Type stubs for Python machine learning libraries
Mypy type stubs for numpy, pandas and matplotlib
This is a PEP-561-compliant stub-only package which provides type information for matplotlib, numpy and pandas. The mypy type checker (or pytype or PyCharm) can recognize the types in these packages by installing this package.
NOTE: This is a work in progress
Lots of functions are already typed, but a lot is still missing (numpy and pandas are huge libraries). Chances are you will see a message from Mypy claiming that a function does not exist when it actually does exist. If you encounter missing functions, we would be very happy for you to send a PR. If you are unsure of how to type a function, we can discuss it.
You can get this package from Pypi:
pip install data-science-types
To get the most up-to-date version, install it directly from GitHub:
pip install git+https://github.com/predictive-analytics-lab/data-science-types
Or clone the repository somewhere and do
pip install -e ..
These are the kinds of things that can be checked:
import numpy as np arr1: np.ndarray[np.int64] = np.array([3, 7, 39, -3]) # OK arr2: np.ndarray[np.int32] = np.array([3, 7, 39, -3]) # Type error arr3: np.ndarray[np.int32] = np.array([3, 7, 39, -3], dtype=np.int32) # OK arr4: np.ndarray[float] = np.array([3, 7, 39, -3], dtype=float) # Type error: the type of ndarray can not be just "float" arr5: np.ndarray[np.float64] = np.array([3, 7, 39, -3], dtype=float) # OK
import numpy as np arr1: np.ndarray[np.int64] = np.array([3, 7, 39, -3]) arr2: np.ndarray[np.int64] = np.array([4, 12, 9, -1]) result1: np.ndarray[np.int64] = np.divide(arr1, arr2) # Type error result2: np.ndarray[np.float64] = np.divide(arr1, arr2) # OK compare: np.ndarray[np.bool_] = (arr1 == arr2)
import numpy as np arr: np.ndarray[np.float64] = np.array([[1.3, 0.7], [-43.0, 5.6]]) sum1: int = np.sum(arr) # Type error sum2: np.float64 = np.sum(arr) # OK sum3: float = np.sum(arr) # Also OK: np.float64 is a subclass of float sum4: np.ndarray[np.float64] = np.sum(arr, axis=0) # OK # the same works with np.max, np.min and np.prod
The goal is not to recreate the APIs exactly. The main goal is to have useful checks on our code. Often the actual APIs in the libraries is more permissive than the type signatures in our stubs; but this is (usually) a feature and not a bug.
We always welcome contributions. All pull requests are subject to CI checks. We check for compliance with Mypy and that the file formatting conforms to our Black specification.
You can install these dev dependencies via
pip install -e '.[dev]'
This will also install numpy, pandas and matplotlib to be able to run the tests.
Running CI locally (recommended)
We include a script that runs the CI checks that will be run when a PR is opened. To test these out locally, you need to install the type stubs in your environment. Typically, you would do this with
pip install -e .
Then use the
check_all.sh script to run all tests:
Below we describe how to run the various checks individually,
check_all.sh should be easier to use.
Checking compliance with Mypy
The settings for Mypy are specified in the
mypy.ini file in the repository.
from the base directory should take these settings into account. We enforce 0 mypy errors.
Formatting with black
We use Black to format the stub files.
black and then run
from the base directory.
python -m pytest -vv tests/
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