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

# Start coding here
workout = pd.read_csv('data/workout.csv')
#three_keywords = pd.read_csv('data/three_keywords.csv')
#workout_geo = pd.read_csv('data/workout_geo.csv')
#three_keywords_geo = pd.read_csv('data/three_keywords_geo.csv')
print(workout.head())
workout.plot(kind='line', x='month', y='workout_worldwide')
plt.show()
year_str = '2020'

three_keywords = pd.read_csv('data/three_keywords.csv')
print(three_keywords.head())
three_keywords.plot(kind='line', x='month')
plt.show()

peak_covid = 'home_workout_worldwide'
current = 'gym_workout_worldwide'

workout_geo = pd.read_csv('data/workout_geo.csv')
print(workout_geo.head())
print(workout_geo[workout_geo['country'].isin(['United States', 'Australia', 'Japan'])][['country','workout_2018_2023']])

top_country = 'United States'