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ColumnTransformer

ColumnTransformer applies different transforms to different columns.

ColumnTransformer

In real tables columns need different processing: numbers are scaled, categories one-hot encoded. ColumnTransformer applies a separate transformer to each column group and concatenates the results side by side:

from sklearn.compose import ColumnTransformer
ct = ColumnTransformer([
    ("sayi", StandardScaler(), ["yas", "gelir"]),
    ("kat", OneHotEncoder(handle_unknown="ignore"), ["sehir"]),
])
Z = ct.fit_transform(df)   # 2 scaled + as many 0/1 columns as cities

Columns can be given by pandas names or integer positions.

A full preprocessing chain

Each column group can have its own sub-pipeline; the whole structure is gathered with the model into a single pipeline:

sayisal = make_pipeline(SimpleImputer(strategy="median"), StandardScaler())
kategorik = make_pipeline(SimpleImputer(strategy="most_frequent"),
                          OneHotEncoder(handle_unknown="ignore"))
ct = ColumnTransformer([("sayi", sayisal, sayi_sutunlar),
                        ("kat", kategorik, kat_sutunlar)])
model = make_pipeline(ct, LogisticRegression())

get_feature_names_out() shows what each column represents after the transformation.

Practice Transform a mixed table

Build a ColumnTransformer processing the yas and gelir columns with StandardScaler and the sehir column with OneHotEncoder. Print the shape of the fit_transform result, then the get_feature_names_out() list.

Starter code

model.py
import numpy as np
import pandas as pd
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import StandardScaler, OneHotEncoder

df = pd.DataFrame({
    "yas": [25, 32, 47, 51],
    "gelir": [3000, 4500, 8000, 6000],
    "sehir": ["Ankara", "İzmir", "Ankara", "Bursa"],
})

# buraya yaz

Sample solution

model.py
import numpy as np
import pandas as pd
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import StandardScaler, OneHotEncoder

df = pd.DataFrame({
    "yas": [25, 32, 47, 51],
    "gelir": [3000, 4500, 8000, 6000],
    "sehir": ["Ankara", "İzmir", "Ankara", "Bursa"],
})

ct = ColumnTransformer([("sayi", StandardScaler(), ["yas", "gelir"]), ("kat", OneHotEncoder(), ["sehir"])])
Z = ct.fit_transform(df)
print(Z.shape)
print(list(ct.get_feature_names_out()))

Expected output

(4, 5)
['sayi__yas', 'sayi__gelir', 'kat__sehir_Ankara', 'kat__sehir_Bursa', 'kat__sehir_İzmir']

Questions and answers

Review the lesson questions together with their answers. To test yourself, solve the lesson interactively.

1 What does ColumnTransformer do by default to columns not named in the list?

  • Scales them
  • Errors
  • Drops them (remainder="drop") Correct answer
  • Passes them through unchanged

Explanation: They can be kept with remainder="passthrough".

2 Keep the columns that aren't named.

Question 
ct = ColumnTransformer([...], remainder=____)
Answer 
ct = ColumnTransformer([...], remainder="passthrough")

Explanation: The default is "drop"; "passthrough" appends the columns unchanged.

3 Match the parts.

Term Meaning
("sayi", StandardScaler(), [...]) Numeric transformation
("kat", OneHotEncoder(), [...]) Categorical transformation
remainder Remaining columns
get_feature_names_out New column names

All lessons in Intermediate

1 scikit-learn basics

  1. What is scikit-learn?
  2. Estimator API
  3. The fit method
  4. The predict method
  5. The score method
  6. Summary - scikit-learn

2 Pipeline

  1. What is a Pipeline?
  2. Steps
  3. fit and predict
  4. Advantages
  5. ColumnTransformer
  6. Summary - Pipeline

3 Cross-validation

  1. What is cross-validation?
  2. K-fold
  3. cross_val_score
  4. Stratified
  5. Advantages
  6. Summary - Cross-validation

4 Grid search

  1. What is grid search?
  2. GridSearchCV
  3. param_grid
  4. best_params_
  5. best_score_
  6. Summary - Grid search

5 SVM

  1. What is SVM?
  2. Margin
  3. Kernel
  4. C parameter
  5. Classification
  6. Summary - SVM

6 Project: SVM

  1. Preparing data
  2. Training model
  3. Prediction
  4. Evaluation

7 Random forest

  1. What is random forest?
  2. Ensemble
  3. n_estimators
  4. Bagging
  5. Feature importance
  6. Summary - Random forest

8 Boosting

  1. What is boosting?
  2. AdaBoost
  3. Gradient boosting
  4. Learning rate
  5. Advantages
  6. Summary - Boosting

9 Clustering

  1. What is clustering?
  2. KMeans
  3. Centroid
  4. Elbow method
  5. Labels
  6. Summary - Clustering

10 KMeans in depth

  1. KMeans algorithm
  2. Initialization
  3. n_init
  4. inertia_
  5. random_state
  6. Summary - KMeans

11 Hierarchical clustering

  1. What is hierarchical clustering?
  2. Dendrogram
  3. Agglomerative
  4. Linkage
  5. Advantages
  6. Summary - Hierarchical clustering

12 Project: Clustering

  1. Preparing data
  2. Training KMeans
  3. Label analysis
  4. Printing results

13 PCA

  1. What is PCA?
  2. Variance
  3. n_components
  4. fit_transform
  5. explained_variance_
  6. Summary - PCA

14 Dimensionality reduction

  1. What is dimensionality reduction?
  2. Why reduce?
  3. PCA vs t-SNE
  4. Visualization
  5. Advantages
  6. Summary - Dimensionality reduction

15 Feature selection

  1. What is feature selection?
  2. Variance threshold
  3. SelectKBest
  4. Recursive elimination
  5. Feature importance
  6. Summary - Feature selection

16 Imbalanced data

  1. What is imbalanced data?
  2. SMOTE
  3. Class weight
  4. Oversampling
  5. Metric selection
  6. Summary - Imbalanced data

17 Project: Features

  1. Feature extraction
  2. Feature selection
  3. Training model
  4. Evaluation

18 Ensemble methods

  1. What is ensemble?
  2. Voting
  3. Bagging
  4. Boosting
  5. Stacking
  6. Summary - Ensemble

19 Regularization

  1. What is regularization?
  2. L1 (Lasso)
  3. L2 (Ridge)
  4. alpha
  5. ElasticNet
  6. Summary - Regularization

20 Intermediate final

  1. Challenge 1
  2. Challenge 2
  3. Challenge 3
  4. Challenge 4