以绘图方式钻取条形图

Question

我正在尝试创建一个条形图，我想在其中钻取区域，然后查看不同城市3年范围内的人口。基本上我找到了这个https://community.plotly.com/t/drill-down-function-for-graphs-embedded-in-dash-app/12290/9，但我无法实现

import dash
import dash_core_components as dcc
import dash_html_components as html
import plotly.graph_objs as go
from dash.dependencies import Output, Input, State
import numpy as np
import pandas as pd
import plotly.figure_factory as ff
from pandas import read_excel

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

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

# app = dash.Dash()
file_name = 'samplePop1.csv'
df = pd.read_csv(file_name)
print(df.head())

colors = {
    'black' : '#000000',
    'text' :  '#696969',
    'plot_color' : '#C0C0C0',
    'white' : '#FFFFF'
}


app.layout = html.Div ([
                        dcc.Graph(    
                            id = 'bar-chart',
                        figure = { 'data' : 
                                    [
                                        {'x' : df['Name'],'y':df['Population Census 1991'],'type':'bar','name':'Population Census 1991'},
                                        {'x' : df['Name'],'y':df['Population Census 2001'],'type':'bar','name':'Population Census 2001'},
                                        {'x' : df['Name'],'y':df['Population Census 2011'],'type':'bar','name':'Population Census 2011'}

                                    ],
                                'layout' : {
                                    'plot_bgcolor' : colors['white'],
                                    'paper_bgcolor' : colors['white'],
                                    'font' : {
                                        'color' : colors['white']
                                    },
                                    'title' : 'Bar Chart',
                                    'orientation':'h'
                                }
                                }
                        )
                    ])
if __name__ == '__main__':
    app.run_server(port =  '8080' , debug ='True')

条形图应该首先显示3年范围内的人口区域，当我点击一个区域时，它将显示区域比较。此外，另一个基本图表，其中他们将是2点击行动，地区和城市智慧，以显示人口为3年的范围，它应该显示清楚的值，更有可能它应该是可滚动的。

指向csv文件https://github.com/9192gks/mapbox/blob/master/samplePop1.csv的链接

Answer 1

在callback_context的帮助下，在Dash中查看这个向下钻取的示例。

​

在这个例子中，我只展示了一个单层的向下钻取，以保持它的简单性，但只需很少的修改，就可以实现多-level向下钻取。有一个后退按钮，用于返回到原始图形。后退按钮仅显示在向下钻取的级别2上，并隐藏在原始底部级别上。

代码：

import dash
import dash_core_components as dcc
import dash_bootstrap_components as dbc
from dash.dependencies import Input, Output
import plotly.express as px
import pandas as pd

app = dash.Dash(__name__, external_stylesheets=[dbc.themes.BOOTSTRAP])

# creating a dummy sales dataframe
product_sales = {'vendors':['VANS','VANS','VANS','VANS','NIKE','NIKE','NIKE','ADIDAS','ADIDAS','CONVERSE','CONVERSE','CONVERSE'],
                 'products': ['Tshirts','Sneakers','Caps','Clothing','Sports Outfit','Sneakers','Caps','Accessories','Bags','Sneakers','Accessories','Tshirts'],
                 'units sold': [2,15,3,8,37,13,7,4,12,7,8,2]
                 }
product_sales_df = pd.DataFrame(product_sales)

# all vendors sales pie chart
def sales_pie():
    df = product_sales_df.groupby('vendors').sum().reset_index()
    fig = px.pie(df, names='vendors',
                 values='units sold', hole=0.4)
    fig.update_layout(template='presentation', title='Sales distribution per Vendor')
    return fig

# creating app layout
app.layout = dbc.Container([
    dbc.Card([
            dbc.Button('', id='back-button', outline=True, size="sm",
                        className='mt-2 ml-2 col-1', style={'display': 'none'}),
            dbc.Row(
                dcc.Graph(
                        id='graph',
                        figure=sales_pie()
                    ), justify='center'
            )
    ], className='mt-3')
])

#Callback
@app.callback(
    Output('graph', 'figure'),
    Output('back-button', 'style'), #to hide/unhide the back button
    Input('graph', 'clickData'),    #for getting the vendor name from graph
    Input('back-button', 'n_clicks')
)
def drilldown(click_data,n_clicks):

    # using callback context to check which input was fired
    ctx = dash.callback_context
    trigger_id = ctx.triggered[0]["prop_id"].split(".")[0]

    if trigger_id == 'graph':

        # get vendor name from clickData
        if click_data is not None:
            vendor = click_data['points'][0]['label']

            if vendor in product_sales_df.vendors.unique():
                # creating df for clicked vendor
                vendor_sales_df = product_sales_df[product_sales_df['vendors'] == vendor]

                # generating product sales bar graph
                fig = px.bar(vendor_sales_df, x='products',
                             y='units sold', color='products')
                fig.update_layout(title='<b>{} product sales<b>'.format(vendor),
                                  showlegend=False, template='presentation')
                return fig, {'display':'block'}     #returning the fig and unhiding the back button

            else:
                return sales_pie(), {'display': 'none'}     #hiding the back button

    else:
        return sales_pie(), {'display':'none'}

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

另请查看此thread以了解更多信息。