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 fill value of the masked array in Numpy
To get the fill value, use the ma.MaskedArray.get_fill_value() method in Python Numpy. The filling value of the masked array is a scalar. 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 and mask some of them as invalid. We have used the fill_value parameter to set the fill value −
maskArr = ma.masked_array(arr, mask =[[1, 1, 0], [ 1, 0, 0], [0, 1, 0], [0, 1, 0]], fill_value = 9999)
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)
To get the fill value, use the ma.MaskedArray.get_fill_value() method. The filling value of the masked array is a scalar −
print("
Result (fill value)...
",maskArr.get_fill_value())Example
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 and mask some of them as invalid
# We have used the fill_value parameter to set the fill value
maskArr = ma.masked_array(arr, mask =[[1, 1, 0], [ 1, 0, 0], [0, 1, 0], [0, 1, 0]], fill_value = 9999)
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)
# To get the fill value, use the ma.MaskedArray.get_fill_value() method
# The filling value of the masked array is a scalar
print("
Result (fill value)...
",maskArr.get_fill_value())Output
Array... [[65 68 81] [93 33 39] [73 88 51] [62 45 67]] Array type... int64 Array Dimensions... 2 Our Masked Array [[-- -- 81] [-- 33 39] [73 -- 51] [62 -- 67]] Our Masked Array type... int64 Our Masked Array Dimensions... 2 Our Masked Array Shape... (4, 3) Elements in the Masked Array... 12 Result (fill value)... 9999
533 Views
