Binary Search | Python (original) (raw)

Last Updated : 20 Dec, 2025

Binary Search is an efficient searching algorithm used to find an element in a **sorted array by repeatedly dividing the search interval in half. It reduces the time complexity to O(log N), making it much faster than linear search.

Here is working of Binary Search

1. Find the middle element of the array.
2. Compare the middle element with the target key. If equal: return index.
3. If the key is smaller: search the left half.
4. If the key is larger: search the right half.
5. Repeat until the element is found or the search space is empty.

Binary Search using the bisect

Python provides the bisect module, which offers built-in functions to maintain a list in sorted order and perform efficient binary searches internally.

Python `

import bisect

def binary_search_bisect(arr, x): i = bisect.bisect_left(arr, x) if i != len(arr) and arr[i] == x: return i else: return -1

arr = [2, 3, 4, 10, 40] x = 10 result = binary_search_bisect(arr, x)

if result != -1: print("Element is present at index", result) else: print("Element is not present in array")

`

Output

Element is present at index 3

**Explanation:

• **bisect_left(arr, x): finds the position where x should be inserted to keep arr sorted.
• Checks if x exists at that position; if yes, returns the index.
• Returns -1 if the element is not found.

Iterative Binary Search

This version manually performs binary search using a while loop - efficient in both time and space.

Python `

def binary_search(arr, x): low = 0 high = len(arr) - 1

while low <= high:
    mid = low + (high - low) // 2

    if arr[mid] < x:
        low = mid + 1
    elif arr[mid] > x:
        high = mid - 1
    else:
        return mid
return -1

arr = [2, 3, 4, 10, 40] x = 10 result = binary_search(arr, x)

if result != -1: print("Element is present at index", result) else: print("Element is not present in array")

`

Output

Element is present at index 3

**Explanation:

• Using a while loop to implement iterative binary search .
• Initialize low and high pointers to define the search range.
• Calculates the middle index mid = low + (high - low) // 2.
• If arr[mid] is less than x, moves search to the right half.
• If arr[mid] is greater than x, moves search to the left half.
• If arr[mid] == x, returns the index.
• Returns -1 if the element is not found.

Recursive Binary Search

Recursion simplifies the logic but increases memory usage due to function call stacks.

Python `

def binary_search(arr, low, high, x): if high >= low: mid = low + (high - low) // 2 if arr[mid] == x: return mid elif arr[mid] > x: return binary_search(arr, low, mid - 1, x) else: return binary_search(arr, mid + 1, high, x) else: return -1

arr = [2, 3, 4, 10, 40] x = 10

result = binary_search(arr, 0, len(arr) - 1, x)

if result != -1: print("Element is present at index", result) else: print("Element is not present in array")

`

Output

Element is present at index 3

**Explanation:

• Using recursion to implement binary search on a sorted list.
• Calculates the middle index and compares its value with x.
• If x matches the middle element, returns its index.
• If x is smaller, searches the left half; if larger, searches the right half.
• Returns -1 if the element is not found.

Related Articles:

• Variants of Binary Search
• Bisect Algorithm Functions in Python