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]])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]])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)Scaling features to a range
# Scaling features to a range using MaxAbsScaler
X_train = np.array([[ 1., -1., 2.],
[ 2., 0., 0.],
[ 0., 1., -1.]])
max_abs_scaler = preprocessing.MaxAbsScaler()
X_train_maxabs = max_abs_scaler.fit_transform(X_train)
X_train_maxabs
# array([[ 0.5, -1., 1. ],
# [ 1. , 0. , 0. ],
# [ 0. , 1. , -0.5]])
X_test = np.array([[ -3., -1., 4.]])
X_test_maxabs = max_abs_scaler.transform(X_test)
X_test_maxabs
# array([[-1.5, -1. , 2. ]])
max_abs_scaler.scale_
# array([2., 1., 2.])Copyright © 2021 Codeinu
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