python/pandas：如何将两个数据帧与分层列索引合并为一个？
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/15989281/
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
python/pandas: how to combine two dataframes into one with hierarchical column index?
提问by behzad.nouri
I have two dataframes which look like this:
我有两个数据框，如下所示：
>>> df1
A B
2000-01-01 1.4 1.4
2000-01-02 1.7 -1.9
2000-01-03 -0.2 -0.8
>>> df2
A B
2000-01-01 0.6 -0.3
2000-01-02 -0.4 0.6
2000-01-03 1.1 -1.0
How can I make one dataframe out of this two with hierarchical column index like below?
如何使用如下所示的分层列索引从这两个数据帧中创建一个数据帧？
df1 df2
A B A B
2000-01-01 1.4 1.4 0.6 -0.3
2000-01-02 1.7 -1.9 -0.4 0.6
2000-01-03 -0.2 -0.8 1.1 -1.0
采纳答案by Jeff
This is a doc example: http://pandas.pydata.org/pandas-docs/stable/merging.html#more-concatenating-with-group-keys
这是一个文档示例：http: //pandas.pydata.org/pandas-docs/stable/merging.html#more-concatenating-with-group-keys
In [9]: df1 = pd.DataFrame(np.random.randn(3,2),columns=list('AB'),index=pd.date_range('20000101',periods=3))
In [10]: df2 = pd.DataFrame(np.random.randn(3,2),columns=list('AB'),index=pd.date_range('20000101',periods=3))
In [11]: df1
Out[11]:
A B
2000-01-01 0.129994 1.189608
2000-01-02 -1.126812 1.087617
2000-01-03 -0.930070 0.253098
In [12]: df2
Out[12]:
A B
2000-01-01 0.535700 -0.769533
2000-01-02 -1.698531 -0.456667
2000-01-03 0.451622 -1.500175
In [13]: pd.concat(dict(df1 = df1, df2 = df2),axis=1)
Out[13]:
df1 df2
A B A B
2000-01-01 0.129994 1.189608 0.535700 -0.769533
2000-01-02 -1.126812 1.087617 -1.698531 -0.456667
2000-01-03 -0.930070 0.253098 0.451622 -1.500175
