如果特定列的一个值为 NaN，有没有办法使用 ffill 替换整个 pandas dataframe 行？

Question

I am trying to sort a dataframe where some rows are all NaN. I want to fill these using ffill. I'm currently trying this although i feel like it's a mismatch of a few commands

df.loc[df['A'].isna(), :] = df.fillna(method='ffill')

This gives an error: AttributeError: 'NoneType' object has no attribute 'fillna'

but I want to filter the NaNs I fill using ffill if one of the columns is NaN. ie

     A    B      C     D   E
0   45    88    NaN   NaN  3
1   62    34    2     86   NaN
2   85    65    11    31   5
3   NaN   NaN   NaN   NaN  NaN
4   90    38    34    93   8
5    0    94    45    10   10
6   58    NaN   23    60   11
7   10    32     5    15   11
8   NaN   NaN   NaN   NaN  NaN

So I would only like to fill a row IFF the value of A is NaN, whilst leaving C,0 and D,0 as NaN. Giving the below dataframe

     A    B      C     D   E
0   45    88    NaN   NaN  3
1   62    34    2     86   NaN
2   85    65    11    31   5
3   85    65    11    31   5
4   90    38    34    93   8
5    0    94    45    10   10
6   58    NaN   23    60   11
7   10    32     5    15   11
8   10    32     5    15   11

So just to clarify, the ONLY rows that get replaced with ffill are 3,8 and the reason is because the value of column A in rows 3 and 8 are NaN Thanks

---Update--- When I'm debugging and evaluate the expression: df.loc[df['A'].isna(), :]

I get

3   NaN   NaN   NaN   NaN  NaN
8   NaN   NaN   NaN   NaN  NaN

So I assume whats happening here is, I then attempt ffill on this new dataframe only containing 3 and 8 and obviously i cant ffill NaNs with NaNs.

Answer 1

Change values only to those row that start with nan

df.loc[df['A'].isna(), :] = df.ffill().loc[df['A'].isna(), :]

      A     B     C     D     E
0  45.0  88.0   NaN   NaN   3.0
1  62.0  34.0   2.0  86.0   NaN
2  85.0  65.0  11.0  31.0   5.0
3  85.0  65.0  11.0  31.0   5.0
4  90.0  38.0  34.0  93.0   8.0
5   0.0  94.0  45.0  10.0  10.0
6  58.0   NaN  23.0  60.0  11.0
7  10.0  32.0   5.0  15.0  11.0
8  10.0  32.0   5.0  15.0  11.0

Answer 2

Try using a mask to identify the relevant rows where column A is null. The take those same rows from the forward filled dataframe.

mask = df['A'].isnull()
df.loc[mask, :] = df.ffill().loc[mask, :]
>>> df
      A     B     C     D     E
0  45.0  88.0   NaN   NaN   3.0
1  62.0  34.0   2.0  86.0   NaN
2  85.0  65.0  11.0  31.0   5.0
3  85.0  65.0  11.0  31.0   5.0
4  90.0  38.0  34.0  93.0   8.0
5   0.0  94.0  45.0  10.0  10.0
6  58.0   NaN  23.0  60.0  11.0
7  10.0  32.0   5.0  15.0  11.0
8  10.0  32.0   5.0  15.0  11.0

Answer 3

you just want to fill ( DataFrame.ffill ) where ( DataFrame.where ) df['A'] is nan and the rest leave it as before ( df ):

df=df.ffill().where(df['A'].isna(),df)
print(df)
      A     B     C     D     E
0  45.0  88.0   NaN   NaN   3.0
1  62.0  34.0   2.0  86.0   NaN
2  85.0  65.0  11.0  31.0   5.0
3  85.0  65.0  11.0  31.0   5.0
4  90.0  38.0  34.0  93.0   8.0
5   0.0  94.0  45.0  10.0  10.0
6  58.0   NaN  23.0  60.0  11.0
7  10.0  32.0   5.0  15.0  11.0
8  10.0  32.0   5.0  15.0  11.0