# 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 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())


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]])

# Get the shape of the Masked Array

# Get the number of elements of the Masked Array

# Return a boolean indicating whether the data is contiguous
print("Check whether the data is contiguous?",maskArr.iscontiguous())

# 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

[[-- -- 81]
[-- 33 39]
[73 -- 51]
[62 -- 67]]

int64

(4, 3)

[ 2 11]