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nptyping

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💡 Type hints for Numpy and Pandas

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🧊 *Type hints for `NumPy`* 
🐼 *Type hints for `pandas.DataFrame`* 
💡 *Extensive dynamic type checks for dtypes shapes and structures* 
🚀 *[Jump to the Quickstart](https://github.com/ramonhagenaars/nptyping/blob/master/USERDOCS.md#Quickstart)*

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"]]

```

### 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:
```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) 
The place to go if you are using this library. 
 
* [Release notes](https://github.com/ramonhagenaars/nptyping/blob/master/HISTORY.md) 
To see what's new, check out the release notes.

* [Contributing](https://github.com/ramonhagenaars/nptyping/blob/master/CONTRIBUTING.md) 
If you're interested in developing along, find the guidelines here.

* [License](https://github.com/ramonhagenaars/nptyping/blob/master/LICENSE) 
If you want to check out how open source this library is.

nptyping

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