
        
    
        var popup_adf978f1b39e4075810600d438b8534b = L.popup({"maxWidth": "100%"});

        
            var html_e918c930a63245b9b3f6343fe61d05b9 = $(`<div id="html_e918c930a63245b9b3f6343fe61d05b9" style="width: 100.0%; height: 100.0%;">[40.68860278398665, -73.96555040867389]</div>`)[0];
            popup_adf978f1b39e4075810600d438b8534b.setContent(html_e918c930a63245b9b3f6343fe61d05b9);
        

        marker_d705cd216eec48b98d7244aeb43ab411.bindPopup(popup_adf978f1b39e4075810600d438b8534b)
        ;

        
    
    
            var marker_9cfb978d81914dab9d5e7393e7b9656a = L.marker(
                [40.798083507525085, -73.868799493102],
                {}
            ).addTo(map_acfa1792315a4d019d5ac53d9e5a4a9d);
        
    
        var popup_1aca6a30dd8c45979671d9a91e631d61 = L.popup({"maxWidth": "100%"});

        
            var html_925ea01c379e482c95ced89ca4b0db66 = $(`<div id="html_925ea01c379e482c95ced89ca4b0db66" style="width: 100.0%; height: 100.0%;">[40.798083507525085, -73.868799493102]</div>`)[0];
            popup_1aca6a30dd8c45979671d9a91e631d61.setContent(html_925ea01c379e482c95ced89ca4b0db66);
        

        marker_9cfb978d81914dab9d5e7393e7b9656a.bindPopup(popup_1aca6a30dd8c45979671d9a91e631d61)
        ;

        
    
    
            var marker_c2539db9f1f54093adfe14a3baa38897 = L.marker(
                [40.65952565241532, -73.77407209883398],
                {}
            ).addTo(map_acfa1792315a4d019d5ac53d9e5a4a9d);
        
    
        var popup_7e4fcb2829be494fa4ddbdacf4c2fdc8 = L.popup({"maxWidth": "100%"});

        
            var html_4c269801cd6848c39a16e176985154fd = $(`<div id="html_4c269801cd6848c39a16e176985154fd" style="width: 100.0%; height: 100.0%;">[40.65952565241532, -73.77407209883398]</div>`)[0];
            popup_7e4fcb2829be494fa4ddbdacf4c2fdc8.setContent(html_4c269801cd6848c39a16e176985154fd);
        

        marker_c2539db9f1f54093adfe14a3baa38897.bindPopup(popup_7e4fcb2829be494fa4ddbdacf4c2fdc8)
        ;

        
    
    
            var marker_9efeae76ab654d31a13464faec444bdb = L.marker(
                [40.73113042965979, -73.9986097081231],
                {}
            ).addTo(map_acfa1792315a4d019d5ac53d9e5a4a9d);
        
    
        var popup_8cba5afaeb024a33994c6ec28c68e4c2 = L.popup({"maxWidth": "100%"});

        
            var html_54979778ac494a6ca3a7ebcfc0c2a5c9 = $(`<div id="html_54979778ac494a6ca3a7ebcfc0c2a5c9" style="width: 100.0%; height: 100.0%;">[40.73113042965979, -73.9986097081231]</div>`)[0];
            popup_8cba5afaeb024a33994c6ec28c68e4c2.setContent(html_54979778ac494a6ca3a7ebcfc0c2a5c9);
        

        marker_9efeae76ab654d31a13464faec444bdb.bindPopup(popup_8cba5afaeb024a33994c6ec28c68e4c2)
        ;

        
    
</script> onload=\"this.contentDocument.open();this.contentDocument.write(atob(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
],
"text/plain": [
"<folium.folium.Map at 0x26401636048>"
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"map"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}