In [2]:
import plotly.plotly as py
import cufflinks as cf
import pandas as pd
import json
with open("/Users/chunqingxu/.plotly/.credentials", 'rb') as fi:
cred= json.load(fi, encoding='utf-8')
myapi = cred['api_key']
username = cred['username']
py.sign_in(username, myapi)
In [13]:
df = pd.read_csv("/Users/chunqingxu/Downloads/jan_hour.csv.csv")
In [33]:
for line in df.line.unique():
tmp = df[df.line == line]['count'].values
if len(tmp) != 24:
print line
else:
d.loc[:,line] = tmp
C
G
GS
S
In [43]:
df[df.line == "C"]
Out[43]:
line
hour
count
192
C
6
215
193
C
7
255
194
C
8
270
195
C
9
416
196
C
10
418
197
C
11
488
198
C
12
482
199
C
13
566
200
C
14
459
201
C
15
520
202
C
16
424
203
C
17
423
204
C
18
556
205
C
19
461
206
C
20
424
207
C
21
409
208
C
22
351
209
C
23
322
In [44]:
df[df.line == "G"] ##6
Out[44]:
line
hour
count
282
G
0
14
283
G
1
13
284
G
2
8
285
G
3
6
286
G
4
4
287
G
5
3
288
G
7
4
289
G
8
3
290
G
9
3
291
G
10
4
292
G
11
2
293
G
12
4
294
G
13
1
295
G
14
5
296
G
15
3
297
G
16
1
298
G
17
4
299
G
18
2
300
G
19
7
301
G
20
3
302
G
21
4
303
G
22
3
304
G
23
4
In [45]:
df[df.line == "GS"] ## 34
Out[45]:
line
hour
count
305
GS
0
11
306
GS
1
5
307
GS
2
4
308
GS
5
1
309
GS
6
3
310
GS
7
3
311
GS
8
10
312
GS
9
16
313
GS
10
6
314
GS
11
21
315
GS
12
10
316
GS
13
10
317
GS
14
16
318
GS
15
11
319
GS
16
11
320
GS
17
6
321
GS
18
5
322
GS
19
8
323
GS
20
6
324
GS
21
6
325
GS
22
8
326
GS
23
14
In [65]:
df[df.line == "S"]['count'].values
Out[65]:
array([1, 2, 1, 1, 2, 1, 1, 1, 2, 1, 1, 5, 2, 1, 2, 2, 4, 2])
In [66]:
counts = [1, 2, 1,0, 1, 2, 0,1, 1, 0,1, 2, 1, 1,0, 5, 0,2, 1, 2,0, 2, 4, 2]
In [67]:
d.loc[:,'S'] = counts
In [46]:
df[df.line == "S"] ## 3 6 9 14 16 20
Out[46]:
line
hour
count
471
S
0
1
472
S
1
2
473
S
2
1
474
S
4
1
475
S
5
2
476
S
7
1
477
S
8
1
478
S
10
1
479
S
11
2
480
S
12
1
481
S
13
1
482
S
15
5
483
S
17
2
484
S
18
1
485
S
19
2
486
S
21
2
487
S
22
4
488
S
23
2
In [56]:
df[df.line == "GS"]['count'].values
Out[56]:
array([11, 5, 4, 1, 3, 3, 10, 16, 6, 21, 10, 10, 16, 11, 11, 6, 5,
8, 6, 6, 8, 14])
In [61]:
counts = [11, 5, 4, 0, 0, 1, 3, 3, 10, 16, 6, 21, 10, 10, 16, 11, 11, 6, 5,
8, 6, 6,8, 14]
In [63]:
d["GS"] = counts
In [38]:
d.loc[6:,'C'] = df[df.line == 'C']['count'].values
In [69]:
df[df.line == "G"]['count'].values
Out[69]:
array([14, 13, 8, 6, 4, 3, 4, 3, 3, 4, 2, 4, 1, 5, 3, 1, 4,
2, 7, 3, 4, 3, 4])
In [70]:
counts = [14, 13, 8, 6, 4, 3, 0, 4, 3, 3, 4, 2, 4, 1, 5, 3, 1, 4,
2, 7, 3, 4, 3, 4]
In [73]:
d2 = d
In [75]:
d2.loc[:5,'C'] = [0, 0, 0, 0, 0, 0]
In [76]:
d2
Out[76]:
1
2
3
4
5
6
7
A
C
D
...
G
GS
J
L
M
N
Q
R
S
Z
0
241
67
68
46
39
171
25
188
0
105
...
