Data Structure
Networking
RDBMS
Operating System
Java
MS Excel
iOS
HTML
CSS
Android
Python
C Programming
C++
C#
MongoDB
MySQL
Javascript
PHP
- Selected Reading
- UPSC IAS Exams Notes
- Developer's Best Practices
- Questions and Answers
- Effective Resume Writing
- HR Interview Questions
- Computer Glossary
- Who is Who
Empty masked array with the properties of an existing array in Numpy
To empty masked array with the properties of an existing array, use the ma.masked_all_like() method in Python Numpy. 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 a new array using the numpy.array() method in Python Numpy −
arr = np.array([[77, 51, 92], [56, 31, 69], [73, 88, 51], [62, 45, 67]], dtype=np.float32)
Displaying our array −
print("Array...
",arr)
Get the datatype −
print("
Array datatype...
",arr.dtype)
To empty masked array with the properties of an existing array, use the ma.masked_all_like() −
arr = ma.masked_all_like(arr)
Displaying our array −
print("
New Array...
",arr)
Get the datatype −
print("
New Array datatype...
",arr.dtype)
Get the dimensions of the Array −
print("
Array Dimensions...
",arr.ndim)
Get the shape of the Array −
print("
Our Array Shape...
",arr.shape)
Get the number of elements of the Array −
print("
Elements in the Array...
",arr.size)
Example
# Python ma.MaskedArray - Empty masked array with the properties of an existing array
import numpy as np
import numpy.ma as ma
# Create a new array using the numpy.array() method in Python Numpy
arr = np.array([[77, 51, 92], [56, 31, 69], [73, 88, 51], [62, 45, 67]], dtype=np.float32)
# Displaying our array
print("Array...
",arr)
# Get the datatype
print("
Array datatype...
",arr.dtype)
# To empty masked array with the properties of an existing array, use the ma.masked_all_like() method in Python Numpy
arr = ma.masked_all_like(arr)
# Displaying our array
print("
New Array...
",arr)
# Get the datatype
print("
New Array datatype...
",arr.dtype)
# Get the dimensions of the Array
print("
Array Dimensions...
",arr.ndim)
# Get the shape of the Array
print("
Our Array Shape...
",arr.shape)
# Get the number of elements of the Array
print("
Elements in the Array...
",arr.size)
Output
Array... [[77. 51. 92.] [56. 31. 69.] [73. 88. 51.] [62. 45. 67.]] Array datatype... float32 New Array... [[-- -- --] [-- -- --] [-- -- --] [-- -- --]] New Array datatype... float32 Array Dimensions... 2 Our Array Shape... (4, 3) Elements in the Array... 12