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# Return range of values from a masked array along a given axis in NumPy

To return the range of values from a masked array, use the **ma.MaskedArray.ptp()** method in Numpy. Peak to peak (maximum - minimum) value along a given axis. The axis is set using the **axis** parameter. The ptp() method returns a new array holding the result, unless out was specified, in which case a reference to out is returned.

The axis parameter is the axis along which to find the peaks. If None (default) the flattened array is used. The out is a parameter, an alternative output array in which to place the result. It must have the same shape and buffer length as the expected output but the type will be cast if necessary.

The keepdims parameter, if 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 array.

## 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([[55, 85, 68, 84], [67, 33, 39, 53], [29, 88, 51, 37], [56, 45, 99, 85]]) print("Array...\n", arr) print("\nArray type...\n", arr.dtype)

Get the dimensions of the Array −

print("Array Dimensions...\n",arr.ndim)

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

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

To return the range of values from a masked array, use the ma.MaskedArray.ptp() method in Numpy. Peak to peak (maximum - minimum) value along a given axis. The axis is set using the axis parameter −

print("\nPeak to peak value (max - min)...\n", np.ptp(maskArr, axis=1))

## Example

import numpy as np import numpy.ma as ma # Create an array with int elements using the numpy.array() method arr = np.array([[55, 85, 68, 84], [67, 33, 39, 53], [29, 88, 51, 37], [56, 45, 99, 85]]) print("Array...\n", arr) print("\nArray type...\n", arr.dtype) # Get the dimensions of the Array print("\nArray Dimensions...\n",arr.ndim) # Create a masked array and mask some of them as invalid maskArr = ma.masked_array(arr, mask =[[1, 1, 0, 0], [ 0, 0, 1, 0], [0, 0, 0, 1], [0, 1, 0, 0]]) print("\nOur Masked Array\n", maskArr) 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("\nElements in the Masked Array...\n",maskArr.size) # To return the range of values from a masked array, use the ma.MaskedArray.ptp() method in Numpy. # Peak to peak (maximum - minimum) value along a given axis. # The axis is set using the axis parameter print("\nPeak to peak value (max - min)...\n", np.ptp(maskArr, axis=1))

## Output

Array... [[55 85 68 84] [67 33 39 53] [29 88 51 37] [56 45 99 85]] Array type... int64 Array Dimensions... 2 Our Masked Array [[-- -- 68 84] [67 33 -- 53] [29 88 51 --] [56 -- 99 85]] Our Masked Array type... int64 Our Masked Array Dimensions... 2 Our Masked Array Shape... (4, 4) Elements in the Masked Array... 16 Peak to peak value (max - min)... [16 34 59 43]

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