Encoding categorical variables in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the library for encoding categorical variables.

Data Analysis Python

from sklearn.preprocessing import [1]

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AMinMaxScaler

BStandardScaler

COneHotEncoder

DPCA

Attempts:

3 left

💡 Hint

Common Mistakes

Importing scalers instead of encoders.

Confusing PCA with encoding.

The OneHotEncoder is used to convert categorical variables into a format that can be provided to ML algorithms.

2fill in blank

medium

Complete the code to create an encoder object for categorical data.

Data Analysis Python

encoder = [1](sparse=False)

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AOneHotEncoder

BMinMaxScaler

CStandardScaler

DLabelEncoder

Attempts:

3 left

💡 Hint

Common Mistakes

Using LabelEncoder which returns single column labels.

Using scalers which are for numeric data.

OneHotEncoder creates binary columns for each category. Setting sparse=False returns a dense array.

3fill in blank

hard

Fix the error in the code to encode the 'color' column of the DataFrame.

Data Analysis Python

encoded = encoder.fit_transform(df[['[1]']])

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AColor

Bcolor

Ccolors

Dcolour

Attempts:

3 left

💡 Hint

Common Mistakes

Using plural or misspelled column names.

Ignoring case sensitivity.

The column name is 'color' exactly as in the DataFrame. Case and spelling must match.

4fill in blank

hard

Fill both blanks to create a dictionary mapping each category to its encoded column index.

Data Analysis Python

mapping = {category: [1] for category, [2] in zip(encoder.categories_[0], range(len(encoder.categories_[0])))}

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Aindex

Bidx

Ccategory

Dvalue

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping variable names.

Using the same name for both variables.

We map each category to its index. The second variable in zip is the index, here named idx.

5fill in blank

hard

Fill all three blanks to create a DataFrame from the encoded array with proper column names.

Data Analysis Python

encoded_df = pd.DataFrame(encoded, columns=[[1] + '_' + str([2]) for [3] in encoder.categories_[0]])

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Acategory

Bi

Ccategories

Didx

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong variable names causing errors.

Mixing up loop variable and index.

We create column names by combining each category with its index i. The loop variable is category.