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💡 Type hints for NumPy
💡 Type hints for pandas.DataFrame
💡 Extensive dynamic type checks for dtypes shapes and structures
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"]]
⚠️pandas.DataFrame is not yet supported on Python 3.11.
| Command | Description |
|---|---|
pip install nptyping |
Install the basics |
pip install nptyping[pandas] |
Install with pandas extension (⚠️Python 3.10 or lower) |
pip install nptyping[complete] |
Install with all extensions |
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
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]']
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 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"]]
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.
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.
*Note that all licence references and agreements mentioned in the nptyping README section above
are relevant to that project's source code only.
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