Python中怎样绘制热力图？

import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np

# 生成一个随机的2D数组
data = np.random.rand(10, 10)

# 使用seaborn绘制热力图
plt.figure(figsize=(10, 8))
sns.heatmap(data, annot=True, fmt=".2f", cmap="YlGnBu")

# 添加标题
plt.title("Random Heatmap")

# 显示图形
plt.show()

import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd

# 创建一个示例DataFrame
data = pd.DataFrame(np.random.rand(10, 10), columns=[f'Col{i}' for i in range(10)], index=[f'Row{i}' for i in range(10)])

# 绘制热力图
plt.figure(figsize=(10, 8))
sns.heatmap(data, annot=True, fmt=".2f", cmap="coolwarm")

# 添加标题
plt.title("DataFrame Heatmap")

# 显示图形
plt.show()

import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np

# 生成一个带有缺失值的2D数组
data = np.random.rand(10, 10)
data[3, 5] = np.nan  # 引入一个NaN值

# 创建一个掩码
mask = np.isnan(data)

# 绘制热力图
plt.figure(figsize=(10, 8))
sns.heatmap(data, mask=mask, annot=True, fmt=".2f", cmap="viridis", vmin=0, vmax=1)

# 添加标题
plt.title("Heatmap with Missing Values")

# 显示图形
plt.show()

import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

# 生成一个随机数据集
np.random.seed(0)
data = pd.DataFrame(np.random.randn(100, 4), columns=['A', 'B', 'C', 'D'])

# 计算相关系数矩阵
corr = data.corr()

# 绘制相关系数矩阵的热力图
plt.figure(figsize=(10, 8))
sns.heatmap(corr, annot=True, fmt=".2f", cmap="coolwarm", vmin=-1, vmax=1)

# 添加标题
plt.title("Correlation Matrix Heatmap")

# 显示图形
plt.show()