如何向 matplotlib 注释添加附加文本

Question

我使用 seaborn 的 titanic 数据集作为我非常大的数据集的代理来创建基于它的图表和数据。

以下代码运行没有任何错误：

import seaborn as sns
import pandas as pd
import numpy as np
sns.set_theme(style="darkgrid")

# Load the example Titanic dataset
df = sns.load_dataset("titanic")

# split fare into decile groups and order them
df['fare_grp'] = pd.qcut(df['fare'], q=10,labels=None, retbins=False, precision=0).astype(str)
df.groupby(['fare_grp'],dropna=False).size()
df['fare_grp_num'] = pd.qcut(df['fare'], q=10,labels=False, retbins=False, precision=0).astype(str)
df.groupby(['fare_grp_num'],dropna=False).size()
df['fare_ord_grp'] = df['fare_grp_num'] + ' ' +df['fare_grp']
df['fare_ord_grp']

# set variables
target = 'survived'
ydim = 'fare_ord_grp'
xdim = 'embark_town'

#del [result]

non_events = pd.DataFrame(df[df[target]==0].groupby([ydim,xdim],as_index=False, dropna=False)[target].count()).rename(columns={target: 'non_events'})
non_events[xdim]=non_events[xdim].replace(np.nan, 'Missing', regex=True)
non_events[ydim]=non_events[ydim].replace(np.nan, 'Missing', regex=True)
non_events_total = pd.DataFrame(df[df[target]==0].groupby([xdim],dropna=False,as_index=False)[target].count()).rename(columns={target: 'non_events_total_by_xdim'}).replace(np.nan, 'Missing', regex=True)

events = pd.DataFrame(df[df[target]==1].groupby([ydim,xdim],as_index=False, dropna=False)[target].count()).rename(columns={target: 'events'})
events[xdim]=events[xdim].replace(np.nan, 'Missing', regex=True)
events[ydim]=events[ydim].replace(np.nan, 'Missing', regex=True)
events_total = pd.DataFrame(df[df[target]==1].groupby([xdim],dropna=False,as_index=False)[target].count()).rename(columns={target: 'events_total_by_xdim'}).replace(np.nan, 'Missing', regex=True)

grand_total = pd.DataFrame(df.groupby([xdim],dropna=False,as_index=False)[target].count()).rename(columns={target: 'total_by_xdim'}).replace(np.nan, 'Missing', regex=True)

grand_total=grand_total.merge(non_events_total, how='left', on=xdim).merge(events_total, how='left', on=xdim)

result = pd.merge(non_events, events, how="outer",on=[ydim,xdim])

result['total'] = result['non_events'].fillna(0) + result['events'].fillna(0)
result[xdim] = result[xdim].replace(np.nan, 'Missing', regex=True)
result = pd.merge(result, grand_total, how="left",on=[xdim])

result['survival rate %'] = round(result['events']/result['total']*100,2)
result['% event dist by xdim'] = round(result['events']/result['events_total_by_xdim']*100,2)
result['% non-event dist by xdim'] = round(result['non_events']/result['non_events_total_by_xdim']*100,2)
result['% total dist by xdim'] = round(result['total']/result['total_by_xdim']*100,2)

display(result)
value_name1 = "% dist by " + str(xdim)
dfl = pd.melt(result, id_vars=[ydim, xdim],value_vars =['% total dist by xdim'], var_name = 'Type',value_name=value_name1).drop(columns='Type')
dfl2 = dfl.pivot(index=ydim, columns=xdim, values=value_name1)
print(dfl2)
title1 = "% dist by " + str(xdim)
ax=dfl2.T.plot(kind='bar', stacked=True, rot=1, figsize=(8, 8), title=title1)
ax.set_xticklabels(ax.get_xticklabels(), rotation=45)
ax.legend(bbox_to_anchor=(1.0, 1.0),title = 'Fare Range')
ax.set_ylabel('% Dist')
for p in ax.patches:
    width, height = p.get_width(), p.get_height()
    x, y = p.get_xy() 
    ax.text(x+width/2, y+height/2,'{:.0f}%'.format(height),horizontalalignment='center', verticalalignment='center')

它会生成以下堆积百分比条形图，其中显示了登船城镇的总分布百分比。

我还想显示存活率以及每个块中的分布百分比。 例如，对于皇后镇，票价范围 1 (7.6, 7.9]，% 总分布为 56%。我想将 37.21% 的存活率显示为 (56%, 37.21%)。我无法弄清楚。请提供任何建议。谢谢。

