The correct answer is sales_df.fillna(value={"PaymentAmount":0.02}, inplace=True).

This call uses DataFrame.fillna with a dictionary that maps the column name to the replacement value and it uses inplace=True so the original DataFrame is updated. fillna will replace NaN entries in the numeric PaymentAmount column with 0.02 without altering other columns.

sales_df.replace(value={"PaymentAmount":0.02}, inplace=True) is incorrect because replace as written does not target NaN specifically. To replace missing values with replace you would need to specify the value to replace such as numpy.nan or use a different mapping. The provided call will not reliably substitute NaN with 0.02.

sales_df.fillna("PaymentAmount"=0.02) is incorrect because it is not valid Python syntax and fillna expects the replacements to be passed with value= or as a dictionary rather than using a quoted name as a keyword. This form would also omit inplace so it would not update the original DataFrame.

sales_df.dropna(inplace=True) is incorrect because dropna removes rows that contain missing values rather than substituting them with a specified value. That would delete records instead of filling PaymentAmount with 0.02.

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