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.
| Column | Description |
|---|---|
Person ID | An identifier for each individual. |
Gender | The gender of the person (Male/Female). |
Age | The age of the person in years. |
Occupation | The 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 Category | The 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 Steps | The average number of steps the person takes per day. |
Sleep Disorder | The presence or absence of a sleep disorder in the person (None, Insomnia, Sleep Apnea). |
# Start coding here
# Use as many cells as you need
import pandas as pd1 - Find occupation with the lowest sleep duration.
# Reading in the dataset
sleep_health_data = pd.read_csv('sleep_health_data.csv')
sleep_health_data.head()sleep_health_data.info()# Group the occupations, then calculate the mean sleep duration for each occupation
sleep_duration = sleep_health_data.groupby('Occupation')['Sleep Duration'].mean()
sleep_duration# Find the occupation with the lowest sleep duration
sleep_duration.sort_values()# Accessing occupation with .index[0] for the first value
lowest_sleep_occ = sleep_duration.sort_values().index[0]
print(lowest_sleep_occ)2 - Find the occupation with the lowest sleep quality
# Group the occupation, then calculate the mean sleep quality for each occupation
sleep_quality = sleep_health_data.groupby('Occupation')['Quality of Sleep'].mean()
sleep_quality# Find the occupation with the lowest sleep quality.
sleep_quality.sort_values()# Accessing occupation with .index[0] for the first value
lowest_sleep_quality_occ = sleep_quality.sort_values().index[0]
print(lowest_sleep_quality_occ)# Check if the same occupation has the lowest average sleep duration and quality
if lowest_sleep_occ == lowest_sleep_quality_occ:
same_occ = True
else:
same_occ = False
print(same_occ)3 - Find what ratio of app users in each BMI category have been diagnosed with insomnia