data-science-types 0.2.4

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 can recognize the types in these packages by installing this package:

pip install data-science-types

To get the most up-to-date version, install it from GitHub directly:

pip install git+https://github.com/predictive-analytics-lab/data-science-types

Or clone the repository somewhere and do pip install -e ..

There is also minor support for Tensorflow and Tensorflow Probability.

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 class hierarchy exactly. The goal is to have useful checks on our code. Often the actual API 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]

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 -l 100 -t py36 -S .

from the base directory.

License

GPL 3