# Reset the fill value of the masked array to default in Numpy

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To reset the fill value of the ma, use the ma.MaskedArray.fill_value() method in Python Numpy and set it to None.

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.

NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. It supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries.

## 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, 39], [73, 88, 51], [62, 45, 67]])
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. We have used the fill_value parameter to set the fill value −

maskArr = ma.masked_array(arr, mask =[[1, 1, 0], [ 1, 0, 0], [0, 1, 0], [0, 1, 0]], fill_value = 12345)
print("\nOur Masked Array type...\n", maskArr.dtype)

Get the dimensions of the Masked Array &minnus;

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)


The filling value of the masked array is a scalar −

print("\nResult (fill value)...\n",maskArr.get_fill_value())

To reset the fill value of the ma, use the ma.MaskedArray.fill_value() method and set it to None −

maskArr.fill_value = None
print("\nResult (After resetting fill value to default)...\n",maskArr.get_fill_value())

## Example

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, 39], [73, 88, 51], [62, 45, 67]])
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
# We have used the fill_value parameter to set the fill value
maskArr = ma.masked_array(arr, mask =[[1, 1, 0], [ 1, 0, 0], [0, 1, 0], [0, 1, 0]], fill_value = 12345)

# Get the dimensions of the Masked Array

# Get the shape of the Masked Array

# Get the number of elements of the Masked Array

# The filling value of the masked array is a scalar
# To reset the fill value of the ma, use the ma.MaskedArray.fill_value() method and set it to None maskArr.fill_value = None
print("\nResult (After resetting fill value to default)...\n",maskArr.get_fill_value())

## Output

Array...
[[65 68 81]
[93 33 39]
[73 88 51]
[62 45 67]]

Array type...
int64

Array Dimensions...
2

[[-- -- 81]
[-- 33 39]
[73 -- 51]
[62 -- 67]]

Our Masked Array type...
int64

Our Masked Array Dimensions...
2

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

Elements in the Masked Array...
12

Result (fill value)...
12345

Result (After resetting fill value to default)...
12345