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Python: Reading And Analyzing Csv Files

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I have a CSV file with student names and their averages for 8 subjects. I have to calculate which students made the honour roll (OVERALL average of 80 or above) and which students

Solution 1:

[EDIT] In collaboration with @edilio I've made a more efficient version that keeps track of ties. There are many of these so it's a rather important distinction. The code is long so I'll host it on a gist.

https://gist.github.com/SamyBencherif/fde7c3bca702545dd22739dd8caf796a

No need for for loops. In fact the syntax in your second for loop was totally botched.

import csv

withopen('C:/Users/rohan/Desktop/Google Drive/honourCSVreader/honour.csv') 
as csv_file:
    csv_list = list(csv.reader(csv_file, delimiter=","))[1:]

    # Subject Awardsprint('The subject award winners are:')
    print('English', max(csv_list, key=lambda row: row[2]))
    print('Math', max(csv_list, key=lambda row: row[3]))
    print('Geography', max(csv_list, key=lambda row: row[4]))

and so on

Solution 2:

My Two cents:

Do both calculations in one loop. Even though using max and lambda looks pretty cool and readable and it still will be O(n), it will also be 9 times slower than the next implementation that uses one loop for both calculations(Honour Roll and Subject Awards):

#!/usr/bin/env pythonimport csv

withopen('/Users/edil3508/Downloads/honours.csv') as csv_file:
    csv_reader = csv.reader(csv_file, delimiter=",")
    next(csv_reader, None)  # skip the headers

    subjects = ['English', 'Math', 'Geography', 'Science', 'Gym', 'History', 'Art', 'Music']
    award_winners = [['', 0], ['', 0], ['', 0], ['', 0], ['', 0], ['', 0], ['', 0], ['', 0]]
    # Honour Rollprint('The honour roll students are:')
    print("-" * 80)
    for row in csv_reader:
        subtotal = 0for i inrange(2, 8 + 2):
            subtotal += int(row[i])
            ifint(row[i]) > award_winners[i-2][1]:
                award_winners[i - 2][0] = row[1] + " " + row[0]
                award_winners[i - 2][1] = int(row[i])
        avg = subtotal / 8if avg > 80:
            print(row[1] + " " + row[0], avg)
    # Subject Awardsprint("-" * 80)
    print('The subject award winners are:')
    print("-" * 80)
    for ix, student_grade inenumerate(award_winners):
        print('{}: {} with {}'.format(subjects[ix], student_grade[0], student_grade[1]))

Output:

The honour roll students are:
----------------------------------------------------------------------
Devin Agorhom 83.75 
Jevon Ahn 84.125 
Chandler Akahira 82.5
Stas Al-Turki 84.25
...

-----------------------------------------------------------------------
The subject award winners are:
-----------------------------------------------------------------------
English: Josiah Gower with99Math: Carson Whicher with99Geography: Ismaila LeBlanc with100Science: Jonathan Emes with100Gym: Woo Taek (James) Irvine with100History: Tami Easterbrook with100Art: Nathan Bryson with100Music: Jamie Bates with100

Solution 3:

Working on a nicer way to do this so that code is clean, modular, and understandable.

https://paiza.io/projects/e/35So9iUPfMdIORGzJTb2NQ

First, read in the student data as a dictionary.

import csv

withopen('data.csv') as csv_file:
    csv_reader = csv.DictReader(csv_file, delimiter=",")
    for line in csv_reader:
        print line

Output:

{'History': '39', 'Last': 'Agalawatte', 'Science': '68', 'Gym': '88', 'Music': '84', 'English': '97', 'Art': '89', 'First': 'Matthew', 'Math': '79', 'Geography': '73'}
{'History': '95', 'Last': 'Agorhom', 'Science': '95', 'Gym': '80', 'Music': '93', 'English': '95', 'Art': '72', 'First': 'Devin', 'Math': '60', 'Geography': '80'}
{'History': '84', 'Last': 'Ahn', 'Science': '98', 'Gym': '71', 'Music': '95', 'English': '91', 'Art': '56', 'First': 'Jevon', 'Math': '95', 'Geography': '83'}
{'History': '97', 'Last': 'Ajagu', 'Science': '69', 'Gym': '82', 'Music': '87', 'English': '60', 'Art': '74', 'First': 'Darion', 'Math': '72', 'Geography': '99'}
{'History': '74', 'Last': 'Akahira', 'Science': '90', 'Gym': '71', 'Music': '79', 'English': '94', 'Art': '86', 'First': 'Chandler', 'Math': '89', 'Geography': '77'}

A lot nicer to work with right?

Now think of each row as a student and then write two functions that evaluate whether the student qualifies for either list.

