Gym Market Analysis

gym

You are a product manager for a fitness studio and are interested in understanding the current demand for digital fitness classes. You plan to conduct a market analysis in Python to gauge demand and identify potential areas for growth of digital products and services.

The Data

You are provided with a number of CSV files in the “Files/data” folder, which offer international and national-level data on Google Trends keyword searches related to fitness and related products.

workout.csv

Column	Description
'month'	Month when the data was measured.
'workout_worldwide'	Index representing the popularity of the keyword ‘workout’, on a scale of 0 to 100.

three_keywords.csv

Column	Description
'month'	Month when the data was measured.
'home_workout_worldwide'	Index representing the popularity of the keyword ‘home workout’, on a scale of 0 to 100.
'gym_workout_worldwide'	Index representing the popularity of the keyword ‘gym workout’, on a scale of 0 to 100.
'home_gym_worldwide'	Index representing the popularity of the keyword ‘home gym’, on a scale of 0 to 100.

workout_geo.csv

Column	Description
'country'	Country where the data was measured.
'workout_2018_2023'	Index representing the popularity of the keyword ‘workout’ during the 5 year period.

three_keywords_geo.csv

Column	Description
'country'	Country where the data was measured.
'home_workout_2018_2023'	Index representing the popularity of the keyword ‘home workout’ during the 5 year period.
'gym_workout_2018_2023'	Index representing the popularity of the keyword ‘gym workout’ during the 5 year period.
'home_gym_2018_2023'	Index representing the popularity of the keyword ‘home gym’ during the 5 year period.

# Import the necessary libraries
import pandas as pd
import matplotlib.pyplot as plt

# Start coding here
import pandas as pd

workout = pd.read_csv('data/workout.csv')
three_kw = pd.read_csv('data/three_keywords.csv')
workout_geo = pd.read_csv('data/workout_geo.csv')
kw_geo = pd.read_csv('data/three_keywords_geo.csv')

workout.head()

	month	workout_worldwide
0	2018-03	59
1	2018-04	61
2	2018-05	57
3	2018-06	56
4	2018-07	51

peak = workout.loc[workout['workout_worldwide'].idxmax()]

year_str = peak.str.split('-')[0][0]
year_str

'2020'

workout.dtypes

month                object
workout_worldwide     int64
dtype: object

covid = workout.loc[(workout['month'] > '2019-12') & (workout['month'] <= '2022-12')]
post_covid = workout.loc[workout['month'] > '2022-12']

peak_covid = three_kw.loc[(workout['month'] > '2019-12') & (workout['month'] <= '2022-12')][['home_workout_worldwide', 'gym_workout_worldwide', 'home_gym_worldwide']].max().idxmax()
current = three_kw.loc[workout['month'] > '2022-12'][['home_workout_worldwide', 'gym_workout_worldwide', 'home_gym_worldwide']].max().idxmax()

peak_covid
current

'gym_workout_worldwide'

top_country = workout_geo.loc[workout_geo['workout_2018_2023'].idxmax()]['country']
top_country

'United States'

kw_geo1 = kw_geo.loc[(kw_geo['Country']=='Philippines') | (kw_geo['Country']=='Malaysia')]
kw_geo1

	Country	home_workout_2018_2023	gym_workout_2018_2023	home_gym_2018_2023
23	Philippines	52.0	38.0	10.0
61	Malaysia	47.0	38.0	15.0

home_workout_geo = kw_geo1.loc[kw_geo1['home_workout_2018_2023'] == kw_geo1['home_workout_2018_2023'].max(), 'Country'].values[0]

home_workout_geo

'Philippines'