Create an array class with possibly masked values and fill in the masked values in Numpy

Create a masked array using the ma.MaskedArray() method. The mask is set using the "mask" parameter. Set to False here. The datatype is set using the "dtype" parameter. The fill_value parameter is used to fill in the masked values when necessary. 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([[77, 51, 92], [56, 31, 69], [73, 88, 51], [62, 45, 67]])
print("Array...<br>", arr)
print("\nArray type...<br>", arr.dtype)

Get the dimensions of the Array −

print("\nArray Dimensions...<br>",arr.ndim)

Create a masked array. The mask is set using the "mask" parameter. Set to False here. The datatype is set using the "dtype" parameter. The fill_value parameter is used to fill in the masked values when necessary −

maskArr = ma.MaskedArray(arr, mask = [[1, 1, 0], [ 1, 0, 0], [0, 1, 0], [0, 1, 0]],
   dtype=float, fill_value = 9999)
print("\nOur Masked Array<br>", maskArr)
print("\nOur Masked Array type...<br>", maskArr.dtype)

Get the dimensions of the Masked Array −

print("\nOur Masked Array Dimensions...<br>",maskArr.ndim)

Get the shape of the Masked Array −

print("\nOur Masked Array Shape...<br>",maskArr.shape)

Get the number of elements of the Masked Array −

print("\nElements in the Masked Array...<br>",maskArr.size)

Displaying the fill_value −

print("\nResult (fill value)...<br>",maskArr.get_fill_value())

Example

# Python ma.MaskedArray - Create an array class with possibly masked values and fill
# in the masked values

import numpy as np
import numpy.ma as ma

# Create an array with int elements using the numpy.array() method
arr = np.array([[77, 51, 92], [56, 31, 69], [73, 88, 51], [62, 45, 67]])
print("Array...<br>", arr)
print("\nArray type...<br>", arr.dtype)

# Get the dimensions of the Array
print("\nArray Dimensions...<br>",arr.ndim)

# Create a masked array
# The mask is set using the "mask" parameter. Set to False here
# The datatype is set using the "dtype" parameter
# The fill_value parameter is used to fill in the masked values when necessary
maskArr = ma.MaskedArray(arr, mask = [[1, 1, 0], [ 1, 0, 0], [0, 1, 0], [0, 1, 0]],
   dtype=float, fill_value = 9999)
print("\nOur Masked Array<br>", maskArr)
print("\nOur Masked Array type...<br>", maskArr.dtype)

# Get the dimensions of the Masked Array
print("\nOur Masked Array Dimensions...<br>",maskArr.ndim)

# Get the shape of the Masked Array
print("\nOur Masked Array Shape...<br>",maskArr.shape)

# Get the number of elements of the Masked Array
print("\nElements in the Masked Array...<br>",maskArr.size)

# Displaying the fill_value
print("\nResult (fill value)...<br>",maskArr.get_fill_value())

Output

Array...
[[77 51 92]
[56 31 69]
[73 88 51]
[62 45 67]]

Array type...
int64

Array Dimensions...
2

Our Masked Array
[[-- -- 92.0]
[-- 31.0 69.0]
[73.0 -- 51.0]
[62.0 -- 67.0]]

Our Masked Array type...
float64

Our Masked Array Dimensions...
2

Our Masked Array Shape...
(4, 3)

Elements in the Masked Array...
12
Result (fill value)...
9999.0