Figure out how you are going to keep track of the results. Here I'm using some nested dictionaries:

import csv
import json

roles = {}
roles['honor role'] = []
subjects = ['History', 'Science','Gym', 'Music', 'English', 'Art', 'Math', 'Geography']
for subject in subjects:
    roles[subject] = {'highest grade':0, 'students':[]}


defisHonorRole(student):
    ''' Test to see if this student has earned the honor role'''returnFalsedefisSubjectAward(subject, student):
    ''' Test to see if this student has earned the honor role'''returnFalsewithopen('data.csv') as csv_file:
    csv_reader = csv.DictReader(csv_file, delimiter=",")
    for student in csv_reader:

        if isHonorRole(student):
            ''' Add to the honor role '''for subject in subjects:
            if isSubjectAward(subject, student):

All right, now we need to implement the logic that classifies who wins the subject awards.

defisSubjectAward(subject, student):
    ''' Test to see if this student has earned the subject award'''
    grade    = float(student[subject])
    highest  = roles[subject]['highest grade']
    students = roles[subject]['students']

    student = (student['First'], student['Last'])

    # is this grade higher than the current highest?if grade > highest:
        # we have a new highest!# clear the list
        students = []
        students.append(student)

        # set new highest
        highest = grade
    elif grade == highest:
        # add to list of students
        students.append(student)
    else:
        return# There where changes to the list
    roles[subject]['highest grade'] = grade
    roles[subject]['students'] = students

print json.dumps(roles, sort_keys=True, indent=4)

Now we have the subject award winners:

{"Art":{"highest grade":100.0,"students":[["Nathan","Bryson"],["Chase","Putnam"]]},"English":{"highest grade":99.0,"students":[["Josiah","Gower"]]},"Geography":{"highest grade":100.0,"students":[["Ismaila","LeBlanc"]]},"Gym":{"highest grade":100.0,"students":[["Woo Taek (James)","Irvine"]]},"History":{"highest grade":100.0,"students":[["Tami","Easterbrook"]]},"Math":{"highest grade":99.0,"students":[["Carson","Whicher"]]},"Music":{"highest grade":100.0,"students":[["Jamie","Bates"],["Michael","Giroux"]]},"Science":{"highest grade":100.0,"students":[["Jonathan","Emes"],["Jack","Hudspeth"]]},"honor role":[]}

Finding the honor role students should be trivial. Especially if we had a few helper functions:

defgetOverallAverage(student):
    ''' Returns the average of all the student's subject grades '''
    total = sum([float(student[subject]) for subject in subjects])
    return total/len(subjects)

defgetName(student):
    '''Extracts the student's first and last name as a tuple'''return' '.join((student['First'], student['Last']))

defisHonorRole(student):
    ''' Test to see if this student has earned the honor role'''
    cutoff = 80if getOverallAverage(student) >= cutoff:
        roles['honor role'].append(getName(student))

    returnFalse

The honor role is:

