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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.
# Start coding here
import pandas as pd
import matplotlib.pyplot as plt

df = pd.read_csv('data/workout.csv')
df.head(5)

plt.figure(figsize=(12, 6))
plt.plot(df['month'], df['workout_worldwide'])
plt.xticks(rotation=90)
plt.show()

year_str = '2020'
# Start coding here
import pandas as pd
import matplotlib.pyplot as plt

df = pd.read_csv('data/three_keywords.csv')
df.head(5)

fig, ax = plt.subplots()
plt.plot(df['month'], df['home_workout_worldwide'], label='home workout worldwide')
plt.plot(df['month'], df['gym_workout_worldwide'], label = 'gym workout worldwide')
plt.plot(df['month'], df['home_gym_worldwide'], label = 'home gym worldwide')
plt.legend()
plt.xticks(rotation=90)
plt.show()

peak_covid = 'home workout'
current = 'gym workout'
import pandas as pd
import matplotlib.pyplot as plt

df = pd.read_csv('data/workout_geo.csv', index_col=0)
print(df.loc['United States'])
print(df.loc['Australia'])
print(df.loc['Japan'])

top_country = 'United States'
import pandas as pd
import matplotlib.pyplot as plt

df = pd.read_csv('data/three_keywords_geo.csv', index_col = 0)
df.head(5)
print(df.loc['Philippines', :])
print(df.loc['Malaysia', :])

home_workout_geo = 'Philippines'