Plotly-Dash：如何基于两个不同的数据框创建图形？
Plotly-Dash: How to create a figure based on two different dataframes?
我正在尝试创建一个来自破折号中两个独立数据框的图表。
对于可复制性，请考虑以下内容：
date = ["2020-01-01","2020-02-01","2020-03-01","2020-04-01","2020-05-01","2020-06-01"
,"2020-07-01","2020-08-01","2020-09-01","2020-10-01","2020-11-01","2020-12-01"
,"2020-01-01","2020-02-01","2020-03-01","2020-04-01","2020-05-01","2020-06-01"
,"2020-07-01","2020-08-01","2020-09-01","2020-10-01","2020-11-01","2020-12-01"
,"2020-01-01","2020-02-01","2020-03-01","2020-04-01","2020-05-01","2020-06-01"
,"2020-07-01","2020-08-01","2020-09-01","2020-10-01","2020-11-01","2020-12-01"
,"2020-01-01","2020-02-01","2020-03-01","2020-04-01","2020-05-01","2020-06-01"
,"2020-07-01","2020-08-01","2020-09-01","2020-10-01","2020-11-01","2020-12-01"
,"2020-01-01","2020-02-01","2020-03-01","2020-04-01","2020-05-01","2020-06-01"
,"2020-07-01","2020-08-01","2020-09-01","2020-10-01","2020-11-01","2020-12-01"
,"2020-01-01","2020-02-01","2020-03-01","2020-04-01","2020-05-01","2020-06-01"
,"2020-07-01","2020-08-01","2020-09-01","2020-10-01","2020-11-01","2020-12-01"]
region = ["Luzon","Luzon","Luzon","Luzon","Luzon","Luzon","Luzon","Luzon","Luzon","Luzon","Luzon","Luzon"
,"Visayas","Visayas","Visayas","Visayas","Visayas","Visayas","Visayas","Visayas","Visayas","Visayas"
,"Visayas","Visayas"
,"Mindanao","Mindanao","Mindanao","Mindanao","Mindanao","Mindanao","Mindanao","Mindanao","Mindanao","Mindanao"
,"Mindanao","Mindanao"
,"Luzon","Luzon","Luzon","Luzon","Luzon","Luzon","Luzon","Luzon","Luzon","Luzon","Luzon","Luzon"
,"Visayas","Visayas","Visayas","Visayas","Visayas","Visayas","Visayas","Visayas","Visayas","Visayas"
,"Visayas","Visayas"
,"Mindanao","Mindanao","Mindanao","Mindanao","Mindanao","Mindanao","Mindanao","Mindanao","Mindanao","Mindanao"
,"Mindanao","Mindanao"]
value = ["1","2","3","4","5","6","7","8","9","10","11","12",
"1","2","3","4","5","6","7","8","9","10","11","12",
"1","2","3","4","5","6","7","8","9","10","11","12"
,"1","2","3","4","5","6","7","8","9","10","11","12",
"1","2","3","4","5","6","7","8","9","10","11","12",
"1","2","3","4","5","6","7","8","9","10","11","12"]
product = ["rice","rice","rice","rice","rice","rice","rice","rice","rice","rice","rice","rice"
,"rice","rice","rice","rice","rice","rice","rice","rice","rice","rice","rice","rice"
,"rice","rice","rice","rice","rice","rice","rice","rice","rice","rice","rice","rice"
,"fish","fish","fish","fish","fish","fish","fish","fish","fish","fish","fish","fish"
,"fish","fish","fish","fish","fish","fish","fish","fish","fish","fish","fish","fish"
,"fish","fish","fish","fish","fish","fish","fish","fish","fish","fish","fish","fish"]
date = [[i] for i in date]
region = [[i] for i in region]
value = [[i] for i in value]
product = [[i] for i in product]
date = pd.DataFrame(date, columns = ["DATE"])
region = pd.DataFrame(region, columns = ["state"])
value = pd.DataFrame(value, columns = ["value"])
product = pd.DataFrame(product, columns = ["product"])
state_level = pd.concat([date,region, value, product], axis=1)
date = ["2020-01-01","2020-02-01","2020-03-01","2020-04-01","2020-05-01","2020-06-01"
,"2020-07-01","2020-08-01","2020-09-01","2020-10-01","2020-11-01","2020-12-01"
,"2020-01-01","2020-02-01","2020-03-01","2020-04-01","2020-05-01","2020-06-01"
,"2020-07-01","2020-08-01","2020-09-01","2020-10-01","2020-11-01","2020-12-01"
,"2020-01-01","2020-02-01","2020-03-01","2020-04-01","2020-05-01","2020-06-01"
,"2020-07-01","2020-08-01","2020-09-01","2020-10-01","2020-11-01","2020-12-01"
,"2020-01-01","2020-02-01","2020-03-01","2020-04-01","2020-05-01","2020-06-01"
,"2020-07-01","2020-08-01","2020-09-01","2020-10-01","2020-11-01","2020-12-01"
,"2020-01-01","2020-02-01","2020-03-01","2020-04-01","2020-05-01","2020-06-01"
,"2020-07-01","2020-08-01","2020-09-01","2020-10-01","2020-11-01","2020-12-01"
,"2020-01-01","2020-02-01","2020-03-01","2020-04-01","2020-05-01","2020-06-01"
,"2020-07-01","2020-08-01","2020-09-01","2020-10-01","2020-11-01","2020-12-01"]
city = ["Metro Manila","Metro Manila","Metro Manila","Metro Manila","Metro Manila","Metro Manila","Metro Manila","Metro Manila","Metro Manila","Metro Manila","Metro Manila","Metro Manila"
,"Metro Cebu","Metro Cebu","Metro Cebu","Metro Cebu","Metro Cebu","Metro Cebu","Metro Cebu","Metro Cebu","Metro Cebu","Metro Cebu"
,"Metro Cebu","Metro Cebu"
,"Metro Davao","Metro Davao","Metro Davao","Metro Davao","Metro Davao","Metro Davao","Metro Davao","Metro Davao","Metro Davao","Metro Davao"
,"Metro Davao","Metro Davao"
,"Metro Manila","Metro Manila","Metro Manila","Metro Manila","Metro Manila","Metro Manila","Metro Manila","Metro Manila","Metro Manila","Metro Manila","Metro Manila","Metro Manila"
,"Metro Cebu","Metro Cebu","Metro Cebu","Metro Cebu","Metro Cebu","Metro Cebu","Metro Cebu","Metro Cebu","Metro Cebu","Metro Cebu"
,"Metro Cebu","Metro Cebu"
,"Metro Davao","Metro Davao","Metro Davao","Metro Davao","Metro Davao","Metro Davao","Metro Davao","Metro Davao","Metro Davao","Metro Davao"
,"Metro Davao","Metro Davao"]
value = ["1","2","3","4","5","6","7","8","9","10","11","12",
"1","2","3","4","5","6","7","8","9","10","11","12",
"1","2","3","4","5","6","7","8","9","10","11","12"
,"1","2","3","4","5","6","7","8","9","10","11","12",
"1","2","3","4","5","6","7","8","9","10","11","12",
"1","2","3","4","5","6","7","8","9","10","11","12"]
product = ["rice","rice","rice","rice","rice","rice","rice","rice","rice","rice","rice","rice"
,"rice","rice","rice","rice","rice","rice","rice","rice","rice","rice","rice","rice"
,"rice","rice","rice","rice","rice","rice","rice","rice","rice","rice","rice","rice"
,"fish","fish","fish","fish","fish","fish","fish","fish","fish","fish","fish","fish"
,"fish","fish","fish","fish","fish","fish","fish","fish","fish","fish","fish","fish"
,"fish","fish","fish","fish","fish","fish","fish","fish","fish","fish","fish","fish"]
date = [[i] for i in date]
city = [[i] for i in city]
value = [[i] for i in value]
product = [[i] for i in product]
date = pd.DataFrame(date, columns = ["DATE"])
city = pd.DataFrame(city, columns = ["city"])
value = pd.DataFrame(value, columns = ["value"])
product = pd.DataFrame(product, columns = ["product"])
city_level = pd.concat([date,city, value, product], axis=1)
考虑 plotly 中的示例图：
import plotly.graph_objects as go
historical = state_level[state_level["state"]=="Visayas"]
historical = historical[historical["product"]=="fish"]
historical
fig = go.Figure()
fig.add_trace(go.Scatter(x=historical["DATE"]
,y=historical["value"]
,name="Historical",
mode="lines"
,line={"color": "grey", "width": 1}
,showlegend=False))
fig.show()
图表是：
我想创建一个动态仪表板，用户可以在州级别或城市级别进行选择，选择州或城市，然后选择产品（大米或鱼），图表将动态变化根据用户输入，我的代码如下：
state = [str(i) for i in state_level.state.unique()]
state.sort(reverse = False)
city = [i for i in city_level.city.unique()]
city.sort(reverse = False)
import plotly.express as px
from jupyter_dash import JupyterDash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output
import pandas as pd
app = JupyterDash(__name__)
all_options = {
'State': state,
'City': city
}
app.layout = html.Div([
dcc.Dropdown(
id='Level1',
options=[{'label': k, 'value': k} for k in all_options.keys()],
value='State'
),
html.Hr(),
dcc.Dropdown(id='Level2'),
html.Hr(),
dcc.Dropdown(id='Product',options=[{'label': k, 'value': k} for k in ["rice","fish"]],
value='Rice'),
html.Hr(),
html.Div(id='display-selected-values')
])
@app.callback(
Output('Level2', 'options'),
[Input('Level1', 'value')])
def set_options(selected_level1):
return [{'label': i, 'value': i} for i in all_options[selected_level1]]
app.run_server(mode='inline')
但鉴于我的来源来自两个不同的数据帧，我不知道该怎么做。
一种方法是将数据帧存储在字典中，如下所示：
dfs = {'state': state_level,
'city': city_level}
然后设置一些动态回调以用作如下图的输入：
# Make a figure based on the selections
@app.callback( # Columns 2m_temp_prod, or....
