Kevin Gonzalez/

Python Project: Getting a Good Night's Sleep


Your client, SleepInc, has shared anonymized sleep data from their hot new sleep tracking app SleepScope. As their data science consultant, your mission is to analyze the lifestyle survey data with Python to discover relationships between exercise, gender, occupation, and sleep quality. See if you can identify patterns leading to insights on sleep quality.

💾 The data: sleep_health_data.csv

SleepInc has provided you with an anonymized dataset of sleep and lifestyle metrics for 374 individuals. This dataset contains average values for each person calculated over the past six months. The data is saved as sleep_health_data.csv.

The dataset includes 13 columns covering sleep duration, quality, disorders, exercise, stress, diet, demographics, and other factors related to sleep health.

Person IDAn identifier for each individual.
GenderThe gender of the person (Male/Female).
AgeThe age of the person in years.
OccupationThe occupation or profession of the person.
Sleep Duration (hours)The average number of hours the person sleeps per day.
Quality of Sleep (scale: 1-10)A subjective rating of the quality of sleep, ranging from 1 to 10.
Physical Activity Level (minutes/day)The average number of minutes the person engages in physical activity daily.
Stress Level (scale: 1-10)A subjective rating of the stress level experienced by the person, ranging from 1 to 10.
BMI CategoryThe BMI category of the person (e.g., Underweight, Normal, Overweight).
Blood Pressure (systolic/diastolic)The average blood pressure measurement of the person, indicated as systolic pressure over diastolic pressure.
Heart Rate (bpm)The average resting heart rate of the person in beats per minute.
Daily StepsThe average number of steps the person takes per day.
Sleep DisorderThe presence or absence of a sleep disorder in the person (None, Insomnia, Sleep Apnea).
# Importing pandas and Reading Data
import pandas as pd
sleep_health_data = pd.read_csv('sleep_health_data.csv')

# group data by occupation and take the average sleep duration
sleep_duration_grouped = sleep_health_data.groupby('Occupation')['Sleep Duration'].mean()
lowest_sleep_occ = sleep_duration_grouped.idxmin()

# group by occupation and take lowest average sleep quality
sleep_quality_grouped = sleep_health_data.groupby('Occupation')['Quality of Sleep'].mean()
lowest_sleep_quality_occ = sleep_quality_grouped.idxmin()

# Check if same occupation has lowest sleep quality and sleep duration
if lowest_sleep_occ == lowest_quality_sleep_occ:
    same_occ = True
    same_occ = False

# Normal BMI
normal = sleep_health_data[(sleep_health_data['BMI Category'] == 'Normal') & (sleep_health_data['Sleep Disorder'] == 'Insomnia')]
total_normal = len(sleep_health_data[sleep_health_data['BMI Category'] == 'Normal'])
normal_insomnia_ratio = round(len(normal) / total_normal, 2)

# Overweight
overweight = sleep_health_data[(sleep_health_data['BMI Category'] == 'Overweight') & (sleep_health_data['Sleep Disorder'] == 'Insomnia')]
total_overweight = len(sleep_health_data[sleep_health_data['BMI Category'] == 'Overweight'])
overweight_insomnia_ratio = round(len(overweight) / total_overweight, 2)

# Obese
obese = sleep_health_data[(sleep_health_data['BMI Category'] == 'Obese') & (sleep_health_data['Sleep Disorder'] == 'Insomnia')]
total_obese = len(sleep_health_data[sleep_health_data['BMI Category'] == 'Obese'])
obese_insomnia_ratio = round(len(obese) / total_obese, 2)

# Save ratios to a dictionary
bmi_insomnia_ratios = {
    'Normal': normal_insomnia_ratio, 
    'Overweight': overweight_insomnia_ratio, 
    'Obese': obese_insomnia_ratio


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