Use matplotlib to realize the practical application of scatter plot of data set

Practical exercise: Use Matplotlib to draw scatter plots of data sets

Matplotlib is one of the commonly used drawing libraries in Python. It provides a wealth of functions that can be drawn Various types of charts. Among them, scatter plot is a commonly used data visualization method to show the relationship between two variables. This article will introduce how to use Matplotlib to draw a scatter plot of a data set, and attach specific code examples.

First, we need to install the Matplotlib library. You can use the pip command to execute the following statement to install:

pip install matplotlib

After the installation is complete, we can import the Matplotlib library and start drawing scatter plots.

import matplotlib.pyplot as plt

# 模拟数据集
x = [1, 2, 3, 4, 5]
y = [5, 4, 3, 2, 1]

# 绘制散点图
plt.scatter(x, y)

# 添加标题和标签
plt.title('Scatter Plot')
plt.xlabel('X-axis')
plt.ylabel('Y-axis')

# 显示图像
plt.show()

The above code first imports the Matplotlib library, and then defines two lists x and y as simulated data sets. Next, we use the scatter function to draw a scatter plot, passing in x and y as parameters.

After drawing the image, we add the title and axis labels by calling the title, xlabel, and ylabel functions. Among them, the title function is used to add a chart title, and the xlabel and ylabel functions are used to add x-axis and y-axis labels respectively.

Finally, the image is displayed by calling the show function.

After running the code, a new window will pop up showing the scatter plot. The abscissa of each point in the figure represents the corresponding element in the x list, and the ordinate represents the corresponding element in the y list. The color and size of the dots can be customized according to actual needs.

In addition to simple scatter plots, we can also add other elements as needed, such as legends, color maps, etc. The following is a slightly more complex sample code:

import matplotlib.pyplot as plt
import numpy as np

# 模拟数据集
x = np.random.rand(100)
y = np.random.rand(100)
colors = np.random.rand(100)
sizes = np.random.randint(10, 100, 100)

# 绘制散点图
plt.scatter(x, y, c=colors, s=sizes, cmap='viridis')

# 添加颜色条
plt.colorbar()

# 添加标题和标签
plt.title('Scatter Plot with Colorbar')
plt.xlabel('X-axis')
plt.ylabel('Y-axis')

# 显示图像
plt.show()

In the above code, we use the random module of the NumPy library to generate more random data, and specify the color and color of the points through the c and s parameters respectively. size. Through the cmap parameter, we can also add a colormap (colormap) to the color to make the image more colorful.

In addition, we also use the colorbar function to add a color bar to represent the range of color changes.

Through the above example code, we can flexibly use the Matplotlib library to draw various forms of scatter plots according to actual needs to achieve visual analysis of the data set.

To sum up, this article introduces how to use Matplotlib to draw scatter plots of data sets, and gives specific code examples. I hope readers can master the use of Matplotlib through practice and achieve richer and more personalized data visualization.

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