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Sort the masked array in-place along axis 0 in NumPy
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([[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 −
maskArr = ma.masked_array(arr, mask =[[1, 1, 0, 0], [ 0, 0, 1, 0], [0, 0, 0, 1], [0, 1, 0, 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)
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. The axis value is set 0 −
maskArr.sort(axis = 0)
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
maskArr = ma.masked_array(arr, mask =[[1, 1, 0, 0], [ 0, 0, 1, 0], [0, 0, 0, 1], [0, 1, 0, 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)
# 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.
# The axis value is set 0
maskArr.sort(axis = 0)
# 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 Our Masked Array [[-- -- 59 77] [67 33 -- 57] [29 88 51 --] [56 -- 99 85]] Our Masked Array type... int64 Our Masked Array Dimensions... 2 Our Masked Array Shape... (4, 4) Elements in the Masked Array... 16 Sorted masked array... [[29 33 51 57] [56 88 59 77] [67 -- 99 85] [-- -- -- --]]
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