- Trending Categories
- Data Structure
- Networking
- RDBMS
- Operating System
- Java
- iOS
- HTML
- CSS
- Android
- Python
- C Programming
- C++
- C#
- MongoDB
- MySQL
- Javascript
- PHP

- Selected Reading
- UPSC IAS Exams Notes
- Developer's Best Practices
- Questions and Answers
- Effective Resume Writing
- HR Interview Questions
- Computer Glossary
- Who is Who

# Compute the differences between consecutive elements of a masked array in Numpy

To compute the differences between consecutive elements of a masked array, use the **MaskedArray.ediff1d()** method in Python Numpy. 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...\n", 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("\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 differences between consecutive elements of a masked array, use the MaskedArray.ediff1d() method in Python Numpy −

print("\nResult..\n.", np.ediff1d(maskArr))

## 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, 0, 0], [ 0, 0, 0], [0, 1, 0], [0, 0, 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 differences between consecutive elements of a masked array, use the MaskedArray.ediff1d() method in Python Numpy print("\nResult..\n.", np.ediff1d(maskArr))

## 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.. . [-- 13 12 -60 43 -3 -- -- 11 -17 22]

- Related Questions & Answers
- Compute the differences between consecutive elements and append an array of numbers in Numpy
- Compute the differences between consecutive elements and append a number in Numpy
- Compute the differences between consecutive elements and prepend numbers in Numpy
- Compute the differences between consecutive elements and prepend & append array of numbers in Numpy
- Compute the median of the masked array elements in Numpy
- Compute the maximum of the masked array elements along a given axis in Numpy
- Compute the minimum of the masked array elements along a given axis in Numpy
- Compute the median of the masked array elements along specified axis in Numpy
- Compute the median of the masked array elements along axis 0 in Numpy
- Compute the maximum of the masked array elements over axis 0 in Numpy
- Compute the maximum of the masked array elements over axis 1 in Numpy
- Compute the minimum of the masked array elements over axis 0 in Numpy
- Compute the minimum of the masked array elements over axis 1 in Numpy
- Count the non-masked elements of the masked array in Numpy
- Repeat elements of a masked array in Numpy