Matplotlib/Seaborn的计算值(Pandas数据框) [英] Matplotlib/Seaborn on calculated value (Pandas Dataframe)

问题描述

我有下表(格式很抱歉):

I have the following table (Sorry for the format):

Date         Service Reference  Document
2018-05-14   A       Null       3542523
2018-05-15   B       01         6234242
2018-05-16   A       09         2342146 
2018-05-16   C       Null       2342342

我有一个计算值[Calculated]就是

I have a calculated value [Calculated] that is the

Reference.count/Document.count()

我想创建一个与下一个相似的图:

I want to create a graph similar to the next one:

在x轴上我有日期，在y轴上我有计算所得的列，但显示有代表不同服务的不同行.

Where in the x-axis I have the date, on the y axis the calculated column but shown with different lines representing the different Services.

到目前为止，我有这个:

So far I have this:

def calculate(df):
    return df.Reference.count() / df.Document.count()

df1 = df.groupby(['Date']).apply(calculate)

但是，如果我尝试将服务添加到groupby中，则无法使用

However if I try to add Services to the groupby I cannot plot it using

sns.lineplot()

是否有另一种或更简便的方法可以将服务"维度添加到图中?

Is there another way or an easier way to add the Services dimension to the plot?

谢谢

推荐答案

一旦您使用以下日期和服务来汇总数据:

Once you aggregate your data by date and service using:

df1 = df.groupby(['Date', 'Service']).apply(calculate)

然后，重置索引以转换为数据帧(来自一系列)

Then, reset the index to convert to a dataframe (from a series)

df1 = df1.reset_index()

然后将其绘制:

sns.lineplot(x='Date', y=0, hue='Service', data=df1)

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