histogram
?
>>> 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
?