我尝试重新创建以下示例，以显示网络上显示的“面向数据科学示例”
我编写了以下代码，对此进行了修改：
import dash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output
import pandas as pd
import plotly.graph_objs as go
# Step 1. Launch the application
app = dash.Dash()
# Step 2. Import the dataset
filepath = 'https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv'
st = pd.read_csv(filepath)
# range slider options
st['Date'] = pd.to_datetime(st.Date)
dates = ['2015-02-17', '2015-05-17', '2015-08-17', '2015-11-17',
'2016-02-17', '2016-05-17', '2016-08-17', '2016-11-17', '2017-02-17']
features = st.columns[1:-1]
opts = [{'label' : i, 'value' : i} for i in features]
# Step 3. Create a plotly figure
trace_1 = go.Scatter(x = st.Date, y = st['AAPL.High'],
name = 'AAPL HIGH',
line = dict(width = 2,
color = 'rgb(229, 151, 50)'))
layout = go.Layout(title = 'Time Series Plot',
hovermode = 'closest')
fig = go.Figure(data = [trace_1], layout = layout)
# Step 4. Create a Dash layout
app.layout = html.Div([
# a header and a paragraph
html.Div([
html.H1("This is my first dashboard"),
html.P("Dash is so interesting!!")
],
style = {'padding' : '50px' ,
'backgroundColor' : '#3aaab2'}),
# adding a plot
dcc.Graph(id = 'plot', figure = fig),
# dropdown
html.P([
html.Label("Choose a feature"),
dcc.Dropdown(
id='opt',
options=opts,
value=features[0],
multi=True
),
# range slider
html.P([
html.Label("Time Period"),
dcc.RangeSlider(id = 'slider',
marks = {i : dates[i] for i in range(0, 9)},
min = 0,
max = 8,
value = [1, 7])
], style = {'width' : '80%',
'fontSize' : '20px',
'padding-left' : '100px',
'display': 'inline-block'})
])
])
# Step 5. Add callback functions
@app.callback(Output('plot', 'figure'),
[Input('opt', 'value'),
Input('slider', 'value')])
def update_figure(input1, input2):
# filtering the data
st2 = st[(st.Date > dates[input2[0]]) & (st.Date < dates[input2[1]])]
# updating the plot
trace_1 = go.Scatter(x = st2.Date, y = st2['AAPL.High'],
name = 'AAPL HIGH',
line = dict(width = 2,
color = 'rgb(229, 151, 50)'))
trace_2 = go.Scatter(x = st2.Date, y = st2[input1],
name = str(input1),
line = dict(width = 2,
color = 'rgb(106, 181, 135)'))
fig = go.Figure(data = [trace_1, trace_2], layout = layout)
return fig
# Step 6. Add the server clause
if __name__ == '__main__':
app.run_server(debug = True)
当我更改要素输入时，它不会正确更新绘图，并且不会在绘图中显示所选要素。
回调函数或第二条曲线的图形初始化有问题。但我不知道问题出在哪里。
答案 0 :(得分：1)
由于您仅在回调中提供两个分散跟踪。从两者来看，对于'AAPL.High'，一个是静态的。因此，您需要将下拉列表值限制为Multi=False。
仅生成用于选择诸如'AAPL.LOW'之类的选项的有效图，而诸如dic之类的其他选项则不会显示第二条迹线。如果始终保留multi=True，则回调将不会终止，如果始终只选择一个选项。选择两个或多个选项后，脚本将失败，因为它将尝试在此处为数据返回块查找错误的数据：
trace_2 = go.Scatter(x = st2.Date, y = st2[**MULTIINPUT**],
name = str(input1),
line = dict(width = 2,
color = 'rgb(106, 181, 135)'))
在 MULTIINPUT处只能传递一个列ID。如果要引入更多跟踪，请使用for循环。
将代码更改为以下内容：
import dash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output
import pandas as pd
import plotly.graph_objs as go
# Step 1. Launch the application
app = dash.Dash()
# Step 2. Import the dataset
filepath = 'https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv'
st = pd.read_csv(filepath)
# range slider options
st['Date'] = pd.to_datetime(st.Date)
dates = ['2015-02-17', '2015-05-17', '2015-08-17', '2015-11-17',
'2016-02-17', '2016-05-17', '2016-08-17', '2016-11-17', '2017-02-17']
features = st.columns
opts = [{'label' : i, 'value' : i} for i in features]
# Step 3. Create a plotly figure
trace_1 = go.Scatter(x = st.Date, y = st['AAPL.High'],
name = 'AAPL HIGH',
line = dict(width = 2,
color = 'rgb(229, 151, 50)'))
layout = go.Layout(title = 'Time Series Plot',
hovermode = 'closest')
fig = go.Figure(data = [trace_1], layout = layout)
# Step 4. Create a Dash layout
app.layout = html.Div([
# a header and a paragraph
html.Div([
html.H1("This is a Test Dashboard"),
html.P("Dash is great!!")
],
style = {'padding' : '50px' ,
'backgroundColor' : '#3aaab2'}),
# adding a plot
dcc.Graph(id = 'plot', figure = fig),
# dropdown
html.P([
html.Label("Choose a feature"),
dcc.Dropdown(
id='opt',
options=opts,
value=features[0],
multi=False
),
# range slider
html.P([
html.Label("Time Period"),
dcc.RangeSlider(id = 'slider',
marks = {i : dates[i] for i in range(0, 9)},
min = 0,
max = 8,
value = [1, 7])
], style = {'width' : '80%',
'fontSize' : '20px',
'padding-left' : '100px',
'display': 'inline-block'})
])
])
# Step 5. Add callback functions
@app.callback(Output('plot', 'figure'),
[Input('opt', 'value'),
Input('slider', 'value')])
def update_figure(input1, input2):
# filtering the data
st2 = st#[(st.Date > dates[input2[0]]) & (st.Date < dates[input2[1]])]
# updating the plot
trace_1 = go.Scatter(x = st2.Date, y = st2['AAPL.High'],
name = 'AAPL HIGH',
line = dict(width = 2,
color = 'rgb(229, 151, 50)'))
trace_2 = go.Scatter(x = st2.Date, y = st2[input1],
name = str(input1),
line = dict(width = 2,
color = 'rgb(106, 181, 135)'))
fig = go.Figure(data = [trace_1, trace_2], layout = layout)
return fig
# Step 6. Add the server clause
if __name__ == '__main__':
app.run_server(debug = True)
我希望这可以解决问题并解决您的问题。 ：）