seaborn简单使用

# 导入相应的python包

import pandas as pdimport warnings # 用来忽略seaborn绘图库产生的warningswarnings.filterwarnings("ignore")import seaborn as snsimport matplotlib.pyplot as pltsns.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        50versicolor    50virginica     50Name: 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_curvesandrews_curves(iris, "species")

绘制parallel coordinates

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

绘制radviz

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