Pro tips for improving the style and effect of matplotlib scatter plots

Pro Tips: Optimize the style and effect of matplotlib scatter plots

Introduction:
matplotlib is a Python library commonly used for data visualization, and scatter plots It is one of the most commonly used chart types. Although matplotlib provides a wealth of functions and setting options, the default scatter plot style may not always meet our needs. In this article, we will introduce some professional techniques for optimizing the style and effect of matplotlib scatter plots, and provide specific code examples.

1. Change the color and size of scatter points

1. Change the color of scatter points: You can use the parameter "c" to specify the color. Commonly used colors include "b" (blue) , "g" (green), "r" (red), "c" (cyan), "m" (magenta), "y" (yellow), "k" (black), etc. For example, you can use "r" to represent red scatter points.

Sample code:

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8, 10]

plt.scatter(x, y, c='r')  # 指定颜色为红色
plt.show()

2. Change the size of the scatter points: You can use the parameter "s" to specify the size of the scatter points. The larger the value, the larger the scatter points. For example, you can use s=100 to represent a scatter point size of 100.

Sample code:

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8, 10]

plt.scatter(x, y, s=100)  # 指定散点的大小为100
plt.show()

2. Add color mapping and size mapping

1. Color mapping and size mapping refer to the numerical size of a variable To automatically adjust the color and size of scatter points to display the data more intuitively. You can use the cmap parameter to specify a color map, or the norm parameter to specify a size map.

Sample code:

import numpy as np
import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8, 10]
colors = [1, 2, 3, 4, 5]  # 颜色映射变量
sizes = np.array([10, 20, 30, 40, 50])  # 大小映射变量

plt.scatter(x, y, c=colors, cmap='rainbow', s=sizes)
plt.colorbar()  # 添加颜色条
plt.show()

3. Adjust the coordinate axis range and scale

1. Adjust the coordinate axis range: You can use plt.xlim( ) and plt.ylim() functions set the range of the x-axis and y-axis respectively.

Sample code:

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8, 10]

plt.scatter(x, y)
plt.xlim(0, 6)  # x轴范围为0到6
plt.ylim(0, 12)  # y轴范围为0到12
plt.show()

2. Adjust the scale: You can use plt.xticks() and plt.yticks() The function sets the scale of the x-axis and y-axis respectively.

Sample code:

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8, 10]

plt.scatter(x, y)
plt.xticks(range(1, 6))  # x轴刻度为1到5
plt.yticks(range(0, 11, 2))  # y轴刻度为0到10，步长为2
plt.show()

4. Add titles and tags
You can use the plt.title() function to add a title, use plt The .xlabel() and plt.ylabel() functions add x-axis and y-axis labels respectively.

Sample code:

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8, 10]

plt.scatter(x, y)
plt.title('Scatter Plot')
plt.xlabel('X')
plt.ylabel('Y')
plt.show()

5. Other style adjustments
In addition to the adjustment methods introduced above, you can also further optimize the style and effect of the scatter plot, such as adding grids and modifying points. Shape, change point edges, add annotations, and more. These operations can be achieved by calling appropriate functions and methods.

Conclusion:
This article introduces some professional techniques for optimizing the style and effect of matplotlib scatter plots, and provides specific code examples. By using these techniques, we can flexibly adjust the appearance of the scatter plot to better suit our needs. I hope this article will be helpful for you to learn and use matplotlib scatter plots.

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