DA0-002 Data Acquisition and Preparation Practice Question
A data analyst is using pandas to clean a DataFrame. They need to replace missing values in the 'age' column with the median age. Which method should they use?
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['age'].fillna(df['age'].median())
fillna() with median() fills NaN values with the median of the column.
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['age'].replace(np.nan, df['age'].mean())
Why it's wrong here
replace() can be used, but mean is not median.
- ✗
df['age'].dropna()
Why it's wrong here
dropna() removes rows with missing values.
- ✓
df['age'].fillna(df['age'].median())
Why this is correct
Correctly fills NaN with median.
- ✗
df['age'].interpolate()
Why it's wrong here
interpolate() fills by interpolation, not median.
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