From beginner to advanced, illustrating Matplotlib drawing methods

Illustrated Matplotlib drawing methods: from basic to advanced, specific code examples are required

Introduction:
Matplotlib is a powerful drawing library commonly used for data visualization . Whether it's a simple line chart, or a complex scatter plot or 3D chart, Matplotlib can meet your needs. This article will introduce Matplotlib's drawing methods in detail, from basic to advanced, and provide specific code examples.

1. Installation and import of Matplotlib

1. Installing Matplotlib
   Use the pip install matplotlib command in the terminal to install Matplotlib.
2. Import Matplotlib
   Use import matplotlib.pyplot as plt to import Matplotlib, and agree on the commonly used alias plt to facilitate subsequent calls.

2. Draw a simple line chart
The following is a simple line chart example, showing the sales changes of a company in the past 12 months.

import matplotlib.pyplot as plt

# 数据
months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
sales = [100, 120, 150, 130, 140, 160, 180, 170, 190, 200, 210, 220]

# 创建图表和画布
plt.figure(figsize=(8, 6))

# 绘制折线图
plt.plot(months, sales, marker='o', linestyle='-', color='blue')

# 设置标题和标签
plt.title('Sales Trend')
plt.xlabel('Months')
plt.ylabel('Sales')

# 显示图表
plt.show()

3. Custom chart style
Matplotlib provides a wealth of chart style settings, which can make your chart more personalized and beautiful.

1. Adjust color and line style

   plt.plot(months, sales, marker='o', linestyle='-', color='blue')

   You can set the mark style through the marker parameter, the linestyle parameter to set the line style, and the color parameter to set the color.

2. Set the legend

   plt.plot(months, sales, marker='o', linestyle='-', color='blue', label='Sales')
   plt.legend()

   Use the label parameter to set the legend label, and then use the plt.legend() method to display the legend.

3. Add grid lines

   plt.grid(True)

   Use the plt.grid(True) method to add grid lines.

4. Draw scatter plots and bar charts
In addition to line charts, Matplotlib also supports drawing scatter plots and bar charts.

1. Drawing a Scatter Plot
   The following is a simple scatter plot example showing the relationship between temperature and rainfall in a city.

import matplotlib.pyplot as plt

# 数据
temperature = [15, 19, 22, 18, 25, 28, 30, 29, 24, 20]
rainfall = [20, 40, 30, 10, 55, 60, 70, 50, 45, 35]

# 创建图表和画布
plt.figure(figsize=(8, 6))

# 绘制散点图
plt.scatter(temperature, rainfall, color='red')

# 设置标题和标签
plt.title('Temperature vs Rainfall')
plt.xlabel('Temperature (°C)')
plt.ylabel('Rainfall (mm)')

# 显示图表
plt.show()

2. Draw a bar chart
   The following is a simple bar chart example that shows the sales of a certain product in different regions.

import matplotlib.pyplot as plt

# 数据
regions = ['North', 'South', 'East', 'West']
sales = [100, 120, 150, 130]

# 创建图表和画布
plt.figure(figsize=(8, 6))

# 绘制条形图
plt.bar(regions, sales, color='blue')

# 设置标题和标签
plt.title('Sales by Region')
plt.xlabel('Region')
plt.ylabel('Sales')

# 显示图表
plt.show()

5. Draw advanced charts
Matplotlib can also draw more complex charts, such as pie charts and 3D charts.

1. Drawing a Pie Chart
   The following is a simple pie chart example that shows the sales proportion of different products in a certain market.

import matplotlib.pyplot as plt

# 数据
products = ['A', 'B', 'C', 'D']
sales = [30, 20, 25, 15]

# 创建图表和画布
plt.figure(figsize=(8, 6))

# 绘制饼图
plt.pie(sales, labels=products, autopct='%.1f%%')

# 设置标题
plt.title('Sales by Product')

# 显示图表
plt.show()

2. Draw 3D graph
   The following is a simple 3D graph example, showing the three-dimensional surface graph of a certain function.

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

# 数据
x = np.linspace(-5, 5, 100)
y = np.linspace(-5, 5, 100)
X, Y = np.meshgrid(x, y)
Z = np.sin(np.sqrt(X**2 + Y**2))

# 创建图表和画布
fig = plt.figure(figsize=(8, 6))
ax = fig.add_subplot(111, projection='3d')

# 绘制3D图
ax.plot_surface(X, Y, Z, cmap='viridis')

# 设置标题和标签
ax.set_title('3D Surface Plot')
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')

# 显示图表
plt.show()

Conclusion:
Through the introduction and examples of this article, we can understand the drawing methods and usage techniques of Matplotlib. Whether it is a simple line chart, or a complex scatter plot and 3D chart, Matplotlib provides a wealth of functions and options to meet different needs for data visualization. I hope this article will be helpful to both beginners and experienced users, so that they can better use Matplotlib for data analysis and display.

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