- 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
Count the number of masked elements along specific axis
To count the number of masked elements along specific axis, use the ma.MaskedArray.count_masked() method. The axis is set using the "axis" parameter. The method returns the total number of masked elements (axis=None) or the number of masked elements along each slice of the given axis.
The axis parameter is the axis along which to count. If None (default), a flattened version of the array is used.
Steps
At first, import the required library −
import numpy as np import numpy.ma as ma
Creating a 4x4 array with int elements using the numpy.arange() method −
arr = np.arange(16).reshape((4,4))
print("Array...
", arr)
print("
Array type...
", arr.dtype)
Get the dimensions of the Array −
print("
Array Dimensions...
",arr.ndim)
Get the shape of the Array −
print("
Our Masked Array Shape...
",arr.shape)
Get the number of elements of the Array −
print("
Elements in the Masked Array...
",arr.size)
Create a masked array −
arr = ma.array(arr) arr[0, 1] = ma.masked arr[1, 1] = ma.masked arr[2, 1] = ma.masked arr[2, 2] = ma.masked arr[3, 0] = ma.masked arr[3, 2] = ma.masked arr[3, 3] = ma.masked
To count the number of masked elements along specific axis, use the ma.MaskedArray.count_masked() method. The axis is set using the "axis" parameter:
print("
Result (number of masked elements)...
",ma.count_masked(arr, axis = 1))
Example
# Python ma.MaskedArray - Count the number of masked elements along specific axis
import numpy as np
import numpy.ma as ma
# Creating a 4x4 array with int elements using the numpy.arange() method
arr = np.arange(16).reshape((4,4))
print("Array...
", arr)
print("
Array type...
", arr.dtype)
# Get the dimensions of the Array
print("
Array Dimensions...
",arr.ndim)
print("
Our Array type...
", arr.dtype)
# Get the shape of the Array
print("
Our Masked Array Shape...
",arr.shape)
# Get the number of elements of the Array
print("
Elements in the Masked Array...
",arr.size)
# Create a masked array
arr = ma.array(arr)
arr[0, 1] = ma.masked
arr[1, 1] = ma.masked
arr[2, 1] = ma.masked
arr[2, 2] = ma.masked
arr[3, 0] = ma.masked
arr[3, 2] = ma.masked
arr[3, 3] = ma.masked
# To count the number of masked elements along specific axis, use the ma.MaskedArray.count_masked() method
# The axis is set using the "axis" parameter
print("
Result (number of masked elements)...
",ma.count_masked(arr, axis = 1))
Output
Array... [[ 0 1 2 3] [ 4 5 6 7] [ 8 9 10 11] [12 13 14 15]] Array type... int64 Array Dimensions... 2 Our Array type... int64 Our Masked Array Shape... (4, 4) Elements in the Masked Array... 16 Result (number of masked elements)... [1 1 2 3]
138 Views
