Matplotlib热图绘制|极客笔记

Matplotlib热图绘制

热图（heatmap）是一种数据可视化的图表类型，通常用于显示矩阵数据的密度。热图通过颜色的深浅展示数据的大小，能够帮助人们快速发现数据中的规律和关联性。在Python中，使用Matplotlib库的plt模块可以方便地绘制热图。

1. 绘制简单的热图

import matplotlib.pyplot as plt
import numpy as np

data = np.random.random((10, 10))
plt.imshow(data, cmap='hot', interpolation='nearest')
plt.colorbar()
plt.show()

Output:

Matplotlib热图绘制

2. 修改热图颜色映射

import matplotlib.pyplot as plt
import numpy as np

data = np.random.random((10, 10))
plt.imshow(data, cmap='cool', interpolation='nearest')
plt.colorbar()
plt.show()

Output:

Matplotlib热图绘制

3. 调整热图插值方式

import matplotlib.pyplot as plt
import numpy as np

data = np.random.random((10, 10))
plt.imshow(data, cmap='plasma', interpolation='bicubic')
plt.colorbar()
plt.show()

Output:

Matplotlib热图绘制

4. 翻转热图颜色映射

import matplotlib.pyplot as plt
import numpy as np

data = np.random.random((10, 10))
plt.imshow(data, cmap='viridis', interpolation='nearest', aspect='auto')
plt.colorbar()
plt.show()

Output:

Matplotlib热图绘制

5. 自定义热图刻度

import matplotlib.pyplot as plt
import numpy as np

data = np.random.random((10, 10))
plt.imshow(data, cmap='inferno', interpolation='nearest')
plt.colorbar(ticks=[0.25, 0.5, 0.75])
plt.show()

Output:

Matplotlib热图绘制

6. 在热图上显示数值

import matplotlib.pyplot as plt
import numpy as np

data = np.random.random((10, 10))
plt.imshow(data, cmap='cividis', interpolation='nearest')
plt.colorbar(format='%.1f')
plt.show()

Output:

Matplotlib热图绘制

7. 调整热图坐标轴

import matplotlib.pyplot as plt
import numpy as np

data = np.random.random((10, 10))
plt.imshow(data, cmap='magma', interpolation='nearest')
plt.colorbar()
plt.xticks(ticks=[0, 5, 9], labels=['A', 'B', 'C'])
plt.yticks(ticks=[0, 5, 9], labels=['X', 'Y', 'Z'])
plt.show()

Output:

Matplotlib热图绘制

8. 更改热图的透明度

import matplotlib.pyplot as plt
import numpy as np

data = np.random.random((10, 10))
plt.imshow(data, cmap='twilight', interpolation='nearest', alpha=0.5)
plt.colorbar()
plt.show()

Output:

Matplotlib热图绘制

9. 自定义热图的大小

import matplotlib.pyplot as plt
import numpy as np

data = np.random.random((10, 10))
plt.figure(figsize=(8, 6))
plt.imshow(data, cmap='twilight_shifted', interpolation='nearest')
plt.colorbar()
plt.show()

Output:

Matplotlib热图绘制

10. 在热图上添加标题

import matplotlib.pyplot as plt
import numpy as np

data = np.random.random((10, 10))
plt.imshow(data, cmap='turbo', interpolation='nearest')
plt.colorbar()
plt.title('Heatmap of Random Data')
plt.show()

Output:

Matplotlib热图绘制