Python plotly Express 直方图：图表未显示所有唯一 TIME_BUCKET 值，它以每小时值的形式聚集 TIME_BUCKETs
Python plotly Express Histogram: Graph not showing all unique TIME_BUCKET values, it clubbing TIME_BUCKETs in hourly value
我的 CSV 内容有 1.1K 列这样的三列。这具有 5 分钟 TIME_BUCKET 的值，例如 03:40:00+00:00、03:45:00+00:00 等。
我希望图表绘制所有这些不同的 TIME_BUCKETS 的直方图，但它实际上绘制了每小时时间桶的图表，如 03:00、04:00 等
我的代码如下
import pandas as pd
import plotly.express as px
df = pd.read_csv("D:/Work/WORK/2021/FEB21/BiPA/nqquery/Issue/5MinTimeBucketHistogramNotWorking1.csv")
graph = px.histogram(df, x='TIME_BUCKET', color='REPORT_NAME', title='Report Category Wise Execution Count (5 minuntes sample size)')
graph.show()
我的 CSV 内容如下所示，有 1.1K 列。共享整个 CSV here 以供参考。
,REPORT_NAME,TIME_BUCKET
23,DashboardReport,2021-01-20 03:30:00+00:00
33,DashboardReport,2021-01-20 03:40:00+00:00
69,ExportReport,2021-01-20 03:40:00+00:00
74,ExportReport,2021-01-20 03:40:00+00:00
97,ExportReport,2021-01-20 03:40:00+00:00
98,ExportReport,2021-01-20 03:40:00+00:00
99,ExportReport,2021-01-20 03:40:00+00:00
101,ExportReport,2021-01-20 03:40:00+00:00
103,ExportReport,2021-01-20 03:40:00+00:00
2821,DashboardReport,2021-01-20 15:40:00+00:00
2822,DashboardReport,2021-01-20 15:40:00+00:00
2823,DashboardReport,2021-01-20 15:45:00+00:00
2896,DashboardReport,2021-01-20 16:15:00+00:00
3283,SQLReport,2021-01-20 19:00:00+00:00
3285,DashboardReport,2021-01-20 19:00:00+00:00
3288,DashboardReport,2021-01-20 19:05:00+00:00
3289,DashboardReport,2021-01-20 19:05:00+00:00
3292,ImportReport,2021-01-20 19:05:00+00:00
3293,DashboardReport,2021-01-20 19:05:00+00:00
3294,DashboardReport,2021-01-20 19:05:00+00:00
3295,DashboardReport,2021-01-20 19:10:00+00:00
3297,DashboardReport,2021-01-20 19:10:00+00:00
3298,SQLReport,2021-01-20 19:10:00+00:00
3300,DashboardReport,2021-01-20 19:10:00+00:00
3303,SQLReport,2021-01-20 19:15:00+00:00
3307,ImportReport,2021-01-20 19:15:00+00:00
3309,DashboardReport,2021-01-20 19:15:00+00:00
3312,DashboardReport,2021-01-20 19:15:00+00:00
3313,DashboardReport,2021-01-20 19:15:00+00:00
3314,SQLReport,2021-01-20 19:15:00+00:00
3315,DashboardReport,2021-01-20 19:15:00+00:00
3316,DashboardReport,2021-01-20 19:15:00+00:00
3317,DashboardReport,2021-01-20 19:15:00+00:00
3318,ImportReport,2021-01-20 19:15:00+00:00
3319,DashboardReport,2021-01-20 19:15:00+00:00
3324,DashboardReport,2021-01-20 19:20:00+00:00
3328,SQLReport,2021-01-20 19:20:00+00:00
3331,ImportReport,2021-01-20 19:20:00+00:00
3332,ImportReport,2021-01-20 19:20:00+00:00
3335,DashboardReport,2021-01-20 19:20:00+00:00
3336,ImportReport,2021-01-20 19:20:00+00:00
3337,DashboardReport,2021-01-20 19:20:00+00:00
3339,DashboardReport,2021-01-20 19:20:00+00:00
3344,DashboardReport,2021-01-20 19:20:00+00:00
3345,DashboardReport,2021-01-20 19:20:00+00:00
3349,DBReport,2021-01-20 19:20:00+00:00
3350,SQLReport,2021-01-20 19:20:00+00:00
3354,DashboardReport,2021-01-20 19:20:00+00:00
3355,DashboardReport,2021-01-20 19:20:00+00:00
3356,DashboardReport,2021-01-20 19:20:00+00:00
3357,DashboardReport,2021-01-20 19:20:00+00:00
