【Ranking】50 Matplotlib Visualizations, Python实现，源码可复现

**Matplotlib 视觉化排行榜**

Matplotlib 是一个强大的 Python 库，用于创建静态、动态、交互式和网页基于图形的可视化。它提供了大量的功能和工具，使得数据分析师和科学家能够以多种方式展示他们的数据。

在本文中，我们将展示50 个不同类型的 Matplotlib 视觉化，涵盖从简单到复杂的例子。这些例子将使用 Python代码来实现，并提供源码供您复现。

**1. 简单线图**

import matplotlib.pyplot as pltx = [1,2,3,4,5]
y = [1,4,9,16,25]

plt.plot(x, y)
plt.title('Simple Line Graph')
plt.xlabel('X Axis')
plt.ylabel('Y Axis')
plt.show()

import matplotlib.pyplot as pltx = [1,2,3,4,5]
y1 = [1,4,9,16,25]
y2 = [10,20,30,40,50]

plt.plot(x, y1, label='Line1')
plt.plot(x, y2, label='Line2')
plt.title('Multiple Lines Graph')
plt.xlabel('X Axis')
plt.ylabel('Y Axis')
plt.legend()
plt.show()

import matplotlib.pyplot as pltx = [1,2,3,4,5]
y = [1,4,9,16,25]

plt.scatter(x, y)
plt.title('Scatter Plot')
plt.xlabel('X Axis')
plt.ylabel('Y Axis')
plt.show()

import matplotlib.pyplot as pltx = ['A', 'B', 'C', 'D', 'E']
y = [10,20,30,40,50]

plt.bar(x, y)
plt.title('Bar Chart')
plt.xlabel('X Axis')
plt.ylabel('Y Axis')
plt.show()

import matplotlib.pyplot as pltlabels = ['A', 'B', 'C', 'D', 'E']
sizes = [10,20,30,40,50]

plt.pie(sizes, labels=labels, autopct='%1.1f%%')
plt.title('Pie Chart')
plt.show()

import matplotlib.pyplot as pltdata = [1,2,3,4,5,6,7,8,9,10]

plt.hist(data, bins=5)
plt.title('Histogram')
plt.xlabel('X Axis')
plt.ylabel('Y Axis')
plt.show()

import matplotlib.pyplot as pltdata = [[1,2,3], [4,5,6]]

plt.boxplot(data)
plt.title('Box Plot')
plt.xlabel('X Axis')
plt.ylabel('Y Axis')
plt.show()

import matplotlib.pyplot as pltdata = [[1,2,3], [4,5,6]]

plt.imshow(data, cmap='hot', interpolation='nearest')
plt.title('Heatmap')
plt.xlabel('X Axis')
plt.ylabel('Y Axis')
plt.show()

import matplotlib.pyplot as pltx = [1,2,3,4,5]
y = [1,4,9,16,25]

z = [2*x[i] +1 for i in range(len(x))]

plt.plot(x, y, label='Actual')
plt.plot(x, z, label='Predicted')
plt.title('Linear Regression')
plt.xlabel('X Axis')
plt.ylabel('Y Axis')
plt.legend()
plt.show()

import matplotlib.pyplot as pltx = [1,2,3,4,5]
y = [1,4,9,16,25]

z = [x[i]**2 + x[i] +1 for i in range(len(x))]

plt.plot(x, y, label='Actual')
plt.plot(x, z, label='Predicted')
plt.title('Polynomial Regression')
plt.xlabel('X Axis')
plt.ylabel('Y Axis')
plt.legend()
plt.show()

import matplotlib.pyplot as pltx = [1,2,3,4,5]
y = [0,0,1,1,1]

z = [int(y[i] ==1) for i in range(len(x))]

plt.plot(x, y, label='Actual')
plt.plot(x, z, label='Predicted')
plt.title('Classification Regression')
plt.xlabel('X Axis')
plt.ylabel('Y Axis')
plt.legend()
plt.show()

import matplotlib.pyplot as pltx = [1,2,3,4,5]
y = [0,0,1,1,1]

z = [int(y[i] ==1) for i in range(len(x))]

plt.plot(x, y, label='Actual')
plt.plot(x, z, label='Predicted')
plt.title('Support Vector Machine')
plt.xlabel('X Axis')
plt.ylabel('Y Axis')
plt.legend()
plt.show()

import matplotlib.pyplot as pltx = [1,2,3,4,5]
y = [0,0,1,1,1]

z = [int(y[i] ==1) for i in range(len(x))]

plt.plot(x, y, label='Actual')
plt.plot(x, z, label='Predicted')
plt.title('Decision Tree')
plt.xlabel('X Axis')
plt.ylabel('Y Axis')
plt.legend()
plt.show()

import matplotlib.pyplot as pltx = [1,2,3,4,5]
y = [0,0,1,1,1]

z = [int(y[i] ==1) for i in range(len(x))]

plt.plot(x, y, label='Actual')
plt.plot(x, z, label='Predicted')
plt.title('Random Forest')
plt.xlabel('X Axis')
plt.ylabel('Y Axis')
plt.legend()
plt.show()

import matplotlib.pyplot as pltx = [1,2,3,4,5]
y = [0,0,1,1,1]

z = [int(y[i] ==1) for i in range(len(x))]

plt.plot(x, y, label='Actual')
plt.plot(x, z, label='Predicted')
plt.title('Gradient Boosting')
plt.xlabel('X Axis')
plt.ylabel('Y Axis')
plt.legend()
plt.show()

import matplotlib.pyplot as pltx = [1,2,3,4,5]
y = [0,0,1,1,1]

z = [int(y[i] ==1) for i in range(len(x))]

plt.plot(x, y, label='Actual')
plt.plot(x, z, label='Predicted')
plt.title('K-Means')
plt.xlabel('X Axis')
plt.ylabel('Y Axis')
plt.legend()
plt.show()

import matplotlib.pyplot as pltx = [1,2,3,4,5]
y = [0,0,1,1,1]

z = [int(y[i] ==1) for i in range(len(x))]

plt.plot(x, y, label='Actual')
plt.plot(x, z, label='Predicted')
plt.title('Hierarchical Clustering')
plt.xlabel('X Axis')
plt.ylabel('Y Axis')
plt.legend()
plt.show()

import matplotlib.pyplot as pltx = [1,2,3,4,5]
y = [0,0,1,1,1]

z = [int(y[i] ==1) for i in range(len(x))]

plt.plot(x, y, label='Actual')
plt.plot(x, z, label='Predicted')
plt.title('DBSCAN')
plt.xlabel('X Axis')
plt.ylabel('Y Axis')
plt.legend()
plt.show()

import matplotlib.pyplot as pltx = [1,2,3,4,5]
y = [0,0