如何向 matplotlib 注释添加附加文本

Posted 2023-03-12

【中文标题】如何向 matplotlib 注释添加附加文本

【英文标题】：How to add additional text to matplotlib annotations

【发布时间】：2021-08-01 08:53:11

【问题描述】：

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，其中显示了各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]，总分布百分比为 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。

这里是输出汇总表供参考

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

【参考方案1】：

dfl2.T 正在绘制中，但 'survival rate %' 在 result 中。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

都是 不是唯一的

，我们不能使用匹配的key-values 中的dict。 为'survival rate %'创建一个对应的pivot DataFrame，然后将其展平。所有值的顺序都与来自dfl2.T 的'% total dist by xdim' 值的顺序相同。因此，它们可以被编入索引。 相对于dfl2.T，plot API 按列顺序绘制，这意味着必须使用.flatten(order='F') 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')

注意事项

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