如何整理数据在Python中正确绘制圆形折线图

问题描述 #

用户编写Python代码，预期绘制ydata与xdata的圆形关系图，包含带标记的折线图和散点图两个子图，但输出的折线图点未正确连接，无法形成圆形。原代码如下：

import matplotlib.pyplot as plt


xdata = [-1.9987069285852805, -1.955030386765729, -1.955030386765729, -1.8259096357678795, -1.8259096357678795, -1.6169878720004491, -1.6169878720004491, -1.3373959790579202, -1.3373959790579202, -0.9993534642926399, -0.9993534642926399, -0.6176344077078071, -0.6176344077078071, -0.20892176376743077, -0.20892176376743077, 0.20892176376743032, 0.20892176376743032, 0.6176344077078065, 0.6176344077078065, 0.999353464292642, 0.999353464292642, 1.3373959790579217, 1.3373959790579217, 1.6169878720004487, 1.6169878720004487, 1.8259096357678786, 1.8259096357678786, 1.9550303867657255, 1.9550303867657255, 1.9987069285852832]
 
ydata = (0.0, -0.038801795445724575, 0.038801795445724575, -0.07590776623879933, 0.07590776623879933, -0.10969620340136318, 0.10969620340136318, -0.13869039009450249, 0.13869039009450249, -0.16162314123018345, 0.16162314123018345, -0.1774921855402276, 0.1774921855402276, -0.18560396964016201, 0.18560396964016201, -0.185603969640162, 0.185603969640162, -0.17749218554022747, 0.17749218554022747, -0.16162314123018337, 0.16162314123018337, -0.13869039009450224, 0.13869039009450224, -0.10969620340136294, 0.10969620340136294, -0.0759077662387991, 0.0759077662387991, -0.038801795445725006, 0.038801795445725006, 0.0)



fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16,6))
fig.suptitle('Plot comparison: line vs scatter'+ 3*'\n')
fig.subplots_adjust(wspace=1, hspace=3)
fig.supxlabel('x')
fig.supylabel('y')


ax1.plot(xdata, ydata, 'o-', c='blue')
ax1.set_title('Line-point plot', c='blue')

for i in range(len(xdata)):
   ax2.scatter(xdata, ydata, c='orange')
   ax2.set_title('Scatter plot', c='orange')
plt.savefig('line_vs_scatter_plot.png')
plt.show()

输出图中，折线图的点在相同x值的正负y之间反复连线，无法形成完整的圆形轮廓。

问题原因 #

原数据的排列顺序是：按x从左到右，每个x值对应正负两个y值依次排列。而Matplotlib的plot函数是按数据列表的顺序依次连接相邻点，导致相同x的正负y点之间被连线，随后直接跳到下一个x值的点，破坏了圆形的连续路径。

解决方案 #

可以通过重新整理数据顺序，让点按圆形的顺时针或逆时针路径排列，同时优化散点图的冗余代码。

修改后的代码 #

import matplotlib.pyplot as plt

xdata = [-1.9987069285852805, -1.955030386765729, -1.955030386765729, -1.8259096357678795, -1.8259096357678795, -1.6169878720004491, -1.6169878720004491, -1.3373959790579202, -1.3373959790579202, -0.9993534642926399, -0.9993534642926399, -0.6176344077078071, -0.6176344077078071, -0.20892176376743077, -0.20892176376743077, 0.20892176376743032, 0.20892176376743032, 0.6176344077078065, 0.6176344077078065, 0.999353464292642, 0.999353464292642, 1.3373959790579217, 1.3373959790579217, 1.6169878720004487, 1.6169878720004487, 1.8259096357678786, 1.8259096357678786, 1.9550303867657255, 1.9550303867657255, 1.9987069285852832]
ydata = (0.0, -0.038801795445724575, 0.038801795445724575, -0.07590776623879933, 0.07590776623879933, -0.10969620340136318, 0.10969620340136318, -0.13869039009450249, 0.13869039009450249, -0.16162314123018345, 0.16162314123018345, -0.1774921855402276, 0.1774921855402276, -0.18560396964016201, 0.18560396964016201, -0.185603969640162, 0.185603969640162, -0.17749218554022747, 0.17749218554022747, -0.16162314123018337, 0.16162314123018337, -0.13869039009450224, 0.13869039009450224, -0.10969620340136294, 0.10969620340136294, -0.0759077662387991, 0.0759077662387991, -0.038801795445725006, 0.038801795445725006, 0.0)

# 重新整理数据：先按x从左到右取负y点，再按x从右到左取正y点，形成顺时针圆形路径
neg_y_points = list(zip(xdata[::2], ydata[::2]))  # 提取偶数索引的点（x从左到右，y为负或0）
pos_y_points = list(zip(xdata[1::2], ydata[1::2]))[::-1]  # 提取奇数索引的点，反转后x从右到左，y为正

# 合并成连续的圆形路径数据
sorted_x = [x for x, y in neg_y_points + pos_y_points]
sorted_y = [y for x, y in neg_y_points + pos_y_points]

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16,6))
fig.suptitle('Plot comparison: line vs scatter' + 3*'\n')
fig.subplots_adjust(wspace=1, hspace=3)
fig.supxlabel('x')
fig.supylabel('y')

# 使用整理后的数据绘制折线图
ax1.plot(sorted_x, sorted_y, 'o-', c='blue')
ax1.set_title('Line-point plot', c='blue')

# 散点图无需循环，直接绘制所有点
ax2.scatter(xdata, ydata, c='orange')
ax2.set_title('Scatter plot', c='orange')

plt.savefig('line_vs_scatter_plot.png')
plt.show()

额外优化方案：生成标准圆形数据 #

如果不需要使用现有手动整理的x/ydata，也可以通过极坐标转直角坐标的方式生成标准圆形数据，更简洁且不易出错：

import numpy as np
import matplotlib.pyplot as plt

# 生成圆形数据：极坐标转直角坐标
theta = np.linspace(0, 2*np.pi, 100)  # 0到2π的角度，共100个点
radius = 2  # 半径匹配原数据范围
x = radius * np.cos(theta)
y = radius * np.sin(theta)

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16,6))
fig.suptitle('Plot comparison: line vs scatter' + 3*'\n')
fig.subplots_adjust(wspace=1, hspace=3)
fig.supxlabel('x')
fig.supylabel('y')

ax1.plot(x, y, 'o-', c='blue')
ax1.set_title('Line-point plot (standard circle)', c='blue')

ax2.scatter(x, y, c='orange')
ax2.set_title('Scatter plot (standard circle)', c='orange')

plt.savefig('line_vs_scatter_plot.png')
plt.show()

内容的提问来源于stack exchange，提问作者hbaromega