Pandas : Fillna for all columns, except two-CodePudding

I wonder how can we fill the NaNs from all columns of a dataframe, except some. For example, I have a dataframe with 20 columns, I want to fill the NaN for all except two columns (in my case, NaN are replaced by the mean).

df = df.drop(['col1','col2], 1).fillna(df.mean())

I tried this, but I don't think it's the best way to achieve this (also, i want to avoid the inplace=true arg).

Thank's

CodePudding user response：

You can select which columns to use fillna on. Assuming you have 20 columns and you want to fill all of them except 'col1' and 'col2' you can create a list with the ones you want to fill:

f = [c for c in df.columns if c not in ['col1','col2']]
df[f] = df[f].fillna(df[f].mean())

print(df)

   col1  col2      col3  col4  ...     col17  col18     col19  col20
0   1.0   1.0  1.000000   1.0  ...  1.000000      1  1.000000      1
1   NaN   NaN  2.666667   2.0  ...  2.000000      2  2.000000      2
2   NaN   3.0  3.000000   1.5  ...  2.333333      3  2.333333      3
3   4.0   4.0  4.000000   1.5  ...  4.000000      4  4.000000      4

              (2.66666) was the mean

# Initial DF:

{'col1': {0: 1.0, 1: nan, 2: nan, 3: 4.0},
 'col2': {0: 1.0, 1: nan, 2: 3.0, 3: 4.0},
 'col3': {0: 1.0, 1: nan, 2: 3.0, 3: 4.0},
 'col4': {0: 1.0, 1: 2.0, 2: nan, 3: nan},
 'col5': {0: 1.0, 1: nan, 2: 3.0, 3: 4.0},
 'col6': {0: 1, 1: 2, 2: 3, 3: 4},
 'col7': {0: nan, 1: 2.0, 2: 3.0, 3: 4.0},
 'col8': {0: 1.0, 1: 2.0, 2: nan, 3: 4.0},
 'col9': {0: 1, 1: 2, 2: 3, 3: 4},
 'col10': {0: 1.0, 1: 2.0, 2: nan, 3: 4.0},
 'col11': {0: 1.0, 1: nan, 2: 3.0, 3: 4.0},
 'col12': {0: 1, 1: 2, 2: 3, 3: 4},
 'col13': {0: 1.0, 1: 2.0, 2: nan, 3: 4.0},
 'col14': {0: 1.0, 1: nan, 2: 3.0, 3: 4.0},
 'col15': {0: 1, 1: 2, 2: 3, 3: 4},
 'col16': {0: 1.0, 1: nan, 2: 3.0, 3: nan},
 'col17': {0: 1.0, 1: 2.0, 2: nan, 3: 4.0},
 'col18': {0: 1, 1: 2, 2: 3, 3: 4},
 'col19': {0: 1.0, 1: 2.0, 2: nan, 3: 4.0},
 'col20': {0: 1, 1: 2, 2: 3, 3: 4}}