Common Usage Examples

Importing the Fordham Dataset

This requires approval by the department first. After that, write a python file in the same directory as the dataset, with the correct file name:

Python

from edmlib import gradeData, classCorrelationData

df = gradeData("fordhams_dataset_fileName.csv")
df.defineWorkingColumns('OTCM_FinalGradeN', 'SID', 'REG_term', 'REG_CourseCrn',
                        'REG_Programcode', 'REG_Numbercode', 'GRA_MajorAtGraduation')

Filtering Data to Certain Classes or Majors

For majors, define a python list with major or department names, making sure they are spelled the same in the defined ‘classDept’ column (this is the ‘REG_Programcode’ column in the fordam dataset).

For classes, define a python list with class names that match the defined ‘classCode’ column. If ‘classCode’ was not defined but instead both ‘classDept’ and ‘classNumber’ were (like in the fordham dataset), these columns are added to define the ‘classCode’ column automatically, so use the concatenation of the two columns (e.g. ‘Psych1000’ from ‘Psych’ and ‘1000’).

This example filters to Computer Science classes and “core” classes, as defined by Fordham.

Python

from edmlib import gradeData, classCorrelationData

df = gradeData("fordhams_dataset_fileName.csv")
df.defineWorkingColumns('OTCM_FinalGradeN', 'SID', 'REG_term', 'REG_CourseCrn',
                        'REG_Programcode', 'REG_Numbercode', 'GRA_MajorAtGraduation')

majorsToFilterTo = ['Computer and Info Science',
                    'Psychology']
coreClasses = [ 'Philosophy1000',
                'Theology1000',
                'English1102',
                'English1101',
                'History1000',
                'Theology3200',
                'VisualArts1101',
                'Physics1201',
                'Chemistry1101']

df.filterToMultipleMajorsOrClasses(majorsToFilterTo, coreClasses)

A similar filter is available for the correlational data class:

Python

data = classCorrelationData('outputCorrelation.csv')
data.filterToMultipleMajorsOrClasses(majorsToFilterTo, coreClasses)

Filtering Data to Students of Specific Majors

For filtering data to students who have declared certain majors, the ‘studentMajor’ column should have been defined with ‘defineWorkingColumns’. It should be noted that if a student ever declared one of these majors, they will be included. The syntax is very similar:

Python

from edmlib import gradeData, classCorrelationData

df = gradeData("fordhams_dataset_fileName.csv")
df.defineWorkingColumns('OTCM_FinalGradeN', 'SID', 'REG_term', 'REG_CourseCrn',
                        'REG_Programcode', 'REG_Numbercode', 'GRA_MajorAtGraduation')

                        # function takes a 'list'
                        df.filterStudentsByMajors(['Psychology', 'Economics'])

Exporting Correlation Data

Correlations can be obtained with the following example, which filters by GPA deviation first:

Python

from edmlib import gradeData, classCorrelationData

df = gradeData('yourDataSet.csv')
df.defineWorkingColumns('OTCM_FinalGradeN', 'SID', 'REG_term', 'REG_CourseCrn',
                        'REG_Programcode', 'REG_Numbercode', 'GRA_MajorAtGraduation')
df.filterByGpaDeviationMoreThan(0.2)


# The first parameter is the output file name, the second is the minimum
# number of students classes must share to calculate a correlation

df.exportCorrelationsWithMinNSharedStudents('outputCorrelation.csv', 20)

Furthermore, this correlational data can be made into a chord graph like on the front page (EDMLib) by using the Correlational Data class, which outputs to HTML and PNG in the same directory:

Python

data = classCorrelationData('outputCorrelation.csv')
data.chordGraphByMajor()

This is currently limited to averaging data by major. Options for this function can be found here: Export / Graphs.

Getting Started EDMLib Functions