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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 representing 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 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

ColumnDescription
'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

ColumnDescription
'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_df = pd.read_csv("data/workout.csv")
plt.figure(figsize=(12,6))
plt.plot(workout_df["month"], workout_df["workout_worldwide"])
plt.xticks(rotation=90)
plt.show()

year_str = "2020"


keywords_df = pd.read_csv("data/three_keywords.csv")
plt.figure(figsize=(12,6))
plt.plot(keywords_df["month"], keywords_df["home_workout_worldwide"], label="Home Workout")
plt.plot(keywords_df["month"], keywords_df["gym_workout_worldwide"], label="Gym Workout")
plt.plot(keywords_df["month"], keywords_df["home_gym_worldwide"], label="Home Gym")
plt.xticks(rotation=90)
plt.show()

peak_covid = "home workout"
current ="gym workout"

workout_geo_df = pd.read_csv("data/workout_geo.csv", index_col = 0)
print(workout_geo_df.loc["United States"])
print(workout_geo_df.loc["Australia"])
print(workout_geo_df.loc["Japan"])

top_country = "United States"


three_keywords_geo_df = pd.read_csv("data/three_keywords_geo.csv", index_col = 0)
print(three_keywords_geo_df.loc["Philippines", :])
print(three_keywords_geo_df.loc["Malaysia", :])

home_workout_geo = "Philippines"