# Binary Search

#### In this tutorial, you will learn how Binary Search sort works. Also, you will find working examples of Binary Search in Python.

Binary Search is a searching algorithm for finding an element’s position in a sorted array.

In this approach, the element is always searched in the middle of a portion of an array.

Binary search can be implemented only on a sorted list of items. If the elements are not sorted already, we need to sort them first.

## Binary Search Working

Binary Search Algorithm can be implemented in two ways which are discussed below.

- Iterative Method
- Recursive Method

The recursive method follows the divide and conquer approach.

The general steps for both methods are discussed below.

- The array in which searching is to be performed is:

Let

`x = 4`

be the element to be searched. - Set two pointers low and high at the lowest and the highest positions respectively.

- Find the middle element
`mid`of the array ie.`(arr[low + high]) / 2 = 6`

.

- If x == mid, then return mid.Else, compare the element to be searched with m.
- If
`x > mid`

, compare`x`with the middle element of the elements on the right side of`mid`. This is done by setting`low`to`low = mid + 1`

. - Else, compare
`x`with the middle element of the elements on the left side of`mid`. This is done by setting`high`to`high = mid - 1`

.

- Repeat steps 3 to 6 until low meets high.

`x = 4`

is found.

## Binary Search Algorithm

### Iteration Method

do until the pointers low and high meet each other. mid = (low + high)/2 if (x == arr[mid]) return mid else if (x > A[mid]) // x is on the right side low = mid + 1 else // x is on the left side high = mid - 1

### Recursive Method

binarySearch(arr, x, low, high) if low > high return False else mid = (low + high) / 2 if x == arr[mid] return mid else if x < data[mid] // x is on the right side return binarySearch(arr, x, mid + 1, high) else // x is on the right side return binarySearch(arr, x, low, mid - 1)

## Python Examples (Iterative Method)

```
/* Binary Search in python */
def binarySearch(array, x, low, high):
/* Repeat until the pointers low and high meet each other */
while low <= high:
mid = low + (high - low)//2
if array[mid] == x:
return mid
elif array[mid] < x:
low = mid + 1
else:
high = mid - 1
return -1
array = [3, 4, 5, 6, 7, 8, 9]
x = 4
result = binarySearch(array, x, 0, len(array)-1)
if result != -1:
print("Element is present at index " + str(result))
else:
print("Not found")
```

## Python Examples (Recursive Method)

```
/* Binary Search in python */
def binarySearch(array, x, low, high):
if high >= low:
mid = low + (high - low)//2
/* If found at mid, then return it */
if array[mid] == x:
return mid
/* Search the left half */
elif array[mid] > x:
return binarySearch(array, x, low, mid-1)
/* Search the right half */
else:
return binarySearch(array, x, mid + 1, high)
else:
return -1
array = [3, 4, 5, 6, 7, 8, 9]
x = 4
result = binarySearch(array, x, 0, len(array)-1)
if result != -1:
print("Element is present at index " + str(result))
else:
print("Not found")
```

## Binary Search Complexity

**Time Complexities**

**Best case complexity**:`O(1)`

**Average case complexity**:`O(log n)`

**Worst case complexity**:`O(log n)`

**Space Complexity**

The space complexity of the binary search is `O(n)`

.

## Binary Search Applications

- In libraries of Java, .Net, C++ STL
- While debugging, the binary search is used to pinpoint the place where the error happens.

# Python Example for Beginners

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There are two sides to machine learning:

**Practical Machine Learning:**This is about querying databases, cleaning data, writing scripts to transform data and gluing algorithm and libraries together and writing custom code to squeeze reliable answers from data to satisfy difficult and ill defined questions. It’s the mess of reality.**Theoretical Machine Learning**: This is about math and abstraction and idealized scenarios and limits and beauty and informing what is possible. It is a whole lot neater and cleaner and removed from the mess of reality.

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