关于特定列的逐行填充? [英] Row-by-row fillna with respect to a specific column?

问题描述

我有以下pandas数据框，我想用D列中的值以行的方式填充AC列中的NaN.是否有明确的方法来执行此操作，因此我可以定义所有NaN都应依赖D列中的值是否按行排列?我找不到在fillna()中显式执行此操作的方法.

I have the following pandas dataframe and I would like to fill the NaNs in columns A-C in a row-wise fashion with values from columns D. Is there an explicit way to do this where I can define that all the NaNs should depend row-wise on values in column D? I couldn't find a way to explicitly do this in fillna().

请注意，还有另外的列E-Z ，它们具有自己的NaN，并且可能具有其他填写NaN的规则，因此应保持不变.

Note that there are additional columns E-Z which have their own NaNs and may have other rules for filling in NaNs, and should be left untouched.

A        B        C        D       E
158      158      158      177     ...  
158      158      158      177     ...
NaN      NaN      NaN      177     ...   
158      158      158      177     ...
NaN      NaN      NaN      177     ...  

仅希望将其用于A-C列:

Would like to have this for columns A-C only:

A        B        C        D       E
158      158      158      177     ...  
158      158      158      177     ...
177      177      177      177     ...   
158      158      158      177     ...
177      177      177      177     ...  

谢谢.

推荐答案

使用fillna函数:

df.fillna(axis=1, method='backfill')

如果其他列中没有NaN，则

会执行.
如果有并且您希望不受影响，我认为这种方式的唯一选择是对数据帧的子集执行fillna.带有示例数据框:

will do if there are no NaN's in the other columns.
If there are and you want to leave them untouched, I think the only option in this way is to perform the fillna on a subset of your dataframe. With example dataframe:

In [45]: df
Out[45]: 
     A    B    C    D   E   F
0  158  158  158  177   1  10
1  158  158  158  177   2  20
2  NaN  NaN  NaN  177   3  30
3  158  158  158  177 NaN  40
4  NaN  NaN  NaN  177   5  50

In [48]: df[['A', 'B', 'C', 'D']] = df[['A', 'B', 'C', 'D']].fillna(axis=1, method='backfill')

In [49]: df
Out[49]: 
     A    B    C    D   E   F
0  158  158  158  177   1  10
1  158  158  158  177   2  20
2  177  177  177  177   3  30
3  158  158  158  177 NaN  40
4  177  177  177  177   5  50

---

日期:如果不想依赖列顺序，还可以指定用于填充每一行的值(例如.fillna(value=df['D']).唯一的问题是，这仅适用于Series(当它是一个数据框时，它将尝试映射不同的值以填充到不同的列，而不是行).因此，通过逐列应用，它可以工作:

---

Udate: If you don't want to depend on the column order, you can also specify the values to use to fill for each row (like .fillna(value=df['D']). The only problem is that this only works for Series (when it is a dataframe, it tries to map the different values to fill to the different columns, not the rows). So with an apply to do it column by column, it works:

In [60]: df[['A', 'B', 'C']].apply(lambda x: x.fillna(value=df['D']))
Out[60]: 
     A    B    C
0  158  158  158
1  158  158  158
2  177  177  177
3  158  158  158
4  177  177  177

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