LoslegenKostenlos loslegen

Date picker for sales data

The interactive sales dashboard you created was a smash hit; however, it is vital for the sales team to be able to monitor daily sales to analyze patterns and trends. You have been asked if you can extend your work to include a filter to view sales by major category on a particular date?

This sounds like a job for a date picker!

Diese Übung ist Teil des Kurses

Building Dashboards with Dash and Plotly

Kurs anzeigen

Anleitung zur Übung

  • Create a date picker component called dcc.DatePickerSingle with the identifier sale_date below line 18.
  • Set the date parameter to April 11, 2011 below line 23 to show this as the initial visible date.

Interaktive Übung

Versuche dich an dieser Übung, indem du diesen Beispielcode vervollständigst.

from dash import Dash, dcc, html, Input, Output, callback
import plotly.express as px
import pandas as pd
from datetime import datetime, date
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'
ecom_sales['InvoiceDate'] = pd.to_datetime(ecom_sales['InvoiceDate'])

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('Sale Date Select'),
        # Create a date picker with identifier
        ____(
            id='____',
            min_date_allowed=ecom_sales['InvoiceDate'].min(),
            max_date_allowed=ecom_sales['InvoiceDate'].max(),
            # Set the initial visible date
            date=date(____),
            initial_visible_month=date(2011, 4, 11),
            style={'width': '200px', 'margin': '0 auto'})],
        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('Daily 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='sale_date', component_property='date')
)
def update_plot(input_date):
    sales = ecom_sales.copy(deep=True)
    if input_date:
        sales = sales[sales['InvoiceDate'] == input_date]
    ecom_bar_major_cat = sales.groupby('Major Category')['OrderValue'].agg('sum').reset_index(name='Total Sales ($)')
    bar_fig_major_cat = px.bar(
        title=f'Sales on: {input_date}', data_frame=ecom_bar_major_cat, orientation='h',
        x='Total Sales ($)', y='Major Category')
    return bar_fig_major_cat

if __name__ == '__main__':
    app.run(debug=True)
Code bearbeiten und ausführen