- 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

# Check which element in a masked array is greater than a given value

To check which element in a masked array is greater than the given value, use the **ma.MaskedArray.__gt__()** method. True is returned for every array element greater than the given value val. 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.

NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. It supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries.

## 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([[55, 85, 68, 84], [67, 33, 39, 53], [29, 88, 51, 37], [56, 45, 99, 85]]) 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 −

maskArr = ma.masked_array(arr, mask =[[1, 1, 0, 0], [ 0, 0, 1, 0], [0, 0, 0, 1], [0, 1, 0, 0]]) 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)

The value to be compared −

val = 61 print("

The given value to be compared with the masked array elements...

",val)

To check which element in a masked array is greater than the given value, use the ma.MaskedArray.__gt__() method. Returns with boolean type i.e. True and False. True is returned for every array element greater than the given value val −

print("

Display True for each element greater than the given value val...

", maskArr.__gt__(val))

## Example

import numpy as np import numpy.ma as ma # Create an array with int elements using the numpy.array() method arr = np.array([[55, 85, 68, 84], [67, 33, 39, 53], [29, 88, 51, 37], [56, 45, 99, 85]]) 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 maskArr = ma.masked_array(arr, mask =[[1, 1, 0, 0], [ 0, 0, 1, 0], [0, 0, 0, 1], [0, 1, 0, 0]]) 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) # The value to be compared val = 61 print("

The given value to be compared with the masked array elements...

",val) # To check which element in a masked array is greater than the given value, use the ma.MaskedArray.__gt__() method # Returns with boolean type i.e. True and False. # True is returned for every array element greater than the given value val print("

Display True for each element greater than the given value val...

", maskArr.__gt__(val))

## Output

Array... [[55 85 68 84] [67 33 39 53] [29 88 51 37] [56 45 99 85]] Array type... int64 Array Dimensions... 2 Our Masked Array [[-- -- 68 84] [67 33 -- 53] [29 88 51 --] [56 -- 99 85]] Our Masked Array type... int64 Our Masked Array Dimensions... 2 Our Masked Array Shape... (4, 4) Elements in the Masked Array... 16 The given value to be compared with the masked array elements... 61 Display True for each element greater than the given value val... [[-- -- True True] [True False -- False] [False True False --] [False -- True True]]

- Related Articles
- Check which element in a masked array is greater than or equal to a given value in NumPy
- Check which element in a masked array is less than the given value in Numpy
- Check which element in a masked array is less than or equal to a given value in Numpy
- Check which element in a masked array is equal to a given value in NumPy
- Check which element in a masked array is not equal to a given value in NumPy
- Mask array elements greater than a given value in Numpy
- How to check if a list element is greater than a certain value in R?
- XOR every element of a masked array by a given scalar value in Python
- XOR a given scalar value with every element of a masked array in Python
- AND every element of a masked array by a given scalar value in Python
- AND a given scalar value with every element of a masked array in Python
- OR every element of a masked array by a given scalar value in Python
- OR a given scalar value with every element of a masked array in Python
- Mask array elements greater than or equal to a given value in Numpy
- Left Shift every element of a masked array by a given scalar value in NumPy