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

import pandas as pd

# Start coding here
workout = pd.read_csv('data/workout.csv')

# Convert 'month' column to datetime
workout['month'] = pd.to_datetime(workout['month'])

# Group by 'month' column and count the occurrences
workout_grouped = workout.groupby(workout['month'].dt.year).agg(sum)
year_str="2020"

keys=pd.read_csv('data/three_keywords.csv')
plt.bar( pd.to_datetime(keys['month']).dt.year,keys['home_workout_worldwide'],label='1')
plt.bar( pd.to_datetime(keys['month']).dt.year,keys['home_gym_worldwide'],label='2')
plt.bar( pd.to_datetime(keys['month']).dt.year,keys['gym_workout_worldwide'],label='3')
plt.legend()
plt.grid(True)

current = 'gym_workout_worldwide'
peak_covid = 'home_workout_worldwide'

import pandas as pd

country = pd.read_csv('data/workout_geo.csv')

top_country = country.groupby('country').agg(sum).reset_index()
top_country = top_country.sort_values(by='workout_2018_2023')
top_country  = top_country.iloc[-1,0]