Chapter 3 Quiz: Python for the Business Professional

Test your understanding of Python fundamentals, pandas basics, and the business case for coding. Select the best answer for each question.

Question 1

What is the primary advantage of Python over Excel for business data analysis?

A) Python is faster for all calculations B) Python can handle larger datasets, automate workflows, and integrate with AI/ML tools C) Python produces better-looking charts D) Python does not require any learning curve

Question 2

What does the following code output?

revenue = 500000
costs = 350000
margin = (revenue - costs) / revenue * 100
print(f"Margin: {margin}%")

A) Margin: 30% B) Margin: 30.0% C) Margin: 0.3% D) An error, because you cannot multiply by 100

Question 3

Which of the following is a valid Python variable name?

A) 2nd_quarter B) second-quarter C) second_quarter D) second quarter

Question 4

What error does this code produce?

store_name = "Downtown"
store_count = 47
message = store_name + " has " + store_count + " employees"

A) NameError B) SyntaxError C) TypeError D) ValueError

Question 5

What does the following code print?

scores = [85, 92, 78, 95, 88]
print(scores[1])

A) 85 B) 92 C) 78 D) An error, because lists start at index 1

Question 6

What is the output of this code?

quarterly_revenue = 280000
target = 300000

if quarterly_revenue >= target:
    status = "Met"
elif quarterly_revenue >= target * 0.9:
    status = "Near"
else:
    status = "Missed"

print(status)

A) Met B) Near C) Missed D) An error

Question 7

What does pd refer to in the following code?

import pandas as pd
df = pd.read_csv("sales.csv")

A) A built-in Python module B) An alias (nickname) for the pandas library C) A variable that stores the CSV file path D) The name of the DataFrame

Question 8

Which pandas method shows the first 5 rows of a DataFrame?

A) df.first() B) df.top(5) C) df.head() D) df.preview()

Question 9

What is the correct way to filter a pandas DataFrame for rows where revenue exceeds $50,000?

A) df.filter(revenue > 50000) B) df[df["revenue"] > 50000] C) df.where("revenue" > 50000) D) df.select(revenue > 50000)

Question 10

What does the following code return?

sales_df.groupby("region")["revenue"].mean()

A) The total revenue for each region B) The average revenue for each region C) A filtered DataFrame showing only the "region" and "revenue" columns D) A single number — the overall average revenue

Question 11

Which of the following is NOT a valid pandas data type?

A) int64 B) float64 C) object (used for strings) D) currency

Question 12

What is wrong with this code?

northeast = sales_df[sales_df["region"] == "Northeast" & sales_df["revenue"] > 50000]

A) Nothing — the code is correct B) Each condition must be wrapped in parentheses when using & C) You should use and instead of & D) You cannot filter on two conditions at once in pandas

Question 13

What does df.shape return?

A) The number of rows B) The number of columns C) A tuple of (rows, columns) D) A description of the DataFrame's data types

Question 14

What does the following function return when called with calculate_discount(100, 0.15)?

def calculate_discount(price, rate=0.10):
    return price * (1 - rate)

A) 90.0 B) 85.0 C) 15.0 D) 100.0

Question 15

You receive this error:

KeyError: 'revenues'

What is the most likely cause?

A) The pandas library is not installed B) You misspelled a column name — the column is probably revenue, not revenues C) The CSV file is corrupted D) Your DataFrame is empty

Question 16

What is a Jupyter notebook cell?

A) A single unit of code or text within a notebook B) A type of spreadsheet cell C) A function that runs automatically D) A pandas data structure

Question 17

Which keyboard shortcut runs a cell and moves to the next one in Jupyter?

A) Ctrl+R B) Shift+Enter C) Alt+R D) F5

Question 18

What does this list comprehension produce?

prices = [10, 25, 50, 75, 100]
result = [p * 1.08 for p in prices if p >= 50]

A) [10.8, 27.0, 54.0, 81.0, 108.0] B) [54.0, 81.0, 108.0] C) [50, 75, 100] D) An error

Question 19

A colleague argues that learning Python is unnecessary because their company already uses Tableau for visualization. Which response best captures the argument from this chapter?

