>>> 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. ]))
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:bins
contains number
result
[0.-0.4)
0
1
[0.4-0.8)
n/a
0
[0.8-1.2)
1
1
[1.2-1.6)
n/a
0
[1.6-2.)
n/a
0
[2.-2.4)
2
1
[2.4-2.8)
n/a
0
[2.8-3.2)
3
1
[3.2-3.6)
n/a
0
[3.6-4.]
4
1
[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
以上就是numpy api analysis的详细内容。