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İşlem Hattı İçinde ColumnTransformer

Farklı türlerdeki ham verilerin elle bölünmeden doğrudan işlenmesini sağlamak için sayısal ve kategorik verilerin birlikte ön işlenmesini gerçekleştiren ColumnTransformer'ı bir Pipeline içine yerleştireceksiniz.

İşlem Hattı İçinde ColumnTransformer, CoddyKit'te ücretsiz bir Machine Learning Academy dersidir. Bu, 4 dersinin 2. dersidir. Aşağıdan dersin tamamını ücretsiz okuyabilir, sonra tarayıcıda yerleşik kod editörü ve 7/24 yapay zeka koçu ile uygulamalı olarak pratik yapabilirsin. Bu, Machine Learning Academy öğrenme yolunun bir parçasıdır ve ilerlemeniz web ve CoddyKit uygulaması arasında senkronize olur. Machine Learning Academy kursu toplamda 4 dersten oluşur.

Bu dersin bazı bölümleri henüz çevrilmemiş olup İngilizce olarak gösterilmektedir.

The Mixed-Data Problem

Real-world tabular datasets almost always contain a mix of numeric columns (age, salary, temperature) and categorical columns (city, product category, gender). Each type needs different preprocessing: numeric columns need scaling or imputation, while categorical columns need encoding. ColumnTransformer lets you apply different transformers to different subsets of columns in parallel, producing a single clean feature matrix.

ColumnTransformer: The Basic Structure

ColumnTransformer takes a list of (name, transformer, columns) triples. The columns can be a list of column names, a list of integer indices, a boolean mask, or a sklearn selector like make_column_selector. After transformation, the results from all transformers are horizontally concatenated.

from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import StandardScaler, OneHotEncoder

ct = ColumnTransformer([
    ('num', StandardScaler(), ['age', 'salary']),
    ('cat', OneHotEncoder(handle_unknown='ignore'), ['city', 'education'])
])

print('Transformers:', [name for name, _, _ in ct.transformers])

Working with a Real Mixed Dataset

Let us create a small mixed DataFrame and apply a ColumnTransformer to see the output shape and values. This illustrates how numeric and categorical outputs are concatenated into a single NumPy array that any sklearn estimator can consume.

import pandas as pd
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import StandardScaler, OneHotEncoder

df = pd.DataFrame({
    'age': [25, 40, 35, 55],
    'salary': [50000, 80000, 65000, 90000],
    'city': ['NYC', 'LA', 'NYC', 'SF'],
    'education': ['BSc', 'MSc', 'BSc', 'PhD']
})

ct = ColumnTransformer([
    ('num', StandardScaler(), ['age', 'salary']),
    ('cat', OneHotEncoder(handle_unknown='ignore', sparse_output=False), ['city', 'education'])
])

X_transformed = ct.fit_transform(df)
print('Output shape:', X_transformed.shape)
print('Output:\n', X_transformed.round(2))

Automatic Column Selection

Instead of listing columns manually, use make_column_selector to automatically select columns by dtype. dtype_include=np.number selects all numeric columns; dtype_exclude=np.number selects non-numeric (categorical/string) columns. This is especially helpful for wide datasets where listing every column name would be impractical.

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

df = pd.DataFrame({
    'age': [25, 40, 35], 'salary': [50000, 80000, 65000],
    'city': ['NYC', 'LA', 'NYC']
})

ct = ColumnTransformer([
    ('num', StandardScaler(), make_column_selector(dtype_include=np.number)),
    ('cat', OneHotEncoder(), make_column_selector(dtype_exclude=np.number))
])

print(ct.fit_transform(df).shape)

Nesting ColumnTransformer Inside a Pipeline

The real power comes from nesting ColumnTransformer as a preprocessing step inside a Pipeline. The full pipeline — preprocessing and classifier — becomes a single estimator. All sklearn tooling (cross_val_score, GridSearchCV, joblib serialisation) works on this combined object.

from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer, make_column_selector
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.linear_model import LogisticRegression
import numpy as np

preprocessor = ColumnTransformer([
    ('num', StandardScaler(), make_column_selector(dtype_include=np.number)),
    ('cat', OneHotEncoder(handle_unknown='ignore'),
     make_column_selector(dtype_exclude=np.number))
])

pipe = Pipeline([
    ('preprocessor', preprocessor),
    ('clf', LogisticRegression(max_iter=300))
])

print('Pipeline steps:', [name for name, _ in pipe.steps])

