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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# Start coding here
import pandas as pd
import matplotlib.pyplot as plt
workout = pd.read_csv('data/workout.csv')
plt.figure(figsize= (12,6))
plt.xlabel('Month')
plt.ylabel('workout_worldwide')
plt.title('Yearly Home Gym Usage')
plt.plot(workout['month'], workout['workout_worldwide'])
plt.xticks(rotation = 90)
plt.show()
year_str = '2020'
plt.clf()
keyword = pd.read_csv('data/three_keywords.csv')
plt.subplots(3,1)
plt.figure(figsize=(12,6))
plt.plot(keyword['month'], keyword['home_workout_worldwide'], label = 'Home Workout')
plt.plot(keyword['month'], keyword['gym_workout_worldwide'], label = 'Gym Workout')
plt.plot(keyword['month'], keyword['home_gym_worldwide'], label = 'Home gym')
plt.xticks(rotation = 90)
plt.legend()
plt.show()
plt.clf()
peak_covid = 'Home Workout'
current = 'Gym Workout'
Country = pd.read_csv('data/workout_geo.csv', index_col = 0)
print(Country.loc['United States', :])
print(Country.loc['Australia', :])
print(Country.loc['Japan', :])
top_country = 'United States'
Home = pd.read_csv('data/three_keywords_geo.csv', index_col= 0)
print(Home.loc['Philippines', :])
print(Home.loc['Malaysia', :])
home_workout_geo = 'Philippines'