** numpy.MaskedArray.median() ** is used to calculate the median along the specified axis of the masked array. Returns the median of array elements.

Syntax:`numpy.ma.median (arr, axis = None, out = None, overwrite_input = False, keepdims = False )`

Parameters:

arr:[ndarray] Input masked array.

axis:[int, optional] Axis along which the median is computed. The default (None) is to compute the median over the flattened array.

dtype:[dtype, optional] Type of the returned array, as well as of the accumulator in which the elements are multiplied.

out:[ndarray, optional] A location into which the result is stored.

– & gt; If provided, it must have a shape that the inputs broadcast to.

– & gt; If not provided or None, a freshly-allocated array is returned.

overwrite_input:[bool, optional] If True, then allow use of memory of input array for calculations. The input array will be modified by the call to median. This will save memory when you do not need to preserve the contents of the input array. Treat the input as undefined, but it will probably be fully or partially sorted. Default is False. Note that, if overwrite_input is True, and the input is not already an ndarray, an error will be raised.

keepdims:[bool, optional] If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the input array.

Return:[median_along_axis, ndarray] A new array holding the result is returned unless out is specified, in which case a reference to out is returned.

** Code # 1: **

` `

```
```
` # Program Python explaining `

` # numpy.MaskedArray.median () method `

` # import numy as geek `

` # and numpy.ma module as ma `

` import `

` numpy as geek `

` import `

` numpy.ma as ma `

` # create input array `

in_arr ` = `

` geek.array ([[`

` 1 `

`, `

` 2 `

`], [`

` 3 `

`, `

` - `

` 1 `

`], [`

` 5 `

`, `

` - `

` 3 `

`]]) `

` print `` (`

` "Input array:" `

`, in_arr) `

```
```

` # Now we create a masked array. `

` # invalidating the entry. `

` mask_arr `

` = `

` ma.masked_array (in_arr, mask `

` = `

` [[`

` 1 `

`, `` 0 `

`], [`

` 1 `

` , `

` 0 `

`], [`

` 0 `

`, `

` 0 `

`]]) `

```
``` ` print `

` (`

`" Masked array: "`

`, mask_arr) `

` # apply MaskedArray.median `

` # methods of the masked array `

` out_arr `

` = `

` ma.median (mask_arr) `

` `` print `

` (`

` "median of masked array along default axis:" `

, out_arr)

` `

** Output: **

Input array: [[1 2] [3 -1] [5 -3]] Masked array: [[- 2] [- -1] [5 -3]] median of masked array along default axis: 0.5

** Code # 2: **

` `

```
```
` # Python program, explaining `

` # numpy.MaskedArray.median () method `

` # import numy as geek `

` # and numpy.ma module as ma `

` import `

` numpy as geek `

` import `

` numpy.ma as ma `

` # creating input array `

` in_arr `

= ` geek.array ([[[`

` 1 `

`, `

` 0 `

`, `

` 3 `

`], [`

` 4 `

`, `

` 1 `

`, `

` 6 `

`]]) `

` print `

` (`

` "Input array:" `

`, in_arr ) `

` # Now we create a masked array. `

` # invalidating one entry. `

` mask_arr `

` = `

` ma.masked_array (in_arr, mask `

` = `

` [[`

` 0 `

`, `

` 0 `

`, `

` 0 `

`], [`

` 0 `

`, `

` 0 ``, `

` 1 `

`]]) `

```
``` ` print `

` (`

` "Masked array:" `

` , mask_arr) `

` # applying MaskedArray.median methods `

` # to the masked array `

` out_arr1 `

` = `

` ma.median (mask_arr, axis `

` = `

` 0 `

`) `

` `` print `

` (`

` "median of masked array along 0 axis:" `

, out_arr1)

` out_arr2 `

` = `

` ma.median (mask_arr, axis `

` = `

` 1 `

`) `

` print `

` (`

`" median of masked array along 1 axis: "`

`, out_arr2) `

` `

** Output:**

Input array: [[1 0 3] [4 1 6]] Masked array: [[1 0 3] [4 1 -]] median of masked array along 0 axis: [2.5 0.5 3.0] median of masked array along 1 axis: [1.0 2.5]

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