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# Compute the median of the masked array elements along axis 0 in Numpy

To compute the median of the masked array elements along specific axis, use the **MaskedArray.median()** method in Python Numpy −

- The axis is set using the "axis" parameter
- The axis is axis along which the medians are computed.
- The default (None) is to compute the median along a flattened version of the array.

The overwrite_input parameter, if True, then allow use of memory of input array (a) 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.

## Steps

At first, import the required library −

import numpy as np import numpy.ma as ma

Create an array with int elements using the numpy.array() method

arr = np.array([[65, 68, 81], [93, 33, 76], [73, 88, 51], [62, 45, 67]]) print("Array...\n", arr)

Create a masked array and mask some of them as invalid −

maskArr = ma.masked_array(arr, mask =[[1, 1, 0], [ 0, 0, 0], [0, 1, 0], [0, 1, 0]]) print("\nOur Masked Array...\n", maskArr)

Get the type of the masked array −

print("\nOur Masked Array type...\n", maskArr.dtype)

Get the dimensions of the Masked Array −

print("\nOur Masked Array Dimensions...\n",maskArr.ndim)

Get the shape of the Masked Array −

print("\nOur Masked Array Shape...\n",maskArr.shape)

Get the number of elements of the Masked Array −

print("\nNumber of elements in the Masked Array...\n",maskArr.size)

To compute the median of the masked array elements along specific axis, use the MaskedArray.median() method. The axis is set using the "axis" parameter. The axis is axis along which the medians are computed. The default (None) is to compute the median along a flattened version of the array −

resArr = np.ma.median(maskArr, axis = 0) print("\nResultant Array..\n.", resArr)

## Example

import numpy as np import numpy.ma as ma # Create an array with int elements using the numpy.array() method arr = np.array([[65, 68, 81], [93, 33, 76], [73, 88, 51], [62, 45, 67]]) print("Array...\n", arr) # Create a masked array and mask some of them as invalid maskArr = ma.masked_array(arr, mask =[[1, 1, 0], [ 0, 0, 0], [0, 1, 0], [0, 1, 0]]) print("\nOur Masked Array...\n", maskArr) # Get the type of the masked array print("\nOur Masked Array type...\n", maskArr.dtype) # Get the dimensions of the Masked Array print("\nOur Masked Array Dimensions...\n",maskArr.ndim) # Get the shape of the Masked Array print("\nOur Masked Array Shape...\n",maskArr.shape) # Get the number of elements of the Masked Array print("\nNumber of elements in the Masked Array...\n",maskArr.size) # To compute the median of the masked array elements along specific axis, use the MaskedArray.median() method in Python Numpy # The axis is set using the "axis" parameter # The axis is axis along which the medians are computed. # The default (None) is to compute the median along a flattened version of the array. resArr = np.ma.median(maskArr, axis = 0) print("\nResultant Array..\n.", resArr)

## Output

Array... [[65 68 81] [93 33 76] [73 88 51] [62 45 67]] Our Masked Array... [[-- -- 81] [93 33 76] [73 -- 51] [62 -- 67]] Our Masked Array type... int64 Our Masked Array Dimensions... 2 Our Masked Array Shape... (4, 3) Number of elements in the Masked Array... 12 Resultant Array.. . [73.0 33.0 71.5]

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