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 represeting 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 represeting the popularity of the keyword 'home workout', on a scale of 0 to 100.
`'gym_workout_worldwide'`	Index represeting the popularity of the keyword 'gym workout', on a scale of 0 to 100.
`'home_gym_worldwide'`	Index represeting 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 represeting 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 represeting the popularity of the keyword 'home workout' during the 5 year period.
`'gym_workout_2018_2023'`	Index represeting the popularity of the keyword 'gym workout' during the 5 year period.
`'home_gym_2018_2023'`	Index represeting 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 he

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')

workout.head()

# Find the row with the maximum workout_worldwide value to get the month of peak interest
peak_month = workout.loc[workout['workout_worldwide'].idxmax(), 'month']

# Extract the year from the peak month
year_str = peak_month.split("-")[0]

year_str

three_keywords.head()

three_keywords.plot()

workout_geo.head()

peak_covid = 'home_workout_worldwide'
current = 'gym_workout_worldwide'

top_country = workout_geo[workout_geo['country'].isin(["United States", "Australia", "Japan"])]
top_country = top_country[top_country["workout_2018_2023"] == top_country["workout_2018_2023"].max()]

top_country = top_country['country'].values[0]
top_country

three_keywords_geo.head()