3358,DashboardReport,2021-01-20 19:20:00+00:00
3359,DashboardReport,2021-01-20 19:20:00+00:00
3360,DashboardReport,2021-01-20 19:20:00+00:00
3368,DashboardReport,2021-01-20 19:25:00+00:00
3370,DashboardReport,2021-01-20 19:25:00+00:00
3375,DashboardReport,2021-01-20 19:25:00+00:00
3377,DashboardReport,2021-01-20 19:30:00+00:00
3379,DashboardReport,2021-01-20 19:30:00+00:00
3381,DashboardReport,2021-01-20 19:30:00+00:00
3384,DashboardReport,2021-01-20 19:30:00+00:00
3396,ImportReport,2021-01-20 19:40:00+00:00
3398,DashboardReport,2021-01-20 19:40:00+00:00
3403,DashboardReport,2021-01-20 19:45:00+00:00
3404,DashboardReport,2021-01-20 19:45:00+00:00
3408,DashboardReport,2021-01-20 19:45:00+00:00
3410,DashboardReport,2021-01-20 19:45:00+00:00
3418,DashboardReport,2021-01-20 19:50:00+00:00
3419,SQLReport,2021-01-20 19:50:00+00:00
3421,DashboardReport,2021-01-20 19:50:00+00:00
3422,DashboardReport,2021-01-20 19:50:00+00:00
3429,DashboardReport,2021-01-20 19:50:00+00:00
3434,DashboardReport,2021-01-20 19:55:00+00:00
3443,ImportReport,2021-01-20 20:00:00+00:00
3444,ImportReport,2021-01-20 20:00:00+00:00
3450,DBReport,2021-01-20 20:05:00+00:00
3451,DBReport,2021-01-20 20:05:00+00:00
3489,SQLReport,2021-01-20 20:20:00+00:00
3490,ImportReport,2021-01-20 20:20:00+00:00
3496,DashboardReport,2021-01-20 20:20:00+00:00
3499,ImportReport,2021-01-20 20:25:00+00:00
3501,DashboardReport,2021-01-20 20:25:00+00:00
3505,DashboardReport,2021-01-20 20:25:00+00:00
3513,SQLReport,2021-01-20 20:30:00+00:00
3514,DashboardReport,2021-01-20 20:35:00+00:00
3521,SQLReport,2021-01-20 20:35:00+00:00
3522,DashboardReport,2021-01-20 20:35:00+00:00
3523,DashboardReport,2021-01-20 20:35:00+00:00
3527,DashboardReport,2021-01-20 20:40:00+00:00
3537,DashboardReport,2021-01-20 20:40:00+00:00
3538,DashboardReport,2021-01-20 20:40:00+00:00
3540,DashboardReport,2021-01-20 20:45:00+00:00
3549,DashboardReport,2021-01-20 20:50:00+00:00
3552,DashboardReport,2021-01-20 20:55:00+00:00
3555,SQLReport,2021-01-20 20:55:00+00:00
3556,DashboardReport,2021-01-20 20:55:00+00:00
3557,SQLReport,2021-01-20 20:55:00+00:00
3558,DashboardReport,2021-01-20 20:55:00+00:00
输出如下所示
您的 df['TIME_BUCKETS'] 毫不奇怪地被 plotly 解释为连续时间，并在连续的 x 轴上显示为连续时间。如果您想显示存储桶 类别 的值，就像它们出现在您的数据框中一样，只需添加：
fig.update_xaxes(type='category')
如果你也稍微调整一下 ticktext 的 font size，那么你会得到这样的结果：
请注意，我在以下位置使用了 df['TIME_BUCKETS'] 的格式化版本：
df['buckets'] = [dat[11:16] for dat in df['TIME_BUCKET']]
如果你不这样做，你会得到这样的结果：
带有数据示例的完整代码：
import pandas as pd
import plotly.express as px
df.to_dict()
df = pd.DataFrame({' ': {0: 23,
1: 33,
2: 69,
3: 74,
4: 97,
5: 98,
6: 99,
7: 101,
8: 103,
9: 2821,
10: 2822,
11: 2823,
12: 2896,
13: 3283,
14: 3285,
15: 3288,
16: 3289,
17: 3292,
18: 3293,
19: 3294,
20: 3295,
21: 3297,
22: 3298,
23: 3300,
24: 3303,
25: 3307,
26: 3309,
27: 3312,
28: 3313,
29: 3314,
30: 3315,
31: 3316,
32: 3317,
33: 3318,
34: 3319,
35: 3324,
36: 3328,
37: 3331,
38: 3332,
39: 3335,
40: 3336,
41: 3337,
42: 3339,
43: 3344,
44: 3345,
45: 3349,
46: 3350,
47: 3354,
