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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.

ColumnDescription
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).
# Start coding here
# Use as many cells as you need
import pandas as pd
sleep_data=pd.read_csv("sleep_health_data.csv")
sleep_data.head()
sleep_data.info()

Which occupation has the lowest average sleep duration?

lowest_sleep=sleep_data.groupby("Occupation")["Sleep Duration"].mean()
lowest_sleep_occ=lowest_sleep.sort_values().index[0]

Which occupation has the lowest average sleep quality?

sleep_quality=sleep_data.groupby("Occupation")["Quality of Sleep"].mean()
lowest_sleep_quality_occ=sleep_quality.sort_values().index[0]
lowest_sleep_quality_occ

Did the occupation with the lowest sleep duration also have the lowest sleep quality?

if lowest_sleep_quality_occ==lowest_sleep_occ :
    same_occ=True
else:
    same_occ=False
        
same_occ   
sleep_data["BMI Category"].unique()

Ratio of people having Insomnia in each BMI Category

overweight=sleep_data[sleep_data["BMI Category"]=="Overweight"]["Person ID"].count() #Total Overweight people

overweight_insomnia=sleep_data[(sleep_data["BMI Category"]=="Overweight") & (sleep_data["Sleep Disorder"]=="Insomnia")]["Person ID"].count() #overweight people with insomnia

overweight_ratio=overweight_insomnia/overweight #ratio of overweight with insomnia over total overweight

obese=sleep_data[sleep_data["BMI Category"]=="Obese"]["Person ID"].count() #Total Obese people
obese_insomnia=overweight_insomnia=sleep_data[(sleep_data["BMI Category"]=="Obese") & (sleep_data["Sleep Disorder"]=="Insomnia")]["Person ID"].count() #Obese people with Insomnia

obese_ratio=obese_insomnia/obese #Ratio of obese people with insomnia to total obese people

Normal=sleep_data[sleep_data["BMI Category"]=="Normal"]["Person ID"].count() #Total Normal people
Normal_insomnia=overweight_insomnia=sleep_data[(sleep_data["BMI Category"]=="Normal") & (sleep_data["Sleep Disorder"]=="Insomnia")]["Person ID"].count() #Normal people with insomnia

normal_ratio=Normal_insomnia/Normal #Ratio of normal people with insomnia to Total Normal people

bmi_insomnia_ratios={"Normal":round(normal_ratio,2),"Overweight":round(overweight_ratio,2),"Obese":round(obese_ratio,2)}

bmi_insomnia_ratios