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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

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.

Load data

# Import the necessary libraries
import pandas as pd
import matplotlib.pyplot as plt
import pandas as pd

# Load the data
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')

Check data

# workout
print(workout.head())
# three_keywords
print(three_keywords.head())
# workout_geo
print(workout_geo.head())
# three_keywords_geo
print(three_keywords_geo.head())

Exploratory analysis: year of peak interest

# Creating a year column
workout['year'] = workout['month'].str.strip().str[:-3]

# Grouping per year
workout.groupby('year')['workout_worldwide'].sum().sort_values(ascending=False)

# Taking the most searched year
year_str = workout.groupby('year')['workout_worldwide'].sum().sort_values(ascending=False).index[0]
print(f'The most searched year is: {year_str}')

Exploratory analysis: popular keyword during COVID

# Creating date subsets for COVID and current year
three_keywords_covid = three_keywords[(three_keywords['month'] < '2023-05') & (three_keywords['month'] >= '2020-01')]

three_keywords_now = three_keywords[three_keywords['month'].isin(['2023-01', '2023-02', '2023-03'])]
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