计数带色调的堆积条形图 [英] count plot with stacked bars per hue

问题描述

我正在寻找一种根据色相"绘制带有堆叠条形图的计数图的有效方法. 标准的色相行为是根据第二列的值将计数分成多个平行条，我正在寻找一种有效的方式来堆叠色相条以便快速比较总数.

I am looking for an efficient way of drawing a count plot with stacked bars according to "hue". Standard hue behavior is to split a count into parallel bars according to the value of a second column, what I am looking for is an efficient way to have the hue bars stacked in order to quickly compare totals.

让我用泰坦尼克号数据集的示例进行解释:

Let me explain with an example from the titanic dataset:

import pandas as pd
import numpy as np
import seaborn as sns
%matplotlib inline

df = sns.load_dataset('titanic')
sns.countplot(x='survived',hue='class',data=df)

通过计数图和色相给出标准的Seaborn行为

gives standard Seaborn behavior with countplot and hue

我正在寻找的是每色调的堆积条

what I am looking for is something like stacked bars per hue

要获取最后一张图片，我使用了以下代码

to get the last image I used the following code

def aggregate(rows,columns,df):
    column_keys = df[columns].unique()
    row_keys = df[rows].unique()

    agg = { key : [ len(df[(df[rows]==value) & (df[columns]==key)]) for value in row_keys]
               for key in column_keys }

    aggdf = pd.DataFrame(agg,index = row_keys)
    aggdf.index.rename(rows,inplace=True)

    return aggdf

aggregate('survived','class',df).plot(kind='bar',stacked=True)

我确信有一些更有效的方法. 我知道seaborn并不是非常友好的堆叠式吧台，所以我尝试用我的函数重新排列数据集并使用matplotlib，但是我想还有一种更聪明的方法.

I am sure there is some more efficient way. I know seaborn is not very stacked bars friendly... so I tried to rearrange the dataset with my function and used matplotlib, but I guess there is a more clever way to do that as well.

非常感谢！

推荐答案

您的最后一部分基本上就在那儿，将DataFrame.plot()与bar和stacked=True一起使用.

You were basically there with your last part, using DataFrame.plot() with bar and stacked=True.

您可以用groupby + pivot代替您的aggregate函数.

Instead of your aggregate function, you can accomplish what you want with a groupby + pivot.

df_plot = df.groupby(['class', 'survived']).size().reset_index().pivot(columns='class', index='survived', values=0)

class     First  Second  Third
survived                      
0            80      97    372
1           136      87    119

在这里，您可以使用stacked=True参数将其绘制为bar

From here you can just plot it as a bar with the stacked=True argument

df_plot.plot(kind='bar', stacked=True)

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