使用分组的多列熊猫绘制堆积条形图

我有两个数据框，我需要获取它们之间的差异，然后在该差异之上绘制其中一个数据框。这是一个最小的例子：

import pandas as pd
import matplotlib.pyplot as plt

df1 = pd.DataFrame([[2,5,7,6,7],[4,4,4,4,3],[8,8,7,3,4],[16,10,12,13,16]], columns=["N", "A", "B", "C", "D"])
df2 = pd.DataFrame([[2,1,3,6,5],[4,1,2,3,2],[8,2,2,3,3],[16,8,10,3,11]], columns=["N", "A", "B", "C", "D"])

dfDiff = df1 - df2
dfDiff['N'] = df1['N']

# Individual barchart
colors = ['#6c8ebf', '#82b366', '#F7A01D', '#9876a7']
df1.set_index('N')[["A", "B", "C", "D"]].plot.bar(color=colors)
df2.set_index('N')[["A", "B", "C", "D"]].plot.bar(color=colors)

dfStacked = pd.DataFrame(columns=["N", "A", "A_diff", "B", "B_diff"])
dfStacked["N"] = df2["N"]
dfStacked["A"] = df2["A"]
dfStacked["B"] = df2["B"]
dfStacked["C"] = df2["C"]
dfStacked["D"] = df2["D"]
dfStacked["A_diff"] = dfDiff["A"]
dfStacked["B_diff"] = dfDiff["B"]
dfStacked["C_diff"] = dfDiff["C"]
dfStacked["D_diff"] = dfDiff["D"]

dfStacked.set_index('N').plot.bar(stacked=True)

plt.show()

The dataframes look like this: The thing is that now the new stacked one ends up with everything merged. I want to have "A" stacked with "A_diff", "B", stacked with "B_diff", "C" stacked with "C_diff" and "D" stacked with "D_diff". For example, I changed the code to do it with "A" and "A_diff" as dfStacked.set_index('N')[["A", "A_diff"]].plot.bar(stacked=True) which looks correct, but I want A,B,C and D grouped by N like in the first two figures.

我是否需要一个新的数据框，例如dfStacked？如果可以，应该以什么形式添加内容？我怎样才能保留same颜色但添加hatch="/" only对于“顶部”堆叠栏？

数据框如下所示会更好吗？：

df3 = pd.DataFrame(columns=["N", "Algorithm", "df1", "dfDiff"])
df3.loc[len(df3)] = [2, "A", 20, 10]
df3.loc[len(df3)] = [2, "A", 1, 4]
df3.loc[len(df3)] = [4, "A", 2, 3]
df3.loc[len(df3)] = [4, "A", 3, 4]
df3.loc[len(df3)] = [2, "B", 1, 3]
df3.loc[len(df3)] = [2, "B", 2, 4]
df3.loc[len(df3)] = [4, "B", 3, 3]
df3.loc[len(df3)] = [4, "B", 4, 2]

但如何按“N”和“算法”对它们进行分组呢？我的意思是，每一行对应一个条，只是它们应该按“N”与所有“算法”分组，最后两列是每个条的两个“部分”。颜色与前两个数字（对于“算法”）相匹配会很好，但是条形图的顶部有hatch="/"例如。

我将从df1, df2并得到dfStacked以稍微不同的方式：

import pandas as pd

df1 = pd.DataFrame(
    [
        [2,5,7,6,7],
        [4,4,4,4,3],
        [8,8,7,3,4],
        [16,10,12,13,16]
    ], 
    columns=["N", "A", "B", "C", "D"]
).set_index('N')

df2 = pd.DataFrame(
    [
        [2,1,3,6,5],
        [4,1,2,3,2],
        [8,2,2,3,3],
        [16,8,10,3,11]
    ], 
    columns=["N", "A", "B", "C", "D"]
).set_index('N')

dfStacked = pd.concat(
    [df1, df1-df2], 
    axis=1, 
    keys=['raw','diff']
).reorder_levels([1,0], axis=1)

现在我们有这个DataFrame:

要在按第一级堆叠的条形图中绘制这些数据，我们可以使用两个DataFrame.plot的特点——ax and bottom。第一个是应绘制条形图的轴的位置，第二个是条形底线应开始的值。详情请运行help(plt.bar)阅读有关bottom and help(pd.DataFrame.plot)阅读有关ax.

import matplotlib.pyplot as plt
from matplotlib.colors import TABLEAU_COLORS

plt.figure(figsize=(10,7))
ax = plt.gca()

names = dfStacked.columns.levels[0]
n = len(names)
color = iter(TABLEAU_COLORS)
w = 1/(n+2)       # width
h = '/'*5         # hatch for diff values
for i, name in enumerate(names):
    c = next(color)   # color
    p = n/2 - i       # position
    dfStacked[name]['raw'].plot.bar(
        ax=ax, 
        position=p, 
        width=w, 
        color=c,
        label=f'{name} raw'
    )
    dfStacked[name]['diff'].plot.bar(
        ax=ax, 
        bottom=dfStacked[name]['raw'], 
        hatch=h,
        position=p,
        width=w, 
        color=c,
        label=f'{name} diff'
    )

ax.set_xlim([-1, n])
ax.tick_params(axis='x', rotation=0)
ax.legend();

这是输出：