Five_Question_Summary.py

# Code for generating Five-Question Summary reports. 

# Import basic packages
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

# Read in student response data; change name of file as needed
col_names = ["Challenge", "Support", "Competence", "Autonomy", 
             "Relatedness"]
df = pd.read_csv('student_responses.csv', names = col_names)

# Create 2x2 for Challenge vs Support 

cs = df.groupby(["Challenge", "Support"]).size().reset_index(name="Count")
plt.axes()
plt.xlim([0,6])
plt.ylim([0,6])
plt.xticks([1,2,3,4,5])
plt.yticks([1,2,3,4,5])
plt.xlabel("Challenge")
plt.ylabel("Support")
plt.grid(True)
plt.axvline(x=3, color='black')
plt.axhline(y=3, color='black')
cmap = plt.cm.get_cmap("cool")
plt.scatter(cs.Challenge, cs.Support, c = cs['Count'], s=300, cmap = cmap)

# Create 2x2 for Competence and Relatedness

comp_rel = df.groupby(["Competence", "Relatedness"]).size().reset_index(name="Count")
plt.axes()
plt.xlim([0,6])
plt.ylim([0,6])
plt.xticks([1,2,3,4,5])
plt.yticks([1,2,3,4,5])
plt.xlabel("Competence")
plt.ylabel("Relatedness")
plt.grid(True)
plt.axvline(x=3, color='black')
plt.axhline(y=3, color='black')
cmap = plt.cm.get_cmap("cool")
plt.scatter(comp_rel.Competence, comp_rel.Relatedness, c = comp_rel['Count'], cmap = cmap, s = 300)

# Create Autonomy boxplot

df.boxplot(['Autonomy'])