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List Comprehensions and Generators

Think of list comprehensions as your musical score, where you can compose a list of notes in a single, elegant line. It's like writing a melody that flows seamlessly. With list comprehensions, you can create new lists by applying an expression to each item in an existing iterable, such as a list or a range.

Here's a simple example: Imagine you have a list of note durations in seconds, and you want to convert them to milliseconds. Instead of writing a loop, you can use a list comprehension:


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note_durations_sec = [0.5, 1.0, 1.5, 2.0]
note_durations_ms = [duration * 1000 for duration in note_durations_sec]

print("Note Durations in ms:", note_durations_ms)

In this example, each note duration is multiplied by 1000 to convert it to milliseconds, all in one concise line. It's like hitting the perfect chord with minimal effort.

Now, let's talk about generators. Imagine you're creating an endless playlist of music that streams without taking up much space on your device. Generators are like that playlist—they allow you to iterate over data without storing it all in memory at once.

Generators are defined using functions and the yield keyword. Here's how you can create a simple generator that produces an infinite sequence of beats:


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def infinite_beats():
    beat = 1
    while True:
        yield beat
        beat += 1

beat_generator = infinite_beats()

# Let's play the first 5 beats
for _ in range(5):
    print("Beat:", next(beat_generator))

In this example, the infinite_beats generator function yields a new beat each time it's called. It's like having a drummer who never stops playing, providing you with a continuous rhythm.

List comprehensions and generators can work together harmoniously. For instance, you can use a generator to produce a large dataset and then apply a list comprehension to filter or transform it.

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Complete the normalize_popularity function that normalizes a list of popularity scores. This normalization process scales the scores to a range between 0 and 1, making it easier to compare and analyze them.

Inputs:

popularity_scores: A list of integers representing the popularity scores to be normalized.

Steps:

Identify Minimum and Maximum Scores: Determine the minimum and maximum values from the popularity_scores list. These values will be used to scale the scores.
Normalize Scores: Use a list comprehension to iterate over each score in popularity_scores. For each score, apply the normalization formula
Normalization Formula: (score - min_score) / (max_score - min_score).

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solution.py

main.py

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