Frame the study table and plot experiment

Success is not “I followed the tutorial.” Success is producing evidence that: CSV summaries and a saved plot file have explicit axes and a reproducible path. The accepted input is narrow on purpose: a small CSV with one learner per row (hours, passed). That narrowness is what lets you inspect every field and prevents a toy demo from being narrated as a production system.

Record the baseline you must beat: hand-computed mean of the four-row fixture. If the finished artifact cannot beat that baseline on the fixture below, stop and revise the claim before writing more code.

import io,pandas as pd
csv='hours,passed\n1,0\n3,0\n5,1\n12,1\n'
df=pd.read_csv(io.StringIO(csv))
print(df.shape, list(df.columns))

Expected evidence: (4, 2) and column names. Treat the printout as a claim about this fixture, not as proof that the toolchain merely started.

For the study-hours table plus hours-vs-pass plot, a decorative win often looks like a clean run that never checks mean hours, max hours, and plot file exists with xlabel/ylabel set. Write the metric down now so later pages cannot redefine success after the fact. Also note the operational threat you will eventually gate on: publishing learner names from the CSV in a shared plot title.

For data-tables-and-plots, write the claim on a sticky note in this exact shape: “Given a small CSV with one learner per row (hours, passed), the study table and plot will …”. Fill the ellipsis with the observable part of: CSV summaries and a saved plot file have explicit axes and a reproducible path. Tape the baseline beside it: hand-computed mean of the four-row fixture. If someone later replaces your metric with a vibe check, the sticky note is how you push back.

Also sketch the one-sentence user story: a person uses this output to inspect typical hours, an extreme value, and the hours/pass relationship. If that sentence needs a dashboard, a model zoo, or five services, the lab scope is too wide—shrink the fixture (four-row study.csv with hours and passed columns) until the story fits on one screen.

Decide now whether live network calls are allowed on page 1. For this lab they usually are not; inventory and contracts should run offline against four-row study.csv with hours and passed columns. Note the metric you will eventually require (mean hours, max hours, and plot file exists with xlabel/ylabel set) so page 4 cannot invent a softer target. The characteristic failure to keep in mind is wrong column names, or a plot that saves without axis labels.

At the experiment brief 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 falsifiable claim and baseline written before coding 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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