numpy.percentile()

1.函数

百分位数是统计中使用的度量，表示小于这个值的观察值的百分比。 函数numpy.percentile()接受以下参数。

np.percentile(a, q, axis=None, out=None, overwrite_input=False, interpolation='linear', keepdims=False) 
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2.参数说明：

• a: 输入数组
• q: 要计算的百分位数，在 0 ~ 100 之间
• axis: 沿着它计算百分位数的轴
• keepdims :bool是否保持维度不变
• 首先明确百分位数：第 p 个百分位数是这样一个值，它使得至少有 p% 的数据项小于或等于这个值，且至少有 (100-p)% 的数据项大于或等于这个值。

【注】举个例子：高等院校的入学考试成绩经常以百分位数的形式报告。比如，假设某个考生在入学考试中的语文部分的原始分数为 54 分。相对于参加同一考试的其他学生来说，他的成绩如何并不容易知道。但是如果原始分数54分恰好对应的是第70百分位数，我们就能知道大约70%的学生的考分比他低，而约30%的学生考分比他高。这里的 p = 70。

• a : array_like
  Input array or object that can be converted to an array.
• q : array_like of float
  Percentile or sequence of percentiles to compute, which must be between 0 and 100 inclusive.
• axis : {int, tuple of int, None}, optional
  Axis or axes along which the percentiles are computed. The default is to compute the percentile(s) along a flattened version of the array.
  Changed in version 1.9.0: A tuple of axes is supported
• out : ndarray, optional
  Alternative output array in which to place the result. It must have the same shape and buffer length as the expected output, but the type (of the output) will be cast if necessary.
• overwrite_input : bool, optional
  If True, then allow the input array a to be modified by intermediate calculations, to save memory. In this case, the contents of the input a after this function completes is undefined.
• interpolation : {‘linear’, ‘lower’, ‘higher’, ‘midpoint’, ‘nearest’}
  This optional parameter specifies the interpolation method to use when the desired percentile lies between two data points i < j:
  ‘linear’: i + (j - i) * fraction, where fraction is the fractional part of the index surrounded by i and j.
  ‘lower’: i.
  ‘higher’: j.
  ‘nearest’: i or j, whichever is nearest.
  ‘midpoint’: (i + j) / 2.
  New in version 1.9.0.
• keepdims : bool, optional
  If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the original array a.

3.实例分析

import numpy as np 
 
a = np.array([[10, 7, 4], [3, 2, 1]])
print ('我们的数组是：')
print (a)
 
print ('调用 percentile() 函数：')
# 50% 的分位数，就是 a 里排序之后的中位数
print (np.percentile(a, 50)) 
 
# axis 为 0，在纵列上求
print (np.percentile(a, 50, axis=0)) 
 
# axis 为 1，在横行上求
print (np.percentile(a, 50, axis=1)) 
 
# 保持维度不变
print (np.percentile(a, 50, axis=1, keepdims=True))
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输出结果

我们的数组是：
[[10  7  4]
 [ 3  2  1]]
调用 percentile() 函数：
3.5
[6.5 4.5 2.5]
[7. 2.]
[[7.]
 [2.]]
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4.更多例子

>> a = np.array([[10, 7, 4], [3, 2, 1]])
>>> a
array([[10,  7,  4],
      [ 3,  2,  1]])
>>> np.percentile(a, 50)
3.5
>>> np.percentile(a, 50, axis=0)
array([6.5, 4.5, 2.5])
>>> np.percentile(a, 50, axis=1)
array([7.,  2.])
>>> np.percentile(a, 50, axis=1, keepdims=True)
array([[7.],
      [2.]])
>>> m = np.percentile(a, 50, axis=0)
>>> out = np.zeros_like(m)
>>> np.percentile(a, 50, axis=0, out=out)
array([6.5, 4.5, 2.5])
>>> m
array([6.5, 4.5, 2.5])
>>> b = a.copy()
>>> np.percentile(b, 50, axis=1, overwrite_input=True)
array([7.,  2.])
>>> assert not np.all(a == b)
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参考：
1.https://docs.scipy.org/doc/numpy/reference/generated/numpy.percentile.html
2.https://www.runoob.com/numpy/numpy-statistical-functions.html