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
- 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
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 only estimates 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.
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.
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).
If you want to ignore a whole file or directory, use the
Flake8 noqa comments
For compatibility with flake8, Vulture
supports the F401 and
codes for ignoring unused imports (
# noqa: F401) and unused local
# noqa: F841). However, we recommend using whitelists instead
noqa comments, since
noqa comments add visual noise to the code and
make it harder to read.
You can use
--ignore-names foo*,ba[rz] to let Vulture ignore all names
foo and the names
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
We recommend using whitelists instead of
--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 tuple assignments or function signatures. Vulture will ignore these
variables if they start with an underscore (e.g.,
_x, y = get_pos() or
def my_method(self, widget, **_kwargs)).
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.
If Vulture complains about code like
if False:, you can use a Boolean
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
[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
Version control integration
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
pre-commit install. Finally, create a
in your repository and specify all files that Vulture should check under
[tool.vulture] --> paths (see above).
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
raise statements, and by searching
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
Consider the following Python script (
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()
$ 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.
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
dead_code.py:1: unused import 'os' (90% confidence) dead_code.py:8: unused variable 'message' (60% confidence)
|0||No dead code found|
|1||Dead code found|
|1||Invalid input (file missing, syntax error, wrong encoding)|
|2||Invalid command line arguments|
- pyflakes finds unused imports
and unused local variables (in addition to many other programmatic
- 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,
- 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.