如何在分组的柱形图上显示自定义数值？

huangapple
go评论114阅读模式
英文:

How can I display custom values over grouped bars?

问题

这是你要翻译的内容：

  1. I have a barplot with this values in a `dict` for the C/C):
  3. {'Country Name': {0: 'United States',
  4.   1: 'United States',
  5.   2: 'United States',
  6.   3: 'United States',
  7.   4: 'Russian Federation',
  8.   5: 'Russian Federation',
  9.   6: 'Russian Federation',
  10.   7: 'Russian Federation',
  11.   8: 'Japan',
  12.   9: 'Japan',
  13.   10: 'Japan',
  14.   11: 'Japan',
  15.   12: 'Germany',
  16.   13: 'Germany',
  17.   14: 'Germany',
  18.   15: 'Germany',
  19.   16: 'France',
  20.   17: 'France',
  21.   18: 'France',
  22.   19: 'France'},
  23.  'Indicator Name': {0: 'Population, total',
  24.   1: 'Population, ages 0-14, total',
  25.   2: 'Population, ages 15-64, total',
  26.   3: 'Population, ages 65+, total',
  27.   4: 'Population, total',
  28.   5: 'Population, ages 0-14, total',
  29.   6: 'Population, ages 15-64, total',
  30.   7: 'Population, ages 65+, total',
  31.   8: 'Population, total',
  32.   9: 'Population, ages 0-14, total',
  33.   10: 'Population, ages 15-64, total',
  34.   11: 'Population, ages 65+, total',
  35.   12: 'Population, total',
  36.   13: 'Population, ages 0-14, total',
  37.   14: 'Population, ages 15-64, total',
  38.   15: 'Population, ages 65+, total',
  39.   16: 'Population, total',
  40.   17: 'Population, ages 0-14, total',
  41.   18: 'Population, ages 15-64, total',
  42.   19: 'Population, ages 65+, total'},
  43.  'Valeur': {0: 320896618.0,
  44.   1: 61653419.0,
  45.   2: 212262832.0,
  46.   3: 46980367.0,
  47.   4: 144096870.0,
  48.   5: 24255306.0,
  49.   6: 100404879.0,
  50.   7: 19436685.0,
  51.   8: 127141000.0,
  52.   9: 16517168.0,
  53.   10: 77547638.0,
  54.   11: 33076194.0,
  55.   12: 81686611.0,
  56.   13: 10716271.0,
  57.   14: 53720119.0,
  58.   15: 17250221.0,
  59.   16: 66624068.0,
  60.   17: 12168975.0,
  61.   18: 41837530.0,
  62.   19: 12617563.0}}
  64. Code for the barplot  (with the ax because I tried containers):
  66. ax = plt.figure(figsize=(10,5))
  67. ax = sns.barplot(data = Graph1values, x = 'Country Name', y = 'Valeur', hue = 'Indicator Name',palette="dark")
  68. ax = plt.xlabel('')
  69. ax = plt.ylabel('Nombre de personnes', size = 15)
  70. ax = plt.title('Etude de la répartition de la population', size = 20)
  72. ax = plt.show()
  74. [![enter image description here][1]][1]
  76. I want to add these values on top of the bars
  78. x = ['100%', '19%', '66%', '15%', '100%', '17%', '70%', '13%', '100%', '13%', '61%', '26%', '100%',
  79.  '13%', '66%', '21%', '100%', '18%', '63%', '19%']
  81. I tried with containers, but it does not work.

只提供翻译，不包括问题的回答：

  1. 我有一个条形图，其中包含一个`dict`中的这些值，用于C/C）：
  3. {'Country Name': {0: '美国',
  4.   1: '美国',
  5.   2: '美国',
  6.   3: '美国',
  7.   4: '俄罗斯联邦',
  8.   5: '俄罗斯联邦',
  9.   6: '俄罗斯联邦',
  10.   7: '俄罗斯联邦',
  11.   8: '日本',
  12.   9: '日本',
  13.   10: '日本',
  14.   11: '日本',
  15.   12: '德国',
  16.   13: '德国',
  17.   14: '德国',
  18.   15: '德国',
  19.   16: '法国',
  20.   
  22. <details>
  23. <summary>英文:</summary>
  25. I have a barplot with this values in a `dict` for the C/C):
  27.     {&#39;Country Name&#39;: {0: &#39;United States&#39;,
  28.       1: &#39;United States&#39;,
  29.       2: &#39;United States&#39;,
  30.       3: &#39;United States&#39;,
  31.       4: &#39;Russian Federation&#39;,
  32.       5: &#39;Russian Federation&#39;,
  33.       6: &#39;Russian Federation&#39;,
  34.       7: &#39;Russian Federation&#39;,
  35.       8: &#39;Japan&#39;,
  36.       9: &#39;Japan&#39;,
  37.       10: &#39;Japan&#39;,
  38.       11: &#39;Japan&#39;,
  39.       12: &#39;Germany&#39;,
  40.       13: &#39;Germany&#39;,
  41.       14: &#39;Germany&#39;,
  42.       15: &#39;Germany&#39;,
  43.       16: &#39;France&#39;,
  44.       17: &#39;France&#39;,
  45.       18: &#39;France&#39;,
  46.       19: &#39;France&#39;},
  47.      &#39;Indicator Name&#39;: {0: &#39;Population, total&#39;,
  48.       1: &#39;Population, ages 0-14, total&#39;,
  49.       2: &#39;Population, ages 15-64, total&#39;,
  50.       3: &#39;Population, ages 65+, total&#39;,
  51.       4: &#39;Population, total&#39;,
  52.       5: &#39;Population, ages 0-14, total&#39;,
  53.       6: &#39;Population, ages 15-64, total&#39;,
  54.       7: &#39;Population, ages 65+, total&#39;,
  55.       8: &#39;Population, total&#39;,
  56.       9: &#39;Population, ages 0-14, total&#39;,
  57.       10: &#39;Population, ages 15-64, total&#39;,
  58.       11: &#39;Population, ages 65+, total&#39;,
  59.       12: &#39;Population, total&#39;,
  60.       13: &#39;Population, ages 0-14, total&#39;,
  61.       14: &#39;Population, ages 15-64, total&#39;,
  62.       15: &#39;Population, ages 65+, total&#39;,
  63.       16: &#39;Population, total&#39;,
  64.       17: &#39;Population, ages 0-14, total&#39;,
  65.       18: &#39;Population, ages 15-64, total&#39;,
  66.       19: &#39;Population, ages 65+, total&#39;},
  67.      &#39;Valeur&#39;: {0: 320896618.0,
  68.       1: 61653419.0,
  69.       2: 212262832.0,
  70.       3: 46980367.0,
  71.       4: 144096870.0,
  72.       5: 24255306.0,
  73.       6: 100404879.0,
  74.       7: 19436685.0,
  75.       8: 127141000.0,
  76.       9: 16517168.0,
  77.       10: 77547638.0,
  78.       11: 33076194.0,
  79.       12: 81686611.0,
  80.       13: 10716271.0,
  81.       14: 53720119.0,
  82.       15: 17250221.0,
  83.       16: 66624068.0,
  84.       17: 12168975.0,
  85.       18: 41837530.0,
  86.       19: 12617563.0}}
  88. Code for the barplot  (with the ax because I tried containers):
  90.     ax = plt.figure(figsize=(10,5))
  91.     ax = sns.barplot(data = Graph1values, x = &#39;Country Name&#39;, y = &#39;Valeur&#39;, hue = &#39;Indicator Name&#39;,palette=&quot;dark&quot;)
  92.     ax = plt.xlabel(&#39;&#39;)
  93.     ax = plt.ylabel(&#39;Nombre de personnes&#39;, size = 15)
  94.     ax = plt.title(&#39;Etude de la r&#233;partition de la population&#39;, size = 20)
  95.         
  96.     ax = plt.show()
  98. [![enter image description here][1]][1]
  100. **I want to add these values on top of the bars**
  102.     x = [&#39;100%&#39;, &#39;19%&#39;, &#39;66%&#39;, &#39;15%&#39;, &#39;100%&#39;, &#39;17%&#39;, &#39;70%&#39;, &#39;13%&#39;, &#39;100%&#39;, &#39;13%&#39;, &#39;61%&#39;, &#39;26%&#39;, &#39;100%&#39;,
  103.      &#39;13%&#39;, &#39;66%&#39;, &#39;21%&#39;, &#39;100%&#39;, &#39;18%&#39;, &#39;63%&#39;, &#39;19%&#39;]
  105. I tried with containers, but it does not work.
  108.   [1]: https://i.stack.imgur.com/mjYqF.png
  110. </details>
  113. # 答案1
  114. **得分**: 1
  116. 以下是翻译的内容：
  118. - 似乎`dict`必须是DataFrame的输出，但这不在问题中显示。
  119. - 使用[`.bar_label`][1]来在每个条形图的顶部添加自定义标签。[如何在条形图上添加值标签][2]中有该方法的详细解释。
  120.   - 使用条形图的高度(`h`)，`country`和容器`label`，以及DataFrame上的[布尔索引][3]，以获取正确的`'percents'`来注释条形图。
  121.   - [此问题][4]的答案没有展示如何获取条形图的自定义值。
  122. - 不要将所有行分配给`ax`，因为它们不返回`matplotlib.axes`。这会覆盖`ax`，因此`ax.bar_label`不起作用。
  123.   - `plt.xlabel`，`plt.ylabel`和`plt.title`返回`matplotlib.text.Text`。
  124.   - 使用`ax = plt.show()`，`type(ax) → NoneType`，因为`plt.show`不返回任何内容。
  125. - **在`python 3.11`，`pandas 1.5.3`，`matplotlib 3.7.1`，`seaborn 0.12.2`中进行了测试**
  127. [![在此输入图片描述][5]][5]
  129. ## `df`
  131. ```python
  132.           Country Name                 Indicator Name       Valeur percents
  133. 0        United States              Population, total  320896618.0     100%
  134. 1        United States   Population, ages 0-14, total   61653419.0      19%
  135. 2        United States  Population, ages 15-64, total  212262832.0      66%
  136. 3        United States    Population, ages 65+, total   46980367.0      15%
  137. 4   Russian Federation              Population, total  144096870.0     100%
  138. 5   Russian Federation   Population, ages 0-14, total   24255306.0      17%
  139. 6   Russian Federation  Population, ages 15-64, total  100404879.0      70%
  140. 7   Russian Federation    Population, ages 65+, total   19436685.0      13%
  141. 8                Japan              Population, total  127141000.0     100%
