如何正确地为 Dash Web 应用程序执行回调和函数

Question

非常感谢有关如何为我的 Dash Web 应用程序进行正确回调的一些帮助。我在下面有以下代码。它应该返回一个图表，其中包含有关股票财务状况的各种线条。它从 API 获取数据。但我不确定如何为应用回调定义我的函数。我尝试过遵循在线教程，但取得了任何成功。如果它太混乱或效率低下，我深表歉意，我是新手。'''#!/usr/bin/env pythonimport pandas as pdimport requestsimport jsonimport plotlyimport chart_studio.plotly as pyimport plotly.graph_objs as goimport plotly.express as pximport dashimport dash_core_components as dccimport dash_html_components as htmlfrom dash.dependencies import Output, Inputticker = input("Insert company ticker: ")qoy = input("QUARTERLY or YEARLY: ")if qoy == "Yearly" or qoy == "yearly" or qoy == "YEARLY":    IS = requests.get("https://financialmodelingprep.com/api/v3/financials/income-statement/" + ticker)elif qoy == "Quarterly" or qoy == "quarterly" or qoy == "QUARTERLY":    IS = requests.get(        "https://financialmodelingprep.com/api/v3/financials/income-statement/" + ticker + "?period=quarter")IS = IS.json()IS = IS['financials']IS = pd.DataFrame.from_dict(IS)IS = IS.set_index("date")if qoy == "Yearly" or qoy == "yearly" or qoy == "YEARLY":    BS = requests.get("https://financialmodelingprep.com/api/v3/financials/balance-sheet-statement/" + ticker)elif qoy == "Quarterly" or qoy == "quarterly" or qoy == "QUARTERLY":    BS = requests.get(        "https://financialmodelingprep.com/api/v3/financials/balance-sheet-statement/" + ticker + "?period=quarter")BS = BS.json()BS = BS['financials']BS = pd.DataFrame.from_dict(BS)BS = BS.set_index("date")if qoy == "Yearly" or qoy == "yearly" or qoy == "YEARLY":    CF = requests.get("https://financialmodelingprep.com/api/v3/financials/cash-flow-statement/" + ticker)elif qoy == "Quarterly" or qoy == "quarterly" or qoy == "QUARTERLY":    CF = requests.get(        "https://financialmodelingprep.com/api/v3/financials/cash-flow-statement/" + ticker + "?period=quarter")CF = CF.json()CF = CF['financials']CF = pd.DataFrame.from_dict(CF)CF = CF.set_index("date")df_FS = pd.concat([IS, BS, CF], axis=1, sort=True)Date = df_FS.indexdf_FS.fillna(0, inplace=True)print(df_FS)

Answer 1

您必须在回调中执行所有数据处理，因为它们需要输入和按钮值。有多种方法可以解决此问题，具体取决于您希望应用程序如何响应输入。一种方法是这样。在这里，主要输入是qoy，回调使用stock input. 因此，在您选择 之前，输入股票输入不会更新应用程序qoy。

#!/usr/bin/env python

from plotly.subplots import make_subplots

import pandas as pd

import requests

import json

import plotly

import chart_studio.plotly as py

import plotly.graph_objs as go

import plotly.express as px

import dash

import dash_core_components as dcc

import dash_html_components as html

from dash.dependencies import Output, Input, State

from dash.exceptions import PreventUpdate

external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']

app = dash.Dash(__name__, external_stylesheets=external_stylesheets)

app.layout = html.Div([

    # Input the stock ticker

    html.Div([

        dcc.Input(id="stock-input",

                  placeholder='Please insert stock', type="text"),

        dcc.RadioItems(

            id="quarterlyoryearly",

            options=[

                {'label': 'Quarterly', 'value': 'quarterly'},

                {'label': 'Yearly', 'value': 'yearly'}

            ]

        )

    ]),

    # Banner of app

    html.Div([

        html.H2("Stock App")

    ], className="banner"),

    # Graphs

    html.Div([

        # Earnings & Revenue Graph

        html.Div([

            dcc.Graph(

                id="Earnings & Revenue",

                # figure=fig

            )

        ], className="six columns"),

    ], className="row")

])

