Each example is a complete, real-world workflow built with tsb — the kind of analysis you'd typically reach for pandas to do. Click any card to open its interactive page; every code block runs live in your browser.
The dataset in each example is small, inline, and editable — change the numbers and re-run to see the analysis update instantly.
Scenario: You're an analyst at a regional retail chain. Q1 sales just landed in a CSV. Find top-performing regions and products, then visualize revenue with a quick ASCII bar chart.
Scenario: A junior quant wants to inspect a price history: compute daily returns, a 5-day rolling mean and volatility, and detect a simple moving-average crossover signal.
Scenario: A climate journalist has a year of daily temperature observations and wants monthly averages, the hottest month, and a quick visual of the warming curve.
Scenario: A SaaS growth team wants to know how many customers signed up each month, the cumulative customer base, and which cohort grew fastest.
Scenario: You ran a 30-person survey asking which programming language people prefer, broken down by experience level. Build a contingency table and a percentage breakdown.
Scenario: An on-call engineer wants to know how many 5xx errors hit the API per hour during the last incident, broken down by status code class.
Scenario: A product manager just shipped a new checkout button (variant B) to half of users. Compare conversion rates and order values between the control (A) and the variant (B).
Scenario: A digital marketer has a stream of pageview events tagged with traffic source and device type. Pivot the data to see how each source performs across devices.
Scenario: An amateur football league played a round-robin. Build the league table from match results: wins, goal difference, points, and final rank.
Scenario: An e-commerce merchandiser wants to bucket the catalogue into 4 price tiers (Budget / Mid / Premium / Luxury) and see the count and average margin per tier.
Scenario: A payments risk team joins transactions with merchant chargeback history, scores expected loss, and builds a segment/device heatmap plus a manual review queue.
Scenario: A retail operations analyst combines daily sell-through with SKU master data to calculate reorder points and summarize replenishment risk.
Scenario: A growth team reshapes campaign spend, joins conversion facts, and compares return on ad spend by region and channel.