DA0-002 Data Acquisition and Preparation Practice Question
A data analyst is using pandas in Python to clean a dataset. Which method is most appropriate to replace missing numerical values with the median of the column?
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.fillna(df.median())
fillna with median replaces missing values with median.
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.fillna(df.median())
Why this is correct
fillna with median replaces nulls with median.
- ✗
df.interpolate()
Why it's wrong here
interpolate estimates missing values based on neighboring values.
- ✗
df.dropna()
Why it's wrong here
dropna removes rows with missing values.
- ✗
df.replace(np.nan, df.mean())
Why it's wrong here
replace can replace values but using mean, not median.
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