Find the indices of the first and last unmasked values in Numpy



To find the indices of the first and last unmasked values, use the ma.flatnotmasked_edges() method in Python Numpy. Returns the indices of first and last non-masked value in the array. Returns None if all values are masked.

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
import numpy.ma as ma

Create an array with int elements using the numpy.array() method −

arr = np.array([[65, 68, 81], [93, 33, 39], [73, 88, 51], [62, 45, 67]])
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], [ 1, 0, 0], [0, 1, 0], [0, 1, 0]])
print("
Our Masked Array
", maskArr) print("
Our Masked Array type...
", maskArr.dtype)

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)

Return a boolean indicating whether the data is contiguous −

print("
Check whether the data is contiguous?
",maskArr.iscontiguous())

Find contiguous unmasked data in a masked array −

print("
Contiguous unmasked data...
",np.ma.flatnotmasked_contiguous(maskArr))

To find the indices of the first and last unmasked values, use the ma.flatnotmasked_edges() method in Python Numpy −

print("
Result...
",np.ma.flatnotmasked_edges(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, 39], [73, 88, 51], [62, 45, 67]])
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], [ 1, 0, 0], [0, 1, 0], [0, 1, 0]]) print("
Our Masked Array
", maskArr) print("
Our Masked Array type...
", maskArr.dtype) # 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) # Return a boolean indicating whether the data is contiguous print("
Check whether the data is contiguous?
",maskArr.iscontiguous()) # Find contiguous unmasked data in a masked array print("
Contiguous unmasked data...
",np.ma.flatnotmasked_contiguous(maskArr)) # To find the indices of the first and last unmasked values, use the ma.flatnotmasked_edges() method in Python Numpy print("
Result...
",np.ma.flatnotmasked_edges(maskArr))

Output

Array...
[[65 68 81]
[93 33 39]
[73 88 51]
[62 45 67]]

Array type...
int64

Array Dimensions...
2

Our Masked Array
[[-- -- 81]
[-- 33 39]
[73 -- 51]
[62 -- 67]]

Our Masked Array type...
int64

Our Masked Array Shape...
(4, 3)

Elements in the Masked Array...
12

Check whether the data is contiguous?
True

Contiguous unmasked data...
[slice(2, 3, None), slice(4, 7, None), slice(8, 10, None), slice(11, 12, None)]

Result...
[ 2 11]

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