在两个表之间绘制箭头（作为图像）（Python）

我想绘制两个相邻表的绘图，其中箭头以特定方式在它们之间射出（在

matplotlib

中）。到目前为止，我或多或少知道如何使用

plt.arrow

来让箭头做我想做的事情，我发现了一篇文章，展示了如何仅绘制

plt.table

的表格部分

源代码，如果链接失效：

import numpy as np
import matplotlib.pyplot as plt
title_text = 'Loss by Disaster'
footer_text = 'June 24, 2020'
fig_background_color = 'skyblue'
fig_border = 'steelblue'
data =  [
            [         'Freeze', 'Wind', 'Flood', 'Quake', 'Hail'],
            [ '5 year',  66386, 174296,   75131,  577908,  32015],
            ['10 year',  58230, 381139,   78045,   99308, 160454],
            ['20 year',  89135,  80552,  152558,  497981, 603535],
            ['30 year',  78415,  81858,  150656,  193263,  69638],
            ['40 year', 139361, 331509,  343164,  781380,  52269],
        ]
# Pop the headers from the data array
column_headers = data.pop(0)
row_headers = [x.pop(0) for x in data]
# Table data needs to be non-numeric text. Format the data
# while I'm at it.
cell_text = []
for row in data:
    cell_text.append([f'{x/1000:1.1f}' for x in row])
# Get some lists of color specs for row and column headers
rcolors = plt.cm.BuPu(np.full(len(row_headers), 0.1))
ccolors = plt.cm.BuPu(np.full(len(column_headers), 0.1))
# Create the figure. Setting a small pad on tight_layout
# seems to better regulate white space. Sometimes experimenting
# with an explicit figsize here can produce better outcome.
plt.figure(linewidth=2,
           edgecolor=fig_border,
           facecolor=fig_background_color,
           tight_layout={'pad':1},
           #figsize=(5,3)
          )
# Add a table at the bottom of the axes
the_table = plt.table(cellText=cell_text,
                      rowLabels=row_headers,
                      rowColours=rcolors,
                      rowLoc='right',
                      colColours=ccolors,
                      colLabels=column_headers,
                      loc='center')
# Scaling is the only influence we have over top and bottom cell padding.
# Make the rows taller (i.e., make cell y scale larger).
the_table.scale(1, 1.5)
# Hide axes
ax = plt.gca()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
# Hide axes border
plt.box(on=None)
# Add title
plt.suptitle(title_text)
# Add footer
plt.figtext(0.95, 0.05, footer_text, horizontalalignment='right', size=6, weight='light')
# Force the figure to update, so backends center objects correctly within the figure.
# Without plt.draw() here, the title will center on the axes and not the figure.
plt.draw()
# Create image. plt.savefig ignores figure edge and face colors, so map them.
fig = plt.gcf()
plt.savefig('pyplot-table-demo.png',
            #bbox='tight',
            edgecolor=fig.get_edgecolor(),
            facecolor=fig.get_facecolor(),
            dpi=150
            )

但是，我无法让两个表相邻打印并看起来很漂亮，并且两个表之间的箭头不会穿过两个打印区域

import numpy as np
import matplotlib.pyplot as plt

fig, (ax1, ax2) = plt.subplots(1, 2)


title_text = 'Loss by Disaster'
footer_text = 'June 24, 2020'
fig_background_color = 'skyblue'
fig_border = 'steelblue'
data =  [
            [         'Freeze', 'Wind', 'Flood', 'Quake', 'Hail'],
            [ '5 year',  66386, 174296,   75131,  577908,  32015],
            ['10 year',  58230, 381139,   78045,   99308, 160454],
            ['20 year',  89135,  80552,  152558,  497981, 603535],
            ['30 year',  78415,  81858,  150656,  193263,  69638],
            ['40 year', 139361, 331509,  343164,  781380,  52269],
        ]
# Pop the headers from the data array
column_headers = data.pop(0)
row_headers = [x.pop(0) for x in data]
# Table data needs to be non-numeric text. Format the data
# while I'm at it.
cell_text = []
for row in data:
    cell_text.append([f'{x/1000:1.1f}' for x in row])
# Get some lists of color specs for row and column headers
rcolors = plt.cm.BuPu(np.full(len(row_headers), 0.1))
ccolors = plt.cm.BuPu(np.full(len(column_headers), 0.1))
# Create the figure. Setting a small pad on tight_layout
# seems to better regulate white space. Sometimes experimenting
# with an explicit figsize here can produce better outcome.
# ax1.figure(linewidth=2,
#            edgecolor=fig_border,
#            facecolor=fig_background_color,
#            tight_layout={'pad':1},
#            #figsize=(5,3)
#           )
# Add a table at the bottom of the axes
the_table = ax1.table(cellText=cell_text,
                      rowLabels=row_headers,
                      rowColours=rcolors,
                      rowLoc='right',
                      colColours=ccolors,
                      colLabels=column_headers,
                      loc='center')
# Scaling is the only influence we have over top and bottom cell padding.
# Make the rows taller (i.e., make cell y scale larger).
the_table.scale(1, 1.5)
# Hide axes
ax1 = plt.gca()
ax1.get_xaxis().set_visible(False)
ax1.get_yaxis().set_visible(False)
#
# Do it again
#
the_table = ax2.table(cellText=cell_text,
                      rowLabels=row_headers,
                      rowColours=rcolors,
                      rowLoc='right',
                      colColours=ccolors,
                      colLabels=column_headers,
                      loc='center')
# Scaling is the only influence we have over top and bottom cell padding.
# Make the rows taller (i.e., make cell y scale larger).
the_table.scale(1, 1.5)
# Hide axes
ax2 = plt.gca()
ax2.get_xaxis().set_visible(False)
ax2.get_yaxis().set_visible(False)
#
plt.arrow(0.5, 0.5, -5, 1)

