# Sort the masked array in-place along last axis in NumPy

NumpyServer Side ProgrammingProgramming

#### Python Data Science basics with Numpy, Pandas and Matplotlib

Most Popular

63 Lectures 6 hours

#### Data Analysis using NumPy and Pandas

19 Lectures 8 hours

#### Numpy with Python

Most Popular

12 Lectures 3 hours

To sort the masked array in-place, use the ma.MaskedArray.sort() method in Numpy. The axis parameter sets the axis along which to sort.

The method returns an array of the same type and shape as array. When the array is a structured array, the order parameter specifies which fields to compare first, second, and so on. This list does not need to include all of the fields.

The endwith parameter, suggests whether missing values (if any) should be treated as the largest values (True) or the smallest values (False) When the array contains unmasked values sorting at the same extremes of the datatype, the ordering of these values and the masked values is undefined. The fill_value is a 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...", 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 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 Sort the masked array in-place, use the ma.MaskedArray.sort() method in Numpy. The axis parameter sets the axis along which to sort. If None, the array is flattened before sorting. The default is -1, which sorts along the last axis −

maskArr.sort(axis = -1)

Display the Sorted Masked Array −

print("Sorted masked array...",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([[55, 85, 59, 77], [67, 33, 39, 57], [29, 88, 51, 37], [56, 45, 99, 85]])
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
[0, 0, 0, 1], [0, 1, 0, 0]])

# Get the dimensions of the Masked Array

# Get the shape of the Masked Array

# Get the number of elements of the Masked Array

# To Sort the masked array in-place, use the ma.MaskedArray.sort() method in Numpy
# The axis parameter sets the axis along which to sort.
# If None, the array is flattened before sorting. The default is -1, which sorts along the last axis.
# Display the Sorted Masked Array
print("Sorted masked array...",maskArr)

## Output

Array...
[[55 85 59 77]
[67 33 39 57]
[29 88 51 37]
[56 45 99 85]]

Array type...
int64

Array Dimensions...
2

[[-- -- 59 77]
[67 33 -- 57]
[29 88 51 --]
[56 -- 99 85]]

int64

2

[56 85 99 --]]