DA0-002 Data Acquisition and Preparation Practice Question
A data analyst is using pandas to clean a DataFrame that contains missing values in the 'age' and 'income' columns. Which THREE pandas methods are appropriate for handling missing data? (Select THREE).
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
dropna()
Common pandas methods for missing data include dropna (remove rows with NaN), fillna (replace NaN with a value), and apply with a custom function. Merge is for combining DataFrames; pivot_table is for reshaping.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
dropna()
Why this is correct
Removes rows with missing values.
- ✗
pivot_table()
Why it's wrong here
Used for creating pivot tables, not for missing data handling.
- ✗
merge()
Why it's wrong here
Used to join DataFrames, not for missing data.
- ✓
apply() with a custom function
Why this is correct
Can be used to impute missing values based on conditions.
- ✓
fillna()
Why this is correct
Fills missing values with a specified value.
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