Implement the happy path

Build the complete path once without adding optional features. Enter the example exactly, predict the expected output, run it, and compare. Then change one meaningful value connected to a four-row study dataset must be inspected for typical hours, an extreme value, and the relationship between hours and passing and explain why the result should change.

The deliverable for this step is a reproducible pandas summary and labeled matplotlib scatter plot saved as study-results.png.

import pandas as pd
import matplotlib.pyplot as plt

df = pd.read_csv("study.csv")
required = {"hours", "passed"}
if missing := required - set(df.columns):
    raise ValueError(f"missing columns: {sorted(missing)}")

print(df["hours"].describe()[["count", "mean", "max"]])
df.plot.scatter(x="hours", y="passed", title="Study hours and outcome")
plt.savefig("study-results.png", dpi=150, bbox_inches="tight")

Expected evidence:

count    4.0
mean     4.5
max      8.0
Name: hours, dtype: float64

The output may include version-specific details such as hashes, paths, fitted thresholds, or final decimal places. Compare the structural facts described here rather than copying placeholders. If the structure differs, stop and inspect the earliest unexpected line.

1. read_csv creates a DataFrame and infers column types from file values.
2. set subtraction catches misspelled or absent columns before plotting.
3. describe computes several summaries and the bracket selection keeps the three used here.
4. plot.scatter names both axes from columns; savefig creates a reviewable artifact without relying on an interactive window.

These lines form one chain: a CSV table with one learner per row becomes validated column summaries plus a plot whose axes and file path are explicit. Change only one input first. When several values change together, you cannot tell which change caused the new behavior.

• First failure: KeyError: 'hours' means the header differs, perhaps by capitalization or whitespace. Re-run the smallest command that proves the repair.
• Second failure: a mean that becomes NaN may indicate an empty or nonnumeric column; inspect df.info() and conversion failures. Preserve the failing input as a test when it represents a realistic mistake.
• Misleading success: a blank saved image often occurs when savefig runs after closing or clearing the figure. A clean-looking final line cannot cancel contradictory intermediate evidence.

When debugging, copy the exact error text and inspect names, paths, shapes, types, and versions. Explain the cause in one sentence before changing code. That discipline prevents a guessed repair from creating a second defect.

Deliver the CSV schema, analysis script, printed summary, and PNG together. A reviewer should be able to rerun the command and get the same measured values.

For the current build step, save the smallest useful evidence: the relevant command, its output, and the input that produced it. Do not use a screenshot as the only record when text can be copied and searched. Keep generated artifacts separate from source inputs so rerunning the example does not destroy the evidence it is meant to evaluate.

Keep the example small enough to inspect manually. Small does not mean careless: boundary values, file locations, feature order, and held-out data still determine whether the result means what you claim.

Return to your opening prediction. Mark it correct or rewrite it with the condition you missed. Then explain the difference between a successful execution and a trustworthy result for this specific example.