如何在python中创建交互式绘图，根据单击的位置生成新绘图？

我有各个商店、每个月和不同类别产品的销售数据

假设我有一张各商店的销售数量热图，每个轴上都有几个月

现在，当我在与特定商店和月份对应的地点单击热图时，我需要生成一个新的条形图，显示该月份和该商店中每个类别的销售额

我在SAS VA也做过类似的事情。我相信这就是所谓的互动效应

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我曾尝试搜索matplotlib的文档，但plotly还没有得到任何有用的信息。

下面是一个在Bokeh v1.1.0中如何做到这一点的示例

from bokeh.plotting import figure, show
from bokeh.models import TapTool, CustomJS, ColumnDataSource, Row, ColorBar, LinearColorMapper, BasicTicker
from bokeh.models.sources import ColumnDataSource
from bokeh.transform import transform
from bokeh.palettes import Viridis256
import random

stores = ["store 1", "store 2", "store 3"]
months = ["january", "fabruary", "march"]
x = ["store 1", "store 2", "store 3", "store 1", "store 2", "store 3", "store 1", "store 2", "store 3"]
y = ["january", "january", "january", "fabruary", "fabruary", "fabruary", "march", "march", "march"]
colors = ["#0B486B", "#79BD9A", "#CFF09E", "#79BD9A", "#0B486B", "#79BD9A", "#CFF09E", "#79BD9A", "#0B486B" ]

p1 = figure(title = "Categorical Heatmap", tools = "tap", toolbar_location = None,
            x_range = stores, y_range = months)
p1.rect(x = x, y = y, color = colors, width = 1, height = 1)

categories = ['shoes', 'pants', 'suits']
category_sales = {}
for store in stores:
    category_sales[store] = {}
    for month in months:
        category_sales[store][month] = [random.choice([i for i in range(10000)]) for r in range(3)]

dummy_category_sales = [1000, 1100, 1200]
data = {'x': categories, 'y': dummy_category_sales}
source = ColumnDataSource(data)

p2 = figure(x_range = categories)
bars = p2.vbar(x = 'x', top = 'y', source = source, bottom = 0, width = 0.5)
bars.visible = False

code = '''if (cb_data.source.selected.indices.length > 0){
            bars.visible = true;
            selected_index = cb_data.source.selected.indices[0];    
            store = cb_data.source.data['x'][selected_index]
            month = cb_data.source.data['y'][selected_index]

            bars.data_source.data['y'] = category_sales[store][month]
            bars.data_source.change.emit(); 
        }'''

p1.select_one(TapTool).callback = CustomJS(args = dict(bars = bars, category_sales = category_sales), code = code)

plots = Row(p1, p2)
show(plots)

结果: