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 csv
workout_data = pd.read_csv("./data/workout.csv")
workout_data = workout_data.dropna()
workout_data.head()

# three_keywords csv
three_keywords_data = pd.read_csv("./data/three_keywords.csv")
three_keywords_data = three_keywords_data.dropna()
three_keywords_data.head()

# workout_geo_data csv
workout_geo_data = pd.read_csv("./data/workout_geo.csv")
workout_geo_data = workout_geo_data.dropna()
workout_geo_data.head()

# three_keywords_geo csv
three_keywords_geo_data = pd.read_csv("./data/three_keywords_geo.csv")
three_keywords_geo_data = three_keywords_geo_data.dropna()
three_keywords_geo_data.head()

Now that we imported the data our goal is to find the peak workout year

workout_worldwide_max = workout_data["workout_worldwide"].max()
print(workout_worldwide_max)
print(workout_data[workout_data["workout_worldwide"] == workout_worldwide_max])

year_str = "2020" # most searches for workouts were made in 2020 april

# Convert 'month' to datetime for better handling
three_keywords_data["month"] = pd.to_datetime(three_keywords_data["month"])

# Define a function to plot the data
def plot_keyword_trend(data, keyword, title):
    plt.figure(figsize=(12, 6))
    plt.plot(data["month"], data[keyword], label=title)
    plt.xlabel("Year")
    plt.ylabel("Search Interest")
    plt.title(f"Search Interest Over Time for {title}")
    plt.legend()
    plt.xticks(
        data["month"][::12],  # Show only the first month of each year
        data["month"].dt.year[::12],  # Extract the year
        rotation=45
    )
    plt.show()

# Plot Home Workout
plot_keyword_trend(three_keywords_data, "home_workout_worldwide", "Home Workout")

# Plot Gym Workout
plot_keyword_trend(three_keywords_data, "gym_workout_worldwide", "Gym Workout")

# Plot Home Gym
plot_keyword_trend(three_keywords_data, "home_gym_worldwide", "Home Gym")

peak_covid = "home_workout_worldwide"
current = "gym_workout_worldwide"

sorted_workout_geo = workout_geo_data.sort_values(by="workout_2018_2023", ascending=False)
print(sorted_workout_geo.head())

top_country = "United States"