Update 5.md

docs/5.md

@@ -117,10 +117,11 @@ sns.rugplot(x);
 
 ![http://seaborn.pydata.org/_images/distributions_22_0.png](img/63e498131614f726dd72a90161b58971.jpg)
 
-### Fitting parametric distributions
+### 拟合参数分布
 
-You can also use [`distplot()`](../generated/seaborn.distplot.html#seaborn.distplot "seaborn.distplot") to fit a parametric distribution to a dataset and visually evaluate how closely it corresponds to the observed data:
+您还可以使用 [`distplot()`](../generated/seaborn.distplot.html#seaborn.distplot "seaborn.distplot")
 
+将参数分布拟合到数据集上，并直观地评估其与观测数据的对应程度:
 ```py
 x = np.random.gamma(6, size=200)
 sns.distplot(x, kde=False, fit=stats.gamma);
@@ -129,9 +130,9 @@ sns.distplot(x, kde=False, fit=stats.gamma);
 
 ![http://seaborn.pydata.org/_images/distributions_24_0.png](img/cf48dc45f5484db58f3d310e434b11a2.jpg)
 
-## Plotting bivariate distributions
+## 绘制二元分布
 
-It can also be useful to visualize a bivariate distribution of two variables. The easiest way to do this in seaborn is to just use the [`jointplot()`](../generated/seaborn.jointplot.html#seaborn.jointplot "seaborn.jointplot") function, which creates a multi-panel figure that shows both the bivariate (or joint) relationship between two variables along with the univariate (or marginal) distribution of each on separate axes.
+它对于可视化两个变量的二元分布也很有用。在seaborn中，最简单的方法就是使用[`jointplot()`](../generated/seaborn.jointplot.html#seaborn.jointplot "seaborn.jointplot")函数，它创建了一个多面板图形，显示了两个变量之间的二元(或联合)关系，以及每个变量在单独轴上的一元(或边际)分布。
 
 ```py
 mean, cov = [0, 1], [(1, .5), (.5, 1)]
@@ -140,9 +141,9 @@ df = pd.DataFrame(data, columns=["x", "y"])
 
 ```
 
-### Scatterplots
+### 散点图
 
-The most familiar way to visualize a bivariate distribution is a scatterplot, where each observation is shown with point at the _x_ and _y_ values. This is analgous to a rug plot on two dimensions. You can draw a scatterplot with the matplotlib `plt.scatter` function, and it is also the default kind of plot shown by the [`jointplot()`](../generated/seaborn.jointplot.html#seaborn.jointplot "seaborn.jointplot") function:
+可视化二元分布最常见的方法是散点图，其中每个观察点都以_x_和_y_值表示。 这类似于二维rug plot。 您可以使用matplotlib的`plt.scatter` 函数绘制散点图, 它也是 [`jointplot()`](../generated/seaborn.jointplot.html#seaborn.jointplot "seaborn.jointplot")函数显示的默认类型的图:
 
 ```py
 sns.jointplot(x="x", y="y", data=df);
@@ -151,7 +152,7 @@ sns.jointplot(x="x", y="y", data=df);
 
 ![http://seaborn.pydata.org/_images/distributions_28_0.png](img/66ba868aeef60b82d90c872e188217ed.jpg)
 
-### Hexbin plots
+### 六角形箱(Hexbin)图
 
 The bivariate analogue of a histogram is known as a “hexbin” plot, because it shows the counts of observations that fall within hexagonal bins. This plot works best with relatively large datasets. It’s available through the matplotlib `plt.hexbin` function and as a style in [`jointplot()`](../generated/seaborn.jointplot.html#seaborn.jointplot "seaborn.jointplot"). It looks best with a white background: