matplotlib Python PieChart(是否可以进行标注标签)

vu8f3i0k  于 2022-11-30  发布在  Python
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是否有使用Python、Matplotlib等执行标注标注的示例

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像上面这样有一条线和一个标签从饼图外面指向..看不到任何可能的例子,它是用Mathplotlib完成的....这能用Python完成吗?

8i9zcol2

8i9zcol21#

可以使用matplotlib**annotations**创建文本标签和注解线。

手动贴标

下面是一个示例,其中标签在数据坐标中手动定位,即饼图的中心是(0,0)坐标。

import matplotlib.pyplot as plt 

fig, ax= plt.subplots(figsize=(4,4))
plt.subplots_adjust(bottom=0.3)
total = [13,87]
plt.title('How to spot intellectuals on TV')
plt.gca().axis("equal")
pie = plt.pie(total, startangle=93)
labels = ["1. They say sophisticated things", "2. They sit in front of a bookshelf"]

bbox_props = dict(boxstyle="square,pad=0.3", fc="w", ec="k", lw=0.72)
arrowprops=dict(arrowstyle="-",connectionstyle="angle,angleA=0,angleB=90")
kw = dict(xycoords='data',textcoords='data',
          arrowprops=arrowprops, bbox=bbox_props, zorder=0)
plt.gca().annotate("2", xy=(0, 0), xytext=( 1.1, -0.8), **kw )
plt.gca().annotate("1", xy=(0, 0), xytext=(-1.1,  0.8), **kw )

plt.legend(pie[0],labels, loc="center", bbox_to_anchor=(0.5,-0.1))
plt.show()

自动贴标:

我们可以使用楔形块的Angular 在合适的位置自动创建标签。

import matplotlib.pyplot as plt
import numpy as np 

fig, ax= plt.subplots(figsize=(4,4))
plt.subplots_adjust(bottom=0.3)
total = [12,15,12,13,16]
plt.title('My repair strategies')
plt.gca().axis("equal")
patches, texts = pie = plt.pie(total, startangle=5)
labels = ["1. roaring at it", 
          "2. hitting it",
          "3. using superglue",
          "4. using duct tape",
          "5. dismantling it, then ditch it"]

bbox_props = dict(boxstyle="square,pad=0.3", fc="w", ec="k", lw=0.72)
arrowprops=dict(arrowstyle="-",connectionstyle="angle,angleA=0,angleB=90")
kw = dict(xycoords='data',textcoords='data',arrowprops=arrowprops, 
          bbox=bbox_props, zorder=0, va="center")

for i, p in enumerate(patches):
    ang = (p.theta2 - p.theta1)/2.+p.theta1
    y = np.sin(ang/180.*np.pi)
    x = 1.35*np.sign(np.cos(ang/180.*np.pi))
    plt.gca().annotate(str(1+i), xy=(0, 0), xytext=( x, y), **kw )

plt.legend(pie[0],labels, loc="center", bbox_to_anchor=(0.5,-0.2))
plt.show()

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