pandas 在列级连接数据帧时添加键 [英] Pandas add keys while concatenating dataframes at column level
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问题描述
根据Pandas 0.19.2文档,我可以提供 keys 参数来创建结果的多索引DataFrame.一个示例(来自pandas文档)是:
As per Pandas 0.19.2 documentation, I can provide keys argument to create a resulting multi-index DataFrame. An example (from pandas documents ) is :
result = pd.concat(frames, keys=['x', 'y', 'z'])
如何连接数据框,以便可以在列级而不是索引级提供键?
How would I concat the dataframe so that I can provide the keys at the column level instead of index level ?
我基本上需要的是这样的东西:
What I basically need is something like this :
其中df1和df2是连续的.
where df1 and df2 are to be concat.
推荐答案
pd.concat
,当指定axis=1
时:
This is supported by keys
parameter of pd.concat
when specifying axis=1
:
df1 = pd.DataFrame(np.random.random((4, 4)), columns=list('ABCD'))
df2 = pd.DataFrame(np.random.random((4, 3)), columns=list('BDF'), index=[2, 3, 6, 7])
df = pd.concat([df1, df2], keys=['X', 'Y'], axis=1)
结果输出:
X Y
A B C D B D F
0 0.654406 0.495906 0.601100 0.309276 NaN NaN NaN
1 0.020527 0.814065 0.907590 0.924307 NaN NaN NaN
2 0.239598 0.089270 0.033585 0.870829 0.882028 0.626650 0.622856
3 0.983942 0.103573 0.370121 0.070442 0.986487 0.848203 0.089874
6 NaN NaN NaN NaN 0.664507 0.319789 0.868133
7 NaN NaN NaN NaN 0.341145 0.308469 0.884074
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