>>> from sklearn import preprocessing >>> >>> data = [100, 10, 2, 32, 31, 949] >>> >>> preprocessing.normalize([data]) array([[0.10467389, 0.01046739, 0.00209348, 0.03349564, 0.03244891,0.99335519]])
import pandas as pd from sklearn import preprocessing x = df.values #returns a numpy array min_max_scaler = preprocessing.MinMaxScaler() x_scaled = min_max_scaler.fit_transform(x) df = pd.DataFrame(x_scaled)
# define a method to scale data, looping thru the columns, and passing a scaler def scale_data(data, columns, scaler): for col in columns: data[col] = scaler.fit_transform(data[col].values.reshape(-1, 1)) return data