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

Similar codes

0 vote223 views1 answer
Python pandas rellenar con ceros a la izquierda
Python
3 votes252 views1 answer
Drop duplicates pandas first column
Python
0 vote213 views1 answer
Reg.fit(x_train, y_train) TypeError: fit() missing 1 required positional argument: 'y'
Python
24 votes231 views4 answers
Select rows which have nan values python
Python
0 vote220 views1 answer
Find highest value in array python
Python
1 vote235 views1 answer
Python pandas save df to xlsx file
Python
1 vote241 views1 answer
Internet Explorer Selenium
Python
4 votes241 views4 answers
Recursion python examples
Python
0 vote216 views1 answer
-1 / -1
Python
3 votes273 views1 answer
Sort two lists by one python
Python