Pythonで学ぶMachine Learningの前処理
James Chapman
Curriculum Manager, DataCamp
print(df)
col1 col2 col3 0 1.00 48.0 100.0 1 1.20 45.5 101.3 2 0.75 46.2 103.5 3 1.60 50.0 104.0
print(df.var())
col1 0.128958 col2 4.055833 col3 3.526667 dtype: float64
from sklearn.preprocessing import StandardScaler scaler = StandardScaler() df_scaled = pd.DataFrame(scaler.fit_transform(df), columns=df.columns)
from sklearn.preprocessing import StandardScaler scaler = StandardScaler()
df_scaled = pd.DataFrame(scaler.fit_transform(df), columns=df.columns)
print(df_scaled)
col1 col2 col3 0 -0.442127 0.329683 -1.352726 1 0.200967 -1.103723 -0.553388 2 -1.245995 -0.702369 0.799338 3 1.487156 1.476409 1.106776
print(df_scaled.var())
col1 1.333333 col2 1.333333 col3 1.333333 dtype: float64
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