Life's a garden. Dig it.
You own a national restaurant chain called Applewasps. To increase sales, you decide to launch a multi-regional television marketing campaign.
At the end of the campaign you have a table of commercials indicating when and where each commercial aired, and a table of sales indicating when and where customers generated sales.
import numpy as np
import pandas as pd
generator = np.random.default_rng(5555)
regions = ['north', 'south', 'east', 'west']
commercials = pd.DataFrame({
'commercial_id': range(10),
'region': generator.choice(regions, size=10),
'date_time': pd.to_datetime('2020-01-01') + pd.to_timedelta(generator.integers(240, size=10), unit='h')
})
sales = pd.DataFrame({
'sale_id': range(10),
'region': generator.choice(regions, size=10),
'date_time': pd.to_datetime('2020-01-01') + pd.to_timedelta(generator.integers(240, size=10), unit='h'),
'revenue': np.round(generator.normal(loc=20, scale=5, size=10), 2)
})
print(commercials)
# commercial_id region date_time
# 0 0 west 2020-01-10 12:00:00
# 1 1 north 2020-01-10 16:00:00
# 2 2 south 2020-01-09 01:00:00
# 3 3 east 2020-01-10 19:00:00
# 4 4 south 2020-01-08 22:00:00
# 5 5 east 2020-01-03 02:00:00
# 6 6 south 2020-01-07 15:00:00
# 7 7 west 2020-01-05 22:00:00
# 8 8 east 2020-01-03 04:00:00
# 9 9 west 2020-01-05 04:00:00
print(sales)
# sale_id region date_time revenue
# 0 0 west 2020-01-05 08:00:00 20.14
# 1 1 east 2020-01-08 22:00:00 22.98
# 2 2 south 2020-01-07 21:00:00 22.98
# 3 3 west 2020-01-05 17:00:00 16.82
# 4 4 west 2020-01-02 12:00:00 20.47
# 5 5 east 2020-01-10 09:00:00 26.93
# 6 6 north 2020-01-08 19:00:00 20.25
# 7 7 south 2020-01-01 08:00:00 23.38
# 8 8 south 2020-01-01 17:00:00 25.74
# 9 9 south 2020-01-10 22:00:00 22.28In order to analyze the performance of each commercial, map each sale to the commercial that aired prior to the sale, in the same region.
Solution¶
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