要计算一个系列的百分位数，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}

我引导数据，然后绘制出10个样本的置信区间。置信区间表示概率在5%到95%之间的范围。

 import pandas as pd
 import matplotlib.pyplot as plt
 import seaborn as sns
 import numpy as np
 import json
 import dc_stat_think as dcst

 data = [154, 400, 1124, 82, 94, 108]
 #print (np.percentile(data,[0.5,95])) # gives the 95th percentile

 bs_data = dcst.draw_bs_reps(data, np.mean, size=6*10)

 #print(np.reshape(bs_data,(24,6)))

 x= np.linspace(1,6,6)
 print(x)
 for (item1,item2,item3,item4,item5,item6) in bs_data.reshape((10,6)):
     line_data=[item1,item2,item3,item4,item5,item6]
     ci=np.percentile(line_data,[.025,.975])
     mean_avg=np.mean(line_data)
     fig, ax = plt.subplots()
     ax.plot(x,line_data)
     ax.fill_between(x, (line_data-ci[0]), (line_data+ci[1]), color='b', alpha=.1)
     ax.axhline(mean_avg,color='red')
     plt.show()

顺便说一下，有一个百分位数函数的纯python实现，以防人们不想依赖scipy。函数复制如下:

## {{{ 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/ }}}

您可能会对SciPy Stats包感兴趣。它有你所追求的百分位数函数和许多其他统计上的好处。

Percentile()在numpy中也可用。

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percentile()集成到numpy中。

如果你需要答案是输入numpy数组的成员:

再加上numpy中的百分位数函数默认情况下将输出计算为输入向量中两个相邻项的线性加权平均。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开始，您可以使用“插值”选项，使用“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

后者是向量中的一个实际条目，而前者是与百分位数相邻的两个向量条目的线性插值

要计算一个系列的百分位数，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}