How to normalize only continuous variables in dataframe?

Question

I'm trying to normalize the continuous variables from my dataframe but I'm getting the following error

ValueError: could not convert string to float: 'M'

I know that the error occurs because I have some categorical variables so how can I ignore the categorical variables. This is my code

X=data
mms=MinMaxScaler()
mms.fit(X)
Xnorm=mms.transform(X)
print(Xnorm.min(axis=0))
print(Xnorm.max(axis=0))
print(Xnorm.shape)

Select the columns you want to normalize instead of using all columns. Something like X = data[['col1', 'col2']]. — matias
– matias, Commented Jun 2, 2022 at 18:19
@matias but that way isn't the dataset reduzed to only that columns? — P.Brito
– P.Brito, Commented Jun 2, 2022 at 18:23
@matias but if I want to normalize in the dataset I need to normalize in data not in X. — P.Brito
– P.Brito, Commented Jun 2, 2022 at 18:33

Shubham Sharma · Accepted Answer · 2022-06-02 18:32:55Z

4

Select the numerical columns, then apply min-max transformation and store the result back into selected numerical columns

X = data.select_dtypes(np.number)
data[X.columns] = MinMaxScaler().fit_transform(X)

answered Jun 2, 2022 at 18:32

Shubham Sharma

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4 Comments

P.Brito Over a year ago

@Subham Sharma I'm getting only 0's and 1's. [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. nan 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. nan] [ 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. nan 1. 1. 0. 1. 1. 1. 1. 1. 1. 1. 1. nan] It's normal?

Shubham Sharma Over a year ago

@P.Brito This depends on your input data, btw what is the shape of input dataframe data.shape?

P.Brito Over a year ago

this is the shape of the data (60398, 64)

Shubham Sharma Over a year ago

@P.Brito What's the version of scikit-learn you are using? Also check the shape of X?

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