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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# Import the necessary libraries
import pandas as pd
import matplotlib.pyplot as plt
# Find the peak for global 'workout' searches
df_workout = pd.read_csv("data/workout.csv")
plt.figure(figsize=(12, 6))
plt.plot(df_workout["month"], df_workout["workout_worldwide"])
plt.xticks(rotation=90)
plt.show()
year_str = "2020"
# Find the most popular keywords for the current year and during covid
df_keywords = pd.read_csv("data/three_keywords.csv")
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"
# Find the country with the highest interest for workouts
df_workout_geo = pd.read_csv("data/workout_geo.csv", index_col = 0)
print(df_workout_geo.loc["United States"])
print(df_workout_geo.loc["Australia"])
print(df_workout_geo.loc["Japan"])
top_country = "United States"
# Who has the highest interest in home workouts, Philippines or Malaysia?
df_keywords_geo = pd.read_csv("data/three_keywords_geo.csv", index_col = 0)
print(df_keywords_geo.loc["Philippines", :])
print(df_keywords_geo.loc["Malaysia", :])
home_workout_geo = "Philippines"