14
11
44
60
191
209
181
219
1
32
1
195
63
58
24
19
111
29
127
0
73
...
13
5
30
101
148
133
109
149
2
20
2
134
39
39
22
16
87
27
113
0
55
...
8
4
34
60
92
98
84
109
1
20
3
86
36
35
9
5
51
19
83
0
31
...
6
0
17
71
83
72
60
95
0
14
4
81
36
36
7
6
46
15
84
0
33
...
4
0
17
50
63
58
57
59
1
15
5
96
42
41
18
17
68
11
59
0
52
...
3
1
2
29
91
52
47
61
2
2
6
177
56
57
22
22
187
22
120
215
60
...
0
3
6
38
132
104
87
123
0
5
7
190
70
70
24
19
197
54
156
255
74
...
4
3
6
43
143
118
86
137
1
2
8
264
61
63
60
52
281
46
157
270
78
...
3
10
9
37
169
148
112
175
1
8
9
338
73
73
64
48
313
50
220
416
159
...
3
16
13
52
250
205
159
232
0
11
10
336
71
74
80
59
390
43
191
418
176
...
4
6
7
61
264
194
132
250
1
7
11
396
99
97
96
79
441
62
248
488
220
...
2
21
15
61
324
294
230
333
2
15
12
431
99
98
73
59
419
52
237
482
212
...
4
10
18
67
321
317
242
366
1
18
13
363
67
67
96
83
373
48
262
566
243
...
1
10
24
55
345
336
242
399
1
24
14
449
88
86
96
85
392
44
218
459
224
...
5
16
17
72
337
382
283
434
0
17
15
400
74
73
84
74
366
44
260
520
213
...
3
11
19
62
286
367
282
428
5
17
16
381
68
66
80
74
331
29
225
424
199
...
1
11
13
44
287
331
253
365
0
13
17
410
72
72
60
50
384
31
201
423
168
...
4
6
16
85
281
317
228
351
2
16
18
502
90
89
68
57
416
24
252
556
217
...
2
5
12
97
323
390
306
435
1
10
19
490
99
100
60
52
362
23
229
461
186
...
7
8
16
81
284
378
287
405
2
13
20
376
78
77
46
39
309
18
220
424
147
...
3
6
21
76
226
311
236
337
0
21
21
358
84
86
44
41
283
30
206
409
141
...
4
6
26
66
241
307
221
333
2
21
22
356
84
83
33
30
243
31
174
351
100
...
3
8
17
70
194
272
209
299
4
14
23
288
70
70
37
35
243
22
191
322
124
...
4
14
23
69
207
208
167
240
2
16
24 rows × 22 columns
In [71]:
d["G"] = counts
In [78]:
d = d2
d
Out[78]:
1
2
3
4
5
6
7
A
C
D
...
G
GS
J
L
M
N
Q
R
S
Z
0
241
67
68
46
39
171
25
188
0
105
...
14
11
44
60
191
209
181
219
1
32
1
195
63
58
24
19
111
29
127
0
73
...
13
5
30
101
148
133
109
149
2
20
2
134
39
39
22
16
87
27
113
0
55
...
8
4
34
60
92
98
84
109
1
20
3
86
36
35
9
5
51
19
83
0
31
...
6
0
17
71
83
72
60
95
0
14
4
81
36
36
7
6
46
15
84
0
33
...
4
0
17
50
63
58
57
59
1
15
5
96
42
41
18
17
68
11
59
0
52
...
3
1
2
29
91
52
47
61
2
2
6
177
56
57
22
22
187
22
120
215
60
...
0
3
6
38
132
104
87
123
0
5
7
190
70
70
24
19
197
54
156
255
74
...
4
3
6
43
143
118
86
137
1
2
8
264
61
63
60
52
281
46
157
270
78
...
3
10
9
37
169
148
112
175
1
8
9
338
73
73
64
48
313
50
220
416
159
...
3
16
13
52
250
205
159
232
0
11
10
336
71
74
80
59
390
43
191
418
176
...
4
6
7
61
264
194
132
250
1
7
11
396
99
97
96
79
441
62
248
488
220
...
2
21
15
61
324
294
230
333
2
15
12
431
99
98
73
59
419
52
237
482
212
...
4
10
18
67
321
317
242
366
1
18
13
363
67
67
96
83
373
48
262
566
243
...
1
10
24
55
345
336
242
399
1
24
14
449
88
86
96
85
392
44
218
459
224
...
5
16
17
72
337
382
283
434
0
17
15
400
74
73
84
74
366
44
260
520
213
...
