Exam will include short-answer questions and essay type questions and they will cover materials from the reading, Discussion, and the additional course materials. The list of exam questions will be shared by the end of Week 5 to help you prepare. Exams will be proctored via Honorlock.Please ensure that your answers are between 150-200 words for each question, including in-text citations for the articles read in class. You are expected to take exams as scheduled. Failure to do so will result in a zero for the missed exam. Only under exceptional circumstances will make-up exams be permitted. A new and alternative exam will be written for this purpose, and the content and format may differ from the original exam format. Make-up exams will only be granted when all three of the following conditions are met: 1) the student notifies me at least 24 hours prior to the scheduled exam time; 2) the circumstances are extenuating; and 3) the student presents proof of the extenuating circumstances.

Given: def divide(num1, num2): if num2 != 0: return num1 / num2 …and: In [11]: divide(a, b) In [12]: What is the value of the variable a?

families is a data frame consisting of records about families, including their number of children and their income (on a scale of 0 – 9). What expression returns a DataFrame containing all records where the family has more than 2 children and all records where the family’s income is above 7?

Consider: 0 not in [n % i for i in range(2,n)] This expression…

Consider: x[~np.isnan(x)] This expression demonstrates what concept we learned in class?

Given: a = np.loadtxt(filename, delimiter=”,”) How does NumPy determine the data type of the array a?

What method can you call to get basic statistics (count, mean, median, std, etc) after grouping a DataFrame?

The pandas DataFrame candidates has, among other columns, the name of applicants for a job opening and a logical (or Boolean) column named complete which indicates whether or not the candidate’s application is complete. Which expression returns a DataFrame consisting of all candidates with complete applications?

The DataFrame df contains, among other columns, a column representing the salary of each entry in the data. To return a DataFrame sorted by salary, you use: df.sort_values(‘salary’) What is the best way to modify this statement such that the record with the highest salary appears first in the output?

Plaintiffs who sue under Section 1981 of the Civil Rights Act of 1866 are NOT subject to the same damage award caps as plaintiffs who sue under Title 7 of the Civil Rights Act of 1964.