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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# When was the global search for 'workout' at its peak?
wrk = pd.read_csv('data/workout.csv')
plt.figure(figsize=(12, 6))
plt.plot(wrk["month"], wrk["workout_worldwide"])
plt.xticks(rotation=90)
plt.show()
year_str = '2020'
# Of the keywords available, what was the most popular during the covid pandemic, and what is the most popular now?
wrds = pd.read_csv('data/three_keywords.csv')
wrds.plot()
plt.title('Keywords Trends')
plt.xlabel('Year')
plt.ylabel('Searches')
peak_covid = 'home_workout_worldwide'
current = 'gym_workout_worldwide'
# What country has the highest interest for workouts among the following: United States, Australia, or Japan?
wrkgeo = pd.read_csv('data/workout_geo.csv', index_col = 0)
topthree = wrkgeo.loc[["United States", "Australia", "Japan"], :]
top_country = topthree.iloc[0]
print(top_country)
top_country = "United States"
# Philippines or Malaysia. Which of the two countries has the highest interest in home workouts?
keywordsgeo = pd.read_csv('data/three_keywords_geo.csv', index_col = 0)
print(keywordsgeo.loc[['Philippines', 'Malaysia'], ['home_workout_2018_2023', 'home_gym_2018_2023']])
home_workout_geo = 'Philippines'