3
11
19
62
286
367
282
428
5
17
16
381
68
66
80
74
331
29
225
424
199
...
1
11
13
44
287
331
253
365
0
13
17
410
72
72
60
50
384
31
201
423
168
...
4
6
16
85
281
317
228
351
2
16
18
502
90
89
68
57
416
24
252
556
217
...
2
5
12
97
323
390
306
435
1
10
19
490
99
100
60
52
362
23
229
461
186
...
7
8
16
81
284
378
287
405
2
13
20
376
78
77
46
39
309
18
220
424
147
...
3
6
21
76
226
311
236
337
0
21
21
358
84
86
44
41
283
30
206
409
141
...
4
6
26
66
241
307
221
333
2
21
22
356
84
83
33
30
243
31
174
351
100
...
3
8
17
70
194
272
209
299
4
14
23
288
70
70
37
35
243
22
191
322
124
...
4
14
23
69
207
208
167
240
2
16
24 rows × 22 columns
In [18]:
d = pd.DataFrame(index=range(24),columns=df.line.unique())
In [23]:
df.columns
Out[23]:
Index([u'line', u'hour', u'count'], dtype='object')
In [ ]:
In [81]:
df = d.copy()
In [84]:
figure = df.iplot(kind='scatter', asFigure=True, )
# print figure.to_string()
In [87]:
df.iplot?
In [92]:
figure['data'][1]
Out[92]:
{'line': {'color': 'rgba(55, 128, 191, 1.0)', 'dash': 'solid', 'width': 1.3},
'mode': 'lines',
'name': 'Trace 1',
'text': '',
'type': 'scatter',
'x': array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19, 20, 21, 22, 23]),
'y': array([67, 63, 39, 36, 36, 42, 56, 70, 61, 73, 71, 99, 99, 67, 88, 74, 68,
72, 90, 99, 78, 84, 84, 70])}
In [94]:
df.iplot?
In [95]:
figure
Out[95]:
{'data': [{'line': {'color': 'rgba(255, 153, 51, 1.0)',
'dash': 'solid',
'width': 1.3},
'mode': 'lines',
'name': '1',
'text': '',
'type': 'scatter',
'x': array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19, 20, 21, 22, 23]),
'y': array([241, 195, 134, 86, 81, 96, 177, 190, 264, 338, 336, 396, 431,
363, 449, 400, 381, 410, 502, 490, 376, 358, 356, 288])},
{'line': {'color': 'rgba(55, 128, 191, 1.0)', 'dash': 'solid', 'width': 1.3},
'mode': 'lines',
'name': '2',
'text': '',
'type': 'scatter',
'x': array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19, 20, 21, 22, 23]),
'y': array([67, 63, 39, 36, 36, 42, 56, 70, 61, 73, 71, 99, 99, 67, 88, 74, 68,
72, 90, 99, 78, 84, 84, 70])},
{'line': {'color': 'rgba(50, 171, 96, 1.0)', 'dash': 'solid', 'width': 1.3},
'mode': 'lines',
'name': '3',
'text': '',
'type': 'scatter',
'x': array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19, 20, 21, 22, 23]),
'y': array([ 68, 58, 39, 35, 36, 41, 57, 70, 63, 73, 74, 97, 98,
67, 86, 73, 66, 72, 89, 100, 77, 86, 83, 70])},
{'line': {'color': 'rgba(128, 0, 128, 1.0)', 'dash': 'solid', 'width': 1.3},
'mode': 'lines',
'name': '4',
'text': '',
'type': 'scatter',
'x': array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19, 20, 21, 22, 23]),
'y': array([46, 24, 22, 9, 7, 18, 22, 24, 60, 64, 80, 96, 73, 96, 96, 84, 80,
60, 68, 60, 46, 44, 33, 37])},
{'line': {'color': 'rgba(219, 64, 82, 1.0)', 'dash': 'solid', 'width': 1.3},
'mode': 'lines',
'name': '5',
'text': '',
'type': 'scatter',
'x': array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19, 20, 21, 22, 23]),
'y': array([39, 19, 16, 5, 6, 17, 22, 19, 52, 48, 59, 79, 59, 83, 85, 74, 74,
50, 57, 52, 39, 41, 30, 35])},
{'line': {'color': 'rgba(0, 128, 128, 1.0)', 'dash': 'solid', 'width': 1.3},
'mode': 'lines',
'name': '6',
'text': '',
'type': 'scatter',
'x': array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19, 20, 21, 22, 23]),
'y': array([171, 111, 87, 51, 46, 68, 187, 197, 281, 313, 390, 441, 419,
