Data Structure
Networking
RDBMS
Operating System
Java
MS Excel
iOS
HTML
CSS
Android
Python
C Programming
C++
C#
MongoDB
MySQL
Javascript
PHP
Physics
Chemistry
Biology
Mathematics
English
Economics
Psychology
Social Studies
Fashion Studies
Legal Studies
- Selected Reading
- UPSC IAS Exams Notes
- Developer's Best Practices
- Questions and Answers
- Effective Resume Writing
- HR Interview Questions
- Computer Glossary
- Who is Who
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("
Array type...
", arr.dtype)
print("
Array 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("
Our Masked Array
", maskArr)
print("
Our Masked Array type...
", maskArr.dtype)Get the dimensions of the Masked Array −
print("
Our Masked Array Dimensions...
",maskArr.ndim)
Get the itemsize of the Masked Array −
print("
Our Masked Array itemsize...
", maskArr.itemsize)Get the total bytes consumed by the masked array, use the ma.MaskedArray.nbytes attribute in Numpy −
print("
Our 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("
Array type...
", arr.dtype)
print("
Array 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("
Our Masked Array
", maskArr)
print("
Our Masked Array type...
", maskArr.dtype)
# Get the dimensions of the Masked Array
print("
Our Masked Array Dimensions...
",maskArr.ndim)
# Get the itemsize of the Masked Array
print("
Our Masked Array itemsize...
", maskArr.itemsize)
# To get the total bytes consumed by the masked array, use the ma.MaskedArray.nbytes attribute in Numpy
print("
Our 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
557 Views
