In this assignment, you will TBD.

Notes

You dont need to use input() to get started on this assignment. Use the values shown in the sample sessions to get your solution started, then add input later.

Many of the calculations require you to convert units (from millimeters to meters, from millimeters to inches, from hours to minutes to seconds). Make sure that youre converting the values before doing the calculation!

Use 2 decimal places of precision for speed and 4 decimal places of precision for all other values in your output.

Do not create your own value for . Use the constant that comes from the _math module.

Part 2 covers the same outcomes as part 1, but adds complex math functions from the math module. While you are encouraged to complete part 2 for practice, completing part 1

shows proficiency at the outcomes defined above.

Programmed by

Use the following code snippet at the end of your script to print out the time and end of processing message:

Question 1: Required

In this question, you will calculate some statistics for a csv file named reducedweather.csv. The file contains tabular data which includes Manitoba daily minimum and maximum temperatures from Jan until Sept 17 in 2019. The first row is a header record (row), with headings Max Temp and Min Temp. After the header row, each row is a day where maximum and minimum temperature for that day is recorded. From this file, you need to calculate mean, standard deviation, highest and lowest temperature for each column.

Step 1 Reading file:

The first step is to read the file. The file contains two columns. Even though we already know how many columns are contained in this file, the script that you produce for reading this file must be generic. This means it will work for a .csv file with any number of columns if the initial header record is given.

To accomplish this, you cannot hard-code the data structures to work with a fixed number of columns. You can use lists. For example, after reading the header row, you can split the header with comma. As the split function returns a list, that list will automatically contain all the header but in a separate index of the list.

Once you have read the header record, you know how many columns are contained in the file (by using _{len(headings)} ). Now, you can create another list to hold the data. Suppose, this list is called observations . The observations list contains lists too. The number of list inside the observations list should be equal to the length of headings . So, each list inside the data

corresponds to a column in the file.

To obtain this data structure, you may create an empty _observations list first using observations = [] . Then, you can use a for loop that will iterate n times (considering n is the

length of headings ) to append n empty lists inside observations . Then you can use another for loop to iterate through the rest of the data and append the data to the to appropriate list inside o bservations .

Step 2 Calculating mean, highest and lowest temperature:

After creating the nested list, for each of the lists in the nested list find the mean. You can use the sum(list) function on each of the lists and then divide each sum by each lists length to get the

mean. As there will be multiple means (the number of means will be equal to number of columns), you can store the means in a list too. You can also apply max(list) and min(list) functions on each list of the nested list to obtain highest and lowest temperature of each column. You can save the highest temperatures and lowest temperatures in two different lists.

Step 3 Calculating Standard Deviation

After calculating the mean, you need to calculate the squared differences between the mean and each of the elements of each nested lists. You can create a new nested list to store the squared differences. After finding the squared differences, you can calculate the standard deviation. The formula for standard deviation:

Step 4 Printing Output:

The output needs to be nicely formatted in a tabular form like the sample output given below. The header of the table should be center aligned and the values under each column should be right aligned.

Data source: http://climate.weather.gc.ca/climate_data/daily_data_e.html?StationID=27174 Sample output:

Question 2: Optional (Dont hand in)

Social Insurance Numbers (SINs) are issued by the Canadian Government for administering various government programs. For example, a SIN is required for reporting of earned income from employment. A SIN consists of 9 digits (eg. 046454286). Not all 9-digit numbers represent a valid SIN. There is a simple validation process to determine if a 9-digit number is a valid SIN. Below, we use an example will illustrate how the validation process works.

Suppose the 9-digit number to be validated is 046454286. Take the number 121212121 and write it below 046454286 to get

046454286

121212121

Next, for each of the 9 digit positions, multiply the top and bottom digit to get

0 8 6 8 5 8 2 16 6

Next, add up all the digits together (note that 16 contains the digits 1 and 6) to get

0 + 8 + 6 + 8 + 5 + 8 + 2 + 1 + 6 + 6 = 50

If the sum from the previous line is evenly divisible by 10 (that is, no remainder), then the 9-digit number is a valid SIN. If the sum from the previous line is not evenly divisible by 10, then it is not a valid SIN. For the example 9-digit number 046454286, it is a valid SIN, since 50 is evenly divisible by 10.

Write a program that reads in a 9-digit number and determine if it is a valid SIN.

Try your program with various 9-digit numbers including 630308902, 666234872, 056123456.