在 Matplotlib 中为 Boxplot 提供自定义四分位距 [英] Giving Custom inter quartile range for Boxplot in Matplotlib

问题描述

Matplotlib或Seaborn箱形图给出了介于25％和75％之间的四分位数范围.有没有一种方法可以为Boxplot提供自定义的四分位数范围?我需要得到箱线图，使得四分位距介于 10% 和 90% 之间.在谷歌和其他来源上查找，开始了解在箱线图上获取自定义胡须，而不是自定义四分位间距.希望这里能提供一些有用的解决方案.

The Matplotlib or Seaborn box plot gives the interquartile range between the 25th percentile and 75th percentile. Is there a way to give custom interquartile range for the Boxplot ? I need to get the box plot such that the interquartile range is between 10th percentile and 90th percentile. Looked up on google and other sources, came to know about getting custom whiskers on the box plot but not custom interquartile range. Hoping would get some useful solutions here.

推荐答案

是的，可以在您想要的任何百分位数处绘制带有框边的箱线图.

Yes, it is possible to plot a boxplot with box edges at any percentiles you desire.

对于箱线图，通常绘制数据的第 25 个和第 75 个百分位数.因此，您应该意识到，违反此约定会使您面临误导读者的风险.您还应该仔细考虑改变框的百分位数对离群值分类和框图的晶须的意义.

With box and whisker plots it is convention to plot the 25th and 75th percentiles of the data. Thus, you should be aware that departing from this convention puts you at risk of misleading readers. You should also carefully consider what altering the box percentiles means to outlier classification and the whiskers of the boxplot.

一种快速解决方案(忽略晶须位置的任何影响)是计算所需的箱线图统计信息，更改 q1 和 q3 的位置，然后使用<代码>ax.bxp:

A quick fix (ignoring any implications for whisker locations) is to compute the boxplot statistics we desire, alter the locations of q1 and q3, and then plot with ax.bxp:

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]
stats['C'] = cbook.boxplot_stats(data, labels='C')[0]

# For box A compute the 1st and 99th percentiles
stats['A']['q1'], stats['A']['q3'] = np.percentile(data, [1, 99])
# For box B compute the 10th and 90th percentiles
stats['B']['q1'], stats['B']['q3'] = np.percentile(data, [10, 90])
# For box C compute the 25th and 75th percentiles (matplotlib default)
stats['C']['q1'], stats['C']['q3'] = np.percentile(data, [25, 75])

fig, ax = plt.subplots(1, 1)
# Plot boxplots from our computed statistics
ax.bxp([stats['A'], stats['B'], stats['C']], positions=range(3))

但是，查看所产生的图，我们发现更改 q1 和 q3 而保持晶须不变可能不是一个明智的主意.您可以通过重新计算例如来解决这个问题. stats ['A'] ['iqr'] 和晶须位置 stats ['A'] ['whishi'] 和 stats ['A']['whislo'].

However, viewing the plot produced we see that altering q1 and q3 whilst leaving the whiskers unchanged may not be a sensible idea. You could counter this by recomputing eg. stats['A']['iqr'] and the whisker locations stats['A']['whishi'] and stats['A']['whislo'].

通过查看matplotlib的源代码，我们发现matplotlib使用 matplotlib.cbook.boxplot_stats 来计算箱线图中使用的统计信息.

Looking through matplotlib's source code we find that matplotlib uses matplotlib.cbook.boxplot_stats to compute the statistics used in the boxplot.

在 boxplot_stats 中，我们找到代码 q1, med, q3 = np.percentile(x, [25, 50, 75]).这是我们可以更改的线，以更改绘制的百分位数.

Within boxplot_stats we find the code q1, med, q3 = np.percentile(x, [25, 50, 75]). This is the line we can alter to change the plotted percentiles.

因此，一个潜在的解决方案是制作 matplotlib.cbook.boxplot_stats 的副本，并根据需要更改它.在这里，我调用函数 my_boxplot_stats 并添加一个参数 percents 以便于更改 q1 和 q3 的位置>.

