如何在条形图上添加多个注释

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本文介绍了如何在条形图上添加多个注释的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！问题描述

除了计数之外，我想将百分比值添加到我的熊猫程序中.但是，我无法这样做.我的代码如下所示，到目前为止，我可以显示计数值.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?

import matplotlib导入matplotlib.pyplot作为plt%matplotlib 内联plt.style.use('ggplot')将 seaborn 作为 sns 导入sns.set_style(白色")无花果= plt.figure()fig.set_figheight(5)fig.set_figwidth(10)ax = fig.add_subplot(111)计数 = [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)#用值注释条形图对于我在ax.patches中: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?

解决方案

使用 pandas

使用 pandas v1.2.4 进行测试

导入和加载数据

将熊猫作为pd导入导入matplotlib.pyplot作为plt# 从 OP 中的值创建数据框计数= [29227、102492、53269、504028、802994]df = pd.DataFrame(data=counts, columns=['counts'], index=['A','B','C','D','E'])# 添加百分比列df ['％'] = df.counts.div(df.counts.sum()).mul(100).round(2)# 显示(df)计数 %一个29227 1.96乙 102492 6.87C 53269 3.57D 504028 33.78E 802994 53.82

使用 3.4.2 版中的 matplotlib 绘图

使用
注释资源 - 来自 matplotlib v3.4.2
- 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();
  The output of my code is shown below. How can one add % values next to each count value displayed?
  
  解决方案
  With pandas
  - Tested with pandas v1.2.4
  Imports and Load Data
  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
  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()}\n {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()
  Annotation Resources - from matplotlib v3.4.2
  - Adding value labels on a matplotlib bar chart
  - How to annotate each segment of a stacked bar chart
  - Stacked Bar Chart with Centered Labels
  - How to plot and annotate multiple data columns in a seaborn barplot
  - How to annotate a seaborn barplot with the aggregated value
  - stack bar plot in matplotlib and add label to each section
  - How to plot and annotate a grouped bar chart
  Plot use matplotlib before version 3.4.2
  # 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()
  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()
  - You can play with the positioning.
  - Other formatting options mentioned by JohanC
  - Print both parts of the text in one string with a \n in between to get a "natural" line spacing:
  - str(f'{round((y.get_width()), 1)}\n{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.