Create a function called create_ones that takes an integer n and returns a list of n ones using list comprehension.

Write a function called clone_list that takes a list and returns a new list (clone) using list comprehension.

Use list comprehension to create a list of squares of numbers from 0 to 9.

Use list comprehension to create a list of even numbers from 0 to 20.

Use dictionary comprehension to create a dictionary mapping numbers from 0 to 9 to their squares.

Use set comprehension to create a set of squares of numbers from -5 to 5. Note how duplicates are automatically removed.

Use dictionary comprehension with a condition to create a dictionary mapping even numbers from 0 to 20 to their halves.

Use nested list comprehension to create a multiplication table (list of lists) for numbers 1 to 5.

Use list comprehension with a conditional expression to create a list where even numbers are kept as-is and odd numbers are squared.

Given a list of strings, use dictionary comprehension to create a dictionary mapping each string to its length.

Write a function called flatten that takes a list of lists and returns a flattened list using nested list comprehension.

Use list comprehension to create a list of prime numbers less than 50. A prime number is only divisible by 1 and itself. Use a helper function is_prime to check if a number is prime.

Use dictionary comprehension to create a dictionary that counts the frequency of each character in a string. Read a string from input.

Use nested list comprehension to transpose a matrix (swap rows and columns). Read the number of rows, then read each row.

What is the output of the following code? Analyze each comprehension step by step.

1
A0 = dict(zip(('a','b','c','d','e'),(1,2,3,4,5)))
2
A1 = range(10)
3
A2 = sorted([i for i in A1 if i in A0])
4
A3 = sorted([A0[s] for s in A0])
5
A4 = [i for i in A1 if i in A3]
6
A5 = {i : i * i for i in A1}
7
A6 = [[i , i * i] for i in A1]
8

9
print(A0, A1, A2, A3, A4, A5, A6)

Write a function called cartesian_product that takes two lists and returns the Cartesian product (all possible pairs) using nested list comprehension.

Use dictionary comprehension to create a dictionary from two lists: one for keys and one for values. Read the number of elements, then read keys and values.

Use set comprehension to create a set of numbers from 1 to 100 that are divisible by both 3 and 5.

Use nested dictionary comprehension to create a dictionary where keys are tuples (i, j) and values are i * j, for i and j from 1 to 3.

Use nested list comprehension to create a list of lists where each inner list contains numbers from 1 to n, for n from 1 to 5.

Use dictionary comprehension with a conditional expression to create a dictionary mapping numbers 1 to 10, where values are “even” for even numbers and “odd” for odd numbers.

Use list comprehension with multiple conditions to create a list of numbers from 1 to 50 that are divisible by 3 but not by 5.

Use dictionary comprehension to invert a dictionary (swap keys and values). If multiple keys have the same value, handle it appropriately. Read the number of entries, then read key-value pairs.

Use list comprehension to create a list of dictionaries. Given lists of names and ages, create a list where each dictionary has ‘name’ and ‘age’ keys.

Write a function process_students that takes a list of dictionaries where each dictionary has nested dictionaries (e.g., {'name': 'Alice', 'scores': {'math': 85, 'science': 90, 'english': 80}}). The function should use sorted() with a complex lambda key that sorts by the sum of all nested score values (descending), then by name (alphabetically). Return a dictionary comprehension that maps each name to their average score.

Write a function process_data that takes a list of tuples where each tuple contains (name, (x, y) coordinates, score). The function should use sorted() with a lambda key that sorts by the Euclidean distance from origin (x**2 + y**2)**0.5 (ascending), then by score (descending). Return a dictionary comprehension where keys are tuples (name, score) and values are the coordinates.

Write a function process_nested that takes a nested list of lists where each inner list contains numbers. The function should use map() with a lambda to compute the sum of each inner list, then use filter() with a lambda to keep only sums greater than 10. Return a dictionary comprehension mapping each filtered sum to a tuple of (original_length, doubled_sum). You’ll need to track which list corresponds to which sum.

Write a function process_items that takes a list of dictionaries where each dictionary has a ‘name’ key and a ‘data’ key containing a nested list of numbers. The function should use sorted() with a lambda key that sorts by the maximum value in the nested list (descending), then by the length of the nested list (ascending), then by name (alphabetically). Return a dictionary comprehension mapping each name to a tuple of (max_value, sum_of_list).

Write a function process_data that takes a list of dictionaries with structure {'id': (x, y), 'values': [n1, n2, ...], 'meta': {'count': c, 'sum': s}}. The function should use sorted() with a lambda key that sorts by the product of tuple coordinates x * y (descending), then by the ‘count’ in meta (ascending). Return a dictionary comprehension where keys are tuples (id_tuple, max_value) and values are nested dictionaries with ‘avg’ and ‘total’ keys.

Write a function process_complex that takes a list of dictionaries with structure {'key': (a, b, c), 'nested': {'list': [x, y, z], 'dict': {'p': p_val, 'q': q_val}}}. The function should use sorted() with a complex lambda key that sorts by a * b + c (descending), then by the sum of the nested list (ascending), then by p_val * q_val (descending). Return a dictionary comprehension where keys are tuples (key_tuple, nested_sum) and values are dictionaries with ‘product’ (p*q) and ‘list_max’ keys.