The Correlation Between Sleeping Habits and Tiredness

As someone who often feels that they did not get enough sleep, I want to explore how the things that people do around their bedtime affect how well they sleep and how tired they feel the next day. I feel like many people (especially young adults) are always complaining about how tired they are and how reliant they have become on caffiene, so I want to find a way to change that. Many sources that I have read online including MedlinePlus and the CDC suggest that screens, such as the ones on our phones, are the main culprit for this issue, and I want to explore whether that is true. I also wanted to see whether having breakfast in the morning affected how tired someone felt because I usually find myself in a rush in the morning with no time to eat. My dataset is from Kaggle.

import pandas as pd
import csv

# opens the csv file
with open('SleepStudyData.csv', 'r') as file:
    
    # stores the csv file as a dictionary
    reader = csv.DictReader(file)
    
    # create an empty dictionary to store the data
    sleep_dict = {}
    
    # iterates through each row of the data
    for row in reader:
        
        # iterates through each key-value pair in the row dictionary
        for column, value in row.items():
            
            # adds the value to a list for the corresponding column key in the data_dict dictionary
            sleep_dict.setdefault(column, []).append(value)
    
   

 # uses dictionary to create a dataframe
df_sleep = pd.DataFrame.from_dict(sleep_dict)

df_sleep

# Column Meanings:

# Enough - do they think that they got enough sleep?
# Hours - on average, how many hours of sleep do they get on weeknights
# PhoneReach - do they sleep with their phone within arms reach?
# PhoneTime - do they use their phone within 30 minutes of falling asleep?
# Tired - on a scale from 1 to 5 how tired do they feel throughout the day?
# Breakfast - do they typically eat breakfast?

	Enough	Hours	PhoneReach	PhoneTime	Tired	Breakfast
0	Yes	8	Yes	Yes	3	Yes
1	No	6	Yes	Yes	3	No
2	Yes	6	Yes	Yes	2	Yes
3	No	7	Yes	Yes	4	No
4	No	7	Yes	Yes	2	Yes
...	...	...	...	...	...	...
99	No	7	Yes	Yes	2	Yes
100	No	7	No	Yes	3	Yes
101	Yes	8	Yes	Yes	3	Yes
102	Yes	7	Yes	Yes	2	Yes
103	Yes	6	Yes	Yes	3	Yes

104 rows × 6 columns

My plan is to make multiple color-coded histograms or bar graphs so that related variables such as "Hours" and "Enough" can be clearly compared.