Winsorizing data by column in pandas with NaN

Question

I\'d like to winsorize several columns of data in a pandas Data Frame. Each column has some NaN, which affects the winsorization, so they need to be removed. The only way I

Answer 1

As often happens, simply creating the MWE helped clarify. I need to use clip() in combination with quantile() as below:

df2 = df.clip(lower=df.quantile(0.01), upper=df.quantile(0.99), axis=1)
df2.quantile([0, 0.01, 0.25, 0.5, 0.75, 0.99, 1])

Output:

               one       two      three          four
0.00  9.862626e-07  0.000974   0.975807   1003.814520
0.01  9.862666e-07  0.000974   0.975816   1003.820092
0.25  2.485043e-05  0.024975  25.200378  25099.994780
0.50  4.975859e-05  0.049810  50.290946  50374.548980
0.75  7.486737e-05  0.074842  74.794537  75217.343920
0.99  9.897462e-05  0.098986  98.978245  98991.436977
1.00  9.897463e-05  0.098986  98.978263  98991.438985

In [384]: df2.count()
Out[384]:
one       90700
two       91600
three     63500
four     100000
dtype: int64

The numbers are different from above because I have maintained all of the data in each column that is not missing (NaN).