python - 在 Matplotlib 中为 Boxplot 提供自定义四分位数范围

Question

Matplotlib 或 Seaborn 箱线图给出了第 25 个百分位数和第 75 个百分位数之间的四分位数范围。有没有办法为 Boxplot 提供自定义四分位数范围？我需要获得箱线图，使四分位数范围位于第 10 个百分位数和第 90 个百分位数之间。在谷歌和其他来源上查找，开始了解如何在箱形图上获取自定义晶须，而不是自定义四分位数范围。希望能在这里得到一些有用的解决方案。

Answer 1

是的，可以在您想要的任何百分位处绘制箱线边缘的箱线图。

约定

对于箱线图和须线图，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，背离此约定会使您面临误导读者的风险。您还应该仔细考虑更改箱形百分位数对异常值分类和箱线图的须线意味着什么。

快速解决方案

一个快速解决方案(忽略对晶须位置的任何影响)是计算我们想要的箱线图统计数据，改变 q1 和 q3 的位置，然后用 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，查看生成的图，我们发现改变 q1 和 q3 同时保持 mustache 不变可能不是一个明智的想法。您可以通过重新计算来解决这个问题，例如。 stats['A']['iqr'] 和须线位置 stats['A']['whishi'] 和 stats['A'] ['whislo'].

更完整的解决方案

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数据。

在 boxplot_stats 中，我们找到代码 q1, med, q3 = np.percentile(x, [25, 50, 75])。我们可以改变这条线来改变绘制的百分位数。

因此，一个潜在的解决方案是制作 matplotlib.cbook.boxplot_stats 的副本并根据我们的需要进行更改。在这里，我调用函数 my_boxplot_stats 并添加参数 percents 以便轻松更改 q1 和 q3 的位置>.

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，我们就可以计算统计数据，然后使用 plt.bxp 进行绘图。

# 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，通过此解决方案，我们的函数中的须线根据我们选择的百分位数进行了调整。: