01 · Validate the data
Reconcile booking facts, workshop labels, and independent daily completeness receipts.
The final volume · Part V
Python Foundations Workshop V
Turn synthetic Workshop Desk bookings into an attendance report. Check the source, calculate defensible metrics, and ship a report another person can reproduce.
1,000pages
96runnable cases
12chapters
3connected projects
12 chapters
Find a topic
12 chapters
01 Data grain, keys and measurement units
Identify the meaning of one row before counting. Check keys, units and missing values. Keep an event log distinct from a snapshot.
Pages 5–77 · 8 cases02 Time, reporting windows and completeness
Declare the event window, calendar and data freeze. Distinguish a missing day from a delivered day that legitimately has zero observations.
Pages 78–150 · 8 cases03 Reconcile source and analytical records
Reconcile counts as records move through validation. Hold conflicting facts, assign reason codes and preserve provenance.
Pages 151–223 · 8 cases04 Joins and dimensional modeling
Use dimension keys and time-valid attributes to enrich facts. Check for fan-out, unmatched rows and incomplete dimension coverage.
Pages 224–296 · 8 cases05 Build trustworthy rates and KPIs
Write metric definitions with an explicit numerator and denominator. Separate unavailable rates, genuine zeros, changes in points and relative changes.
Pages 337–409 · 8 cases06 Distributions, spread and outliers
Describe center and spread without hiding unusual observations. State the quantile method, sample size and how flagged values were handled.
Pages 410–482 · 8 cases07 Cohorts, trends and segment comparisons
Define time and segment cohorts consistently. Compare changes within groups and check whether the overall result moved because the mix changed.
Pages 483–555 · 8 cases08 Uncertainty and honest inference
Represent uncertainty without overstating what the data can support. Check sample assumptions, association and reproducibility.
Pages 556–628 · 8 cases09 Compare experiments and observational periods
Compare periods or experiment groups using common definitions. Consider assignment, crossover, guardrails and outside changes before claiming an effect.
Pages 669–741 · 8 cases10 Chart design and accessible reporting
Choose a chart that expresses the measure. Use honest scales, label the unit and provide equivalent values in an accessible table.
Pages 742–814 · 8 cases11 Disclosure control and publication boundaries
Review what an aggregate release could reveal. Keep small values out of all output forms and look for totals or overlapping releases that expose them.
Pages 815–887 · 8 cases12 Reproducible reports and series handoff
Freeze report definitions and compare reruns. Ship the fixtures, source, output and checks someone else needs to reproduce the result.
Pages 888–960 · 8 cases
Three connected projects
Build a reproducible attendance report
Open the worked attendance report
02 · Define the metrics
Calculate rates with explicit denominators, compare segments, and suppress small groups in the classroom report.
03 · Deliver the report
Build accessible HTML and SVG from one report object, then verify repeat builds with a manifest.
Look inside
See an actual workbook page
Try a data-grain case in the free 16-page excerpt. Preview the source pages from each project.
The series
Continue from the earlier workbooks
Get the complete workbook
The PDF and ZIP include 96 runnable cases with assertions and recorded output, a project suite with tests, and a curriculum index. Use Python 3.12 or newer.
Gumroad listing pending · $20 USD
The free sample and worked report are available now.