lapipe 1.0.0

Tiny value-first pipe operator with optional '_' placeholder

lapipe

A tiny, zero-dependency “value-first” pipe helper for Python  —  inspired by Unix pipes and F#’s |>, but with an optional _ placeholder so you can drop the value exactly where you need it.

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Why?

• Chain data-transform steps without intermediate variables
• Keep left-to-right reading order (like functional pipes)
• Explicitly mark the slot with _ only when you need to
• Works with any callables — Pandas, NumPy, matplotlib, your own functions — no monkey-patching or heavy frameworks

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Installation

pip install lapipe

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Quick start

from lapipe import pipe, _

def add(a, b):      return a + b
def mul(a, b):      return a * b

result = (
    pipe(2)               # wrap the value
    | (add, 3)            # 2 -> first arg: add(2, 3)
    | (mul, 4, _)         # 5 -> second arg: mul(4, 5)
)
print(result.value)        # 20

Rules in one table

syntax	how the value is passed
`	func`	first positional arg
`	(func, *args)`	first positional arg
`	(func, …, _)`	replaces that _
`	(func, {..., k=_})`	replaces that kwarg _
returns	new Pipe each step

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Examples

1. NumPy vector math

import numpy as np
from lapipe import pipe, _

vec = np.arange(5)                # [0 1 2 3 4]

out = (
    pipe(vec)
    | (np.add, 10)                # [10 11 12 13 14]
    | (np.multiply, 2, _)         # [20 22 24 26 28]
    | (np.power, _, 2)            # square each
)
print(out.value)
# [400 484 576 676 784]

2. Pandas transformation + plot

import pandas as pd, matplotlib.pyplot as plt
from lapipe import pipe, _

df = pd.DataFrame({
    "year": range(2010, 2025),
    "sales": [42, 45, 47, 44, 50, 54, 57, 60, 64, 66, 70, 74, 78, 81, 85]
})

(
    pipe(df)
    | (pd.DataFrame.copy, _)              # keep original intact
    | (pd.DataFrame.set_index, _, "year")
    | (pd.DataFrame.assign, growth=lambda d: d.sales.pct_change())
    | (pd.DataFrame.rolling, 3)           # 3-year moving mean
    | (pd.core.window.Rolling.mean, _)    # computes .mean()
    | (pd.DataFrame.plot, _, y="sales")   # `_` is DataFrame here
)
plt.title("3-year moving average sales")
plt.show()

3. Keyword placeholder

def greet(who, *, punct="!"):
    return f"Hello {who}{punct}"

pipe("?") | (greet, "world", {'punct': _})
# Pipe('Hello world?')

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API

pipe(value)        # wrap any value → returns Pipe
Pipe | func        # call func(value, …)
Pipe | (func, ...) # tuple form with args / kwargs
_                  # sentinel placeholder
Pipe.value         # access the wrapped result

That’s it! No other symbols, no hidden globals.

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Design notes

• No string → AST tricks. Just normal Python calls.
• No runtime import hacks. You decide which libraries to use.
• Stateless. Each step returns a new Pipe; the original stays unchanged.
• Pure Python 3.8+. One file, ~80 lines.

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Contributing

Issues and PRs are super welcome! If you find an edge-case that breaks the placeholder logic (looking at you, Pandas & NumPy dtypes), open an issue or send a failing test.

git clone https://github.com/yourname/lapipe
pytest

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License

MIT — do what you want, just keep the copyright and don’t blame us.

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Designed so your data can flow like water, not spaghetti. Enjoy piping! 🚰