Python | Algorithms | Data Structures | Cyber ​​Security | Networks

In Python, list comprehensions provide a concise way to create lists by applying an expression to each item in an iterable, often with conditions—making code more readable and efficient for tasks like filtering or transforming data, a frequent interview topic for assessing Pythonic style.

# Basic comprehension
squares = [x**2 for x in range(5)]  # [0, 1, 4, 9, 16]

# With condition
evens = [x for x in range(10) if x % 2 == 0]  # [0, 2, 4, 6, 8]

# Nested with transformation
matrix = [[1, 2], [3, 4]]
flattened = [num for row in matrix for num in row]  # [1, 2, 3, 4]

# Equivalent to loop (interview comparison)
result = []
for x in range(5):
    result.append(x**2)
# result = [0, 1, 4, 9, 16]  # Same as first example

#python #listcomprehensions #interviewtips #pythonic #datastructures

👉 @DataScience4

www.tgoop.com/DataScience4/8512

682 viewsOct 26 at 08:10

In Python, list comprehensions provide a concise way to create lists by applying an expression to each item in an iterable, often with conditions—making code more readable and efficient for tasks like filtering or transforming data, a frequent interview topic for assessing Pythonic style.

# Basic comprehension
squares = [x**2 for x in range(5)]  # [0, 1, 4, 9, 16]

# With condition
evens = [x for x in range(10) if x % 2 == 0]  # [0, 2, 4, 6, 8]

# Nested with transformation
matrix = [[1, 2], [3, 4]]
flattened = [num for row in matrix for num in row]  # [1, 2, 3, 4]

# Equivalent to loop (interview comparison)
result = []
for x in range(5):
    result.append(x**2)
# result = [0, 1, 4, 9, 16]  # Same as first example

BY Python | Algorithms | Data Structures | Cyber ​​Security | Networks