Measure whether the study table and plot works

Translate the claim into assertions or a tiny eval harness. The metric to protect is: mean hours, max hours, and plot file exists with xlabel/ylabel set. Always record the denominator (how many cases) beside any rate. A percentage without a denominator is marketing, not measurement.

from pathlib import Path
assert Path('hours_vs_pass.png').exists()
print({'plot_exists':True,'xlabel':'hours','ylabel':'passed'})

Expected evidence: plot_exists True. A passing assertion proves only the behavior it names; broader usefulness still needs the chapter’s full limits.

Be explicit: beating the baseline (hand-computed mean of the four-row fixture) on this fixture does not prove behavior under wrong column names, or a plot that saves without axis labels. Label observations separately from conclusions so the next page inherits honest evidence about the study table and plot.

Compute mean hours, max hours, and plot file exists with xlabel/ylabel set with the denominator written beside the rate every time. For this chapter, the evaluation set is intentionally tiny; that is allowed only if you say so in the evidence. Compare against hand-computed mean of the four-row fixture before celebrating.

Add one negative case aimed at wrong column names, or a plot that saves without axis labels. A suite with only happy cases cannot protect the study table and plot when the characteristic failure appears in review.

If a check is expensive or flaky, shrink it until it is deterministic on four-row study.csv with hours and passed columns. Flaky green builds teach the team to ignore gates. Record what this page does not prove so security-ops and mastery-ship inherit honest limits.

At the evaluation 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 metrics with explicit denominators and a negative case 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

Previous · Next

For data-tables-and-plots, write a transfer example that differs in one constraint from the chapter scenario. Keep the quality bar fixed. Explain which check still applies.

For data-tables-and-plots, write a transfer example that differs in one constraint from the chapter scenario. Keep the quality bar fixed. Explain which check still applies.

For data-tables-and-plots, write a transfer example that differs in one constraint from the chapter scenario. Keep the quality bar fixed. Explain which check still applies.