Seasonality and moving averages
Stepping back, we will now look at the overall revenue data for our meditation app. We saw strong purchase growth in one of our products, and now we want to see if that is leading to a corresponding rise in revenue. As you may expect, revenue is very seasonal, so we want to correct for that and unlock macro trends.
In this exercise, we will correct for weekly, monthly, and yearly seasonality and plot these over our raw data. This can reveal trends in a very powerful way.
The revenue data is loaded for you as daily_revenue.
This exercise is part of the course
Customer Analytics and A/B Testing in Python
Exercise instructions
- Using the
.rolling()method, find the rolling average of the data with a 7 day window and store it in a column7_day_rev. - Find the monthly (28 days) rolling average and store it in a column
28_day_rev. - Find the yearly (365 days) rolling average and store it in a column
365_day_rev. - Hit 'Submit Answer' to plot the three calculated rolling averages together along with the raw data.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Compute 7_day_rev
daily_revenue['7_day_rev'] = daily_revenue.revenue.____(window=____,center=False).____
# Compute 28_day_rev
daily_revenue['28_day_rev'] = daily_revenue.revenue.____(window=____,center=False).____
# Compute 365_day_rev
daily_revenue['365_day_rev'] = daily_revenue.revenue.____(window=____,center=False).____
# Plot date, and revenue, along with the 3 rolling functions (in order)
daily_revenue.plot(x='date', y=['revenue', '7_day_rev', '28_day_rev', '365_day_rev', ])
plt.show()