Description
Description
In this assignment, you will TBD.
Notes
You don’t 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 you’re 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
import time
print (“\nProgrammed by ******YOU******”)
print (“Date: ” + time.ctime())
print (“End of processing”)
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 list’s 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:
headings = infile.readline().strip().split(‘,’)
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 (Don’t 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
Enter file name: reducedweather.csv
Column Names | Mean | Std Deviation | Highest Score | Lowest Score
————–+—————+—————+—————+—————
Max Temp| 10.10| 16.91| 36.60| -29.80
————–+—————+—————+—————+—————
Min Temp| -2.20| 15.17| 21.30| -39.90
————–+—————+—————+—————+—————
Programmed By Instructor: Sheikh Jubair
University of Manitoba
Computer Science
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.
Handing in
You must complete the Blanket Honesty Declaration checklist{target=”_blank”} before you can
submit your assignment.
An assignment submission area has been created for Assignment 1. Click on the “Assessments”
menu, then click on “Assignments”.
The only thing you’re required to turn in for this assignment is the .py script that you create.
Please name your script file with the following convention:
FirstnameLastnameA1Q1.py