Starter code provided in 3rd picture and file provided in 4th picture. Looking for help on (c) and (g).

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Question 1 (14 Points): Purpose: To practice Arithmetic with arrays. Degree of Difficulty: Easy to Moderate. In this question, you will use information from Statistics Canada to analyse percentage changes of Canada national population grouped by ages from year 2013 to year 2017 On Moodle, you will find a CSV file age_statistics.csvwith all the information that you require. Also, a starter python file a5q1_starter.py is provided. Write your assignment based on the given starter file. The provided starter file opens the CSV file and reads the data it contains. You can just run the starter file to take a look at the data. You can see that the CSV file is a tabular file with commas for delimiters (if you want a better view of what the data in the CSV file looks like, open it in Excel. OpenOffice, or similar spreadsheet program; this will nicely visualize the data in columns for you) To complete the assignment, do the following: (a) To analyze data in a file, you first need to separate the data lines/rows from the header lines/rows. If you examine the CSV file, you'll see that first actual data row is the 10th row. Write python code to extract only these data rows from the variable data and assign the result to a variable data1. (b) As you can see, the first “column” of data in data1 gives the age group as a string, e g. “o to 4 years”. We do not need this column for our analysis. Write python code to remove the data in the first column”, convert all of the remaining data to integers (instead of strings), and assign the result to the variable data2. (c) Although we don't want the age group strings in the data that we will use for computation, we still want to refer back to them when we want to output thee result of our analysis. Writee python code to define a dictionary row_age_dct mapping the row index to age group strings. The dictionary should look like: fo '0 to 4 years', 1: '5 to 9 years', 2: '10 to 14 years', '15 to 19 years', 4: '20 to 24 years', 5: '25 to 29 years', 6: 3: 7: 35 to 39 years', 8: '40 to 44 years', 50 to 54 years 30 to 34 years', 45 to 49 years', 12: '60 to 64 years', 11: '55 to 59 years', 9: 10 65 to 69 years', 14: 70 to 74 years', 17: '85 to 89 years', 13: 80 to 84 years', 15: '75 to 79 years', 16: 18: 90 to 94 years', '95 to 99 years', 20: '100 years and over'} 19 (d) Convert the list data2 to a 2D numpy array and assign the result ot the variable data_array. (e) We know that numpy arrays have some commonly used attributes such as the number of dimensions, shape, size and the data type of an array. Print out these 4 attributes of the array data_array. (f) Now let's do a little bit of calculation on the array data_array. Note that the columns of data that we retained are the population for each age group for the years 2013 through 2017. Use the sum() method defined in numpy module (not the built-in sum() function) to get the total population of each year (sum over all age groups), and print them out to the console. You can use help (np.sum) to check how to use this function to get the results required here. You should print out something like this: The total population in year 2013 is 35152370 The total population in year 2014 is 35535348 The total population in year 2015 is 35832513 The total population The total population in year 2016 is 36264604 in year 2017 is 36708083 (g) We would now like to determine year-over-year percentage change in population for the different age groups. Write a function called percentage_change (), which takes two parameters, one is a 2D array (containing the population data) and the other one is an integer which refers to a row index in the 2D array, and calculate the year-over-year percentage change of the age group at the given row index for all years. The return value of the function percentage_change() should be a 1-D array. Document it with a doctring. The year over year percentage change for a population is: (current year population previous year population) previous year population x 100 Hint: you can't compute the year-over-year percentage change for 2013 because you don't have the population data for 2012, so your returned array should be of length 4 and contain the year-over-year percentage changes for 2014 through 2017 Hint: try to use operators on arrays instead of loops to calculate the year-over-year-percentage- changes. (h) Print the following examples to the console by calling the function. print (percentage_change (data_array , print (percentage_change (data_array print (percentage_change (data_array , 19)) print (percentage_change (data_array, 20)) O)) 10)) If you did everything right, the function calls above should produce: [ 0.11459547 [ 0.71633971 [8.1507 2093 0.56734682] 0.1811961 0.90501734 -1.87950579 -2.61922781] 8.53062629] 7.64227642] -0.3930734 10.6718783 10.95626277 [ 2.8125567 6.6782307 1.74726438 (i) Finally, write code to determine which age group had the largest absolute (positive or negative) year- over-year population change from 2013 to 2017 and print this to the console, like this The age group with the highest absolute year-over – year – percentage – change is 95 to 99 years. Hint: You can use loops to call the percentage_change () function to get all the year-over-year-percentage- change for all age groups. Use the row index of the largest percentage change to look up the age group as a string from the row_age_dct dictionary. import.. # put the csv file in the same folder as your program f open('age_statistics.csv', 'r') CSvreader = csv. reader(f, delimiter=',') data = [] for row in csvreader: row1 = [item.replace',', ') for item in rowl # This is used to remove the thousand separator, in each row data.append (row1) print(data) Population estimates on July 1st”,” by age and sex”” 1 2 3 4″,,,, Annual,,,, Table: 17-10-0005-01 (formerly CANSIM 051-0001),,,,, “Geography: Canada, Province or territory”,,,.. י, ,יי Canada,,, Both sexes, , , Age group,2013,2014,2015,2016,2017 ,Persons,m 0 to 4 years,”1,918,924″,”1,921,123″,”1,924,604″,”1,942,022″,”1,953,040″ 5 to 9 years,”1,882,687″,”1,918,323″,”1,952, 041″,”1,985, 144″,”2,003, 143″ 10 to 14 years,”1,868,495″,”1,865,818″,”1,864,760″,”1,886,340″,”1,920,898″| 15 to 19 years,”2,178,288″,”2,137, 784″,”2,097, 043″,”2,066, 404″,”2,056, 445″ 20 to 24 years,”2,445,559″,”2,470,054″,”2,460,317″,”2,467,287″,”2,476,338″| 25 to 29years,”2,408,813″,”2,437,377″,”2,464,184″, “2,515,993”,”2,574,384″ 30 to 34 years,”2,434,220″,”2,479,427″,”2,499,523″,”2,529,348″, “2,553,635” 35 to 39 years,”2,326,722″,”2,367,428″,”2,401,531″,”2,455,403″,”2,506,165″ 40 to 44 years,”2,371,026″,”2, 358, 616'” , “2, 349,528″,”2,345,732″,”2,364, 959'” 45 to 49 years,”2,568,593″,”2,492,188″, “2,432,391”,”2,415,365″,”2,405,165″| 50 to 54 years,”2,754,559″,”2,774,291″,”2,763,386″,”2,711,448″,”2,640,429″ 55 to 59 years,”2,501,797″,”2,557,158″,”2,602,741″,”2,653,893″,”2,683,302″| 60 to 64 years,”2,110,161″,”2,167,664″,”2,234,388″,”2,300,327″,”2,374,636″ 65 to 69 years,”1,747,711″,”1,831,749″,”1,911,216″,”1,976,211″,”1,997,090″ 70 to 74 years,”1,256,700″,”1,315,039″,”1,371,962″,”1,438,585″,”1,547,668″ 75 to 79 years,”947,393″,”973,989″,”1,000, 838″,”1,035, 621″, “1,077, 431″ 80 to 84 years,”729,397″,”738,240″,”745,302'”,”753,852″,”763,413″ 85 to 89 years,”452,747″,”464,667″,”477,845″,”492,434″,”504,232″ 90 to 94 years,”199,304″,”211,417″,”220,767″,”228,925″,”236,012″| 95 to 99 years,”43,763″,”47,330″,”52,381″,”58,120″, “63,078” 100 years and over,”5,511″,”5,666″,”5,765″,”6,150″, “6,620”