# NumPy median()

The `numpy.median()` method computes the median along an array's specified axis.

### Example

``````import numpy as np

# create an array
array1 = np.array([0, 1, 2, 3, 4, 5, 6, 7])

# calculate the median of the array
median1 = np.median(array1)

print(median1)

# Output: 3.5``````

## median() Syntax

The syntax of the `numpy.median()` method is:

``numpy.median(array, axis = None, out = None, overwrite_input = False, keepdims = <no value>)``

## median() Arguments

The `numpy.median()` method takes following arguments:

• `array` - array containing numbers whose median we need to compute (can be `array_like`)
• `axis` (optional) - axis or axes along which the medians are computed (`int` or `tuple of int`)
• `out` (optional) - output array in which to place the result (`ndarray`)
• `override_input` (optional) - `bool` value that determines if intermediate calculations can modify an array
• `keepdims` (optional) - specifies whether to preserve the shape of the original array (`bool`)

Notes: The default values of `numpy.median()` have the following implications:

• `axis = None` - the median of the entire array is taken.
• By default, `keepdims` will not be passed.

## median() Return Value

The `numpy.median()` method returns the median of the array.

## Example 1: Find the median of a ndArray

``````import numpy as np

# create an array
array1 = np.array([[[0, 1],
[2, 3]],
[[4, 5],
[6, 7]]])

# find the median of the entire array
median1 = np.median(array1)

# find the median across axis 0
median2 = np.median(array1, 0)

# find the median across axis 0 and 1
median3 = np.median(array1, (0, 1))

print('\nmedian of the entire array:', median1)
print('\nmedian across axis 0:\n', median2)
print('\nmedian across axis 0 and 1', median3)``````

Output

```median of the entire array: 3.5

median across axis 0:
[[2. 3.]
[4. 5.]]

median across axis 0 and 1 [3. 4.]```

## Example 2: Using Optional keepdims Argument

If `keepdims` is set to `True`, the resultant median array is of the same number of dimensions as the original array.

``````import numpy as np

array1 = np.array([[1, 2, 3],
[4, 5, 6]])

# keepdims defaults to False
result1 = np.median(array1, axis = 0)

# pass keepdims as True
result2 = np.median(array1, axis = 0, keepdims = True)

print('Dimensions in original array:', array1.ndim)
print('Without keepdims:', result1, 'with dimensions', result1.ndim)
print('With keepdims:', result2, 'with dimensions', result2.ndim)``````

Output

```Dimensions in original array: 2
Without keepdims: [2.5 3.5 4.5] with dimensions 1
With keepdims: [[2.5 3.5 4.5]] with dimensions 2```

## Example 3: Using Optional out Argument

The `out` parameter allows to specify an output array where the result will be stored.

``````import numpy as np

array1 = np.array([[1, 2, 3],
[4, 5, 6]])

# create an output array
output = np.zeros(3)

# compute median and store the result in the output array
np.median(array1, out = output, axis = 0)

print('median:', output)``````

Output

`median: [2.5 3.5 4.5]`