在 pandas 中的 groupby 之后绘制多个时间序列

2022-01-11 00:00:00 python pandas time-series group-by

问题描述

假设我在 valgdata DataFrame 上创建了一个 groupby，如下所示:

Suppose I made a groupby on the valgdata DataFrame like below:

grouped_valgdata = valgdata.groupby(['news_site','dato_uden_tid']).mean()

现在我明白了:

                                  sentiment
news_site          dato_uden_tid           
dr.dk              2015-06-15     54.777183
                   2015-06-16     54.703167
                   2015-06-17     54.948775
                   2015-06-18     54.424881
                   2015-06-19     53.290554
eb.dk              2015-06-15     53.279251
                   2015-06-16     53.285643
                   2015-06-17     53.558753
                   2015-06-18     52.854750
                   2015-06-19     54.415988
jp.dk              2015-06-15     56.590428
                   2015-06-16     55.313752
                   2015-06-17     53.771377
                   2015-06-18     53.218408
                   2015-06-19     54.392638
pol.dk             2015-06-15     54.759532
                   2015-06-16     55.182641
                   2015-06-17     55.001800
                   2015-06-18     56.004326
                   2015-06-19     54.649052

现在我想为每个 news_site 制作一个时间序列，其中 dato_uden_tid 在 X 轴上，而情绪在 Y 轴上.

Now I want to make a timeseries for each of the news_site, where dato_uden_tid is on the X axis and sentiment is on Y axis.

实现这一目标的最佳和最简单的方法是什么?

What is the best and easiest way to accomplish that?

谢谢！

解决方案

(有点好笑，因为这个问题让我做了同样的事情.)

(Am a bit amused, as this question caught me doing the exact same thing.)

你可以这样做

valgdata
    .groupby([valgdata.dato_uden_tid.name, valgdata.news_site.name])
    .mean()
    .unstack()

这会

  • 反向分组

  • reverse the groupby

将新网站取消堆叠为列

要绘图，只需执行前面的代码片段，紧跟 .plot():

To plot, just do the previous snippet immediately followed by .plot():

valgdata
    .groupby([valgdata.dato_uden_tid.name, valgdata.news_site.name])
    .mean()
    .unstack()
    .plot()

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