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.

3 hidden cells

wgeo= pd.read_csv('data/workout_geo.csv')
print(wgeo.head())

# Convert 'workout_2018_2023' to numeric, forcing invalid parsing to NaN, then drop NaN values
wgeo['workout_2018_2023'] = pd.to_numeric(wgeo['workout_2018_2023'], errors='coerce')
wgeo1 = wgeo.dropna(subset=['workout_2018_2023'])

print((wgeo1.shape))
fig, ax = plt.subplots()
fig.set_size_inches([12,5])
ax.bar(wgeo1['country'],wgeo1['workout_2018_2023'])
ax.set_xticklabels(wgeo1['country'], rotation = 90)

plt.show()

Hidden output

wgeo3kw= pd.read_csv('data/three_keywords_geo.csv')
print(wgeo3kw.head())

# Convert 'workout_2018_2023' to numeric, forcing invalid parsing to NaN, then drop NaN values
wgeo3kw = wgeo3kw[wgeo3kw['Country'].isin( ['Singapore', 'Malaysia'])]

print(wgeo3kw.iloc[0:4])


fig, ax = plt.subplots()
ax.hist(wgeo3kw['Country'],(wgeo3kw['home_workout_2018_2023']+wgeo3kw['home_gym_2018_2023']))
# ax.bar(wgeo3kw['Country'],wgeo3kw['gym_workout_2018_2023'])
# ax.bar(wgeo3kw['Country'],)

ax.set_xticklabels(wgeo3kw['Country'], rotation = 90)
plt.show()