A common task in data cleaning is managing "NaN" (Not a number) values. Which…
2026
A common task in data cleaning is managing "NaN" (Not a number) values. Which Pandas function allows you to replace all missing values in a DataFrame with a specific value, such as 0?
Answer: C. df.fillna() — The correct function to replace missing values (NaN) with a specific value like 0 in Pandas is fillna(). For example, df.fillna(0) replaces all NaN values…
- A.
df.dropna()
- B.
df.isna()
- C.
df.fillna()
- D.
df.replace_nan()
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Correct answer: C
The correct function to replace missing values (NaN) with a specific value like 0 in Pandas is fillna(). For example, df.fillna(0) replaces all NaN values with 0. Other functions like dropna() remove rows containing missing values, and isna() checks for them without replacing.
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