Keep pandas structure with numpy/scikit functions

Question

I'm using the excellent read_csv()function from pandas, which gives:

In [31]: data = pandas.read_csv("lala.csv", delimiter=",")

In [32]: data
Out[32]: 
<class 'pandas.core.frame.DataFrame'>
Int64Index: 12083 entries, 0 to 12082
Columns: 569 entries, REGIONC to SCALEKER
dtypes: float64(51), int64(518)

but when i apply a function from scikit-learn i loose the informations about columns:

from sklearn import preprocessing
preprocessing.scale(data)

gives numpy array.

Is there a way to apply scikit or numpy function to DataFrames without loosing the information?

Answer 1

This can be done by wrapping the returned data in a dataframe, with index and columns information in.

import pandas as pd
pd.DataFrame(preprocessing.scale(data), index = data.index, columns = data.columns)