在Pandas Dataframe pd.concat之后，我得到NaNs [英] After Pandas Dataframe pd.concat I get NaNs

问题描述

我有三只熊猫，其中一只已被排移动，第一个元素是空的。
当我连接三个df以获得一个单列的3列数据框时，我得到所有的NaN在三列中的两列：

df1：

  S 
 2010-12-31 True 
 2011-01-01 False 
 2011-01-02假
  

df2：

  P 
 2010-12-31 
 2011-01-01在
 2011-01-02在
  

df3：

  C 
 2010-12- 31 On 
 2011-01-01在
 2011-01-02在
  

res = pd.concat（[df1，df2，df3]）：

  PCS 
 2010-12-31 NaN NaN True 
 2011-01-01 NaN NaN False 
 2011-01-02 NaN NaN False 
  

订单似乎也是颠倒的...

非常感谢

在[3]中：df1 = pd.DataFrame（{'S'：[True，False，False]}，index = index）

在[4]中：df2 = pd.DataFrame（{'P'：[''，'On'，'On']}，index = index）

在[5] df3 = pd.DataFrame（{'C'：['On'，'On'，'On']}，index = index）

如果您的DataFrames定义如上，那么 pd.concat 与 axis = 1 应该工作：

 在[7]中：pd.concat（[df1，df2，df3]，axis = 1）
输出[7]：
 SPC 
 2010-12-31 True On 
 2011-01-01 False On On 
 2011-01-02 False On On 
 
 [3行×3列] 
  

I have three pandas df one of them has been 'row'-shifted and the first element is empty. When I concatenate the three df to obtain a single 3-column dataframe I get all NaN in two out of three columns:

df1:

                    S
2010-12-31         True
2011-01-01        False
2011-01-02        False

df2:

               P
2010-12-31           
2011-01-01    On
2011-01-02    On

df3:

              C
2010-12-31    On
2011-01-01    On
2011-01-02    On

res = pd.concat([df1, df2, df3]):

                    P         C           S
2010-12-31        NaN        NaN         True
2011-01-01        NaN        NaN        False
2011-01-02        NaN        NaN        False

The order seems to be inverted as well...

Many thanks

解决方案

In [2]: index = pd.DatetimeIndex(['2010-12-31', '2011-01-01', '2011-01-02'])

In [3]: df1 = pd.DataFrame({'S':[True,False,False]}, index=index)

In [4]: df2 = pd.DataFrame({'P':['','On','On']}, index=index)

In [5]: df3 = pd.DataFrame({'C':['On','On','On']}, index=index)

If your DataFrames are defined as above, then pd.concat with axis=1 should work:

In [7]: pd.concat([df1,df2,df3], axis=1)
Out[7]: 
                S   P   C
2010-12-31   True      On
2011-01-01  False  On  On
2011-01-02  False  On  On

[3 rows x 3 columns]

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