Online Sales Data Dashboard

This is a interaction dashboard using Panel in Python. This is the result:

Below is step by step how I create this dashboad. To serve the dashboard locally, clone this repo: https://github.com/rsm-dtt006/Interaction-Dashboard and use the command:

panel serve interactive_dashboard.ipynb

import pandas as pd
import numpy as np
import panel as pn
pn.extension('tabulator')

import hvplot.pandas

df = pd.read_csv('Online Sales Data.csv')

Some minor data preprocessing

df['Date'] = pd.to_datetime(df['Date'])  # Ensure the Date column is in datetime format
df['Month'] = df['Date'].dt.month  # Extract the month
df['Day Name'] = df['Date'].dt.strftime('%A')
df

	Transaction ID	Date	Product Category	Product Name	Units Sold	Unit Price	Total Revenue	Region	Payment Method	Month	Day Name
0	10001	2024-01-01	Electronics	iPhone 14 Pro	2	999.99	1999.98	North America	Credit Card	1	Monday
1	10002	2024-01-02	Home Appliances	Dyson V11 Vacuum	1	499.99	499.99	Europe	PayPal	1	Tuesday
2	10003	2024-01-03	Clothing	Levi's 501 Jeans	3	69.99	209.97	Asia	Debit Card	1	Wednesday
3	10004	2024-01-04	Books	The Da Vinci Code	4	15.99	63.96	North America	Credit Card	1	Thursday
4	10005	2024-01-05	Beauty Products	Neutrogena Skincare Set	1	89.99	89.99	Europe	PayPal	1	Friday
...	...	...	...	...	...	...	...	...	...	...	...
235	10236	2024-08-23	Home Appliances	Nespresso Vertuo Next Coffee and Espresso Maker	1	159.99	159.99	Europe	PayPal	8	Friday
236	10237	2024-08-24	Clothing	Nike Air Force 1 Sneakers	3	90.00	270.00	Asia	Debit Card	8	Saturday
237	10238	2024-08-25	Books	The Handmaid's Tale by Margaret Atwood	3	10.99	32.97	North America	Credit Card	8	Sunday
238	10239	2024-08-26	Beauty Products	Sunday Riley Luna Sleeping Night Oil	1	55.00	55.00	Europe	PayPal	8	Monday
239	10240	2024-08-27	Sports	Yeti Rambler 20 oz Tumbler	2	29.99	59.98	Asia	Credit Card	8	Tuesday

240 rows × 11 columns

Total Sales over time by Regions

#Define Panel Widget
month_slider = pn.widgets.IntSlider(name='Month Slider', start= 1, end=8, step=1, value=8)
# Make DataFrame Pipeline Interactive
idf = df.interactive()

regions = df['Region'].unique().tolist()
regions

['North America', 'Europe', 'Asia']

revenue_pipeline = (
    idf[
        (idf.Month <= month_slider) &
        (idf['Region'].isin(regions))
    ]
    .groupby(['Region', 'Month'])['Total Revenue'].sum()
    .to_frame()
    .reset_index()
    .sort_values(by = 'Month')
    .reset_index(drop = True)
)
revenue_plot = revenue_pipeline.hvplot(x = 'Month', by = 'Region', y = 'Total Revenue', line_width = 2, title = 'Total Revenue over months by Regions')
revenue_plot

Table - Total Sales Overtime by Regions

revenue_table = revenue_pipeline.pipe(pn.widgets.Tabulator, pagination='remote', page_size = 10, sizing_mode='stretch_width') 
revenue_table

Total Revenue by Date

revenue_date = df.groupby('Date')['Total Revenue'].sum().hvplot(grid=True, figsize=(12, 6), title='Total Revenue Over Time', xlabel='Date', ylabel='Total Revenue')
revenue_date

WARNING:param.main: hvPlot does not have the concept of a figure, and the figsize keyword will be ignored. The size of each subplot in a layout is set individually using the width and height options.

Barchart : Unit sales by Product Category

day_name = df['Day Name'].unique().tolist()
products = df['Product Category'].unique().tolist()

day_select = pn.widgets.Select(name='Day Select', options= day_name)
day_select

unit_sale_pipeline = (
    idf[
        (idf['Day Name'] == day_select) &
        idf['Product Category'].isin(products)
    ]
    .groupby(['Day Name', 'Product Category'])['Units Sold'].sum()  # Corrected line
    .to_frame()
    .reset_index()
    .reset_index(drop = True)
)
unit_sale_pipeline

unit_plot = unit_sale_pipeline.hvplot(x = 'Product Category', y = 'Units Sold', kind = 'bar', title = 'Unit Sold by Product Categories Over Day', rot = 45)
unit_plot

Creating Dashboard

template = pn.template.FastListTemplate(
    title='Online Sales Data dashboard', 
    sidebar=[pn.pane.Markdown("# Online Sales Data"), 
             pn.pane.Markdown("This dataset provides a comprehensive overview of online sales transactions across different product categories. Each row represents a single transaction with detailed information such as the order ID, date, category, product name, quantity sold, unit price, total price, region, and payment method."),
             month_slider
             ],
    main=[pn.Row(pn.Column(
                           revenue_plot, margin=(0,25)), 
                 revenue_table.panel(width=500)), 
          pn.Row(pn.Column(revenue_date, margin=(0,25)), 
                 pn.Column(day_select, unit_plot))],
    accent_base_color="#88d8b0",
    header_background="#88d8b0",
)
# template.show()
template.servable();