Python Pandas replace multiple columns zero to Nan

Question

List with attributes of persons loaded into pandas dataframe df2. For cleanup I want to replace value zero (0 or '0') by np.nan.

df2.dtypes  ID                   object Name                 object Weight              float64 Height              float64 BootSize             object SuitSize             object Type                 object dtype: object 

Working code to set value zero to np.nan:

df2.loc[df2['Weight'] == 0,'Weight'] = np.nan df2.loc[df2['Height'] == 0,'Height'] = np.nan df2.loc[df2['BootSize'] == '0','BootSize'] = np.nan df2.loc[df2['SuitSize'] == '0','SuitSize'] = np.nan 

Believe this can be done in a similar/shorter way:

df2[["Weight","Height","BootSize","SuitSize"]].astype(str).replace('0',np.nan) 

However the above does not work. The zero's remain in df2. How to tackle this?

Answer 1

I think you need replace by dict:

cols = ["Weight","Height","BootSize","SuitSize","Type"] df2[cols] = df2[cols].replace({'0':np.nan, 0:np.nan})