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# Compute the differences between consecutive elements and append a number in Numpy

To compute the differences between consecutive elements of a masked array, use the **MaskedArray.ediff1d()** method in Python Numpy. The "**to_end**" parameter sets the number(s) to append at the end of the returned differences.

This function is the equivalent of numpy.ediff1d that takes masked values into account, see numpy.ediff1d for details.

A masked array is the combination of a standard numpy.ndarray and a mask. A mask is either nomask, indicating that no value of the associated array is invalid, or an array of booleans that determines for each element of the associated array whether the value is valid or not.

## Steps

At first, import the required library −

import numpy as np

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...

", arr)

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

maskArr = ma.masked_array(arr, mask =[[1, 0, 0], [ 0, 0, 0], [0, 1, 0], [0, 0, 0]]) print("

Our Masked Array...

", maskArr)

Get the type of the masked array −

print("

Our Masked Array type...

", maskArr.dtype)

Get the dimensions of the Masked Array −

print("

Our Masked Array Dimensions...

",maskArr.ndim)

Get the shape of the Masked Array −

print("

Our Masked Array Shape...

",maskArr.shape)

Get the number of elements of the Masked Array −

print("

Number of elements in the Masked Array...

",maskArr.size)

To compute the differences between consecutive elements of a masked array, use the MaskedArray.ediff1d() method in Python Numpy. The "to_end" parameter sets the number(s) to append at the end of the returned differences:

print("

Result..

.", np.ediff1d(maskArr, to_end=999))

## 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...

", arr) # Create a masked array and mask some of them as invalid maskArr = ma.masked_array(arr, mask =[[1, 0, 0], [ 0, 0, 0], [0, 1, 0], [0, 0, 0]]) print("

Our Masked Array...

", maskArr) # Get the type of the masked array print("

Our Masked Array type...

", maskArr.dtype) # Get the dimensions of the Masked Array print("

Our Masked Array Dimensions...

",maskArr.ndim) # Get the shape of the Masked Array print("

Our Masked Array Shape...

",maskArr.shape) # Get the number of elements of the Masked Array print("

Number of elements in the Masked Array...

",maskArr.size) # To compute the differences between consecutive elements of a masked array, use the MaskedArray.ediff1d() method in Python Numpy # The "to_end" parameter sets the number(s) to append at the end of the returned differences. print("

Result..

.", np.ediff1d(maskArr, to_end=999))

## Output

Array... [[65 68 81] [93 33 76] [73 88 51] [62 45 67]] Our Masked Array... [[-- 68 81] [93 33 76] [73 -- 51] [62 45 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 Result.. . [ 3 13 12 -60 43 -3 15 -37 11 -17 22 999]

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