如何为每个索引注释分组条形图的百分比

Question

我用以下代码创建了一个条形图。我想为每个用户标记添加到 ~100% 的百分比，就像我在下面的 user1 和 user2 中所做的那样：

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

users = ['user1', 'user2', 'user3', 'user4', 'user5', 'user6', 'user7',\
         'user8', 'user9', 'user10', 'user11', 'user12']
NEG = [433, 1469, 1348, 2311, 522, 924, 54, 720, 317, 135, 388, 9]
NEU = [2529, 4599, 4617, 4297, 1782, 2742, 61, 2640, 1031, 404, 1723, 76]
POS = [611, 1149, 1262, 1378, 411, 382, 29, 513, 421, 101, 584, 49]

data = {'Negative': NEG, 'Neutral': NEU, 'Positive': POS}
df = pd.DataFrame(data, index=users)
ax = df.plot(kind='bar', ylabel='Number of Messages\nw/ <= 128 Characters',\
             xlabel='Username', title='Discord Sentiment Analysis',\
             color=['coral', 'khaki', 'skyblue'])

plt.tight_layout()
plt.show()

根据这个帖子的建议，我尝试了以下方法：

方法一

ax = df.plot(kind='bar', ylabel='Number of Messages\nw/ <= 128 Characters',\
             xlabel='Username', title='Discord Sentiment Analysis',\
             color=['coral', 'khaki', 'skyblue'])

for p in ax.containers:
    ax.bar_label(p, fmt='%.1f%%', label_type='edge')`

plt.tight_layout()
plt.show()

尝试 2

ax = df.plot(kind='bar', ylabel='Number of Messages\nw/ <= 128 Characters',\
             xlabel='Username', title='Discord Sentiment Analysis',\
             color=['coral', 'khaki', 'skyblue'])

for p in ax.patches:
    width = p.get_width()
    height = p.get_height()
    x, y = p.get_xy() 
    ax.annotate(f'{height:.0%}', (x + width/2, y + height*1.02), ha='center')

plt.tight_layout()
plt.show()

尝试三

ax = df.plot(kind='bar', ylabel='Number of Messages\nw/ <= 128 Characters',\
             xlabel='Username', title='Discord Sentiment Analysis',\
             color=['coral', 'khaki', 'skyblue'])

for p in ax.containers:
    ax.bar_label(p, fmt='%.1f%%', label_type='edge')
    
plt.tight_layout()
plt.show()

- Aaron

Answer 1

• 绘制 df，但创建一个百分比的数据框，可用作 .bar_label 的自定义标签。
• 有关详细解释和其他示例，请参见 如何在条形图上添加值标签 中的.bar_label。

# create a dataframe of percents
percent = df.div(df.sum(axis=1), axis=0).mul(100).round(1)

# plot
ax = df.plot(kind='bar', ylabel='Number of Messages\nw/ <= 128 Characters',\
             xlabel='Username', title='Discord Sentiment Analysis',\
             color=['coral', 'khaki', 'skyblue'], width=0.9, figsize=(10, 8))

# add annotations
for p in ax.containers:
    # get the current legend label, which is the column name
    label = p.get_label()
    # use the column name to access the correct labels from percent 
    labels = percent[label].astype(str).add('%')
    # add the bar labels
    ax.bar_label(p, labels=labels, label_type='edge', rotation=90, fontsize=10, padding=3)
    
# pad the spacing between the number and the edge of the figure
ax.margins(y=0.1)

plt.tight_layout()
plt.show()

---

对于分组的水平条形图，注释的代码kind='barh'是完全相同的。

# create a dataframe of percents
percent = df.div(df.sum(axis=1), axis=0).mul(100).round(1)

# plot
ax = df.plot(kind='barh', ylabel='Number of Messages\nw/ <= 128 Characters',\
             xlabel='Username', title='Discord Sentiment Analysis',\
             color=['coral', 'khaki', 'skyblue'], width=0.9, figsize=(10, 8))

# add annotations
for p in ax.containers:
    label = p.get_label()
    labels = percent[label].astype(str).add('%')
    ax.bar_label(p, labels=labels, label_type='edge', rotation=0, fontsize=10, padding=3)
    
# pad the spacing between the number and the edge of the figure
ax.margins(x=0.1)

plt.tight_layout()
plt.show()

---

df

        Negative  Neutral  Positive
user1        433     2529       611
user2       1469     4599      1149
user3       1348     4617      1262
user4       2311     4297      1378
user5        522     1782       411
user6        924     2742       382
user7         54       61        29
user8        720     2640       513
user9        317     1031       421
user10       135      404       101
user11       388     1723       584
user12         9       76        49

百分比

        Negative  Neutral  Positive
user1       12.1     70.8      17.1
user2       20.4     63.7      15.9
user3       18.7     63.9      17.5
user4       28.9     53.8      17.3
user5       19.2     65.6      15.1
user6       22.8     67.7       9.4
user7       37.5     42.4      20.1
user8       18.6     68.2      13.2
user9       17.9     58.3      23.8
user10      21.1     63.1      15.8
user11      14.4     63.9      21.7
user12       6.7     56.7      36.6

- Trenton McKinney