nptyping alternatives and similar packages
Based on the "Machine Learning" category.
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Write Clean Python Code. Always.
* Code Quality Rankings and insights are calculated and provided by Lumnify.
They vary from L1 to L5 with "L5" being the highest.
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README
💡 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.
Installation
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 
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.
*Note that all licence references and agreements mentioned in the nptyping README section above
are relevant to that project's source code only.