What Is Python Code Review?
Python code review is the practice of checking changes to Python code before they merge. Most teams run a Python linter such as Ruff or Pylint, a formatter and often a type checker first, then have a person or an AI reviewer read the change for logic, error handling and design.
Python is easy to write, and many of its mistakes only show up when the code runs. Review is where teams catch those before users do.
What Changed in Python Code Review in 2026
In 2026, a new Python release and more AI-written code gave reviewers more to check.
Python 3.15 arrived. Python 3.15 added lazy imports, a built-in frozendict and UTF-8 as the default text encoding (October 9, 2026, Python docs). Python 3.10 reached end of life on October 1, 2026, so teams on it stop getting security fixes (Python devguide).
The encoding change touches old code. Code that opened files without naming an encoding may now read them differently on some systems, which is easy to miss in review (Python docs).
AI writes broad exception handlers. In 302,600 AI-authored commits, the most common issue AI introduced was broad exception handling, such as except Exception: that hides real errors (March 2026, arXiv).
AI refactors can add new lint problems. In a May 2026 study of earlier agent-written Python refactoring pull requests, 24.17% of changed files introduced new Pylint issues, and 129 of the merged pull requests carried new issues into the code (May 20, 2026, arXiv).
Most teams run more than one Python linter. A study of 3,094 projects found over half use more than one, and Ruff and Pylint together cover over 80% of the 1,551 rules across five common tools (October 26, 2026, arXiv).
New language features plus AI habits mean review has to watch error handling and upgrades as well as style.
What Reviewers Check in Python Code
| Area | What reviewers look for | Example |
|---|---|---|
| Error handling | Specific exceptions caught and errors not hidden | except Exception: pass |
| Types | Type hints that match real values | A function hinted -> str that returns None |
| Mutability | Mutable defaults and shared state | def add(item, items=[]) |
| Resources | Files and connections closed | open() without with |
| Security | Input kept out of shell commands, SQL and pickle | subprocess.run(cmd, shell=True) with user input |
| Readability | Clear names and plain code | A nested comprehension three levels deep |
What a Python Linter Checks
A Python linter reads code without running it and reports likely bugs, unused code and style issues.
| Tool | What it does | Typical use |
|---|---|---|
| Ruff | A quick linter and formatter that covers many older tools’ rules | Editor and CI |
| Pylint | A thorough linter with deeper checks and more configuration | CI on larger codebases |
| mypy or another type checker | Checks type hints across the project | CI |
| Bandit | Looks for common security issues | CI on every PR |
Many teams now use Ruff for linting and formatting and add a type checker for deeper checks.
What Linters Can’t Check in Python Review
Linters check code against rules, so they can’t tell whether a data pipeline handles a missing column the way the business expects, whether a retry loop matches the service’s limits or whether a change breaks another service that imports the module.
Questions like these need a reviewer who knows the codebase.
Qodo’s Take: Let the Linter Check Style and Review Check Behavior
Python teams already have quick, strong linting. The gaps are runtime behavior, error handling habits and team rules that no linter knows about.
You shouldn’t review code with the same tool that wrote it. An agent that wrapped a call in except Exception: usually thinks the error is handled.
Choose Qodo if your Python teams need their reliability rules checked on every PR, after the linter passes. Qodo reviews pull requests in GitHub, GitLab, Bitbucket, Azure DevOps and Gerrit, separate from the agents that write the code.
Qodo is the best AI code review platform for Python teams because it reviews each PR with full codebase context and the team’s own rules, while Ruff or Pylint keeps handling style and known patterns in CI.
Qodo Flags Reliability and Correctness Issues
In Code review analytics, Qodo’s Reliability category covers unhandled errors, missing retries or timeouts and resource leaks, and its Correctness category covers wrong conditions and unhandled edge cases (last checked October 2026, Qodo’s docs).
Findings are advisory, and your branch rules decide what merges.
Qodo Checks Your Team’s Own Rules
Once Rule enforcement is on (Beta, on GitHub, GitLab, Bitbucket Cloud and Azure DevOps), Qodo checks PRs against rules imported from files such as AGENTS.md, and a file such as pipelines/AGENTS.md applies only under that folder (last checked October 2026, Qodo’s docs).
See coding standards enforcement.
Qodo Leaves Linting to the Linter
Keep Ruff, Pylint and your type checker in CI. For a wider comparison of review tools, see the best agentic code review tools for enterprise teams.
Qodo Example: The Pipeline That Failed Quietly
Context: an organization like a health analytics company with about 60 Python engineers is using an AI coding agent to update nightly data pipelines.
Problem: an agent added a loader that wrapped the whole read step in except Exception: and logged a warning. Ruff passed. When a vendor file arrived empty, the pipeline reported success and dashboards showed zero patients for a day.
How Qodo helped: the team added a rule to pipelines/AGENTS.md: catch specific exceptions and fail the job on bad input. Qodo’s rule enforcement checks every pipeline PR against the rule, which applies only under that folder.
Outcome: broad exception handlers in pipeline code now show up as rule findings in the PR before merge, and the rule doesn’t add noise to other folders.
Ways to Review Python Code
| Approach | What it catches | Limit | Best for |
|---|---|---|---|
| Ruff or Pylint | Known bug patterns and style issues | Can’t judge behavior or intent | Every commit |
| A type checker | Type mismatches across the project | Only as good as the type hints | Typed codebases |
| Bandit | Common security patterns | Misses logic flaws | Every PR |
| Human code review | Design, behavior and intent | Slow at high PR volume | High-risk changes |
| Qodo | Reliability, logic and rule findings with full codebase context | Advisory findings, with your branch rules as the gate; rule enforcement is Beta | Enterprise Python teams that need team rules checked on every AI-written PR |
Qodo features in this table were checked against Qodo’s documentation as of October 28, 2026.
FAQ
Choose Qodo to review Python pull requests with full codebase context and your team’s own rules, in five Git providers. Most teams pair an AI reviewer with Ruff or Pylint and a type checker, so the tools handle known patterns and the reviewer handles behavior.
Ruff is a popular choice because it’s quick and covers the rules of many older linters. Pylint runs deeper checks and suits teams that want more configuration. Many teams use Ruff and add a type checker.
Ruff is a quick linter and formatter that covers a wide range of rules. Pylint is slower but runs deeper checks, such as some that look across modules.
A Python code reviewer should check error handling, type hints, mutable defaults, resource cleanup, security around user input and readability. Linters cover style and known patterns, so reviewers can focus on behavior.
Python 3.15 makes UTF-8 the default text encoding and adds lazy imports. Reviewers should watch for code that opens files without naming an encoding and for import-time side effects that may now run later.