Which of the following Pandas statements prints the institutions that contain the string “Univ” in their name? 

What is the output of the following code? list(rankings.iloc)

Consider the following ancestry dictionary:  ancestry = {   “name”: “John”,    “age”: 28,    “born”: “Sheboygan”,    “parent”: {        “name”: “Dean”,        “age”: 53,        “born”: “Milwaukee”,        “parent”: {            “name”: “Mike”,            “age”: 74,            “born”: “Racine”,            “parent”: {                “name”: “Bill”,                “age”: 96,                “born”: “La Crosse”            }        }    }} Which line of code prints “Bill”

What is the output of the following code:  query(“””SELECT COUNT(DISTINCT(Year)) FROM rankings”””).iloc

Assume you want to parse the html code from the course office hours page  “https://cs220.cs.wisc.edu/sum25/office_hours.html” and will respond to requests by returning an HTML file. Which of the options below could replace the ??? in the code to successfully save the HTML code to a file on the user’s machine? import requestsfrom bs4 import BeautifulSoupdef download(url):    try:        r = requests.get(url)       f = open(“office_hours.html”, “w”, encoding=”utf-8″)       f.write(???)        print(“Success!”)    except requests.HTTPError:        print(“Error!”)download(“https://cs220.cs.wisc.edu/sum25/office_hours.html”)

Consider a Dataframe df of fire hydrants, with two columns: (1) year, and (2) BarrelDiameter that represent the year of construction of each fire hydrant and the barrel diameter. How can we draw a horizontal bar plot of the number of hydrants of each year?

Assume you have an html unordered list of stores with their links and the following code is provided: from bs4 import BeautifulSoup html_string = “””Stores    Store 1    Store 2    Store 3″””bs_obj = BeautifulSoup(html_string, “html.parser”)items = bs_obj.find_all(“a”)stores = ???print(stores) Which of the following answers could replace the ???’s so that the output of this code is: ‘www.store1.com’    

Which of the following Pandas statements is equivalent to the following SQL query: query(” SELECT `National Rank` + `Education Rank` + `Employability Rank` FROM rankings WHERE country=’USA'”)

Consider the following code: df = pd.DataFrame({“Player”:, “Score”:, “Games”: })df = df We want to only keep the rows of df which have a Score greater than 50 and Games more than 1. Which of the following code snippets could replace the ??? to accomplish this?

What will be the output of the following code: def func(f, n):    for i in range(n):        f()def dog():   print(“Woof”, end=” “)def cat():   print(“Meow”, end=” “)func(dog, 3)func(cat, 3)