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 = 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.plot(kind = 'line', x = 'month', y = 'workout_worldwide')

peak_workout = workout[workout['workout_worldwide'] == workout['workout_worldwide'].max()]

year = str(peak_workout['month'].iloc[0])

year_str = year[0:4]

three_keywords.plot(kind = 'line', x = 'month', y = ["home_workout_worldwide", "gym_workout_worldwide", "home_gym_worldwide"])

peak_covid = "home_workout_worldwide"
current = "gym_workout_worldwide"

top_country = workout_geo[workout_geo["workout_2018_2023"] == workout_geo["workout_2018_2023"].max()]["country"].iloc[0]

top_country