Explanation:
The correct code snippet to perform one-hot encoding on the 'Color' feature while handling missing values is 'df = pd.get_dummies(df, columns=['Color'], dummy_na=True)'. The 'dummy_na=True' parameter ensures that a separate binary column is created for missing values, allowing the model to differentiate between the original missing data and the encoded categories.
Ultimate access to all questions.
No comments yet.
Given a dataset with a categorical feature 'Color' having values 'Red', 'Blue', 'Green', and missing values, write a code snippet to perform one-hot encoding on this feature using Python and the pandas library. Explain how this process handles missing values.
A
df = pd.get_dummies(df, columns=['Color'], dummy_na=True)
B
df = pd.get_dummies(df, columns=['Color'], dummy_na=False)
C
df = pd.get_dummies(df, columns=['Color'], drop_first=True)
D
df = pd.get_dummies(df, columns=['Color'], drop_first=False)