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

NumpyServer Side ProgrammingProgramming

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...
", arr) print("
Array type...
", arr.dtype)

Get the dimensions of the Array −

print("
Array Dimensions...
",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("
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)

Displaying the fill_value −

print("
Result (fill value)...
",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...
", arr) print("
Array type...
", arr.dtype) # Get the dimensions of the Array print("
Array Dimensions...
",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("
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) # Displaying the fill_value print("
Result (fill value)...
",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
raja
Updated on 03-Feb-2022 11:02:45

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