从具有类似索引的其他DataFrame的列创建pandas DataFrame

I have 2 DataFrames df1 and df2 with the same column names ['a','b','c'] and indexed by dates. The date index can have similar values. I would like to create a DataFrame df3 with only the data from columns ['c'] renamed respectively 'df1' and 'df2' and with the correct date index. My problem is that I cannot get how to merge the index properly.

我有2个DataFrames df1和df2,它们具有相同的列名['a','b','c']并按日期索引。日期索引可以具有类似的值。我想创建一个DataFrame df3,只有来自列['c']的数据分别重命名为'df1'和'df2',并使用正确的日期索引。我的问题是我无法正确合并索引。

df1 = pd.DataFrame(np.random.randn(5,3), index=pd.date_range('01/02/2014',periods=5,freq='D'), columns=['a','b','c'] )
df2 = pd.DataFrame(np.random.randn(8,3), index=pd.date_range('01/01/2014',periods=8,freq='D'), columns=['a','b','c'] )
df1
                 a        b            c
2014-01-02   0.580550    0.480814    1.135899
2014-01-03  -1.961033    0.546013    1.093204
2014-01-04   2.063441   -0.627297    2.035373
2014-01-05   0.319570    0.058588    0.350060
2014-01-06   1.318068   -0.802209   -0.939962

df2
                 a        b            c
2014-01-01   0.772482    0.899337    0.808630
2014-01-02   0.518431   -1.582113    0.323425
2014-01-03   0.112109    1.056705   -1.355067
2014-01-04   0.767257   -2.311014    0.340701
2014-01-05   0.794281   -1.954858    0.200922
2014-01-06   0.156088    0.718658   -1.030077
2014-01-07   1.621059    0.106656   -0.472080
2014-01-08  -2.061138   -2.023157    0.257151

The df3 DataFrame should have the following form :

df3 DataFrame应具有以下形式:

df3
                 df1        df2
2014-01-01   NaN        0.808630
2014-01-02   1.135899   0.323425
2014-01-03   1.093204   -1.355067
2014-01-04   2.035373   0.340701
2014-01-05   0.350060   0.200922
2014-01-06   -0.939962  -1.030077
2014-01-07   NaN        -0.472080
2014-01-08   NaN        0.257151

But with NaN in the df1 column as the date index of df2 is wider. (In this example, I would get NaN for the ollowing dates : 2014-01-01, 2014-01-07 and 2014-01-08)

但是在df1列中使用NaN作为df2的日期索引更宽。 (在这个例子中,我会得到NaN的日期:2014-01-01,2014-01-07和2014-01-08)

Thanks for your help.

谢谢你的帮助。

2 个解决方案

#1

You can use concat:

你可以使用concat:

In [11]: pd.concat([df1['c'], df2['c']], axis=1, keys=['df1', 'df2'])
Out[11]: 
                 df1       df2
2014-01-01       NaN -0.978535
2014-01-02 -0.106510 -0.519239
2014-01-03 -0.846100 -0.313153
2014-01-04 -0.014253 -1.040702
2014-01-05  0.315156 -0.329967
2014-01-06 -0.510577 -0.940901
2014-01-07       NaN -0.024608
2014-01-08       NaN -1.791899

[8 rows x 2 columns]

The axis argument determines the way the DataFrames are stacked:

axis参数确定DataFrame堆叠的方式:

df1 = pd.DataFrame([1, 2, 3])
df2 = pd.DataFrame(['a', 'b', 'c'])

pd.concat([df1, df2], axis=0)
   0
0  1
1  2
2  3
0  a
1  b
2  c

pd.concat([df1, df2], axis=1)

   0  0
0  1  a
1  2  b
2  3  c

#2

Well, I'm not sure that merge would be the way to go. Personally I would build a new data frame by creating an index of the dates and then constructing the columns using list comprehensions. Possibly not the most pythonic way, but it seems to work for me!

好吧,我不确定合并是否可行。我个人会通过创建日期索引然后使用列表推导构建列来构建新的数据框。可能不是最pythonic的方式,但它似乎对我有用!

import pandas as pd
import numpy as np

df1 = pd.DataFrame(np.random.randn(5,3), index=pd.date_range('01/02/2014',periods=5,freq='D'), columns=['a','b','c'] )
df2 = pd.DataFrame(np.random.randn(8,3), index=pd.date_range('01/01/2014',periods=8,freq='D'), columns=['a','b','c'] )

# Create an index list from the set of dates in both data frames
Index = list(set(list(df1.index) + list(df2.index)))
Index.sort()

df3 = pd.DataFrame({'df1': [df1.loc[Date, 'c'] if Date in df1.index else np.nan for Date in Index],\
                'df2': [df2.loc[Date, 'c'] if Date in df2.index else np.nan for Date in Index],},\
                index = Index)

df3

#1