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 pandas as pd
import matplotlib.pyplot as plt

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()

Finding the time of peak searches for workout

workout.info()

As we see here, the data named month contains data from 2018 to 2023.

workout_set = workout.sort_values(by=["month"], ascending=False)
workout_set

To group by year, I separated the months and years.

workout[['year', 'month']] = workout['month'].str.split('-', expand=True)
workout.head()

yearly_total = workout.groupby('year')['workout_worldwide'].sum().reset_index()
yearly_total.sort_values(by=["workout_worldwide"], ascending=False)

import seaborn as sns

sns.set(style = 'whitegrid')

sns.relplot(x ="year",
            y ="workout_worldwide",
            kind ="line",
            data = yearly_total)
plt.show()

max_year = yearly_total.loc[yearly_total['workout_worldwide'].idxmax(), 'year']
year_str = str(max_year)
year_str

Finding the most popular keywords for the current year and during covid