Python Requirements Cleanup: Remove Stale Packages From Apps and Jobs

Python requirements cleanup starts by separating runtime imports from tooling, notebooks, migrations, optional extras, and scheduled jobs. A package can disappear from application code while still being loaded by Celery tasks, Alembic migrations, pytest plugins, or deployment hooks.

The useful output is a Python requirements cleanup pull request with import evidence, process checks, constraints diff, CI output, and rollback notes. Keep the review concrete: Remove direct requirements only after checking workers, migrations, notebooks, and CI plugins, then make the next action visible to the team that owns the risk. That matters because the cleanup can still go wrong when removing dependencies used by optional jobs.

Key takeaways

Treat each cleanup candidate as an owned system with dependencies, not anonymous clutter.
Use one release cycle plus the longest scheduled-job, migration, and notebook refresh interval before deciding that “quiet” means “unused.”
Prefer reversible changes first when removing dependencies used by optional jobs is still plausible.
Leave behind a Python requirements cleanup pull request with import evidence, process checks, constraints diff, CI output, and rollback notes so the next review starts with context.
Measure the result as lower spend, lower risk, less operational drag, or clearer ownership.

Map Python Execution Modes

Start with one Python service, job, or monorepo package where requirements files, pyproject metadata, imports, entry points, notebooks, tests, and container builds can be reviewed together. The best cleanup scope is small enough that owners can answer quickly but wide enough to include the attachments that make removal risky.

Field	Why it matters
Owner	Cleanup needs a person or team that can accept the decision
Current purpose	A short reason to keep the item, written in present tense
Last meaningful use	last use, permission scope, owner, rotation age, and reachable systems
Dependency evidence	audit logs, deployment references, identity provider records, and service owners
Risk if wrong	The outage, data loss, access failure, or rollback gap the review must avoid
Next action	Keep, reduce, archive, disable, remove, or investigate

Do not make the inventory larger than the decision. A short list with owners and evidence beats a perfect spreadsheet that nobody is willing to act on.

Requirements Evidence

The useful question is not “how old is it?” It is “what would break, become harder to recover, or lose accountability if this disappeared?” For Python requirements cleanup, collect enough evidence to answer that without relying on naming conventions.

Check	What to look for	Cleanup signal
Manifest location	requirements.txt, constraints files, pyproject groups, extras, Dockerfiles, and CI install steps	The package is listed without a current install reason
Import paths	Static imports, dynamic imports, entry points, management commands, notebooks, and plugins	No supported path imports or loads the package
Job coverage	Web app, workers, cron jobs, migrations, tests, and one-off scripts	All known execution modes pass without the dependency
Risk and replacement	Vulnerability, license, abandoned package, duplicate library, and pinned transitive parent	Removing or upgrading lowers risk without losing functionality

Use several signals together. Activity can miss monthly jobs and incident-only paths. Ownership can be stale. Cost can distract from security or recovery risk. The strongest case combines runtime data, dependency checks, owner review, and a rollback plan.

If the evidence conflicts, label the item “investigate” with a named owner and review date. That is still progress because the next review starts with a narrower question.

Example Evidence Check

Search imports and process entry points before pruning a Python requirement.

rg "^${PACKAGE_NAME}([<=> ]|$)" requirements*.txt constraints*.txt pyproject.toml
rg "import ${MODULE_NAME}|from ${MODULE_NAME} import" src tests jobs notebooks scripts
rg "celery|rq|alembic|pytest_plugins|entry_points" src tests pyproject.toml setup.cfg

Treat the output as a candidate list. Do not pipe these checks into delete commands; add owner review, dependency checks, and a rollback path first.

Prune With Process Checks

Use the least permanent move that proves the decision. In Python requirements cleanup, removal is only one possible outcome; reducing size, narrowing permission, shortening retention, archiving, or disabling a trigger may produce the same benefit with less risk.

Remove direct requirements only after checking workers, migrations, notebooks, and CI plugins.
Regenerate lock or constraints files with the package diff visible.
Run representative commands for every process type, not just the web app test suite.

