12.6.1: Review Questions
- Page ID
- 58347
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Review Questions
Multiple Choice
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Which pandas function reads a CSV file into a DataFrame?
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a) pd.load_csv() b) pd.read_csv() c) pd.import_csv() d) pd.open_csv()
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In R's dplyr, which verb is used to create new calculated columns?
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a) filter() b) select() c) mutate() d) arrange()
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A SQL window function like ROW_NUMBER() OVER (PARTITION BY dept ORDER BY salary DESC) does what?
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a) Deletes duplicate rows b) Assigns a rank within each department by salary c) Groups and aggregates by department d) Joins two tables
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Short Answer
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Compare pandas (Python) and dplyr (R) for data manipulation. Write equivalent code in both languages to: filter rows where region = "North", group by product, and sum revenue.
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Explain what a Jupyter Notebook is and why it's valuable for analytics. How does it support reproducibility?
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When would you use SQL for analytics versus Python/R? Describe a scenario where SQL is the better choice and one where Python is preferable.
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What is a SQL window function? How does it differ from GROUP BY aggregation? Provide an example showing a running total.
True/False
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True or False: pandas DataFrames can only hold numeric data.
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True or False: R Markdown documents can include Python code chunks alongside R code.
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True or False: SQL CTEs (WITH clauses) create permanent tables in the database.
Answer Key:
1-b, 2-c, 3-b, 4. Python: df[df['region']=='North'].groupby('product')['revenue'].sum(). R: df %>% filter(region == "North") %>% group_by(product) %>% summarise(total = sum(revenue)). 5. Jupyter = interactive document with code cells, markdown text, and outputs (charts, tables) in one place. Supports reproducibility because anyone can re-run the notebook to regenerate all results; combines code, documentation, and output. 6. SQL better: querying large databases directly, aggregation/joins that the database engine optimizes (e.g., summarizing 100M rows in a warehouse). Python better: complex data transformations, ML modeling, custom visualizations, working with unstructured data (text, images). 7. Window function computes a value for each row using related rows WITHOUT collapsing the result — unlike GROUP BY which reduces rows. Example: SUM(revenue) OVER (ORDER BY date ROWS UNBOUNDED PRECEDING) gives a running total where each row shows cumulative revenue up to that date. 8. False, 9. True, 10. False (CTEs are temporary, existing only for the query).