seaborn简单使用

# 导入相应的python包

import pandas as pd
import warnings # 用来忽略seaborn绘图库产生的warnings
warnings.filterwarnings("ignore")
import seaborn as sns
import matplotlib.pyplot as plt
sns.set(style="white", color_codes=True)
%matplotlib inline

# 读取数据

iris = pd.read_csv("iris.csv") 

iris.head()

	sepal_length	sepal_width	petal_length	petal_width	species
0	5.1	3.5	1.4	0.2	setosa
1	4.9	3.0	1.4	0.2	setosa
2	4.7	3.2	1.3	0.2	setosa
3	4.6	3.1	1.5	0.2	setosa
4	5.0	3.6	1.4	0.2	setosa

iris["species"].value_counts()

setosa        50
versicolor    50
virginica     50
Name: species, dtype: int64

绘制散点图

sns.jointplot(x="sepal_length", y="sepal_width", data=iris, kind='reg', size=6)

sns.FacetGrid(iris, hue="species", size=5).map(plt.scatter, "sepal_length", "sepal_width").add_legend()

绘制箱线图

fig = plt.figure()
fig.set_size_inches(8, 5)
sns.boxplot(x="species", y="sepal_length", data=iris)

fig = plt.figure()
fig.set_size_inches(8, 5)
sns.boxplot(x="species", y="sepal_length", data=iris)
sns.stripplot(x="species", y="sepal_length", data=iris, jitter=True, edgecolor="gray")

fig = plt.figure()
fig.set_size_inches(8, 5)
sns.violinplot(x="species", y="petal_length", data=iris, size=6)

绘制KDE分布

sns.FacetGrid(iris, hue="species", size=5).map(sns.kdeplot, "petal_width").add_legend()

绘制特征之间的散点图

sns.pairplot(iris, hue="species", size=3)

绘制特征之间的kde分布图

sns.pairplot(iris, hue="species", size=3, diag_kind="kde")

iris.boxplot(by="species", figsize=(12, 6))

绘制andrews curves

from pandas.tools.plotting import andrews_curves
andrews_curves(iris, "species")

绘制parallel coordinates

from pandas.tools.plotting import parallel_coordinates
parallel_coordinates(iris, "species")

绘制radviz

from pandas.tools.plotting import radviz
radviz(iris, "species")