Question 2: In the next cell, load the School1.csv\School2.csv data, which contain 1000 trait values for 2 schools.

school1 = pd.read_csv("school1.csv") 

school2 = pd.read_csv("school2.csv") 

Compute the dissimilarity (according to the above method) between School1 and School2. Call your answer dissimilarity. Use a single line of code to compute the dissimilarity answer.

dissimilarity = sum([abs(number) for number in (np.array(school1["Trait value"])-np.array(school2["Trait value"]))]) #Use only a single line of code

dissimilarity 

14060.558701067917 

Question 3. Suppose you've already decided on a weight for each trait. You saved the weights in a weights.csv file. Load them into a table called weights in the cell below.

weights = pd.read_csv("weights.csv")

weights 

	Weight
0	0.001
1	0.200
2	0.050
3	0.050
4	0.200
...	...
995	0.050
996	0.050
997	0.050
998	0.050
999	0.200

1000 rows 1 columns

Question 4. Now use the revised method to compute a revised dissimilarity between School 1 and School 2.

Hint: Using array arithmetic, your answer should be almost as short as in question 1.

revised_dissimilarity = ... #Use only a single line of code

revised_dissimilarity