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gradeAnalysisFunc.py
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from prompt_toolkit import prompt
from prompt_toolkit.completion import PathCompleter
from prompt_toolkit.completion import WordCompleter
import string
import random
from timeit import repeat
from pandas.core.reshape.concat import concat
from tkinter import ttk
import pandas as pd
import csv
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import os
import tkinter as tk
from requests.packages import target
import gradeAnalysisWidgets as gaw
import dictionary as dic
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg, NavigationToolbar2Tk
from matplotlib.figure import Figure
from tabulate import tabulate
from sklearn.preprocessing import (
StandardScaler,
MinMaxScaler,
RobustScaler,
MaxAbsScaler,
Normalizer,
)
def file_path(file):
return os.path.join(os.path.dirname(os.path.realpath(__file__)), file)
plt.rcParams.update({"font.size": 14})
# save the data file as df
# get useful list of all unique departments, majors, instructors, courses, UniqueCourseID, and students
def get_unique_dept(df):
return df["Department"].unique()
def get_unique_major(df):
return df["Major"].unique()
def get_unique_inst(df):
return df["FacultyID"].unique()
def get_unique_crs(df):
return df["CourseTitle"].unique()
def get_unique_crscode(df):
return df["CourseCode"].unique()
def get_unique_crsid(df):
return df["UniqueCourseID"].unique()
def get_unique_stud(df):
return df["SID"].unique()
# returns filtered dataframe. Each condition should be passed as column name = LIST of targets
# e.g. "filter(df, crsTitle = ['PHYSICS I LAB'], facultyID = ['F18125', 'F97128'])" returns df with 84 rows
def filter(df, **kwargs):
for key in kwargs.keys():
df = df[(df[key]).isin(kwargs.get(key))]
return df
# All this is done to make a csv file with all the unique entries appearing in the given dataset. It is
# not used and is only there for refrence purposes.
def save_unique_entries(df, user_directory):
uniquevalue = get_unique_dept(df)
save_path = os.path.join(user_directory, "unique_entries.csv")
with open(save_path, "w", newline="") as f:
writer = csv.writer(f)
# Write headers
writer.writerow(
[
"These courses are purely informational and do not correlate between columns."
]
)
writer.writerow(
[
"They are the unique courses that appear in the dataset for the given column."
]
)
writer.writerow(
[
"Departments:",
"Majors:",
"FacultyIDs:",
"CourseTitles:",
"UniqueCourseIDs:",
"SIDs:",
]
)
max_length = max(
len(uniquevalue),
len(get_unique_major(df)),
len(get_unique_inst(df)),
len(get_unique_crs(df)),
len(get_unique_crsid(df)),
len(get_unique_stud(df)),
)
for i in range(max_length):
row = [
uniquevalue[i] if i < len(uniquevalue) else "",
get_unique_major(df)[i] if i < len(get_unique_major(df)) else "",
get_unique_inst(df)[i] if i < len(get_unique_inst(df)) else "",
get_unique_crs(df)[i] if i < len(get_unique_crs(df)) else "",
get_unique_crsid(df)[i] if i < len(get_unique_crsid(df)) else "",
get_unique_stud(df)[i] if i < len(get_unique_stud(df)) else "",
]
writer.writerow(row)
print("\n\nFile Created:", f" {save_path}\n\n")
def drop_courses_by_threshold(df, column, min_threshold=None, max_threshold=None):
if min_threshold is not None:
df = df[df[column] >= min_threshold]
if max_threshold is not None:
df = df[df[column] <= max_threshold]
return df
# average & std grades of all students taking courses in specific department
def check_list_is_subset(target_list, check_list):
return all(item in check_list for item in target_list)
def DepartmentAnalysis(
df,
user_directory,
target_values=None,
min_enrollments=100,
max_enrollments=None,
min_sections=None,
legend=None,
max_sections=None,
csv=False,
generate_grade_dist=False,
):
col = find_column_by_value(df, list(target_values.keys())[1])
deptTable = pandas_df_agg(df, "Department")
deptTable = drop_courses_by_threshold(
deptTable, "Enrollments", min_enrollments, max_enrollments
)
deptTable = drop_courses_by_threshold(
deptTable, "Sections", min_sections, max_sections
)
if csv:
save_path = os.path.join(user_directory, "deptTable.csv")
print("\n\nFile Created:", f" {save_path}\n\n")
deptTable.to_csv(save_path, encoding="utf-8-sig")
deptTable['color'] = deptTable[col].map(target_values)
if dic.department_analysis_options["Department vs GPA"]:
plotter = gaw.tkMatplot(
title="Average Department GPA",
window_width=800,
window_height=700,
x_label="Department",
y_label="GPA",
plot_type="bar",
colors=target_values,
legend=legend,
color='black',
x_plot="Department",
y_plot="GPAW",
df=deptTable,
output_directory=user_directory,
)
plotter.plot()
if dic.department_analysis_options["Department vs Enrollment"]:
plotter = gaw.tkMatplot(
title="Average Department Enrollment",
window_width=800,
window_height=700,
x_label="Department",
y_label="Enrollment",
plot_type="scatter",
colors=target_values,
legend=legend,
color="teal",
x_plot="Department",
y_plot="Enrollments",
df=deptTable,
output_directory=user_directory,
)
plotter.plot()
if dic.department_analysis_options["Department vs Section #"]:
plotter = gaw.tkMatplot(
title="# Sections offered by Department",
window_width=800,
window_height=700,
x_label="Department",
y_label="Sections",
plot_type="scatter",
colors=target_values,
legend=legend,
color="teal",
x_plot="Department",
y_plot="Sections",
df=deptTable,
output_directory=user_directory,
)
plotter.plot()
if dic.department_analysis_options["Department vs Course #"]:
plotter = gaw.tkMatplot(
title="# Courses offered by Department",
window_width=800,
window_height=700,
x_label="Department",
y_label="Courses",
plot_type="bar",
colors=target_values,
color="teal",
legend=legend,
x_plot="Department",
y_plot="Courses",
df=deptTable,
output_directory=user_directory,
)
plotter.plot()
if dic.department_analysis_options["Standard Deviation vs Enrollment"]:
plotter = gaw.tkMatplot(
title="GPA Standard Deviation vs Department Enrollment Number",
window_width=800,
window_height=700,
x_label="Standard Deviation",
y_label="Enrollment",
plot_type="scatter",
colors=target_values,
legend=legend,
color="teal",
x_plot="stddev",
y_plot="Enrollments",
df=deptTable,
)
plotter.plot()
if dic.department_analysis_options["Enrollment vs GPA"]:
plotter = gaw.tkMatplot(
title="Department Enrollment vs GPA",
window_width=800,
window_height=700,
x_label="Enrollment",
y_label="GPA",
plot_type="scatter",
colors=target_values,
legend=legend,
color="teal",
x_plot="Enrollments",
y_plot="GPAW",
df=deptTable,
)
plotter.plot()
if dic.department_analysis_options["GPA vs Standard Deviation"]:
plotter = gaw.tkMatplot(
title="Department GPA vs Department Standard Deviation",
window_width=800,
window_height=700,
x_label="GPA",
y_label="Standard Deviation",
plot_type="scatter",
colors=target_values,
legend=legend,
color="teal",
x_plot="GPAW",
y_plot="stddev",
df=deptTable,
)
plotter.plot()
if generate_grade_dist:
graph_grade_distribution(
df=deptTable,
column="Department",
)
dic.reset_all_false()
def return_filtered_dataframe(df: pd.DataFrame, column: str, values: list) -> pd.DataFrame:
mask = df[column].isin(values)
return df[mask]
def find_column_by_value(df, value):
for col in df.columns:
if df[col].eq(value).any():
return col
return None
def InstructorAnalysis(
df,
user_directory,
min_enrollments=None,
max_enrollments=None,
target_values=None,
min_sections=None,
max_sections=None,
legend=None,
csv=False,
generate_grade_dist=False,
):
col = find_column_by_value(df, list(target_values.keys())[1])
if col != 'FacultyID':
instTable = pandas_df_agg(df, ["FacultyID", col])
else:
instTable = pandas_df_agg(df, 'FacultyID')
instTable = drop_courses_by_threshold(
instTable, "Enrollments", min_enrollments, max_enrollments
)
instTable = drop_courses_by_threshold(
instTable, "Sections", min_sections, max_sections
)
if col == 'FacultyID':
instTable = instTable.drop_duplicates(subset=["FacultyID"])
if csv:
save_path = os.path.join(user_directory, "instTable.csv")
print("\n\nFile Created:", f" {save_path}\n\n")
instTable.to_csv(save_path, encoding="utf-8-sig")
instTable['color'] = instTable[col].map(target_values)
if generate_grade_dist:
graph_grade_distribution(
df=instTable,
column="FacultyID",
)
if dic.instructor_analysis_options["Instructor vs GPA"]:
plotter = gaw.tkMatplot(
title="Average Instructor GPA",
window_width=800,
window_height=700,
x_label="Instructor",
y_label="GPA",
plot_type="bar",
color=gaw.get_random_values(gaw.get_non_red_colors())[0],
colors=target_values,
legend=legend,
x_plot="FacultyID",
y_plot="GPA",
df=instTable,
output_directory=user_directory,
)
plotter.plot()
if dic.instructor_analysis_options["Instructor vs Enrollment"]:
plotter = gaw.tkMatplot(
title="# Enrollment per Instructor",
window_width=800,
window_height=700,
x_label="Instructor",
y_label="Enrollment",
plot_type="scatter",
color=gaw.get_random_values(gaw.get_non_red_colors())[0],
legend=legend,
colors=target_values,
x_plot="FacultyID",
y_plot="Enrollments",
df=instTable,
output_directory=user_directory,
)
plotter.plot()
if dic.instructor_analysis_options["Instructor vs Section #"]:
plotter = gaw.tkMatplot(
title="# Sections taught per Instructor",
window_width=800,
window_height=700,
x_label="Instructor",
y_label="Sections",
plot_type="scatter",
color=gaw.get_random_values(gaw.get_non_red_colors())[0],
legend=legend,
x_plot="FacultyID",
colors=target_values,
y_plot="Sections",
df=instTable,
output_directory=user_directory,
)
plotter.plot()
if dic.instructor_analysis_options["Instructor vs Course #"]:
plotter = gaw.tkMatplot(
title="# Courses taught per Instructor",
window_width=800,
window_height=700,
x_label="Instructor",
y_label="Courses",
plot_type="bar",
color=gaw.get_random_values(gaw.get_non_red_colors())[0],
legend=legend,
colors=target_values,
x_plot="FacultyID",
y_plot="Courses",
df=instTable,
output_directory=user_directory
)
plotter.plot()
if dic.instructor_analysis_options["Instructor vs Standard Deviation"]:
plotter = gaw.tkMatplot(
title="GPA Standard Deviation per Instructor",
window_width=800,
window_height=700,
x_label="Instructor",
y_label="Standard Deviation",
plot_type="bar",
color=gaw.get_random_values(gaw.get_non_red_colors())[0],
legend=legend,
colors=target_values,
x_plot="FacultyID",
y_plot="stddev",
df=instTable,
output_directory=user_directory,
)
plotter.plot()
if dic.instructor_analysis_options["Enrollment vs GPA"]:
plotter = gaw.tkMatplot(
title="Instructor's Average GPA vs Total Enrollment",
window_width=800,
window_height=700,
x_label="Instructor GPA",
y_label="Enrollment #",
plot_type="scatter",
colors=target_values,
color=gaw.get_random_values(gaw.get_non_red_colors())[0],
legend=legend,
x_plot="GPA",
y_plot="Enrollments",
df=instTable,
)
plotter.plot()
if dic.instructor_analysis_options["GPA vs Standard Deviation"]:
plotter = gaw.tkMatplot(
title="Instructor's GPA vs Instructor's GPA Standard Deviation",
window_width=800,
window_height=700,
x_label="GPA",
y_label="Standard Deviation",
plot_type="scatter",
color=gaw.get_random_values(gaw.get_non_red_colors())[0],
legend=legend,
colors=target_values,
x_plot="GPAW",
y_plot="stddev",
df=instTable,
)
plotter.plot()
dic.reset_all_false()
def MajorAnalysis(
df,
user_directory,
min_enrollments=None,
max_enrollments=None,
min_sections=None,
max_sections=None,
csv=False,
target_values=None,
generate_grade_dist=False,
legend=None,
):
col = find_column_by_value(df, list(target_values.keys())[1])
mjrTable = pandas_df_agg(df, "Major")
mjrTable = drop_courses_by_threshold(
mjrTable, "Enrollments", min_enrollments, max_enrollments
)
mjrTable = drop_courses_by_threshold(
mjrTable, "Sections", min_sections, max_sections
)
mjrTable['Major'].replace("", pd.NA, inplace=True)
mjrTable.dropna(subset=['Major'], inplace=True)
if csv:
save_path = os.path.join(user_directory, "majorTable.csv")
print("\n\nFile Created:", f" {save_path}\n\n")
mjrTable.to_csv(save_path, encoding="utf-8-sig")
mjrTable['color'] = mjrTable[col].map(target_values)
if dic.major_analysis_options["Major vs GPA"]:
plotter = gaw.tkMatplot(
title="Major's Average GPA",
window_width=800,
window_height=700,
x_label="Major",
y_label="GPA",
plot_type="bar",
color=gaw.get_random_values(gaw.get_non_red_colors())[0],
legend=legend,
colors=target_values,
x_plot="Major",
y_plot="GPAW",
df=mjrTable,
output_directory=user_directory,
)
plotter.plot()
if dic.major_analysis_options["Major vs Enrollment"]:
plotter = gaw.tkMatplot(
title="# Enrollment per Major",
window_width=800,
window_height=700,
x_label="Major",
y_label="Enrollment",
plot_type="scatter",
legend=legend,
color="teal",
colors=target_values,
x_plot="Major",
y_plot="Enrollments",
df=mjrTable,
output_directory=user_directory
)
plotter.plot()
if dic.major_analysis_options["Major vs Section #"]:
plotter = gaw.tkMatplot(
title="# Sections taken by Major",
window_width=800,
window_height=700,
x_label="Major",
y_label="Sections",
plot_type="scatter",
color=gaw.get_random_values(gaw.get_non_red_colors())[0],
legend=legend,
colors=target_values,
x_plot="Major",
y_plot="Sections",
df=mjrTable,
output_directory=user_directory
)
plotter.plot()
if dic.major_analysis_options["Major vs Course #"]:
plotter = gaw.tkMatplot(
title="# Courses taken by Major",
window_width=800,
window_height=700,
x_label="Major",
y_label="Courses",
plot_type="bar",
color=gaw.get_random_values(gaw.get_non_red_colors())[0],
legend=legend,
colors=target_values,
x_plot="Major",
y_plot="Courses",
df=mjrTable,
output_directory=user_directory
)
plotter.plot()
if dic.major_analysis_options["GPA vs Enrollment"]:
plotter = gaw.tkMatplot(
title="Major's Average GPA vs Major's Total Enrollment",
window_width=800,
