Handling categorical variables in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to convert the 'color' column to categorical type.

ML Python

df['color'] = df['color'].[1]()

Drag options to blanks, or click blank then click option'

Ato_numeric

Bastype('category')

Cfillna

Ddropna

Attempts:

3 left

💡 Hint

Common Mistakes

Using methods like to_numeric which are for numeric conversion.

Trying to fill missing values instead of converting type.

Using astype('category') converts the column to categorical type, which is efficient for categorical data.

2fill in blank

medium

Complete the code to create one-hot encoded columns for the 'color' categorical variable.

ML Python

encoded_df = pd.get_dummies(df['color'], [1]=False)

Drag options to blanks, or click blank then click option'

Adrop_first

Bprefix

Ccolumns

Ddummy_na

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'prefix' which adds prefixes but does not drop columns.

Using 'dummy_na' which adds a column for NaNs instead of dropping first.

The drop_first=True option avoids multicollinearity by dropping the first category.

3fill in blank

hard

Fix the error in encoding the 'size' column using LabelEncoder.

ML Python

from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
df['size_encoded'] = le.[1](df['size'])

Drag options to blanks, or click blank then click option'

Afit

Btransform

Cfit_transform

Dencode

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'transform' without fitting first causes an error.

Using 'encode' which is not a method of LabelEncoder.

fit_transform fits the encoder and transforms the data in one step, which is needed here.

4fill in blank

hard

Fill both blanks to create a dictionary mapping categories to numbers using pandas.

ML Python

mapping = {cat: num for num, cat in enumerate(df['category'].[1]())}
encoded = df['category'].[2](mapping)

Drag options to blanks, or click blank then click option'

Aunique

Bmap

Cvalue_counts

Dreplace

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'value_counts' which returns counts, not unique categories.

Using 'replace' which can work but is not the method asked here.

unique() gets unique categories, and map() applies the mapping to encode the column.

5fill in blank

hard

Fill all three blanks to create a pipeline that imputes missing values, encodes categories, and fits a model.

ML Python

from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OneHotEncoder
pipeline = Pipeline([
    ('imputer', SimpleImputer(strategy=[1])),
    ('encoder', OneHotEncoder(handle_unknown=[2])),
    ('model', [3]())
])

Drag options to blanks, or click blank then click option'

A'most_frequent'

B'ignore'

CLogisticRegression

D'mean'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'most_frequent' for numeric imputation which may not be ideal.

Using 'error' instead of 'ignore' for unknown categories causing errors.

Not importing or using a model class for the last step.

Use 'mean' strategy to fill missing numeric values, 'ignore' to handle unknown categories gracefully, and LogisticRegression as the model.

Practice

1. Why do we need to convert categorical variables into numbers before using them in machine learning models?

easy

A. Because most machine learning algorithms only understand numbers, not words.

B. Because numbers take less memory than words.

C. Because categorical variables are always continuous data.

D. Because converting to numbers makes the data look nicer.

Solution

1. Step 1: Understand machine learning input requirements

   Most machine learning models work with numerical data and cannot process text or categories directly.

2. Step 2: Reason why conversion is necessary

   Converting categories to numbers allows models to perform calculations and learn patterns from the data.

3. Final Answer:

   Because most machine learning algorithms only understand numbers, not words. -> Option A

4. Quick Check:

   Machine learning needs numbers = D [OK]

Hint: Remember: ML models need numbers, not words [OK]

Common Mistakes:

• Thinking categorical variables are continuous data
• Believing conversion is just for memory saving
• Assuming conversion is for aesthetics

2. Which of the following is the correct way to apply one-hot encoding using pandas on a column named color?

easy

A. pd.encode_onehot(df.color)

B. df['color'].label_encode()

C. df.one_hot_encode('color')

D. pd.get_dummies(df['color'])

Solution

1. Step 1: Recall pandas one-hot encoding method

   Pandas provides get_dummies() to convert categorical columns into one-hot encoded columns.

2. Step 2: Match correct syntax

   The correct syntax is pd.get_dummies(df['color']). Other options are not valid pandas methods.

3. Final Answer:

   pd.get_dummies(df['color']) -> Option D

4. Quick Check:

   Use pd.get_dummies for one-hot encoding [OK]

Hint: Use pd.get_dummies for one-hot encoding in pandas [OK]

Common Mistakes:

• Using non-existent pandas methods
• Confusing label encoding with one-hot encoding
• Calling methods on dataframe incorrectly

3. Given this code snippet:

from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
df['size_encoded'] = le.fit_transform(df['size'])
print(df['size_encoded'].tolist())

If df['size'] contains ['small', 'medium', 'large', 'medium'], what will be the printed output?

medium

A. [0, 1, 2, 1]

B. [2, 1, 0, 1]

C. [1, 2, 0, 2]

D. [0, 2, 1, 2]

Solution

1. Step 1: Understand LabelEncoder mapping

   LabelEncoder assigns integers in alphabetical order: 'large'->0, 'medium'->1, 'small'->2.

2. Step 2: Map each value in the list

   Input list: ['small', 'medium', 'large', 'medium'] maps to [2, 1, 0, 1].

3. Final Answer:

   [2, 1, 0, 1] -> Option B

4. Quick Check:

   Alphabetical order encoding = A [OK]

Hint: LabelEncoder assigns numbers alphabetically [OK]

Common Mistakes:

• Assuming encoding follows input order
• Mixing up label encoding with one-hot encoding
• Forgetting zero-based indexing

4. You tried to apply one-hot encoding on a categorical column but got an error: ValueError: Input contains NaN, infinity or a value too large for dtype('float64'). What is the most likely cause and fix?

medium

A. The column has missing values; fill or drop them before encoding.

B. The column is numeric; convert it to string first.

C. The one-hot encoder only works on integers; convert categories to integers first.

D. The dataset is too large; reduce its size.

Solution

1. Step 1: Analyze the error message

   The error mentions NaN (missing) or infinite values, which cause encoding to fail.

2. Step 2: Identify fix for missing values

   Fill missing values with a placeholder or drop rows before encoding to avoid errors.

3. Final Answer:

   The column has missing values; fill or drop them before encoding. -> Option A

4. Quick Check:

   Handle missing data before encoding [OK]

Hint: Check for missing values before encoding [OK]

Common Mistakes:

• Ignoring missing values in categorical columns
• Trying to encode numeric columns as categories
• Assuming dataset size causes this error

5. You have a dataset with a categorical feature education_level having values: ['high school', 'bachelor', 'master', 'phd']. You want to use this feature in a regression model where order matters. Which encoding method should you choose and why?

hard

A. One-hot encoding, because it creates separate columns for each category.

B. Random encoding, to avoid bias in the model.

C. Label encoding, because it preserves the order of categories as numbers.

D. No encoding needed, use categories as strings directly.

Solution

1. Step 1: Understand the nature of the feature

   Education levels have a natural order: high school < bachelor < master < phd.

2. Step 2: Choose encoding that preserves order

   Label encoding assigns numbers reflecting order, suitable for regression models that consider magnitude.

3. Final Answer:

   Label encoding, because it preserves the order of categories as numbers. -> Option C

4. Quick Check:

   Ordered categories use label encoding [OK]

Hint: Use label encoding for ordered categories [OK]

Common Mistakes:

• Using one-hot encoding for ordered categories
• Ignoring order importance in encoding
• Trying to use strings directly in models