Skip to content
Project: Data-Driven Product Management: Conducting a Market Analysis
  • AI Chat
  • Code
  • Report
  • 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

    ColumnDescription
    'month'Month when the data was measured.
    'workout_worldwide'Index represeting the popularity of the keyword 'workout', on a scale of 0 to 100.

    three_keywords.csv

    ColumnDescription
    'month'Month when the data was measured.
    'home_workout_worldwide'Index represeting the popularity of the keyword 'home workout', on a scale of 0 to 100.
    'gym_workout_worldwide'Index represeting the popularity of the keyword 'gym workout', on a scale of 0 to 100.
    'home_gym_worldwide'Index represeting the popularity of the keyword 'home gym', on a scale of 0 to 100.

    workout_geo.csv

    ColumnDescription
    'country'Country where the data was measured.
    'workout_2018_2023'Index represeting the popularity of the keyword 'workout' during the 5 year period.

    three_keywords_geo.csv

    ColumnDescription
    'country'Country where the data was measured.
    'home_workout_2018_2023'Index represeting the popularity of the keyword 'home workout' during the 5 year period.
    'gym_workout_2018_2023'Index represeting the popularity of the keyword 'gym workout' during the 5 year period.
    'home_gym_2018_2023'Index represeting 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
    # Start coding he
    
    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.head()
    # Find the row with the maximum workout_worldwide value to get the month of peak interest
    peak_month = workout.loc[workout['workout_worldwide'].idxmax(), 'month']
    
    # Extract the year from the peak month
    year_str = peak_month.split("-")[0]
    
    year_str
    three_keywords.head()
    three_keywords.plot()
    workout_geo.head()
    peak_covid = 'home_workout_worldwide'
    current = 'gym_workout_worldwide'
    top_country = workout_geo[workout_geo['country'].isin(["United States", "Australia", "Japan"])]
    top_country = top_country[top_country["workout_2018_2023"] == top_country["workout_2018_2023"].max()]
    
    top_country = top_country['country'].values[0]
    top_country
    three_keywords_geo.head()
    three_keywords_geo = three_keywords_geo[three_keywords_geo['Country'].isin(["Philippines", "Malaysia"])]
    
    
    home_workout_geo = three_keywords_geo[three_keywords_geo["home_workout_2018_2023"] == three_keywords_geo["home_workout_2018_2023"].max()]
    
    home_workout_geo = home_workout_geo['Country'].values[0]
    
    
    home_workout_geo