DS0-001 Data Querying And Analysis Practice Question
You are performing data transformation in Python using Pandas. You have a 'DataFrame' with missing values in the 'Revenue' column. Which method should be used to replace these missing values with the column mean?
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
✓
fillna()
The fillna() method is the standard way to replace NaN values in a Pandas DataFrame.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
replace()
Why it's wrong here
replace() is general-purpose but less efficient for NaN-specific handling.
- ✓
fillna()
Why this is correct
fillna() allows replacement of missing values with a specified statistic.
- ✗
dropna()
Why it's wrong here
dropna() removes rows rather than filling values.
- ✗
interpolate()
Why it's wrong here
interpolate() estimates values based on adjacent points, not the mean.
- ✗
map()
Why it's wrong here
map() applies a function to series elements.
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Last reviewed August 2026 · checked against the official CompTIA exam blueprint
This DS0-001 practice question is part of Courseiva's free CompTIA certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DS0-001 exam.