A DataFrame has a 'Score' column with values that include 'N/A', 'NA', 'null', and actual numbers. The analyst wants to treat all these non-numeric strings as missing values and then impute them. What is the most efficient way to handle this?

Drop all rows where 'Score' is not numeric

Use df['Score'].astype(float) and catch the ValueError

Use pd.to_numeric(df['Score'], errors='coerce') to convert invalid entries to NaN, then impute

Replace each string individually using df.replace() for 'N/A', 'NA', and 'null'

Python InstitutePCAD - Certified Associate in Data Analytics with Python

Domain 1Objective 4

Data Preparation Techniques PCAD-31 Practice Questions (Page 6)

Part of the Data Acquisition and Pre-Processing domain, which accounts for 29.2% of the PCAD-31 exam. Python Institute does not publish an official question count, but from its 60-minute exam (~25–40 total, ~7–7 in this domain), expect 2–2 from this objective — we provide 27 practice questions to prepare you well beyond it. (estimate)

27questions here

6free pages

6concepts

29.2%of the exam

Questions 26–27

26foundation · easy
After loading a DataFrame, which pandas method displays the first few rows to quickly inspect the data?
Select an answer first
27expert · hard
A DataFrame has a 'Score' column with values that include 'N/A', 'NA', 'null', and actual numbers. The analyst wants to treat all these non-numeric strings as missing values and then impute them. What is the most efficient way to handle this?
Select an answer first

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Data Preparation Techniques PCAD-31 Practice Questions (Page 6)

Questions 26–27

After loading a DataFrame, which pandas method displays the first few rows to quickly inspect the data?

A DataFrame has a 'Score' column with values that include 'N/A', 'NA', 'null', and actual numbers. The analyst wants to treat all these non-numeric strings as missing values and then impute them. What is the most efficient way to handle this?

Keep studying — Data Acquisition and Pre-Processing