如何更新IntSlider和InsSlider框以自动适应样本大小

问题描述

我真的希望我能找到解决问题的办法。我一直在努力寻找一个，但是看起来我肯定被卡住了。

下面有一段代码，生成了两个营养素的交互式散点图。

根据“下拉列表”框中的营养对进行更新，但也根据另一个“下拉列表”框中的产品类型进行更新，更重要的是，更新为一个人选择的总体n个样本的子集（如果选择“全部”，则选择整个总体） ）。

问题是，如果选择“全部”，则nsample的数量取决于每种产品类型的样品数量或总人口。 我一直在努力寻找如何可靠地传递给IntSlider及其框的信息，这是在下拉菜单中选择产品类型时更新后的最大样品数。

非常感谢

class App:
    
    def __init__(self,df):
        self._df = df
        self._x_dropdown = self._create_indicator_dropdown(all_nutriments,1,"Nutriment1")
        self._y_dropdown = self._create_indicator_dropdown(all_nutriments,"Nutriment2")        
        self._pnns_dropdown = self._create_indicator_dropdown(all_products,"Product type")
        self._plot_container = widgets.Output()
        if self._pnns_dropdown.value == "All":
            vmax = df.shape[0]
        else:
            vmax = df[df["pnns_groups_2"] == self._pnns_dropdown.value].shape[0]
        self._nsample_slider,self._nsample_slider_box = self._create_sample_slider(200,100,vmax,100)

        _app_container = widgets.VBox([
            widgets.HBox([self._x_dropdown,self._y_dropdown]),self._plot_container,self._nsample_slider_box,self._pnns_dropdown],layout=widgets.Layout(align_items='center',flex='3 0 auto'))
        self.container = widgets.VBox([
            widgets.HTML(('<h1>Nutriment indicators for product categories</h1>'),margin='1 0 5em 0')),widgets.HBox([_app_container,widgets.HTML(USAGE,layout=widgets.Layout(margin='0 0 0 2em'))])],layout=widgets.Layout(flex='1 1 auto',margin='0 auto 0 auto',max_width='1024px'))
        self._update_app()
        
    @classmethod
    def from_url(cls,url):
        df = pd.read_csv(url,sep=";")
        return cls(df)
        
    def _create_indicator_dropdown(self,indicators,initial_index,description):
        dropdown = widgets.Dropdown(options=indicators,value=indicators[initial_index],description=description)
        dropdown.observe(self._on_change,names=['value'])
        return dropdown
    
    def _create_sample_slider(self,value,min_sample,max_sample,step):
        sample_slider_label = widgets.Label('Number of samples: ')
        sample_slider = widgets.IntSlider(value=value,min=min_sample,max=max_sample,step=step,layout=widgets.Layout(width='500px'))
        sample_slider.observe(self._on_change,names=['value'])        
        sample_slider_box = widgets.HBox([sample_slider_label,sample_slider])
        sample_slider_box.observe(self._on_change,names=['value'])
#        sample_slider.observe(self._update_nsample,names=['value'])
        return sample_slider,sample_slider_box

#    def _update_nsample(change):
#        ns = change.new
#        self._nsample_slider.max = ns

    def _create_plot(self,x_indicator,y_indicator,pnns_group,nsample):
        if pnns_group == "All":
            df = self._df
        else:
            df = self._df[self._df['pnns_groups_2'] == pnns_group]
        xs = df[x_indicator]
        ys = df[y_indicator]
        plt.rcParams["figure.figsize"] = [12,12]
        plt.rcParams.update({'font.size': 18})
        fig,ax = plt.subplots()
        colorpalette=["#008a4b","#7fc241","#feca07","#f58221","#ef3e23"]
        sns.set_palette(sns.color_palette(colorpalette))
        sns.scatterplot(x=xs,y=ys,data=df.sample(nsample),hue="nutriscore_grade",s=80,hue_order=["a","b","c","d","e"],alpha=0.9)
        ax.set_xlabel(x_indicator.split("_")[0].capitalize() + " content (g) for 100g" if x_indicator != "energy_100g" \
                      else x_indicator.split("_")[0].capitalize() + " (kcal) for 100g")
        ax.set_ylabel(y_indicator.split("_")[0].capitalize() + " content (g) for 100g" if y_indicator != "energy_100g" \
                      else y_indicator.split("_")[0].capitalize() + " (kcal) for 100g")
        
    def _on_change(self,_):
        self._update_app()
        
    def _update_app(self):
        x_indicator = self._x_dropdown.value
        y_indicator = self._y_dropdown.value
        pnns_group = self._pnns_dropdown.value
        self._nsample_slider.observe(self._update_nsample,names=['value'])
        nsample = self._nsample_slider.value
        self._plot_container.clear_output(wait=True)
        with self._plot_container:
            self._create_plot(x_indicator,nsample)
            plt.show()

解决方法

我没有定义样本数量，而是定义了一个滑块，该滑块占样本总数的百分比为：

self._nsample_slider,self._nsample_slider_box = self._create_sample_slider(10,1,100,1)

然后在_update_app函数中，使用此％来获取要绘制的实际样本数（我不得不再次使用数据框）：

def _update_app(self):
    df = self._df
    x_indicator = self._x_dropdown.value
    y_indicator = self._y_dropdown.value
    pnns_group = self._pnns_dropdown.value
    if self._pnns_dropdown.value != "All":
        nsample = int((self._nsample_slider.value / 100) * df[df["pnns_groups_2"] == self._pnns_dropdown.value].shape[0])
    else:
        nsample = int((self._nsample_slider.value / 100) * df.shape[0])
    self._plot_container.clear_output(wait=True)
    with self._plot_container:
        self._create_plot(x_indicator,y_indicator,pnns_group,nsample)
        plt.show()