回调说 x 和 y 不能同时是列引用列表或 o 列列表的问题

Question

考虑以下在 flask 应用程序中使用的破折号应用程序：

"""Instantiate a Dash app."""
import dash
from dash import dcc, Input, Output, html
import pandas as pd
import plotly.graph_objects as go
import plotly.express as px

from .layout import html_layout
from .data import create_cluster_dataframe, create_stacked_dataframe


def init_dashboard(server):
    """Create a Plotly Dash dashboard."""
    dash_app = dash.Dash(
        server=server,
        routes_pathname_prefix="/dashapp/",
        external_stylesheets=[
            "/static/dist/css/styles.css",
            "https://fonts.googleapis.com/css?family=Lato",
        ],
    )

    # Load DataFrame
    df = create_stacked_dataframe()
    df1 = create_cluster_dataframe()

    # Custom HTML layout
    dash_app.index_string = html_layout

    # Create Layout
    dash_app.title = "ActiSleep Monitoring App"
    dash_app.layout = html.Div(
        children=[
            dcc.Graph(
                id="histogram-graph",
                figure=go.Figure(
                    data=[
                        go.Bar(
                            x=df.loc[df["Label"] == "Sleep", "time"],
                            y=df.loc[df["Label"] == "Sleep", "percentage"],
                            name="Sleep",
                            marker_color=df.loc[df["Label"] == "Sleep", "color"].iloc[
                                0
                            ],
                        ),
                        go.Bar(
                            x=df.loc[df["Label"] == "Vigorous", "time"],
                            y=df.loc[df["Label"] == "Vigorous", "percentage"],
                            name="Vigorous",
                            marker_color=df.loc[
                                df["Label"] == "Vigorous", "color"
                            ].iloc[0],
                        ),
                        go.Bar(
                            x=df.loc[df["Label"] == "Moderate", "time"],
                            y=df.loc[df["Label"] == "Moderate", "percentage"],
                            name="Moderate",
                            marker_color=df.loc[
                                df["Label"] == "Moderate", "color"
                            ].iloc[0],
                        ),
                        go.Bar(
                            x=df.loc[df["Label"] == "Light", "time"],
                            y=df.loc[df["Label"] == "Light", "percentage"],
                            name="Light",
                            marker_color=df.loc[df["Label"] == "Light", "color"].iloc[
                                0
                            ],
                        ),
                        go.Bar(
                            x=df.loc[df["Label"] == "Sedentary", "time"],
                            y=df.loc[df["Label"] == "Sedentary", "percentage"],
                            name="Sedentary",
                            marker_color=df.loc[
                                df["Label"] == "Sedentary", "color"
                            ].iloc[0],
                        ),
                    ],
                    layout=go.Layout(
                        barmode="stack",
                        title="Activity percentage during the day(average)",
                        xaxis=dict(title="Hour of the day"),
                        yaxis=dict(title="Duration %"),
                    ),
                ),
                style={"height": "600px"},
            ),
            html.Hr(),
            html.H3(
                "Cluster Data and Projections using tsne and umap",
                style={"margin-left": "20px"},
            ),
            html.Div(
                className="row",
                children=[
                    html.Div(
                        children=[
                            html.Label(
                                ["Algorithm:"],
                                style={"font-weight": "bold", "text-align": "center"},
                            ),
                            dcc.Dropdown(
                                ["Agglomerative", "K-Medoid", "Spectral"],
                                id="algorithm",
                                value="Agglomerative"
                            ),
                        ],
                        style={"width": "33.33%", "display": "inline-block"},
                    ),
                    html.Div(
                        children=[
                            html.Label(
                                ["Metric:"],
                                style={"font-weight": "bold", "text-align": "center"},
                            ),
                            dcc.Dropdown(
                                ["Edit", "Euclidean", "Wasserstein"],
                                id="metric",
                                value="Edit"
                            ),
                        ],
                        style={"width": "33.33%", "display": "inline-block"},
                    ),
                    html.Div(
                        children=[
                            html.Label(
                                ["No. of Clusters:"],
                                style={"font-weight": "bold", "text-align": "center"},
                            ),
                            dcc.Dropdown(
                                [2, 3, 4, 5],
                                id="n_clusters",
                                value=3
                            ),
                        ],
                        style={"width": "33.33%", "display": "inline-block"},
                    ),
                ],
                style={"width": "90%", "margin": "auto"},
            ),
            dcc.Graph(
                id="tsne",
                figure=go.Figure(
                    data=px.scatter(
                        x=df1.loc[
                            (df1["algorithm_name"] == "spectral_clustering_edit")
                            & (df1["n_clusters"] == 3)
                            & (df1["metric"] == "edit"),
                            "tsne_x",
                        ],
                        y=df1.loc[
                            (df1["algorithm_name"] == "spectral_clustering_edit")
                            & (df1["n_clusters"] == 3)
                            & (df1["metric"] == "edit"),
                            "tsne_y",
                        ],
                        color=[x for x in df1["hue"]],
                    )
                ),
                style={"display": "inline-block"},
            ),
            dcc.Graph(
                id="umap",
                figure=go.Figure(
                    data=px.scatter(
                        x=df1.loc[
                            (df1["algorithm_name"] == "spectral_clustering_edit")
                            & (df1["n_clusters"] == 3)
                            & (df1["metric"] == "edit"),
                            "umap_x",
                        ],
                        y=df1.loc[
                            (df1["algorithm_name"] == "spectral_clustering_edit")
                            & (df1["n_clusters"] == 3)
                            & (df1["metric"] == "edit"),
                            "umap_y",
                        ],
                        color=[x for x in df1["hue"]],
                    )
                ),
                style={"display": "inline-block"},
            ),
        ]
    )
    init_callbacks(dash_app)

