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.csv with all the information that you require. ALso, a starter python file a6q1 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 datai gives the age group as a string, e.g. "0 to 4 years" We do notneed this column for our analysis. Write python code to remove the data in the first "column" 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 the result of our analysis. Write python code to define a dictionary row_age_dct mapping the row index to age group strings. The dictionary should look like: CO 0 to 4 years', 1: 5 to 9 years', 2 '10 to 14 years 8: 15 to 19 years', 4:'20 to 24 yeare', 5 25 tc 29 years, 6 30 to 34 years', 7 35 to 39 yearg', 8 40 tc 44 years 9: 45 to 49 years ', 10: 50 tc 54 years',11 55 to 59 years' , 12: ,60 to 64 years, , 13: ,65 ;o 69 years, , 14: ,70 t 74 years', 15: 75 to 79 years ', 16: 80 co 84 years', 17 85 to 89 years', 18: 90 to 94 years ', 19: '95 to 99 years', 20: 100 years and over'h (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 in year 2016 is 36264604 The total population in year 2017 is 36708083 (g) We would now like to determine year-over-year percentage change in population for the different age tage_change), Which takes two parameters, one is a 2D array in the 2D ow inde Write a f ed groups (containing the population data) and the other one is a array, and calc pe teger which r te the year-over r percentage change of the age group at the giv