End-to-End Example with Titanic Data

Let us apply a ColumnTransformer pipeline to a Titanic-style dataset. Numeric columns (Age, Fare) get median imputation then standard scaling; categorical columns (Sex, Embarked) get most-frequent imputation then one-hot encoding. The pipeline is then fitted and scored.

import pandas as pd
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.impute import SimpleImputer
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score
import numpy as np

# Simulated Titanic subset
df = pd.DataFrame({
    'Age': [22, 38, None, 35, 28],
    'Fare': [7.25, 71.83, 7.92, 53.1, 8.05],
    'Sex': ['male', 'female', 'female', 'male', 'male'],
    'Embarked': ['S', 'C', 'S', None, 'S'],
    'Survived': [0, 1, 1, 1, 0]
})

X = df.drop('Survived', axis=1)
y = df['Survived']

num_pipe = Pipeline([('impute', SimpleImputer(strategy='median')),
                     ('scale', StandardScaler())])
cat_pipe = Pipeline([('impute', SimpleImputer(strategy='most_frequent')),
                     ('encode', OneHotEncoder(handle_unknown='ignore'))])

preprocessor = ColumnTransformer([
    ('num', num_pipe, ['Age', 'Fare']),
    ('cat', cat_pipe, ['Sex', 'Embarked'])
])

pipe = Pipeline([('prep', preprocessor), ('clf', LogisticRegression())])
pipe.fit(X, y)
print('Fitted successfully. Output features:', pipe['prep'].transform(X).shape[1])

The remainder Parameter

By default, ColumnTransformer drops columns not explicitly listed. Set remainder='passthrough' to pass through any unlisted columns unchanged, or remainder=StandardScaler() to apply a transformer to them. This is useful when you have many numeric columns and only want to specially handle a few categorical ones.

import pandas as pd
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder

df = pd.DataFrame({
    'age': [25, 40, 35],
    'salary': [50000, 80000, 65000],
    'city': ['NYC', 'LA', 'NYC']
})

# Only encode city; pass through numeric columns
ct = ColumnTransformer([
    ('cat', OneHotEncoder(), ['city'])
], remainder='passthrough')

print(ct.fit_transform(df))

Getting Feature Names After Transformation

After fitting, call columntransformer.get_feature_names_out() to retrieve names for all output columns. OneHotEncoder contributes names like cat__city_NYC; StandardScaler produces names like num__age. These names are essential for interpreting feature importances in tree models trained on the transformed data.

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

df = pd.DataFrame({
    'age': [25, 40, 35],
    'salary': [50000, 80000, 65000],
    'city': ['NYC', 'LA', 'NYC']
})

ct = ColumnTransformer([
    ('num', StandardScaler(), ['age', 'salary']),
    ('cat', OneHotEncoder(sparse_output=False), ['city'])
])
ct.fit(df)

print('Output feature names:')
print(ct.get_feature_names_out())

Grid-Searching Pipeline with ColumnTransformer

To tune parameters of steps inside a ColumnTransformer nested in a Pipeline, use the double-underscore chain: preprocessor__num__scale__with_std or preprocessor__cat__encode__drop. The naming convention is pipeline_step__ct_step__substep__param. It looks verbose but is completely consistent.

from sklearn.model_selection import GridSearchCV
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.linear_model import LogisticRegression

# (assuming preprocessor and pipe are defined as above)
param_grid = {
    'clf__C': [0.1, 1.0, 10.0],
    # 'prep__num__scale__with_std': [True, False]
}

grid = GridSearchCV(pipe, param_grid, cv=3)
# grid.fit(X, y)  # would run on real data
print('Grid ready with params:', list(param_grid.keys()))

ColumnTransformer with Imputation Substeps

For each column type, you can nest its own sub-Pipeline inside the ColumnTransformer to handle multiple sequential operations. The numeric sub-pipe imputes then scales; the categorical sub-pipe imputes (to handle missing strings) then encodes. This structure keeps all preprocessing logic in one auditable object.

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

num_subpipe = Pipeline([
    ('imputer', SimpleImputer(strategy='median')),
    ('scaler', StandardScaler())
])

cat_subpipe = Pipeline([
    ('imputer', SimpleImputer(strategy='most_frequent')),
    ('encoder', OneHotEncoder(handle_unknown='ignore', sparse_output=False))
])

print('Numeric sub-pipeline steps:', [s[0] for s in num_subpipe.steps])
print('Categorical sub-pipeline steps:', [s[0] for s in cat_subpipe.steps])

Validating the Full Pipeline

After building a complex pipeline, run a quick sanity check: fit on a small synthetic dataset, confirm the output shape matches expectations, and verify that predictions are sensible. Catching shape errors early saves debugging time later.

import pandas as pd
import numpy as np
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.impute import SimpleImputer
from sklearn.linear_model import LogisticRegression

X = pd.DataFrame({
    'num1': np.random.randn(100),
    'num2': np.random.randn(100),
    'cat1': np.random.choice(['A', 'B', 'C'], 100)
})
y = np.random.randint(0, 2, 100)

ct = ColumnTransformer([
    ('num', Pipeline([('imp', SimpleImputer()), ('sc', StandardScaler())]), ['num1', 'num2']),
    ('cat', Pipeline([('imp', SimpleImputer(strategy='most_frequent')),
                      ('enc', OneHotEncoder(sparse_output=False))]), ['cat1'])
])

pipe = Pipeline([('prep', ct), ('clf', LogisticRegression())])
pipe.fit(X, y)
print('Accuracy:', pipe.score(X, y).round(4))
print('Transformed shape:', ct.transform(X).shape)

Quick Check

Test your understanding of ColumnTransformer from this lesson.

Lesson Recap

In this lesson you learned: ColumnTransformer applies different transformers to different column subsets simultaneously, nesting ColumnTransformer inside a Pipeline creates one auditable, leak-proof object for mixed-type preprocessing, and double-underscore notation lets you tune any nested parameter via GridSearchCV. Next up we cross-validate and grid-search a full pipeline to find optimal hyperparameters without leakage.

Sıkça Sorulan Sorular

“İşlem Hattı İçinde ColumnTransformer” dersi ücretsiz mi?

Evet — “İşlem Hattı İçinde ColumnTransformer” dersin tüm metni burada web'de ücretsiz olarak okunabilir. Etkileşimli olarak pratik yapmak (yerleşik kod editörü ve 7/24 yapay zeka koçu) ve Machine Learning Academy kursunun geri kalanını açmak için CoddyKit PRO'ya yükselt. Machine Learning Academy kursu toplamda 4 dersten oluşur.

“İşlem Hattı İçinde ColumnTransformer” dersinde ne öğreneceğim?

Farklı türlerdeki ham verilerin elle bölünmeden doğrudan işlenmesini sağlamak için sayısal ve kategorik verilerin birlikte ön işlenmesini gerçekleştiren ColumnTransformer'ı bir Pipeline içine yerleşt… Machine Learning Academy ile uygulamalı kodu tarayıcıda doğrudan çalıştırarak pratik yaparsın ve 7/24 yapay zeka koçu dersi çalışırken sorularını yanıtlar.

Machine Learning Academy öğrenmeye başlamak için deneyim gerekli mi?

Önceden deneyim gerekmez. CoddyKit'te Machine Learning Academy, başlangıçtan ileri seviyeye kadar yapılandırıldığı için buradan başlayabilir veya başından başlayıp kendi hızında ilerleme yapabilirsin. Bu, 4 dersinin 2. dersidir.

“İşlem Hattı İçinde ColumnTransformer” dersi ne kadar sürer?

Çoğu CoddyKit dersi yaklaşık 5–10 dakika sürer. Her biri kısa ve etkileşimli olduğu için sabit ilerleme yaparsın ve web ile uygulama arasında tam olarak bıraktığın yerden devam edebilirsin.

Bu Machine Learning Academy dersinde kod yazıp çalıştırabilir miyim?

Evet. Her Machine Learning Academy dersi yerleşik bir kod editörü içerir, bu sayede tarayıcıda gerçek kod yazıp çalıştırabilir ve anlık yapay zeka geri bildirimi alırsın — yerel kurulum gerekli değildir.

Bu kursun tüm dersleri

  1. İlk İşlem Hattınızı Oluşturma: Ölçekleyici ve Sınıflandırıcı
  2. İşlem Hattı İçinde ColumnTransformer
  3. Tam İşlem Hattını Çapraz Doğrulama ve Izgara Aramasıyla Değerlendirme
  4. joblib ile İşlem Hattını Kaydetme ve Yükleme
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