  142. 9                Japan   Population, ages 0-14, total   16517168.0      13%
  143. 10               Japan  Population, ages 15-64, total   77547638.0      61%
  144. 11               Japan    Population, ages 65+, total   33076194.0      26%
  145. 12             Germany              Population, total   81686611.0     100%
  146. 13             Germany   Population, ages 0-14, total   10716271.0      13%
  147. 14             Germany  Population, ages 15-64, total   53720119.0      66%
  148. 15             Germany    Population, ages 65+, total   17250221.0      21%
  149. 16              France              Population, total   66624068.0     100%
  150. 17              France   Population, ages 0-14, total   12168975.0      18%
  151. 18              France  Population, ages 15-64, total   41837530.0      63%
  152. 19              France    Population, ages 65+, total   12617563.0      19%
英文:
  • It seems the dict must be the output of a DataFrame, which isn't shown in the OP.
  • Use .bar_label to add custom labels to the top of each bar. How to add value labels on a bar chart has a thorough explanation of the method.
    • Use the bar height (h), country, and container label, with Boolean indexing on df, to get the correct &#39;percents&#39; to annotate the bars.
    • The answers to this question do not demonstrate how to get the custom values for the bars.
  • Do not assign all lines to ax, as they do not return a matplotlib.axes. This overwrites ax, so ax.bar_label won't work.
    • plt.xlabel, plt.ylabel, and plt.title return matplotlib.text.Text.
    • With ax = plt.show(), type(ax) → NoneType because plt.show doesn't return anything.
  • Tested in python 3.11, pandas 1.5.3, matplotlib 3.7.1, seaborn 0.12.2
  1. import pandas as pd
  2. import matplotlib.pyplot as plt
  3. import seaborn as sns
  5. # create the dataframe
  6. data = {&#39;Country Name&#39;: {0: &#39;United States&#39;, 1: &#39;United States&#39;, 2: &#39;United States&#39;, 3: &#39;United States&#39;, 4: &#39;Russian Federation&#39;, 5: &#39;Russian Federation&#39;, 6: &#39;Russian Federation&#39;, 7: &#39;Russian Federation&#39;, 8: &#39;Japan&#39;, 9: &#39;Japan&#39;, 10: &#39;Japan&#39;, 11: &#39;Japan&#39;, 12: &#39;Germany&#39;, 13: &#39;Germany&#39;, 14: &#39;Germany&#39;, 15: &#39;Germany&#39;, 16: &#39;France&#39;, 17: &#39;France&#39;, 18: &#39;France&#39;, 19: &#39;France&#39;},
  7.         &#39;Indicator Name&#39;: {0: &#39;Population, total&#39;, 1: &#39;Population, ages 0-14, total&#39;, 2: &#39;Population, ages 15-64, total&#39;, 3: &#39;Population, ages 65+, total&#39;, 4: &#39;Population, total&#39;, 5: &#39;Population, ages 0-14, total&#39;, 6: &#39;Population, ages 15-64, total&#39;, 7: &#39;Population, ages 65+, total&#39;, 8: &#39;Population, total&#39;, 9: &#39;Population, ages 0-14, total&#39;, 10: &#39;Population, ages 15-64, total&#39;, 11: &#39;Population, ages 65+, total&#39;, 12: &#39;Population, total&#39;, 13: &#39;Population, ages 0-14, total&#39;, 14: &#39;Population, ages 15-64, total&#39;, 15: &#39;Population, ages 65+, total&#39;, 16: &#39;Population, total&#39;, 17: &#39;Population, ages 0-14, total&#39;, 18: &#39;Population, ages 15-64, total&#39;, 19: &#39;Population, ages 65+, total&#39;},
  8.         &#39;Valeur&#39;: {0: 320896618.0, 1: 61653419.0, 2: 212262832.0, 3: 46980367.0, 4: 144096870.0, 5: 24255306.0, 6: 100404879.0, 7: 19436685.0, 8: 127141000.0, 9: 16517168.0, 10: 77547638.0, 11: 33076194.0, 12: 81686611.0, 13: 10716271.0, 14: 53720119.0, 15: 17250221.0, 16: 66624068.0, 17: 12168975.0, 18: 41837530.0, 19: 12617563.0}}
  9. df = pd.DataFrame(data)
  11. # add the list of percent values to the dataframe, which assumes they&#39;re in the correct order (as they appear to be)
  12. df[&#39;percents&#39;] = [&#39;100%&#39;, &#39;19%&#39;, &#39;66%&#39;, &#39;15%&#39;, &#39;100%&#39;, &#39;17%&#39;, &#39;70%&#39;, &#39;13%&#39;, &#39;100%&#39;, &#39;13%&#39;, &#39;61%&#39;, &#39;26%&#39;, &#39;100%&#39;, &#39;13%&#39;, &#39;66%&#39;, &#39;21%&#39;, &#39;100%&#39;, &#39;18%&#39;, &#39;63%&#39;, &#39;19%&#39;]
  14. # create the plot
  15. fig, ax = plt.subplots(figsize=(10, 5))
  16. sns.barplot(data=df, x=&#39;Country Name&#39;, y=&#39;Valeur&#39;, hue=&#39;Indicator Name&#39;, palette=&quot;dark&quot;, ax=ax)
  18. # customize the plot labels and title
  19. plt.xlabel(&#39;&#39;)
  20. plt.ylabel(&#39;Nombre de personnes&#39;, size=15)
  21. plt.title(&#39;Etude de la r&#233;partition de la population&#39;, size=20)
  23. # list of countries for selecting data from df
  24. countries = [v.get_text() for v in ax.get_xticklabels()]  # countries = df[&#39;Country Name&#39;].unique() also works
  26. # iterate through the axes bar containers
  27. for c in ax.containers:
  28.     
  29.     # get the label of the current containers
  30.     label = c.get_label()
  32.     # use the height of the bar, country, and label name to get the corresponding percent for the container
  33.     labels = [df.loc[df[&#39;Indicator Name&#39;].eq(label) &amp;
  34.                      df[&#39;Country Name&#39;].eq(country) &amp;
  35.                      df[&#39;Valeur&#39;].eq(h) , &#39;percents&#39;].iloc[0] if (h := v.get_height()) else &#39;&#39; for (v, country) in zip(c, countries)]
  36.     
  37.     # add the bar label with the custom labels
  38.     ax.bar_label(c, labels=labels)

