我认为这里有一些问题。第一个问题是，当您绘制由"Category"（例如"Big"或"Small"）过滤的数据时，您将绘制重复日期的数据，这就是导致线在时间上倒退的原因，这使得绘制很难解释。
相反，您可以通过对col_color, col_filter执行groupby来添加记录道—在您的示例中，如果按["Animal", "Category"]分组，则应该有四个唯一的组，因此有四个记录道。
然后，我认为您可以重新设计按钮，根据跟踪的名称是否包含特定的col_filter来切换每个跟踪的可见性。例如，如果您的记录道被称为("Cat","Big"),("Cat,"Small"),("Dog","Big"),("Dog","Small")，当您单击按钮“Big”时，它会将四条记录道的可见性切换为m1n 6o1p。而"All"按钮会将所有记录道的可视性设置为m1 n8o1p。

import pandas as pd
import plotly.graph_objects as go
df = pd.DataFrame({"Date": ["2022-10-01","2022-10-02","2022-10-03","2022-10-01","2022-10-02","2022-10-03","2022-10-01","2022-10-02","2022-10-03","2022-10-01","2022-10-02","2022-10-03"],
                   "Animal" :["Cat","Cat","Cat","Cat","Cat","Cat","Dog","Dog","Dog","Dog","Dog","Dog"],
                   "Category":["Small","Small","Small","Big","Big","Big","Small","Small","Small","Big","Big","Big"],
                   "Quantity":[2,4,3,5,1,2,6,5,6,4,2,1]})
df["Date"] = df["Date"].astype('datetime64')
def plot_line_go_graph(df,col_x,col_y,col_color = None,col_filter = None,add_points = False) :
    df_graph = df.copy()
    if add_points :
        param_mode='lines+markers'
        param_name='lines+markers'
    else :
        param_mode='lines'
        param_name='lines'
    fig = go.Figure()
    if col_filter is None :
        if col_color is None :
            fig.add_trace(go.Scatter(x=df_graph[col_x], y=df_graph[col_y],mode=param_mode,name=param_name))
        else :
            for c in df_graph[col_color].unique() :
                fig.add_trace(go.Scatter(x=df_graph[df_graph[col_color]==c][col_x], y=df_graph[df_graph[col_color]==c][col_y],mode=param_mode,name=c))
    else :
        df_graph[col_filter] = df_graph[col_filter].fillna("NaN")
        if col_color is None :
            fig.add_trace(go.Scatter(x=df_graph[col_x], y=df_graph[col_y],mode=param_mode,name=param_name,visible = True))
        else :
            for group, df_group in df_graph.groupby([col_color, col_filter]):
                fig.add_trace(go.Scatter(
                    x=df_group[col_x], 
                    y=df_group[col_y],
                    mode=param_mode,
                    name=f"{group}",
                    visible=True
                ))
        updatemenu = []
        buttons = []
        # button with one option for each dataframe
        buttons.append(dict(method='restyle',
                                label="All",
                                visible=True,
                                args=[{'visible' : [True]*len(fig.data)}]
                                )
                          )
        for group, df_filter in df_graph.groupby([col_filter]):
            visible_traces = [group in trace['name'] for trace in fig.data]
            buttons.append(dict(method='restyle',
                                label=group,
                                visible=True,
                                args=[{'visible' : visible_traces}]
                            ))
        # some adjustments to the updatemenus
        updatemenu = []
        your_menu = dict()
        updatemenu.append(your_menu)
        updatemenu[0]['buttons'] = buttons
        updatemenu[0]['direction'] = 'down'
        updatemenu[0]['showactive'] = True
        # add dropdown menus to the figure
        fig.update_layout(updatemenus=updatemenu)
        if col_color is None :
            fig.update_layout(showlegend=False)
    fig.update_layout({
            'plot_bgcolor': 'rgba(0,0,0,0)',
            'paper_bgcolor': 'rgba(0,0,0,0)',
        },
        hoverlabel=dict(
            #bgcolor="white", 
            font_size=12, 
            #font_family="Rockwell"
        ),
        hovermode = "x"
    )
    fig.update_xaxes(showspikes=True, spikecolor = 'black', showline=True, linewidth=1,linecolor='black', ticks = "outside", tickwidth = 1, tickcolor = 'black',ticklen = 5)
    fig.update_yaxes(showspikes=True, spikecolor = 'black', showline=True, linewidth=1,linecolor='black', ticks = "outside", tickwidth = 1, tickcolor = 'black',ticklen = 5)
    return fig
fig = plot_line_go_graph(df,"Date","Quantity",col_color = "Animal", col_filter = "Category",add_points = False)
fig.show()

小时