window_height=700,
x_label="GPA",
y_label="Enrollment",
plot_type="scatter",
color=gaw.get_random_values(gaw.get_non_red_colors())[0],
legend=legend,
colors=target_values,
x_plot="GPAW",
y_plot="Enrollments",
df=mjrTable,
output_directory=user_directory
)
plotter.plot()
if dic.major_analysis_options["Standard Deviation vs Enrollment"]:
plotter = gaw.tkMatplot(
title="Major's GPA Standard Deviation vs Major's Total Enrollment",
window_width=800,
window_height=700,
x_label="Standard Deviation",
y_label="Enrollment",
plot_type="scatter",
color=gaw.get_random_values(gaw.get_non_red_colors())[0],
legend=legend,
colors=target_values,
x_plot="stddev",
y_plot="Enrollments",
df=mjrTable,
output_directory=user_directory
)
plotter.plot()
if generate_grade_dist:
graph_grade_distribution(
df=mjrTable,
column="Major",
)
dic.reset_all_false()
def pandas_df_agg(df, index=["Major"]):
"""Sections: Unique instances of a CourseID for a given index
Courses: Unique instances of a CourseTitle for a given index
GPA: Average GPA for a given index
stddev: Standard deviation of GPA for a given index
kurtosis: Kurtosis of GPA for a given index
skewness: Skewness of GPA for a given index
"""
df = df[~df['FinLetterGrade'].isin(['P'])] if 'P' in df['FinLetterGrade'].values else df
if isinstance(index, str):
index = [index]
df_enrollments = (
df.groupby(index)["SID"].nunique().reset_index(name="Enrollments")
)
df_agg = (
df.groupby(index)
.agg(
Sections=("UniqueCourseID", "nunique"),
Courses=("CourseCode", "nunique"),
GPA=("FinNumericGrade", "mean"),
GPAW=('FinNumericGrade', lambda x: np.average(x, weights=df.loc[x.index, 'CredHrs']) if df.loc[x.index, 'CredHrs'].sum() != 0 else np.nan),
stddev=("FinNumericGrade", "std"),
kurtosis=("FinNumericGrade", lambda x: x.kurt()),
skewness=("FinNumericGrade", lambda x: x.skew()),
CoV=("FinNumericGrade", lambda x: ((x.std() / x.mean()) * 100) if x.mean() != 0 else np.nan), #coefficent of variation
ModeGPA=("FinNumericGrade", lambda x: x.mode()[0] if not x.mode().empty else np.nan),
)
.rename(columns={'CoV': 'CoV(%)'})
.reset_index()
)
df_grade = grade_distribution_df(df, index)
df_final = pd.merge(df_enrollments, df_agg, on=index)
df_final = pd.merge(df_final, df_grade, on=index)
delta_gpa_ret_df = pd.DataFrame()
delta_gpa_ret_df[index] = df_final[index]
if not any(item in ['UniqueCourseID', 'SID'] for item in index):
delta_gpa = df.groupby(index + ['Semester']).agg(
GPA=("FinNumericGrade", "mean"),
GPAW=('FinNumericGrade', lambda x: np.average(
x, weights=df.loc[x.index, 'CredHrs']
) if df.loc[x.index, 'CredHrs'].sum() != 0 else np.nan)
)
temp_copy = delta_gpa.copy()
delta_gpa['DELTA(GPA)'] = temp_copy.groupby(index)['GPA'].diff().fillna(0)
delta_gpa['DELTA(GPAW)'] = temp_copy.groupby(index)['GPAW'].diff().fillna(0)
delta_gpa_ret_df = delta_gpa.groupby(index).agg(
avg_gpa_change=('DELTA(GPA)', 'mean'),
avg_gpaw_change=('DELTA(GPAW)', 'mean')
).reset_index()
df_final = pd.merge(df_final, delta_gpa_ret_df, on=index)
float_cols = df_final.select_dtypes(include="float").columns
df_final[float_cols] = df_final[float_cols].round(3)
return df_final
def section_analysis(
df: pd.DataFrame,
user_directory,
target_courses,
csv=False,
min_enrollments=None,
max_enrollments=None,
min_gpa=None,
max_gpa=None,
legend=None,
generate_grade_dist=False,
):
col = find_column_by_value(df, list(target_courses.keys())[1])
if col != 'UniqueCourseID':
