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WitrynaOr, if they're actually NaNs (which, it seems is unlikely), then use fillna: df.fillna(0, inplace=True) Or, to handle both situations at the same time, use apply + pd.to_numeric (slightly slower but guaranteed to work in any case): Witryna3 lis 2024 · from sklearn.preprocessing import Normalizer, StandardScaler import numpy as np data = np.array ( [0,1,2,np.nan, 3,4]) scaler = StandardScaler (with_mean=True, with_std=True) scaler.fit_transform (data.reshape (-1,1)) normalizer = Normalizer (norm='l2') normalizer.fit_transform (data.reshape (-1,1)) python numpy scikit-learn … penndot interactive map

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