Student Happiness Survey Data

Explore the student happiness survey dataset and publish your findings.

# Load packages
import numpy as np 
import pandas as pd 
import matplotlib.pyplot as plt
import seaborn as sns
color = sns.color_palette()

Load your data

# Upload data as a .csv file 
df = pd.read_csv('survey.csv', index_col = 'response_id')
df.head()

Understand your variables

# Rename your column names to be more succinct 
to_rename = [column for column in df.columns[9:]]
acronyms = ['Volunteer', 'Participate', 'Interest', 'Passion', 'Stress', 'Satisfaction', 'Interaction', 'Events', 'Ideal'] 
mapping = {key: value for key, value in zip(to_rename,acronyms)}
df = df.rename(columns = mapping)
df.columns

Index(['Career', 'Citizenship', 'Nationality', 'Year since Matriculation',
  'Year of Study', 'Primary Programme', 'Gender', 'Department',
  'Housing Type', 'Volunteer', 'Participate', 'Interest', 'Passion',
  'Stress', 'Satisfaction', 'Interaction', 'Events', 'Ideal'],
dtype='object')

# Understand your variables
variables = pd.DataFrame(columns=['Variable','Number of unique values','Values'])

for i, var in enumerate(df.columns):
    variables.loc[i] = [var, df[var].nunique(), df[var].unique().tolist()]
    
variables

Identify what variables are worth analyzing further

Create a heatmap to identify correlations

# Generate correlation matrix
corr = df.corr(method='pearson')

# Generate a mask for the upper triangle
mask = np.triu(np.ones_like(corr, dtype=bool))

# Set up the matplotlib figure
fig, ax = plt.subplots(figsize=(11, 9))                    # Set figure size

# Generate a custom diverging colormap
cmap = sns.diverging_palette(230, 20, as_cmap=True)

# Draw the heatmap with the mask 
sns.heatmap(corr, 
            mask = mask, 
            cmap = cmap, 
            vmax = 1,                                      # Set scale min value
            vmin = -1,                                     # Set scale min value
            center = 0,                                    # Set scale min value
            square = True,                                 # Ensure perfect squares
            linewidths = 1.5,                              # Set linewidth between squares
            cbar_kws = {"shrink": .8},                     # Set size of color bar
            annot = True                                   # Include values within squares
           );

plt.xticks(rotation=90)                                    # Rotate x labels
plt.title('Diagonal Correlation Plot', size=20, y=1.05);   # Set plot title and position

Answer interesting questions:

Now you get to explore this exciting dataset! Can't think of where to start? No worries we've got you covered. Try your hand at these questions:

• Are international students happier than domestic students?
• How does the amount of events students attend, influence their stress levels?
• Does the type of housing influence stress levels, passion and happiness?

# Start coding