ColumnTransformer for mixed types in ML Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of ColumnTransformer with numeric and categorical features

What is the shape of the transformed data after applying this ColumnTransformer?

ML Python

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

X = np.array([[1, 'red'], [2, 'blue'], [3, 'green']])

ct = ColumnTransformer(
    transformers=[
        ('num', StandardScaler(), [0]),
        ('cat', OneHotEncoder(), [1])
    ]
)

X_transformed = ct.fit_transform(X)
print(X_transformed.shape)

A (3, 4)

B (3, 3)

C (3, 5)

D (3, 2)

💡 Hint

Remember that OneHotEncoder creates one column per unique category.

The numeric column is scaled to 1 feature. The categorical column has 3 unique values, so OneHotEncoder creates 3 columns. Total columns = 1 + 3 = 4. Rows remain 3.

❓ Model Choice

intermediate

Choosing transformers for mixed data types

You have a dataset with numeric, categorical, and text columns. Which set of transformers is best to use in a ColumnTransformer?

A RobustScaler for numeric, OrdinalEncoder for categorical, HashingVectorizer for text

B StandardScaler for numeric, OneHotEncoder for categorical, TfidfVectorizer for text

C MinMaxScaler for numeric, LabelEncoder for categorical, CountVectorizer for text

D Normalizer for numeric, OneHotEncoder for categorical, LabelEncoder for text

💡 Hint

Text data needs vectorization, categorical data needs encoding, numeric data needs scaling.

StandardScaler is common for numeric data. OneHotEncoder is suitable for categorical data to avoid ordinal assumptions. TfidfVectorizer is a good choice for text data to capture word importance.

❓ Hyperparameter

advanced

Effect of 'remainder' parameter in ColumnTransformer

What happens if you set remainder='passthrough' in a ColumnTransformer?

A An error is raised if columns are missing in transformers

B Columns not specified are dropped from the output

C All columns are transformed regardless of specification

D Columns not specified in transformers are passed through without changes

💡 Hint

Think about how to keep columns unchanged in the output.

Setting remainder='passthrough' keeps columns not listed in transformers unchanged and includes them in the output.

🔧 Debug

advanced

Error when fitting ColumnTransformer with incompatible data types

What error will this code raise and why? from sklearn.compose import ColumnTransformer from sklearn.preprocessing import StandardScaler, OneHotEncoder import numpy as np X = np.array([[1, 'red'], [2, 'blue'], [3, 4]]) ct = ColumnTransformer( transformers=[ ('num', StandardScaler(), [0]), ('cat', OneHotEncoder(), [1]) ] ) ct.fit(X)

ML Python

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

X = np.array([[1, 'red'], [2, 'blue'], [3, 4]])

ct = ColumnTransformer(
    transformers=[
        ('num', StandardScaler(), [0]),
        ('cat', OneHotEncoder(), [1])
    ]
)

ct.fit(X)

A TypeError because column 1 has mixed types (strings and integers)

B TypeError because StandardScaler cannot process strings

C No error, the code runs successfully

D IndexError because column 1 does not exist

💡 Hint

Check the data types in the categorical column.

Column 1 has mixed types: strings ('red', 'blue') and an integer (4). OneHotEncoder expects consistent types and raises TypeError.

🧠 Conceptual

expert

Why use ColumnTransformer in a machine learning pipeline?

Which is the best explanation for why ColumnTransformer is useful when working with mixed data types?

A It replaces the need for feature selection by removing irrelevant columns

B It automatically detects data types and chooses the best model

C It allows applying different preprocessing steps to different columns in one step, simplifying pipelines

D It converts all data to numeric format without any user input

💡 Hint

Think about handling numeric and categorical data differently.

ColumnTransformer lets you apply different transformations to different columns in a single step, making preprocessing easier and cleaner.

Practice

1. What is the main purpose of using ColumnTransformer in machine learning?

easy

A. To train multiple models on the same dataset

B. To apply different preprocessing steps to different columns in a dataset

C. To visualize data distributions

D. To split data into training and testing sets

Solution

1. Step 1: Understand the role of ColumnTransformer

   ColumnTransformer allows applying different transformations to different columns, such as scaling numbers and encoding text.

2. Step 2: Compare with other options

   Training models, visualizing data, or splitting data are different tasks not handled by ColumnTransformer.

3. Final Answer:

   To apply different preprocessing steps to different columns in a dataset -> Option B

4. Quick Check:

   ColumnTransformer = Different preprocessing per column [OK]

Hint: Think: Different columns, different treatments [OK]

Common Mistakes:

• Confusing ColumnTransformer with model training
• Thinking it splits data instead of transforming
• Assuming it visualizes data

2. Which of the following is the correct way to import ColumnTransformer from scikit-learn?

easy

A. from sklearn.feature_extraction import ColumnTransformer

B. from sklearn.preprocessing import ColumnTransformer

C. from sklearn.pipeline import ColumnTransformer

D. from sklearn.compose import ColumnTransformer

Solution

1. Step 1: Recall the module for ColumnTransformer

   ColumnTransformer is part of the compose module in scikit-learn.

