Return an ndarray of indices that sort the masked array along the specified axis in NumPy

NumpyServer Side ProgrammingProgramming

To return an ndarray of indices that sort the array, use the ma.MaskedArray.argsort() method in Numpy. The axis is set using the "axis" parameter i.e. the Axis along which to sort.

Returns an Array of indices that sort a along the specified axis. In other words, a[index_array] yields a sorted a. The axis is the axis along which to sort. If None, the default, the flattened array is used. The order is when a is an array with fields defined, this argument specifies which fields to compare first, second, etc. Not all fields need be specified. The fill_value is the value used internally for the masked values. If fill_value is not None, it supersedes endwith.

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...\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 =[[0, 0, 1], [ 0, 1, 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)

Return an ndarray of indices that sort the array, use the ma.MaskedArray.argsort() method in Numpy. The axis is set using the "axis" parameter i.e the Axis along which to sort. If None, the default, the flattened array is used −

print("\nResult...\n",maskArr.argsort(axis = None))

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...\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, 0], [ 0, 0], [0, 0], [0,
1]])
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 an ndarray of indices that sort the array, use the ma.MaskedArray.argsort() method in Numpy
# The axis is set using the "axis" parameter i.e the Axis along which to sort.
# If None, the default, the flattened array is used.
print("\nResult...\n",maskArr.argsort(axis = None))

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 3 6 2 1 5 0 7]
raja
Updated on 04-Feb-2022 06:39:48

Advertisements