FacertGrid()的使用

查看数据的前五行

tips = sns.load_dataset("tips")

tips.head()

 

引入数据，布置横向画布

g = sns.FacetGrid(tips, col='time')

 

 

g = sns.FacetGrid(tips, col='time')
g.map(plt.hist, "tip")  #以tip为横轴画柱状图

 

g = sns.FacetGrid(tips, col="sex", hue="smoker")
g.map(plt.scatter, "total_bill", "tip", alpha=.7)   #绘制散点图，设置横纵轴，设置透明度
g.add_legend()                                                 #加上如下图标注的图例

 

 

g = sns.FacetGrid(tips, row="smoker", col="time", margin_titles=True)              #设置行列布局方式
g.map(sns.regplot, "size", "total_bill", color=".1", fit_reg=True, x_jitter=.1)         #fit_reg画出回归线，x_jitter为摆动程度

 

画出柱形图

g = sns.FacetGrid(tips, col="day", size=4, aspect=.5)
g.map(sns.barplot, "sex", "total_bill")

 

 

from pandas import Categorical

ordered_days = tips.day.value_counts().index

print(ordered_days)

查看day的排列顺序

CategoricalIndex(['Sat', 'Sun', 'Thur', 'Fri'], categories=['Thur', 'Fri', 'Sat', 'Sun'], ordered=False, dtype='category')

重新设置行的排列顺序

ordered_days = Categorical(["Thur", "Sun", "Fri", "Sat"])

g = sns.FacetGrid(tips, row="day", row_order=ordered_days, size=1.7, aspect=4)
g.map(sns.boxplot, "total_bill")

(盒图能够自动识别哪个变量是离散型，哪个是连续型，然后对连续型构造盒图。）

例如以下代码

import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib as mpl
from pandas import Categorical
import matplotlib.pyplot as plt

tips = sns.load_dataset("tips")   #seaborn内置数据集，DaraFram类型
print(tips.head())
ordered_days = Categorical(["Thur", "Sun", "Fri", "Sat"])
print(type(ordered_days))
print(ordered_days)
g = sns.FacetGrid(tips, row="day", row_order=ordered_days, size=1.7, aspect=4)
g.map(sns.boxplot, "total_bill", "sex")

plt.show()

运行结果如下，函数识别出sex是离散型变量，所以对sex进行分类，然后在每一个类别上对连续型变量total_bill构造盒图。

 还可以用FacertGrid的palette参数给hue的列的不同类设置不同颜色，代码如下

import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib as mpl
from pandas import Categorical
import matplotlib.pyplot as plt

tips = sns.load_dataset("tips")   #seaborn内置数据集，DaraFram类型
print(tips.head())
pal = dict(Lunch="seagreen", Dinner="gray")
g = sns.FacetGrid(data=tips, hue="time", palette=pal, size=5)
g.map(plt.scatter, "total_bill", "tip", s=50, alpha=0.7, linewidths=0.5, edgecolors="white")
g.add_legend()
plt.show()

运行结果如下

如果再设置marker参数，可指定用什么图标画散点，可以是三角形或圆形等

import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib as mpl
from pandas import Categorical
import matplotlib.pyplot as plt

tips = sns.load_dataset("tips")   #seaborn内置数据集，DaraFram类型
print(tips.head())
pal = dict(Lunch="seagreen", Dinner="gray")
g = sns.FacetGrid(data=tips, hue="time", palette=pal, size=5, hue_kws={"marker":['^', 'v']})
g.map(plt.scatter, "total_bill", "tip", s=50, alpha=0.7, linewidths=0.5, edgecolors="white")
g.add_legend()
plt.show()

还有一些小调整：set_axis_labels()函数可以自定义x和y轴名字，set(xticks, yticks)可以自定义x和y轴的刻度。fig.subplots_adjust()函数可以调整子图之间

的间隔和距离边框的大小。edgecolors可以设置散点周围的边缘颜色。

import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib as mpl
from pandas import Categorical
import matplotlib.pyplot as plt

tips = sns.load_dataset("tips")   #seaborn内置数据集，DaraFram类型
print(tips.head())
with sns.axes_style("white"):
    g = sns.FacetGrid(tips, row="sex", col="smoker", margin_titles=True, size=2.5)
g.map(plt.scatter, "total_bill", "tip", color="#334488", edgecolors="white", lw=0.5)
g.set_axis_labels("Total_bill", "Tip")
g.set(xticks=[10, 30, 50], yticks=[2, 6, 10])
g.fig.subplots_adjust(wspace=0.25, hspace=0.25)
plt.show()

可以用PairGrid对数据中的列进行两两配对绘制散点图，当然也可以指定要配对的列。

import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib as mpl
from pandas import Categorical
import matplotlib.pyplot as plt

iris = sns.load_dataset("iris")
g = sns.PairGrid(data=iris, vars=["sepal_length", "sepal_width"], hue="species")
g.add_legend()
g.map_offdiag(plt.scatter)
g.map_diag(plt.hist)
plt.show()

函数PairGrid（）中的vars参数指定要两两进行绘图的列，这些列是数据集的子列。map_offdiag和map_diag分别设置非对角的和对角的图使用的统计图类型。

import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib as mpl
from pandas import Categorical
import matplotlib.pyplot as plt

iris = sns.load_dataset("iris")
g = sns.PairGrid(data=iris, vars=["sepal_length", "sepal_width"], hue="species")
g.add_legend()
g.map_offdiag(plt.scatter)
g.map_diag(plt.hist)
plt.show()