Track the cleanup candidate with a simple priority score:

Score	Good sign	Bad sign
Impact	Meaningful spend, risk, toil, noise, or confusion disappears	The item is cheap and low-risk but politically distracting
Confidence	Owner, purpose, and dependency path are understood	The team is guessing from age or name
Reversibility	Restore, recreate, re-enable, or rollback path exists	Deletion would be the first real test
Prevention	A rule can stop recurrence	The same pattern will return next month

Start with high-impact, high-confidence, reversible candidates. Defer confusing items only if they get an owner and a date; otherwise “defer” becomes another word for keeping waste permanently.

Packages Loaded Outside Web Code

Some cleanup candidates are supposed to look quiet. Do not rush these cases:

Packages imported by Celery or RQ workers, Alembic/Django migrations, pytest plugins, and notebooks.
Optional database, cloud, image, crypto, or ML dependencies loaded only in specific environments.
Pinned packages that keep a transitive dependency compatible with an older runtime.

For these cases, use a longer observation window, explicit owner approval, and a staged reduction. The point is not to avoid cleanup; it is to avoid making the first proof of dependency an outage.

Run the Requirements PR

Run Python requirements cleanup as a decision review, not an open-ended hygiene project.

Pick the narrow scope and export the candidate list.
Add owner, current purpose, last-use evidence, dependency checks, and risk if wrong.
Remove obvious false positives, then ask owners to choose keep, reduce, archive, disable, remove, or investigate.
Apply the least permanent useful change first.
Watch the signals that would reveal a bad decision.
Complete the final removal only after the review window closes.
Save a Python requirements cleanup pull request with import evidence, process checks, constraints diff, CI output, and rollback notes.

For broader cleanup planning, use the cleanup library to pair this guide with related notes. If the cleanup has infrastructure impact, pair it with a visible owner, a rollback path, and a measurable business case. For infrastructure cleanup, the main cloud cost optimization checklist is a useful companion.

Group Dependencies by Purpose

Prevention should change the creation path, not just the cleanup path. For Python requirements cleanup, the useful prevention fields are owner, expiry date, least-privilege scope, rotation schedule, and removal notes. Make those fields part of normal creation and review.

Group dependencies by runtime, dev, job, and notebook purpose.
Require new requirements to include owner and import reason in the pull request.
Retire requirements when scripts, jobs, and notebooks are archived.

The recurring review should be short: sort by impact, pick the unclear items, assign owners, and close the loop on anything nobody claims. If the review keeps producing the same class of candidate, fix the creation path instead of celebrating repeated cleanup.

Example Decision Record

Use a compact record so the cleanup can be reviewed later without reconstructing the whole investigation.

Field	Example entry for this cleanup
Candidate	Unused Python packages in Python services
Why it looked stale	Low recent activity, unclear owner, or no current consumer after the first review
Evidence checked	Manifest location, Import paths, and owner confirmation
First reversible move	Remove direct requirements only after checking workers, migrations, notebooks, and CI plugins
Watch signal	The metric, alert, job, route, query, or owner complaint that would show the cleanup was wrong
Final action	Keep, reduce, archive, disable, or remove after one release cycle plus the longest scheduled-job, migration, and notebook refresh interval
Prevention rule	Group dependencies by runtime, dev, job, and notebook purpose

This record is intentionally small. If the decision needs a long narrative, the candidate is probably not ready for removal yet. Keep investigating until the owner, evidence, reversible move, and prevention rule are clear.

FAQ

How often should teams do Python requirements cleanup?

Use one release cycle plus the longest scheduled-job, migration, and notebook refresh interval for the first decision, then set a recurring cadence based on change rate. Fast-moving non-production systems may need monthly review; slower systems can be quarterly if every unclear item has an owner and a review date.

What is the safest first action?

The safest first action is usually ownership repair plus evidence collection. After that, remove direct requirements only after checking workers, migrations, notebooks, and ci plugins. That creates a visible test before permanent deletion.

What should not be removed quickly?

Do not rush anything connected to packages imported by celery or rq workers, alembic/django migrations, pytest plugins, and notebooks. Also slow down when the cleanup affects recovery, compliance, customer-specific behavior, rare schedules, or security response.

How do you make the decision useful later?

Write the decision as a small operational record: candidate, owner, evidence, chosen action, watch signals, rollback path, final date, and prevention rule. That format helps future engineers, search engines, and AI assistants understand the cleanup without guessing.