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

Load Global Workout Interest Data

# Load the global workout trends
workout = pd.read_csv("data/workout.csv")
workout.head()

Find the Peak Time for "Workout" Searches

# Find the row with the highest popularity index
peak_row = workout.loc[workout['workout_worldwide'].idxmax()]
year_str = peak_row['month'][:4]  # Format: "yyyy"
print("Peak workout interest year:", year_str)

Analyze Keyword Popularity During COVID and Now

# Load trends for three keywords
three_keywords = pd.read_csv("data/three_keywords.csv")

# Plot the trends
three_keywords.plot(x='month', figsize=(12,6), title="Keyword Trends Over Time")
plt.ylabel("Popularity Index")
plt.grid(True)
plt.show()

# COVID period: 2020–2021
covid_period = three_keywords[three_keywords['month'].between("2020-01", "2021-12")]
avg_covid = covid_period[['home_workout_worldwide', 'gym_workout_worldwide', 'home_gym_worldwide']].mean()

peak_covid = avg_covid.idxmax()

# Current period: 2023
current_period = three_keywords[three_keywords['month'].str.startswith("2023")]
avg_current = current_period[['home_workout_worldwide', 'gym_workout_worldwide', 'home_gym_worldwide']].mean()

current = avg_current.idxmax()

print("Most popular keyword during COVID:", peak_covid)
print("Most popular keyword now:", current)

Highest Workout Interest Among Selected Countries

# Load geo-based workout data
workout_geo = pd.read_csv("data/workout_geo.csv")

# Filter for selected countries
selected = workout_geo[workout_geo['country'].isin(['United States', 'Australia', 'Japan'])]

# Find the country with the highest interest
top_country = selected.sort_values(by='workout_2018_2023', ascending=False).iloc[0]['country']
print("Country with highest workout interest:", top_country)

Compare Philippines and Malaysia on Home Workouts

# Load keyword geo data
three_keywords_geo = pd.read_csv("data/three_keywords_geo.csv")

# Check the column names to avoid similar errors in the future
print("Columns available:", three_keywords_geo.columns)

# Use the correct column name 'Country'
ph_my = three_keywords_geo[three_keywords_geo['Country'].isin(['Philippines', 'Malaysia'])]

# Determine which country has higher home workout interest
home_workout_geo = ph_my.sort_values(by='home_workout_2018_2023', ascending=False).iloc[0]['Country']
print("Best country to expand home workouts:", home_workout_geo)

Summary of Variables