f22-final

CS 220 - Fall 2022 Instructors: Meenakshi Syamkumar, Mike Doescher, and Gurmail Singh Final — 10% (Last) Surname: (First) Given name: NetID (email): @wisc.edu Fill in these fields (left to right) on the scantron form (use #2 pencil): 1. LAST NAME (surname) and FIRST NAME (given name), fill in bubbles 2. IDENTIFICATION NUMBER is your Campus ID number, fill in bubbles 3. Under ABC of SPECIAL CODES, write your lecture number, fill in bubbles: 001 - MWF 11:00am (Meena) 002 - MWF 1:20pm (Meena) 003 - MWF 8:50am (Gurmail) 004 - MWF 9:55am (Mike) 005 - MWF 3:30pm (Mike) 006 - MWF 1:20pm (Gurmail) 4. Under F of SPECIAL CODES, write A and fill in bubble 6 If you miss step 4 above (or do it wrong), the system may not grade you against the correct answer key, and your grade will be no better than if you were to randomly guess on each question. So don’t forget! You may only reference your note sheet. You may not use books, your neighbors, calculators, or other electronic devices during this exam. Please place your student ID face up on your desk. Turn off and put away portable electronics (including smart watches) now. Use a #2 pencil to mark all answers. When you’re done, please hand in the exam and note sheet and your filled-in scantron form. The note sheet will not be returned. Page 1 of 18

Smartphones (Pandas) Consider the following code: import pandas as pd smartphones_dict = { "Model": ["iPhone 13", "iPhone 13 Pro Max", "iPhone 12", \ "Galaxy S22 Ultra", "Galaxy A13"], "Manufacturer": ["Apple", "Apple", "Apple", "Samsung", "Samsung"], "Release Year": [2021, 2021, 2020, 2022, 2022], "Cost ($)": [799, 1099, 599, 955, 149], "Market Share (in %)": [5.5, 3.4, 1.6, 1.4, 1.4] } smartphones = pd.DataFrame(smartphones_dict) 1. What data type does smartphones["Manufacturer"].value counts() return? A. int B. dict C. str D. Series E. DataFrame 2. What will the inner data structures in smartphones dict represent in the smartphones DataFrame? A. rows B. columns C. DataFrames D. lists 3. How can we get the name of the most expensive Model ? A. smartphones["Cost ( $ )"].max()["Model"] B. smartphones["Model"]["Cost ( $ )"].max() C. smartphones[smartphones["Cost ( $ )"] == smartphones["Cost ( $ )"].max()]["Model"] D. smartphones["Cost ( $ )"] == smartphones["Cost ( $ )"].max()["Model"] 4. How can we get the name of the least expensive Manufacturer for each Release Year ? A. smartphones["Cost ( $ )"].value counts().min()[["Manufacturer", "Release Year"]] B. smartphones[["Manufacturer", "Release Year", "Cost ( $ )"]]["Cost ( $ )"].value counts().min() C. smartphones[smartphones["Cost ( $ )"] == smartphones["Cost ( $ )"].min()][["Manufacturer", "Release Year"]] D. smartphones["Release Year"].groupby("Release Year")[["Manufacturer", "Cost ( $ )"]].min() E. smartphones[["Manufacturer", "Release Year", "Cost ( $ )"]].groupby("Release Year").min() Page 2 of 18

