pandas replace nan
data["Gender"].fillna("No Gender", inplace = True)pandas replace nan
data["Gender"].fillna("No Gender", inplace = True)replace outliers with nan python
df.mask(df.sub(df.mean()).div(df.std()).abs().gt(2))
c1 c2 c3
0 NaN 1.0 2.0
1 1.0 3.0 4.0
2 2.0 5.0 6.0
3 3.0 1.0 NaN
4 4.0 3.0 4.0
5 5.0 5.0 6.0
6 6.0 NaN 6.0Copyright © 2021 Codeinu
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