Pandas version 0.23.4, python version 3.7.1
I have a dataframe df as below
df = pd.DataFrame([[0.1, 2, 55, 0,np.nan],
                   [0.2, 4, np.nan, 1,99],
                   [0.3, np.nan, 22, 5,88],
                   [0.4, np.nan, np.nan, 4,77]],
                   columns=list('ABCDE'))
     A    B     C  D     E
0  0.1  2.0  55.0  0   NaN
1  0.2  4.0   NaN  1  99.0
2  0.3  NaN  22.0  5  88.0
3  0.4  NaN   NaN  4  77.0
I want to replace Na values in columns B and C with value in column `A'.  
Expected output is
     A   B      C    D      E 
0   0.1  2.0    55.0   0    NaN 
1   0.2  4.0    0.2    1    99.0 
2   0.3  0.3    22.0   5    88.0 
3   0.4  0.4    0.4    4    77.0
I have tried fillna using fill along axis 0, but its not giving expected output,  (its filling from the above column)
df.fillna(method='ffill',axis=0, inplace = True)
    A    B     C   D     E
0  0.1  2.0  55.0  0   NaN
1  0.2  4.0  55.0  1  99.0
2  0.3  4.0  22.0  5  88.0
3  0.4  4.0  22.0  4  77.0  
df.fillna(method='ffill',axis=1, inplace = True)
output: NotImplementedError:
Also tried
df[['B','C']] = df[['B','C']].fillna(df.A)
output:
    A    B     C   D     E
0  0.1  2.0  55.0  0   NaN
1  0.2  4.0   NaN  1  99.0
2  0.3  NaN  22.0  5  88.0
3  0.4  NaN   NaN  4  77.0
Tried to fill all Na's in B and Cwith 0, using inplace, but this also is not giving expected output 
df[['B','C']].fillna(0,inplace=True)
output:
     A    B     C  D     E
0  0.1  2.0  55.0  0   NaN
1  0.2  4.0   NaN  1  99.0
2  0.3  NaN  22.0  5  88.0
3  0.4  NaN   NaN  4  77.0
filling 0 to slice of data frame will work if assigned back to the same subset
df[['B','C']] = df[['B','C']].fillna(0)
output:
     A    B     C  D     E
0  0.1  2.0  55.0  0   NaN
1  0.2  4.0   0.0  1  99.0
2  0.3  0.0  22.0  5  88.0
3  0.4  0.0   0.0  4  77.0
1) How to fill na values in columns BandC using values from column A from the given data frame ?
2) Also why is inlace not working when using fillna on a subset of the data frame.
3) How to do ffill along the rows(is it implemented)?
 
     
    