Return the default fill value for the argument object in Numpy

NumpyServer Side ProgrammingProgramming

To return the default fill value for the argument object, 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 int elements using the numpy.array() method −

arr = np.array([[65, 68, 81], [93, 33, 76], [73, 88, 51], [62, 45, 67]])
print("Array...\n", 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("\nOur Masked Array...\n", maskArr)

Get the type of the masked array −

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("\nNumber of elements in the Masked Array...\n",maskArr.size)

To return the default fill value for the argument object, 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 7minus;

print("\nThe default fill value...\n",np.ma.default_fill_value(maskArr))

Example

# Python ma.MaskedArray - Return the default fill value for the argument object

import numpy as np
import numpy.ma as ma

# Create an array with int elements using the numpy.array() method
arr = np.array([[65, 68, 81], [93, 33, 76], [73, 88, 51], [62, 45, 67]])
print("Array...\n", 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("\nOur Masked Array...\n", maskArr)

# Get the type of the masked array
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("\nNumber of elements in the Masked Array...\n",maskArr.size)

# To return the default fill value for the argument object, 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("\nThe default fill value...\n",np.ma.default_fill_value(maskArr))

Output

Array...
[[65 68 81]
[93 33 76]
[73 88 51]
[62 45 67]]

Our Masked Array...
[[-- -- 81]
[93 33 76]
[73 -- 51]
[62 -- 67]]

Our Masked Array type...
int64

Our Masked Array Dimensions...
2

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

Number of elements in the Masked Array...
12

The default fill value...
999999
raja
Updated on 04-Feb-2022 10:56:03

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