- Trending Categories
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
Initialize the mask to homogeneous boolean array by passing in a scalar boolean value in Numpy
The mask is initialized to homogeneous boolean array with the same shape as data by passing in a scalar boolean value. True indicates a masked (i.e. invalid) data. The "mask" parameter is used to set the mask. Create a masked array using the ma.MaskedArray() method.
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 −
arr = np.array([[65, 68, 81], [93, 33, 39], [73, 88, 51], [62, 45, 67]]) print("Array...
", arr) print("
Array type...
", arr.dtype)
Get the dimensions of the Array −
print("
Array Dimensions...
",arr.ndim)
Create a masked array. The "mask" parameter is used to set the mask. The mask is initialized to homogeneous boolean array with the same shape as data by passing in a scalar boolean value. True indicates a masked (i.e. invalid) data:
maskArr = ma.MaskedArray(arr, mask =True) 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 shape of the Masked Array −
print("
Our Masked Array Shape...
",maskArr.shape)
Get the number of elements of the Masked Array −
print("
Elements in the Masked Array...
",maskArr.size)
Example
# Python ma.MaskedArray - Initialize the mask to homogeneous boolean array by passing in a scalar boolean value 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...
", arr) print("
Array type...
", arr.dtype) # Get the dimensions of the Array print("
Array Dimensions...
",arr.ndim) # Create a masked array # The mask is set using the "mask" parameter # The mask initialized to homogeneous boolean array with the same shape as data by passing in a scalar boolean value: # True indicates a masked (i.e. invalid) data. maskArr = ma.MaskedArray(arr, mask =True) 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 shape of the Masked Array print("
Our Masked Array Shape...
",maskArr.shape) # Get the number of elements of the Masked Array print("
Elements in the Masked Array...
",maskArr.size)
Output
Array... [[65 68 81] [93 33 39] [73 88 51] [62 45 67]] Array type... int64 Array Dimensions... 2 Our Masked Array [[-- -- --] [-- -- --] [-- -- --] [-- -- --]] Our Masked Array type... int64 Our Masked Array Dimensions... 2 Our Masked Array Shape... (4, 3) Elements in the Masked Array... 12
- Related Articles
- Create a boolean mask from an array in Numpy
- How to initialize a boolean array in JavaScript?
- Return the mask of a masked array or full boolean array of False in Numpy
- How can we initialize a boolean array in Java?\n
- Compute the bit-wise NOT of a boolean array in Numpy
- Create a Boolean object from Boolean value in Java
- Explain non-boolean value coercion to a boolean one in JavaScript?
- Mask array elements equal to a given value in Numpy
- Java Program to convert boolean value to Boolean
- Mask array elements not equal to a given value in Numpy
- Mask array elements greater than a given value in Numpy
- Mask array elements less than a given value in Numpy
- A Boolean Array Puzzle in C?
- Multiply each element of a masked Array by a scalar value in-place in Numpy
- Updating boolean value in MySQL?
