Hover to update another plot

The global e-commerce company loves the innovation and new life your work is breathing into their tired old reports. The manager for whom you recently built the key stats hover dashboard has sent you a new challenge. While those words provided a great snapshot, a picture is worth a thousand words! The manager has asked if you could produce graphs showing different subsets depending on what is being hovered over.

Your previous experience with using callbacks to filter data and regenerate figures, in combination with your recent work on hover triggering callbacks, should be just the perfect combination.

This exercise is part of the course

Building Dashboards with Dash and Plotly

View Course

Exercise instructions

Create a callback to update the minor_cat graph when hovering over the scatter plot below line 26.
Create a callback to update the major_cat graph when hovering over the scatter plot below line 41.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

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'
ecom_country = ecom_sales.groupby('Country')['OrderValue'].agg(['sum', 'count']).reset_index().rename(columns={'count':'Sales Volume', 'sum':'Total Sales ($)'})
ecom_scatter = px.scatter(ecom_country, x='Total Sales ($)', y='Sales Volume', color='Country', width=350, height=550, custom_data=['Country'])
ecom_scatter.update_layout({'legend':dict(orientation='h', y=-0.7,x=1, yanchor='bottom', xanchor='right')})

app = Dash()

app.layout = [
  html.Img(src=logo_link, style={'margin':'30px 0px 0px 0px' }),
  html.H1('Sales breakdowns'),
  html.Div([
    html.H3('Sales Volume vs Sales Amount by Country'),
    dcc.Graph(id='scatter', figure=ecom_scatter)],
    style={'width':'350px', 'height':'650px', 'display':'inline-block', 
           'vertical-align':'top', 'border':'1px solid black', 'padding':'20px'}),
  html.Div([
    dcc.Graph(id='major_cat'),
    dcc.Graph(id='minor_cat')],
    style={'width':'700px', 'height':'650px','display':'inline-block'})
]

# Create a callback to update the minor category plot
@callback(
    Output('____', '____'),
    Input('scatter', '____'))

def update_min_cat_hover(hoverData):
    hover_country = 'Australia'
    if hoverData:
        hover_country = hoverData['points'][0]['customdata'][0]
    minor_cat_df = ecom_sales[ecom_sales['Country'] == hover_country]
    minor_cat_agg = minor_cat_df.groupby('Minor Category')['OrderValue'].agg('sum').reset_index(name='Total Sales ($)')
    ecom_bar_minor_cat = px.bar(minor_cat_agg, x='Total Sales ($)', y='Minor Category', orientation='h', height=450, title=f'Sales by Minor Category for: {hover_country}')
    ecom_bar_minor_cat.update_layout({'yaxis':{'dtick':1, 'categoryorder':'total ascending'}, 'title':{'x':0.5}})
    return ecom_bar_minor_cat

# Create a callback to update the major category plot
@callback(
    Output('____', '____'),
    Input('scatter', '____'))

def update_major_cat_hover(hoverData):
    hover_country = 'Australia'
    if hoverData:
        hover_country = hoverData['points'][0]['customdata'][0]
    major_cat_df = ecom_sales[ecom_sales['Country'] == hover_country]
    major_cat_agg = major_cat_df.groupby('Major Category')['OrderValue'].agg('sum').reset_index(name='Total Sales ($)')
    ecom_bar_major_cat = px.bar(major_cat_agg, x='Total Sales ($)', 
                                y='Major Category', height=300, 
                                title=f'Sales by Major Category for: {hover_country}', color='Major Category',
                                color_discrete_map={'Clothes':'blue','Kitchen':'red', 'Garden':'green', 'Household':'yellow'})
    ecom_bar_major_cat.update_layout({'margin':dict(l=10,r=15,t=40,b=0), 'title':{'x':0.5}})
    return ecom_bar_major_cat

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

Edit and Run Code