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https://github.com/wearepal/data-science-types

Mypy stubs, i.e., type information, for numpy, pandas and matplotlib
https://github.com/wearepal/data-science-types

matplotlib mypy mypy-stubs numpy pandas python stubs type-stubs

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Mypy stubs, i.e., type information, for numpy, pandas and matplotlib

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# Mypy type stubs for NumPy, pandas, and Matplotlib



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⚠️  **this project has mostly stopped development** ⚠️



The pandas team and the numpy team are both in the process of integrating type stubs into their codebases,

and we don't see the point of competing with them.



---



This is a [PEP-561][pep-561]-compliant stub-only package

which provides type information for [matplotlib][matplotlib], [numpy][numpy] and [pandas][pandas].

The [mypy][mypy] type checker (or pytype or PyCharm) can [recognize][mypy-docs] 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:



```bash

pip install data-science-types

```



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



```bash

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

```



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



## Examples



These are the kinds of things that can be checked:



### Array creation



```python

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



```python

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



```python

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



```bash

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



```bash

pip install -e .

```



Then use the `check_all.sh` script to run all tests:



```bash

./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



```bash

mypy tests

```



from the base directory should take these settings into account.

We enforce 0 Mypy errors.



### Formatting with black



We use [Black][black] to format the stub files.

First, install `black` and then run



```bash

black .

```

from the base directory.



### Pytest



```bash

python -m pytest -vv tests/

```



### Flake8



```bash

flake8 *-stubs

```



## License



[Apache 2.0](LICENSE)



[pep-561]: https://www.python.org/dev/peps/pep-0561/

[matplotlib]: https://matplotlib.org

[numpy]: https://numpy.org

[pandas]: https://pandas.pydata.org

[mypy]: http://www.mypy-lang.org/

[mypy-docs]: https://mypy.readthedocs.io/en/latest/installed_packages.html

[black]: https://github.com/psf/black