python - Pandas - Replacing NaN by aggregate of non-null values -


suppose have dataframe nan -

import pandas pd l = [{'c1':-6,'c3':2},      {'c2':-6,'c3':3},      {'c1':-6.3,'c2':8,'c3':9},      {'c2':-7}] df1 = pd.dataframe(l,     index=['r1','r2','r3','r4']) print(df1)       c1   c2   c3 r1 -6.0  nan  2.0 r2  nan -6.0  3.0 r3 -6.3  8.0  9.0 r4  nan -7.0  nan

problem - if there nan value in row cell has replaced aggregate of non-null values same row. instance, in first row, value of (r1,c2) should = (-6+2)/2 = -2

expected output -

     c1   c2   c3 r1 -6.0 -4.0  2.0 r2 -1.5 -6.0  3.0 r3 -6.3  8.0  9.0 r4 -7.0 -7.0 -7.0

use apply axis=1 process rows:

df1 = df1.apply(lambda x: x.fillna(x.mean()), axis=1) print(df1)       c1   c2   c3 r1 -6.0 -2.0  2.0 r2 -1.5 -6.0  3.0 r3 -6.3  8.0  9.0 r4 -7.0 -7.0 -7.0

also works:

df1 = df1.t.fillna(df1.mean(1)).t print(df1)      c1   c2   c3 r1 -6.0 -2.0  2.0 r2 -1.5 -6.0  3.0 r3 -6.3  8.0  9.0 r4 -7.0 -7.0 -7.0

because:

df1 = df1.fillna(df1.mean(1), axis=1) print(df1)

notimplementederror: can fill dict/series column column


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