Print first m multiples of n without using any loop in Python

Given n and m, print first m multiples of a m number without using any loops in Python. Examples:

Input : n = 2, m = 3
Output : 2 4 6 

Input : n = 3, m = 4
Output : 3 6 9 12 

We can use range() function in Python to store the multiples in a range. First we store the numbers till m multiples using range() function in an array, and then print the array with using (*a) which print the array without using loop. Below is the Python implementation of the above approach: 

Python3

# function to print the first m multiple
# of a number n without using loop.
def multiple(m, n):

    # inserts all elements from n to
    # (m * n)+1 incremented by n.
    a = range(n, (m * n)+1, n)

    print(*a)

# driver code
m = 4
n = 3
multiple(m, n)

Output:

3 6 9 12

Note : In Python 3, print(*(range(x)) is equivalent to print(” “.join([str(i) for i in range(x)]))

Approach: Using numpy arange() with reshape()

note: install numpy module using command “pip install numpy”

Here’s a method to solve this problem using the NumPy library’s arange() function and the reshape() function:

Algorithm:

Create a numpy array using arange() function with start as n and stop as (m*n)+n, and step as n.
Reshape the numpy array using reshape() function to a (m,1) matrix.
Convert the numpy array to a list using tolist() function.
Print the list using * operator.

Python3

# Import the NumPy library
import numpy as np

# Define a function to print the first m multiples of n without using loop
def multiple(m, n):

    # Use NumPy's arange() function to create an array of multiples
    a = np.arange(n, m*n+1, n)

    # Use NumPy's reshape() function to reshape the array into a single row
    a = a.reshape(1, -1)

    # Convert the array to a string with space-separated values
    result = ' '.join(a[0].astype(str))

    # Print the resulting string
    print(result)

# Test the function with example inputs
m = 4
n = 3
multiple(m, n)

Output:

3 6 9 12

Time Complexity: The time complexity of this algorithm is O(m) as we are creating a numpy array with m elements and then reshaping it, which takes constant time.

Auxiliary Space: The space complexity of this algorithm is O(m) as we are creating a numpy array with m elements and then reshaping it, which takes constant space.