highlambder 0.1.0

lambda with some syntactic sugar

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.

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

pip install highlambder

Quick Examples

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

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