python - Pandas 堆积条形图中元素的排序

Question

我正在尝试绘制有关一个地区 5 个地区的特定行业的家庭收入部分的信息。

我使用 groupby 按地区对数据框中的信息进行排序:

df = df_orig.groupby('District')['Portion of income'].value_counts(dropna=False)
df = df.groupby('District').transform(lambda x: 100*x/sum(x))
df = df.drop(labels=math.nan, level=1)
ax = df.unstack().plot.bar(stacked=True, rot=0)
ax.set_ylim(ymax=100)

display(df.head())

    District  Portion of income
    A         <25%                 12.121212
              25 - 50%              9.090909
              50 - 75%              7.070707
              75 - 100%             2.020202

由于此收入属于类别，因此我想以合乎逻辑的方式对堆叠条中的元素进行排序。 Pandas 生成的图表如下。现在，顺序(从每个条形的底部开始)是:

25 - 50%

50 - 75%

75 - 100%

<25%

不确定

我意识到这些是按字母顺序排序的，并且很好奇是否有办法设置自定义排序。为了直观，我希望顺序是(同样，从栏的底部开始):

不确定

<25%

25 - 50%

50 - 75%

75 - 100%

然后，我想翻转图例以显示与此顺序相反的顺序(即，我希望图例顶部有 75 - 100，因为这将位于条形图的顶部)。

Answer 1

要对收入类别施加自定义排序顺序，一种方法是将它们转换为 CategoricalIndex .

要反转 matplotlib 图例条目的顺序，请使用 get_legend_handles_labels来自这个问题的方法:Reverse legend order pandas plot

import pandas as pd
import numpy as np
import math

np.random.seed(2019)

# Hard-code the custom ordering of categories
categories = ['unsure', '<25%', '25 - 50%', '50 - 75%', '75 - 100%']

# Generate some example data
# I'm not sure if this matches your input exactly
df_orig = pd.DataFrame({'District': pd.np.random.choice(list('ABCDE'), size=100), 
                        'Portion of income': np.random.choice(categories + [np.nan], size=100)})

# Unchanged from your code. Note that value_counts() returns a 
# Series, but you name it df
df = df_orig.groupby('District')['Portion of income'].value_counts(dropna=False)
df = df.groupby('District').transform(lambda x: 100*x/sum(x))

# In my example data, np.nan was cast to the string 'nan', so 
# I have to drop it like this
df = df.drop(labels='nan', level=1)

# Instead of plotting right away, unstack the MultiIndex
# into columns, then convert those columns to a CategoricalIndex 
# with custom sort order
df = df.unstack()

df.columns = pd.CategoricalIndex(df.columns.values, 
                                 ordered=True, 
                                 categories=categories)

# Sort the columns (axis=1) by the new categorical ordering
df = df.sort_index(axis=1)

# Plot
ax = df.plot.bar(stacked=True, rot=0)
ax.set_ylim(ymax=100)

# Matplotlib idiom to reverse legend entries 
handles, labels = ax.get_legend_handles_labels()
ax.legend(reversed(handles), reversed(labels))