Is the Level of Fitness and Importance of Exercise determined by Age and Gender?

Introduction

Exercise and Generally Body Movement as we all know is a vital part of life and one of the Pillars of Holistic Wellness.In this post I curiously explore and analyse the correlation of Exercise vs (Age and Gender )

The Data.

This particular dataset is from Kaggle. Here is the dirty dataset. This was a survey analysis done to answer a couple of questions as it pertains to Exercise.

The data contains the following columns: (These are the shortened columns after I cleaned up the data)

TimeStamp', 'Name', 'Gender', 'Age', 'Exercise_Importance',
       'LevelOfFitness', 'ExerciseFrequency', 'Barriers', 'ExercisesTypes',
       'ExerciseOrNot', 'ExerciseTimePreference', 'Time_Spent', 'DietType',
       'Prevents_GoodDiet', 'Health_Level', 'Recommend_Fitness',
       'EquipmentPurchase', 'YourMotivation'

These were the original column names:

'Timestamp', 'Your name ', 'Your gender ', 'Your age ',
       'How important is exercise to you ?',
       'How do you describe your current level of fitness ?',
       'How often do you exercise?',
       'What barriers, if any, prevent you from exercising more regularly?           (Please select all that apply)',
       'What form(s) of exercise do you currently participate in ?                        (Please select all that apply)',
       'Do you exercise ___________ ?',
       'What time if the day do you prefer to exercise?',
       'How long do you spend exercising per day ?',
       'Would you say you eat a healthy balanced diet ?',
       'What prevents you from eating a healthy balanced diet, If any?                         (Please select all that apply)',
       'How healthy do you consider yourself?',
       'Have you ever recommended your friends to follow a fitness routine?',
       'Have you ever purchased a fitness equipment?',
       'What motivates you to exercise?         (Please select all that applies )'

The Cleaning and Analysis.

I started of by importing the data and reading it into to create a Data Set. I noticed that there were no null values thankully but the column names were just too long.

Here is a before vs after of the column names after i renamed and replaced the columns to shorter names

Old Column Name vs New Column Names

For this DataSet, I didn’t need The TimeStamp and Names, so I dropped them.The First thing I was really curious about was the Importance of Exercise for different age groups?. I decided to use a bar graph for this analysis.

Importance of Exercise vs Age Groups

I am also keen to see, how many men and women are in this DataSet?

Women vs Men

Seems like more women workout? Or is it just that more women take surveys hence we have a higher number of women? Anyway, the next thing I would really love to know is, who is fitter? or who thinks they are fitter? Men or Women. But before we make that comparison, let’s see how the levels of Fitness compare across the board.

I used a counter for this one

To make this visually appealing, I converted the numbers to percentages and used a PieChart

Level of Fitnes of Participants

So now Men vs Women — Who is Fitter?

For this, I replaced the Gender names with numbers for easier analysis

Remember Male is 0, Female is 1

Women are Fitter than Men :-)

Lastly, I am curious about two things: Athlete Age vs Level of Fitness and FrequencyOfExercise vs Level of Fitness.

Looks Ugly.Texts overlapping

With the texts overlapping, I used the .replace()again for a cleaner look.

Rememeber:
#0 is I don’t really exercise
#1 is 30 minutes
#2 is 1hour
#3 is 2 hours
#4 is 3 hours and Above

Here is the Output

Level Of Exercise vs Frequency

For the Athlete Age vs Level of Fitness analysis, I used a Bar Chart as well.

In Conclusion,

From the Analysis:

More Women seem to be taking up Exercise, probably why they have a higher level of fitness, or maybe just many women actually take surveys.

As far as Exercise Importance vs Age
19–25 importance varies
40+ Your health and fitness becomes something you consider very important we see a spike in the graph
26–30 there is a bit of a steady rise

Age 19–25 have a higher level of Fitness,
Age 26–30 have the lowest level of Fitness, probabbly due to lack Motivation

Less really is More. People seem to stick to Exercise consistently if they spend only 30 minutes.

Here is the Source Code if you would like to dig deeper into it.