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

# Start coding here
workout_df = pd.read_csv('data/workout.csv')
three_keywords_df = pd.read_csv('data/three_keywords.csv')
workout_geo_df = pd.read_csv('data/workout_geo.csv', index_col=0)
three_keywords_geo_df = pd.read_csv('data/three_keywords_geo.csv', index_col=0)

print(workout_df.info())
print(workout_df.head())

print(workout_geo_df.isna().sum())

print(workout_geo_df.info())
print(workout_geo_df.head())

print(three_keywords_df.info())
print(three_keywords_df.isna().sum())
print(three_keywords_df.head())

print(three_keywords_geo_df.info())
print(three_keywords_geo_df.isna().sum())
print(three_keywords_geo_df.head())

fig, ax = plt.subplots()
ax.plot(workout_df['month'], workout_df['workout_worldwide'])
ax.plot(three_keywords_df['month'], three_keywords_df['home_workout_worldwide'], label='Home Workout')
ax.plot(three_keywords_df['month'], three_keywords_df['gym_workout_worldwide'], label='Gym Workout')
ax.plot(three_keywords_df['month'], three_keywords_df['home_gym_worldwide'], label='Home Gym')
fig.set_figwidth(15)
plt.xticks(rotation=90)
ax.legend()
plt.show()

year_str = '2020'
peak_covid = 'home_workout_worldwide'
current = 'gym_workout_worldwide'

print(workout_geo_df.loc['United States'])
print(workout_geo_df.loc['Australia'])
print(workout_geo_df.loc['Japan'])

top_country = 'United States'

print(three_keywords_geo_df.loc["Philippines", :])
print(three_keywords_geo_df.loc['Malaysia', :])