How can I normalize the data in a range of columns in my pandas dataframe

Question

Suppose I have a pandas data frame surveyData:

I want to normalize the data in each column by performing:

surveyData_norm = (surveyData - surveyData.mean()) / (surveyData.max() - surveyData.min()) 

This would work fine if my data table only contained the columns I wanted to normalize. However, I have some columns containing string data preceding like:

Name  State  Gender  Age  Income  Height Sam   CA     M        13   10000    70 Bob   AZ     M        21   25000    55 Tom   FL     M        30   100000   45 

I only want to normalize the Age, Income, and Height columns but my above method does not work becuase of the string data in the name state and gender columns.

Answer 1

You can perform operations on a sub set of rows or columns in pandas in a number of ways. One useful way is indexing:

# Assuming same lines from your example cols_to_norm = ['Age','Height'] survey_data[cols_to_norm] = survey_data[cols_to_norm].apply(lambda x: (x - x.min()) / (x.max() - x.min())) 

This will apply it to only the columns you desire and assign the result back to those columns. Alternatively you could set them to new, normalized columns and keep the originals if you want.

Answer 2

I think it's better to use 'sklearn.preprocessing' in this case which can give us much more scaling options. The way of doing that in your case when using StandardScaler would be:

from sklearn.preprocessing import StandardScaler cols_to_norm = ['Age','Height'] surveyData[cols_to_norm] = StandardScaler().fit_transform(surveyData[cols_to_norm])