outlier removal
q1 = df['column'].quantile(0.25)
q3 = df['column'].quantile(0.75)
iqr = q3 - q1
df.loc[df['column'] > (q3 + 1.5 * iqr) | df['column'] < (q1 - 1.5 * iqr), 'column'] = df['column'].mean()outlier removal
q1 = df['column'].quantile(0.25)
q3 = df['column'].quantile(0.75)
iqr = q3 - q1
df.loc[df['column'] > (q3 + 1.5 * iqr) | df['column'] < (q1 - 1.5 * iqr), 'column'] = df['column'].mean()outliers removal
#Removing outliers first then skewness
from scipy.stats import zscore
z=abs(zscore(df))
print(z.shape)
df=df[(z<3).all(axis=1)]
df.shapeCopyright © 2021 Codeinu
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