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DS-1000 / 855 / I have a pandas DataFrame data

Problem

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Problem:

I have a pandas DataFrame data
it has about 12k rows and more than 500 columns, each column has its unique name
However, when I used sklearn preprocessing, I found the result lose the information about the columns
Here's the code

from sklearn import preprocessing
preprocessing.scale(data)
outputs a numpy array.

So my question is, how to apply preprocessing.scale to DataFrames, and don't lose the information(index, columns)?


A:

<code>
import numpy as np
import pandas as pd
from sklearn import preprocessing
data = load_data()
</code>
df_out = ... # put solution in this variable
BEGIN SOLUTION
<code>

library

Sklearn

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Source and history

upstream_id / Original problem ID
855
task_id
f41e7183-f489-5586-a1cb-d5337bb466ab
task_key
default--test--855
task_revision_id
1
config / split
default / test

Official source

initial import