5 Best Ways to Convert an Integer to a Binary Array in Python

💡 Problem Formulation: In Python, converting an integer to a binary array requires representing the integer as an array of bits, where each bit is a binary digit (0 or 1). For instance, converting the integer 5 into binary gives us ‘101’, and as a binary array, it would be [1, 0, 1]. The aim of this article is to explore different methods to achieve this conversion.

Method 1: Using the bin() function and List Comprehension

This method uses the built-in bin() function to convert an integer to a binary string format and then employs list comprehension to convert this string to an array of binary digits.

Here’s an example:

number = 5
binary_array = [int(digit) for digit in bin(number)[2:]]
print(binary_array)

Output:

[1, 0, 1]

This code snippet converts the integer 5 to a binary string using bin(), slices off the ‘0b’ prefix and iterates over the string using list comprehension to create an array of integers.

Method 2: Bitwise Operations

By using bitwise shift and bitwise AND operations, an integer can be converted to a binary array directly without converting it to a string representation first.

Here’s an example:

number = 5
binary_array = []
while number:
    binary_array.insert(0, number & 1)
    number = number >> 1
print(binary_array)

Output:

[1, 0, 1]

Here, the code performs a bitwise AND with 1 to determine the least significant bit and inserts it at the start of the array. It then shifts the bits of number one position to the right. This continues until the number is reduced to 0.

Method 3: Using the format() Function

The format() function can be used to convert an integer to a binary string formatted without the ‘0b’ prefix, followed by generating the binary array using list comprehension, similar to Method 1.

Here’s an example:

number = 5
binary_array = [int(digit) for digit in format(number, 'b')]
print(binary_array)

Output:

[1, 0, 1]

The format() function here takes two arguments: the number to be formatted and the format specification ‘b’ for binary. A list comprehension is then applied to create the binary array.

Method 4: Recursion

A recursive function can be devised to convert an integer to a binary array by dividing the integer by 2 and collecting the remainders in reverse order.

Here’s an example:

def int_to_binary_array(number):
    return [number] if number < 2 else int_to_binary_array(number // 2) + [number % 2]

binary_array = int_to_binary_array(5)
print(binary_array)

Output:

[1, 0, 1]

The recursive function int_to_binary_array calls itself with the quotient of the number divided by 2, until the base case (number less than 2) is reached. The remainder is then collected in a list which represents the binary array.

Bonus One-Liner Method 5: Using numpy.binary_repr() and numpy.fromstring()

A compact and efficient way uses the numpy library functions binary_repr(), which returns a binary string, and fromstring() which can convert this string into an array.

Here’s an example:

import numpy as np

number = 5
binary_string = np.binary_repr(number)
binary_array = np.fromstring(binary_string, 'u1') - ord('0')
print(binary_array)

Output:

[1 0 1]

In this one-liner, np.binary_repr() returns a binary string for the number, and the np.fromstring() function is used to convert this string into a numpy array of integers, after which ‘0’ is subtracted to get the actual numerical values.

Summary/Discussion

• Method 1: Using bin() and List Comprehension. Pros – Simple and easy to understand. Cons – It requires string manipulation, which might not be the most efficient.
• Method 2: Bitwise Operations. Pros – Direct bit manipulation which can be more efficient. Cons – Somewhat more complex to understand and implement.
• Method 3: Using format(). Pros – Cleaner and more Pythonic alternative to bin(). Cons – Similar to method 1, also involves string handling.
• Method 4: Recursion. Pros – Elegant mathematical approach. Cons – May lead to stack overflow for very large integers due to recursive calls.
• Method 5 (Bonus): One-Liner with numpy. Pros – Utilizes the power of numpy for a compact one-liner solution, very fast. Cons – Relies on an external library, which is not always desired.