column_transformer = ColumnTransformer([("min_max_scaler", MinMaxScaler(), ["bill_length_mm", "bill_depth_mm", "flipper_length_mm"]), ("standard_scaler", StandardScaler(), ["body_mass_g"])])

Please note that the MinMaxScaler() works on “bill_length_mm”, “bill_depth_mm”, and “flipper_length_mm”. And the StandardScaler() works on “body_mass_g”.

After initializing the column transformer, we are calling the fit_transform() method that learns from the data and then, transforms the data.

The output of the above program will be:

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 344 entries, 0 to 343
Data columns (total 7 columns):
 #   Column             Non-Null Count  Dtype  
---  ------             --------------  -----  
 0   species            344 non-null    object 
 1   island             344 non-null    object 
 2   bill_length_mm     342 non-null    float64
 3   bill_depth_mm      342 non-null    float64
 4   flipper_length_mm  342 non-null    float64
 5   body_mass_g        342 non-null    float64
 6   sex                333 non-null    object 
dtypes: float64(4), object(3)
memory usage: 18.9+ KB
None
  species     island  bill_length_mm  ...  flipper_length_mm  body_mass_g     sex
0  Adelie  Torgersen        0.254545  ...           0.152542    -0.564142    Male
1  Adelie  Torgersen        0.269091  ...           0.237288    -0.501703  Female
2  Adelie  Torgersen        0.298182  ...           0.389831    -1.188532  Female
3  Adelie  Torgersen             NaN  ...                NaN          NaN     NaN
4  Adelie  Torgersen        0.167273  ...           0.355932    -0.938776  Female

[5 rows x 7 columns]