Update 22.md

docs/22.md

 # seaborn.jointplot
 
+> 译者：[Stuming](https://github.com/Stuming)
+
 ```py
 seaborn.jointplot(x, y, data=None, kind='scatter', stat_func=None, color=None, height=6, ratio=5, space=0.2, dropna=True, xlim=None, ylim=None, joint_kws=None, marginal_kws=None, annot_kws=None, **kwargs)
 ```
 
-Draw a plot of two variables with bivariate and univariate graphs.
+绘制两个变量的双变量及单变量图。
 
-This function provides a convenient interface to the [`JointGrid`](seaborn.JointGrid.html#seaborn.JointGrid "seaborn.JointGrid") class, with several canned plot kinds. This is intended to be a fairly lightweight wrapper; if you need more flexibility, you should use [`JointGrid`](seaborn.JointGrid.html#seaborn.JointGrid "seaborn.JointGrid") directly.
+这个函数提供调用[`JointGrid`](seaborn.JointGrid.html#seaborn.JointGrid "seaborn.JointGrid")类的便捷接口，以及一些封装好的绘图类型。这是一个轻量级的封装，如果需要更多的灵活性，应当直接使用[`JointGrid`](seaborn.JointGrid.html#seaborn.JointGrid "seaborn.JointGrid").
 
-参数：`x, y`：strings or vectors
+参数：`x, y`：strings或者vectors
 
-> Data or names of variables in `data`.
+> `data`中的数据或者变量名。
 
-`data`：DataFrame, optional
+`data`：DataFrame, 可选
 
-> DataFrame when `x` and `y` are variable names.
+> 当`x`和`y`为变量名时的DataFrame.
 
-`kind`：{ “scatter” | “reg” | “resid” | “kde” | “hex” }, optional
+`kind`：{ “scatter” | “reg” | “resid” | “kde” | “hex” }, 可选
 
-> Kind of plot to draw.
+> 绘制图像的类型。
 
-`stat_func`：callable or None, optional
+`stat_func`：可调用的，或者None, 可选
 
-> _Deprecated_
+> 已过时
 
-`color`：matplotlib color, optional
+`color`：matplotlib颜色, 可选
 
-> Color used for the plot elements.
+> 用于绘制元素的颜色。
 
-`height`：numeric, optional
+`height`：numeric, 可选
 
-> Size of the figure (it will be square).
+> 图像的尺寸（方形）。
 
-`ratio`：numeric, optional
+`ratio`：numeric, 可选
 
-> Ratio of joint axes height to marginal axes height.
+>  中心轴的高度与侧边轴高度的比例
 
-`space`：numeric, optional
+`space`：numeric, 可选
 
-> Space between the joint and marginal axes
+> 中心和侧边轴的间隔大小
 
-`dropna`：bool, optional
+`dropna`：bool, 可选
 
-> If True, remove observations that are missing from `x` and `y`.
+> 如果为True, 移除`x`和`y`中的缺失值。
 
-`{x, y}lim`：two-tuples, optional
+`{x, y}lim`：two-tuples, 可选
 
-> Axis limits to set before plotting.
+> 绘制前设置轴的范围。
 
-`{joint, marginal, annot}_kws`：dicts, optional
+`{joint, marginal, annot}_kws`：dicts, 可选
 
-> Additional keyword arguments for the plot components.
+> 额外的关键字参数。
 
-`kwargs`：key, value pairings
+`kwargs`：键值对
 
-> Additional keyword arguments are passed to the function used to draw the plot on the joint Axes, superseding items in the `joint_kws` dictionary.
+> 额外的关键字参数会被传给绘制中心轴图像的函数，取代`joint_kws`字典中的项。
 
 
 返回值：`grid`：[`JointGrid`](seaborn.JointGrid.html#seaborn.JointGrid "seaborn.JointGrid")
 
-> [`JointGrid`](seaborn.JointGrid.html#seaborn.JointGrid "seaborn.JointGrid") object with the plot on it.
+> [`JointGrid`](seaborn.JointGrid.html#seaborn.JointGrid "seaborn.JointGrid")对象.
 
