Pandas dataframe fillna only some columns in place

Question

I am trying to fill none values in a Pandas dataframe with 0 s for only some subset of columns   When I do   import pandas as pd df   pd DataFrame data   a   1 2 3 None   b   4 5 None 6   c   None None 7 8    print df df fillna value 0  inplace True  print df   The output        a    b    c 0  1 0  4 0  NaN 1  2 0  5 0  NaN 2  3 0  NaN  7 0 3  NaN  6 0  8 0      a    b    c 0  1 0  4 0  0 0 1  2 0  5 0  0 0 2  3 0  0 0  7 0 3  0 0  6 0  8 0   It replaces every None with 0 s  What I want to do is  only replace Nones in columns a and b  but not c   What is the best way of doing this

User · Answer

You can using dict   fillna with different value for different column  df fillna   a  0  b  0   Out 829         a    b    c 0  1 0  4 0  NaN 1  2 0  5 0  NaN 2  3 0  0 0  7 0 3  0 0  6 0  8 0   After assign it back   df df fillna   a  0  b  0   df Out 831         a    b    c 0  1 0  4 0  NaN 1  2 0  5 0  NaN 2  3 0  0 0  7 0 3  0 0  6 0  8 0

User · Answer

For some odd reason this DID NOT work  using Pandas   0 25 1   df   col1    col2    fillna value 0  inplace True   Another solution  subset cols     col1   col2    df col  fillna 0  inplace True  for col in subset cols   Example  df   pd DataFrame data   col1   1 2 np nan     col2   1 np nan 3    col3   np nan 2 3     output     col1  col2  col3 0  1 00  1 00   nan 1  2 00   nan  2 00 2   nan  3 00  3 00  Apply list comp  to fillna values  subset cols     col1   col2    df col  fillna 0  inplace True  for col in subset cols   Output     col1  col2  col3 0  1 00  1 00   nan 1  2 00  0 00  2 00 2  0 00  3 00  3 00

User · Answer

Sometimes this syntax wont work   df   col1   col2      df   col1   col2    fillna     Use the following instead   df  col1   col2

User · Answer

You can select your desired columns and do it by assignment   df   a    b      df   a   b    fillna value 0    The resulting output is as expected        a    b    c 0  1 0  4 0  NaN 1  2 0  5 0  NaN 2  3 0  0 0  7 0 3  0 0  6 0  8 0

User · Answer

You can avoid making a copy of the object using Wen s solution and inplace True   df fillna   a  0   b  0   inplace True  print df    Which yields        a    b    c 0  1 0  4 0  NaN 1  2 0  5 0  NaN 2  3 0  0 0  7 0 3  0 0  6 0  8 0

User · Answer

Or something like   df loc df  a   isnull    a   0 df loc df  b   isnull    b   0   and if there is more   for i in your list      df loc df i  isnull   i  0

User · Answer

Here s how you can do it all in one line   df   a    b    fillna value 0  inplace True    Breakdown  df   a    b    selects the columns you want to fill NaN values for  value 0 tells it to fill NaNs with zero  and inplace True will make the changes permanent  without having to make a copy of the object

User · Answer

using the top answer produces a warning about making changes to a copy of a df slice  Assuming that you have other columns  a better way to do this is to pass a dictionary   df fillna   A    NA    B    NA    inplace True

[python] Pandas dataframe fillna() only some columns in place

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