（也许可以用一个函数来整理

再做一次
。）


最后，我想得到下面的草图


我愿意对我的方法进行一些小的调整，以完成我正在做的事情，甚至不使用
matplotlib
，但我如何才能将我的想法整合到一些代码中，从而产生类似于我的草图的东西呢？
你就快到了（可能同时已经解决了这个问题），缺少的要点是，您需要创建一个箭头（或者更确切地说是一个
连接补丁
），该箭头需要与整个图形进行变换（即
transform=fig.transFigure
），而不是仅使用单个子图作为参考。请注意，此解决方案是并且是一些相关文档

完整代码：

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import ConnectionPatch

fig, (ax1, ax2) = plt.subplots(1, 2,figsize=(10,5))

title_text = 'Loss by Disaster'
footer_text = 'June 24, 2020'
fig_background_color = 'skyblue'
fig_border = 'steelblue'
data =  [
            [         'Freeze', 'Wind', 'Flood', 'Quake', 'Hail'],
            [ '5 year',  66386, 174296,   75131,  577908,  32015],
            ['10 year',  58230, 381139,   78045,   99308, 160454],
            ['20 year',  89135,  80552,  152558,  497981, 603535],
            ['30 year',  78415,  81858,  150656,  193263,  69638],
            ['40 year', 139361, 331509,  343164,  781380,  52269],
        ]
# Pop the headers from the data array
column_headers = data.pop(0)
row_headers = [x.pop(0) for x in data]
# Table data needs to be non-numeric text. Format the data
# while I'm at it.
cell_text = []
for row in data:
    cell_text.append([f'{x/1000:1.1f}' for x in row])
# Get some lists of color specs for row and column headers
rcolors = plt.cm.BuPu(np.full(len(row_headers), 0.1))
ccolors = plt.cm.BuPu(np.full(len(column_headers), 0.1))
# Create the figure. Setting a small pad on tight_layout
# seems to better regulate white space. Sometimes experimenting
# with an explicit figsize here can produce better outcome.
# ax1.figure(linewidth=2,
#            edgecolor=fig_border,
#            facecolor=fig_background_color,
#            tight_layout={'pad':1},
#            #figsize=(5,3)
#           )
# Add a table at the bottom of the axes
the_table = ax1.table(cellText=cell_text,
                      rowLabels=row_headers,
                      rowColours=rcolors,
                      rowLoc='right',
                      colColours=ccolors,
                      colLabels=column_headers,
                      loc='center')
# Scaling is the only influence we have over top and bottom cell padding.
# Make the rows taller (i.e., make cell y scale larger).
the_table.scale(1, 1.5)

# Do it again
#
the_table = ax2.table(cellText=cell_text,
                      rowLabels=row_headers,
                      rowColours=rcolors,
                      rowLoc='right',
                      colColours=ccolors,
                      colLabels=column_headers,
                      loc='center')
# Scaling is the only influence we have over top and bottom cell padding.
# Make the rows taller (i.e., make cell y scale larger).
the_table.scale(1, 1.5)

### actually turn axes off:
for a in [ax1,ax2]:
    a.set_axis_off()

con = ConnectionPatch(xyA=(0.38,0.4), xyB=(0.43, 0.52), 
                      coordsA="data", coordsB="data",
                      axesA=ax1, axesB=ax2,
                      shrinkA=1, shrinkB=1,
                      ec="red", fc="w", linewidth=2, alpha=1,
                      arrowstyle="-|>",connectionstyle="arc3", 
                      mutation_scale=20, )
ax2.add_artist(con)

plt.show()

生成此图像：

注意箭头起点和终点是如何在子批次坐标中给出的（例如
（0.38,0.4）
，即您应该能够通过鼠标悬停快速找到好的点