Normalize Pandas DataFrame at specific columns

Question

i have a Pandas DataFrame with the following structure.

Feature 1  | Feature 2  | Feature 3
10         | 200        | True
30         | 233        | False
45         | 344        | True

any idea how i can do normalization for feature 1 and feature 2 only? without changing the index of original DataFrame.

i already try this following code, but it's normalize all columns and change the index of dataframe to 0,1,2

x = df.values
min_max_scaler = preprocessing.MinMaxScaler()
x_scaled = min_max_scaler.fit_transform(x)
dataset = pd.DataFrame(x_scaled)

Yuca · Accepted Answer · 2018-10-10 19:50:57Z

4

Just create a view of your dataframe:

x = df[['Feature 1', 'Feature 2']]
min_max_scaler = preprocessing.MinMaxScaler()
x_scaled = min_max_scaler.fit_transform(x)
dataset = pd.DataFrame(x_scaled)
dataset['Feature 3'] = df['Feature 3']

edited Oct 10, 2018 at 19:50

answered Oct 10, 2018 at 19:46

Yuca

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2 Comments

kerato2323 Over a year ago

it's work, but i have to include 'feature 3' too (without normalization) in new dataset.

Yuca Over a year ago

check the update (notice that your sample data has no index, so it should work)

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