48: 3355,
49: 3356,
50: 3357,
51: 3358,
52: 3359,
53: 3360,
54: 3368,
55: 3370,
56: 3375,
57: 3377,
58: 3379,
59: 3381,
60: 3384,
61: 3396,
62: 3398,
63: 3403,
64: 3404,
65: 3408,
66: 3410,
67: 3418,
68: 3419,
69: 3421,
70: 3422,
71: 3429,
72: 3434,
73: 3443,
74: 3444,
75: 3450,
76: 3451,
77: 3489,
78: 3490,
79: 3496,
80: 3499,
81: 3501,
82: 3505,
83: 3513,
84: 3514,
85: 3521,
86: 3522,
87: 3523,
88: 3527,
89: 3537,
90: 3538,
91: 3540,
92: 3549,
93: 3552,
94: 3555,
95: 3556,
96: 3557,
97: 3558},
'REPORT_NAME': {0: 'DashboardReport',
1: 'DashboardReport',
2: 'ExportReport',
3: 'ExportReport',
4: 'ExportReport',
5: 'ExportReport',
6: 'ExportReport',
7: 'ExportReport',
8: 'ExportReport',
9: 'DashboardReport',
10: 'DashboardReport',
11: 'DashboardReport',
12: 'DashboardReport',
13: 'SQLReport',
14: 'DashboardReport',
15: 'DashboardReport',
16: 'DashboardReport',
17: 'ImportReport',
18: 'DashboardReport',
19: 'DashboardReport',
20: 'DashboardReport',
21: 'DashboardReport',
22: 'SQLReport',
23: 'DashboardReport',
24: 'SQLReport',
25: 'ImportReport',
26: 'DashboardReport',
27: 'DashboardReport',
28: 'DashboardReport',
29: 'SQLReport',
30: 'DashboardReport',
31: 'DashboardReport',
32: 'DashboardReport',
33: 'ImportReport',
34: 'DashboardReport',
35: 'DashboardReport',
36: 'SQLReport',
37: 'ImportReport',
38: 'ImportReport',
39: 'DashboardReport',
40: 'ImportReport',
41: 'DashboardReport',
42: 'DashboardReport',
43: 'DashboardReport',
44: 'DashboardReport',
45: 'DBReport',
46: 'SQLReport',
47: 'DashboardReport',
48: 'DashboardReport',
49: 'DashboardReport',
50: 'DashboardReport',
51: 'DashboardReport',
52: 'DashboardReport',
53: 'DashboardReport',
54: 'DashboardReport',
55: 'DashboardReport',
56: 'DashboardReport',
57: 'DashboardReport',
58: 'DashboardReport',
59: 'DashboardReport',
60: 'DashboardReport',
61: 'ImportReport',
62: 'DashboardReport',
63: 'DashboardReport',
64: 'DashboardReport',
65: 'DashboardReport',
66: 'DashboardReport',
67: 'DashboardReport',
68: 'SQLReport',
69: 'DashboardReport',
70: 'DashboardReport',
71: 'DashboardReport',
72: 'DashboardReport',
73: 'ImportReport',
74: 'ImportReport',
75: 'DBReport',
76: 'DBReport',
77: 'SQLReport',
78: 'ImportReport',
79: 'DashboardReport',
80: 'ImportReport',
81: 'DashboardReport',
82: 'DashboardReport',
83: 'SQLReport',
84: 'DashboardReport',
85: 'SQLReport',
86: 'DashboardReport',
87: 'DashboardReport',
88: 'DashboardReport',
89: 'DashboardReport',
90: 'DashboardReport',
91: 'DashboardReport',
92: 'DashboardReport',
93: 'DashboardReport',
94: 'SQLReport',
95: 'DashboardReport',
96: 'SQLReport',
97: 'DashboardReport'},
'TIME_BUCKET': {0: '2021-01-20 03:30:00+00:00',
1: '2021-01-20 03:40:00+00:00',
2: '2021-01-20 03:40:00+00:00',
3: '2021-01-20 03:40:00+00:00',
4: '2021-01-20 03:40:00+00:00',
5: '2021-01-20 03:40:00+00:00',
6: '2021-01-20 03:40:00+00:00',
7: '2021-01-20 03:40:00+00:00',
8: '2021-01-20 03:40:00+00:00',
9: '2021-01-20 15:40:00+00:00',
10: '2021-01-20 15:40:00+00:00',
11: '2021-01-20 15:45:00+00:00',