373, 392, 366, 331, 384, 416, 362, 309, 283, 243, 243])},
{'line': {'color': 'rgba(255, 255, 51, 1.0)', 'dash': 'solid', 'width': 1.3},
'mode': 'lines',
'name': '7',
'text': '',
'type': 'scatter',
'x': array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19, 20, 21, 22, 23]),
'y': array([25, 29, 27, 19, 15, 11, 22, 54, 46, 50, 43, 62, 52, 48, 44, 44, 29,
31, 24, 23, 18, 30, 31, 22])},
{'line': {'color': 'rgba(128, 128, 0, 1.0)', 'dash': 'solid', 'width': 1.3},
'mode': 'lines',
'name': 'A',
'text': '',
'type': 'scatter',
'x': array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19, 20, 21, 22, 23]),
'y': array([188, 127, 113, 83, 84, 59, 120, 156, 157, 220, 191, 248, 237,
262, 218, 260, 225, 201, 252, 229, 220, 206, 174, 191])},
{'line': {'color': 'rgba(251, 128, 114, 1.0)',
'dash': 'solid',
'width': 1.3},
'mode': 'lines',
'name': 'C',
'text': '',
'type': 'scatter',
'x': array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19, 20, 21, 22, 23]),
'y': array([ 0, 0, 0, 0, 0, 0, 215, 255, 270, 416, 418, 488, 482,
566, 459, 520, 424, 423, 556, 461, 424, 409, 351, 322])},
{'line': {'color': 'rgba(128, 177, 211, 1.0)',
'dash': 'solid',
'width': 1.3},
'mode': 'lines',
'name': 'D',
'text': '',
'type': 'scatter',
'x': array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19, 20, 21, 22, 23]),
'y': array([105, 73, 55, 31, 33, 52, 60, 74, 78, 159, 176, 220, 212,
243, 224, 213, 199, 168, 217, 186, 147, 141, 100, 124])},
{'line': {'color': 'rgba(128, 177, 211, 0.89999999999999991)',
'dash': 'solid',
'width': 1.3},
'mode': 'lines',
'name': 'E',
'text': '',
'type': 'scatter',
'x': array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19, 20, 21, 22, 23]),
'y': array([246, 173, 137, 85, 78, 105, 180, 206, 221, 320, 304, 334, 343,
414, 320, 317, 324, 339, 452, 393, 352, 343, 296, 265])},
{'line': {'color': 'rgba(255, 153, 51, 0.89999999999999991)',
'dash': 'solid',
'width': 1.3},
'mode': 'lines',
'name': 'F',
'text': '',
'type': 'scatter',
'x': array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19, 20, 21, 22, 23]),
'y': array([186, 157, 91, 73, 61, 93, 137, 167, 181, 299, 294, 359, 359,
389, 394, 336, 325, 299, 358, 306, 252, 259, 218, 230])},
{'line': {'color': 'rgba(55, 128, 191, 0.89999999999999991)',
'dash': 'solid',
'width': 1.3},
'mode': 'lines',
'name': 'G',
'text': '',
'type': 'scatter',
'x': array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19, 20, 21, 22, 23]),
'y': array([14, 13, 8, 6, 4, 3, 0, 4, 3, 3, 4, 2, 4, 1, 5, 3, 1,
4, 2, 7, 3, 4, 3, 4])},
{'line': {'color': 'rgba(50, 171, 96, 0.89999999999999991)',
'dash': 'solid',
'width': 1.3},
'mode': 'lines',
'name': 'GS',
'text': '',
'type': 'scatter',
'x': array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19, 20, 21, 22, 23]),
'y': array([11, 5, 4, 0, 0, 1, 3, 3, 10, 16, 6, 21, 10, 10, 16, 11, 11,
6, 5, 8, 6, 6, 8, 14])},
{'line': {'color': 'rgba(128, 0, 128, 0.89999999999999991)',
'dash': 'solid',
'width': 1.3},
'mode': 'lines',
'name': 'J',
'text': '',
'type': 'scatter',
'x': array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19, 20, 21, 22, 23]),
'y': array([44, 30, 34, 17, 17, 2, 6, 6, 9, 13, 7, 15, 18, 24, 17, 19, 13,
16, 12, 16, 21, 26, 17, 23])},
{'line': {'color': 'rgba(219, 64, 82, 0.89999999999999991)',
'dash': 'solid',
'width': 1.3},
'mode': 'lines',
'name': 'L',
'text': '',
'type': 'scatter',