FIFA (Database) Consider the following code, and the corresponding output: import sqlite3 import pandas as pd conn = sqlite3.connect("fifa.db") fifa_scores = pd.read_sql("SELECT * from fifa_top_scorers", conn) fifa_scores player name country goals scored year 0 Alex Morgan USA 6 2019 1 Harry Kane England 6 2018 2 James Rodriguez Colombia 6 2014 3 Thomas Muller Germany 5 2010 4 David Villa Spain 5 2010 5 Miroslav Klose Germany 5 2006 9. Which SQL clause enables us to narrow down columns of interest? A. SELECT B. FROM C. WHERE D. GROUP BY E. LIMIT 10. How many rows will the following SQL query return? SELECT COUNT(player_name) FROM fifa_top_scorers GROUP BY goals_scored LIMIT 4 A. 0 B. 1 C. 2 D. 3 E. 4 11. Sort by descending order of year , the rows of players whose goals scored is more than 5. A. SELECT FROM fifa top scorers WHERE goals scored > 5 ORDER BY year DESC B. SELECT * FROM fifa top scorers WHERE goals scored > 5 ORDER BY DESC year C. SELECT * FROM fifa top scorers WHERE goals scored > 5 ORDER BY year DSC D. SELECT * FROM fifa top scorers WHERE goals scored > 5 ORDER BY year DESC 12. Which player name will be excluded when we execute the below SQL query? SELECT player_name FROM fifa_top_scorers WHERE year >= 2010 AND year < 2020 A. Alex Morgan B. Thomas Muller C. David Villa D. Miroslav Klose Page 4 of 18

13. Which SQL query produces a similar output to the following pandas expression? fifa_scores[fifa_scores["country"] == "Germany"]["goals_scored"].mean() A. SELECT goals scored FROM fifa top scorers WHERE country = "Germany" ORDER BY goals scored DESC B. SELECT AVG(goals scored) FROM fifa top scorers WHERE country = "Germany" C. SELECT SUM(goals scored)/MAX(goals scored) FROM fifa top scorers WHERE country = "Germany" D. SELECT AVG(goals scored) FROM fifa top scorers GROUP BY country 14. Which pandas expression will produce the closest results to the following SQL query? SELECT country, MAX(goals_scored) FROM fifa_top_scorers GROUP BY country A. fifa scores["country"].value counts().max() B. fifa scores["goals scored"].value counts().max() C. fifa scores[fifa scores["goals scored"] == fifa scores["goals scored"].max()] D. fifa scores[["country", "goals scored"]].groupby("country").max() 15. Which SQL query answers the following question: Which year (s) had more than 6 goals scored in total? A. SELECT year, SUM(goals scored) AS total goals FROM fifa top scorers WHERE HAVING total goals > 6 GROUP BY year B. SELECT year, SUM(goals scored) AS total goals FROM fifa top scorers GROUP BY year HAVING total goals > 6 C. SELECT year, SUM(goals scored) AS total goals FROM fifa top scorers GROUP BY year ORDER BY total goals DESC D. SELECT year, SUM(goals scored) AS total goals FROM fifa top scorers GROUP BY total goals LIMIT 1 Page 5 of 18

18. Assume that majors df DataFrame column Major contains 4 unique values. In the below code snippet, what should replace the ??? (argument passed to the ax parameter) to produce the below scatter plots? from matplotlib import pyplot as plt majors = list(set(majors_df["Major"])) fig, axes = plt.subplots(ncols=4, sharex=True, sharey=True) plot_area = None for i in range(len(majors)): major = majors[i] major_data = majors_df[majors_df["Major"] == major] # assume that appropriate arguments are passed to other relevant parameters plot_area = major_data.plot.scatter(x="Time studied (hours)", y="GPA", \ ax=???, ...) A. plot area B. axes C. axes[i] D. range(i) E. None 19. When we invoke majors df["GPA"].plot.bar() , which of the following represents the y- axis? A. GPA values B. row indices C. column names D. row integer positions E. column integer positions Page 7 of 18

20. Which of the following options would create the below bar plot representing the number of students in each Major having a GPA above 3.8? A. ax = majors df[majors df["GPA"] > 3.8].value counts().plot.bar() B. ax = majors df[majors df["GPA"] > 3.8].plot.bar() C. ax = majors df[majors df["GPA"] > 3.8]["Major"].plot.bar() D. ax = majors df[majors df["GPA"] > 3.8]["Major"].value counts().plot.bar() 21. Consider the bar plot from the previous question. If we want to change the angle of the x-axis tick labels to 45 degrees, what additional keyword argument should we pass to the below method call? # assume the variable ‘indices’ stores a reference # to the list of x-axis tick labels ax.set_xticklabels(indices, ???) A. flip=45 B. move=45 C. turn=45 D. tilt=45 E. rotation=45 Page 8 of 18

