Vulture - Find dead code

Vulture finds unused code in Python programs. This is useful for cleaning up and finding errors in large code bases. If you run Vulture on both your library and test suite you can find untested code.

Due to Python's dynamic nature, static code analyzers like Vulture are likely to miss some dead code. Also, code that is only called implicitly may be reported as unused. Nonetheless, Vulture can be a very helpful tool for higher code quality.

Features

• fast: uses static code analysis
• tested: tests itself and has complete test coverage
• complements pyflakes and has the same output syntax
• sorts unused classes and functions by size with --sort-by-size

Installation

$ pip install vulture

Usage

$ vulture myscript.py # or $ python3 -m vulture myscript.py $ vulture myscript.py mypackage/ $ vulture myscript.py --min-confidence 100 # Only report 100% dead code.

The provided arguments may be Python files or directories. For each directory Vulture analyzes all contained \*.py files.

After you have found and deleted dead code, run Vulture again, because it may discover more dead code.

Types of unused code

In addition to finding unused functions, classes, etc., Vulture can detect unreachable code. Each chunk of dead code is assigned a confidence value between 60% and 100%, where a value of 100% signals that it is certain that the code won't be executed. Values below 100% are very rough estimates (based on the type of code chunk) for how likely it is that the code is unused.

| Code type | Confidence value | |--------------------------------------------------------|------------------| | function/method/class argument, unreachable code | 100% | | import | 90% | | attribute, class, function, method, property, variable | 60% |

You can use the --min-confidence flag to set the minimum confidence for code to be reported as unused. Use --min-confidence 100 to only report code that is guaranteed to be unused within the analyzed files.

Handling false positives

When Vulture incorrectly reports chunks of code as unused, you have several options for suppressing the false positives. If fixing your false positives could benefit other users as well, please file an issue report.

Whitelists

The recommended option is to add used code that is reported as unused to a Python module and add it to the list of scanned paths. To obtain such a whitelist automatically, pass --make-whitelist to Vulture:

$ vulture mydir --make-whitelist > whitelist.py $ vulture mydir whitelist.py

Note that the resulting whitelist.py file will contain valid Python syntax, but for Python to be able to run it, you will usually have to make some modifications.

We collect whitelists for common Python modules and packages in vulture/whitelists/ (pull requests are welcome).

Ignoring files

If you want to ignore a whole file or directory, use the --exclude parameter (e.g., --exclude "settings.py,/docs/.py,/test_.py,/.venv/*.py"). The exclude patterns are matched against absolute paths.

Flake8 noqa comments

For compatibility with flake8, Vulture supports the F401 and F841 error codes for ignoring unused imports (# noqa: F401) and unused local variables (# noqa: F841). However, we recommend using whitelists instead of noqa comments, since noqa comments add visual noise to the code and make it harder to read.

Ignoring names

You can use --ignore-names foo*,ba[rz] to let Vulture ignore all names starting with foo and the names bar and baz. Additionally, the --ignore-decorators option can be used to ignore the names of functions decorated with the given decorator (but not their arguments or function body). This is helpful for example in Flask projects, where you can use --ignore-decorators "@app.route" to ignore all function names with the @app.route decorator. Note that Vulture simplifies decorators it cannot parse: @foo.bar(x, y) becomes "@foo.bar" and @foo.bar(x, y).baz becomes "@" internally.

We recommend using whitelists instead of --ignore-names or --ignore-decorators whenever possible, since whitelists are automatically checked for syntactic correctness when passed to Vulture and often you can even pass them to your Python interpreter and let it check that all whitelisted code actually still exists in your project.

Marking unused variables

There are situations where you can't just remove unused variables, e.g., in function signatures. The recommended solution is to use the del keyword as described in the PyLint manual and on StackOverflow:

def foo(x, y):
    del y
    return x + 3

Vulture will also ignore all variables that start with an underscore, so you can use x, y = getpos() to mark unused tuple assignments or function arguments, e.g., def foo(x, _y).

Minimum confidence

Raise the minimum confidence value with the --min-confidence flag.

Unreachable code

If Vulture complains about code like if False:, you can use a Boolean flag debug = False and write if debug: instead. This makes the code more readable and silences Vulture.

Forward references for type annotations

See #216. For example, instead of def foo(arg: "Sequence"): ..., we recommend using

python
from future import annotations
def foo(arg: Sequence):     ...

Configuration

You can also store command line arguments in pyproject.toml under the tool.vulture section. Simply remove leading dashes and replace all remaining dashes with underscores.

Options given on the command line have precedence over options in pyproject.toml.

Example Config:

toml
[tool.vulture]
exclude = ["file.py", "dir/"]
ignoredecorators = ["@app.route", "@require*"]
ignorenames = ["visit", "do_"]
make_whitelist = true
min_confidence = 80
paths = ["myscript.py", "mydir", "whitelist.py"]
sortbysize = true
verbose = true

Vulture will automatically look for a pyproject.toml in the current working directory.

To use a pyproject.toml in another directory, you can use the --config path/to/pyproject.toml flag.

Integrations

You can use a pre-commit hook to run Vulture before each commit. For this, install pre-commit and add the following to the .pre-commit-config.yaml file in your repository:

repos:
  - repo: https://github.com/jendrikseipp/vulture
    rev: 'v2.3'  # or any later Vulture version
    hooks:
      - id: vulture

Then run pre-commit install. Finally, create a pyproject.toml file in your repository and specify all files that Vulture should check under [tool.vulture] --> paths (see above).

There's also a GitHub Action for Vulture, a VS Code extension and you can use Vulture programmatically. For example:

python
import vulture
v = vulture.Vulture() v.scavenge(['.']) unusedcode = v.getunused_code()  # returns a list of Item objects

How does it work?

Vulture uses the ast module to build abstract syntax trees for all given files. While traversing all syntax trees it records the names of defined and used objects. Afterwards, it reports the objects which have been defined, but not used. This analysis ignores scopes and only takes object names into account.

Vulture also detects unreachable code by looking for code after return, break, continue and raise statements, and by searching for unsatisfiable if- and while-conditions.

Sort by size

When using the --sort-by-size option, Vulture sorts unused code by its number of lines. This helps developers prioritize where to look for dead code first.

Examples

Consider the following Python script (dead_code.py):

python
import os
class Greeter:     def greet(self):         print("Hi")
def hello_world():     message = "Hello, world!"     greeter = Greeter()     func_name = "greet"     greetfunc = getattr(greeter, funcname)     greet_func()
if name == "main":     hello_world()

Calling :

$ vulture dead_code.py

results in the following output:

dead_code.py:1: unused import 'os' (90% confidence) dead_code.py:4: unused function 'greet' (60% confidence) dead_code.py:8: unused variable 'message' (60% confidence)

Vulture correctly reports os and message as unused but it fails to detect that greet is actually used. The recommended method to deal with false positives like this is to create a whitelist Python file.

Preparing whitelists

In a whitelist we simulate the usage of variables, attributes, etc. For the program above, a whitelist could look as follows:

python
whitelistdeadcode.py
from dead_code import Greeter
Greeter.greet

Alternatively, you can pass --make-whitelist to Vulture and obtain an automatically generated whitelist.

Passing both the original program and the whitelist to Vulture

$ vulture deadcode.py whitelistdead_code.py

makes Vulture ignore the greet method:

dead_code.py:1: unused import 'os' (90% confidence) dead_code.py:8: unused variable 'message' (60% confidence)

Exit codes

| Exit code | Description | |-----------|------------------------------------------------------------| | 0 | No dead code found | | 1 | Invalid input (file missing, syntax error, wrong encoding) | | 2 | Invalid command line arguments | | 3 | Dead code found |

Similar programs

- pyflakes finds unused imports and unused local variables (in addition to many other programmatic errors). - coverage finds unused code more reliably than Vulture, but requires all branches of the code to actually be run. - uncalled finds dead code by using the abstract syntax tree (like Vulture), regular expressions, or both. - dead finds dead code by using the abstract syntax tree (like Vulture).

Participate

Please visit to report any issues or to make pull requests.

- Contributing guide: CONTRIBUTING.md - Release notes: CHANGELOG.md - Roadmap: TODO.md