Plotly foundations: interactive charts and useful hover data

What you will learn

Build interactive bar, scatter and line charts with deliberate hover fields and export them without a hosted account.

Before you start

You need basic variables, lists and function calls, not advanced Python algorithms. If these are new, visit Python foundations first.

1. Create a local environment: python -m venv .venv. Activate it for your OS and install python -m pip install jupyterlab numpy pandas matplotlib seaborn plotly.
2. Download the 12-row sales CSV into your notebook folder. Keep its filename unchanged.
3. Start Jupyter with jupyter lab. Run the setup below in a fresh notebook before every workshop.

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
raw = pd.read_csv('data-science-sales.csv', parse_dates=['date'])
df = raw.copy()
df['revenue'] = df['units'] * df['unit_price']
net = df.loc[df['returned'].eq(0)].copy()

Data dictionary: each row is one order for one product; units is a positive count; unit_price is INR per unit; returned is 0 or 1. Revenue is units × unit_price. For this workshop, net sales exclude the entire returned order (not partial returns). The CSV has 12 rows, 4950 gross revenue, 4450 net revenue and 35 net units. This small fictional sample teaches technique; it does not justify population or causal claims.

Save your notebook and written interpretation. There is no browser code judge and no automatic grading of visual design.

Official Plotly documentation

Learn the idea

Plotly Express produces a Figure from a DataFrame. Define x, y, colour and hover fields deliberately: interactive does not mean understandable. Aggregate revenue before a business-total bar chart. update_layout sets titles and legend labels. write_html(include_plotlyjs=True) creates a larger self-contained file that works offline; using 'cdn' reduces file size but requires internet. These labs use local HTML and do not require Dash, an API key or a hosting service.

Follow a worked example

summary=net.groupby('region',as_index=False)['revenue'].sum()
fig=px.bar(summary,x='region',y='revenue',text='revenue',labels={'revenue':'Net revenue (INR)'},title='Regional net sales; returns excluded')
fig.write_html('regional-interactive.html',include_plotlyjs=True)
fig.show()

Expected result

Three interactive bars with totals 1350,1700,1400 and a standalone HTML file.

Skill-development workshop

These are practical assignments, not MCQs or code-judge puzzles. Work in your own notebook or MySQL client. Complete the levels in order; if a task feels difficult, follow the hint and compare with the reference only after making an attempt. Keep your result and a short explanation of what it means.

Easy: Create a product revenue bar chart.

Success criteria: Four bars, Lamp 2400, labelled INR.

summary=net.groupby('product',as_index=False)['revenue'].sum()
fig=px.bar(summary,x='product',y='revenue',labels={'revenue':'Net revenue (INR)'},title='Product net sales'); fig.show()

Medium: Include order id, product and region in scatter hover.

Success criteria: 11 points; hovering identifies an order without exposing any personal data.

fig=px.scatter(net,x='units',y='revenue',color='region',hover_data=['order_id','product'],labels={'revenue':'Order revenue (INR)'},title='Order units and revenue'); fig.show()

Hard: Plot sorted monthly net revenue with custom labels.

Success criteria: 1100,1550,1800 in chronological order with markers; month labels preserved.

m=net.assign(month=net['date'].dt.to_period('M').astype(str)).groupby('month',as_index=False)['revenue'].sum().sort_values('month')
fig=px.line(m,x='month',y='revenue',markers=True,labels={'revenue':'Net revenue (INR)'},title='Monthly net sales'); fig.show()

Professional: Deliver an offline interactive report plus a data alternative.

Success criteria: HTML includes plotly.js, CSV reconciles to 4450, file opens without a hosted account. State that embedding JS makes the file larger.

s=net.groupby('region',as_index=False)['revenue'].sum()
fig=px.bar(s,x='region',y='revenue',text='revenue',title='Q1 2026 net revenue (INR); returns excluded')
fig.write_html('offline-sales.html',include_plotlyjs=True,full_html=True)
s.to_csv('offline-sales-data.csv',index=False)
assert s['revenue'].sum()==4450

Reflection and submission

Save your queries/notebook and outputs. Write three sentences: what you changed, how you checked it, and one limitation. A correct chart or query without a clear interpretation is not a completed professional assignment.