Type stubs for Python machine learning libraries
Project description
Mypy type stubs for NumPy, pandas, and Matplotlib
This is a PEP561compliant stubonly 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
Many 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 does exist. If you encounter missing functions, we would be delighted for you to send a PR. If you are unsure of how to type a function, we can discuss it.
Installing
You can get this package from PyPI:
pip install datasciencetypes
To get the most uptodate version, install it directly from GitHub:
pip install git+https://github.com/predictiveanalyticslab/datasciencetypes
Or clone the repository somewhere and do pip install e .
.
Examples
These are the kinds of things that can be checked:
Array creation
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
Operations
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)
Reductions
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
Philosophy
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.
Contributing
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 for running the CI checks that are triggered 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:
./check_all.sh
Below we describe how to run the various checks individually,
but 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.
Just running
mypy tests
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.
First, install black
and then run
black .
from the base directory.
Pytest
python m pytest vv tests/
Flake8
flake8 *stubs
License
Project details
Release history Release notifications  RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for datasciencetypes0.2.23.tar.gz
Algorithm  Hash digest  

SHA256  8096b9a35a8a187bf9a122b4707c97de841d810744690ee2a4ac30c6462e0d16 

MD5  c899a88e472d21eb7ccdaa382fe0bc65 

BLAKE2256  ae5f6c4888c17fa53c551df740d93806c68f1d5eed6b6167747087415e50dccb 
Hashes for data_science_types0.2.23py3noneany.whl
Algorithm  Hash digest  

SHA256  bca319abc0e53a0316f9fcb887937e942477cb9e5fc63c8581e0b0438903b977 

MD5  df35aa8171cfc8374c8f227a57e1de5a 

BLAKE2256  9f663f48f40f1aaa0508732706271db4285c25977ee0e00d6c3582e2a6ec2f01 