Update 4.md

docs/4.md

 # Plotting with categorical data
 
-In the [relational plot tutorial](relational.html#relational-tutorial) we saw how to use different visual representations to show the relationship between multiple variables in a dataset. In the examples, we focused on cases where the main relationship was between two numerical variables. If one of the main variables is “categorical” (divided into discrete groups) it may be helpful to use a more specialized approach to visualization.
+在[绘制关系图](relational.html#relational-tutorial)的教程中，我们学习了如何使用不同的可视化方法来展示数据集中多个变量之间的关系。在示例中，我们专注于两个数值变量之间的主要关系。如果其中一个主要变量是“可分类的”（可以被分为不同的组），那么我们可以使用更专业的可视化方法。
 
 In seaborn, there are several different ways to visualize a relationship involving categorical data. Similar to the relationship between [`relplot()`](../generated/seaborn.relplot.html#seaborn.relplot "seaborn.relplot") and either [`scatterplot()`](../generated/seaborn.scatterplot.html#seaborn.scatterplot "seaborn.scatterplot") or [`lineplot()`](../generated/seaborn.lineplot.html#seaborn.lineplot "seaborn.lineplot"), there are two ways to make these plots. There are a number of axes-level functions for plotting categorical data in different ways and a figure-level interface, [`catplot()`](../generated/seaborn.catplot.html#seaborn.catplot "seaborn.catplot"), that gives unified higher-level access to them.
 
@@ -316,4 +316,4 @@ g.set(xscale="log");
 
 ```
 
-![http://seaborn.pydata.org/_images/categorical_54_0.png](img/64c8d092f1c51150e58e0a41dfcad834.jpg)
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+![http://seaborn.pydata.org/_images/categorical_54_0.png](img/64c8d092f1c51150e58e0a41dfcad834.jpg)