Retaining Headings after Nomalizing using sklearn Normalizer

Question

I am trying to normalize a dataset using min- max normalizer.

from sklearn import preprocessing
x = df1.values #returns a numpy array
min_max_scaler = preprocessing.MinMaxScaler()
x_scaled = min_max_scaler.fit_transform(x)
df1 = pd.DataFrame(x_scaled)

Inupt

PID | FNID | ID
-----------------
10  | 15   | 20
11  | 16   | 21

Expected Output

PID | FNID |ID
---------------
0.1 | 0.15 | 0.2
0.11| 0.16 | 0.21

But I am getting output like..

0   | 1    | 2
----------------
0.1 | 0.15 | 0.2
0.11| 0.16 |0.21

I want the headers as in the original dataset. I tried This.

Answer 1

I tried.

x = df1.values #returns a numpy array
min_max_scaler = preprocessing.MinMaxScaler()
x_scaled = min_max_scaler.fit_transform(x)
df11 = pd.DataFrame(x_scaled, columns = df1.columns)
df11.head()

And got expected result.

Thank you!