Make cleaning explicit in code

Make cleaning explicit in code — guided practice

Record exclusion reasons and reject unexpected values.

Use free local JupyterLab with a Python 3 kernel, or an equivalent notebook environment that can read uploaded local files. Put analysis.ipynb and attendance.csv in the same new practice folder; no extra Python packages are used by the analysis. The CSV rows are S1 north 8 held; S2 south 6 held; S3 north blank held; S4 south 0 canceled; S5 north 7 held; S6 south 5 held; S7 north 4 held; S8 south 9 held. The supplied analyze.py runs the same calculation with Python from this folder. Use only these fictional records.

1. 1. Read the branch order

   Locate canceled handling before blank-count handling.

   You should see: S4 is excluded as canceled; S3 as missing attendance.

   Distinct reasons describe why each row was omitted.

2. 2. Run the notebook cell

   Execute the code cell with the supplied input.

   You should see: Raw=8, included=6, excluded=2, total=39.

   The program applies the written rules to the exact fixture.

3. 3. Inspect exclusions

   Open output/result.json and read the excluded entries.

   You should see: S3 missing attendance; S4 canceled.

   A trace makes data loss visible.

4. 4. Try a bad value

   In a temporary copy replace S1 count 8 with -1, run, observe ValueError, then restore 8.

   You should see: The unexpected negative count is rejected rather than silently used.

   A failed run is useful evidence that a guard catches invalid input.