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

# Assuming the CSV file is located in the 'data/' folder and named 'workout.csv'
file_path = 'data/workout.csv'

# Load the CSV file into a DataFrame
workout_df = pd.read_csv(file_path)

# Convert the 'Week' column to datetime format
workout_df['month'] = pd.to_datetime(workout_df['month'])

# Plot the data
plt.figure(figsize=(12, 6))
plt.plot(workout_df['month'], workout_df['workout_worldwide'], marker='o', linestyle='')
plt.xticks(rotation=90)
plt.show()
year_str = '2020'

# Find the most popular keywords for the current year and during covid
df_keywords = pd.read_csv("data/three_keywords.csv")

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

peak_covid = "home workout"
current = "gym workout"

# Find the country with the highest interest for workouts
df_workout_geo = pd.read_csv("data/workout_geo.csv", index_col = 0)
print(df_workout_geo.loc["United States"])
print(df_workout_geo.loc["Australia"])
print(df_workout_geo.loc["Japan"])

top_country = "United States"

# Who has the highest interest in home workouts, Philippines or Malaysia?
df_keywords_geo = pd.read_csv("data/three_keywords_geo.csv", index_col = 0)
print(df_keywords_geo.loc["Philippines", :])
print(df_keywords_geo.loc["Malaysia", :])