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 vote188 views2 answers
Change date format to yyyy mm dd in django template datepicker
Python
16 votes196 views2 answers
What is my python working directory
Python
1 vote197 views1 answer
Replace space with . pyhton
Python
4 votes209 views3 answers
No such table: django_session
Python
1 vote190 views2 answers
Python flask api
Python
8 votes188 views2 answers
Find max in a dataframe
Python
3 votes147 views1 answer
Flask secret key generator
Python
15 votes201 views3 answers
Count number of occurrences of all elements in list python
Python
1 vote191 views1 answer
Random forrest plotting feature importance function
Python
0 vote193 views1 answer
Pytest temp directory
Python