So a potential solution would be to make a copy of matplotlib.cbook.boxplot_stats and alter it as we desire. Here I call the function my_boxplot_stats and add an argument percents to make it easy to alter the locations of q1 and q3.

import itertools
from matplotlib.cbook import _reshape_2D
import matplotlib.pyplot as plt
import numpy as np

# Function adapted from matplotlib.cbook
def my_boxplot_stats(X, whis=1.5, bootstrap=None, labels=None,
                  autorange=False, percents=[25, 75]):

    def _bootstrap_median(data, N=5000):
        # determine 95% confidence intervals of the median
        M = len(data)
        percentiles = [2.5, 97.5]

        bs_index = np.random.randint(M, size=(N, M))
        bsData = data[bs_index]
        estimate = np.median(bsData, axis=1, overwrite_input=True)

        CI = np.percentile(estimate, percentiles)
        return CI

    def _compute_conf_interval(data, med, iqr, bootstrap):
        if bootstrap is not None:
            # Do a bootstrap estimate of notch locations.
            # get conf. intervals around median
            CI = _bootstrap_median(data, N=bootstrap)
            notch_min = CI[0]
            notch_max = CI[1]
        else:

            N = len(data)
            notch_min = med - 1.57 * iqr / np.sqrt(N)
            notch_max = med + 1.57 * iqr / np.sqrt(N)

        return notch_min, notch_max

    # output is a list of dicts
    bxpstats = []

    # convert X to a list of lists
    X = _reshape_2D(X, "X")

    ncols = len(X)
    if labels is None:
        labels = itertools.repeat(None)
    elif len(labels) != ncols:
        raise ValueError("Dimensions of labels and X must be compatible")

    input_whis = whis
    for ii, (x, label) in enumerate(zip(X, labels)):

        # empty dict
        stats = {}
        if label is not None:
            stats['label'] = label

        # restore whis to the input values in case it got changed in the loop
        whis = input_whis

        # note tricksyness, append up here and then mutate below
        bxpstats.append(stats)

        # if empty, bail
        if len(x) == 0:
            stats['fliers'] = np.array([])
            stats['mean'] = np.nan
            stats['med'] = np.nan
            stats['q1'] = np.nan
            stats['q3'] = np.nan
            stats['cilo'] = np.nan
            stats['cihi'] = np.nan
            stats['whislo'] = np.nan
            stats['whishi'] = np.nan
            stats['med'] = np.nan
            continue

        # up-convert to an array, just to be safe
        x = np.asarray(x)

        # arithmetic mean
        stats['mean'] = np.mean(x)

        # median
        med = np.percentile(x, 50)
        ## Altered line
        q1, q3 = np.percentile(x, (percents[0], percents[1]))

        # interquartile range
        stats['iqr'] = q3 - q1
        if stats['iqr'] == 0 and autorange:
            whis = 'range'

        # conf. interval around median
        stats['cilo'], stats['cihi'] = _compute_conf_interval(
            x, med, stats['iqr'], bootstrap
        )

        # lowest/highest non-outliers
        if np.isscalar(whis):
            if np.isreal(whis):
                loval = q1 - whis * stats['iqr']
                hival = q3 + whis * stats['iqr']
            elif whis in ['range', 'limit', 'limits', 'min/max']:
                loval = np.min(x)
                hival = np.max(x)
            else:
                raise ValueError('whis must be a float, valid string, or list '
                                 'of percentiles')
        else:
            loval = np.percentile(x, whis[0])
            hival = np.percentile(x, whis[1])

        # get high extreme
        wiskhi = np.compress(x <= hival, x)
        if len(wiskhi) == 0 or np.max(wiskhi) < q3:
            stats['whishi'] = q3
        else:
            stats['whishi'] = np.max(wiskhi)

        # get low extreme
        wisklo = np.compress(x >= loval, x)
        if len(wisklo) == 0 or np.min(wisklo) > q1:
            stats['whislo'] = q1
        else:
            stats['whislo'] = np.min(wisklo)

        # compute a single array of outliers
        stats['fliers'] = np.hstack([
            np.compress(x < stats['whislo'], x),
            np.compress(x > stats['whishi'], x)
        ])

        # add in the remaining stats
        stats['q1'], stats['med'], stats['q3'] = q1, med, q3

    return bxpstats

有了这个，我们就可以计算我们的统计数据，然后用 plt.bxp 绘图.

With this in place we can compute our statistics and then plot with plt.bxp.

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

stats = {}

# Compute the boxplot stats with our desired percentiles
stats['A'] = my_boxplot_stats(data, labels='A', percents=[1, 99])[0]
stats['B'] = my_boxplot_stats(data, labels='B', percents=[10, 90])[0]
stats['C'] = my_boxplot_stats(data, labels='C', percents=[25, 75])[0]

fig, ax = plt.subplots(1, 1)
# Plot boxplots from our computed statistics
ax.bxp([stats['A'], stats['B'], stats['C']], positions=range(3))

看到使用此解决方案，根据我们选择的百分位数在我们的函数中调整胡须.

See that with this solution the whiskers are adjusted in our function based on our selected percentiles.:

这篇关于在 Matplotlib 中为 Boxplot 提供自定义四分位距的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！