numpy.ma.masked_object()

numpy.ma.masked_object(x, value, copy=True, shrink=True) [source]

Mask the array x where the data are exactly equal to value.

This function is similar to masked_values, but only suitable for object arrays: for floating point, use masked_values instead.

Parameters:

Parameters:	x : array_like Array to mask value : object Comparison value copy : {True, False}, optional Whether to return a copy of `x`. shrink : {True, False}, optional Whether to collapse a mask full of False to nomask
Returns:	result : MaskedArray The result of masking `x` where equal to `value`.

x : array_like

Array to mask

value : object

Comparison value

copy : {True, False}, optional

Whether to return a copy of x.

shrink : {True, False}, optional

Whether to collapse a mask full of False to nomask

Returns:

result : MaskedArray

The result of masking x where equal to value.

Examples

>>> import numpy.ma as ma
>>> food = np.array(['green_eggs', 'ham'], dtype=object)
>>> # don't eat spoiled food
>>> eat = ma.masked_object(food, 'green_eggs')
>>> print(eat)
[-- ham]
>>> # plain ol` ham is boring
>>> fresh_food = np.array(['cheese', 'ham', 'pineapple'], dtype=object)
>>> eat = ma.masked_object(fresh_food, 'green_eggs')
>>> print(eat)
[cheese ham pineapple]

Note that mask is set to nomask if possible.

>>> eat
masked_array(data = [cheese ham pineapple],
      mask = False,
      fill_value=?)

Links:

https://docs.scipy.org/doc/numpy-1.11.0/reference/generated/numpy.ma.masked_object.html

doc_NumPy

2025-01-10 15:47:30

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