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
import matplotlib.pyplot as plt

# create workout dataframe (table) from "workout.csv" file inside "data" folder
workout_df = pd.read_csv("data/workout.csv")

# Highest Google Search Time (Year) for "workout" keyword in Google (Global Standard)
# April 2020 is the Highest Google Search Year/Month for "workout" keyword
max_value = workout_df["workout_worldwide"].agg("max")
workout_df[workout_df["workout_worldwide"] == max_value]
year_str = "2020"

# Find the most popular during Covid Vs Now
three_keywords_df = pd.read_csv("data/three_keywords.csv")
# Compare three numerical variables with line plot over time
three_keywords_df.plot(kind = "line", x = "month", grid = True)
plt.show()

# Among 3 keywords ["home_workout_worldwide", "gym_workout_worldwide", "home_gym_worldwide"], the most popular keywords during Covid and Current Time
peak_covid = "home_workout_worldwide"
current = "gym_workout_worldwide"

workout_geo_df = pd.read_csv("data/workout_geo.csv")
set_workout_geo = workout_geo_df.set_index("country").sort_index()
uaj = set_workout_geo.loc[["United States","Australia","Japan"], "workout_2018_2023"]
uaj

# Country with the highest interest rate for workouts among 3 countries (US, Australia, Japan)
top_country = "United States"

three_keywords_geo = pd.read_csv("data/three_keywords_geo.csv")
last = three_keywords_geo.set_index("Country").sort_index()
last.loc[["Philippines", "Malaysia"], "home_workout_2018_2023"]