Problem 2 My Solutions > The MAT file called Postlab2Data.mat contains data pertaining to three metallic samples from a corrosion experiment. These samples are represented as structure-class MATLAB variables: sample1 sample2 sample3 sample1, sample2, and sample3 each have four fields, specified by the names: Alloy ■ Mass ■ Media ■ Thickness Write a MATLAB script that completes the following Loads the variables contained in the Postlab2Data.mat file into the worspace ■ Defines a new variable called copperMass that contains the mass of sample2 ■ ■ Defies a new variable called thkDiff that contains the absolute-value of the difference in thickness between sample1 and sample3 Changes the media of sample2 to 'Saline Solution' Creates a new variable called sample4 that has the following attributes: Alloy = 'Ti-6Al-4V'; Mass = 1.5524; Media = 'None'; Thickness = 1.72;

Please elaborate on the benefits of using high-order, pure functions.

Seq is a sequence object that can be imported from Biopython using the following statement: from Bio.Seq import Seq If my_seq is a Seq object, what is the correct Biopython code to print the reverse complement of my_seq? Hint. Use the built-in function help you find out the methods of the Seq object. print('reverse complement is %s' % complement(my_seq.reverse())) print('reverse complement is %s' % my_seq.reverse()) print('reverse complement is %s' % my_seq.reverse_complement()) print('reverse complement is %s' % reverse(my_seq.complement()))

I need to merge three datasets so that I can create a custom function that would do the following: Create a function called theme_by_year that takes as input a year (as an integer) and shows the theme ids and theme names (listed in order by theme id) that were in sets that year. The column names must be id and name_themes (to differentiate between the name of a theme and the name of a set) in that order. The index should be reset and go from 0 to n-1. Each theme should only be listed once even if it appeared in more than one set from that year -- duplicate themes should be based on theme id and not name since there are some themes with the same name but with a different id. Hint: It will help if you were to think about merging appropriate DataFrames to help you get this answer. The desired output should look like this: theme_by_year(1960) Output: id    name_themes0    371    Supplemental1    497    Books2    513    Classic Here's what I tried, but I did not get the desired output: