python如何实现可视化热力图

#fmt(字符串格式代码，矩阵上标识数字的数据格式，比如保留小数点后几位数字)import numpy as np

np.random.seed(0)

x = np.random.randn(4,4)

f, (ax1, ax2) = plt.subplots(figsize=(6,6),nrows=2)

sns.heatmap(x, annot=True, ax=ax1)

sns.heatmap(x, annot=True, fmt='.1f', ax=ax2)

#linewidths(矩阵小块的间隔),linecolor(切分热力图矩阵小块的线的颜色)import matplotlib.pyplot as plt

f, ax = plt.subplots(figsize = (6,4))

cmap = sns.cubehelix_palette(start = 1, rot = 3, gamma=0.8, as_cmap = True)  

sns.heatmap(pt, cmap = cmap, linewidths = 0.05, linecolor= 'red', ax = ax)  

ax.set_title('Amounts per kind and region')

ax.set_xlabel('region')

ax.set_ylabel('kind')

#xticklabels，yticklabels横轴和纵轴的标签名输出import matplotlib.pyplot as plt

f, (ax1,ax2) = plt.subplots(figsize = (5,5),nrows=2)

cmap = sns.cubehelix_palette(start = 1.5, rot = 3, gamma=0.8, as_cmap = True)

p1 = sns.heatmap(pt, ax=ax1, cmap=cmap, center=None, xticklabels=False)

ax1.set_title('xticklabels=None',fontsize=8)

p2 = sns.heatmap(pt, ax=ax2, cmap=cmap, center=None, xticklabels=2, yticklabels=list(range(5)))

ax2.set_title('xticklabels=2, yticklabels is a list',fontsize=8)

ax2.set_xlabel('region')

#mask对某些矩阵块的显示进行覆盖

f, (ax1,ax2) = plt.subplots(figsize = (5,5),nrows=2)

cmap = sns.cubehelix_palette(start = 1.5, rot = 3, gamma=0.8, as_cmap = True)

p1 = sns.heatmap(pt, ax=ax1, cmap=cmap, xticklabels=False, mask=None)

ax1.set_title('mask=None')

ax1.set_ylabel('kind')

p2 = sns.heatmap(pt, ax=ax2, cmap=cmap, xticklabels=True, mask=(pt<800))  

#mask对pt进行布尔型转化,结果为True的位置用白色覆盖

ax2.set_title('mask: boolean DataFrame')

ax2.set_xlabel('region')

ax2.set_ylabel('kind')

f,(ax1,ax2) = plt.subplots(figsize=(4,6),nrows=2)

x = np.array([[1,2,3],[2,0,1],[-1,-2,0]])

sns.heatmap(x, annot=True, ax=ax1)

sns.heatmap(x, mask=x < 1, ax=ax2, annot=True, annot_kws={"weight": "bold"})   #把小于1的区域覆盖掉