List indexing and slicing in Python: reach in by position

Positive indexes count from the front, starting at 0. Negative indexes count from the back, starting at -1:

daily_tees = [42, 57, 38, 64, 71, 95, 88]
print("First day:", daily_tees[0])
print("Last day:", daily_tees[-1])
print("Day before that:", daily_tees[-2])

First day: 42
Last day: 88
Day before that: 95

[-1] is always the last item, however long the list is. That's far safer than [6], which breaks the moment an eighth day is added.

A slice takes a section. [:3] means "from the start, up to but not including position 3," so it returns three items:

size_sales = [18, 64, 120, 97, 41]
ranked = sorted(size_sales, reverse=True)

print("Ranked:", ranked)
print("Top three:", ranked[:3])
print("The rest:", ranked[3:])

Ranked: [120, 97, 64, 41, 18]
Top three: [120, 97, 64]
The rest: [41, 18]

ranked[:3] and ranked[3:] split the list cleanly with no overlap, because the stop index is excluded from the first and the start index is included in the second.

Compute an index and you can find the middle. Sort the order values, then take the item at len // 2: the median, which one huge order can't distort:

order_values = [38, 38, 52, 76, 38, 114, 45, 38, 600]
order_values.sort()

mean = sum(order_values) / len(order_values)
median = order_values[len(order_values) // 2]
print(f"Mean order: ${mean:.2f}")
print(f"Median order: ${median}")

Mean order: $115.44
Median order: $45

One $600 bulk order drags the mean above $115, while the median stays at $45. For "what does a typical customer spend?", the median is the honest answer.