Output('Products_graph', 'figure'),
[Input('countries-radio', 'value'),
Input('cities-radio', 'value'),
Input('products-radio', 'value')])
def make_graph(level, area, product):
# level='city'
# area='Metro Manila'
# product = 'fish'
df = dfs[level]
df = df[df[df.columns[1]]==area]
df = df[df['product']==product]
fig=go.Figure()
fig.add_traces(go.Scatter(x=df.DATE, y =df['value'] ))
return fig
你的价值观变化很小，这让你很难看出动态是否像他们所展示的那样起作用，因为你总是以一条直线结束。但是，当您有时间查看下面代码片段中的详细信息时，我们可以讨论详细信息。这是生成的应用程序：
在此设置中，区域选项将根据城市或州级别而变化
完整代码：
import dash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output
from jupyter_dash import JupyterDash
import plotly.graph_objects as go
# data
import pandas as pd
date = ["2020-01-01","2020-02-01","2020-03-01","2020-04-01","2020-05-01","2020-06-01"
,"2020-07-01","2020-08-01","2020-09-01","2020-10-01","2020-11-01","2020-12-01"
,"2020-01-01","2020-02-01","2020-03-01","2020-04-01","2020-05-01","2020-06-01"
,"2020-07-01","2020-08-01","2020-09-01","2020-10-01","2020-11-01","2020-12-01"
,"2020-01-01","2020-02-01","2020-03-01","2020-04-01","2020-05-01","2020-06-01"
,"2020-07-01","2020-08-01","2020-09-01","2020-10-01","2020-11-01","2020-12-01"
,"2020-01-01","2020-02-01","2020-03-01","2020-04-01","2020-05-01","2020-06-01"
,"2020-07-01","2020-08-01","2020-09-01","2020-10-01","2020-11-01","2020-12-01"
,"2020-01-01","2020-02-01","2020-03-01","2020-04-01","2020-05-01","2020-06-01"
,"2020-07-01","2020-08-01","2020-09-01","2020-10-01","2020-11-01","2020-12-01"
,"2020-01-01","2020-02-01","2020-03-01","2020-04-01","2020-05-01","2020-06-01"
,"2020-07-01","2020-08-01","2020-09-01","2020-10-01","2020-11-01","2020-12-01"]
region = ["Luzon","Luzon","Luzon","Luzon","Luzon","Luzon","Luzon","Luzon","Luzon","Luzon","Luzon","Luzon"
,"Visayas","Visayas","Visayas","Visayas","Visayas","Visayas","Visayas","Visayas","Visayas","Visayas"
,"Visayas","Visayas"
,"Mindanao","Mindanao","Mindanao","Mindanao","Mindanao","Mindanao","Mindanao","Mindanao","Mindanao","Mindanao"
,"Mindanao","Mindanao"
,"Luzon","Luzon","Luzon","Luzon","Luzon","Luzon","Luzon","Luzon","Luzon","Luzon","Luzon","Luzon"
,"Visayas","Visayas","Visayas","Visayas","Visayas","Visayas","Visayas","Visayas","Visayas","Visayas"
,"Visayas","Visayas"
,"Mindanao","Mindanao","Mindanao","Mindanao","Mindanao","Mindanao","Mindanao","Mindanao","Mindanao","Mindanao"
,"Mindanao","Mindanao"]
value = ["1","2","3","4","5","6","7","8","9","10","11","12",
"1","2","3","4","5","6","7","8","9","10","11","12",
"1","2","3","4","5","6","7","8","9","10","11","12"
,"1","2","3","4","5","6","7","8","9","10","11","12",
"1","2","3","4","5","6","7","8","9","10","11","12",
"1","2","3","4","5","6","7","8","9","10","11","12"]
product = ["rice","rice","rice","rice","rice","rice","rice","rice","rice","rice","rice","rice"
,"rice","rice","rice","rice","rice","rice","rice","rice","rice","rice","rice","rice"
,"rice","rice","rice","rice","rice","rice","rice","rice","rice","rice","rice","rice"
,"fish","fish","fish","fish","fish","fish","fish","fish","fish","fish","fish","fish"
,"fish","fish","fish","fish","fish","fish","fish","fish","fish","fish","fish","fish"
,"fish","fish","fish","fish","fish","fish","fish","fish","fish","fish","fish","fish"]
date = [[i] for i in date]
region = [[i] for i in region]
value = [[i] for i in value]
product = [[i] for i in product]
date = pd.DataFrame(date, columns = ["DATE"])
region = pd.DataFrame(region, columns = ["state"])
value = pd.DataFrame(value, columns = ["value"])
product = pd.DataFrame(product, columns = ["product"])
state_level = pd.concat([date,region, value, product], axis=1)
date = ["2020-01-01","2020-02-01","2020-03-01","2020-04-01","2020-05-01","2020-06-01"
,"2020-07-01","2020-08-01","2020-09-01","2020-10-01","2020-11-01","2020-12-01"
,"2020-01-01","2020-02-01","2020-03-01","2020-04-01","2020-05-01","2020-06-01"
,"2020-07-01","2020-08-01","2020-09-01","2020-10-01","2020-11-01","2020-12-01"
,"2020-01-01","2020-02-01","2020-03-01","2020-04-01","2020-05-01","2020-06-01"
,"2020-07-01","2020-08-01","2020-09-01","2020-10-01","2020-11-01","2020-12-01"