@app.callback(Output("quarterlyoryearly", "value"),

              [Input("stock-input", "n_submit")],

              [State("quarterlyoryearly", "value")])

def enter_key(n_sub, qoy):

    if n_sub:

        return qoy

@app.callback(dash.dependencies.Output("Earnings & Revenue", "figure"),

              [dash.dependencies.Input("quarterlyoryearly", "value")],

              [dash.dependencies.State("stock-input", "value")]

              )

def update_fig(*args):

    if not any(args):

        raise PreventUpdate

    else:

        qoy, ticker = args

        if qoy.lower() == "yearly":

            IS = requests.get(

                "https://financialmodelingprep.com/api/v3/financials/income-statement/" + ticker)

        elif qoy.lower() == "quarterly":

            IS = requests.get(

                "https://financialmodelingprep.com/api/v3/financials/income-statement/" + ticker + "?period=quarter")

        IS = IS.json()

        IS = IS['financials']

        IS = pd.DataFrame.from_dict(IS)

        IS = IS.set_index("date")

        if qoy == "Yearly" or qoy == "yearly" or qoy == "YEARLY":

            BS = requests.get(

                "https://financialmodelingprep.com/api/v3/financials/balance-sheet-statement/" + ticker)

        elif qoy == "Quarterly" or qoy == "quarterly" or qoy == "QUARTERLY":

            BS = requests.get(

                "https://financialmodelingprep.com/api/v3/financials/balance-sheet-statement/" + ticker + "?period=quarter")

        BS = BS.json()

        BS = BS['financials']

        BS = pd.DataFrame.from_dict(BS)

        BS = BS.set_index("date")

        if qoy == "Yearly" or qoy == "yearly" or qoy == "YEARLY":

            CF = requests.get(

                "https://financialmodelingprep.com/api/v3/financials/cash-flow-statement/" + ticker)

        elif qoy == "Quarterly" or qoy == "quarterly" or qoy == "QUARTERLY":

            CF = requests.get(

                "https://financialmodelingprep.com/api/v3/financials/cash-flow-statement/" + ticker + "?period=quarter")

        CF = CF.json()

        CF = CF['financials']

        CF = pd.DataFrame.from_dict(CF)

        CF = CF.set_index("date")

        df_FS = pd.concat([IS, BS, CF], axis=1, sort=True)

        Date = df_FS.index

        df_FS.fillna(0, inplace=True)

        # EARNINGS & REVENUE

        fig = make_subplots(specs=[[{"secondary_y": True}]])

        fig.add_trace(go.Scatter(x=Date, y=df_FS['Revenue'],

                                 mode='lines+markers',

                                 name='Revenue'), secondary_y=False, )

        fig.add_trace(go.Bar(x=Date, y=df_FS['Profit Margin'],

                             opacity=0.2,

                             name='Profit Margin'), secondary_y=True, )

        fig.add_trace(go.Scatter(x=Date, y=df_FS['Consolidated Income'],

                                 mode='lines+markers',

                                 name='Earnings'), secondary_y=False, )

        fig.add_trace(go.Scatter(x=Date, y=df_FS['Operating Cash Flow'],

                                 mode='lines+markers',

                                 name='Operating Cash Flow'), secondary_y=False, )

        fig.add_trace(go.Scatter(x=Date, y=df_FS['Free Cash Flow'],

                                 mode='lines+markers',

                                 name='Free Cash Flow'), secondary_y=False, )

        fig.add_trace(go.Scatter(x=Date, y=df_FS['Operating Expenses'],

                                 mode='lines+markers',

                                 name='Operating Expenses'), secondary_y=False, )

        fig.update_layout(title="EARNINGS & REVENUE",

                          barmode='group', hovermode='x')

        fig.update_yaxes(title_text="in USD", secondary_y=False)

        fig.update_yaxes(title_text="Profit Margin", secondary_y=True)

        fig.update_xaxes(rangeslider_visible=True)

        return fig

if __name__ == '__main__':

    app.run_server(debug=False)

你可以玩转输入和状态选项，看看应用程序是如何响应的。干杯!

PS：我添加了另一个回调，所以在第一个qoy和ticker值之后，您可以简单地更改ticker输入字段并点击输入按钮（因为qoy已经选择了）来更新您的应用程序。希望这能让您更深入地了解回调是如何工作的。