ALSO, PLEASE USE THE GIVEN INFORMATION for AGES:

https://ufile.io/ryviq

the program should be I'm Python 3.

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, csv with all the information that you require. Also, a starter python file a6a1-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) lo 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. "0 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 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: 10 '0 to 4 years', 1 '5 to 9 years', 2 10 to 14 years, 3: 15 to 19 years', 4: '20 to 24 years', 5 25 to 29 years', 6: 30 to 34 years', 7: '35 to 39 years', 840 to 44 years ' , 9 45 to 49 years', 10 '50 to 54 years', 11: '55 to 59 years' , 12 '60 to 64 years', 13: 65 to 69 years',14: 70 to 74 years', 15 75 to 79 years', 16: '80 to 84 years', 17: 85 to 89 years', 18 '90 to 94 years', 19: '95 to 99 years', 20: '100 years and over') (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 351 52370 The total population in year 2014 is 35535348 The total population in year 2015 is 358 32513 The total population in year 2016 is 3626460 4 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 groups. Write a function called per centage_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 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, 0)) print (percentage_change (data_array, 10)) print (percen tage_change (data_array, 19)) print (percentage_change (data_array, 20)) If you did everything right, the function calls above should produce 0.11459547 0.1811961 0.90501734 0.56734682] 0.71633971 -0.3930734 1.87950579 2.619227 81] 8.15072093 10.6718783 10.95626277 8.53062629] 2.81255671.74726438 6.67823077.64227642] (i) Finally, write code to determine which age group across ALL age groups, not just the 4 from part h. had the largest absolute (positive or negative) year-over-year population change from 2013 to 2017 (that is the age group that had the SINGLE largest change in any year as opposed to the age group that had the highest average change), 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. int: You can use loops to call the per centage_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 # continue your codes based on this starter file mport numpy as np import csv 9 10 | # put the csv file in the same folder as your program open ('age statistics.csv r' ssvreader-csv.reader (f, delimiter-,') data [ for row in csvreader: 12 13 14 15 1 6 rowi- [item. replace(', ', '') for item in row] This is used to remove the thousand separator , in each row data.append (rowl) print (dat.a) 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, csv with all the information that you require. Also, a starter python file a6a1-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) lo 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. "0 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 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: 10 '0 to 4 years', 1 '5 to 9 years', 2 10 to 14 years, 3: 15 to 19 years', 4: '20 to 24 years', 5 25 to 29 years', 6: 30 to 34 years', 7: '35 to 39 years', 840 to 44 years ' , 9 45 to 49 years', 10 '50 to 54 years', 11: '55 to 59 years' , 12 '60 to 64 years', 13: 65 to 69 years',14: 70 to 74 years', 15 75 to 79 years', 16: '80 to 84 years', 17: 85 to 89 years', 18 '90 to 94 years', 19: '95 to 99 years', 20: '100 years and over') (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 351 52370 The total population in year 2014 is 35535348 The total population in year 2015 is 358 32513 The total population in year 2016 is 3626460 4 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 groups. Write a function called per centage_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 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, 0)) print (percentage_change (data_array, 10)) print (percen tage_change (data_array, 19)) print (percentage_change (data_array, 20)) If you did everything right, the function calls above should produce 0.11459547 0.1811961 0.90501734 0.56734682] 0.71633971 -0.3930734 1.87950579 2.619227 81] 8.15072093 10.6718783 10.95626277 8.53062629] 2.81255671.74726438 6.67823077.64227642] (i) Finally, write code to determine which age group across ALL age groups, not just the 4 from part h. had the largest absolute (positive or negative) year-over-year population change from 2013 to 2017 (that is the age group that had the SINGLE largest change in any year as opposed to the age group that had the highest average change), 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. int: You can use loops to call the per centage_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 # continue your codes based on this starter file mport numpy as np import csv 9 10 | # put the csv file in the same folder as your program open ('age statistics.csv r' ssvreader-csv.reader (f, delimiter-,') data [ for row in csvreader: 12 13 14 15 1 6 rowi- [item. replace(', ', '') for item in row] This is used to remove the thousand separator , in each row data.append (rowl) print (dat.a)