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A list of 480 superheroes has been loaded into your session (called heroes) as well as a list of each hero's corresponding publisher (called publishers).You'd like to filter the heroes list based on a hero's specific publisher, but are unsure which of the below functions is more efficient.def get_publisher_heroes(heroes, publishers, desired_publisher): desired_heroes = [] for i,pub in enumerate(publishers): if pub == desired_publisher: desired_heroes.append(heroes[i]) return desired_heroesdef get_publisher_heroes_np(heroes, publishers, desired_publisher): heroes_np = np.array(heroes) pubs_np = np.array(publishers) desired_heroes = heroes_np[pubs_np == desired_publisher] return desired_heroesInstructions 1/425 XP1234Use the get_publisher_heroes() function and the get_publisher_heroes_np() function to collect heroes from the Star Wars universe. The desired_publisher for Star Wars is 'George Lucas'.

Question

A list of 480 superheroes has been loaded into your session (called heroes) as well as a list of each hero's corresponding publisher (called publishers).You'd like to filter the heroes list based on a hero's specific publisher, but are unsure which of the below functions is more efficient.def get_publisher_heroes(heroes, publishers, desired_publisher): desired_heroes = [] for i,pub in enumerate(publishers): if pub == desired_publisher: desired_heroes.append(heroes[i]) return desired_heroesdef get_publisher_heroes_np(heroes, publishers, desired_publisher): heroes_np = np.array(heroes) pubs_np = np.array(publishers) desired_heroes = heroes_np[pubs_np == desired_publisher] return desired_heroesInstructions 1/425 XP1234Use the get_publisher_heroes() function and the get_publisher_heroes_np() function to collect heroes from the Star Wars universe. The desired_publisher for Star Wars is 'George Lucas'.

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Solution

The question seems to be asking which of the two provided functions is more efficient for filtering a list of superheroes based on their publisher. The two functions are get_publisher_heroes() and get_publisher_heroes_np().

The first function, get_publisher_heroes(), uses a for loop and the built-in Python function enumerate() to iterate over the list of publishers. If the publisher matches the desired publisher, the corresponding hero is appended to the desired_heroes list.

The second function, get_publisher_heroes_np(), converts the lists of heroes and publishers to NumPy arrays. It then uses Boolean indexing to select the heroes that correspond to the desired publisher.

In terms of efficiency, the second function is likely to be faster, especially for large lists. This is because NumPy arrays are stored more efficiently than Python lists and operations on them are executed more quickly.

To use these functions to collect heroes from the Star Wars universe, you would call them with 'George Lucas' as the desired_publisher. Here's how you could do it:

star_wars_heroes = get_publisher_heroes(heroes, publishers, 'George Lucas')
star_wars_heroes_np = get_publisher_heroes_np(heroes, publishers, 'George Lucas')

Both of these lines of code will return a list of heroes from the Star Wars universe.

This problem has been solved

Similar Questions

A random sample of 25,000 superheroes has been loaded into your session as an array called sample_indices. This sample is a list of indices that corresponds to each superhero's index selected from the heroes list.A function named calc_bmi_lists has also been created and saved to a file titled bmi_lists.py. For convenience, it is displayed below:def calc_bmi_lists(sample_indices, hts, wts): # Gather sample heights and weights as lists s_hts = [hts[i] for i in sample_indices] s_wts = [wts[i] for i in sample_indices] # Convert heights from cm to m and square with list comprehension s_hts_m_sqr = [(ht / 100) ** 2 for ht in s_hts] # Calculate BMIs as a list with list comprehension bmis = [s_wts[i] / s_hts_m_sqr[i] for i in range(len(sample_indices))] return bmisNotice that this function performs all necessary calculations using list comprehension (hence the name calc_bmi_lists()). Dig deeper into this function and analyze the memory footprint for performing your calculations using lists:Load the memory_profiler package into your IPython session.Import calc_bmi_lists from bmi_lists.Once you've completed the above steps, use %mprun to profile the calc_bmi_lists() function acting on your superheroes data. The hts array and wts array have already been loaded into your session.After you've finished coding, answer the following question:How much memory do the list comprehension lines of code consume in the calc_bmi_lists() function? (i.e., what is the total sum of the Increment column for these four lines of code?)

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