# Numpy numpy.median Function

Python Numpy`numpy.median()` calculates the median of the given NumPy array over the specified axis.

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

``````numpy.median(a,
axis=None,
out=None,
overwrite_input=False,
keepdims=False)
``````

### Parameters

`a` Array or Object, which could be converted into an array whose median is to be calculated.
`axis` find median along the `row` (axis=0) or `column` (axis=1). By default, the median is calculated by flattening the array.
`out` placeholder for the result of the `np.median()` method
`overwrite_input` Boolean. The input array will be modified by the call to the median() method (`overwrite_input=True`)
`keepdims` Boolean. Make the dimensions of the output the same as the input (`keepdims=True`).

### Return

Array with medians along the specified axis.

## Example Codes: `numpy.median()` Method to Find Median of an Array

``````import numpy as np

a=np.array([[2,3,4],
[5,6,7],
[8,9,10]])

median=np.median(a)

print(median)
``````

Output:

``````6.0
``````

It calculates the median of the array by flattening the array.

By flattening the array, we mean placing all the rows one after another to convert the given array to 1-D array.

## Example Codes: Set `axis` Parameter in `numpy.median()` Method to Find Median of an Array Along Particular Axis

### Example Codes: `numpy.median()` Method to Find Median of an Array Along Column Axis

To find the mean of the array along the column axis, we set `axis=0`.

``````import numpy as np

a=np.array([[2,3],
[5,6],
[8,9]])

median=np.median(a,axis=0)

print(median)
``````

Output:

``````[5. 6.]
``````

It calculates the median for both the columns and finally returns an array with each column’s median.

### Example Codes: `numpy.median()` Method to Find Median of an Array Along Row Axis

To find the median of the array along the row axis, we set `axis=1`.

``````import numpy as np

a=np.array([[2,3],
[5,6],
[8,9]])

median=np.median(a,axis=1)

print(median)
``````

Output:

``````[2.5 5.5 8.5]
``````

It calculates the median for all three rows and finally returns an array with each row’s median.

## Example Codes: Set `out` Parameter in `numpy.median()` Method

``````
import numpy as np

a=np.array([[2,3],
[5,6],
[8,9]])

median=np.zeros(np.median(a,axis=1).shape)
print(f"median before calculation: {median}")

np.median(a,axis=1,out=median)
print(f"median after calculation: {median}")

``````

Output:

``````[2.5 5.5 8.5]
``````

It saves the result of the method in the `median` variable.

We must ensure that the variable’s dimension to which output is to be assigned is of the same size as that of output.

## Example Codes: Set `keepdims` Parameter in `numpy.median()` Method

``````
import numpy as np

a=np.array([[2,3],
[5,6],
[8,9]])

print(f"Dimension of Input Array: {median.ndim}")

median=np.median(a,axis=1)
print(f"Dimension of median with 'keepdims=False': {median.ndim}")

median=np.median(a,axis=1,keepdims=True)
print(f"Dimension of median with 'keepdims=True': {median.ndim}")

``````

Output:

``````Dimension of Input Array: 2
Dimension of median with 'keepdims=False': 1
Dimension of median with 'keepdims=True': 2
``````

Setting `keepdims=True` preserves the number of dimensions in the output array.

Here, input array `a` is 2-dimensional. If `keepdims=False` (default value), the dimensions of `median` might get altered but setting `keepdims=True` preserves the number of dimensions in output of `np.median()` method.