Return the default fill value for a masked array with float datatype in Numpy

NumpyServer Side ProgrammingProgramming

To return the default fill value for an array with float datatype, use the ma.default_fill_value() method in Python Numpy. The default filling value depends on the datatype of the input array or the type of the input scalar −

datatypedefault
boolTrue
int999999
float1.e20
complex1.e20+0j
object'?'
string'N/A'

For structured types, a structured scalar is returned, with each field the default fill value for its type. For subarray types, the fill value is an array of the same size containing the default scalar fill value.

Steps

At first, import the required library −

import numpy as np
import numpy.ma as ma

Create an array with float elements using the numpy.array() method −

arr = np.array([[72.7, 68.2, 81.6], [93.4, 33.4, 76.2], [73.6, 88.1, 51.8], [62.3, 45.5, 67.9]])
print("Array...
", arr)

Create a masked array and mask some of them as invalid −

maskArr = ma.masked_array(arr, mask =[[1, 1, 0], [ 0, 0, 0], [0, 1, 0], [0, 1, 0]])
print("
Our Masked Array...
", maskArr)

Get the type of the masked array −

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("
Number of elements in the Masked Array...
",maskArr.size)

To return the default fill value for an array with float datatype, use the ma.default_fill_value() method in Python Numpy. The default filling value depends on the datatype of the input array or the type of the input scalar −

print("
The default fill value...
",np.ma.default_fill_value(maskArr))

Example

import numpy as np
import numpy.ma as ma

# Create an array with float elements using the numpy.array() method
arr = np.array([[72.7, 68.2, 81.6], [93.4, 33.4, 76.2], [73.6, 88.1, 51.8], [62.3, 45.5, 67.9]])
print("Array...
", arr) # Create a masked array and mask some of them as invalid maskArr = ma.masked_array(arr, mask =[[1, 1, 0], [ 0, 0, 0], [0, 1, 0], [0, 1, 0]]) print("
Our Masked Array...
", maskArr) # Get the type of the masked array 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("
Number of elements in the Masked Array...
",maskArr.size) # To return the default fill value for an array with float datatype, use the ma.default_fill_value() method in Python Numpy # The default filling value depends on the datatype of the input array or the type of the input scalar print("
The default fill value...
",np.ma.default_fill_value(maskArr))

Output

Array...
[[72.7 68.2 81.6]
[93.4 33.4 76.2]
[73.6 88.1 51.8]
[62.3 45.5 67.9]]

Our Masked Array...
[[-- -- 81.6]
[93.4 33.4 76.2]
[73.6 -- 51.8]
[62.3 -- 67.9]]

Our Masked Array type...
float64

Our Masked Array Dimensions...
2

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

Number of elements in the Masked Array...
12

The default fill value...
1e+20
raja
Updated on 04-Feb-2022 10:59:51

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