What if I have a column called "numpy_array" how can I get that one only passed through?

from sklearn.compose import ColumnTransformer

ct = ColumnTransformer(
    transformers=[
        ('np_array_transform', 'passthrough', ['numpy_array']),
    ],
    remainder='drop',
)

It is a bit strange to encode continuous data as Salary. It makes no sense unless you have binned your salary to certain ranges/categories. If I were you I would do:

import pandas as pd
import numpy as np

from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler, OneHotEncoder



numeric_features = ['Salary']
numeric_transformer = Pipeline(steps=[
    ('imputer', SimpleImputer(strategy='median')),
    ('scaler', StandardScaler())])

categorical_features = ['Age','Country']
categorical_transformer = Pipeline(steps=[
    ('imputer', SimpleImputer(strategy='constant', fill_value='missing')),
    ('onehot', OneHotEncoder(handle_unknown='ignore'))])

preprocessor = ColumnTransformer(
    transformers=[
        ('num', numeric_transformer, numeric_features),
        ('cat', categorical_transformer, categorical_features)])

from here you can pipe it with a classifier e.g.

clf = Pipeline(steps=[('preprocessor', preprocessor),
                  ('classifier', LogisticRegression(solver='lbfgs'))])  
                  

Use it as so:

clf.fit(X_train,y_train)

this will apply the preprocessor and then pass transformed data to the predictor.

Updates:

If we want to select data types on the fly, we can modify our preprocessor to use column selector by data dtypes:

from sklearn.compose import make_column_selector as selector

preprocessor = ColumnTransformer(
    transformers=[
        ('num', numeric_transformer, selector(dtype_include="numeric")),
        ('cat', categorical_transformer, selector(dtype_include="category"))])

Using GridSearch

param_grid = {
    'preprocessor__num__imputer__strategy': ['mean', 'median'],
    'classifier__C': [0.1, 1.0, 10, 100],
    'classifier__solver': ['lbfgs', 'sag'],
}

grid_search = GridSearchCV(clf, param_grid, cv=10)
grid_search.fit(X_train,y_train)

Getting names of features

preprocessor = ColumnTransformer(
    transformers=[
        ('num', numeric_transformer, selector(dtype_include="numeric")),
        ('cat', categorical_transformer, selector(dtype_include="category"))],
    verbose_feature_names_out=False, # added this line
)

# now we can access feature names with

clf[:-1]. get_feature_names_out() # step before estimator