python/pandas: how to combine two dataframes into one with hierarchical column index?
Question:
I have two dataframes which look like this:
>>> 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?
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
Answers:
This is a doc example: 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
I have two dataframes which look like this:
>>> 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?
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
This is a doc example: 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