DA0-002 Data Acquisition and Preparation Practice Question
You are using pandas in Python to clean a dataset. You notice several rows with missing values in the 'age' column. Which method would you use to remove those rows?
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
✓
df.dropna()
df.dropna() removes rows with any missing values by default. df.fillna() fills missing values, df.isna() returns a boolean mask, df.drop_duplicates() removes duplicate rows.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
df.drop_duplicates()
Why it's wrong here
Removes duplicate rows, not missing values.
- ✓
df.dropna()
Why this is correct
Removes rows with any missing values by default.
- ✗
df.fillna(0)
Why it's wrong here
Fills missing values with 0, does not remove rows.
- ✗
df.isna()
Why it's wrong here
Returns a DataFrame of booleans indicating missing values.
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