np2typing 2.6.3

Type hints for NumPy.

🧊 Type hints for NumPy
🐼 Type hints for pandas.DataFrame
💡 Extensive dynamic type checks for dtypes shapes and structures
🚀 Jump to the Quickstart

Ramon Hagenaars' original nptyping package is available via pip install nptyping. This fork has been updated to work with NumPy version 2 and above, and is available via pip install np2typing followed by import nptyping (not import np2typing). The vast majority of the code in this package remains unchanged from Ramon's original work.

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 np2typing	Install the basics
pip install np2typing[pandas]	Install with pandas extension
pip install np2typing[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.