Data normalization python

Normalize data python

>>> 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]])

Source: educative.io

Feature scaling in python

from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
from sklearn.linear_model import Ridge
X_train, X_test, y_train, y_test = train_test_split(X_data, y_data,
                                                   random_state = 0)

X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

Data normalization python

from sklearn import preprocessing
normalizer = preprocessing.Normalizer().fit(X_train)  
X_train = normalizer.transform(X_train)
X_test = normalizer.transform(X_test)

Data wrangling python

pd.merge(left, right, how='inner', on=None, left_on=None, right_on=None,
left_index=False, right_index=False, sort=True)

Source: tutorialspoint.com

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