Sorting is the process in which we try to put elements in an ordered sequence.

## numpy.sort():

It is used to return a sorted copy of an array.

### Syntax:

`numpy.sort(a, axis=- 1, kind=None, order=None)`

### Parameters:

**a-**Array to be sorted.**axis-**Axis along which array is to be sorted.**kind-**Sorting algorithm[‘quicksort’, ‘mergesort’, ‘heapsort’]. The default value is set to 'quicksort'.**order-**This specify which field is to compare first.

### Returns:

It is used to return the sorted array.

#### Example:

```
import numpy as np
# sorting along the first axis
a = np.array([[1,7,4],[2,8,5],[9,3,6]])
b=np.sort(a, axis = 0)
print ("Along first axis-",b)
#sorting along last axis
c=np.sort(a, axis = -1)
print("Along first axis-",c)
#sorting in 1-D array
d=np.sort(a, axis = None)
print("Along none axis-",d)
```

**Output:**

Along first axis- [[1 3 4] [2 7 5] [9 8 6]] Along first axis- [[1 4 7] [2 5 8] [3 6 9]] Along none axis- [1 2 3 4 5 6 7 8 9]

**Note: **As we know, that there are various types of sorting algorithms that are characterised by their worst-case performance, workspace size, average speed whether they are stable or not. A stable sort is used to keeps items with the same key in the same relative order.

kind | speed | worst-case | workspace | stable |

‘quicksort’ | 1 | O(n^2) | 0 | no |

'mergesort’ | 2 | O(n*log(n)) | ~n/2 | yes |

‘heapsort’ | 3 | O(n*log(n)) | no | no |

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