Metadata-Version: 2.4
Name: highlambder
Version: 0.1.0
Summary: lambda with some syntactic sugar
Author-email: Dario Colombotto <dario.colombotto@outlook.com>
License: MIT
Project-URL: Homepage, https://github.com/colomb8/highlambder
Description-Content-Type: text/markdown
License-File: LICENSE
Dynamic: license-file

# Highlambder - there can be only one... lambda to rule them all

Syntactic sugar for Python lambda expressions.
Make your anonymous functions shorter, more expressive, and (let's be honest) just more fun.
<p align="center"><img src="media/highlambder.png" alt="there can be only one" width="400"/></p>

>Image used under fair use, for illustrative and non-commercial purposes. All rights to the character and image belong to their respective owners.

## What is Highlambder?

**Highlambder** lets you write more elegant and compact code by replacing Python’s `lambda` keyword with something cleaner and more intuitive: a symbolic placeholder that behaves like a function.

You get concise, readable lambdas with operator overloading — no more `lambda x:` boilerplate.

## Installation

```bash
pip install highlambder
```

## Quick Examples

```python
from highlambder import L as λ

λ (10)  # -> 10

(λ + 5) (10)  # -> 15

(λ * 5) (10)  # -> 50

(3 + λ * 2) (10)  # -> 23

(40 / λ / 5) (2)  # -> 4

(10 * λ[1]) ([1, 2, 3])  # -> 20

(-1 + λ * 5 / λ + 1) (13)  # -> 5

(λ * 2 + λ * 4 + λ) (10)  # -> 70

(λ['A'] + λ['B']) ({'A': 3, 'B': 4})  # -> 7

(λ + λ) (2)  # -> 4

("It's a Me, " + λ) ('Mario!') # ->  'It's a Me, Mario!'
```

## Work-in-progress: pandas and NumPy support

```python
import pandas as pd
import numpy as np
from highlambder import L as λ

# pandas:

df = pd.DataFrame({
    'A': [1, 1, 2, 2],
    'B': [5, 6, 7, 8],
    'C': ['banana', 'apple', 'kiwi', 'orange'],
})

# Traditional lambda
assert pd.DataFrame.equals(
    df.assign(D=lambda d: d.A + 20),
    df.assign(D=λ.A + 20)
)

# String operations
assert pd.DataFrame.equals(
    df.assign(D=lambda d: d['C'].str.len() * 2),
    df.assign(D=λ['C'].str.len * 2)
)

# NumPy:

assert (λ + λ)(np.int64(2)) == 4

assert (λ.max - λ.min)(np.array([3, 4, 5, 6, 7, 8])) == 5
```

## Limitations (for now)

- Only single-argument functions are supported.
- len(L) is not supported due to Python's limitations on overriding __len__ without a concrete value.

These limitations may be lifted in future versions.

## License

[MIT](LICENSE)
