Return the data portion of the masked array as a hierarchical Python list

NumpyServer Side ProgrammingProgramming

To return the data portion of the masked array as a hierarchical Python list, use the ma.MaskedArray.tolist() method in Numpy. Data items are converted to the nearest compatible Python type.

Masked values are converted to fill_value. If fill_value is None, the corresponding entries in the output list will be None. The method returns the Python list representation of the masked array.

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...\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)

To return the data portion of the masked array as a hierarchical Python list, use the ma.MaskedArray.tolist() method in Numpy −

print("\nResult of the transformation...\n",maskArr.tolist())

Example

# Python ma.MaskedArray - Return the data portion of the masked array as a hierarchical Python list

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("\nArray 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)

# To return the data portion of the masked array as a hierarchical Python list, use the ma.MaskedArray.tolist() method in Numpy
# Data items are converted to the nearest compatible Python type.
# Masked values are converted to "fill_value" parameter.
# We have set fill_value as None, i.e. the corresponding entries in the output list will be None.
print("\nResult of the transformation...\n",maskArr.tolist())

Output

Array...
[[49 85 45]
[67 33 59]]

Array type...
int64

Array Dimensions...
2

Our Masked Array
[[49 85 --]
[67 -- 59]]

Our Masked Array type...
int64

Our Masked Array Dimensions...
2

Our Masked Array Shape...
(2, 3)

Elements in the Masked Array...
6

Result of the transformation...
[[49, 85, None], [67, None, 59]]
raja
Updated on 02-Feb-2022 07:33:49

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