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Slider for sales data

Analysis of trends and patterns in the global e-commerce company's data has been greatly enhanced with your date pickers. There is a further request to see what categories are driving the 'big ticket' orders.

The company has asked you to build a tool whereby they can select a value (any value, really!) and a graph will show how many sales had an order value greater than that value, split by major category.

You know this would be facilitated well by a range input.

Diese Übung ist Teil des Kurses

Building Dashboards with Dash and Plotly

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Anleitung zur Übung

  • Add a slider component called dcc.Slider with the identifier value_slider below line 16 to select a minimum order value.
  • Set the value parameter of the slider to 0 below line 20.
  • Set the step parameter of the slider to 50 below line 22.

Interaktive Übung

Vervollständige den Beispielcode, um diese Übung erfolgreich abzuschließen.

from dash import Dash, dcc, html, Input, Output, callback
import plotly.express as px
import pandas as pd
ecom_sales = pd.read_csv('/usr/local/share/datasets/ecom_sales.csv')
logo_link = 'https://assets.datacamp.com/production/repositories/5893/datasets/fdbe0accd2581a0c505dab4b29ebb66cf72a1803/e-comlogo.png'

app = Dash()

app.layout = [
  html.Img(src=logo_link, style={'margin':'30px 0px 0px 0px'}),
  html.H1('Sales breakdowns'),
  html.Div([
        html.H2('Controls'),
        html.Br(),
        html.H3('Minimum OrderValue Select'),
        # Add a slider input
        ____(id='value_slider',
            min=ecom_sales['OrderValue'].min(),
            max=ecom_sales['OrderValue'].max(),
            # Set the starting value of the slider
            ____=____,
            # Set the step increment of the slider
            ____=____,
            vertical=False)],
        style={'width':'350px', 'height':'350px', 'display':'inline-block', 'vertical-align':'top', 'border':'1px solid black', 'padding':'20px'}),
  html.Div([
		dcc.Graph(id='sales_cat'),
		html.H2('Sales by Major Category', style={ 'border':'2px solid black', 'width':'400px', 'margin':'0 auto'})],
		style={'width':'700px','display':'inline-block'})
]

@callback(
    Output(component_id='sales_cat', component_property='figure'),
    Input(component_id='value_slider', component_property='value')
)
def update_plot(min_val):
    sales = ecom_sales.copy(deep=True)
    if min_val:
        sales = sales[sales['OrderValue'] >= min_val]
    ecom_bar_major_cat = sales.groupby('Major Category')['OrderValue'].size().reset_index(name='Total Sales Volume')
    bar_fig_major_cat = px.bar(
        title=f'Sales with order value: {min_val}',data_frame=ecom_bar_major_cat, orientation='h', 
        x='Total Sales Volume', y='Major Category')
    return bar_fig_major_cat
  
if __name__ == '__main__':
    app.run(debug=True)
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