    return dash_app.server

def init_callbacks(dash_app):
    @dash_app.callback(
        [Output("tsne", "figure1"),
        Output("umap", "figure2")],
        [Input("algorithm", "value"),
        Input("metric", "value"),
        Input("n_clusters", "value")],
    )
    def update_plots(algorithm, metric, n_clusters):
        df1 = create_cluster_dataframe()
        figure1 = go.Figure(
            data=px.scatter(
                x=df1.loc[
                    (df1["algorithm_name"] == algorithm)
                    & (df1["n_clusters"] == n_clusters)
                    & (df1["metric"] == metric),
                    "tsne_x",
                ],
                y=df1.loc[
                    (df1["algorithm_name"] == algorithm)
                    & (df1["n_clusters"] == n_clusters)
                    & (df1["metric"] == metric),
                    "tsne_y",
                ],
                color=[x for x in df1["hue"]],
            )
        )
        figure2 = go.Figure(
            data=px.scatter(
                x=df1.loc[
                    (df1["algorithm_name"] == algorithm)
                    & (df1["n_clusters"] == n_clusters)
                    & (df1["metric"] == metric),
                    "umap_x",
                ],
                y=df1.loc[
                    (df1["algorithm_name"] == algorithm)
                    & (df1["n_clusters"] == n_clusters)
                    & (df1["metric"] == metric),
                    "umap_y",
                ],
                color=[x for x in df1["hue"]],
            )
        )

        return figure1, figure2

它适用于初始图，但在回调中它给出错误 ValueError: Cannot accept list of column references or list of columns for both xandy.

Answer 1

我必须在回调中使用数据之前过滤数据。现在看起来像下面这样：

def init_callbacks(dash_app):
    @dash_app.callback(
        [Output("tsne", "figure1"), Output("umap", "figure2")],
        [
            Input("algorithm", "value"),
            Input("metric", "value"),
            Input("n_clusters", "value"),
        ],
    )
    def update_plots(algorithm, metric, n_clusters):
        df1 = create_cluster_dataframe()
        print(df1)
        filtered_df = df1.loc[
            (df1["algorithm_name"] == algorithm)
            & (df1["n_clusters"] == n_clusters)
            & (df1["metric"] == metric)
        ]
        figure1 = go.Figure(
            data=px.scatter(
                filtered_df,
                x="tsne_x",
                y="tsne_y",
                color=[x for x in df1["hue"]],
            )
        )
        figure2 = go.Figure(
            data=px.scatter(
                filtered_df,
                x="umap_x",
                y="umap_y",
                color=[x for x in df1["hue"]],
            )
        )
        return figure1, figure2

回调说 x 和 y 不能同时是列引用列表或 o 列列表的问题

Ploty problem with callbacks saying x and y cannot be both list of column references or list o columns

plotly-dash