如何在分组的柱形图上显示自定义数值？

df

  1.           Country Name                 Indicator Name       Valeur percents
  2. 0        United States              Population, total  320896618.0     100%
  3. 1        United States   Population, ages 0-14, total   61653419.0      19%
  4. 2        United States  Population, ages 15-64, total  212262832.0      66%
  5. 3        United States    Population, ages 65+, total   46980367.0      15%
  6. 4   Russian Federation              Population, total  144096870.0     100%
  7. 5   Russian Federation   Population, ages 0-14, total   24255306.0      17%
  8. 6   Russian Federation  Population, ages 15-64, total  100404879.0      70%
  9. 7   Russian Federation    Population, ages 65+, total   19436685.0      13%
  10. 8                Japan              Population, total  127141000.0     100%
  11. 9                Japan   Population, ages 0-14, total   16517168.0      13%
  12. 10               Japan  Population, ages 15-64, total   77547638.0      61%
  13. 11               Japan    Population, ages 65+, total   33076194.0      26%
  14. 12             Germany              Population, total   81686611.0     100%
  15. 13             Germany   Population, ages 0-14, total   10716271.0      13%
  16. 14             Germany  Population, ages 15-64, total   53720119.0      66%
  17. 15             Germany    Population, ages 65+, total   17250221.0      21%
  18. 16              France              Population, total   66624068.0     100%
  19. 17              France   Population, ages 0-14, total   12168975.0      18%
  20. 18              France  Population, ages 15-64, total   41837530.0      63%
  21. 19              France    Population, ages 65+, total   12617563.0      19%

huangapple
  • 本文由 发表于 2023年3月15日 19:01:14
  • 转载请务必保留本文链接：https://go.coder-hub.com/75743826.html
  • grouped-bar-chart
  • matplotlib
  • plot-annotations
  • python
  • seaborn
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