12: '2021-01-20 16:15:00+00:00',
13: '2021-01-20 19:00:00+00:00',
14: '2021-01-20 19:00:00+00:00',
15: '2021-01-20 19:05:00+00:00',
16: '2021-01-20 19:05:00+00:00',
17: '2021-01-20 19:05:00+00:00',
18: '2021-01-20 19:05:00+00:00',
19: '2021-01-20 19:05:00+00:00',
20: '2021-01-20 19:10:00+00:00',
21: '2021-01-20 19:10:00+00:00',
22: '2021-01-20 19:10:00+00:00',
23: '2021-01-20 19:10:00+00:00',
24: '2021-01-20 19:15:00+00:00',
25: '2021-01-20 19:15:00+00:00',
26: '2021-01-20 19:15:00+00:00',
27: '2021-01-20 19:15:00+00:00',
28: '2021-01-20 19:15:00+00:00',
29: '2021-01-20 19:15:00+00:00',
30: '2021-01-20 19:15:00+00:00',
31: '2021-01-20 19:15:00+00:00',
32: '2021-01-20 19:15:00+00:00',
33: '2021-01-20 19:15:00+00:00',
34: '2021-01-20 19:15:00+00:00',
35: '2021-01-20 19:20:00+00:00',
36: '2021-01-20 19:20:00+00:00',
37: '2021-01-20 19:20:00+00:00',
38: '2021-01-20 19:20:00+00:00',
39: '2021-01-20 19:20:00+00:00',
40: '2021-01-20 19:20:00+00:00',
41: '2021-01-20 19:20:00+00:00',
42: '2021-01-20 19:20:00+00:00',
43: '2021-01-20 19:20:00+00:00',
44: '2021-01-20 19:20:00+00:00',
45: '2021-01-20 19:20:00+00:00',
46: '2021-01-20 19:20:00+00:00',
47: '2021-01-20 19:20:00+00:00',
48: '2021-01-20 19:20:00+00:00',
49: '2021-01-20 19:20:00+00:00',
50: '2021-01-20 19:20:00+00:00',
51: '2021-01-20 19:20:00+00:00',
52: '2021-01-20 19:20:00+00:00',
53: '2021-01-20 19:20:00+00:00',
54: '2021-01-20 19:25:00+00:00',
55: '2021-01-20 19:25:00+00:00',
56: '2021-01-20 19:25:00+00:00',
57: '2021-01-20 19:30:00+00:00',
58: '2021-01-20 19:30:00+00:00',
59: '2021-01-20 19:30:00+00:00',
60: '2021-01-20 19:30:00+00:00',
61: '2021-01-20 19:40:00+00:00',
62: '2021-01-20 19:40:00+00:00',
63: '2021-01-20 19:45:00+00:00',
64: '2021-01-20 19:45:00+00:00',
65: '2021-01-20 19:45:00+00:00',
66: '2021-01-20 19:45:00+00:00',
67: '2021-01-20 19:50:00+00:00',
68: '2021-01-20 19:50:00+00:00',
69: '2021-01-20 19:50:00+00:00',
70: '2021-01-20 19:50:00+00:00',
71: '2021-01-20 19:50:00+00:00',
72: '2021-01-20 19:55:00+00:00',
73: '2021-01-20 20:00:00+00:00',
74: '2021-01-20 20:00:00+00:00',
75: '2021-01-20 20:05:00+00:00',
76: '2021-01-20 20:05:00+00:00',
77: '2021-01-20 20:20:00+00:00',
78: '2021-01-20 20:20:00+00:00',
79: '2021-01-20 20:20:00+00:00',
80: '2021-01-20 20:25:00+00:00',
81: '2021-01-20 20:25:00+00:00',
82: '2021-01-20 20:25:00+00:00',
83: '2021-01-20 20:30:00+00:00',
84: '2021-01-20 20:35:00+00:00',
85: '2021-01-20 20:35:00+00:00',
86: '2021-01-20 20:35:00+00:00',
87: '2021-01-20 20:35:00+00:00',
88: '2021-01-20 20:40:00+00:00',
89: '2021-01-20 20:40:00+00:00',
90: '2021-01-20 20:40:00+00:00',
91: '2021-01-20 20:45:00+00:00',
92: '2021-01-20 20:50:00+00:00',
93: '2021-01-20 20:55:00+00:00',
94: '2021-01-20 20:55:00+00:00',
95: '2021-01-20 20:55:00+00:00',
96: '2021-01-20 20:55:00+00:00',
97: '2021-01-20 20:55:00+00:00'}})
df['buckets'] = [dat[11:16] for dat in df['TIME_BUCKET']]
fig = px.histogram(df, x='TIME_BUCKET', color='REPORT_NAME', title='Report Category Wise Execution Count (5 minuntes sample size)')
