56 lines
1.4 KiB
Python
56 lines
1.4 KiB
Python
"""
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=============
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Masked Arrays
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=============
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Arrays sometimes contain invalid or missing data. When doing operations
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on such arrays, we wish to suppress invalid values, which is the purpose masked
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arrays fulfill (an example of typical use is given below).
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For example, examine the following array:
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>>> x = np.array([2, 1, 3, np.nan, 5, 2, 3, np.nan])
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When we try to calculate the mean of the data, the result is undetermined:
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>>> np.mean(x)
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nan
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The mean is calculated using roughly ``np.sum(x)/len(x)``, but since
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any number added to ``NaN`` [1]_ produces ``NaN``, this doesn't work. Enter
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masked arrays:
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>>> m = np.ma.masked_array(x, np.isnan(x))
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>>> m
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masked_array(data = [2.0 1.0 3.0 -- 5.0 2.0 3.0 --],
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mask = [False False False True False False False True],
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fill_value=1e+20)
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Here, we construct a masked array that suppress all ``NaN`` values. We
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may now proceed to calculate the mean of the other values:
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>>> np.mean(m)
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2.6666666666666665
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.. [1] Not-a-Number, a floating point value that is the result of an
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invalid operation.
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.. moduleauthor:: Pierre Gerard-Marchant
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.. moduleauthor:: Jarrod Millman
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"""
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from __future__ import division, absolute_import, print_function
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from . import core
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from .core import *
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from . import extras
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from .extras import *
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__all__ = ['core', 'extras']
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__all__ += core.__all__
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__all__ += extras.__all__
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from numpy.testing import _numpy_tester
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test = _numpy_tester().test
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bench = _numpy_tester().bench
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