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.

• 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
• supports Python >= 3.6

$ pip install vulture

$ 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.

Vulture assigns each chunk of dead code a confidence value. A confidence value of 100% means that the code will never 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.

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

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/).

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 functions decorated with the given decorator. This is helpful for example in Flask projects, where you can use --ignore-decorators "@app.route" to ignore all functions with the @app.route decorator.

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 = get_pos() to mark unused tuple assignments or function arguments, e.g., def foo(x, _y).

Minimum confidence

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.

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

from __future__ import annotations

def foo(arg: Sequence):
    ...

if you're using Python 3.7+.

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:

[tool.vulture]
exclude = ["file*.py", "dir/"]
ignore_decorators = ["@app.route", "@require_*"]
ignore_names = ["visit_*", "do_*"]
make_whitelist = true
min_confidence = 80
paths = ["myscript.py", "mydir"]
sort_by_size = true
verbose = true

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).

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.

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.

Consider the following Python script (dead_code.py):

import os

class Greeter:
    def greet(self):
        print("Hi")

def hello_world():
    message = "Hello, world!"
    greeter = Greeter()
    greet_func = getattr(greeter, "greet")
    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:

# whitelist_dead_code.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 dead_code.py whitelist_dead_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)

• 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).

Please visit https://github.com/jendrikseipp/vulture to report any issues or to make pull requests.

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