'x': array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19, 20, 21, 22, 23]),
'y': array([ 60, 101, 60, 71, 50, 29, 38, 43, 37, 52, 61, 61, 67,
55, 72, 62, 44, 85, 97, 81, 76, 66, 70, 69])},
{'line': {'color': 'rgba(0, 128, 128, 0.89999999999999991)',
'dash': 'solid',
'width': 1.3},
'mode': 'lines',
'name': 'M',
'text': '',
'type': 'scatter',
'x': array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19, 20, 21, 22, 23]),
'y': array([191, 148, 92, 83, 63, 91, 132, 143, 169, 250, 264, 324, 321,
345, 337, 286, 287, 281, 323, 284, 226, 241, 194, 207])},
{'line': {'color': 'rgba(255, 255, 51, 0.89999999999999991)',
'dash': 'solid',
'width': 1.3},
'mode': 'lines',
'name': 'N',
'text': '',
'type': 'scatter',
'x': array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19, 20, 21, 22, 23]),
'y': array([209, 133, 98, 72, 58, 52, 104, 118, 148, 205, 194, 294, 317,
336, 382, 367, 331, 317, 390, 378, 311, 307, 272, 208])},
{'line': {'color': 'rgba(128, 128, 0, 0.89999999999999991)',
'dash': 'solid',
'width': 1.3},
'mode': 'lines',
'name': 'Q',
'text': '',
'type': 'scatter',
'x': array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19, 20, 21, 22, 23]),
'y': array([181, 109, 84, 60, 57, 47, 87, 86, 112, 159, 132, 230, 242,
242, 283, 282, 253, 228, 306, 287, 236, 221, 209, 167])},
{'line': {'color': 'rgba(251, 128, 114, 0.89999999999999991)',
'dash': 'solid',
'width': 1.3},
'mode': 'lines',
'name': 'R',
'text': '',
'type': 'scatter',
'x': array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19, 20, 21, 22, 23]),
'y': array([219, 149, 109, 95, 59, 61, 123, 137, 175, 232, 250, 333, 366,
399, 434, 428, 365, 351, 435, 405, 337, 333, 299, 240])},
{'line': {'color': 'rgba(251, 128, 114, 0.79999999999999982)',
'dash': 'solid',
'width': 1.3},
'mode': 'lines',
'name': 'S',
'text': '',
'type': 'scatter',
'x': array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19, 20, 21, 22, 23]),
'y': array([1, 2, 1, 0, 1, 2, 0, 1, 1, 0, 1, 2, 1, 1, 0, 5, 0, 2, 1, 2, 0, 2, 4,
2])},
{'line': {'color': 'rgba(128, 177, 211, 0.79999999999999982)',
'dash': 'solid',
'width': 1.3},
'mode': 'lines',
'name': 'Z',
'text': '',
'type': 'scatter',
'x': array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19, 20, 21, 22, 23]),
'y': array([32, 20, 20, 14, 15, 2, 5, 2, 8, 11, 7, 15, 18, 24, 17, 17, 13,
16, 10, 13, 21, 21, 14, 16])}],
'layout': {'legend': {'bgcolor': '#F5F6F9', 'font': {'color': '#4D5663'}},
'paper_bgcolor': '#F5F6F9',
'plot_bgcolor': '#F5F6F9',
'titlefont': {'color': '#4D5663'},
'xaxis1': {'gridcolor': '#E1E5ED',
'showgrid': True,
'tickfont': {'color': '#4D5663'},
'title': '',
'titlefont': {'color': '#4D5663'},
'zerolinecolor': '#E1E5ED'},
'yaxis1': {'gridcolor': '#E1E5ED',
'showgrid': True,
'tickfont': {'color': '#4D5663'},
'tickprefix': '$',
'title': 'Price',
'titlefont': {'color': '#4D5663'},
'zerolinecolor': '#E1E5ED'}}}
In [111]:
#df=cf.datagen.lines(3,columns=['a','b','c'])
figure['layout']['yaxis1'].update({'title': 'Counts', 'tickprefix': ''})
figure['layout']['xaxis1'].update({'title': 'Hours', 'tickprefix': ''})
figure['layout']['title'] = 'How many people prefer yellow cab than subway? by hour, Jan 2016'
for i, trace in enumerate(figure['data']):
trace['name'] = '{}'.format(d.columns.values[i])
py.iplot(figure)
Out[111]:
Content source: djfan/why_yellow_taxi
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