 
 
-See also
+参考
 
-The Grid class used for drawing this plot. Use it directly if you need more flexibility.
+绘制图像的Grid类。如果需要更多的灵活性，可以直接使用Grid类。
 
-Examples
+示例
 
-Draw a scatterplot with marginal histograms:
+绘制带有侧边直方图的散点图:
 
 ```py
 >>> import numpy as np, pandas as pd; np.random.seed(0)
@@ -81,7 +83,7 @@ Draw a scatterplot with marginal histograms:
 
 ![http://seaborn.pydata.org/_images/seaborn-jointplot-1.png](img/48d5020fcbaa6d2f36aae520f84a6234.jpg)
 
-Add regression and kernel density fits:
+添加回归线及核密度拟合:
 
 ```py
 >>> g = sns.jointplot("total_bill", "tip", data=tips, kind="reg")
@@ -90,7 +92,7 @@ Add regression and kernel density fits:
 
 ![http://seaborn.pydata.org/_images/seaborn-jointplot-2.png](img/8434c101b75c73a9e1b8dfb89975a2b5.jpg)
 
-Replace the scatterplot with a joint histogram using hexagonal bins:
+将散点图替换为六角形箱体图:
 
 ```py
 >>> g = sns.jointplot("total_bill", "tip", data=tips, kind="hex")
@@ -99,7 +101,7 @@ Replace the scatterplot with a joint histogram using hexagonal bins:
 
 ![http://seaborn.pydata.org/_images/seaborn-jointplot-3.png](img/6d5c569bf97b1f683a2ec921e1031112.jpg)
 
-Replace the scatterplots and histograms with density estimates and align the marginal Axes tightly with the joint Axes:
+将散点图和直方图替换为密度估计，并且将侧边轴与中心轴对齐:
 
 ```py
 >>> iris = sns.load_dataset("iris")
@@ -110,7 +112,7 @@ Replace the scatterplots and histograms with density estimates and align the mar
 
 ![http://seaborn.pydata.org/_images/seaborn-jointplot-4.png](img/c2c70e8889861b837b4fd45d707a6616.jpg)
 
-Draw a scatterplot, then add a joint density estimate:
+绘制散点图，添加中心密度估计:
 
 ```py
 >>> g = (sns.jointplot("sepal_length", "sepal_width",
@@ -121,7 +123,7 @@ Draw a scatterplot, then add a joint density estimate:
 
 ![http://seaborn.pydata.org/_images/seaborn-jointplot-5.png](img/b6895c87c4fa5a7fa1dc151dc3e5b385.jpg)
 
-Pass vectors in directly without using Pandas, then name the axes:
+不适用Pandas, 直接传输向量，随后给轴命名:
 
 ```py
 >>> x, y = np.random.randn(2, 300)
@@ -132,7 +134,7 @@ Pass vectors in directly without using Pandas, then name the axes:
 
 ![http://seaborn.pydata.org/_images/seaborn-jointplot-6.png](img/72b1f526c884ba4a6a285f1e8723013e.jpg)
 
-Draw a smaller figure with more space devoted to the marginal plots:
+绘制侧边图空间更大的图像:
 
 ```py
 >>> g = sns.jointplot("total_bill", "tip", data=tips,
@@ -142,7 +144,7 @@ Draw a smaller figure with more space devoted to the marginal plots:
 
 ![http://seaborn.pydata.org/_images/seaborn-jointplot-7.png](img/ddcf0a83320e56c75f2d5d15ff29c874.jpg)
 
-Pass keyword arguments down to the underlying plots:
+传递关键字参数给后续绘制函数:
 
 ```py
 >>> g = sns.jointplot("petal_length", "sepal_length", data=iris,