Python Data Science Jobs & Interviews

In Python, loops are essential for repeating code efficiently: for loops iterate over known sequences (like lists or ranges) when you know the number of iterations, while loops run based on a condition until it's false (ideal for unknown iteration counts or sentinel values), and nested loops handle multi-dimensional data by embedding one inside another—use break/continue for control, and comprehensions for concise alternatives in interviews.

# For loop: Use for fixed iterations over iterables (e.g., processing lists)
fruits = ["apple", "banana", "cherry"]
for fruit in fruits:  # Iterates each element
    print(fruit)      # Output: apple \n banana \n cherry

for i in range(3):    # Numeric sequence (start=0, stop=3)
    print(i)          # Output: 0 \n 1 \n 2

# While loop: Use when iterations depend on a dynamic condition (e.g., user input, convergence)
count = 0
while count < 3:      # Runs as long as condition is True
    print(count)
    count += 1       # Increment to avoid infinite loop! Output: 0 \n 1 \n 2

# Nested loops: Use for 2D data (e.g., matrices, grids); outer for rows, inner for columns
matrix = [[1, 2], [3, 4]]
for row in matrix:    # Outer: each sublist
    for num in row:   # Inner: elements in row
        print(num)    # Output: 1 \n 2 \n 3 \n 4

# Control statements: break (exit loop), continue (skip iteration)
for i in range(5):
    if i == 2:
        continue    # Skip 2
    if i == 4:
        break       # Exit at 4
    print(i)        # Output: 0 \n 1 \n 3

# List comprehension: Concise for loop alternative (use for simple transformations/filtering)
squares = [x**2 for x in range(5) if x % 2 == 0]  # Even squares
print(squares)  # Output: [0, 4, 16]

#python #loops #forloop #whileloop #nestedloops #comprehensions #interviewtips #controlflow

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131 viewsedited  Oct 27 at 07:39

In Python, loops are essential for repeating code efficiently: for loops iterate over known sequences (like lists or ranges) when you know the number of iterations, while loops run based on a condition until it's false (ideal for unknown iteration counts or sentinel values), and nested loops handle multi-dimensional data by embedding one inside another—use break/continue for control, and comprehensions for concise alternatives in interviews.

# For loop: Use for fixed iterations over iterables (e.g., processing lists)
fruits = ["apple", "banana", "cherry"]
for fruit in fruits:  # Iterates each element
    print(fruit)      # Output: apple \n banana \n cherry

for i in range(3):    # Numeric sequence (start=0, stop=3)
    print(i)          # Output: 0 \n 1 \n 2

# While loop: Use when iterations depend on a dynamic condition (e.g., user input, convergence)
count = 0
while count < 3:      # Runs as long as condition is True
    print(count)
    count += 1       # Increment to avoid infinite loop! Output: 0 \n 1 \n 2

# Nested loops: Use for 2D data (e.g., matrices, grids); outer for rows, inner for columns
matrix = [[1, 2], [3, 4]]
for row in matrix:    # Outer: each sublist
    for num in row:   # Inner: elements in row
        print(num)    # Output: 1 \n 2 \n 3 \n 4

# Control statements: break (exit loop), continue (skip iteration)
for i in range(5):
    if i == 2:
        continue    # Skip 2
    if i == 4:
        break       # Exit at 4
    print(i)        # Output: 0 \n 1 \n 3

# List comprehension: Concise for loop alternative (use for simple transformations/filtering)
squares = [x**2 for x in range(5) if x % 2 == 0]  # Even squares
print(squares)  # Output: [0, 4, 16]

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