如何使用python / numpy计算百分位数？

8 回答

• 2

  您可能对SciPy Stats包感兴趣 . 它有the percentile function你正在追求和许多其他统计的好东西 .

  percentile() is available在 numpy 中也是如此 .

  import numpy as np
  a = np.array([1,2,3,4,5])
  p = np.percentile(a, 50) # return 50th percentile, e.g median.
  print p
  3.0
  

  这张票让我相信他们不会很快将百分之百（）变成numpy .

  回复于 2024-04-27T04:41:52+08:00
• 209

  顺便说一下，如果一个人不想依赖scipy，那就有a pure-Python implementation of percentile function . 该功能复制如下：

  ## {{{ http://code.activestate.com/recipes/511478/ (r1)
  import math
  import functools
  
  def percentile(N, percent, key=lambda x:x):
      """
      Find the percentile of a list of values.
  
      @parameter N - is a list of values. Note N MUST BE already sorted.
      @parameter percent - a float value from 0.0 to 1.0.
      @parameter key - optional key function to compute value from each element of N.
  
      @return - the percentile of the values
      """
      if not N:
          return None
      k = (len(N)-1) * percent
      f = math.floor(k)
      c = math.ceil(k)
      if f == c:
          return key(N[int(k)])
      d0 = key(N[int(f)]) * (c-k)
      d1 = key(N[int(c)]) * (k-f)
      return d0+d1
  
  # median is 50th percentile.
  median = functools.partial(percentile, percent=0.5)
  ## end of http://code.activestate.com/recipes/511478/ }}}
  

  回复于 2024-04-27T04:41:52+08:00
• 7

  import numpy as np
  a = [154, 400, 1124, 82, 94, 108]
  print np.percentile(a,95) # gives the 95th percentile
  

  回复于 2024-04-27T04:41:52+08:00
• 57

  我通常看到的百分位数的定义结果是所提供的列表中的值，其中P％的值被找到...这意味着结果必须来自集合，而不是集合元素之间的插值 . 为此，您可以使用更简单的功能 .

  def percentile(N, P):
      """
      Find the percentile of a list of values
  
      @parameter N - A list of values.  N must be sorted.
      @parameter P - A float value from 0.0 to 1.0
  
      @return - The percentile of the values.
      """
      n = int(round(P * len(N) + 0.5))
      return N[n-1]
  
  # A = (1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
  # B = (15, 20, 35, 40, 50)
  #
  # print percentile(A, P=0.3)
  # 4
  # print percentile(A, P=0.8)
  # 9
  # print percentile(B, P=0.3)
  # 20
  # print percentile(B, P=0.8)
  # 50
  

  如果您希望从提供的列表中获取值，或者在其中找到P％的值，则使用以下简单修改：

  def percentile(N, P):
      n = int(round(P * len(N) + 0.5))
      if n > 1:
          return N[n-2]
      else:
          return N[0]
  

  或者@ijustlovemath建议的简化：

  def percentile(N, P):
      n = max(int(round(P * len(N) + 0.5)), 2)
      return N[n-2]
  

  回复于 2024-04-27T04:41:52+08:00
• 23

  检查scipy.stats模块：

  scipy.stats.scoreatpercentile
  

  回复于 2024-04-27T04:41:52+08:00
• 0

  这里是如何在没有numpy的情况下完成它，只使用python来计算百分位数 .

  import math
  
  def percentile(data, percentile):
      size = len(data)
      return sorted(data)[int(math.ceil((size * percentile) / 100)) - 1]
  
  p5 = percentile(mylist, 5)
  p25 = percentile(mylist, 25)
  p50 = percentile(mylist, 50)
  p75 = percentile(mylist, 75)
  p95 = percentile(mylist, 95)
  

  回复于 2024-04-27T04:41:52+08:00
• 6

  要计算系列的百分位数，请运行：

  from scipy.stats import rankdata
  import numpy as np
  
  def calc_percentile(a, method='min'):
      if isinstance(a, list):
          a = np.asarray(a)
      return rankdata(a, method=method) / float(len(a))
  

  例如：

  a = range(20)
  print {val: round(percentile, 3) for val, percentile in zip(a, calc_percentile(a))}
  >>> {0: 0.05, 1: 0.1, 2: 0.15, 3: 0.2, 4: 0.25, 5: 0.3, 6: 0.35, 7: 0.4, 8: 0.45, 9: 0.5, 10: 0.55, 11: 0.6, 12: 0.65, 13: 0.7, 14: 0.75, 15: 0.8, 16: 0.85, 17: 0.9, 18: 0.95, 19: 1.0}
  

  回复于 2024-04-27T04:41:52+08:00
• 8

  In case you need the answer to be a member of the input numpy array:

  只是补充一点，默认情况下numpy中的百分位函数将输出计算为输入向量中两个相邻条目的线性加权平均值 . 在某些情况下，人们可能希望返回的百分位数是向量的实际元素，在这种情况下，从v1.9.0起，您可以使用“插值”选项，“低”，“高”或“最近” .

  import numpy as np
  x=np.random.uniform(10,size=(1000))-5.0
  
  np.percentile(x,70) # 70th percentile
  
  2.075966046220879
  
  np.percentile(x,70,interpolation="nearest")
  
  2.0729677997904314
  

  后者是向量中的实际条目，而前者是边界百分位数的两个向量条目的线性插值

  回复于 2024-04-27T04:41:52+08:00