我有一个破折号应用程序，我尝试在其中上传任意csv，然后通过下拉菜单访问所述数据文件的列。我有两个下拉菜单。访问列之后，我想计算两者之间的相关性并绘制它们。但是，我一直试图找出如何访问下拉列表中的上传数据列。我有一个工作的Shiny应用程序可以执行此操作，但是我正在尝试使用Dash复制它。我对Dash真的很陌生，所以这可能是一个简单的修复。我的代码在下面！
import os
import io
import json
import dash
import base64
import plotly
import datetime
import operator
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import dash_core_components as dcc
import dash_html_components as html
import dash_table_experiments as dte
from dash.dependencies import Input, Output, State
app = dash.Dash()
app.scripts.config.serve_locally = True
app.config['suppress_callback_exceptions'] = True
app.layout = html.Div([
html.H5("Upload Files"),
dcc.Upload(
id='upload-data',
children=html.Div([
'Drag and Drop or ',
html.A('Select Files')
]),
style={
'width': '15%',
'height': '60px',
'lineHeight': '60px',
'borderWidth': '1px',
'borderStyle': 'dashed',
'borderRadius': '5px',
'textAlign': 'left',
'margin': '10px'
},
multiple=False),
html.Div([
html.H5("First Column"),
dcc.Dropdown(
id = 'y-dropdown',
options = [],
)
]),
html.Div([
html.H5("Second Column"),
dcc.Dropdown(
id = 'x-dropdown',
options = [],
)
]),
html.Br(),
html.Button(
id = 'propagate-button',
n_clicks = 0,
children = 'Propagate Table Data'
),
html.Br(),
html.H5("Updated Table"),
html.Div(
dte.DataTable(rows = [{}], id = 'table')
),
html.Div(
dcc.Graph(
id = 'graph'
)
)
])
## Functions
# File upload function
def parse_contents(contents, filename):
content_type, content_string = contents.split(',')
decoded = base64.b64decode(content_string)
try:
if 'csv' in filename:
# Assume that the user uploaded a CSV file
df = pd.read_csv(
io.StringIO(decoded.decode('utf-8')))
elif 'xls' in filename:
# Assume that the user uploaded an excel file
df = pd.read_excel(io.BytesIO(decoded))
except Exception as e:
print(e)
return None
return df
## Callbacks
# Table creation
@app.callback(Output('table', 'rows'),
[Input('upload-data', 'contents'),
Input('upload-data', 'filename')])
def update_output(contents, filename):
if contents is not None:
df = parse_contents(contents, filename)
if df is not None:
return df.to_dict('records')
else:
return [{}]
else:
return [{}]
app.css.append_css({
"external_url": "https://codepen.io/chriddyp/pen/bWLwgP.css"
})
if __name__ == '__main__':
app.run_server(debug=True)
答案 0 :(得分：0)
我认为您无法创建具有多个输出的单个回调，因此您需要为每个下拉列表和表格创建一个单独的回调。请参考下面的代码。
import os
import io
import json
import dash
import base64
import plotly
import datetime
import operator
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import dash_core_components as dcc
import dash_html_components as html
import dash_table_experiments as dte
from dash.dependencies import Input, Output, State
app = dash.Dash()
app.scripts.config.serve_locally = True
app.config['suppress_callback_exceptions'] = True
app.layout = html.Div([
html.H5("Upload Files"),
dcc.Upload(
id='upload-data',
children=html.Div([
'Drag and Drop or ',
html.A('Select Files')
]),
style={
'width': '15%',
'height': '60px',
'lineHeight': '60px',
'borderWidth': '1px',
'borderStyle': 'dashed',
'borderRadius': '5px',
'textAlign': 'left',
'margin': '10px'
},
multiple=False),
html.Div([
html.H5("First Column"),
dcc.Dropdown(
id = 'y-dropdown',
options = [],
)
]),
html.Div([
html.H5("Second Column"),
dcc.Dropdown(
id = 'x-dropdown',
options = [],
)
]),
html.Br(),
html.Button(
id = 'propagate-button',
n_clicks = 0,
children = 'Propagate Table Data'
),
html.Br(),
html.H5("Updated Table"),
html.Div(
dte.DataTable(rows = [{}], id = 'table')
),
html.Div(
dcc.Graph(
id = 'graph'
)
)
])
## Functions
# File upload function
def parse_contents(contents, filename):
content_type, content_string = contents.split(',')
decoded = base64.b64decode(content_string)
try:
if 'csv' in filename:
# Assume that the user uploaded a CSV file
df = pd.read_csv(io.StringIO(decoded.decode('utf-8')))
elif 'xls' in filename:
# Assume that the user uploaded an excel file
df = pd.read_excel(io.BytesIO(decoded))
except Exception as e:
print(e)
return None
return df
## Callbacks
# Table creation
@app.callback(Output('table', 'rows'),
[Input('upload-data', 'contents'),
Input('upload-data', 'filename')])
def update_output(contents, filename):
if contents is not None:
df = parse_contents(contents, filename)
columns = df.columns.values.tolist()
if df is not None:
return df.to_dict('records')
else:
return [{}]
else:
return [{}]
# update y-dropdown
@app.callback(Output('y-dropdown', 'options'),
[Input('upload-data', 'contents'),
Input('upload-data', 'filename')])
def update_y_dropdown(contents, filename):
if contents is not None:
df = parse_contents(contents, filename)
columns = df.columns.values.tolist()
if df is not None:
return [ {'label': x, 'value': x} for x in columns ]
else:
return []
else:
return []
# update x-dropdown
@app.callback(Output('x-dropdown', 'options'),
[Input('upload-data', 'contents'),
Input('upload-data', 'filename')])
def update_x_dropdown(contents, filename):
if contents is not None:
df = parse_contents(contents, filename)
columns = df.columns.values.tolist()
if df is not None:
return [ {'label': x, 'value': x} for x in columns ]
else:
return []
else:
return []
if __name__ == '__main__':
app.run_server(debug=True)