IQR 的 Python 箱线图大小从 50% 到 70%

发布于08月08日

我想知道是否有可能把70%的人口放在盒子里，就像红色的盒子里一样？我知道Q3-Q1=IQR，但不知道这对我有什么帮助. 我正在使用matplotlib绘制我的框图.

def boxplot_one_micro_competence(one_micro):
     """
     show the boxplot corresponding to the list in parameters
     --------
     Parameters :
     one_micro - list - list of questions_id for the micro desired 
     --------
     >>> boxplot_one_micro_competence(sere)
     """    
     plt.subplots(figsize=(4, 4))
     plt.boxplot(df_micro_competence_groupe(one_micro)['score'], showcaps = False, whis = False, showfliers = False, labels = [one_micro])

     plt.ylim(-0.1, 4.1)
     plt.show()

boxplot_one_micro_competence(sere)

result个

目前，我的代码看起来是这样的.

任何帮助我们都将不胜感激！

如果我的解释不够清楚，请让我知道；)

谢谢!

推荐答案

我引用了this solution作为参考:

import matplotlib.cbook as cbook
import matplotlib.pyplot as plt
import numpy as np

# Generate some random data to visualise
np.random.seed(2019)
data = np.random.normal(size=100)

stats = {}
# Compute the boxplot stats (as in the default matplotlib implementation)
stats['A'] = cbook.boxplot_stats(data, labels='A')[0]
stats['B'] = cbook.boxplot_stats(data, labels='B')[0]


# For box A compute the 15th and 85th percentiles
stats['A']['q1'], stats['A']['q3'] = np.percentile(data, [25, 75])
# For box B compute the 15th and 85th percentiles
stats['B']['q1'], stats['B']['q3'] = np.percentile(data, [15, 85])


fig, ax = plt.subplots(1, 1)
# Plot boxplots from our computed statistics
ax.bxp([stats['A'], stats['B']], positions=range(2), vert=False)

Output:个