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<a href='https://https://pypi.org/project/nptyping/'>
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💡 *Type hints for `NumPy`* <br/>
💡 *Type hints for `pandas.DataFrame`* <br/>
💡 *Extensive dynamic type checks for dtypes shapes and structures* <br/>
Example of a hinted `numpy.ndarray`:
```python
>>> from nptyping import NDArray, Int, Shape
>>> arr: NDArray[Shape["2, 2"], Int]
```
Example of a hinted `pandas.DataFrame`:
```python
>>> 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:
```python
>>> 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:
```python
>>> 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`:
```python
>>> from nptyping import Structure
>>> Structure["name: Str, age: Int"]
Structure['age: Int, name: Str']
```
Here is an example to see it in action:
```python
>>> 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:
```python
>>> 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.
```python
>>> 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`.
```python
>>> 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`:
```python
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](https://github.com/ramonhagenaars/nptyping/blob/master/USERDOCS.md#Examples).
## Documentation
* [User documentation](https://github.com/ramonhagenaars/nptyping/blob/master/USERDOCS.md) <br/>
The place to go if you are using this library. <br/><br/>
* [Release notes](https://github.com/ramonhagenaars/nptyping/blob/master/HISTORY.md) <br/>
To see what's new, check out the release notes. <br/><br/>
* [Contributing](https://github.com/ramonhagenaars/nptyping/blob/master/CONTRIBUTING.md) <br/>
If you're interested in developing along, find the guidelines here. <br/><br/>
* [License](https://github.com/ramonhagenaars/nptyping/blob/master/LICENSE) <br/>
If you want to check out how open source this library is.
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