python数据分析与机器学习实战-18.热力图绘制

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

data = np.random.rand(3,3)
print(data)
heatmap = sns.heatmap(data)
[[0.79738421 0.16230573 0.06197724]
 [0.79989397 0.73146068 0.17731879]
 [0.26202278 0.78645707 0.26536331]]





<matplotlib.axes._subplots.AxesSubplot at 0x10d042a90>

1 2	#设置数据最小值到最大值界限 ax = sns.heatmap(data,vmin=0.1,vmax=0.5)

#当数据有正负时，可以指定以0为中心
data = np.random.randn(3,3)
print(data)
ax = sns.heatmap(data,center=0)
[[ 0.65257565 -0.38550847  0.55620784]
 [ 0.28429021 -1.68905002 -0.29115511]
 [-1.09649997 -0.81622564  0.47706824]]

1 2	flights = sns.load_dataset("flights") flights.head()

	year	month	passengers
0	1949	January	112
1	1949	February	118
2	1949	March	132
3	1949	April	129
4	1949	May	121

#数据转换
flights = flights.pivot("month","year","passengers")
#热力图
sns.heatmap(flights)
<matplotlib.axes._subplots.AxesSubplot at 0x1a1a0a5ac8>

1 2	#将数据显示在heatmap中，annot=True,将fmt的值设置为'd',否则数据会出现乱码。 ax = sns.heatmap(flights,annot=True,fmt='d')

1 2	#heatmap格与格之间增加间隙 ax = sns.heatmap(flights,linewidth=0.5)

1 2	#设置自定义颜色 ax = sns.heatmap(flights,linewidth=0.5,cmap="YlGnBu")

# seaborn # heatmap