# Mask an array where a condition is met in Numpy

To mask an array where a condition is met, use the numpy.ma.masked_where() method in Python Numpy. Return the array to mask as an array masked where condition is True. Any masked values of a or condition are also masked in the output.

The condition parameter sets the masking condition. When condition tests floating point values for equality, consider using masked_values instead. The copy parameter, If True (default) make a copy of a in the result. If False modify a in place and return a view.

## 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([[71, 55, 91], [82, 33, 39], [73, 82, 51], [90, 45, 82]])
print("Array...", arr)

Get the type pf array −

print("Array type...", 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("Number of Elements in the Array...",arr.size)

To mask an array where a condition is met, use the numpy.ma.masked_where() method in Python Numpy. Here, all the elements above 60 will get masked −

print("Result...",np.ma.masked_where(arr > 60, arr))


## Example

import numpy as np
import numpy.ma as ma

# Create an array with int elements using the numpy.array() method
arr = np.array([[71, 55, 91], [82, 33, 39], [73, 82, 51], [90, 45, 82]])
print("Array...", arr)

# Get the type pf array
print("Array type...", 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("Number of Elements in the Array...",arr.size)

# To mask an array where a condition is met, use the numpy.ma.masked_where() method in Python Numpy
# Here, all the elements above 60 will get masked
print("Result...",np.ma.masked_where(arr > 60, arr))

## Output

Array...
[[71 55 91]
[82 33 39]
[73 82 51]
[90 45 82]]

Array type...
int64

Array Dimensions...
2

Our Array Shape...
(4, 3)

Number of Elements in the Array...
12

Result...
[[-- 55 --]
[-- 33 39]
[-- -- 51]
[-- 45 --]]