normalize data python pandas
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)normalize data python pandas
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)standardize columns in pandas
columns = ['A', 'B','C'] #specify the column names
for col in columns:
df[col] = (df[col] - df[col].mean())/df[col].std()function to scale features in dataframe
# 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 dataCopyright © 2021 Codeinu
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