Return the minimum value that can be represented by the dtype of an object in Numpy

To return the minimum value that can be represented by the dtype of an object, use the ma.maximum_fill_value() method in Python Numpy. This function is useful for calculating a fill value suitable for taking the minimum of an array with a given dtype. It returns the minimum representable value.

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 7minus;

arr = np.array([[65, 68, 81], [93, 33, 76], [73, 88, 51], [62, 45, 67]])
print("Array...<br>", 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...<br>", maskArr)

Get the type of the masked array −

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("\nNumber of elements in the Masked Array...<br>",maskArr.size)

To return the minimum value that can be represented by the dtype of an object, use the ma.maximum_fill_value() method. This function is useful for calculating a fill value suitable for taking the minimum of an array with a given dtype. It returns the maximum representable value:

print("\nResult..<br>",np.ma.maximum_fill_value(maskArr))

Example

# Python ma.MaskedArray - Return the minimum value that can be represented by the dtype of an 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...<br>", 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...<br>", maskArr)

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

# To return the minimum value that can be represented by the dtype of an object, use the ma.maximum_fill_value() method in Python Numpy

# This function is useful for calculating a fill value suitable for taking the maximum of an array with a given dtype
# It returns the minimum representable value.
print("\nResult..<br>",np.ma.maximum_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

Result..
-9223372036854775808