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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
import numpy as np
import seaborn as sns
#read csv
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.info()
workout["month"] = pd.to_datetime(workout["month"])
peak_month = workout.loc[workout["workout_worldwide"].idxmax(), "month"]
year_str = str(peak_month.year)

three_keywords["month"] = pd.to_datetime(three_keywords["month"])

# During COVID (2020–2021) 
covid_period = three_keywords[
    (three_keywords["month"].dt.year >= 2020) &
    (three_keywords["month"].dt.year <= 2021)
]

peak_covid = covid_period[
    ["home_workout_worldwide", "gym_workout_worldwide", "home_gym_worldwide"]
].mean().idxmax()

# Current (most recent month)
latest = three_keywords.iloc[-1]

# the values are numeric before using idxmax
current = latest[
    ["home_workout_worldwide", "gym_workout_worldwide", "home_gym_worldwide"]
].astype(float).idxmax()
top_country = (
    workout_geo[
        workout_geo["country"].isin(["United States", "Australia", "Japan"])
    ]
    .sort_values("workout_2018_2023", ascending=False)
    .iloc[0]["country"]
)

# the actual column names and use the correct one
print(three_keywords_geo.columns)  # To inspect the actual column names



home_workout_geo = (
    three_keywords_geo[
        three_keywords_geo["Country"].isin(["Philippines", "Malaysia"])
    ]
    .sort_values("home_workout_2018_2023", ascending=False)
    .iloc[0]["Country"]
)