# Return the indices of unmasked elements that are not zero and group the indices by element in NumPy

To return the indices of unmasked elements that are not zero, use the ma.MaskedArray.nonzero() method in Numpy. To group the indices by element, we have used the nonzero() method in the transpose().

Returns a tuple of arrays, one for each dimension, containing the indices of the non-zero elements in that dimension. The corresponding non-zero values can be obtained with −

a[a.nonzero()]

To group the indices by element, rather than dimension, use instead −

np.transpose(a.nonzero())


The result of this is always a 2d array, with a row for each non-zero element.

## 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, 59, 77], [67, 33, 39, 57], [29, 88, 51, 37], [56, 45, 99, 85]])
print("Array...", arr)
print("Array type...", arr.dtype)

Get the dimensions of the Array −

print("Array Dimensions...",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("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("Elements in the Masked Array...",maskArr.size)


To return the indices of unmasked elements that are not zero, use the ma.MaskedArray.nonzero() method in Numpy. Returns a tuple of arrays, one for each dimension, containing the indices of the non-zero elements in that dimension. To group the indices by element, we have used the nonzero() method in the transpose() −

print("Result...",np.transpose(maskArr.nonzero()))

## 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, 59, 77], [67, 33, 39, 57], [29, 88, 51, 37], [56, 45, 99, 85]])
print("Array...", arr)
print("Array type...", arr.dtype)

# Get the dimensions of the Array
print("Array Dimensions...",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]])

# Get the dimensions of the Masked Array

# Get the shape of the Masked Array

# Get the number of elements of the Masked Array

# To return the indices of unmasked elements that are not zero, use the ma.MaskedArray.nonzero() method in Numpy
# Returns a tuple of arrays, one for each dimension, containing the indices of the non-zero elements in that dimension.
# To and group the indices by element, we have used the nonzero() method in the transpose()
print("Result...",np.transpose(maskArr.nonzero()))

## Output

Array...
[[55 85 59 77]
[67 33 39 57]
[29 88 51 37]
[56 45 99 85]]

Array type...
int64

Array Dimensions...
2

[[-- -- 59 77]
[67 33 -- 57]
[29 88 51 --]
[56 -- 99 85]]

int64

2

(4, 4)

16

Result...
[[0 2]
[0 3]
[1 0]
[1 1]
[1 3]
[2 0]
[2 1]
[2 2]
[3 0]
[3 2]
[3 3]]

Updated on: 05-Feb-2022

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