,"2020-01-01","2020-02-01","2020-03-01","2020-04-01","2020-05-01","2020-06-01"
,"2020-07-01","2020-08-01","2020-09-01","2020-10-01","2020-11-01","2020-12-01"
,"2020-01-01","2020-02-01","2020-03-01","2020-04-01","2020-05-01","2020-06-01"
,"2020-07-01","2020-08-01","2020-09-01","2020-10-01","2020-11-01","2020-12-01"
,"2020-01-01","2020-02-01","2020-03-01","2020-04-01","2020-05-01","2020-06-01"
,"2020-07-01","2020-08-01","2020-09-01","2020-10-01","2020-11-01","2020-12-01"]
city = ["Metro Manila","Metro Manila","Metro Manila","Metro Manila","Metro Manila","Metro Manila","Metro Manila","Metro Manila","Metro Manila","Metro Manila","Metro Manila","Metro Manila"
,"Metro Cebu","Metro Cebu","Metro Cebu","Metro Cebu","Metro Cebu","Metro Cebu","Metro Cebu","Metro Cebu","Metro Cebu","Metro Cebu"
,"Metro Cebu","Metro Cebu"
,"Metro Davao","Metro Davao","Metro Davao","Metro Davao","Metro Davao","Metro Davao","Metro Davao","Metro Davao","Metro Davao","Metro Davao"
,"Metro Davao","Metro Davao"
,"Metro Manila","Metro Manila","Metro Manila","Metro Manila","Metro Manila","Metro Manila","Metro Manila","Metro Manila","Metro Manila","Metro Manila","Metro Manila","Metro Manila"
,"Metro Cebu","Metro Cebu","Metro Cebu","Metro Cebu","Metro Cebu","Metro Cebu","Metro Cebu","Metro Cebu","Metro Cebu","Metro Cebu"
,"Metro Cebu","Metro Cebu"
,"Metro Davao","Metro Davao","Metro Davao","Metro Davao","Metro Davao","Metro Davao","Metro Davao","Metro Davao","Metro Davao","Metro Davao"
,"Metro Davao","Metro Davao"]
value = ["1","2","3","4","5","6","7","8","9","10","11","12",
"1","2","3","4","5","6","7","8","9","10","11","12",
"1","2","3","4","5","6","7","8","9","10","11","12"
,"1","2","3","4","5","6","7","8","9","10","11","12",
"1","2","3","4","5","6","7","8","9","10","11","12",
"1","2","3","4","5","6","7","8","9","10","11","12"]
product = ["rice","rice","rice","rice","rice","rice","rice","rice","rice","rice","rice","rice"
,"rice","rice","rice","rice","rice","rice","rice","rice","rice","rice","rice","rice"
,"rice","rice","rice","rice","rice","rice","rice","rice","rice","rice","rice","rice"
,"fish","fish","fish","fish","fish","fish","fish","fish","fish","fish","fish","fish"
,"fish","fish","fish","fish","fish","fish","fish","fish","fish","fish","fish","fish"
,"fish","fish","fish","fish","fish","fish","fish","fish","fish","fish","fish","fish"]
date = [[i] for i in date]
city = [[i] for i in city]
value = [[i] for i in value]
product = [[i] for i in product]
date = pd.DataFrame(date, columns = ["DATE"])
city = pd.DataFrame(city, columns = ["city"])
value = pd.DataFrame(value, columns = ["value"])
product = pd.DataFrame(product, columns = ["product"])
city_level = pd.concat([date,city, value, product], axis=1)
# Dash
app = JupyterDash(__name__)
app.config['suppress_callback_exceptions'] = True
dfs = {'state': state_level,
'city': city_level}
all_options = {
'state': list(dfs['state']['state'].unique()),
'city': list(dfs['city']['city'].unique())
}
app.layout = html.Div([
dcc.RadioItems(
id='countries-radio',
options=[{'label': k, 'value': k} for k in all_options.keys()],
value='state'
),
html.Hr(),
dcc.RadioItems(id='cities-radio'),
html.Hr(),
dcc.RadioItems(
id='products-radio',
options=[{'label': k, 'value': k} for k in dfs['state']['product'].unique()],
value='rice'
),
html.Hr(),
html.Div(id='display-selected-values'),
dcc.Graph(id="Products_graph")
])
@app.callback(
Output('cities-radio', 'options'),
[Input('countries-radio', 'value')])
def set_cities_options(selected_country):
return [{'label': i, 'value': i} for i in all_options[selected_country]]
@app.callback(
Output('cities-radio', 'value'),
[Input('cities-radio', 'options')])
def set_cities_value(available_options):
return available_options[0]['value']
@app.callback(
Output('display-selected-values', 'children'),
[Input('countries-radio', 'value'),
Input('cities-radio', 'value')])
def set_display_children(selected_country, selected_city):
return 'Level: ' + u'{} | Area: {}'.format(
# selected_city, selected_country,
selected_country, selected_city,
)
# Make a figure based on the selections
@app.callback( # Columns 2m_temp_prod, or....
Output('Products_graph', 'figure'),
[Input('countries-radio', 'value'),
Input('cities-radio', 'value'),
Input('products-radio', 'value')])
def make_graph(level, area, product):
# level='city'
# area='Metro Manila'
# product = 'fish'
df = dfs[level]
df = df[df[df.columns[1]]==area]
df = df[df['product']==product]
fig=go.Figure()
fig.add_traces(go.Scatter(x=df.DATE, y =df['value'] ))
return fig
app.run_server(mode='external', port = 8070, dev_tools_ui=True,
dev_tools_hot_reload =True, threaded=True)
我正在尝试创建一个来自破折号中两个独立数据框的图表。 对于可复制性，请考虑以下内容：
date = ["2020-01-01","2020-02-01","2020-03-01","2020-04-01","2020-05-01","2020-06-01"
,"2020-07-01","2020-08-01","2020-09-01","2020-10-01","2020-11-01","2020-12-01"
,"2020-01-01","2020-02-01","2020-03-01","2020-04-01","2020-05-01","2020-06-01"
,"2020-07-01","2020-08-01","2020-09-01","2020-10-01","2020-11-01","2020-12-01"
,"2020-01-01","2020-02-01","2020-03-01","2020-04-01","2020-05-01","2020-06-01"
,"2020-07-01","2020-08-01","2020-09-01","2020-10-01","2020-11-01","2020-12-01"
,"2020-01-01","2020-02-01","2020-03-01","2020-04-01","2020-05-01","2020-06-01"
,"2020-07-01","2020-08-01","2020-09-01","2020-10-01","2020-11-01","2020-12-01"
,"2020-01-01","2020-02-01","2020-03-01","2020-04-01","2020-05-01","2020-06-01"
,"2020-07-01","2020-08-01","2020-09-01","2020-10-01","2020-11-01","2020-12-01"
,"2020-01-01","2020-02-01","2020-03-01","2020-04-01","2020-05-01","2020-06-01"
,"2020-07-01","2020-08-01","2020-09-01","2020-10-01","2020-11-01","2020-12-01"]
region = ["Luzon","Luzon","Luzon","Luzon","Luzon","Luzon","Luzon","Luzon","Luzon","Luzon","Luzon","Luzon"
,"Visayas","Visayas","Visayas","Visayas","Visayas","Visayas","Visayas","Visayas","Visayas","Visayas"
,"Visayas","Visayas"
,"Mindanao","Mindanao","Mindanao","Mindanao","Mindanao","Mindanao","Mindanao","Mindanao","Mindanao","Mindanao"
,"Mindanao","Mindanao"
,"Luzon","Luzon","Luzon","Luzon","Luzon","Luzon","Luzon","Luzon","Luzon","Luzon","Luzon","Luzon"
,"Visayas","Visayas","Visayas","Visayas","Visayas","Visayas","Visayas","Visayas","Visayas","Visayas"
,"Visayas","Visayas"
,"Mindanao","Mindanao","Mindanao","Mindanao","Mindanao","Mindanao","Mindanao","Mindanao","Mindanao","Mindanao"
,"Mindanao","Mindanao"]
value = ["1","2","3","4","5","6","7","8","9","10","11","12",
"1","2","3","4","5","6","7","8","9","10","11","12",
"1","2","3","4","5","6","7","8","9","10","11","12"
,"1","2","3","4","5","6","7","8","9","10","11","12",
"1","2","3","4","5","6","7","8","9","10","11","12",
"1","2","3","4","5","6","7","8","9","10","11","12"]
product = ["rice","rice","rice","rice","rice","rice","rice","rice","rice","rice","rice","rice"
,"rice","rice","rice","rice","rice","rice","rice","rice","rice","rice","rice","rice"
,"rice","rice","rice","rice","rice","rice","rice","rice","rice","rice","rice","rice"
,"fish","fish","fish","fish","fish","fish","fish","fish","fish","fish","fish","fish"
,"fish","fish","fish","fish","fish","fish","fish","fish","fish","fish","fish","fish"
,"fish","fish","fish","fish","fish","fish","fish","fish","fish","fish","fish","fish"]
date = [[i] for i in date]
region = [[i] for i in region]
value = [[i] for i in value]
product = [[i] for i in product]
date = pd.DataFrame(date, columns = ["DATE"])
region = pd.DataFrame(region, columns = ["state"])
value = pd.DataFrame(value, columns = ["value"])
product = pd.DataFrame(product, columns = ["product"])
state_level = pd.concat([date,region, value, product], axis=1)
date = ["2020-01-01","2020-02-01","2020-03-01","2020-04-01","2020-05-01","2020-06-01"
,"2020-07-01","2020-08-01","2020-09-01","2020-10-01","2020-11-01","2020-12-01"
,"2020-01-01","2020-02-01","2020-03-01","2020-04-01","2020-05-01","2020-06-01"
,"2020-07-01","2020-08-01","2020-09-01","2020-10-01","2020-11-01","2020-12-01"
,"2020-01-01","2020-02-01","2020-03-01","2020-04-01","2020-05-01","2020-06-01"
,"2020-07-01","2020-08-01","2020-09-01","2020-10-01","2020-11-01","2020-12-01"
,"2020-01-01","2020-02-01","2020-03-01","2020-04-01","2020-05-01","2020-06-01"
,"2020-07-01","2020-08-01","2020-09-01","2020-10-01","2020-11-01","2020-12-01"
