In-depth learning: Master the advanced techniques of matplotlib for drawing scatter plots

Advanced Guide: Mastering Matplotlib Advanced Scatter Plot Drawing Skills

Introduction:
Matplotlib is a powerful, flexible and easy-to-use drawing library that provides Rich graphics drawing functions. Among them, scatter plot is a commonly used data visualization method, which can more intuitively display the relationship between data. This article will introduce the techniques of drawing advanced scatter plots in Matplotlib and provide specific code examples.

1. Basic scatter plot drawing
Before using Matplotlib to draw a scatter plot, you need to import the relevant libraries and data. The following is a basic scatter plot drawing example:

import matplotlib.pyplot as plt
import numpy as np

# 生成随机数据
np.random.seed(1)
x = np.random.randn(100)
y = np.random.randn(100)

# 绘制散点图
plt.scatter(x, y)

# 添加标题和标签
plt.title("Basic Scatter Plot")
plt.xlabel("X")
plt.ylabel("Y")

# 显示图形
plt.show()

Running the above code will generate a basic scatter plot, in which the x and y axes represent the two dimensions of the data respectively.

2. Adjust the scatter point style
You can adjust the scatter diagram style by modifying parameters to make the graph more eye-catching. The following are some commonly used parameter settings:

# 绘制散点图（修改参数）
plt.scatter(x, y, c='red', s=100, alpha=0.5, marker='o', edgecolors='black')

# 添加标题和标签
plt.title("Customized Scatter Plot")
plt.xlabel("X")
plt.ylabel("Y")

# 显示图形
plt.show()

In the above code, we use the c parameter to set the color of the scatter points to red, and the s parameter to set the size of the scatter points. is 100, the alpha parameter sets the transparency of the scatter points to 0.5, the marker parameter sets the shape of the scatter points to a circle, the edgecolors parameter sets the boundary color of the scatter points is black.

3. Draw multiple sets of scatter plots
In some cases, we need to draw multiple sets of scatter plots at the same time to show the relationship between different data. The following is an example of drawing multiple sets of scatter plots:

# 生成随机数据
np.random.seed(1)
x1 = np.random.randn(100)
y1 = np.random.randn(100)
x2 = np.random.randn(100)
y2 = np.random.randn(100)

# 绘制散点图（多组）
plt.scatter(x1, y1, c='red', label='Group 1')
plt.scatter(x2, y2, c='blue', label='Group 2')

# 添加标题和标签
plt.title("Multiple Scatter Plots")
plt.xlabel("X")
plt.ylabel("Y")

# 添加图例
plt.legend()

# 显示图形
plt.show()

In the above code, we draw two sets of scatter plots by calling the scatter function multiple times, using red and blue respectively. express. Set the label of each set of scatter plots through the label parameter, and use the legend function to add a legend to the graph.

4. Use color mapping
When the data has a specific meaning, color can be represented as an additional dimension. The following is an example of using color mapping to draw a scatter plot:

# 生成随机数据
np.random.seed(1)
x = np.random.randn(100)
y = np.random.randn(100)
colors = np.random.rand(100)

# 绘制散点图（使用颜色映射）
plt.scatter(x, y, c=colors, cmap='viridis')

# 添加颜色映射说明
cbar = plt.colorbar()
cbar.set_label("Color")

# 添加标题和标签
plt.title("Scatter Plot with Color Mapping")
plt.xlabel("X")
plt.ylabel("Y")

# 显示图形
plt.show()

In the above code, we pass an array as the basis for color mapping through the c parameter, and then pass cmapThe parameter specifies the color mapping scheme used. Then use the colorbar function to add color mapping instructions.

Conclusion:
Through the introduction of this article, we have learned how to use Matplotlib to draw advanced scatter plots. We can use techniques such as adjusting styles, drawing multiple sets of scatter plots, and using color mapping to show the relationship between data. I hope this article has been helpful to you in data visualization.

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