如何设置条形图悬停以显示 x 轴的标签？

Question

我已经生成了一个简单的条形图。为了使其更具交互性，我在图表中添加了 Hovertool。from bokeh.io import show, output_notebookfrom bokeh.plotting import figure, output_filefrom bokeh.models.glyphs import HBarfrom bokeh.models import ColumnDataSource, Legend, HoverTooloutput_notebook()# Set x and yfunctions = ['func_1', 'func_2', 'func_3']percentage = [233.14, 312.03, 234.00]# Set data source (color needs to be set precisely with len(category))source = ColumnDataSource(data=dict(functions=functions, percentage=percentage))# Set the x_range to the list of categories abovep = figure(x_range=functions, plot_height=600, plot_width=800, title="The Overall Use of my functions",          x_axis_label='Functions', y_axis_label='Percentage')# Categorical values can also be used as coordinatesp.vbar(x='functions', top='percentage', width=0.9, source=source)p.add_tools(HoverTool(tooltips=[('Percentage', "@percentage")]))show(p)虽然它正确显示了 y 轴的值，但我想显示 x 轴的标签（例如，Func1：9.45）。就像链接中显示的图片一样（我还不能发布图片）：更新#1 我试图想出一些东西，这就是我得到的：# Set Hoverpercentage = list(map(lambda i: str(i), percentage))my_hover = list(zip(functions, percentage))p.add_tools(HoverTool(tooltips=my_hover))事实证明它显示了每个条中的每个细节，如下所示

Answer 1

此代码适用于 Bokeh v1.0.4。

import math

import numpy as np

import pandas as pd

from bokeh.models import ColumnDataSource, HoverTool

from bokeh.plotting import figure, show

from bokeh.palettes import Category10

df = pd.DataFrame(data = np.random.rand(10, 1), columns = ['percentage'], index = ['Func {}'.format(nmb) for nmb in range(10)])

df['color'] = Category10[10]

source = ColumnDataSource(data = dict(functions = df.index.values, percentage = df['percentage'].values, color = df['color'].values))

p = figure(x_range = df.index.values, plot_height = 600, plot_width = 800, title = "The Overall Use of my functions",

           x_axis_label = 'functions', y_axis_label = 'percentage')

p.vbar(x = 'functions', top = 'percentage', width = 0.9, color = 'color', source = source)

p.add_tools(HoverTool(tooltips = '<font color=blue>@functions:</font><font color=red> @percentage</font>'))

p.xgrid.grid_line_color = None

p.xaxis.major_label_orientation = math.pi / 4  # Rotate axis' labels

show(p)

结果：