使用公共轴绘制两个变量的仓位相关性 [英] Plotting binned correlation of two variables using common axis

问题描述

我已经将三个列表加载到了熊猫数据框中.

I have three lists that I have loaded into a pandas dataframe.

import pandas as pd
df = pd.DataFrame({'x': location})
df = df.assign(y1 = variable1)
df = df.assign(y2 = variable2)

我想画出y1与y2的相关性，其中x是共同的x轴.也就是说，实际上，我想根据x位置对y1和y2值进行分类，在每个bin中找到y1与y2的相关性，然后在整个x域中绘制一条相关性线.因此，我的最终绘图将在y轴上具有相关性，而在x轴上将具有相关性.

I would like to plot the correlation of y1 with y2 with x being the common x-axis. That is, really, I would like to bin y1 and y2 values according to x location, find the correlation of y1 with y2 within each bin and then plot a line of the correlations across the whole x domain. So my final plot will have correlation on the y-axis and location on the x-axis.

我以前已经使用scipy binned_statistics 函数绘制条件均值来完成与此操作不完全不同的操作，但是我认为我不能轻易将其扩展到相关性.无论如何，我也想在使用熊猫方面做得更好一些，因此，我尽可能地避免使用这条路线.

I have previously done something not completely dissimilar to this using the scipy binned_statistics function to plot conditional means but I don't think I can easily extend that to correlations. I would also like to get a bit better at using pandas anyway so I'm trying to avoid that route if at all possible.

我敢肯定，这已经被问过了，但是我遇到的所有事情似乎都在研究多个分布图.

I'm sure this has been asked before but everything that I have come across seems to be looking at multiple distribution plots.

推荐答案

我已经或多或少地提出了解决方案.在此处实施类似于的操作:

I've more or less arrived at a solution. Implementing something similar to what was used here I have:

nbins = 20
df['bins'] = pd.qcut(df['x'], q=nbins)
plotdatadf = df.groupby('bins')[['y1', 'y2']].corr().iloc[0::2, -1]

这为我提供了一个数据框，每个仓的相关系数分别为 y1 和 y2 ，其中，仓沿 x 平均分配就每个箱的观察而言.

This provides me with a data frame with a correlation coefficient of y1 and y2 for each bin, where bins are evenly divided along x in terms of observations per bin.

我现在可以返回到先前的数据帧，并使用这些相关值添加原始长度的另一列，条件是 if bin [1]然后corr = corr [1] 类型的复制.然后可以将此列绘制为y，而将我已经存在的x绘制为折线图.

I can now go back to my previous dataframe and add another column of the original length with these correlation values, conditional on if bin[1] then corr = corr[1]-type copying. This column can then be plotted as y against my already existing x as a line plot.

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