Python plotly Express Histogram：图表未显示所有唯一的 TIME_BUCKET 值，它以每小时值将 TIME_BUCKET 分组 完整代码和数据样本：

问题描述

我的 CSV 内容包含三列，例如 1.1K 列。这具有 5 分钟 TIME_BUCKET 的值，例如 03:40:00+00:00、03:45:00+00:00 等。 我希望图表能够为所有这些不同的 TIME_BUCKETS 绘制直方图，但它实际上是为每小时时间段绘制图表，例如 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
33,2021-01-20 03:40:00+00:00
69,ExportReport,2021-01-20 03:40:00+00:00
74,2021-01-20 03:40:00+00:00
97,2021-01-20 03:40:00+00:00
98,2021-01-20 03:40:00+00:00
99,2021-01-20 03:40:00+00:00
101,2021-01-20 03:40:00+00:00
103,2021-01-20 03:40:00+00:00
2821,2021-01-20 15:40:00+00:00
2822,2021-01-20 15:40:00+00:00
2823,2021-01-20 15:45:00+00:00
2896,2021-01-20 16:15:00+00:00
3283,sqlReport,2021-01-20 19:00:00+00:00
3285,2021-01-20 19:00:00+00:00
3288,2021-01-20 19:05:00+00:00
3289,2021-01-20 19:05:00+00:00
3292,ImportReport,2021-01-20 19:05:00+00:00
3293,2021-01-20 19:05:00+00:00
3294,2021-01-20 19:05:00+00:00
3295,2021-01-20 19:10:00+00:00
3297,2021-01-20 19:10:00+00:00
3298,2021-01-20 19:10:00+00:00
3300,2021-01-20 19:10:00+00:00
3303,2021-01-20 19:15:00+00:00
3307,2021-01-20 19:15:00+00:00
3309,2021-01-20 19:15:00+00:00
3312,2021-01-20 19:15:00+00:00
3313,2021-01-20 19:15:00+00:00
3314,2021-01-20 19:15:00+00:00
3315,2021-01-20 19:15:00+00:00
3316,2021-01-20 19:15:00+00:00
3317,2021-01-20 19:15:00+00:00
3318,2021-01-20 19:15:00+00:00
3319,2021-01-20 19:15:00+00:00
3324,2021-01-20 19:20:00+00:00
3328,2021-01-20 19:20:00+00:00
3331,2021-01-20 19:20:00+00:00
3332,2021-01-20 19:20:00+00:00
3335,2021-01-20 19:20:00+00:00
3336,2021-01-20 19:20:00+00:00
3337,2021-01-20 19:20:00+00:00
3339,2021-01-20 19:20:00+00:00
3344,2021-01-20 19:20:00+00:00
3345,2021-01-20 19:20:00+00:00
3349,DBReport,2021-01-20 19:20:00+00:00
3350,2021-01-20 19:20:00+00:00
3354,2021-01-20 19:20:00+00:00
3355,2021-01-20 19:20:00+00:00
3356,2021-01-20 19:20:00+00:00
3357,2021-01-20 19:20:00+00:00
3358,2021-01-20 19:20:00+00:00
3359,2021-01-20 19:20:00+00:00
3360,2021-01-20 19:20:00+00:00
3368,2021-01-20 19:25:00+00:00
3370,2021-01-20 19:25:00+00:00
3375,2021-01-20 19:25:00+00:00
3377,2021-01-20 19:30:00+00:00
3379,2021-01-20 19:30:00+00:00
3381,2021-01-20 19:30:00+00:00
3384,2021-01-20 19:30:00+00:00
3396,2021-01-20 19:40:00+00:00
3398,2021-01-20 19:40:00+00:00
3403,2021-01-20 19:45:00+00:00
3404,2021-01-20 19:45:00+00:00
3408,2021-01-20 19:45:00+00:00
3410,2021-01-20 19:45:00+00:00
3418,2021-01-20 19:50:00+00:00
3419,2021-01-20 19:50:00+00:00
3421,2021-01-20 19:50:00+00:00
3422,2021-01-20 19:50:00+00:00
3429,2021-01-20 19:50:00+00:00
3434,2021-01-20 19:55:00+00:00
3443,2021-01-20 20:00:00+00:00
3444,2021-01-20 20:00:00+00:00
3450,2021-01-20 20:05:00+00:00
3451,2021-01-20 20:05:00+00:00
3489,2021-01-20 20:20:00+00:00
3490,2021-01-20 20:20:00+00:00
3496,2021-01-20 20:20:00+00:00
3499,2021-01-20 20:25:00+00:00
3501,2021-01-20 20:25:00+00:00
3505,2021-01-20 20:25:00+00:00
3513,2021-01-20 20:30:00+00:00
3514,2021-01-20 20:35:00+00:00
3521,2021-01-20 20:35:00+00:00
3522,2021-01-20 20:35:00+00:00
3523,2021-01-20 20:35:00+00:00
3527,2021-01-20 20:40:00+00:00
3537,2021-01-20 20:40:00+00:00
3538,2021-01-20 20:40:00+00:00
3540,2021-01-20 20:45:00+00:00
3549,2021-01-20 20:50:00+00:00
3552,2021-01-20 20:55:00+00:00
3555,2021-01-20 20:55:00+00:00
3556,2021-01-20 20:55:00+00:00
3557,2021-01-20 20:55:00+00:00
3558,2021-01-20 20:55:00+00:00

输出如下所示

解决方法

您的 df['TIME_BUCKETS'] 不出所料地被 plotly 解释为连续时间，并显示在连续的 x 轴上。如果您想显示存储桶 categories 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勾选文本的 font size，那么你最终会得到这样的结果：

请注意，我在以下内容中使用了 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：

完整代码和数据样本：

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()