检查scipy。统计模块:

 scipy.stats.scoreatpercentile

我通常看到的百分位数的定义期望从所提供的列表中找到P个百分比的值…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，可以使用一个更简单的函数。

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

如果你想从所提供的列表中获得等于或低于P百分比的值，那么使用以下简单的修改:

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

或者使用@ijustlovemath建议的简化:

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

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。

对于系列:用于描述函数

假设df具有以下列sales和id。你想计算销售额的百分比，它是这样工作的，

df['sales'].describe(percentiles = [0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1])

0.0: .0: minimum
1: maximum 
0.1 : 10th percentile and so on

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()

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}