Assuming we have a `df` as follows:

```
id A  B
50 1  5
60 2  6
70 3  7
80 4  8

```

I would like to know as to how can normalize just the `column B`, between 0 and 1, while keeping the other columns `id` and `column A` completely unaffected?

**Edit 1:** If I do the following

```
import pandas as pd
df = pd.DataFrame({ 'id' : ['50', '60', '70', '80'],
        'A' : ['1', '2', '3', '4'],
        'B' : ['5', '6', '7', '8']
        })

from sklearn import preprocessing
min_max_scaler = preprocessing.MinMaxScaler()
X_minmax = min_max_scaler.fit_transform(df.values[:,[2]])

```

I get the `X_minmax` as follows

```
0
0.333333
0.666667
1

```

I want these 4 values to be placed in place of the column `B` in the dataframe `df` without changing the other 2 columns looking as below:

```
    id A  B
    50 1  0
    60 2  0.333333
    70 3  0.666667
    80 4  1

```