如何向条形图添加多个注释 [英] How to add multiple annotations to a barplot

问题描述

我想将百分比值 - 除了计数 - 添加到我的 Pandas 条形图中.但是，我无法这样做.我的代码如下所示，到目前为止，我可以获得要显示的计数值.有人可以帮我在每个条形显示的计数值旁边/下方添加相对百分比值吗?

I would like to add percent values - in addition to counts - to my pandas barplot. However, I am not able to do so. My code is shown below and thus far I can get count values to display. Can somebody please help me add relative % values next to/below the count values displayed for each bar?

import matplotlib
import matplotlib.pyplot as plt
%matplotlib inline
plt.style.use('ggplot')

import seaborn as sns
sns.set_style("white")

fig = plt.figure()
fig.set_figheight(5)
fig.set_figwidth(10)

ax = fig.add_subplot(111)

counts = [29227, 102492,  53269, 504028, 802994]

y_ax = ('A','B','C','D','E')
y_tick = np.arange(len(y_ax))

ax.barh(range(len(counts)), counts, align = "center", color = "tab:blue")
ax.set_yticks(y_tick)
ax.set_yticklabels(y_ax, size = 8)

#annotate bar plot with values
for i in ax.patches:
    ax.text(i.get_width()+.09, i.get_y()+.3, str(round((i.get_width()), 1)), fontsize=8)

sns.despine()
plt.show();

我的代码的输出如下所示.如何在显示的每个计数值旁边添加 % 值?

The output of my code is shown below. How can one add % values next to each count value displayed?

推荐答案

With pandas

• 使用 pandas v1.2.4 进行测试

import pandas as pd
import matplotlib.pyplot as plt

# create the dataframe from values in the OP
counts = [29227, 102492,  53269, 504028, 802994]
df = pd.DataFrame(data=counts, columns=['counts'], index=['A','B','C','D','E'])

# add a percent column
df['%'] = df.counts.div(df.counts.sum()).mul(100).round(2)

# display(df)
   counts      %
A   29227   1.96
B  102492   6.87
C   53269   3.57
D  504028  33.78
E  802994  53.82

使用 3.4.2 版本的 matplotlib 绘图

• 使用matplotlib.pyplot.bar_label
• 有关其他格式选项，请参阅 matplotlib:条形标签演示页面.
• 使用 pandas v1.2.4 进行测试，使用 matplotlib 作为绘图引擎.
• 一些格式化可以使用 fmt 参数完成，但更复杂的格式化应该使用 labels 参数完成.

Plot use matplotlib from version 3.4.2

• Use matplotlib.pyplot.bar_label
• See the matplotlib: Bar Label Demo page for additional formatting options.
• Tested with pandas v1.2.4, which is using matplotlib as the plot engine.
• Some formatting can be done with the fmt parameter, but more sophisticated formatting should be done with the labels parameter.

ax = df.plot(kind='barh', y='counts', figsize=(10, 5), legend=False, width=.75,
             title='This is the plot generated by all code examples in this answer')

# customize the label to include the percent
labels = [f' {v.get_width()}
 {df.iloc[i, 1]}%' for i, v in enumerate(ax.containers[0])]

# set the bar label
ax.bar_label(ax.containers[0], labels=labels, label_type='edge', size=13)

ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
plt.show()

• 在 matplotlib 条形图上添加值标签
• 如何注释堆积条形图的每一段
• 带有居中标签的堆积条形图
• 如何在 seaborn 条形图中绘制和注释多个数据列
• 如何使用聚合值注释 seaborn 条形图
• matplotlib 中的堆栈条形图并为每个部分添加标签
• 如何绘制和注释分组条形图

# plot the dataframe
ax = df.plot(kind='barh', y='counts', figsize=(10, 5), legend=False, width=.75)
for i, y in enumerate(ax.patches):

    # get the percent label
    label_per = df.iloc[i, 1]
    
    # add the value label
    ax.text(y.get_width()+.09, y.get_y()+.3, str(round((y.get_width()), 1)), fontsize=10)
    
    # add the percent label here
    ax.text(y.get_width()+.09, y.get_y()+.1, str(f'{round((label_per), 2)}%'), fontsize=10)

ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
plt.show()

没有pandas的原始答案

• 使用 matplotlib v3.3.4 进行测试

Original Answer without pandas

• Tested with matplotlib v3.3.4

import matplotlib.pyplot as plt

fig, ax = plt.subplots(figsize=(10, 5))

counts = [29227, 102492,  53269, 504028, 802994]

# calculate percents
percents = [100*x/sum(counts) for x in counts]

y_ax = ('A','B','C','D','E')
y_tick = np.arange(len(y_ax))

ax.barh(range(len(counts)), counts, align = "center", color = "tab:blue")
ax.set_yticks(y_tick)
ax.set_yticklabels(y_ax, size = 8)

#annotate bar plot with values
for i, y in enumerate(ax.patches):
    label_per = percents[i]
    ax.text(y.get_width()+.09, y.get_y()+.3, str(round((y.get_width()), 1)), fontsize=10)
    # add the percent label here
    # ax.text(y.get_width()+.09, y.get_y()+.3, str(round((label_per), 2)), ha='right', va='center', fontsize=10)
    ax.text(y.get_width()+.09, y.get_y()+.1, str(f'{round((label_per), 2)}%'), fontsize=10)

ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
plt.show()

• 你可以玩定位.
• JohanC
提到的其他格式选项
• 在一个字符串中打印文本的两个部分，中间有一个 以获得自然"行间距:
• str(f'{round((y.get_width()), 1)} {round((label_per), 2)}%')
• ax.text(..., va='center') 垂直居中并能够使用稍大的字体.
• ax.set_xlim(0, max(counts) * 1.18) 为文本获得更多空间.
• 以空格开始每行文本以获得自然的水平"填充.
• str(f' {round((label_per), 2)}%')，注意{前的空格.
• y.get_width()+.09 非常接近 y.get_width() 当这些值数以万计时.
• You can play with the positioning.
• Other formatting options mentioned by JohanC
• Print both parts of the text in one string with a in between to get a "natural" line spacing:
• str(f'{round((y.get_width()), 1)} {round((label_per), 2)}%')
• ax.text(..., va='center') to vertically center and be able to use a slightly larger font.
• ax.set_xlim(0, max(counts) * 1.18) to get a bit more space for the text.
• Start each line of text with a space to get a natural "horizontal" padding.
• str(f' {round((label_per), 2)}%'), note the space before {.
• y.get_width()+.09 is extremely close to y.get_width() when these values are in the tens of thousands.

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