Matplotlib：我们可以在同一张图表上绘制直方图和箱线图吗？

Question

我有一个关于绘制直方图和箱线图 Matplotlib 的问题。

我知道我可以单独绘制直方图和箱线图。我的问题是，是否可以将它们绘制在同一个图表上，例如本网站中显示的图表？ Springer Images

非常感谢！

Answer 1

是的，我见过的最好的处理方法是here。 代码和图表的副本：

# Import library and dataset
import seaborn as sns
import matplotlib.pyplot as plt
df = sns.load_dataset('iris')
 
# Cut the window in 2 parts
f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={"height_ratios": (.15, .85)})
 
# Add a graph in each part
sns.boxplot(df["sepal_length"], ax=ax_box)
sns.distplot(df["sepal_length"], ax=ax_hist)
 
# Remove x axis name for the boxplot
ax_box.set(xlabel='')

Answer 2

使用 matplotlib 有几种方法可以实现这一点。 plt.subplots() 方法以及 AxesGrid1 和 gridspec 工具包都提供了非常优雅的解决方案，但可能需要时间来学习。

执行此操作的一种简单的蛮力方法是自己手动将坐标区对象添加到图形中。

import numpy as np
import matplotlib.pyplot as plt

# fake data
x = np.random.lognormal(mean=2.25, sigma=0.75, size=37)

# setup the figure and axes
fig = plt.figure(figsize=(6,4))
bpAx = fig.add_axes([0.2, 0.7, 0.7, 0.2])   # left, bottom, width, height:
                                            # (adjust as necessary)
histAx = fig.add_axes([0.2, 0.2, 0.7, 0.5]) # left specs should match and
                                            # bottom + height on this line should
                                            # equal bottom on bpAx line
# plot stuff
bp = bpAx.boxplot(x, notch=True, vert=False)
h = histAx.hist(x, bins=7)

# confirm that the axes line up 
xlims = np.array([bpAx.get_xlim(), histAx.get_xlim()])
for ax in [bpAx, histAx]:
    ax.set_xlim([xlims.min(), xlims.max()])

bpAx.set_xticklabels([])  # clear out overlapping xlabels
bpAx.set_yticks([])  # don't need that 1 tick mark
plt.show()

Answer 3

我正在寻找类似的东西，这对我有用。图片链接在这里https://raw.githubusercontent.com/preetihemant/preetihemant.github.io/master/images/hist_boxplot.png

plt.subplot(2,1,1)
plt.hist(data,histtype='bar',bins=[values]) 
plt.xlabel('x-label')
plt.ylabel('y-label')
plt.title('Graph title')

plt.subplot(2,1,2)
plt.boxplot(values)

plt.show()

Answer 4

这里有一个通用的解决方案，即包含 17 个 matplotlib 图形实用程序 + 用户指南的免费库：https://www.mlbridgeresearch.com/products/free-article-2。我厌倦了为了编写实用软件而中断研究，因此我积累了满足常见需求的库。该代码有据可查，并且运行良好。这是一个例子。

from sklearn.datasets import load_iris

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

from statistics_utilities import histogram
from statistics_utilities import box_plot_basic

data = load_iris()
# iris, using the label column as a categorical
df = pd.DataFrame(data.data, columns=data.feature_names)
df['label'] = data.target

print(data.feature_names)

# setup the plot grid
plt.style.use('seaborn-darkgrid')
fig, ax = plt.subplots(1, 2)
ax = np.reshape(ax, (1, 2))

variable_name = 'sepal length (cm)'

# Place the histogram on the grid - pass the Axes.
# Plots a single histogram for a quantitative variable using seaborn's distplot().
# See also histogram_grid(), which plots a grid of histograms for a list of
# quantitative variables
hist_type = 'frequency'
# displays summary statistics in a custom legend, set legend=False to turn off.
ax[0, 0] = histogram(df, variable_name=variable_name, bins=20, kde=False, statistics='all',
                     hist_type=hist_type, title=None, ax=ax[0, 0])

# Place the box plot on the grid - pass the Axes
# Plots a single box_plot for a quantitative variable using matplotlib's boxplot().
# See also box_plot() and box_plot_groupby, which plots a quantitative variable
# by one or two categorical variables.
box_orientation = 'vertical'
box_width = .2
ax[0, 1] = box_plot_basic(df, variable_name=variable_name,
                          box_orientation=box_orientation, box_width=box_width, title=None,
                          ax=ax[0, 1])

# adjustments to plot size and spacing
fig.set_size_inches(13, 6)
fig.subplots_adjust(wspace=.55, left=0.035, right=.985, top=.925, bottom=.1)
fig.suptitle('iris dataset', fontsize=13)

plt.show()
plt.close()