fig.update_xaxes(type='category')
fig.layout.xaxis.tickfont.size = 10
fig.show()
我的 CSV 内容有 1.1K 列这样的三列。这具有 5 分钟 TIME_BUCKET 的值，例如 03:40:00+00:00、03:45:00+00:00 等。 我希望图表绘制所有这些不同的 TIME_BUCKETS 的直方图，但它实际上绘制了每小时时间桶的图表，如 03:00、04:00 等
我的代码如下
import pandas as pd
import plotly.express as px
df = pd.read_csv("D:/Work/WORK/2021/FEB21/BiPA/nqquery/Issue/5MinTimeBucketHistogramNotWorking1.csv")
graph = px.histogram(df, x='TIME_BUCKET', color='REPORT_NAME', title='Report Category Wise Execution Count (5 minuntes sample size)')
graph.show()
我的 CSV 内容如下所示，有 1.1K 列。共享整个 CSV here 以供参考。
,REPORT_NAME,TIME_BUCKET
23,DashboardReport,2021-01-20 03:30:00+00:00
33,DashboardReport,2021-01-20 03:40:00+00:00
69,ExportReport,2021-01-20 03:40:00+00:00
74,ExportReport,2021-01-20 03:40:00+00:00
97,ExportReport,2021-01-20 03:40:00+00:00
98,ExportReport,2021-01-20 03:40:00+00:00
99,ExportReport,2021-01-20 03:40:00+00:00
101,ExportReport,2021-01-20 03:40:00+00:00
103,ExportReport,2021-01-20 03:40:00+00:00
2821,DashboardReport,2021-01-20 15:40:00+00:00
2822,DashboardReport,2021-01-20 15:40:00+00:00
2823,DashboardReport,2021-01-20 15:45:00+00:00
2896,DashboardReport,2021-01-20 16:15:00+00:00
3283,SQLReport,2021-01-20 19:00:00+00:00
3285,DashboardReport,2021-01-20 19:00:00+00:00
3288,DashboardReport,2021-01-20 19:05:00+00:00
3289,DashboardReport,2021-01-20 19:05:00+00:00
3292,ImportReport,2021-01-20 19:05:00+00:00
3293,DashboardReport,2021-01-20 19:05:00+00:00
3294,DashboardReport,2021-01-20 19:05:00+00:00
3295,DashboardReport,2021-01-20 19:10:00+00:00
3297,DashboardReport,2021-01-20 19:10:00+00:00
3298,SQLReport,2021-01-20 19:10:00+00:00
3300,DashboardReport,2021-01-20 19:10:00+00:00
3303,SQLReport,2021-01-20 19:15:00+00:00
3307,ImportReport,2021-01-20 19:15:00+00:00
3309,DashboardReport,2021-01-20 19:15:00+00:00
3312,DashboardReport,2021-01-20 19:15:00+00:00
3313,DashboardReport,2021-01-20 19:15:00+00:00
3314,SQLReport,2021-01-20 19:15:00+00:00
3315,DashboardReport,2021-01-20 19:15:00+00:00
3316,DashboardReport,2021-01-20 19:15:00+00:00
3317,DashboardReport,2021-01-20 19:15:00+00:00
3318,ImportReport,2021-01-20 19:15:00+00:00
3319,DashboardReport,2021-01-20 19:15:00+00:00
3324,DashboardReport,2021-01-20 19:20:00+00:00
3328,SQLReport,2021-01-20 19:20:00+00:00
3331,ImportReport,2021-01-20 19:20:00+00:00
3332,ImportReport,2021-01-20 19:20:00+00:00
3335,DashboardReport,2021-01-20 19:20:00+00:00
3336,ImportReport,2021-01-20 19:20:00+00:00
3337,DashboardReport,2021-01-20 19:20:00+00:00
3339,DashboardReport,2021-01-20 19:20:00+00:00
3344,DashboardReport,2021-01-20 19:20:00+00:00
3345,DashboardReport,2021-01-20 19:20:00+00:00
3349,DBReport,2021-01-20 19:20:00+00:00
3350,SQLReport,2021-01-20 19:20:00+00:00
3354,DashboardReport,2021-01-20 19:20:00+00:00
3355,DashboardReport,2021-01-20 19:20:00+00:00
3356,DashboardReport,2021-01-20 19:20:00+00:00
3357,DashboardReport,2021-01-20 19:20:00+00:00
3358,DashboardReport,2021-01-20 19:20:00+00:00
3359,DashboardReport,2021-01-20 19:20:00+00:00
3360,DashboardReport,2021-01-20 19:20:00+00:00
3368,DashboardReport,2021-01-20 19:25:00+00:00
3370,DashboardReport,2021-01-20 19:25:00+00:00
3375,DashboardReport,2021-01-20 19:25:00+00:00
3377,DashboardReport,2021-01-20 19:30:00+00:00
3379,DashboardReport,2021-01-20 19:30:00+00:00
3381,DashboardReport,2021-01-20 19:30:00+00:00
3384,DashboardReport,2021-01-20 19:30:00+00:00
3396,ImportReport,2021-01-20 19:40:00+00:00
3398,DashboardReport,2021-01-20 19:40:00+00:00
3403,DashboardReport,2021-01-20 19:45:00+00:00
3404,DashboardReport,2021-01-20 19:45:00+00:00
3408,DashboardReport,2021-01-20 19:45:00+00:00
3410,DashboardReport,2021-01-20 19:45:00+00:00
3418,DashboardReport,2021-01-20 19:50:00+00:00
3419,SQLReport,2021-01-20 19:50:00+00:00
3421,DashboardReport,2021-01-20 19:50:00+00:00
3422,DashboardReport,2021-01-20 19:50:00+00:00
3429,DashboardReport,2021-01-20 19:50:00+00:00
3434,DashboardReport,2021-01-20 19:55:00+00:00
3443,ImportReport,2021-01-20 20:00:00+00:00
3444,ImportReport,2021-01-20 20:00:00+00:00
3450,DBReport,2021-01-20 20:05:00+00:00
3451,DBReport,2021-01-20 20:05:00+00:00
3489,SQLReport,2021-01-20 20:20:00+00:00
3490,ImportReport,2021-01-20 20:20:00+00:00
3496,DashboardReport,2021-01-20 20:20:00+00:00
3499,ImportReport,2021-01-20 20:25:00+00:00
3501,DashboardReport,2021-01-20 20:25:00+00:00
3505,DashboardReport,2021-01-20 20:25:00+00:00
3513,SQLReport,2021-01-20 20:30:00+00:00
3514,DashboardReport,2021-01-20 20:35:00+00:00
3521,SQLReport,2021-01-20 20:35:00+00:00
3522,DashboardReport,2021-01-20 20:35:00+00:00
3523,DashboardReport,2021-01-20 20:35:00+00:00
3527,DashboardReport,2021-01-20 20:40:00+00:00
3537,DashboardReport,2021-01-20 20:40:00+00:00
3538,DashboardReport,2021-01-20 20:40:00+00:00
3540,DashboardReport,2021-01-20 20:45:00+00:00
3549,DashboardReport,2021-01-20 20:50:00+00:00
3552,DashboardReport,2021-01-20 20:55:00+00:00
3555,SQLReport,2021-01-20 20:55:00+00:00
3556,DashboardReport,2021-01-20 20:55:00+00:00
3557,SQLReport,2021-01-20 20:55:00+00:00
3558,DashboardReport,2021-01-20 20:55:00+00:00
输出如下所示
您的 df['TIME_BUCKETS'] 毫不奇怪地被 plotly 解释为连续时间，并在连续的 x 轴上显示为连续时间。如果您想显示存储桶 类别 的值，就像它们出现在您的数据框中一样，只需添加：
fig.update_xaxes(type='category')
如果你也稍微调整一下 ticktext 的 font size，那么你会得到这样的结果：
请注意，我在以下位置使用了 df['TIME_BUCKETS'] 的格式化版本：
df['buckets'] = [dat[11:16] for dat in df['TIME_BUCKET']]
如果你不这样做，你会得到这样的结果：
带有数据示例的完整代码：
import pandas as pd
import plotly.express as px
df.to_dict()
df = pd.DataFrame({' ': {0: 23,
1: 33,
2: 69,
3: 74,
4: 97,
5: 98,
6: 99,
7: 101,
8: 103,
9: 2821,
10: 2822,
11: 2823,
12: 2896,
13: 3283,
14: 3285,
15: 3288,
16: 3289,
17: 3292,
18: 3293,
19: 3294,
20: 3295,
21: 3297,
22: 3298,
23: 3300,
24: 3303,
25: 3307,
26: 3309,
27: 3312,
28: 3313,
29: 3314,
30: 3315,
31: 3316,
32: 3317,
33: 3318,
34: 3319,
35: 3324,
36: 3328,
37: 3331,
38: 3332,
39: 3335,
40: 3336,
41: 3337,
42: 3339,
43: 3344,
44: 3345,
45: 3349,
46: 3350,
47: 3354,
48: 3355,
49: 3356,
50: 3357,
51: 3358,
52: 3359,
53: 3360,
54: 3368,
55: 3370,
56: 3375,
57: 3377,
58: 3379,
59: 3381,
60: 3384,
61: 3396,
62: 3398,
63: 3403,
64: 3404,
65: 3408,
66: 3410,
67: 3418,
68: 3419,
69: 3421,
70: 3422,
71: 3429,
72: 3434,
73: 3443,
74: 3444,
75: 3450,
76: 3451,
77: 3489,
78: 3490,
79: 3496,
80: 3499,
81: 3501,
82: 3505,
83: 3513,
84: 3514,
85: 3521,
86: 3522,
87: 3523,
88: 3527,
89: 3537,
90: 3538,
91: 3540,
92: 3549,
93: 3552,
94: 3555,
95: 3556,
96: 3557,
97: 3558},
'REPORT_NAME': {0: 'DashboardReport',
1: 'DashboardReport',
2: 'ExportReport',
3: 'ExportReport',
4: 'ExportReport',
5: 'ExportReport',
6: 'ExportReport',
7: 'ExportReport',
8: 'ExportReport',
9: 'DashboardReport',
10: 'DashboardReport',
11: 'DashboardReport',
12: 'DashboardReport',
13: 'SQLReport',
14: 'DashboardReport',
15: 'DashboardReport',
16: 'DashboardReport',
17: 'ImportReport',
18: 'DashboardReport',
19: 'DashboardReport',
20: 'DashboardReport',
21: 'DashboardReport',
22: 'SQLReport',
23: 'DashboardReport',
24: 'SQLReport',
25: 'ImportReport',
26: 'DashboardReport',
27: 'DashboardReport',
28: 'DashboardReport',
29: 'SQLReport',
30: 'DashboardReport',
31: 'DashboardReport',
32: 'DashboardReport',
33: 'ImportReport',
34: 'DashboardReport',
35: 'DashboardReport',
36: 'SQLReport',
37: 'ImportReport',
38: 'ImportReport',
39: 'DashboardReport',
40: 'ImportReport',
41: 'DashboardReport',
42: 'DashboardReport',
43: 'DashboardReport',
44: 'DashboardReport',
45: 'DBReport',
46: 'SQLReport',
47: 'DashboardReport',
48: 'DashboardReport',
49: 'DashboardReport',
50: 'DashboardReport',
51: 'DashboardReport',
52: 'DashboardReport',
53: 'DashboardReport',
54: 'DashboardReport',
55: 'DashboardReport',
56: 'DashboardReport',
57: 'DashboardReport',
58: 'DashboardReport',
59: 'DashboardReport',
60: 'DashboardReport',
61: 'ImportReport',
62: 'DashboardReport',
63: 'DashboardReport',
64: 'DashboardReport',
65: 'DashboardReport',
66: 'DashboardReport',
67: 'DashboardReport',
68: 'SQLReport',
69: 'DashboardReport',
70: 'DashboardReport',
71: 'DashboardReport',
72: 'DashboardReport',
73: 'ImportReport',
74: 'ImportReport',
75: 'DBReport',
76: 'DBReport',
77: 'SQLReport',
78: 'ImportReport',
79: 'DashboardReport',
80: 'ImportReport',
81: 'DashboardReport',
82: 'DashboardReport',
83: 'SQLReport',
84: 'DashboardReport',
85: 'SQLReport',
86: 'DashboardReport',
87: 'DashboardReport',
88: 'DashboardReport',
89: 'DashboardReport',
90: 'DashboardReport',
91: 'DashboardReport',
92: 'DashboardReport',
93: 'DashboardReport',
94: 'SQLReport',
95: 'DashboardReport',
96: 'SQLReport',
97: 'DashboardReport'},
'TIME_BUCKET': {0: '2021-01-20 03:30:00+00:00',
1: '2021-01-20 03:40:00+00:00',
2: '2021-01-20 03:40:00+00:00',
3: '2021-01-20 03:40:00+00:00',
4: '2021-01-20 03:40:00+00:00',
5: '2021-01-20 03:40:00+00:00',
6: '2021-01-20 03:40:00+00:00',
7: '2021-01-20 03:40:00+00:00',
8: '2021-01-20 03:40:00+00:00',
9: '2021-01-20 15:40:00+00:00',
10: '2021-01-20 15:40:00+00:00',
11: '2021-01-20 15:45:00+00:00',
12: '2021-01-20 16:15:00+00:00',
13: '2021-01-20 19:00:00+00:00',
14: '2021-01-20 19:00:00+00:00',