2. Step 2: Verify other options

   Preprocessing, pipeline, and feature_extraction modules do not contain ColumnTransformer.

3. Final Answer:

   from sklearn.compose import ColumnTransformer -> Option D

4. Quick Check:

   ColumnTransformer is in compose module [OK]

Hint: Remember: compose module for combining transformers [OK]

Common Mistakes:

• Importing from preprocessing instead of compose
• Confusing pipeline with compose
• Trying to import from feature_extraction

3. Given the code below, what will be the output of print(transformed_data)?

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

X = np.array([[1, 'red'], [2, 'blue'], [3, 'green']])

ct = ColumnTransformer([
    ('num', StandardScaler(), [0]),
    ('cat', OneHotEncoder(), [1])
])

transformed_data = ct.fit_transform(X)
print(transformed_data)

medium

A. A numpy array with scaled numbers and one-hot encoded colors

B. A list of original values without changes

C. An error because StandardScaler cannot handle strings

D. A numpy array with only scaled numbers

Solution

1. Step 1: Understand ColumnTransformer setup

   Column 0 (numbers) is scaled; column 1 (colors) is one-hot encoded.

2. Step 2: Predict output structure

   Output is a numpy array combining scaled numeric values and one-hot encoded categorical values.

3. Final Answer:

   A numpy array with scaled numbers and one-hot encoded colors -> Option A

4. Quick Check:

   Mixed types transformed correctly = scaled + one-hot [OK]

Hint: Remember: ColumnTransformer applies each transformer to specified columns [OK]

Common Mistakes:

• Expecting original data without transformation
• Thinking StandardScaler will fail on mixed data
• Ignoring one-hot encoding effect

4. What is wrong with the following code snippet using ColumnTransformer?

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

X = np.array([[1, 'red'], [2, 'blue'], [3, 'green']])

ct = ColumnTransformer([
    ('num', StandardScaler(), [0, 1]),
    ('cat', OneHotEncoder(), [1])
])

transformed_data = ct.fit_transform(X)

medium

A. StandardScaler is applied to a string column causing an error

B. OneHotEncoder is applied to a numeric column causing an error

C. ColumnTransformer requires numeric data only

D. No error, code runs fine

Solution

1. Step 1: Check columns assigned to StandardScaler

   StandardScaler is applied to columns 0 and 1, but column 1 contains strings.

2. Step 2: Understand why this causes an error

   StandardScaler cannot process string data, so this will raise a type error.

3. Final Answer:

   StandardScaler is applied to a string column causing an error -> Option A

4. Quick Check:

   Scaler on strings = error [OK]

Hint: Scaler only works on numeric columns [OK]

Common Mistakes:

• Applying scaler to categorical columns
• Assuming ColumnTransformer auto-detects types
• Ignoring column indices in transformer

5. You have a dataset with numeric columns ['age', 'income'] and categorical columns ['city', 'gender']. You want to scale numeric columns and one-hot encode categorical columns using ColumnTransformer. Which code snippet correctly sets this up?

hard

A. ColumnTransformer([('num', OneHotEncoder(), ['age', 'income']), ('cat', StandardScaler(), ['city', 'gender'])])

B. ColumnTransformer([('num', StandardScaler(), ['city', 'gender']), ('cat', OneHotEncoder(), ['age', 'income'])])

C. ColumnTransformer([('num', StandardScaler(), ['age', 'income']), ('cat', OneHotEncoder(), ['city', 'gender'])])

D. ColumnTransformer([('num', StandardScaler(), ['age']), ('cat', OneHotEncoder(), ['income', 'city', 'gender'])])

Solution

1. Step 1: Identify correct transformers for each column type

   Numeric columns should be scaled with StandardScaler; categorical columns should be one-hot encoded.

2. Step 2: Match columns to transformers correctly

   ColumnTransformer([('num', StandardScaler(), ['age', 'income']), ('cat', OneHotEncoder(), ['city', 'gender'])]) assigns numeric columns to StandardScaler and categorical columns to OneHotEncoder correctly.

3. Final Answer:

   ColumnTransformer([('num', StandardScaler(), ['age', 'income']), ('cat', OneHotEncoder(), ['city', 'gender'])]) -> Option C

4. Quick Check:

   Numeric scaled + categorical one-hot = ColumnTransformer([('num', StandardScaler(), ['age', 'income']), ('cat', OneHotEncoder(), ['city', 'gender'])]) [OK]

Hint: Match scaler to numbers, encoder to categories [OK]

Common Mistakes:

• Swapping transformers between numeric and categorical
• Mixing columns in wrong transformer
• Leaving out columns