Ship and explain the study table and plot

A shippable lab artifact includes: how to run it, the metric result (mean hours, max hours, and plot file exists with xlabel/ylabel set), the failure you can still reproduce (wrong column names, or a plot that saves without axis labels), the security gate for publishing learner names from the CSV in a shared plot title, and a rollback note. The user decision it supports remains: inspect typical hours, an extreme value, and the hours/pass relationship.

print({'artifact':'study table+plot','proved':['mean/max','labeled axes'],'unproved':['larger cohort'],'owner':'data-lab'})

Expected evidence: plotting ship note. Store this beside the fixture version so scores remain meaningful after content changes in data-tables-and-plots.

State operating limits for the study table and plot in plain language: fixture size, offline vs live dependencies, and what would require a new eval set. Shipping data-tables-and-plots is honest scoping, not maximal confidence language.

Fill this table in your notes for the study table and plot:

• Proved on four-row study.csv with hours and passed columns: …
• Unproved beyond the fixture: …
• Metric that blocks release: mean hours, max hours, and plot file exists with xlabel/ylabel set
• Failure still reproducible: wrong column names, or a plot that saves without axis labels
• Security gate: publishing learner names from the CSV in a shared plot title
• Rollback: …

Ship the narrative only when the unproved list is honest. Reviewers trust narrow claims that support inspect typical hours, an extreme value, and the hours/pass relationship more than maximal language that collapses under the first production oddity.

Hand your ship note to a peer and ask them to recreate a proved/unproved ship note with rollback without watching you type. If they cannot, your evidence is still tribal knowledge. Tighten the run command and the metric line until a stranger can validate the study table and plot against four-row study.csv with hours and passed columns.

At the mastery and shipping stage for data-tables-and-plots, the job is narrower than finishing a product demo. You are creating one progressive evidence piece about four-row study.csv with hours and passed columns that later pages inherit without redefining success. Keep that fixture small enough to inspect by hand, keep outputs copy-pasteable as text, and refuse to narrate this baseline as if it were a production SLA: hand-computed mean of the four-row fixture.

For this page specifically, success looks like a proved/unproved ship note with rollback while still centering the user decision to inspect typical hours, an extreme value, and the hours/pass relationship. If you cannot point to a file, command, or assertion that proves that for the study table and plot, stay on this page instead of advancing.

Pandas glossary

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