A beginner's guide to making scatter plots with matplotlib

matplotlib is one of the most commonly used data visualization libraries in Python. It offers a variety of plotting options, including line graphs, bar graphs, scatter plots, and more. This article will teach you how to use matplotlib to draw scatter plots, and provide specific code examples to help beginners get started quickly.

1. Import the matplotlib module

Before you start using matplotlib to draw scatter plots, first, you need to import the relevant Python modules. The code is as follows:

import pandas as pd
import matplotlib.pyplot as plt

Among them, for data analysis and processing, we need to use the pandas module. For drawing scatter plots, we need to use the matplotlib.pyplot module.

2. Preparing data

Drawing a scatter plot requires a set of two-dimensional coordinate data. Here, we use the DataFrame object in the pandas module to save the data. The sample code is as follows:

data = pd.DataFrame({'x': [1, 2, 3, 4, 5], 'y': [7.2, 6.4, 9.5, 8.1, 7.7]})

Here we create a DataFrame object data and contains two columns x and y, each column contains 5 data points. For ease of understanding, we create data in the form of a dictionary.

3. Drawing scatter plots

With the data, we can start using matplotlib.pyplot to draw scatter plots. The code is as follows:

plt.scatter(data['x'], data['y'])
plt.show()

In the above code, the plt.scatter() function maps the data to the scatter plot, while the plt.show() function displays the graphics on the screen.

Run the code and we successfully draw a simple scatter plot.

4. Modify the scatter plot style

In addition to data, matplotlib also provides various drawing options to meet different visualization needs. For example, we can flexibly modify the color, size, shape, etc. of the scatter plot according to our needs. The sample code is as follows:

plt.scatter(data['x'], data['y'], color='red', marker='x', s=80)
plt.show()

In the above code, we modify the style of the scatter plot through the color, marker, and s parameters, that is, it becomes a red x-shaped scatter plot with a size of 80.

5. Add axis labels

In order to make the scatter plot easier to interpret, we need to add labels to the x and y axes. By calling the xlabel() and ylabel() functions, we can quickly add labels to the coordinate axes. The sample code is as follows:

plt.scatter(data['x'], data['y'], color='red', marker='x', s=80)
plt.xlabel('x-axis')
plt.ylabel('y-axis')
plt.show()

6. Modify the axis scale and range

In some cases Next, we need to modify the range of the coordinate axis or display a more friendly scale. By calling the xlim() and ylim() functions, we can precisely modify the range of the coordinate axes. At the same time, using the xticks() and yticks() functions, we can customize the position and labels of the ticks.

7. Conclusion

The above is the content introduced in this article. Through the study of this article, beginners can understand how to use matplotlib to draw scatter plots and flexibly modify them according to specific needs. Graphic style. At the same time, it is recommended to practice more during the learning process to enhance your proficiency in the matplotlib module.

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