如何在同一图中绘制多个线性回归 [英] How to plot multiple linear regressions in the same figure

问题描述

鉴于以下内容:

将 numpy 导入为 np将熊猫作为pd导入将 seaborn 作为 sns 导入np.random.seed(365)x1 = np.random.randn(50)y1 = np.random.randn(50)* 100x2 = np.random.randn(50)y2 = np.random.randn(50) * 100df1 = pd.DataFrame({'x1':x1, 'y1': y1})df2 = pd.DataFrame({'x2':x2, 'y2': y2})sns.lmplot('x1'，'y1'，df1，fit_reg = True，ci = None)sns.lmplot('x2'，'y2'，df2，fit_reg = True，ci = None)

这将创建 2 个单独的图.如何将df2中的数据添加到SAME图上?我在网上找到的所有 seaborn 示例似乎都关注如何创建相邻图形(例如，通过 'hue' 和 'col_wrap' 选项).此外，我不喜欢使用可能存在附加列的数据集示例，因为这在我正在从事的项目中没有自然意义.

如果需要混合使用 matplotlib/seaborn 函数来实现这一点，如果有人能帮助说明，我将不胜感激.

解决方案

你可以使用 seaborn 的

如果我理解正确，这就是您所需要的.

请注意，您需要注意 .regplot 参数，并且可能要更改我作为示例输入的值.

该行末尾的
• ; 是禁止显示命令的输出(我在可见的地方使用ipython notebook).
• 文档对.map() 方法.本质上，它就是这样做，将绘图命令与数据映射.然而，它适用于像 regplot 这样的低级"绘图命令，而不是 lmlplot，后者实际上是在幕后调用 regplot.
• 通常 plt.scatter 将采用以下参数: c ='none'， edgecolor ='r'来制作未填充的标记.但是seaborn正在干扰该过程并强制标记变色，因此我看不到一种简单/直观的方法来解决此问题，而是在seaborn生成绘图后操纵 ax 元素，这是最好的作为另一个问题的一部分来解决.

Given the following:

import numpy as np
import pandas as pd
import seaborn as sns

np.random.seed(365)
x1 = np.random.randn(50)
y1 = np.random.randn(50) * 100
x2 = np.random.randn(50)
y2 = np.random.randn(50) * 100

df1 = pd.DataFrame({'x1':x1, 'y1': y1})
df2 = pd.DataFrame({'x2':x2, 'y2': y2})

sns.lmplot('x1', 'y1', df1, fit_reg=True, ci = None)
sns.lmplot('x2', 'y2', df2, fit_reg=True, ci = None)

This will create 2 separate plots. How can I add the data from df2 onto the SAME graph? All the seaborn examples I have found online seem to focus on how you can create adjacent graphs (say, via the 'hue' and 'col_wrap' options). Also, I prefer not to use the dataset examples where an additional column might be present as this does not have a natural meaning in the project I am working on.

If there is a mixture of matplotlib/seaborn functions that are required to achieve this, I would be grateful if someone could help illustrate.

解决方案

You could use seaborn's FacetGrid class to get desired result. You would need to replace your plotting calls with these lines:

# sns.lmplot('x1', 'y1', df1, fit_reg=True, ci = None)
# sns.lmplot('x2', 'y2', df2, fit_reg=True, ci = None)
df = pd.concat([df1.rename(columns={'x1':'x','y1':'y'})
                .join(pd.Series(['df1']*len(df1), name='df')), 
                df2.rename(columns={'x2':'x','y2':'y'})
                .join(pd.Series(['df2']*len(df2), name='df'))],
               ignore_index=True)

pal = dict(df1="red", df2="blue")
g = sns.FacetGrid(df, hue='df', palette=pal, size=5);
g.map(plt.scatter, "x", "y", s=50, alpha=.7, linewidth=.5, edgecolor="white")
g.map(sns.regplot, "x", "y", ci=None, robust=1)
g.add_legend();

This will yield this plot:

Which is if I understand correctly is what you need.

Note that you will need to pay attention to .regplot parameters and may want to change the values I have put as an example.

• ; at the end of the line is to suppress output of the command (I use ipython notebook where it's visible).
• Docs give some explanation on the .map() method. In essence, it does just that, maps plotting command with data. However it will work with 'low-level' plotting commands like regplot, and not lmlplot, which is actually calling regplot behind the scene.
• Normally plt.scatter would take parameters: c='none', edgecolor='r' to make non-filled markers. But seaborn is interfering the process and enforcing color to the markers, so I don't see an easy/straigtforward way to fix this, but to manipulate ax elements after seaborn has produced the plot, which is best to be addressed as part of a different question.

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