[英]Pandas unstack with multiindex columns
I have a pandas dataframe, which can be created with:我有一个 pandas dataframe,可以通过以下方式创建:
pd.DataFrame([[1,'a','green'],[2,'b','blue'],[2,'b','green'],[1,'e','green'],[2,'b','blue']], columns = ['sales','product','color'], index = ['01-01-2020','01-01-2020','01-02-2020','01-03-2020','01-04-2020'])
I would like to unstack the dataframe with the 'color' feature and create a multiindex by product of [green,blue],[sales,product] with the already existing columns as the second level of the column multiindex.我想将具有“颜色”功能的 dataframe 取消堆叠,并通过 [green,blue],[sales,product] 的乘积创建一个多索引,并将现有列作为列多索引的第二级。 The index of the dataframe is a date.
dataframe 的索引是日期。 The resultant dataframe that I would like can be created with the code:
我想要的结果 dataframe 可以使用以下代码创建:
pd.DataFrame([[1,'a',2,'b'],[2,'b',np.nan,np.nan],[1,'e',np.nan,np.nan],[np.nan,np.nan,2,'b']],columns = pd.MultiIndex.from_product([['green','blue'],['sales','product']]), index = ['01-01-2020','01-02-2020','01-03-2020','01-04-2020'])
Please note that the resultant dataframe will be shorter than the original due to the common date indices.请注意,由于通用日期索引,生成的 dataframe 将比原始文件短。
For the life of me, I have been unable to figure out how to pivot/unstack correctly to figure this out.在我的一生中,我一直无法弄清楚如何正确旋转/取消堆叠来解决这个问题。 I am trying to apply this to a very large dataframe, so performance will be key for me.
我正在尝试将其应用于非常大的 dataframe,因此性能对我来说是关键。 Many thanks for any and all help!
非常感谢您的帮助!
Try this:尝试这个:
df.set_index('color', append=True).unstack().swaplevel(0, 1, axis=1).sort_index(axis=1)
Output: Output:
color blue green
product sales product sales
01-01-2020 b 2.0 a 1.0
01-02-2020 NaN NaN b 2.0
01-03-2020 NaN NaN e 1.0
01-04-2020 b 2.0 NaN NaN
Details:细节:
append=True
append=True
“颜色”添加到现有索引As, @QuangHoang states:正如@QuangHoang 所说:
df.set_index('color', append=True).stack().unstack([1,2])
Which is much faster,哪个更快,
4.13 ms ± 274 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
每个循环 4.13 毫秒 ± 274 微秒(平均值 ± 标准偏差。7 次运行,每次 100 次循环)
versus相对
2.78 ms ± 44.7 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
每个循环 2.78 毫秒 ± 44.7 微秒(平均值 ± 标准偏差。7 次运行,每次 100 次循环)
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