15: '2021-01-20 19:05:00+00:00',
16: '2021-01-20 19:05:00+00:00',
17: '2021-01-20 19:05:00+00:00',
18: '2021-01-20 19:05:00+00:00',
19: '2021-01-20 19:05:00+00:00',
20: '2021-01-20 19:10:00+00:00',
21: '2021-01-20 19:10:00+00:00',
22: '2021-01-20 19:10:00+00:00',
23: '2021-01-20 19:10:00+00:00',
24: '2021-01-20 19:15:00+00:00',
25: '2021-01-20 19:15:00+00:00',
26: '2021-01-20 19:15:00+00:00',
27: '2021-01-20 19:15:00+00:00',
28: '2021-01-20 19:15:00+00:00',
29: '2021-01-20 19:15:00+00:00',
30: '2021-01-20 19:15:00+00:00',
31: '2021-01-20 19:15:00+00:00',
32: '2021-01-20 19:15:00+00:00',
33: '2021-01-20 19:15:00+00:00',
34: '2021-01-20 19:15:00+00:00',
35: '2021-01-20 19:20:00+00:00',
36: '2021-01-20 19:20:00+00:00',
37: '2021-01-20 19:20:00+00:00',
38: '2021-01-20 19:20:00+00:00',
39: '2021-01-20 19:20:00+00:00',
40: '2021-01-20 19:20:00+00:00',
41: '2021-01-20 19:20:00+00:00',
42: '2021-01-20 19:20:00+00:00',
43: '2021-01-20 19:20:00+00:00',
44: '2021-01-20 19:20:00+00:00',
45: '2021-01-20 19:20:00+00:00',
46: '2021-01-20 19:20:00+00:00',
47: '2021-01-20 19:20:00+00:00',
48: '2021-01-20 19:20:00+00:00',
49: '2021-01-20 19:20:00+00:00',
50: '2021-01-20 19:20:00+00:00',
51: '2021-01-20 19:20:00+00:00',
52: '2021-01-20 19:20:00+00:00',
53: '2021-01-20 19:20:00+00:00',
54: '2021-01-20 19:25:00+00:00',
55: '2021-01-20 19:25:00+00:00',
56: '2021-01-20 19:25:00+00:00',
57: '2021-01-20 19:30:00+00:00',
58: '2021-01-20 19:30:00+00:00',
59: '2021-01-20 19:30:00+00:00',
60: '2021-01-20 19:30:00+00:00',
61: '2021-01-20 19:40:00+00:00',
62: '2021-01-20 19:40:00+00:00',
63: '2021-01-20 19:45:00+00:00',
64: '2021-01-20 19:45:00+00:00',
65: '2021-01-20 19:45:00+00:00',
66: '2021-01-20 19:45:00+00:00',
67: '2021-01-20 19:50:00+00:00',
68: '2021-01-20 19:50:00+00:00',
69: '2021-01-20 19:50:00+00:00',
70: '2021-01-20 19:50:00+00:00',
71: '2021-01-20 19:50:00+00:00',
72: '2021-01-20 19:55:00+00:00',
73: '2021-01-20 20:00:00+00:00',
74: '2021-01-20 20:00:00+00:00',
75: '2021-01-20 20:05:00+00:00',
76: '2021-01-20 20:05:00+00:00',
77: '2021-01-20 20:20:00+00:00',
78: '2021-01-20 20:20:00+00:00',
79: '2021-01-20 20:20:00+00:00',
80: '2021-01-20 20:25:00+00:00',
81: '2021-01-20 20:25:00+00:00',
82: '2021-01-20 20:25:00+00:00',
83: '2021-01-20 20:30:00+00:00',
84: '2021-01-20 20:35:00+00:00',
85: '2021-01-20 20:35:00+00:00',
86: '2021-01-20 20:35:00+00:00',
87: '2021-01-20 20:35:00+00:00',
88: '2021-01-20 20:40:00+00:00',
89: '2021-01-20 20:40:00+00:00',
90: '2021-01-20 20:40:00+00:00',
91: '2021-01-20 20:45:00+00:00',
92: '2021-01-20 20:50:00+00:00',
93: '2021-01-20 20:55:00+00:00',
94: '2021-01-20 20:55:00+00:00',
95: '2021-01-20 20:55:00+00:00',
96: '2021-01-20 20:55:00+00:00',
97: '2021-01-20 20:55:00+00:00'}})
df['buckets'] = [dat[11:16] for dat in df['TIME_BUCKET']]
fig = px.histogram(df, x='TIME_BUCKET', color='REPORT_NAME', title='Report Category Wise Execution Count (5 minuntes sample size)')
fig.update_xaxes(type='category')
fig.layout.xaxis.tickfont.size = 10
fig.show()