Intended Audience
- Developers
License
- OSI Approved :: MIT License
Operating System
- OS Independent
Natural Language
- English
Programming Language
- Python
- Python :: 3
- Python :: 3.7
- Python :: 3.8
- Python :: 3.9
- Python :: 3.10
- Python :: 3.11
๐ง Type hints for NumPy
๐ผ Type hints for pandas.DataFrame
๐ก Extensive dynamic type checks for dtypes shapes and structures
๐ Jump to the Quickstart
Example of a hinted numpy.ndarray:
>>> from nptyping import NDArray, Int, Shape
>>> arr: NDArray[Shape["2, 2"], Int]
Example of a hinted pandas.DataFrame:
>>> from nptyping import DataFrame, Structure as S
>>> df: DataFrame[S["name: Str, x: Float, y: Float"]]
Installation
| Command | Description |
|---|---|
pip install nptyping |
Install the basics |
pip install nptyping[pandas] |
Install with pandas extension |
pip install nptyping[complete] |
Install with all extensions |
Instance checking
Example of instance checking:
>>> import numpy as np
>>> isinstance(np.array([[1, 2], [3, 4]]), NDArray[Shape["2, 2"], Int])
True
>>> isinstance(np.array([[1., 2.], [3., 4.]]), NDArray[Shape["2, 2"], Int])
False
>>> isinstance(np.array([1, 2, 3, 4]), NDArray[Shape["2, 2"], Int])
False
nptyping also provides assert_isinstance. In contrast to assert isinstance(...), this won't cause IDEs or MyPy
complaints. Here is an example:
>>> from nptyping import assert_isinstance
>>> assert_isinstance(np.array([1]), NDArray[Shape["1"], Int])
True
NumPy Structured arrays
You can also express structured arrays using nptyping.Structure:
>>> from nptyping import Structure
>>> Structure["name: Str, age: Int"]
Structure['age: Int, name: Str']
Here is an example to see it in action:
>>> from typing import Any
>>> import numpy as np
>>> from nptyping import NDArray, Structure
>>> arr = np.array([("Peter", 34)], dtype=[("name", "U10"), ("age", "i4")])
>>> isinstance(arr, NDArray[Any, Structure["name: Str, age: Int"]])
True
Subarrays can be expressed with a shape expression between square brackets:
>>> Structure["name: Int[3, 3]"]
Structure['name: Int[3, 3]']
NumPy Record arrays
The recarray is a specialization of a structured array. You can use RecArray
to express them.
>>> from nptyping import RecArray
>>> arr = np.array([("Peter", 34)], dtype=[("name", "U10"), ("age", "i4")])
>>> rec_arr = arr.view(np.recarray)
>>> isinstance(rec_arr, RecArray[Any, Structure["name: Str, age: Int"]])
True
Pandas DataFrames
Pandas DataFrames can be expressed with Structure also. To make it more concise, you may want to alias Structure.
>>> from nptyping import DataFrame, Structure as S
>>> df: DataFrame[S["x: Float, y: Float"]]
More examples
Here is an example of a rich expression that can be done with nptyping:
def plan_route(
locations: NDArray[Shape["[from, to], [x, y]"], Float]
) -> NDArray[Shape["* stops, [x, y]"], Float]:
...
More examples can be found in the documentation.
Documentation
-
User documentation
The place to go if you are using this library. -
Release notes
To see what's new, check out the release notes. -
Contributing
If you're interested in developing along, find the guidelines here. -
License
If you want to check out how open source this library is.
