Project: Data-Driven Product Management: Conducting a Market Analysis

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

workout['time'] = pd.to_datetime(workout['month'])
workout['year_only'] = workout['time'].dt.year
total_per_year = workout.groupby('year_only')['workout_worldwide'].sum().sort_values(ascending=False).reset_index()
total_per_year['year_only'] = total_per_year['year_only'].astype(str)
year_str = total_per_year.iloc[0, 0]
year_str

workout = pd.read_csv('data/workout.csv')

three_keywords.plot(x='month', y=['gym_workout_worldwide','home_workout_worldwide', 'home_gym_worldwide'], kind='line')
plt.show()
peak_covid = 'home_workout_worldwide'
current = 'gym_workout_worldwide'

# Start coding here
three_keywords = pd.read_csv('data/three_keywords.csv')
three_keywords

specific_countries = ['United States', 'Australia', 'Japan']

countries = workout_geo[workout_geo['country'].isin(specific_countries)].sort_values('country', ascending=False)
top_country = countries.iloc[0, 0]
top_country

workout_geo = pd.read_csv('data/workout_geo.csv')
workout_geo

philip_mal = ['Philippines', 'Malaysia']
filtered_data = three_keywords_geo[three_keywords_geo['Country'].isin(philip_mal)]
home_workout_geo = filtered_data.iloc[0,0]
home_workout_geo