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normalize data python

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Gifted Gull answered on May 14, 2020 Popularity 10/10 Helpfulness 5/10

answer normalize data python

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normalize data python

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from sklearn import preprocessing

data = [[ 1., -1.,  2.],
        [ 2.,  0.,  0.],
        [ 0.,  1., -1.]]
normalize(data)

# produces
array([[ 0.40824829, -0.40824829,  0.81649658],
       [ 1.        ,  0.        ,  0.        ],
       [ 0.        ,  0.70710678, -0.70710678]])

Popularity 10/10 Helpfulness 5/10 Language python

Source: scikit-learn.org

Tags: normalize python

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Contributed on Jun 08 2020

Gifted Gull

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normalize data python pandas

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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)

Popularity 10/10 Helpfulness 8/10 Language python

Source: stackoverflow.com

Tags: normalize norm

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Contributed on May 14 2020

Exuberant Eel

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data normalization python

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

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Source: Grepper

Tags: normalization no

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Contributed on Jan 30 2021

JJSSEEXX

0 Answers Avg Quality 2/10

normalize python

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# Normalize the whole dataset before modeling
from sklearn.preprocessing import StandardScaler
X = StandardScaler()\
	.fit(X)\
	.transform(X.astype(float))
# There are other normalization methods available like MinMaxScaler, Z-score etc

# Alternative approach
from sklearn.preprocessing import normalize
X = normalize(X.astype(float), norm="l1")

# Alternative approach
from sklearn.preprocessing import Normalizer
X = Normalizer()\
	.fit(X)\
	.transform(X.astype(float))

# Alternative approach with scipy : normalize to a standard deviation of 1
from scipy.cluster.vq import whiten
scaled_data = whiten(data) # Works with multi-dimensional data

# Normalizing without library
# Feature Scaling
df["feature_scaled"] = df["col"]/ (df["col"].max())
# Min-max Scaling
df["minmax_scaled"] = (df["col"] - df["col"].min()) / (df["col"].max() - df["col"].min())
# Z-score
df["z_scaled"] = (df["col"] - df["col"].mean()) / df["col"].std()

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Source: Grepper

Tags: normalize norm

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Contributed on Dec 31 2023

Innocent Iguana

0 Answers Avg Quality 2/10

normalize data python

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