ax = sns.boxplot(y=tips["total_bill"]) # 方式2:传入y和data参数 # ax = sns.boxplot(y="total_bill", data=tips) 参数orient In [7]: ax = sns.boxplot(x="day",y="total_bill", data=tips) 改变x-y的位置: ax = sns.boxplot(y="day",x="total_bill", data=tips) 参数order 对...
seaborn.boxplot(x=None, y=None, hue=None, data=None, order=None, hue_order=None, orient=None, color=None, palette=None, saturation=0.75, width=0.8, dodge=True, fliersize=5, linewidth=None, whis=1.5, notch=False, ax=None, **kwargs) 1. 2. 3. 4. 5. 6. 参数解读 x,y,hue:数...
import seaborn as sns import matplotlib.pyplot as plt # 设置样式风格 sns.set(style="whitegrid") # 构建数据 tips = sns.load_dataset("tips") """ 案例5:通过设置order来显式指定分类顺序 order=[字段变量名1,字段变量名2,...] """ sns.boxplot(x="time", y="tip", data=tips, order=["...
seaborn.violinplot(x=None, y=None, hue=None, data=None, order=None, hue_order=None, bw='scott', cut=2, scale='area', scale_hue=True, gridsize=100, width=0.8, inner='box', split=False, dodge=True, orient=None, linewidth=None, color=None, palette=None, saturation=0.75, ax=None,...
seaborn.boxplot(x=None,y=None,hue=None,data=None,order=None,hue_order=None,orient=None,color=None,palette=None,saturation=0.75,width=0.8,dodge=True,fliersize=5,linewidth=None,whis=1.5,ax=None,**kwargs) 绘制箱形图以显示类别的分布。
seaborn.violinplot(x=None, y=None, hue=None, data=None, order=None, hue_order=None, bw='scott', cut=2, scale='area', scale_hue=True, gridsize=100, width=0.8, inner='box', split=False, orient=None, linewidth=None, color=None, palette=None, saturation=0.75, ax=None, **kwargs)...
order参数修改box的次序: ax=sns.boxplot(x="time",y="tip",data=tips,order=["Dinner","Lunch"]) ordering 平行的box: iris=sns.load_dataset("iris")ax=sns.boxplot(data=iris,orient="h",palette="Set2") horizontal.png sns.swarmplot显示出所有的点: ...
violinplot与boxplot扮演类似的角色,它显示了定量数据在一个(或多个)分类变量的多个层次上的分布,这些分布可以进行比较。不像箱形图中所有绘图组件都对应于实际数据点,小提琴绘图以基础分布的核密度估计为特征。具体用法如下: seaborn.violinplot(x=None, y=None, hue=None, data=None, order=None, hue_order=...
order=['Third','Second','First'], hue_order=['child','woman','man'],ax=axes[1]) 可以通过'linewidth'参数,控制线条的粗细。我们把'linewidth'参数设为1,就可以看到整体图形的线条变细,你可以根据自己的需要调节。 sns.boxplot(x='class',y='age',hue='who', ...
boxplot() sns.boxplot( x=None, y=None, hue=None, data=None,order=None, hue_order=None, orient=None, color=None, palette=None, saturation=0.75, width=0.8, dodge=True, fliersize=5, linewidth=None, whis=1.5, notch=False, ax=None,**kwargs, ...