Plotnine (ggplot)：标绘区域外的注释

Question

我在plotnine中有下面的情节。我想在y轴的左边添加一个标签，显示该系列的平均值(基本上与y轴标签一致)。更好的是，如果标签可以按照下面的形状，指向平均线。这个是可能的吗？

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import numpy as np
import pandas as pd
import datetime
from plotnine import *


df = pd.DataFrame({
    'date':pd.date_range(start='1/1/2000', periods=10, freq='A'),
    'a': np.random.choice(range(20),10),
    'b': np.random.choice(range(20),10),
})

mean_a = df['a'].mean()
mean_b = df['b'].mean()

df = pd.melt(df,id_vars=['date'])

p = (ggplot(df, aes(x='date', y='value', fill='variable'))
  + theme_light()
  + geom_col(position='dodge', alpha=0.8)
  + geom_hline(yintercept=mean_a, linetype='dotted', color='#770d50', size=1.5)
  + geom_hline(yintercept=mean_b, linetype='dotted', color='#0055b3', size=1.5)
  + annotate('label', x=datetime.datetime(2010, 10, 1), y=mean_a, label='{:.1f}'.format(mean_a), color='#770d50', size=8, label_size=0.2)
  + annotate('label', x=datetime.datetime(2010, 10, 1), y=mean_b, label='{:.1f}'.format(mean_b), color='#0055b3', size=8, label_size=0.2)
  + annotate('label', x=datetime.datetime(2011, 1, 1), y=mean_b, label='')
  + scale_x_date(expand=(0,0), labels= lambda l: [v.strftime("%Y") for v in l])
  + scale_fill_manual(('#770d50','#0055b3'))
)
p

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Answer 1

正如所描述的这里，您可以使用ggplot对象的draw方法获得matplotlib图。然后，您可以使用轴和传统matplotlib函数来使用ax.text或ax.annotate函数绘制所需的注释，如下所示。您可能需要使用文本的实际x位置。

# original graph
p = (ggplot(df, aes(x='date', y='value', fill='variable'))
  + theme_light()
  + geom_col(position='dodge', alpha=0.8)
  + geom_hline(yintercept=mean_a, linetype='dotted', color='#770d50', size=1.5)
  + geom_hline(yintercept=mean_b, linetype='dotted', color='#0055b3', size=1.5)
  + annotate('label', x=datetime.datetime(2010, 10, 1), y=mean_a, label='{:.1f}'.format(mean_a), color='#770d50', size=8, label_size=0.2)
  + annotate('label', x=datetime.datetime(2010, 10, 1), y=mean_b, label='{:.1f}'.format(mean_b), color='#0055b3', size=8, label_size=0.2)
  + annotate('label', x=datetime.datetime(2011, 1, 1), y=mean_b, label='')
  + scale_x_date(expand=(0,0), labels= lambda l: [v.strftime("%Y") for v in l])
  + scale_fill_manual(('#770d50','#0055b3'))
)

# graph with annotation
fig = p.draw() # get the matplotlib figure object
ax = fig.axes[0] # get the matplotlib axis (may be more than one if faceted)
x_ticks = ax.get_xticks() # get the original x tick locations
x_loc = x_ticks.min() - (sorted(x_ticks)[1] - sorted(x_ticks)[0])*.75 # location for the annotation based on the original xticks
# annotation for mean_a using text
ax.text(x_loc,mean_a,'{:.2f}'.format(mean_a),
        horizontalalignment='right',verticalalignment='center',
        color='#770d50')
# annotation for mean_b using annotate with an arrow
ax.annotate(xy=(x_ticks.min(),mean_b),
            xytext=(x_loc,mean_b+.5),text='{:.2f}'.format(mean_b),
        horizontalalignment='right',verticalalignment='center',
        arrowprops={'arrowstyle':'->'},
        color='#0055b3')

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