From 17fa004b00420d49648b246658e8953c42676a99 Mon Sep 17 00:00:00 2001 From: Stuming <1361046649@qq.com> Date: Mon, 5 Aug 2019 23:09:17 +0800 Subject: [PATCH] Update 22.md --- docs/22.md | 78 ++++++++++++++++++++++++++++-------------------------- 1 file changed, 40 insertions(+), 38 deletions(-) diff --git a/docs/22.md b/docs/22.md index e51314a..c6c61b4 100644 --- a/docs/22.md +++ b/docs/22.md @@ -1,75 +1,77 @@ # 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, -- GitLab