"honor role":["Devin Agorhom","Jevon Ahn","Darion Ajagu","Chandler Akahira","Stas Al-Turki","Bryce Allison","Tucker Allison","Eric Andrews","Henry Angeletti","Harry Apps","Jesse Arnold","Benjamin Aucoin","Matthew Bainbridge","Geordie Ball","Sean Barbe","Dwayne Barida","Jamie Bates","Bradley Baverstock","Adam Beckman","Michael Becq","Joshua Berezny","Aaron Best","Doug Bolsonello","Richard Bolton","Trevor Bolton","Travis Bonellos","Daniel Boulet","Nicholas Bowman","Connor Brent","Michael Britnell","Shu Brooks","Cody Brown","Dylan Brown","Mark Brown","Xinkai (Kevin) Brown","Daniel Bryce","Nathan Bryson","Greg Bull","Eric Burnham","Kevin Burns","Rhys Caldwell","Evan Campbell","Jeremiah Carroll","Ian Cass","Robert Cassidy","Matt Catleugh","Garin Chalmers","Matthew Chan","Ryan Cheeseman","Jack Chen","Phillipe Chester","Cameron Choi","Jason Clare","Brandon Clarke","Justin Clarke","Reid Clarke","Brendan Cleland","Andrew Clemens","Matthew Clemens","Pete Conly","Marc Coombs","Leif Coughlin","Michael Cox","Michael Creighton","Raymond Croke","Andrew Cummins","William Cupillari","James Davidson","Maxim Davis","Peter Davis","Daniel Dearham","Michael Deaville","Andrew Decker","Alex Del Peral","Kobe Dick","Alec Dion","Gaelan Domej","Harrison Dudas","Ted Duncan","Andrew Dunkin","Micah Dupuy","Cameron Dziedzic","Tami Easterbrook","Ethan Ellis","Jonathan Emes","Kevin Ernst","Taylor Evans","Jack Everett","Andrew Fabbri","Les Fawns","Cameron Faya","Patrick Feaver","Josh Ferrando","Aidan Flett","Tommy Flowers","Gregory Friberg","Craig Friesen","Keegan Friesen","Ryan Fullerton","Jason Gainer","Adam Gall","Ryan Gallant","Michael Gasparotto","Scott Gerald","Michael Giroux","Ramanand Gleeson","Jack Goldblatt","Daniel Gonzalez-Stewart","Christopher Got","Josiah Gower","Zachary Grannum","Stuart Gray","Gonzalo Grift-White","Aris Grosvenor","Eric Hager","I\u00c3\u00b1igo Hamel","Davin Hamilton","Matthew Hanafy","Christopher Harpur","Tomas Hart","Gage Haslam","Ross Hayward","Sean Heath","Ryan Hess","Matthew Hessey","Stephen Hewis","Michael Hill","Edward Holbrook","Gavin Holenski","Brendan Holmes","Gregory Houston","Douglas Howarth","Conor Hoyle","Agustin Huang","Jack Hudspeth","James Humfries","David Hunchak","Jesse Im","Steve Inglis","Woo Taek (James) Irvine","Kenny James","Eric Jang","Erik Jeong","Michael Jervis","Brett Johnson","Adam Johnston","Ben Johnstone","Taylor Jones","Braedon Journeay","Neil Karakatsanis","David Karrys","Ryan Keane","Josh Kear","Alexander Kee","Joshua Khan","Matthew Kim","David Kimbell Boddy","Daniel King","Tristan Knappett","Timothy Koornneef","Michael Krikorian","George Kronberg","Danny Kwiatkowski","Chris Lackey","Spenser LaMarre","Matthew Lampi","Craig Landerville","Dallas Lane","Matthew Lanselle","Allen Lapko","Cory Latimer","Ben Lawrence","Matthew Lebel","Ismaila LeBlanc","Christopher Lee","Bailey Legiehn","Andy Lennox","Samuel Leonard","Sam Lockner","Jeffrey MacPherson","Simon Mahoney","Lucas Maier","Trent Manley","Jeremy Manoukas","Nathanial Marsh","Alastair Marshall","Connor Mattucci","Samuel McCormick","Cameron McCuaig","Ronan Mcewan","John McGuire","Brian McNaughton","Christopher McPherson","Alistair McRae","Andrew Medlock","Trevor Meipoom","Justin Metcalfe","Chieh (Jack) Miller","Graham Miller","Josh Miller","Salvador Miller","Max Missiuna","Jack Mitchell","Michael Morris","Paul Morrison","Morgan Moszczynski","Curtis Muir","Christopher Murphy","Mark Murphy","Hiroki Nakajima","Michael Neary","James Nelson","John Nicholson","Stephen Nishida","Michael Nowlan","Jason O'Brien","Manny O'Brien","James O'Donnell","Spencer Olubala Paynter","Daniel Ortiz","Jihwan Ottenhof","Joel Ottenhof","Roger Owen","Jason Ozark","Brent Pardhan","Bernard Park","Jason Parker","Alistair Pasechnyk","James Patrick","Hunter Pellow","Jason Pennings","Brant Perras","Michael Petersen","Jordan Petrov","Don Philp","Adam Piil","Ryan Pirhonen","Alex Pollard","Daniel Postlethwaite","John-Michael Potter","Tim Powell","Chad Power","Jack Pratt","Alexander Price","Tyler Purdie","Andrew Purvis","Colin Purvis","Chase Putnam","Kael Radonicich","Curtis Ravensdale","Brett Ray","Forrest Reid","Aiden Ren","Tyler Rennicks","Alden Revell","Joshua Robinson","Richard Roffey","Michael Rose","Nicholas Roy","Christopher Samuel","Chris Sandilands","Christopher Sarbutt","David Saun","David Scharman","Adam Schoenmaker","Derek Schultz","Rocky Scuralli","Turner Seale","Bryan Senn","Alexander Serena","Seth Shaubel","Alex Shaw","Denroy Shaw","William Sibbald","Curtis Simao","Greg Simm","Nicholas Simon","Stuart Simons","Michael Skarsten","Matthew Skorbinski","Greg Slogan","Lucas Smith","Andrew South","Benjamin Sprowl","Jackson Staley","Reid Stencill-Hohn","Matthew Stevens","Jason Sula","Edward Sunderland","James Suppa","Jason Talbot","Tony Tan","Stuart Tang","Alex Temple","Leonard Theaker","Parker Thomas","Matthew Tisi","Scott Toda","Michael Toth","Zachary Trotter","Matthew Underwood","David Ure","Michael Utts","Joey Van Dyk","Jonathan Van Gaal","Chris Vandervies","Ryan Vickery","Dustin Wain","Brian Walker","Young-Jun Walsh","Brad Walton","Zachary Waugh","Matthew Webster","Samuel Welsh","Coleman West","Alexander Westendorp","Carson Whicher","David Whitney","Samuel Wilkinson","Kevin Williams","Aedan Williamson","Jason Wilson","William Wilson","David Wilton","Isaac Windeler","Liam Winter","Timothy Wong","Vladimir Wong","Robert Workman","Brian Yang","Owen Yates","Devin Young","Paul Young","Joshua Zhao"]

DONE

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