Return array of indices of the minimum values from a masked array in NumPy

To return array of indices of the minimum values, use the ma.MaskedArray.argmin() method in Numpy. For axis, If None, the index is into the flattened array, otherwise along the specified axis. The out is the array into which the result can be placed. Its type is preserved and it must be of the right shape to hold the output.

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([[49, 85, 45], [67, 33, 59]])
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 =[[0, 0, 1], [ 0, 1, 0]])
print("
Our Masked Array
", maskArr)
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)

Return array of indices of the minimum values, use the ma.MaskedArray.argmin() method in Numpy. Masked values are treated as if they had the value "fill_value". The "fill_value" is a parameter i.e. Value used to fill in the masked values. If None, the output of minimum_fill_value(self._data) is used instead −

print("
Result...
",maskArr.argmin())

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], [67, 33], [29, 88], [56, 45]])
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, 0], [ 0, 0], [0, 0], [0,
1]])
print("
Our Masked Array
", maskArr)
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 array of indices of the minimum values, use the ma.MaskedArray.argmin() method in Numpy
# Masked values are treated as if they had the value "fill_value".
# The "fill_value" is a parameter i.e. Value used to fill in the masked values.
# If None, the output of minimum_fill_value(self._data) is used instead.
print("
Result...
",maskArr.argmin())

Output

Array...
[[55 85]
[67 33]
[29 88]
[56 45]]

Array type...
int64

Array Dimensions...
2

Our Masked Array
[[-- 85]
[67 33]
[29 88]
[56 --]]

Our Masked Array type...
int64

Our Masked Array Dimensions...
2

Our Masked Array Shape...
(4, 2)

Elements in the Masked Array...
8

Result...
4