Mask An Array Where A Condition Is Met In Numpy - Tutorialspoint

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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 --]]

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