Python 如何在matplotlib中沿x轴稍微移动重叠的数据点？

我收集了4个不同实验装置的数据点，分别标记为“1”、“2”、“4”和“8”。对于每个实验设置，我收集了10个数据点

我能够成功地绘制这些数据点，并绘制额外的曲线，以显示每个设置的平均值

然而，我在实验中改变了其他设置，我可以画出另一个这样的图形。现在我希望把所有的东西都放在一个单独的图中（两组数据点和两条平均曲线），但是所有的东西看起来都太拥挤了。我的绘图脚本和绘图如下所示：

from numpy import *
import math
import matplotlib.pyplot as plt
import numpy as np

raw_1 = [0.38, 0.49, 0.25, 0.3, 0.4, 0.19, 0.45, 0.93, 0.44, 0.65] 
raw_2 = [0.27, 0.39, 0.09, 0.75, 0.79, 0.77, 0.31, 0.05, 0.73, 0.7]
raw_4 = [0.2, 0.84, 0.83, 0.7, 0.86, 0.2, 0.37, 0.41, 0.72, 0.29]
raw_8 = [0.2, 0.71, 0.31, 0.63, 0.24, 0.07, 0.2, 0.89, 0.34, 0.92]
y = np.array([raw_1, raw_2, raw_4, raw_8])
y = np.transpose(y)
y_mean = [mean(raw_1), mean(raw_2), mean(raw_4), mean(raw_8)]
x = [1,2,4,8]
xx = range(len(x))
plt.plot(xx, y[0], 'rx') 
plt.plot(xx, y[1], 'rx') 
plt.plot(xx, y[2], 'rx') 
plt.plot(xx, y[3], 'rx') 
plt.plot(xx, y[4], 'rx') 
plt.plot(xx, y[5], 'rx') 
plt.plot(xx, y[6], 'rx') 
plt.plot(xx, y[7], 'rx') 
plt.plot(xx, y[8], 'rx') 
plt.plot(xx, y[9], 'rx')

plt.xticks(xx,x)
leg = plt.legend(loc='upper left');

new_raw_1 = [0.217, 0.206, 0.222, 0.271, 0.212, 0.58, 0.333, 0.463, 0.314, 0.59] 
new_raw_2 = [0.511, 0.537, 0.565, 0.597, 0.527, 0.571, 0.505, 0.541, 0.542, 0.517]
new_raw_4 = [0.662, 0.552, 0.772, 0.436, 0.505, 0.577, 0.313, 0.796, 0.582, 0.574]
new_raw_8 = [0.511, 0.587, 0.591, 0.531, 0.522, 0.549, 0.593, 0.544, 0.552, 0.555]
y = np.array([new_raw_1, new_raw_2, new_raw_4, new_raw_8])
y = np.transpose(y)
y_mean_new = [mean(new_raw_1), mean(new_raw_2), mean(new_raw_4), mean(new_raw_8)]
x = [1,2,4,8]
xx = range(len(x))
plt.plot(xx, y[0], 'bo') 
plt.plot(xx, y[1], 'bo') 
plt.plot(xx, y[2], 'bo') 
plt.plot(xx, y[3], 'bo') 
plt.plot(xx, y[4], 'bo') 
plt.plot(xx, y[5], 'bo') 
plt.plot(xx, y[6], 'bo') 
plt.plot(xx, y[7], 'bo') 
plt.plot(xx, y[8], 'bo') 
plt.plot(xx, y[9], 'bo')
plt.plot(xx, y_mean_new, color='C0', marker='D', markersize=10, markerfacecolor='white', label='Avg A')
plt.plot(xx, y_mean, color='C1', marker='H', markersize=10, markerfacecolor='white', label='Avg B')
#plt.xticks(xx,x)
leg = plt.legend();


plt.show()

---

目前，蓝色圆圈和红色X标记相互遮挡。我应该如何修改脚本，在蓝色圆圈和红色x标记之间引入一个小的x轴偏移，同时仍然保持XTICK为“1”、“2”、“4”、“8”，且距离相等？

您可以通过为每个系列定义不同的x坐标列表，向x位置添加偏移量。这不会影响你的记号，因为你只需保持它们原来的x位置。下面是一个例子：

from numpy import *
import math
import matplotlib.pyplot as plt
import numpy as np

raw_1 = [0.38, 0.49, 0.25, 0.3, 0.4, 0.19, 0.45, 0.93, 0.44, 0.65] 
raw_2 = [0.27, 0.39, 0.09, 0.75, 0.79, 0.77, 0.31, 0.05, 0.73, 0.7]
raw_4 = [0.2, 0.84, 0.83, 0.7, 0.86, 0.2, 0.37, 0.41, 0.72, 0.29]
raw_8 = [0.2, 0.71, 0.31, 0.63, 0.24, 0.07, 0.2, 0.89, 0.34, 0.92]
y = np.array([raw_1, raw_2, raw_4, raw_8])
y = np.transpose(y)
y_mean = [mean(raw_1), mean(raw_2), mean(raw_4), mean(raw_8)]
x = [1,2,4,8]
xx = range(len(x))
xxr = [x - 0.1 for x in xx]
plt.plot(xxr, y[0], 'rx')
plt.plot(xxr, y[1], 'rx')
plt.plot(xxr, y[2], 'rx')
plt.plot(xxr, y[3], 'rx')
plt.plot(xxr, y[4], 'rx')
plt.plot(xxr, y[5], 'rx')
plt.plot(xxr, y[6], 'rx')
plt.plot(xxr, y[7], 'rx')
plt.plot(xxr, y[8], 'rx')
plt.plot(xxr, y[9], 'rx')

