使用seaborn绘图(一）

用户6021899

发布于 2020-05-13 13:53:09

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发布于 2020-05-13 13:53:09

文章被收录于专栏：Python编程 pyqt matplotlibPython编程 pyqt matplotlib

seaborn是在matplotlib的基础上进行了封装和扩展，让python的数据可视化功能更加强大。

1. jointplot: 类似于matplotlib的散点图，还可以同时绘制两个变量的分布。

from matplotlib import pyplot as plt
import seaborn
import numpy as np
N = 1000
x= np.random.randn(N)
y= 1.0*x**3+np.random.randn(N)

for kind in ("scatter","reg","reside","kde","hex"):
    r = seaborn.jointplot(x,y,kind=kind)
    plt.show()

共有5种样式：

2. 成对分布图

from matplotlib import pyplot as plt
import seaborn
import numpy as np
import pandas as pd

df = pd.read_excel("testing.xlsx")
print(df)
#df = df.drop(index= range(0,3), axis =0)
pd = df.drop(labels=["PNL_X []","PNL_Y []","PNL_N []"],axis=1)
#df.iloc[3:,1:8]# 行从第3行开始到最后一行，列从1到8号。0 based
#df.iloc[3:,1:8]
#df.loc
g = seaborn.pairplot(df.iloc[3:,1:4])#只取：行3到末尾，列1到3
plt.show()

3. 热力图

from matplotlib import pyplot as plt
import seaborn
import numpy as np
#导入seaborn自带数据集
flights = seaborn.load_dataset("flights")
data = flights.pivot("month","year","passengers")

seaborn.heatmap(data)
plt.title("热力图")
plt.show()