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
这个SO问题的方法:反转图例顺序熊猫情节 https://stackoverflow.com/questions/35373714/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))