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任务是自动化可视化。CSV 文件包含大量特征（列名，例如：32 个，将来可能会增加）。任务是绘制交互式可视化。我发现的所有示例都是针对动态特征选择进行硬编码的。

但要求是使这些东西动态化。如何让它充满活力？请指导。

我已经成功地动态绘制了图形，但无法连接交互部分。代码如下：

import pandas as pd
from bokeh.plotting import figure
from bokeh.io import show
from bokeh.models import CustomJS,HoverTool,ColumnDataSource,Select
from bokeh.models.widgets import CheckboxGroup
from bokeh.models.annotations import Title, Legend
import itertools
from bokeh.palettes import inferno
from bokeh.layouts import row

def creat_plot(dataframe):
    data=dataframe
    #Converting the timestamp Column to Timestamp datatype so that it can be used for Plotting on X-axis
    data['timestamp'] = pd.to_datetime(data['timestamp'])
    
    #Segregating Date and Time from timestamp column. It will be used in Hover Tool
    date = lambda x: str(x)[:10]
    data['date'] = data[['timestamp']].applymap(date)
    time= lambda x: str(x)[11:]
    data['time'] = data[['timestamp']].applymap(time)
    
    #Converting whole dataframe ColumnDatasource for easy usage in hover tool
    source = ColumnDataSource(data)
    
    # List all the tools that you want in your plot separated by comas, all in one string.
    TOOLS="crosshair,pan,wheel_zoom,box_zoom,reset,hover"
    
    # New figure
    t = figure(x_axis_type = "datetime", width=1500, height=600,tools=TOOLS,title="Plot for Interactive Features")
    
    #X-axis Legend Formatter
    t.xaxis.formatter.days = '%d/%m/%Y'
    
    #Axis Labels
    t.yaxis.axis_label = 'Count'
    t.xaxis.axis_label = 'Date and Time Span'
    
    #Grid Line Formatter
    t.ygrid.minor_grid_line_color = 'navy'
    t.ygrid.minor_grid_line_alpha = 0.1
    t.xgrid.visible = True
    t.ygrid.visible= True
    
    #Hover Tool Usage
    t.select_one(HoverTool).tooltips = [('Date', '@date'),('Time', '@time')]
    
    #A color iterator creation
    colors = itertools.cycle(inferno(len(data.columns)))
    
    #A Line type iterator creation
    line_types= ['solid','dashed','dotted','dotdash','dashdot']
    lines= itertools.cycle(line_types)
    
    column_name=[]
    #Looping over the columns to plot the Data
    for m in data.columns[2:len(data.columns)-2]:
        column_name.append(m)
        a=t.line(data.columns[0], m ,color=next(colors),source=source,line_dash=next(lines), alpha= 1)
     
    #Adding Label Selection Check Box List
    column_name= list(column_name)
    checkboxes = CheckboxGroup(labels = column_name, active= [0,1,2])
    
    
    show(row(t,checkboxes))

上述函数可按如下方式使用：

dataframe= pd.read_csv('data.csv')
creat_plot(dataframe)

**以上代码按以下要求执行：

  1. 散景版本：2.2.3
  2. 熊猫版本：1.1.3

该图应与复选框值链接。通过复选框选择的特征应仅绘制。

输出附在此处，但复选框选择的功能不起作用。 要求是选中功能对应的复选框，以便绘制所选功能

上述需求的解决方案如下：

import pandas as pd
from bokeh.plotting import figure
from bokeh.io import show,output_file
from bokeh.models import CustomJS,HoverTool,ColumnDataSource,Select
from bokeh.models.widgets import CheckboxGroup
from bokeh.models.annotations import Title, Legend
import itertools
from bokeh.palettes import inferno
from bokeh.layouts import row

def creat_plot(dataframe):
    data=dataframe
    #Converting the timestamp Column to Timestamp datatype so that it can be used for Plotting on X-axis
    data['timestamp'] = pd.to_datetime(data['timestamp'])
    
    #Segregating Date and Time from timestamp column. It will be used in Hover Tool
    date = lambda x: str(x)[:10]
    data['date'] = data[['timestamp']].applymap(date)
    time= lambda x: str(x)[11:]
    data['time'] = data[['timestamp']].applymap(time)
    
    #Converting whole dataframe ColumnDatasource for easy usage in hover tool
    source = ColumnDataSource(data)
    
    # List all the tools that you want in your plot separated by comas, all in one string.
    TOOLS="crosshair,pan,wheel_zoom,box_zoom,reset,hover"
    
    # New figure
    t = figure(x_axis_type = "datetime", width=1500, height=600,tools=TOOLS,title="Plot for Interactive Visualization")
    
    #X-axis Legend Formatter
    t.xaxis.formatter.days = '%d/%m/%Y'
    
    #Axis Labels
    t.yaxis.axis_label = 'Count'
    t.xaxis.axis_label = 'Date and Time Span'
    
    #Grid Line Formatter
    t.ygrid.minor_grid_line_color = 'navy'
    t.ygrid.minor_grid_line_alpha = 0.1
    t.xgrid.visible = True
    t.ygrid.visible= True
    
    #Hover Tool Usage
    t.select_one(HoverTool).tooltips = [('Date', '@date'),('Time', '@time')]
    
    #A color iterator creation
    colors = itertools.cycle(inferno(len(data.columns)))
    
    #A Line type iterator creation
    line_types= ['solid','dashed','dotted','dotdash','dashdot']
    lines= itertools.cycle(line_types)
    
    feature_lines = []
    column_name=[]
    #Looping over the columns to plot the Data
    for m in data.columns[2:len(data.columns)-2]:
        column_name.append(m)
        #Solution to my question is here
        feature_lines.append(t.line(data.columns[0], m ,color=next(colors),source=source,line_dash=next(lines), alpha= 1, visible=False))
        
         
    #Adding Label Selection Check Box List
    column_name= list(column_name)
    
   #Solution to my question, 
    checkbox = CheckboxGroup(labels=column_name, active=[])
    #Solution to my question
    callback = CustomJS(args=dict(feature_lines=feature_lines, checkbox=checkbox), code="""
                                                            for (let i=0; i<feature_lines.length; ++i) {
                                                                feature_lines[i].visible = i in checkbox.active
                                                            }
                                                            """)
    checkbox.js_on_change('active', callback)
    output_file('Interactive_data_visualization.html')
    show(row(t, checkbox))
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