如何向 matplotlib 注释添加额外的文本
How to add additional text to matplotlib annotations
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，它显示了按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。例如，对于皇后镇，票价范围 1 (7.6, 7.9)，总分布百分比为 56%。我想将生存率 37.21% 显示为 (56%, 37.21%)。我无法弄清楚。请提供任何建议。谢谢。
这里是输出摘要table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
dfl2.T 正在绘制，但 'survival rate %' 在 result 中。因此，来自 dfl2.T 的值的索引不对应于 'survival rate %'.- 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. 的dict
- 为
'survival rate %'创建一个对应的pivoted DataFrame，然后将其展平。所有值的顺序与 dfl2.T 中的 '% total dist by xdim' 值的顺序相同。因此，它们可以被索引。
- 相对于
dfl2.T，绘图 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进行索引。
# 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')
备注
- 这里可以看到
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
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，它显示了按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。例如，对于皇后镇，票价范围 1 (7.6, 7.9)，总分布百分比为 56%。我想将生存率 37.21% 显示为 (56%, 37.21%)。我无法弄清楚。请提供任何建议。谢谢。
这里是输出摘要table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 |
dfl2.T正在绘制，但'survival rate %'在result中。因此，来自dfl2.T的值的索引不对应于'survival rate %'.- 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. 的 - 为
'survival rate %'创建一个对应的pivoted DataFrame，然后将其展平。所有值的顺序与dfl2.T中的'% total dist by xdim'值的顺序相同。因此，它们可以被索引。 - 相对于
dfl2.T，绘图 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进行索引。
dict
# 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')
备注
- 这里可以看到
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