如何通过 DataFrame 扁平化 Pandas group？
声明：本页面是StackOverFlow热门问题的中英对照翻译，遵循CC BY-SA 4.0协议，如果您需要使用它，必须同样遵循CC BY-SA许可，注明原文地址和作者信息，同时你必须将它归于原作者（不是我）：StackOverFlow
原文地址: http://stackoverflow.com/questions/46155173/
Warning: these are provided under cc-by-sa 4.0 license. You are free to use/share it, But you must attribute it to the original authors (not me):
StackOverFlow
How to flatten Pandas groupby DataFrame?
提问by Brylie Christopher Oxley
I have a Pandas DataFrame that is grouped by date and 'outcome':
我有一个按日期和“结果”分组的 Pandas DataFrame：
api_logs.groupby([api_logs.index.date, 'Outcome']).size()
Outcome
2017-04-22 Success 7
2017-04-24 Failure 32
Success 59
2017-04-25 Failure 23
Success 91
2017-04-26 Failure 1
Success 59
2017-04-27 Failure 3
Success 1
2017-04-28 Failure 1
Success 2
2017-04-29 Success 3
2017-05-03 Failure 38
2017-05-04 Failure 6
Success 727
How can I flatten the nested data, so that it is structured as below?
如何展平嵌套数据，使其结构如下？
Failure Success 2017-04-22 7 2017-04-24 32 59 2017-04-25 23 91 2017-04-26 1 59 2017-04-27 3 1 2017-04-28 1 2 2017-04-29 3 2017-05-03 38 2017-05-04 6 727
My end-goal is to plot the failures and successes together in a chart, so perhaps there is a different approach altogether?
我的最终目标是将失败和成功一起绘制在图表中，所以也许有完全不同的方法？
回答by jezrael
Use unstackfor reshape:
使用unstack的重塑：
df = api_logs.groupby([api_logs.index.date, 'Outcome']).size().unstack()
print (df)
Outcome Failure Success
2017-04-22 NaN 7.0
2017-04-24 32.0 59.0
2017-04-25 23.0 91.0
2017-04-26 1.0 59.0
2017-04-27 3.0 1.0
2017-04-28 1.0 2.0
2017-04-29 NaN 3.0
2017-05-03 38.0 NaN
2017-05-04 6.0 727.0
Also is possible replace NaNs to 0by parameter fill_value:
也可以通过参数替换NaNs ：0fill_value
df = api_logs.groupby([api_logs.index.date, 'Outcome']).size().unstack(fill_value=0)
print (df)
Outcome Failure Success
2017-04-22 0 7
2017-04-24 32 59
2017-04-25 23 91
2017-04-26 1 59
2017-04-27 3 1
2017-04-28 1 2
2017-04-29 0 3
2017-05-03 38 0
2017-05-04 6 727
