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

NumpyServer Side ProgrammingProgramming

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...\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 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("\nResult...\n",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...\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 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("\nResult...\n",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

Our Masked Array
[[-- -- 59 77]
[67 33 -- 57]
[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

Result...
[[0 2]
[0 3]
[1 0]
[1 1]
[1 3]
[2 0]
[2 1]
[2 2]
[3 0]
[3 2]
[3 3]]
raja
Updated on 05-Feb-2022 07:31:06

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