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.
- 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 2.7 and Python >= 3.4
$ pip install vulture # from PyPI $ pip install . # from cloned repo
$ 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.
Handling false positives
You can 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
--make-whitelist to Vulture.
$ vulture mydir --make-whitelist > whitelist.py $ vulture mydir whitelist.py
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
--exclude parameter (e.g.,
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
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()).
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.
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 for unsatisfiable
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.
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()
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 "greet" method:
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|
- Vulture can be used together with pyflakes
- The coverage module can find unused code more reliably, but requires all branches of the code to actually be run.
Please visit https://github.com/jendrikseipp/vulture to report any issues or to make pull requests.