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histogram

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>>> a = numpy.arange(5)

>>> hist, bin_edges = numpy.histogram(a,density=False)

>>> hist, bin_edges

(array([1, 0, 1, 0, 0, 1, 0, 1, 0, 1], dtype=int64), array([ 0. , 0.4, 0.8, 1.2, 1.6, 2. , 2.4, 2.8, 3.2, 3.6, 4. ]))

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Analysis:

• Variable a is [0 1 2 3 4]
• After call histogram, it will calculate the total count each number in a= [0 1 2 3 4] according to each bins(阈值), for example:

[0.-0.4) contains 0, so result is 1

[0.4-0.8) does not contain any number in [0 1 2 3 4], so result is 0
[0.8-1.2) contains 1, so result is 1
[1.2-1.6) does not contain any number in [0 1 2 3 4], so result is 0
[1.6-2.) does not contain any number in [0 1 2 3 4], so result is 0

[2.-2.4) contains 2, so result is 1

[2.4-2.8) does not contain any number in [0 1 2 3 4], so result is 0

[2.8-3.2) contains 3, so result is 1

[3.2-3.6) does not contain any number in [0 1 2 3 4], so result is 0

[3.6-4.] contains 4, so result is 1

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Numpy API Analysis

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原文地址：http://www.cnblogs.com/pengpenghappy/p/7095971.html