Assume the following is the content of the file christmas list.html : <h1>A Student’s Christmas List 2022</h1> <ol> <li>Gingerbread cookies</li> <li>Action Figures</li> <li>A pony</li> <li> Good grade in:<a href="https://cs220.cs.wisc.edu/f22/schedule.html">CS 220</a> </li> </ol> For the next two questions, assume that we have executed the below code snippet: from bs4 import BeautifulSoup f = open("christmas_list.html") html = f.read() f.close() doc = BeautifulSoup(html, "html.parser") ol = doc.find("ol") li = ol.find_all("li") a = ol.find("a") 24. What is the output of li[3].get text() ? A. None B. A pony C. Good grade in:<a href="https://cs220.cs.wisc.edu/f22/schedule.html">CS 220</a> D. Good grade in:CS 220 E. TypeError 25. Which of the following will enable us to retrieve the URL found in christmas list.html ? A. a B. a.attrs[0] C. a.attrs["href"] D. a.children E. a.find attributes() Page 10 of 18

Assume that a GET request to https://santaclaus.com/christmas checklist.json produces a successful response (200 status code) with the below content: {"Snowmen":3, "Reindeer":9, "Pine Trees":20, "Snowflakes":2000} Assume that the below code snippet is executed: import requests import json r = requests.get("https://santaclaus.com/christmas_checklist.json") r.raise_for_status() 26. Which of the following will enable us to read the JSON content into a Python data structure? A. json.loads(r) B. r.text C. r.json() D. r.text.json() 27. What is the output of r.text.split(", ")[1][-1] ? A. ‘9’ B. ‘"Reindeer":9’ C. ‘"Snowflakes":2000 } ’ D. ‘2000’ Page 11 of 18

30. Which function call will print 2015 ? A. print(cell(0, "year")) B. print(cell(1, "year")) C. print(cell("Wisconsin", "year")) D. print(cell(0, 1)) E. Both A and C 31. What will be printed by the following code snippet? subjects = ["Math", "History", "Chemistry", "Computer Science"] print(sorted(subjects, key=lambda w: -len(w))) A. ["Chemistry", "Computer Science", "History", "Math"] B. ["Math", "History", "Chemistry", "Computer Science"] C. ["Math", "History", "Computer Science", "Chemistry"] D. ["Math", "Chemistry", "History", "Computer Science"] E. ["Computer Science", "Chemistry", "History", "Math"] 32. What will be printed after the code executes? import copy groceries = { "Apple": [2.5, 1], "Banana": 3, "Cherry": 2 } groceries["Cherry"] = 4 groceries_2 = copy.copy(groceries) groceries_2["Banana"] = 5 groceries_2["Apple"][0] = 0.5 print(groceries) A. { "Apple":[0.5, 1], "Banana":3, "Cherry":4 } B. { "Apple":[0.5, 1], "Banana":5, "Cherry":4 } C. { "Apple":[2.5, 1], "Banana":3, "Cherry":2 } D. { "Apple":[2.5, 1], "Banana":3, "Cherry":4 } E. { "Apple":[2.5, 1], "Banana":5, "Cherry":4 } Page 13 of 18

33. Consider the below definition for the function combo . Which of the following function calls would print out False ? def combo(a, b, c=2): return a == 2 or b == 2 or c == 0 A. print(combo(1, 2, 0)) B. print(combo(2, 1, 0)) C. print(combo(2, 2, 0)) D. print(combo(3, 1, 0)) E. print(combo(3, 1, 1)) 34. What is the output after the following code executes? def mystery(n): if n == 0: print("a", end="") n = 10 return None elif n > 0: print("b", end="") mystery(n-1) return None if n == 1: print("c", end="") return None else: print("d", end="") return None mystery(3) A. bbbca B. bbba C. bbbc D. bdbdbca E. Infinite recursion Page 14 of 18

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