A) Python is always better than Tableau B) Python is free, while Tableau requires a license C) Python handles the full analytics pipeline — data loading, cleaning, analysis, modeling, and automation — while Tableau primarily handles visualization D) Tableau cannot connect to databases

Question 20

What is the output of this code?

store = {"name": "Downtown", "region": "NE", "revenue": 320000}
store["employees"] = 45
print(len(store))

A) 3 B) 4 C) 45 D) An error — you cannot add keys to a dictionary

Question 21

What does df.describe() show?

A) The first 5 rows of the DataFrame B) Column names and data types C) Summary statistics (count, mean, std, min, quartiles, max) for numeric columns D) The total memory usage of the DataFrame

Question 22

Which of the following best describes the relationship between Python and SQL for business analytics?

A) Python has replaced SQL entirely B) SQL is used for querying databases; Python is used for analysis, modeling, and automation — they are complementary C) SQL is better for all data tasks D) Python and SQL do exactly the same things

Question 23

You run the following code and get no output (an empty DataFrame):

result = sales_df[sales_df["revenue"] > 999999]

What is the most likely explanation?

A) The code has a syntax error B) No rows in the dataset have revenue above 999,999 C) The revenue column does not exist D) The DataFrame is corrupted

Question 24

What does the axis=1 parameter mean in pandas operations like df.apply(func, axis=1)?

A) Apply the function to each column B) Apply the function to each row C) Apply the function to the first row only D) Apply the function to both rows and columns

Question 25

Professor Okonkwo says: "You are not learning to be software engineers. You are learning to ask better questions." Which of the following best illustrates this principle?

A) Writing the most computationally efficient Python code possible B) Using Python to load, explore, filter, and summarize business data to answer specific business questions faster than manual methods allow C) Memorizing every pandas function D) Building a full web application in Python

Answer Key

  1. B — Python's advantages are scale, automation, and integration with the broader AI/ML ecosystem, not raw speed for small calculations.

  2. B — The calculation yields 30.0 (a float), and the f-string prints it with the decimal.

  3. C — Variable names cannot start with a number (A), contain hyphens (B), or contain spaces (D).

  4. C — TypeError: Python cannot concatenate a string and an integer with +. You need str(store_count) or an f-string.

  5. B — Python lists are zero-indexed. scores[0] is 85, scores[1] is 92.

  6. B — 280,000 is below 300,000 (not "Met") but above 270,000 (which is 90% of 300,000), so the status is "Near."

  7. B — import pandas as pd loads the pandas library and assigns it the alias pd.

  8. C — df.head() displays the first 5 rows by default. You can pass a number to change this (e.g., df.head(10)).

  9. B — Boolean indexing with df[df["column"] > value] is the standard filtering syntax in pandas.

  10. B — .mean() computes the average. The groupby("region") splits data by region first.

  11. D — There is no currency dtype in pandas. Monetary values are stored as int64 or float64.

  12. B — Without parentheses, Python evaluates "Northeast" & sales_df["revenue"] first due to operator precedence. Correct: (sales_df["region"] == "Northeast") & (sales_df["revenue"] > 50000).

  13. C — .shape returns a tuple like (240, 6) representing (rows, columns).

  14. B — The second argument (0.15) overrides the default (0.10). Result: 100 * (1 - 0.15) = 85.0.

  15. B — A KeyError in pandas almost always means you tried to access a column that does not exist, usually due to a typo.

  16. A — A cell is the basic unit of a Jupyter notebook, containing either code or markdown text.

  17. B — Shift+Enter runs the current cell and advances to the next one.

  18. B — The comprehension filters for prices >= 50 first (keeping 50, 75, 100), then multiplies each by 1.08.

  19. C — Python covers the full analytics pipeline; Tableau is specialized for visualization. They serve different purposes and can complement each other.

  20. B — The dictionary starts with 3 keys; adding "employees" makes it 4.

  21. C — describe() provides summary statistics for all numeric columns.

  22. B — SQL excels at querying and aggregating data in databases; Python excels at analysis, visualization, modeling, and automation. Most analysts use both.

  23. B — An empty result from a filter means no rows matched the condition. The threshold is likely too high for this dataset.

  24. B — axis=1 means "operate across columns for each row." axis=0 (the default) means "operate down rows for each column."

  25. B — The chapter's core message is that Python is a tool for answering business questions, not an end in itself.