sectionTable = pandas_df_agg(df, ["UniqueCourseID", col])
else:
sectionTable = pandas_df_agg(df, 'UniqueCourseID')
sectionTable = sectionTable.drop_duplicates(subset=["FacultyID"])
sectionTable = drop_courses_by_threshold(
sectionTable, "Enrollments", min_enrollments, max_enrollments
)
sectionTable = drop_courses_by_threshold(
sectionTable, "GPA", min_gpa, max_gpa
)
sectionTable['UniqueCourseID'] = sectionTable['UniqueCourseID'].astype(str)
sectionTable = pd.merge(
sectionTable,
df[["UniqueCourseID", "CourseTitle"]],
on="UniqueCourseID",
how="left",
)
sectionTable = pd.merge(sectionTable, df[['FacultyID', 'UniqueCourseID']], on="UniqueCourseID", how="left")
sectionTable = sectionTable.drop_duplicates(subset=["UniqueCourseID"])
sectionTable = drop_courses_by_threshold(
sectionTable, "Enrollments", min_enrollments, max_enrollments
)
sectionTable = drop_courses_by_threshold(
sectionTable, "GPA", min_gpa, max_gpa
)
sectionTable = sectionTable.drop("Sections", axis=1)
sectionTable = sectionTable.drop("Courses", axis=1)
sectionTable['color'] = sectionTable[col].map(target_courses)
if dic.section_analysis_options["Section vs GPA"]:
plotter = gaw.tkMatplot(
title="Individual Section's GPA",
window_width=800,
window_height=700,
x_label="Section",
y_label="GPA",
plot_type="line",
color=gaw.get_random_values(gaw.get_non_red_colors())[0],
legend=legend,
colors=target_courses,
x_plot="UniqueCourseID",
y_plot="GPA",
df=sectionTable,
output_directory=user_directory,
)
plotter.plot()
if dic.section_analysis_options["Section vs Class Size"]:
plotter = gaw.tkMatplot(
title="Individual Section's Class Size",
window_width=800,
window_height=700,
x_label="Section",
y_label="Enrollment",
plot_type="scatter",
color=gaw.get_random_values(gaw.get_non_red_colors())[0],
legend=legend,
colors=target_courses,
x_plot="UniqueCourseID",
y_plot="Enrollments",
df=sectionTable,
output_directory=user_directory,
)
plotter.plot()
if dic.section_analysis_options["GPA vs Class Size"]:
plotter = gaw.tkMatplot(
title="GPA vs ClassSize per Individual Section",
window_width=800,
window_height=700,
x_label="GPA",
y_label="Class Size",
plot_type="scatter",
color=gaw.get_random_values(gaw.get_non_red_colors())[0],
legend=legend,
colors=target_courses,
x_plot="GPA",
y_plot="Enrollments",
df=sectionTable,
output_directory=user_directory,
)
plotter.plot()
elif dic.section_analysis_options["Section vs Standard Deviation"]:
plotter = gaw.tkMatplot(
title="Individual Section's GPA Standard Deviation",
window_width=800,
window_height=700,
x_label="Section",
y_label="Standard Deviation",
plot_type="bar",
color=gaw.get_random_values(gaw.get_non_red_colors())[0],
legend=legend,
colors=target_courses,
x_plot="UniqueCourseID",
y_plot="stddev",
df=sectionTable,
output_directory=user_directory,
)
plotter.plot()
elif dic.section_analysis_options["Enrollment vs Standard Deviation"]:
plotter = gaw.tkMatplot(
title="Enrollment vs GPA Standard Deviation per Individual Section",
window_width=800,
window_height=700,
x_label="Enrollment",
y_label="Standard Deviation",
plot_type="scatter",
color=gaw.get_random_values(gaw.get_non_red_colors())[0],
legend=legend,
colors=target_courses,
x_plot="Enrollments",
y_plot="stddev",
df=sectionTable,
output_directory=user_directory,
)
plotter.plot()
if csv:
save_path = os.path.join(user_directory, "sectionTable.csv")
print("\n\nFile Created:", f" {save_path}\n\n")
sectionTable.to_csv(save_path, encoding="utf-8-sig")
