如何在Python中通过Plotly从Dash的下拉列表中选择数据集列？

我希望获得有关在Python中使用Dlot by Plotly创建仪表板的一些指导。尽管在线上有很多文档和论坛帖子，但我认为他们假设我可能会缺少一些知识，尤其是在正确使用回调实现交互性方面。

我想要实现的是一个仪表板，它将使我能够：

1. 上载Excel档案
2. 从Excel文件中选择一列
3. 运行一个函数来处理该列中的数据
4. 在表中显示结果输出数据

我试图创建一个最小的工作示例，但一直在努力。到目前为止，这是我所管理的：

import base64
import io
import pandas as pd
import dash
import dash_table
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output, State

## 1. Define overall dashboard layout

Dashboard = dash.Dash(__name__, external_stylesheets=['https://codepen.io/chriddyp/pen/bWLwgP.css'])
Dashboard.layout = html.Div([
                             html.H1('Dashboard Prototype', style={'width':'100%', 'textAlign': 'center'}),
                             dcc.Upload(
                                       id       = 'Dashboard_Input',
                                       children = html.A('Select Files'),
                                       style    = {'width': '100%', 'height': '60px', 'lineHeight': '60px', 'borderWidth': '1px',
                                                   'borderStyle': 'dashed', 'borderRadius': '5px', 'textAlign': 'center', 'display': 'inline-block'},
                                       multiple = True
                                       ),
                             html.Br(),
                             html.Div(id='Dashboard_Dropdown'),
                             html.Br(),
                             html.Div(id='Dashboard_Output')
                           ])

## 2. Define functions to upload, process and present data

def selectData(contents, filename, date):

    content_type, content_string = contents.split(',')
    decoded = base64.b64decode(content_string)

    try:
        Raw_Data = pd.read_excel(io.BytesIO(decoded))
    except Exception as e:
        return html.Div(['There was an error processing this file.'])

    Dropdown = html.Div([                      
                         html.H4 (('File Name: ', filename), style={'width':'100%', 'textAlign': 'center'}),
                         html.Br(),
                         dcc.Dropdown(
                                      id          = 'Dataset_and_Column',
                                      options     = [{'label': i, 'value': i} for i in Raw_Data.columns],
                                      style       = {'width': '100%', 'height': '60px', 'lineHeight': '60px', 'borderWidth': '1px',
                                                     'borderStyle': 'dashed', 'borderRadius': '5px', 'textAlign': 'center', 'display': 'inline-block'},
                                      placeholder =  'Select Dataset Column'
                                     ),
                         html.Br(),
                        ])

    return Dropdown

def processData(Raw_Data, Column_Label):

    # DO SOME PROCESSING THAT RESULTS IN 'TABLE':
    Table      = Table
    HTML_Table = html.Div([ 
                           dash_table.DataTable(
                                                data         = Table.to_dict('records'),
                                                columns      = [{'name': i, 'id': i} for i in Table.columns],
                                                style_cell   = {'textAlign': 'center', 'width': '100px'}, 
                                                editable     = False,
                                                style_header = {'fontWeight': 'bold', 'textAlign': 'center'},
                                                style_table  = {'maxHeight': '50ex', 'width': '100%'}
                                               ),
                           html.Hr()
                         ])
    return HTML_Table

## 4. Define callback functions

Dashboard.config.suppress_callback_exceptions = True

@Dashboard.callback(
                    Output('Dashboard_Dropdown', 'children'),
                    [Input('Dashboard_Input'   , 'contents')],
                    [State('Dashboard_Input'   , 'filename'), State('Dashboard_Input', 'last_modified')]
                   )
def createDropdown(list_of_contents, list_of_names, list_of_dates):
    if list_of_contents is not None:
       Dropdown = [selectData(c, n, d) for c, n, d in zip(list_of_contents, list_of_names, list_of_dates)]
       return Dropdown

################################################################################
################################################################################

# THIS BIT DOESN'T WORK #
# Need to make this callback function take an input of Raw_Date and Column_Label (selected from the dropdown) to run the processData() function.

@Dashboard.callback(
                    Output('Dashboard_Output'  , 'children'),
                    [Input('Dataset_and_Column', 'contents'), Input('Dataset_and_Column', 'value')],
                   )
def processSelectedColumn(contents, value):

    # HTML_Table = processData(contents, value)

    return value#HTML_Table

    # ^^^^^^ Note that this allow 'value' to be printed on the dashboard but 
    # when the commented 'HTML_Table' bits are uncommented it fails

################################################################################
################################################################################

## 6. Run dashboard

if __name__ == '__main__':
    Dashboard.run_server(debug=True)

这使我可以选择一个Excel文件，然后打印从中创建的Pandas数据框的选定列，但是我不确定如何将它们全部连接在一起以processData()在选定列上运行函数。

关于如何未正确使用或连接回调，函数等的任何想法吗？