Prospector: python static analysis tool
Prospector is a tool to analyse Python code and output information about errors, potential problems, convention violations and complexity.
The primary aim of Prospector is to be useful ‘out of the box’. A common complaint of other Python analysis tools is that it takes a long time to filter through which errors are relevant or interesting to your own coding style. Prospector provides some default profiles, which hopefully will provide a good starting point and will be useful straight away, and adapts the output depending on the libraries your project uses.
Prospector can be installed using pip by running the following command:
pip install prospector
Optional dependencies for Prospector, such as pyroma can also be installed by running:
pip install prospector[with_pyroma]
For a list of all of the optional dependencies, see the optional extras section on the ReadTheDocs page on Supported Tools Extras.
For more detailed information on installing the tool, see the installation section of the tool’s main page on ReadTheDocs.
Simply run prospector from the root of your project:
This will output a list of messages pointing out potential problems or errors, for example:
prospector.tools.base (prospector/tools/base.py): L5:0 ToolBase: pylint - R0922 Abstract class is only referenced 1 times
Run prospector --help for a full list of options and their effects.
The default output format of prospector is designed to be human readable. For parsing (for example, for reporting), you can use the --output-format json flag to get JSON-formatted output.
Prospector is configurable using “profiles”. These are composable YAML files with directives to disable or enable tools or messages. For more information, read the documentation about profiles.
If your code uses frameworks and libraries
Often tools such as pylint find errors in code which is not an error, for example due to attributes of classes being created at run time by a library or framework used by your project. For example, by default, pylint will generate an error for Django models when accessing objects, as the objects attribute is not part of the Model class definition.
Prospector mitigates this by providing an understanding of these frameworks to the underlying tools.
Prospector will try to intuit which libraries your project uses by detecting dependencies and automatically turning on support for the requisite libraries. You can see which adaptors were run in the metadata section of the report.
If Prospector does not correctly detect your project’s dependencies, you can specify them manually from the commandline:
prospector --uses django celery
Additionally, if Prospector is automatically detecting a library that you do not in fact use, you can turn off autodetection completely:
Note that as far as possible, these adaptors have been written as plugins or augmentations for the underlying tools so that they can be used without requiring Prospector. For example, the Django support is available as a pylint plugin.
Prospector has a configurable ‘strictness’ level which will determine how harshly it searches for errors:
prospector --strictness high
Possible values are verylow, low, medium, high, veryhigh.
Prospector does not include documentation warnings by default, but you can turn this on using the --doc-warnings flag.
If you’d like Prospector to be run automatically when making changes to files in your Git repository, you can install pre-commit and add the following text to your repositories’ .pre-commit-config.yaml:
repos: - repo: https://github.com/PyCQA/prospector rev: 1.1.4 # The version of Prospector to use hooks: - id: prospector
Prospector is available under the GPLv2 License.