plt.xticks(xx,x)
leg = plt.legend(loc='upper left');

new_raw_1 = [0.217, 0.206, 0.222, 0.271, 0.212, 0.58, 0.333, 0.463, 0.314, 0.59] 
new_raw_2 = [0.511, 0.537, 0.565, 0.597, 0.527, 0.571, 0.505, 0.541, 0.542, 0.517]
new_raw_4 = [0.662, 0.552, 0.772, 0.436, 0.505, 0.577, 0.313, 0.796, 0.582, 0.574]
new_raw_8 = [0.511, 0.587, 0.591, 0.531, 0.522, 0.549, 0.593, 0.544, 0.552, 0.555]
y = np.array([new_raw_1, new_raw_2, new_raw_4, new_raw_8])
y = np.transpose(y)
y_mean_new = [mean(new_raw_1), mean(new_raw_2), mean(new_raw_4), mean(new_raw_8)]
x = [1,2,4,8]
xx = range(len(x))
xxb = [x + 0.1 for x in xx]
plt.plot(xxb, y[0], 'bo')
plt.plot(xxb, y[1], 'bo')
plt.plot(xxb, y[2], 'bo')
plt.plot(xxb, y[3], 'bo')
plt.plot(xxb, y[4], 'bo')
plt.plot(xxb, y[5], 'bo')
plt.plot(xxb, y[6], 'bo')
plt.plot(xxb, y[7], 'bo')
plt.plot(xxb, y[8], 'bo')
plt.plot(xxb, y[9], 'bo')
plt.plot(xx, y_mean_new, color='C0', marker='D', markersize=10, markerfacecolor='white', label='Avg A')
plt.plot(xx, y_mean, color='C1', marker='H', markersize=10, markerfacecolor='white', label='Avg B')
#plt.xticks(xx,x)
leg = plt.legend()

您还可以通过删除重复项（特别是numpy的重复导入）和使用循环来保存重复项，从而稍微改进代码：

我在这个版本的理解中也使用了唯一的变量名，因为我认为重用x可能会在Python 2.7中造成问题。

import matplotlib.pyplot as plt
import numpy as np

raw_1 = [0.38, 0.49, 0.25, 0.3, 0.4, 0.19, 0.45, 0.93, 0.44, 0.65] 
raw_2 = [0.27, 0.39, 0.09, 0.75, 0.79, 0.77, 0.31, 0.05, 0.73, 0.7]
raw_4 = [0.2, 0.84, 0.83, 0.7, 0.86, 0.2, 0.37, 0.41, 0.72, 0.29]
raw_8 = [0.2, 0.71, 0.31, 0.63, 0.24, 0.07, 0.2, 0.89, 0.34, 0.92]
y = np.array([raw_1, raw_2, raw_4, raw_8])
y = np.transpose(y)
y_mean = [np.mean(raw_1), np.mean(raw_2), np.mean(raw_4), np.mean(raw_8)]
x = [1,2,4,8]
xx = range(len(x))

xxr = [j - 0.1 for j in xx]
for point in y:
    plt.plot(xxr, point, 'rx')

plt.xticks(xx,x)
leg = plt.legend(loc='upper left');

new_raw_1 = [0.217, 0.206, 0.222, 0.271, 0.212, 0.58, 0.333, 0.463, 0.314, 0.59] 
new_raw_2 = [0.511, 0.537, 0.565, 0.597, 0.527, 0.571, 0.505, 0.541, 0.542, 0.517]
new_raw_4 = [0.662, 0.552, 0.772, 0.436, 0.505, 0.577, 0.313, 0.796, 0.582, 0.574]
new_raw_8 = [0.511, 0.587, 0.591, 0.531, 0.522, 0.549, 0.593, 0.544, 0.552, 0.555]
y = np.array([new_raw_1, new_raw_2, new_raw_4, new_raw_8])
y = np.transpose(y)
y_mean_new = [np.mean(new_raw_1), np.mean(new_raw_2), np.mean(new_raw_4), np.mean(new_raw_8)]

xxb = [k + 0.1 for k in xx]
for point in y:
    plt.plot(xxb, point, 'bo')

plt.plot(xx, y_mean_new, color='C0', marker='D', markersize=10, markerfacecolor='white', label='Avg A')
plt.plot(xx, y_mean, color='C1', marker='H', markersize=10, markerfacecolor='white', label='Avg B')

leg = plt.legend()

---

谢谢你的回复。但是在运行上述代码时，我总是在plt.xticks（xx，x）中遇到以下错误：TypeError:“int”对象不可编辑。

来自numpy import*

@JimWang您没有使用Python 2.7，是吗？这在3.8中运行良好。@SimonN是的，这是python版本的问题。我为jupyter笔记本添加了python3，它现在可以工作了。谢谢如果你有更多的图表，你可以考虑使用它的抖动参数来自动处理这个问题。