if generate_grade_dist:
graph_grade_distribution(
df=sectionTable,
column="UniqueCourseID",
)
dic.reset_all_false()
def CourseAnalysis(
df,
user_directory,
min_enrollments=None,
target_values=None,
max_enrollments=None,
min_sections=None,
max_sections=None,
csv=False,
generate_grade_dist=False,
legend=None,
):
col = find_column_by_value(df, list(target_values.keys())[1])
crsTable = pandas_df_agg(df, "CourseCode")
crsTable = pd.merge(crsTable, df[['CourseCode', 'Department']], on="CourseCode", how="left")
# crsTable = pd.merge(crsTable, df[['CourseCode', 'CourseTitle']], on="CourseCode", how="left")
crsTable = drop_courses_by_threshold(
crsTable, "Enrollments", min_enrollments, max_enrollments
)
crsTable = drop_courses_by_threshold(
crsTable, "Sections", min_sections, max_sections
)
crsTable.drop_duplicates(subset=['CourseCode'], inplace=True)
if csv:
save_path = os.path.join(user_directory, "crsTable.csv")
crsTable.to_csv(save_path, encoding="utf-8-sig")
print("\n\nFile Created:", f" {save_path}\n\n")
crsTable['color'] = crsTable[col].map(target_values)
if dic.course_analysis_options["Course vs GPA"]:
plotter = gaw.tkMatplot(
title="Course Average GPA",
window_width=800,
window_height=700,
x_label="Course",
y_label="GPA",
plot_type="bar",
color=gaw.get_random_values(gaw.get_non_red_colors())[0],
legend=legend,
colors=target_values,
x_plot="CourseCode",
y_plot="GPA",
df=crsTable,
output_directory=user_directory,
)
plotter.plot()
elif dic.course_analysis_options["Course vs Enrollment"]:
plotter = gaw.tkMatplot(
title="# Enrollment per Course",
window_width=800,
window_height=700,
x_label="Course",
y_label="Enrollments",
plot_type="scatter",
color=gaw.get_random_values(gaw.get_non_red_colors())[0],
legend=legend,
colors=target_values,
x_plot="CourseCode",
y_plot="Enrollments",
df=crsTable,
output_directory=user_directory,
)
plotter.plot()
elif dic.course_analysis_options["Course vs Section #"]:
plotter = gaw.tkMatplot(
title="# Sections per Course",
window_width=800,
window_height=700,
x_label="Course",
y_label="Sections",
plot_type="scatter",
color=gaw.get_random_values(gaw.get_non_red_colors())[0],
legend=legend,
colors=target_values,
x_plot="CourseCode",
y_plot="Sections",
df=crsTable,
output_directory=user_directory,
)
plotter.plot()
elif dic.course_analysis_options["Enrollment vs GPA"]:
plotter = gaw.tkMatplot(
title="Course Enrollment vs Average GPA",
window_width=800,
window_height=700,
x_label="Enrollment",
y_label="GPA",
plot_type="scatter",
color=gaw.get_random_values(gaw.get_non_red_colors())[0],
legend=legend,
colors=target_values,
x_plot="Enrollments",
y_plot="GPA",
df=crsTable,
output_directory=user_directory,
)
plotter.plot()
elif dic.course_analysis_options["Course vs Standard Deviation"]:
plotter = gaw.tkMatplot(
title="Course GPA Standard Deviation",
window_width=800,
window_height=700,
x_label="Course",
y_label="Standard Deviation",
plot_type="bar",
color=gaw.get_random_values(gaw.get_non_red_colors())[0],
legend=legend,
colors=target_values,
x_plot="CourseCode",
y_plot="stddev",
df=crsTable,
output_directory=user_directory,
)
plotter.plot()
elif dic.course_analysis_options["GPA vs Standard Deviation"]:
plotter = gaw.tkMatplot(
title="Course GPA vs GPA Standard Deviation",
window_width=800,
window_height=700,
x_label="GPA",
y_label="Standard Deviation",
plot_type="scatter",
color=gaw.get_random_values(gaw.get_non_red_colors())[0],