The term** Median** is basically defined as the value that is used to separate the higher range of data samples from a lower range of data samples.

- The
`numpy.median()`

statistical function in the NumPy library is used to**compute the median along any specified axis**. - Thus this function
**returns the median of the array elements as an output.** - This function is used to calculate the
**median of single-dimensional as well as multi-dimensional array.**

### Syntax of `numpy.median()`

:

The syntax required to use this function is as follows:

`numpy.median(a, axis = None,out,dtype)`

**Parameters:**

Below we have the description of parameters used by this function:

**a**

This parameter is used to indicate the input array.**axis**

This parameter is used to indicate the axis along which we want to calculate the median. By default, the input array is flattened(that is working on all the axis). Here, for value of axis,**axis = 0**means**along the column**and**axis = 1**means**working along the row**.**out**

This is an**optional**parameter that is used to indicate an alternative array in which we want to place the result. The array must have the same dimensions as the expected output.**dtype**

It is an**optional**parameter that is used to indicate the type we desire while computing the median.

**Returned Values:**

This function returns the median of the array (it will return a **scalar value** if the axis is **None**)) or an array with median values along the specified axis.

## Steps used to calculate the Median:

Following are the steps used to calculate the median:

- There are some given data points as Input.
- The second step is, to arrange them in an ascending order.
- If total no. of the terms are odd, then median will be calculated as
**Median = middle term** - If total no. of the terms are even, then median will be calculated as
**Median = Average of the terms in the middle**

## Example 1:

Given below is a basic example showing you the working of this function:

```
import numpy as np
a = [26, 2, 73, 13, 34]
print("The Input Array is : ")
print(a)
print("The median of 1D array is : ")
print(np.median(a))
```

Output:

The Input Array is :

[26, 2, 73, 13, 34]

The median of 1D array is :

26.0

## Example 2:

Now we will apply this method on two-dimensional array and will check the output:

```
import numpy as np
inp = [[1, 17, 19, 33, 49], [14, 6, 87, 8, 19], [34, 2, 54, 4, 7]]
print("\nThe median of array when axis = None : ")
print(np.median(inp))
# calculating median along the axis = 0
print("\nThe median of array when axis = 0 : ")
print(np.median(inp, axis = 0))
#calculating median along the axis = 1
print("\nThe median of array when axis = 1 : ")
print(np.median(inp, axis = 1))
```

Output:

The median of array when axis = None :

17.0

The median of array when axis = 0 :

[14. 6. 54. 8. 19.]

The median of array when axis = 1 :

[19. 14. 7.]