这里是output汇总表供参考

	fare_ord_grp	登船镇	非事件	事件	全部的	total_by_xdim	non_events_total_by_xdim	events_total_by_xdim	存活率 ％	xdim 的 % 事件分布	xdim 的 % 非事件分布	xdim 占总距离的百分比
0	0 (-0.1,7.6]	瑟堡	22	7	29	168	75	93	24.14	7.53	29.33	17.26
1	0 (-0.1,7.6]	皇后镇	4	钠	4	77	47	30	钠	钠	8.51	5.19
2	0 (-0.1,7.6]	南安普敦	53	6	59	644	427	217	10.17	2.76	12.41	9.16
3	1 (7.6,7.9]	皇后镇	27	16	43	77	47	30	37.21	53.33	57.45	55.84
4	1 (7.6,7.9]	南安普敦	34	10	44	644	427	217	22.73	4.61	7.96	6.83
5	2 (7.9,8]	瑟堡	4	1	5	168	75	93	20	1.08	5.33	2.98
6	2 (7.9,8]	南安普敦	83	13	96	644	427	217	13.54	5.99	19.44	14.91
7	3 (8.0,10.5]	瑟堡	2	1	3	168	75	93	33.33	1.08	2.67	1.79
8	3 (8.0,10.5]	皇后镇	2	钠	2	77	47	30	钠	钠	4.26	2.6
9	3 (8.0,10.5]	南安普敦	56	17	73	644	427	217	23.29	7.83	13.11	11.34
10	4 (10.5,14.5]	瑟堡	7	8	15	168	75	93	53.33	8.6	9.33	8.93
11	4 (10.5,14.5]	皇后镇	1	2	3	77	47	30	66.67	6.67	2.13	3.9
12	4 (10.5,14.5]	南安普敦	40	26	66	644	427	217	39.39	11.98	9.37	10.25
13	5 (14.5,21.7]	瑟堡	9	10	19	168	75	93	52.63	10.75	12	11.31
14	5 (14.5,21.7]	皇后镇	5	3	8	77	47	30	37.5	10	10.64	10.39
15	5 (14.5,21.7]	南安普敦	37	24	61	644	427	217	39.34	11.06	8.67	9.47
16	6 (21.7,27]	瑟堡	1	4	5	168	75	93	80	4.3	1.33	2.98
17	6 (21.7,27]	皇后镇	2	3	5	77	47	30	60	10	4.26	6.49
18	6 (21.7,27]	南安普敦	40	39	79	644	427	217	49.37	17.97	9.37	12.27
19	7 (27.0,39.7]	瑟堡	14	10	24	168	75	93	41.67	10.75	18.67	14.29
20	7 (27.0,39.7]	皇后镇	5	钠	5	77	47	30	钠	钠	10.64	6.49
21	7 (27.0,39.7]	南安普敦	38	24	62	644	427	217	38.71	11.06	8.9	9.63
22	8 (39.7,78]	瑟堡	5	19	24	168	75	93	79.17	20.43	6.67	14.29
23	8 (39.7,78]	南安普敦	37	28	65	644	427	217	43.08	12.9	8.67	10.09
24	9 (78.0,512.3]	瑟堡	11	33	44	168	75	93	75	35.48	14.67	26.19
25	9 (78.0,512.3]	皇后镇	1	1	2	77	47	30	50	3.33	2.13	2.6
26	9 (78.0,512.3]	南安普敦	9	30	39	644	427	217	76.92	13.82	2.11	6.06
27	2 (7.9,8]	皇后镇	钠	5	5	77	47	30	100	16.67	钠	6.49
28	9 (78.0,512.3]	失踪	钠	2	2	2	钠	2	100	100	钠	100

Answer 1

• 正在绘制dfl2.T ，但result是'survival rate %' 。 因此，来自dfl2.T的值的索引与'survival rate %'不对应。
• 因为result['% total dist by xdim']中的所有值都不是唯一的，我们不能使用匹配key-values的dict 。
• 为'survival rate %'创建一个对应的旋转 DataFrame ，然后将其展平。 所有值的顺序都与 dfl2.T 中的 '% total dist by dfl2.T '% total dist by xdim'值的顺序相同。 因此，它们可以被索引。
• 关于dfl2.T ， plot API 按列顺序绘制，这意味着.flatten(order='F')必须用于以正确的顺序展平数组以被索引。

# create a corresponding pivoted dataframe for survival rate %
dfl3 = pd.melt(result, id_vars=[ydim, xdim],value_vars =['survival rate %'], var_name = 'Type',value_name=value_name1).drop(columns='Type')
dfl4 = dfl3.pivot(index=ydim, columns=xdim, values=value_name1)

# flatten dfl4.T in column order
dfl4_flattened = dfl4.T.to_numpy().flatten(order='F')

for i, p in enumerate(ax.patches):
    width, height = p.get_width(), p.get_height()
    x, y = p.get_xy() 
    
    # only print values when height is not 0
    if height != 0:
        
        # create the text string
        text = f'{height:.0f}%, {dfl4_flattened[i]:.0f}%'
        
        # annotate the bar segments
        ax.text(x+width/2, y+height/2, text, horizontalalignment='center', verticalalignment='center')

笔记

• 在这里我们可以看到dfl2.T和dfl4.T

# dfl2.T
fare_ord_grp  0 (-0.1, 7.6]  1 (7.6, 7.9]  2 (7.9, 8.0]  3 (8.0, 10.5]  4 (10.5, 14.5]  5 (14.5, 21.7]  6 (21.7, 27.0]  7 (27.0, 39.7]  8 (39.7, 78.0]  9 (78.0, 512.3]
embark_town                                                                                                                                                            
Cherbourg             17.26           NaN          2.98           1.79            8.93           11.31            2.98           14.29           14.29            26.19
Missing                 NaN           NaN           NaN            NaN             NaN             NaN             NaN             NaN             NaN           100.00
Queenstown             5.19         55.84          6.49           2.60            3.90           10.39            6.49            6.49             NaN             2.60
Southampton            9.16          6.83         14.91          11.34           10.25            9.47           12.27            9.63           10.09             6.06

# dfl4.T
fare_ord_grp  0 (-0.1, 7.6]  1 (7.6, 7.9]  2 (7.9, 8.0]  3 (8.0, 10.5]  4 (10.5, 14.5]  5 (14.5, 21.7]  6 (21.7, 27.0]  7 (27.0, 39.7]  8 (39.7, 78.0]  9 (78.0, 512.3]
embark_town                                                                                                                                                            
Cherbourg             24.14           NaN         20.00          33.33           53.33           52.63           80.00           41.67           79.17            75.00
Missing                 NaN           NaN           NaN            NaN             NaN             NaN             NaN             NaN             NaN           100.00
Queenstown              NaN         37.21        100.00            NaN           66.67           37.50           60.00             NaN             NaN            50.00
Southampton           10.17         22.73         13.54          23.29           39.39           39.34           49.37           38.71           43.08            76.92