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")
workout
plt.figure(figsize=(12,6))
plt.plot(workout["month"], workout['workout_worldwide'])
plt.xticks(rotation=90)
plt.show()

year_str = "2020"

keywords = pd.read_csv("data/three_keywords.csv")
keywords

plt.figure(figsize=(12, 6))
plt.plot(keywords["month"], keywords["home_workout_worldwide"], label="Home Workout")
plt.plot(keywords["month"], keywords["gym_workout_worldwide"], label="Gym-Workout")
plt.plot(keywords["month"], keywords["home_gym_worldwide"], label="Home Gym")
plt.xticks(rotation=90)
plt.legend()
plt.show()

peak_covid = "Home Workout"
current = "Gym Workout"

country_workout = pd.read_csv("data/workout_geo.csv", index_col=0)
country_workout
print(country_workout.loc["United States"])
print(country_workout.loc["Australia"])
print(country_workout.loc["Japan"])

top_country = "United States"

keywords_geo = pd.read_csv("data/three_keywords_geo.csv", index_col=0)
keywords_geo
print(keywords_geo.loc["Philippines"])
print(keywords_geo.loc["Malaysia"])

home_workout_geo = "Philippines"