如何在海洋条形图中绘制和注释多个数据列 [英] How to plot and annotate multiple data columns in a seaborn barplot

问题描述

我有一个看起来像这样的数据框:

我已经使用一个小节来表示每一行的订阅者.这就是我所做的:

data = channels.sort_values('subscribers', Ascending=False).head(5)图表 = sns.barplot(x = 'name', y='subscribers',data=data)chart.set_xticklabels(chart.get_xticklabels()，旋转= 90)对于chart.patches中的p:chart.annotate("{:,.2f}".format(p.get_height(), '.2f'), (p.get_x() + p.get_width()/2., p.get_height()), ha = 'center', va = 'center', xytext = (0, 10), textcoords = 'offset points')

现在我想在同一图上显示每个用户的video_count".目标是比较订阅者数量与视频数量之间的关系.我该如何在图表上显示呢?

解决方案

数据

• 需要使用

  注释资源 - 来自 matplotlib v3.4.2

  • 在 matplotlib 条形图上添加值标签
  • 如何注释堆积条形图的每一段
  • 带有居中标签的堆叠条形图
  • 如何使用汇总值注释海洋航海图
  • matplotlib 中的堆栈条形图并为每个部分添加标签
  • 如何在条形图上添加多个注释
  • 如何绘制和注释分组条形图

  具有 seaborn v0.11.1

  的地块
  • 在 3.4.2 版本之前使用 matplotlib
  • 请注意，使用 .annotate 和 .patches 比使用 .bar_label 更冗长.

  # plot无花果，ax = plt.subplots(figsize =(12，6))sns.barplot(x='name', y='values', data=dfl,hue='cats', ax=ax)ax.set_xticklabels(chart.get_xticklabels()，旋转= 0)ax.set_yscale('log')对于ax.patches中的p:ax.annotate(f''{p.get_height():. 0f}''，(p.get_x()+ p.get_width()/2.，p.get_height())，ha='center', va='center', xytext =(0, 7), textcoords='offset points')

  I have a dataframe that looks like this:

  I have used a barplot to represent the subscribers for each row. This is what I did:

  data = channels.sort_values('subscribers', ascending=False).head(5)
  chart = sns.barplot(x = 'name', y='subscribers',data=data)
  chart.set_xticklabels(chart.get_xticklabels(), rotation=90)
  for p in chart.patches:
      chart.annotate("{:,.2f}".format(p.get_height(), '.2f'), (p.get_x() + p.get_width() / 2., p.get_height()), ha = 'center', va = 'center', xytext = (0, 10), textcoords = 'offset points')
  

  Now I want to show the 'video_count' for each user on this same plot. The goal is to compare how the number of subscribers relate to the number of videos. How can I depict this on the chart?

  解决方案

  Data

  • The data needs to be converted to a long format using .melt
  • Because of the scale of values, 'log' is used for the yscale
  • All of the categories in 'cats' are included for the example.
    • Select only the desired columns before melting, or use dfl = dfl[dfl.cats.isin(['sub', 'vc']) to filter for the desired 'cats'.

  import pandas as pd
  import matplotlib.pyplot as plt
  import seaborn as sns
  
  # setup dataframe
  data = {'vc': [76, 47, 140, 106, 246],
          'tv': [29645400, 28770702, 50234486, 30704017, 272551386],
          'sub': [66100, 15900, 44500, 37000, 76700],
          'name': ['a', 'b', 'c', 'd', 'e']}
  df = pd.DataFrame(data)
  
      vc        tv    sub name
  0   76  29645400  66100    a
  1   47  28770702  15900    b
  2  140  50234486  44500    c
  
  # convert to long form
  dfl = (df.melt(id_vars='name', var_name='cats', value_name='values')
         .sort_values('values', ascending=False).reset_index(drop=True))
  
    name cats     values
  0    e   tv  272551386
  1    c   tv   50234486
  2    d   tv   30704017
  

  Updated as of matplotlib v3.4.2

  • Use matplotlib.pyplot.bar_label
  • See the matplotlib: Bar Label Demo page for additional formatting options.
  • Tested with seaborn v0.11.1, which is using matplotlib as the plot engine.

  # plot
  fig, ax = plt.subplots(figsize=(12, 6))
  sns.barplot(x='name', y='values', data=dfl, hue='cats', ax=ax)
  ax.set_xticklabels(ax.get_xticklabels(), rotation=0)
  ax.set_yscale('log')
  
  for c in ax.containers:
      # set the bar label
      ax.bar_label(c, fmt='%.0f', label_type='edge')
  

  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 annotate a seaborn barplot with the aggregated value
  • stack bar plot in matplotlib and add label to each section
  • How to add multiple annotations to a barplot
  • How to plot and annotate a grouped bar chart

  Plot with seaborn v0.11.1

  • Using matplotlib before version 3.4.2
  • Note that using .annotate and .patches is much more verbose than with .bar_label.

  # plot
  fig, ax = plt.subplots(figsize=(12, 6))
  sns.barplot(x='name', y='values', data=dfl, hue='cats', ax=ax)
  ax.set_xticklabels(chart.get_xticklabels(), rotation=0)
  ax.set_yscale('log')
  
  for p in ax.patches:
      ax.annotate(f"{p.get_height():.0f}", (p.get_x() + p.get_width() / 2., p.get_height()),
                  ha='center', va='center', xytext =(0, 7), textcoords='offset points')
  

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