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Compare Online Shopping Experience

Now, experience the power of algorithm efficiency! Run the provided code and compare the performance of four different algorithms (bubble sort and linear search as well as quick sort and binary search).

No coding required. Tweak a parameter, and see how they perform under different conditions. Change the parameter at the top called num_items. (This parameter simulates how many items are in an online shopping catalog).

Run the code and compare the results and notice as we increase from 1000 to 10000, how much longer it takes one option over the other.

Imagine how much better (i.e. faster) the experience would be with one of these pair of algorithms vs the other if you were sorting a list of items by price on an online e-commerce shopping site.

Diese Übung ist Teil des Kurses

Concepts in Computer Science

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Anleitung zur Übung

  • Change the num_items to any number you want to compare. (ex: imagine www.amazon.com has 1,000 items, make num_items = 1000).

Interaktive Übung

Vervollständige den Beispielcode, um diese Übung erfolgreich abzuschließen.

# Change this parameter
num_items = 1000

####### Leave the code below this line alone ########
catalog = [random.randint(0, num_items) for _ in range(num_items)]
total_time_bubble_linear = time_bubble_sort_and_linear_search(catalog)
catalog = [random.randint(0, num_items) for _ in range(num_items)]
total_time_quick_binary = time_quick_sort_and_binary_search(catalog)

df = pd.DataFrame({"Method": ["Bubble Sort + Linear Search", "Quick Sort + Binary Search"],
                   "Total Time (seconds)": [total_time_bubble_linear, total_time_quick_binary]})
print(df)
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