Data visualization is a data presentation technology that converts complex and abstract data into intuitive and easy-to-understand graphics. It can help us quickly grasp the distribution and laws of data, and understand and explore information more easily. In today's era of information explosion, data visualization is getting more and more attention. 

1. Matplotlib

Matplotlib is the most commonly used and most famous data visualization module in Python. The submodule pyplot of this module contains a large number of functions for drawing various charts.

1. Draw basic charts

The most basic charts in daily work include column charts, bar charts, line charts, pie charts, etc. The Matplotlib module provides corresponding drawing functions for these charts. The data used to draw the chart can directly use the provided code, or can be imported from an Excel workbook through the read_excel() function of the pandas module.

1. Draw a histogram

Code file: draw column chart.py

Column charts are usually used to visually compare data and are frequently used in practical work. A column chart can be drawn using the bar() function in the Matplotlib module.

The demo code is as follows:

1   import matplotlib.pyplot as plt
2   x = [1, 2, 3, 4, 5, 6, 7, 8]
3   y = [60, 45, 49, 36, 42, 67, 40, 50]
4   plt.bar(x, y)
5   plt.show()

The first line of code imports the submodule pyplot of the Matplotlib module. The second and third lines of code give the values ​​​​of the x-axis and y-axis of the chart respectively. The fourth line of code uses the bar() function to draw a column chart, and the fifth line The code uses the show() function to display the plotted chart.

The result of running the code is shown in the figure below: