Python_画boxplot 盒图/箱线图 Boxplot图的介绍API介绍示例DemoDemo 1: 绘制简单的BoxplotDemo 2：复杂的Boxplot， 为每个boxplot指定不同的颜色 References
Boxplot图的介绍
箱形图(Box-plot)又称为盒须图/盒式图/箱线图，是一种用作显示一组数据分散情况的统计图。这自然让人想到分位数的概念， 不错, boxplot就是通过分位数来直观展示数据的分散程度。
如上图，几个重要的参数：
下边缘（Q1），表示最小值；
下四分位数（Q2），又称“第一四分位数”，等于该样本中所有数值由小到大排列后第25%的数字；
中位数（Q3），又称“第二四分位数”等于该样本中所有数值由小到大排列后第50%的数字；
上四分位数（Q4），又称“第三四分位数”等于该样本中所有数值由小到大排列后第75%的数字；
上边缘（Q5），表述最大值。
极端异常值，即超出四分位数差3倍距离的异常值，用实心点表示；较为温和的异常值，即处于1.5倍-3倍四分位数差之间的异常值，用空心点表示
API介绍
参数：
x: Array 或者向量序列， Array的话每一行对应一个box, 序列list的话每个子list对应一个box;
labels： 每个box的label, 与x对应
patch_artist： 是否填充box
vert: 控制图的呈现方向 （水平或者垂直）
widths： 指定每个box的宽度
示例Demo Demo 1: 绘制简单的Boxplot import matplotlib.pyplot as pltimport numpy as npfrom matplotlib.patches import Polygon# Fixing random state for reproducibilitynp.random.seed(19680801)# fake up some dataspread = np.random.rand(50) * 100center = np.ones(25) * 50flier_high = np.random.rand(10) * 100 + 100flier_low = np.random.rand(10) * -100data = np.concatenate((spread, center, flier_high, flier_low))fig, axs = plt.subplots(2, 3)# basic plotaxs[0, 0].boxplot(data)axs[0, 0].set_title('basic plot')# notched plotaxs[0, 1].boxplot(data, 1)axs[0, 1].set_title('notched plot')# change outlier point symbolsaxs[0, 2].boxplot(data, 0, 'gD')axs[0, 2].set_title('change outliernpoint symbols')# don't show outlier pointsaxs[1, 0].boxplot(data, 0, '')axs[1, 0].set_title("don't shownoutlier points")# horizontal boxesaxs[1, 1].boxplot(data, 0, 'rs', 0)axs[1, 1].set_title('horizontal boxes')# change whisker lengthaxs[1, 2].boxplot(data, 0, 'rs', 0, 0.75)axs[1, 2].set_title('change whisker length')fig.subplots_adjust(left=0.08, right=0.98, bottom=0.05, top=0.9, hspace=0.4, wspace=0.3)# fake up some more dataspread = np.random.rand(50) * 100center = np.ones(25) * 40flier_high = np.random.rand(10) * 100 + 100flier_low = np.random.rand(10) * -100d2 = np.concatenate((spread, center, flier_high, flier_low))# Making a 2-D array only works if all the columns are the# same length. If they are not, then use a list instead.# This is actually more efficient because boxplot converts# a 2-D array into a list of vectors internally anyway.data = [data, d2, d2[::2]]# Multiple box plots on one Axesfig, ax = plt.subplots()ax.boxplot(data)plt.show()
效果：
Demo 2：复杂的Boxplot， 为每个boxplot指定不同的颜色
一些小技巧：
（1）x_tick的斜体显示
主要是通过ax.text方法的transform参数来指定的：
ax.text(pos[tick], .95, upper_labels[tick],transform=ax.get_xaxis_transform(),
其中ax:
ax.set_xticklabels(np.repeat(random_dists, 2),
rotation=45, fontsize=8)
可以看到旋转角度为45度。
（2）不同的boxplot显示不同的颜色
random_dists = ['Normal(1, 1)', 'Lognormal(1, 1)', 'Exp(1)', 'Gumbel(6, 4)', 'Triangular(2, 9, 11)']N = 500norm = np.random.normal(1, 1, N)logn = np.random.lognormal(1, 1, N)expo = np.random.exponential(1, N)gumb = np.random.gumbel(6, 4, N)tria = np.random.triangular(2, 9, 11, N)# Generate some random indices that we'll use to resample the original data# arrays. For code brevity, just use the same random indices for each arraybootstrap_indices = np.random.randint(0, N, N)data = [ norm, norm[bootstrap_indices], logn, logn[bootstrap_indices], expo, expo[bootstrap_indices], gumb, gumb[bootstrap_indices], tria, tria[bootstrap_indices],]fig, ax1 = plt.subplots(figsize=(10, 6))fig.canvas.manager.set_window_title('A Boxplot Example')fig.subplots_adjust(left=0.075, right=0.95, top=0.9, bottom=0.25)bp = ax1.boxplot(data, notch=0, sym='+', vert=1, whis=1.5)plt.setp(bp['boxes'], color='black')plt.setp(bp['whiskers'], color='black')plt.setp(bp['fliers'], color='red', marker='+')# Add a horizontal grid to the plot, but make it very light in color# so we can use it for reading data values but not be distractingax1.yaxis.grid(True, linestyle='-', which='major', color='lightgrey', alpha=0.5)ax1.set( axisbelow=True, # Hide the grid behind plot objects title='Comparison of IID Bootstrap Resampling Across Five Distributions', xlabel='Distribution', ylabel='Value',)# Now fill the boxes with desired colorsbox_colors = ['darkkhaki', 'royalblue']num_boxes = len(data)medians = np.empty(num_boxes)for i in range(num_boxes): box = bp['boxes'][i] box_x = [] box_y = [] for j in range(5): box_x.append(box.get_xdata()[j]) box_y.append(box.get_ydata()[j]) box_coords = np.column_stack([box_x, box_y]) # Alternate between Dark Khaki and Royal Blue ax1.add_patch(Polygon(box_coords, facecolor=box_colors[i % 2])) # Now draw the median lines back over what we just filled in med = bp['medians'][i] median_x = [] median_y = [] for j in range(2): median_x.append(med.get_xdata()[j]) median_y.append(med.get_ydata()[j]) ax1.plot(median_x, median_y, 'k') medians[i] = median_y[0] # Finally, overplot the sample averages, with horizontal alignment # in the center of each box ax1.plot(np.average(med.get_xdata()), np.average(data[i]), color='w', marker='*', markeredgecolor='k')# Set the axes ranges and axes labelsax1.set_xlim(0.5, num_boxes + 0.5)top = 40bottom = -5ax1.set_ylim(bottom, top)ax1.set_xticklabels(np.repeat(random_dists, 2), rotation=45, fontsize=8)# Due to the Y-axis scale being different across samples, it can be# hard to compare differences in medians across the samples. Add upper# X-axis tick labels with the sample medians to aid in comparison# (just use two decimal places of precision)pos = np.arange(num_boxes) + 1upper_labels = [str(round(s, 2)) for s in medians]weights = ['bold', 'semibold']for tick, label in zip(range(num_boxes), ax1.get_xticklabels()): k = tick % 2 ax1.text(pos[tick], .95, upper_labels[tick], transform=ax1.get_xaxis_transform(), horizontalalignment='center', size='x-small', weight=weights[k], color=box_colors[k])# Finally, add a basic legendfig.text(0.80, 0.08, f'{N} Random Numbers', backgroundcolor=box_colors[0], color='black', weight='roman', size='x-small')fig.text(0.80, 0.045, 'IID Bootstrap Resample', backgroundcolor=box_colors[1], color='white', weight='roman', size='x-small')fig.text(0.80, 0.015, '*', color='white', backgroundcolor='silver', weight='roman', size='medium')fig.text(0.815, 0.013, ' Average Value', color='black', weight='roman', size='x-small')plt.show()
效果：
References
1.https://matplotlib.org/stable/gallery/statistics/boxplot_demo.html#sphx-glr-gallery-statistics-boxplot-demo-py