Define the study table and plot input contract

The accepted input remains: a small CSV with one learner per row (hours, passed). Keep parsing and normalization in functions that do not score, train, or call a model. That split lets a test fail the boundary without blaming the core logic. The user-facing decision stays: inspect typical hours, an extreme value, and the hours/pass relationship.

import pandas as pd
REQUIRED={'hours','passed'}
def load(path):
    df=pd.read_csv(path)
    missing=REQUIRED-set(df.columns)
    if missing: raise ValueError(missing)
    return df
print(sorted(REQUIRED))

Expected evidence: hours, passed. If the contract is silent on a bad value, later debugging will look like an algorithm bug when it is really a data bug.

Write one assertion for a neighboring valid input to the study table and plot so tightening the boundary does not over-reject. Document field names and types the way a teammate would need them on day two of data-tables-and-plots—not as comments you plan to delete.

List every field in four-row study.csv with hours and passed columns and mark each as required, optional, or forbidden. Required fields must fail loudly when missing; optional fields need defaults you can quote in a test; forbidden fields (secrets, raw PII, path escapes) must never be accepted silently. This list is the contract for the study table and plot.

Add one sentence about encoding, units, or timezones if relevant to a small CSV with one learner per row (hours, passed). Contracts that ignore units create “correct” programs that still ship wrong decisions when someone tries to inspect typical hours, an extreme value, and the hours/pass relationship.

Write the error string you want for the most likely bad input. Prefer ValueError('threshold out of range')-style messages over generic invalid input. The contract’s job is to make wrong column names, or a plot that saves without axis labels harder to confuse with a model or algorithm bug later.

At the data contract 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 malformed inputs rejected with field-named errors 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.

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