,"2020-01-01","2020-02-01","2020-03-01","2020-04-01","2020-05-01","2020-06-01"
,"2020-07-01","2020-08-01","2020-09-01","2020-10-01","2020-11-01","2020-12-01"
,"2020-01-01","2020-02-01","2020-03-01","2020-04-01","2020-05-01","2020-06-01"
,"2020-07-01","2020-08-01","2020-09-01","2020-10-01","2020-11-01","2020-12-01"]
city = ["Metro Manila","Metro Manila","Metro Manila","Metro Manila","Metro Manila","Metro Manila","Metro Manila","Metro Manila","Metro Manila","Metro Manila","Metro Manila","Metro Manila"
,"Metro Cebu","Metro Cebu","Metro Cebu","Metro Cebu","Metro Cebu","Metro Cebu","Metro Cebu","Metro Cebu","Metro Cebu","Metro Cebu"
,"Metro Cebu","Metro Cebu"
,"Metro Davao","Metro Davao","Metro Davao","Metro Davao","Metro Davao","Metro Davao","Metro Davao","Metro Davao","Metro Davao","Metro Davao"
,"Metro Davao","Metro Davao"
,"Metro Manila","Metro Manila","Metro Manila","Metro Manila","Metro Manila","Metro Manila","Metro Manila","Metro Manila","Metro Manila","Metro Manila","Metro Manila","Metro Manila"
,"Metro Cebu","Metro Cebu","Metro Cebu","Metro Cebu","Metro Cebu","Metro Cebu","Metro Cebu","Metro Cebu","Metro Cebu","Metro Cebu"
,"Metro Cebu","Metro Cebu"
,"Metro Davao","Metro Davao","Metro Davao","Metro Davao","Metro Davao","Metro Davao","Metro Davao","Metro Davao","Metro Davao","Metro Davao"
,"Metro Davao","Metro Davao"]
value = ["1","2","3","4","5","6","7","8","9","10","11","12",
"1","2","3","4","5","6","7","8","9","10","11","12",
"1","2","3","4","5","6","7","8","9","10","11","12"
,"1","2","3","4","5","6","7","8","9","10","11","12",
"1","2","3","4","5","6","7","8","9","10","11","12",
"1","2","3","4","5","6","7","8","9","10","11","12"]
product = ["rice","rice","rice","rice","rice","rice","rice","rice","rice","rice","rice","rice"
,"rice","rice","rice","rice","rice","rice","rice","rice","rice","rice","rice","rice"
,"rice","rice","rice","rice","rice","rice","rice","rice","rice","rice","rice","rice"
,"fish","fish","fish","fish","fish","fish","fish","fish","fish","fish","fish","fish"
,"fish","fish","fish","fish","fish","fish","fish","fish","fish","fish","fish","fish"
,"fish","fish","fish","fish","fish","fish","fish","fish","fish","fish","fish","fish"]
date = [[i] for i in date]
city = [[i] for i in city]
value = [[i] for i in value]
product = [[i] for i in product]
date = pd.DataFrame(date, columns = ["DATE"])
city = pd.DataFrame(city, columns = ["city"])
value = pd.DataFrame(value, columns = ["value"])
product = pd.DataFrame(product, columns = ["product"])
city_level = pd.concat([date,city, value, product], axis=1)
考虑 plotly 中的示例图：
import plotly.graph_objects as go
historical = state_level[state_level["state"]=="Visayas"]
historical = historical[historical["product"]=="fish"]
historical
fig = go.Figure()
fig.add_trace(go.Scatter(x=historical["DATE"]
,y=historical["value"]
,name="Historical",
mode="lines"
,line={"color": "grey", "width": 1}
,showlegend=False))
fig.show()
图表是：
我想创建一个动态仪表板，用户可以在州级别或城市级别进行选择，选择州或城市，然后选择产品（大米或鱼），图表将动态变化根据用户输入，我的代码如下：
state = [str(i) for i in state_level.state.unique()]
state.sort(reverse = False)
city = [i for i in city_level.city.unique()]
city.sort(reverse = False)
import plotly.express as px
from jupyter_dash import JupyterDash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output
import pandas as pd
app = JupyterDash(__name__)
all_options = {
'State': state,
'City': city
}
app.layout = html.Div([
dcc.Dropdown(
id='Level1',
options=[{'label': k, 'value': k} for k in all_options.keys()],
value='State'
),
html.Hr(),
dcc.Dropdown(id='Level2'),
html.Hr(),
dcc.Dropdown(id='Product',options=[{'label': k, 'value': k} for k in ["rice","fish"]],
value='Rice'),
html.Hr(),
html.Div(id='display-selected-values')
])
@app.callback(
Output('Level2', 'options'),
[Input('Level1', 'value')])
def set_options(selected_level1):
return [{'label': i, 'value': i} for i in all_options[selected_level1]]
app.run_server(mode='inline')
但鉴于我的来源来自两个不同的数据帧，我不知道该怎么做。
一种方法是将数据帧存储在字典中，如下所示：
dfs = {'state': state_level,
'city': city_level}
然后设置一些动态回调以用作如下图的输入：
# Make a figure based on the selections
@app.callback( # Columns 2m_temp_prod, or....
Output('Products_graph', 'figure'),
[Input('countries-radio', 'value'),
Input('cities-radio', 'value'),
Input('products-radio', 'value')])
def make_graph(level, area, product):
# level='city'
# area='Metro Manila'
# product = 'fish'
df = dfs[level]
df = df[df[df.columns[1]]==area]
df = df[df['product']==product]
fig=go.Figure()
fig.add_traces(go.Scatter(x=df.DATE, y =df['value'] ))
return fig
你的价值观变化很小，这让你很难看出动态是否像他们所展示的那样起作用，因为你总是以一条直线结束。但是，当您有时间查看下面代码片段中的详细信息时，我们可以讨论详细信息。这是生成的应用程序：
在此设置中，区域选项将根据城市或州级别而变化
完整代码：
import dash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output
from jupyter_dash import JupyterDash
import plotly.graph_objects as go
# data
import pandas as pd
date = ["2020-01-01","2020-02-01","2020-03-01","2020-04-01","2020-05-01","2020-06-01"
,"2020-07-01","2020-08-01","2020-09-01","2020-10-01","2020-11-01","2020-12-01"
,"2020-01-01","2020-02-01","2020-03-01","2020-04-01","2020-05-01","2020-06-01"
,"2020-07-01","2020-08-01","2020-09-01","2020-10-01","2020-11-01","2020-12-01"
,"2020-01-01","2020-02-01","2020-03-01","2020-04-01","2020-05-01","2020-06-01"
,"2020-07-01","2020-08-01","2020-09-01","2020-10-01","2020-11-01","2020-12-01"
,"2020-01-01","2020-02-01","2020-03-01","2020-04-01","2020-05-01","2020-06-01"
,"2020-07-01","2020-08-01","2020-09-01","2020-10-01","2020-11-01","2020-12-01"
,"2020-01-01","2020-02-01","2020-03-01","2020-04-01","2020-05-01","2020-06-01"
,"2020-07-01","2020-08-01","2020-09-01","2020-10-01","2020-11-01","2020-12-01"
,"2020-01-01","2020-02-01","2020-03-01","2020-04-01","2020-05-01","2020-06-01"
,"2020-07-01","2020-08-01","2020-09-01","2020-10-01","2020-11-01","2020-12-01"]
region = ["Luzon","Luzon","Luzon","Luzon","Luzon","Luzon","Luzon","Luzon","Luzon","Luzon","Luzon","Luzon"
,"Visayas","Visayas","Visayas","Visayas","Visayas","Visayas","Visayas","Visayas","Visayas","Visayas"
,"Visayas","Visayas"
,"Mindanao","Mindanao","Mindanao","Mindanao","Mindanao","Mindanao","Mindanao","Mindanao","Mindanao","Mindanao"
,"Mindanao","Mindanao"
,"Luzon","Luzon","Luzon","Luzon","Luzon","Luzon","Luzon","Luzon","Luzon","Luzon","Luzon","Luzon"
,"Visayas","Visayas","Visayas","Visayas","Visayas","Visayas","Visayas","Visayas","Visayas","Visayas"
,"Visayas","Visayas"
,"Mindanao","Mindanao","Mindanao","Mindanao","Mindanao","Mindanao","Mindanao","Mindanao","Mindanao","Mindanao"
,"Mindanao","Mindanao"]
value = ["1","2","3","4","5","6","7","8","9","10","11","12",
"1","2","3","4","5","6","7","8","9","10","11","12",
"1","2","3","4","5","6","7","8","9","10","11","12"
,"1","2","3","4","5","6","7","8","9","10","11","12",
"1","2","3","4","5","6","7","8","9","10","11","12",
"1","2","3","4","5","6","7","8","9","10","11","12"]
product = ["rice","rice","rice","rice","rice","rice","rice","rice","rice","rice","rice","rice"
,"rice","rice","rice","rice","rice","rice","rice","rice","rice","rice","rice","rice"
,"rice","rice","rice","rice","rice","rice","rice","rice","rice","rice","rice","rice"
,"fish","fish","fish","fish","fish","fish","fish","fish","fish","fish","fish","fish"
,"fish","fish","fish","fish","fish","fish","fish","fish","fish","fish","fish","fish"
,"fish","fish","fish","fish","fish","fish","fish","fish","fish","fish","fish","fish"]
date = [[i] for i in date]
region = [[i] for i in region]
value = [[i] for i in value]
product = [[i] for i in product]
date = pd.DataFrame(date, columns = ["DATE"])
region = pd.DataFrame(region, columns = ["state"])
value = pd.DataFrame(value, columns = ["value"])
product = pd.DataFrame(product, columns = ["product"])
state_level = pd.concat([date,region, value, product], axis=1)
date = ["2020-01-01","2020-02-01","2020-03-01","2020-04-01","2020-05-01","2020-06-01"
,"2020-07-01","2020-08-01","2020-09-01","2020-10-01","2020-11-01","2020-12-01"
,"2020-01-01","2020-02-01","2020-03-01","2020-04-01","2020-05-01","2020-06-01"
,"2020-07-01","2020-08-01","2020-09-01","2020-10-01","2020-11-01","2020-12-01"
,"2020-01-01","2020-02-01","2020-03-01","2020-04-01","2020-05-01","2020-06-01"
,"2020-07-01","2020-08-01","2020-09-01","2020-10-01","2020-11-01","2020-12-01"
,"2020-01-01","2020-02-01","2020-03-01","2020-04-01","2020-05-01","2020-06-01"
,"2020-07-01","2020-08-01","2020-09-01","2020-10-01","2020-11-01","2020-12-01"
,"2020-01-01","2020-02-01","2020-03-01","2020-04-01","2020-05-01","2020-06-01"
,"2020-07-01","2020-08-01","2020-09-01","2020-10-01","2020-11-01","2020-12-01"
,"2020-01-01","2020-02-01","2020-03-01","2020-04-01","2020-05-01","2020-06-01"
,"2020-07-01","2020-08-01","2020-09-01","2020-10-01","2020-11-01","2020-12-01"]
city = ["Metro Manila","Metro Manila","Metro Manila","Metro Manila","Metro Manila","Metro Manila","Metro Manila","Metro Manila","Metro Manila","Metro Manila","Metro Manila","Metro Manila"
,"Metro Cebu","Metro Cebu","Metro Cebu","Metro Cebu","Metro Cebu","Metro Cebu","Metro Cebu","Metro Cebu","Metro Cebu","Metro Cebu"
,"Metro Cebu","Metro Cebu"
,"Metro Davao","Metro Davao","Metro Davao","Metro Davao","Metro Davao","Metro Davao","Metro Davao","Metro Davao","Metro Davao","Metro Davao"
,"Metro Davao","Metro Davao"
,"Metro Manila","Metro Manila","Metro Manila","Metro Manila","Metro Manila","Metro Manila","Metro Manila","Metro Manila","Metro Manila","Metro Manila","Metro Manila","Metro Manila"
,"Metro Cebu","Metro Cebu","Metro Cebu","Metro Cebu","Metro Cebu","Metro Cebu","Metro Cebu","Metro Cebu","Metro Cebu","Metro Cebu"
,"Metro Cebu","Metro Cebu"
,"Metro Davao","Metro Davao","Metro Davao","Metro Davao","Metro Davao","Metro Davao","Metro Davao","Metro Davao","Metro Davao","Metro Davao"
,"Metro Davao","Metro Davao"]
value = ["1","2","3","4","5","6","7","8","9","10","11","12",
"1","2","3","4","5","6","7","8","9","10","11","12",
"1","2","3","4","5","6","7","8","9","10","11","12"
,"1","2","3","4","5","6","7","8","9","10","11","12",
"1","2","3","4","5","6","7","8","9","10","11","12",
"1","2","3","4","5","6","7","8","9","10","11","12"]
product = ["rice","rice","rice","rice","rice","rice","rice","rice","rice","rice","rice","rice"
,"rice","rice","rice","rice","rice","rice","rice","rice","rice","rice","rice","rice"
,"rice","rice","rice","rice","rice","rice","rice","rice","rice","rice","rice","rice"
,"fish","fish","fish","fish","fish","fish","fish","fish","fish","fish","fish","fish"
,"fish","fish","fish","fish","fish","fish","fish","fish","fish","fish","fish","fish"
,"fish","fish","fish","fish","fish","fish","fish","fish","fish","fish","fish","fish"]
date = [[i] for i in date]
city = [[i] for i in city]
value = [[i] for i in value]
product = [[i] for i in product]
date = pd.DataFrame(date, columns = ["DATE"])
city = pd.DataFrame(city, columns = ["city"])
value = pd.DataFrame(value, columns = ["value"])
product = pd.DataFrame(product, columns = ["product"])
city_level = pd.concat([date,city, value, product], axis=1)
# Dash
app = JupyterDash(__name__)
app.config['suppress_callback_exceptions'] = True
dfs = {'state': state_level,
'city': city_level}
all_options = {
'state': list(dfs['state']['state'].unique()),
'city': list(dfs['city']['city'].unique())
}
app.layout = html.Div([
dcc.RadioItems(
id='countries-radio',
options=[{'label': k, 'value': k} for k in all_options.keys()],
value='state'
),
html.Hr(),
dcc.RadioItems(id='cities-radio'),
html.Hr(),
dcc.RadioItems(
id='products-radio',
options=[{'label': k, 'value': k} for k in dfs['state']['product'].unique()],
value='rice'
),
html.Hr(),
html.Div(id='display-selected-values'),
dcc.Graph(id="Products_graph")
])
@app.callback(
Output('cities-radio', 'options'),
[Input('countries-radio', 'value')])
def set_cities_options(selected_country):
return [{'label': i, 'value': i} for i in all_options[selected_country]]
@app.callback(
Output('cities-radio', 'value'),
[Input('cities-radio', 'options')])
def set_cities_value(available_options):
return available_options[0]['value']
@app.callback(
Output('display-selected-values', 'children'),
[Input('countries-radio', 'value'),
Input('cities-radio', 'value')])
def set_display_children(selected_country, selected_city):
return 'Level: ' + u'{} | Area: {}'.format(
# selected_city, selected_country,
selected_country, selected_city,
)
# Make a figure based on the selections
@app.callback( # Columns 2m_temp_prod, or....
Output('Products_graph', 'figure'),
[Input('countries-radio', 'value'),
Input('cities-radio', 'value'),
Input('products-radio', 'value')])
def make_graph(level, area, product):
# level='city'
# area='Metro Manila'
# product = 'fish'
df = dfs[level]
df = df[df[df.columns[1]]==area]
df = df[df['product']==product]
fig=go.Figure()
fig.add_traces(go.Scatter(x=df.DATE, y =df['value'] ))
return fig
app.run_server(mode='external', port = 8070, dev_tools_ui=True,
dev_tools_hot_reload =True, threaded=True)