大 pandas 显示错误的百分位数吗? [英] Is pandas showing the wrong percentile?

问题描述

我正在此处处理这个 WNBA 数据集.我正在分析 Height 变量，下表显示记录的每个高度值的频率、累积百分比和累积频率:

I'm working with this WNBA dataset here. I'm analyzing the Height variable, and below is a table showing frequency, cumulative percentage, and cumulative frequency for each height value recorded:

从表中我可以很容易地得出结论，第一个四分位数(第 25 个百分位数)不能大于 175.

From the table I can easily conclude that the first quartile (the 25th percentile) cannot be larger than 175.

但是，当我使用 Series.describe() 时，我被告知第 25 个百分位数是 176.5.为什么会这样?

However, when I use Series.describe(), I'm told that the 25th percentile is 176.5. Why is that so?

wnba.Height.describe()
count    143.000000
mean     184.566434
std        8.685068
min      165.000000
25%      176.500000
50%      185.000000
75%      191.000000
max      206.000000
Name: Height, dtype: float64

推荐答案

有多种估计分位数的方法.
175.0 与 176.5 与两种不同的方法有关:

There are various ways to estimate the quantiles.
The 175.0 vs 176.5 relates to two different methods:

1. 包括 Q1(得出 176.5)和
2. 不包括 Q1(给出 175.0).

估计不同如下

#1
h = (N − 1)*p + 1 #p being 0.25 in your case
Est_Quantile =  x⌊h⌋ + (h − ⌊h⌋)*(x⌊h⌋ + 1 − x⌊h⌋)

#2
h = (N + 1)*p   
x⌊h⌋ + (h − ⌊h⌋)*(x⌊h⌋ + 1 − x⌊h⌋) 

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