【seaborn.heatmap整理】
用处：将数据绘制为颜色方格（编码矩阵）。

引用形式：

seaborn.heatmap(data, vmin=None, vmax=None, cmap=None, center=None, robust=False, annot=None, fmt=’.2g’, annot_kws=None, linewidths=0, linecolor=‘white’, cbar=True, cbar_kws=None, cbar_ax=None, square=False, xticklabels=‘auto’, yticklabels=‘auto’, mask=None, ax=None, **kwargs)

data : 要显示的数据

ax = sns.heatmap(data)

vmin, vmax : 显示的数据值的最大和最小的范围

ax = sns.heatmap(data,vmin=0, vmax=1)

cmap : matplotlib颜色表名称或对象，或颜色列表，可选从数据值到色彩空间的映射。如果没有提供，默认设置

ax = sns.heatmap(data,vmin=0, vmax=1, cmap = 'GnBu')

center : 指定色彩的中心值

ax = sns.heatmap(data,vmin=0, vmax=1, cmap = 'GnBu', center=0.7)

robust : 如果“Ture”和“ vmin或” vmax不存在，则使用强分位数计算颜色映射范围，而不是极值。

ax = sns.heatmap(data,vmin=0, vmax=1, cmap = 'GnBu', center=0.5, robust=False) #Set1

annot : 如果为True，则将数据值写入每个单元格中

ax = sns.heatmap(data,vmin=0, vmax=1, cmap = 'GnBu', center=0.5, robust=False,annot=True) #Set1

fmt : 表格里显示数据的类型

fmt ='.0%'#显示百分比
fmt ='f' 显示完整数字 = fmt ='g'
fmt ='.3'显示小数的位数 = fmt ='.3f' = fmt ='.3g'

linewidths : 划分每个单元格的线的宽度。

linecolor : 划分每个单元格的线的颜色。

cbar : 是否绘制颜色条：colorbar，默认绘制

square : 为‘True’时，整个网格为一个正方形

xticklabels, yticklabels : 可以以字符串进行命名，也可以调节编号的间隔，也可以不显示坐标

ax = sns.heatmap(uniform_data,cmap = 'RdBu', center=0,cbar = True, square = False,xticklabels =['12','22'])#字符串命名
ax = sns.heatmap(uniform_data,cmap = 'RdBu', center=0,cbar = True, square = False,xticklabels =2)#编号间隔为2
ax = sns.heatmap(uniform_data,cmap = 'RdBu', center=0,cbar = True, square = False,xticklabels =False)#不显示坐标

mask : boolean array or DataFrame, optional

If passed, data will not be shown in cells where mask is True. Cells with missing values are automatically masked.

举例说明：
绘制一个numpy数组的热图：

import numpy as np; np.random.seed(0)
import seaborn as sns; sns.set()
uniform_data = np.random.rand(10, 12)
ax = sns.heatmap(uniform_data)

更改色彩图的限制：

import numpy as np; np.random.seed(0)
import seaborn as sns; sns.set()
uniform_data = np.random.rand(10, 12)
ax = sns.heatmap(uniform_data, vmin=0, vmax=1)

以0为中心的数据绘制热图：

import numpy as np; np.random.seed(0)
import seaborn as sns; sns.set()
normal_data = np.random.randn(10, 12)
ax = sns.heatmap(normal_data, center=0)

用有意义的行和列标签绘制数据框：

import numpy as np; np.random.seed(0)
import seaborn as sns; sns.set()
flights = sns.load_dataset("flights")
flights = flights.pivot("month", "year", "passengers")
ax = sns.heatmap(flights)

使用整数格式用数值注释每个单元格：

import numpy as np; np.random.seed(0)
import seaborn as sns; sns.set()
flights = sns.load_dataset("flights")
flights = flights.pivot("month", "year", "passengers")
ax = sns.heatmap(flights, annot=True, fmt="d")

在每个单元格之间添加行：

import numpy as np; np.random.seed(0)
import seaborn as sns; sns.set()
flights = sns.load_dataset("flights")
flights = flights.pivot("month", "year", "passengers")
ax = sns.heatmap(flights, linewidths=.5)

使用不同的颜色表：

import numpy as np; np.random.seed(0)
import seaborn as sns; sns.set()
flights = sns.load_dataset("flights")
flights = flights.pivot("month", "year", "passengers")
ax = sns.heatmap(flights, cmap="YlGnBu")

以特定值居中色彩图：

import numpy as np; np.random.seed(0)
import seaborn as sns; sns.set()
flights = sns.load_dataset("flights")
flights = flights.pivot("month", "year", "passengers")
ax = sns.heatmap(flights, center=flights.loc["January", 1955])

绘制每个其他列标签并且不要绘制行标签：

import numpy as np; np.random.seed(0)
import seaborn as sns; sns.set()
data = np.random.randn(50, 20)
ax = sns.heatmap(data, xticklabels=2, yticklabels=False)

不要绘制颜色条：

import numpy as np; np.random.seed(0)
import seaborn as sns; sns.set()
data = np.random.randn(10, 10)
ax = sns.heatmap(data, cbar=False)

对色条使用不同的轴：

import numpy as np; np.random.seed(0)
import seaborn as sns; sns.set()
import matplotlib.pyplot as pltflights = sns.load_dataset("flights")
flights = flights.pivot("month", "year", "passengers")
grid_kws = {"height_ratios": (.9, .05), "hspace": .3}
f, (ax, cbar_ax) = plt.subplots(2, gridspec_kw=grid_kws)
ax = sns.heatmap(flights, ax=ax,cbar_ax=cbar_ax,cbar_kws={"orientation": "horizontal"})

使用掩码只绘制矩阵的一部分：

import numpy as np; np.random.seed(0)
import seaborn as sns; sns.set()corr = np.corrcoef(np.random.randn(10, 200))
mask = np.zeros_like(corr)
mask[np.triu_indices_from(mask)] = True
with sns.axes_style("white"):ax = sns.heatmap(corr, mask=mask, vmax=.3, square=True)

import numpy as np; np.random.seed(0)
import seaborn as sns; sns.set()
import matplotlib.pyplot as plt
data = np.random.rand(10, 12)
f, ax = plt.subplots(figsize=(8,5))
ax = sns.heatmap(data,cmap = 'RdBu',ax=ax,vmin=0, vmax=1,annot=True,fmt ='0.1g')#设置坐标字体方向
label_y = ax.get_yticklabels()
plt.setp(label_y, rotation=45, horizontalalignment='right')
label_x = ax.get_xticklabels()
plt.setp(label_x, rotation=45, horizontalalignment='right')
plt.xlabel('x.num')#设置坐标名称
plt.ylabel('y.num')
plt.title('Plotting')#标题
plt.show()

参考资料：
http://seaborn.pydata.org/generated/seaborn.heatmap.html
https://blog.csdn.net/cymy001/article/details/79576019