python - Seaborn Catplot 在条形图上设置值

Question

我像这样在 seaborn 中绘制了一个 catplot

import seaborn as sns
import pandas as pd

data  = {'year': [2016, 2013, 2014, 2015, 2016, 2013, 2014, 2015, 2016, 2013, 2014, 2015, 2016, 2013, 2014, 2015, 2016, 2013, 2014, 2015], 'geo_name': ['Michigan', 'Michigan', 'Michigan', 'Michigan', 'Washtenaw County, MI', 'Washtenaw County, MI', 'Washtenaw County, MI', 'Washtenaw County, MI', 'Ann Arbor, MI', 'Ann Arbor, MI', 'Ann Arbor, MI', 'Ann Arbor, MI', 'Philadelphia, PA', 'Philadelphia, PA', 'Philadelphia, PA', 'Philadelphia, PA', 'Ann Arbor, MI Metro Area', 'Ann Arbor, MI Metro Area', 'Ann Arbor, MI Metro Area', 'Ann Arbor, MI Metro Area'], 'geo': ['04000US26', '04000US26', '04000US26', '04000US26', '05000US26161', '05000US26161', '05000US26161', '05000US26161', '16000US2603000', '16000US2603000', '16000US2603000', '16000US2603000', '16000US4260000', '16000US4260000', '16000US4260000', '16000US4260000', '31000US11460', '31000US11460', '31000US11460', '31000US11460'], 'income': [50803.0, 48411.0, 49087.0, 49576.0, 62484.0, 59055.0, 60805.0, 61003.0, 57697.0, 55003.0, 56835.0, 55990.0, 39770.0, 37192.0, 37460.0, 38253.0, 62484.0, 59055.0, 60805.0, 61003.0], 'income_moe': [162.0, 163.0, 192.0, 186.0, 984.0, 985.0, 958.0, 901.0, 2046.0, 1688.0, 1320.0, 1259.0, 567.0, 424.0, 430.0, 511.0, 984.0, 985.0, 958.0, 901.0]}
df = pd.DataFrame(data)

g = sns.catplot(x='year', y='income', data=df, kind='bar', hue='geo_name', legend=True)
g.fig.set_size_inches(15,8)
g.fig.subplots_adjust(top=0.81,right=0.86)  

我得到如下所示的输出

我想在 K 表示形式的顶部添加每个条形的值。例如在 2013 中，Michigan 的栏位于 48411，所以我想在其上添加值 48.4K酒吧。同样适用于所有条。

Answer 1

• 使用matplotlib.pyplot.bar_label从 matplotlib v3.4.2 开始
  • 参见 Adding value labels on a matplotlib bar chart与新方法相关的其他绘图选项。
  • 查看 matplotlib: Bar Label Demo其他格式选项的页面。
  • 将 fmt 参数用于简单格式，将 labels 参数用于自定义字符串格式。
  • 对于水平条，使用 v.get_width 而不是 v.get_height。
• 在 python 3.11、pandas 1.5.3、matplotlib 3.7.1、seaborn 0.12.2< 中测试

仅适用于单个地 block

g = sns.catplot(data=df, x='year', y='income', kind='bar', hue='geo_name', legend=True, height=6, aspect=2)

# extract the matplotlib axes_subplot objects from the FacetGrid
ax = g.facet_axis(0, 0)  # or ax = g.axes.flat[0]

# iterate through the axes containers
for c in ax.containers:
    labels = [f'{(v.get_height() / 1000):.1f}K' for v in c]
    ax.bar_label(c, labels=labels, label_type='edge')

对于单个或多个地 block

g = sns.catplot(data=df, x='year', y='income', kind='bar', col='geo_name', col_wrap=3)

g.fig.subplots_adjust(top=0.9)
g.fig.suptitle('Bar Count with Annotations')

# iterate through axes
for ax in g.axes.ravel():
    
    # add annotations
    for c in ax.containers:
        labels = [f'{(v.get_height() / 1000):.1f}K' for v in c]
        ax.bar_label(c, labels=labels, label_type='edge')
    ax.margins(y=0.2)

plt.show()

• 也适用于 Facets(子图)中的分组条

df = sns.load_dataset('titanic')
g = sns.catplot(data=df, kind='count', x='survived', hue='sex', col='class')

# iterate through axes
for ax in g.axes.ravel():
    
    # add annotations
    for c in ax.containers:
        # add custom labels with the labels=labels parameter if needed
        # labels = [f'{h}' if (h := v.get_height()) > 0 else '' for v in c]
        ax.bar_label(c, label_type='edge')
    ax.margins(y=0.2)