Article Categories
- All Categories
-
Data Structure
-
Networking
-
RDBMS
-
Operating System
-
Java
-
MS Excel
-
iOS
-
HTML
-
CSS
-
Android
-
Python
-
C Programming
-
C++
-
C#
-
MongoDB
-
MySQL
-
Javascript
-
PHP
Get the total bytes consumed by the elements in Numpy
To get the total bytes consumed by the masked array, use the ma.MaskedArray.nbytes attribute in Numpy. Does not include memory consumed by non-element attributes of the array object.
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 using the numpy.array() method −
arr = np.array([[35, 85], [67, 33]])
print("Array...
", arr)
print("\nArray type...
", arr.dtype)
print("\nArray itemsize...
", arr.itemsize)
Get the dimensions of the Array −
print("Array Dimensions...
",arr.ndim)
Get the total bytes consumed −
print("Array nbytes...
",arr.nbytes)
Create a masked array and mask some of them as invalid −
maskArr = ma.masked_array(arr, mask =[[0, 0], [ 0, 1]])
print("\nOur Masked Array
", maskArr)
print("\nOur Masked Array type...
", maskArr.dtype)
Get the dimensions of the Masked Array −
print("\nOur Masked Array Dimensions...
",maskArr.ndim)
Get the itemsize of the Masked Array −
print("\nOur Masked Array itemsize...
", maskArr.itemsize)
Get the total bytes consumed by the masked array, use the ma.MaskedArray.nbytes attribute in Numpy −
print("\nOur Masked Array nbytes...
",maskArr.nbytes)
Example
import numpy as np
import numpy.ma as ma
arr = np.array([[35, 85], [67, 33]])
print("Array...
", arr)
print("\nArray type...
", arr.dtype)
print("\nArray itemsize...
", arr.itemsize)
# Get the dimensions of the Array
print("Array Dimensions...
",arr.ndim)
# Get the total bytes consumed
print("Array nbytes...
",arr.nbytes)
# Create a masked array and mask some of them as invalid
maskArr = ma.masked_array(arr, mask =[[0, 0], [ 0, 1]])
print("\nOur Masked Array
", maskArr)
print("\nOur Masked Array type...
", maskArr.dtype)
# Get the dimensions of the Masked Array
print("\nOur Masked Array Dimensions...
",maskArr.ndim)
# Get the itemsize of the Masked Array
print("\nOur Masked Array itemsize...
", maskArr.itemsize)
# To get the total bytes consumed by the masked array, use the ma.MaskedArray.nbytes attribute in Numpy
print("\nOur Masked Array nbytes...
",maskArr.nbytes)
Output
Array... [[35 85] [67 33]] Array type... int64 Array itemsize... 8 Array Dimensions... 2 Array nbytes... 32 Our Masked Array [[35 85] [67 --]] Our Masked Array type... int64 Our Masked Array Dimensions... 2 Our Masked Array itemsize... 8 Our Masked Array nbytes... 32
