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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#Read file df_workout
df_workout = pd.read_csv("data/workout.csv")
print(df_workout)Hidden output
#Find the peak of the global search for 'workout'
plt.figure(figsize= (12,6))
plt.plot(df_workout['month'], df_workout['workout_worldwide'])
plt.xticks(rotation=90)
plt.show()Hidden output
year_str = '2020'#Read file three_keywords.csv
df_keywords = pd.read_csv('data/three_keywords.csv')
print(df_keywords)Hidden output
#Find the most popular three_keywords during pandemic covid & current
plt.figure(figsize = (12,6))
plt.plot(df_keywords['month'], df_keywords['home_workout_worldwide'], label = 'Home Workout')
plt.plot(df_keywords['month'], df_keywords['gym_workout_worldwide'], label = 'Gym Workout')
plt.plot(df_keywords['month'], df_keywords['home_gym_worldwide'], label = 'Home Gym')
plt.xticks(rotation = 90)
plt.legend()
plt.show()peak_covid = 'Home Workout'
current = 'Gym Workout'#read file
df_workout_geo = pd.read_csv('data/workout_geo.csv', index_col = 0)
df_workout_geoHidden output
#Find country has the highest for workout
print(df_workout_geo.loc['United States'])
print(df_workout_geo.loc['Australia'])
print(df_workout_geo.loc['Japan'])#The country has the highest interest for workouts
top_country = 'United States'#Read file csv
df_three_keywords_geo = pd.read_csv('data/three_keywords_geo.csv', index_col = 0)
print(df_three_keywords_geo)#Find the country has the highest interest in home workouts
print(df_three_keywords_geo.loc['Malaysia', :])
print(df_three_keywords_geo.loc['Philippines', :])#Philippines has the highest interest in home workouts
home_workout_geo = 'Philippines'