Using Python to analyze the makeup of a given BAS-SD cohort.

cohort_analyzer.py

import csv
from statistics import mean

# set up empty dict containers
associate_type = dict() # row 6
associate_from = dict() # row 7
maths = dict() # row 14
advisors = dict() # row 15

# set up empty list containers
it_201 = [] # row 9
it_206 = [] # row 10
it_207 = [] # row 11
it_219 = [] # row 12
it_220 = [] # row 13

with open("software_student_list.csv") as csv_file:
    csv_reader = csv.reader(csv_file)
    headers = next(csv_reader)
    row_count = 0
    for row in csv_reader:
        row_count += 1
        # read for associate type
        try:
            associate_type[row[6]] += 1
        except KeyError:
            associate_type[row[6]] = 1   

        # read for associate from
        try:
            associate_from[row[7]] += 1
        except KeyError:
            associate_from[row[7]] = 1

        # read for math
        try:
            maths[row[14]] += 1
        except KeyError:
            maths[row[14]] = 1

        # read for advisor
        try:
            advisors[row[15]] += 1
        except KeyError:
            advisors[row[15]] = 1

        # read for IT 201
        try:
            it_201.append(float(row[9]))
        except ValueError:
            pass

        # read for IT 206
        try:
            it_206.append(float(row[10]))
        except ValueError:
            pass
        
        # read for IT 207
        try:
            it_207.append(float(row[11]))
        except ValueError:
            pass
        
        # read for IT 219
        try:
            it_219.append(float(row[12]))
        except ValueError:
            pass
        
        # read for IT 220
        try:
            it_220.append(float(row[13]))
        except ValueError:
            pass
    
# sort dicts into list of tuples, highest to lowest
sorted_associate_type = sorted(associate_type.items(), key=lambda x:x[1], reverse=True)
sorted_associate_from = sorted(associate_from.items(), key=lambda x:x[1], reverse=True)
sorted_maths = sorted(maths.items(), key=lambda x:x[1], reverse=True)
sorted_advisors = sorted(advisors.items(), key=lambda x:x[1], reverse=True)

# print results
print(f"\nNUMBER OF APPLICATIONS RECEIVED:\n")
print(row_count)

print("\nASSOCIATE TYPE:\n")
for k,v in sorted_associate_type:
    print(k, "=", v)

print("\nASSOCIATE FROM:\n")
for k,v in sorted_associate_from:
    print(k, "=", v)

print("\nMATHS EARNED:\n")
for k,v in sorted_maths:
    print(k, "=", v)

print("\nASSIGNED ADVISOR:\n")
for k,v in sorted_advisors:
    print(k, "=", v)

print("\nMEAN GPA FOR PROGRAMMING PREREQS:\n")
print(f"IT 201 GPA: {round(mean(it_201), 2)} ({len(it_201)} reported)")
print(f"IT 206 GPA: {round(mean(it_206), 2)} ({len(it_206)} reported)")
print(f"IT 207 GPA: {round(mean(it_207), 2)} ({len(it_207)} reported)")
print(f"IT 219 GPA: {round(mean(it_219), 2)} ({len(it_219)} reported)")
print(f"IT 220 GPA: {round(mean(it_220), 2)} ({len(it_220)} reported)")