clex-py 0.1.6

An alternative to functional programming

CLEX (Chained-Lambda-Expressions)

CLEX is a lightweight, string-based Domain Specific Language (DSL) for building clean, readable functional pipelines in Python.

If you've ever chained multiple map, filter, or zip calls together, you know how quickly things become unreadable:

result = list(map(lambda x: x * 2, filter(lambda x: x > 2, data)))

CLEX replaces this with a structured, expressive pipeline string that is easier to read, write, and maintain.

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Installation

pip install clex_py

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Quick Start

from CLEX import expression

# Compile a pipeline
add_one = expression("(x + 1:)")

# Execute it
result = add_one(x=[1, 2, 3, 4, 5])
print(result)  # [2, 3, 4, 5, 6]

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Core Concept

A CLEX pipeline is a chain of small, single-purpose operations, written inside a string.

Function Format

(input operation value; return_variable)

Shortcut:

(input operation value:)

→ Automatically assigns the result to the default register _

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Anatomy of a Function

Each function has four parts:

1. Input Iterable (x)

   • The active dataset

2. Operation

   • The transformation, condition, or function applied

3. Acting Value

   • A scalar (broadcasted), or another iterable (zipped)

4. Return Variable

   • Where the result is stored

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Execution Model

• Operations run element-wise
• Scalars are automatically broadcasted
• Iterables are zipped together
• Execution stops at the shortest iterable
• The final step’s result is automatically returned

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Operators

Arithmetic

Operator	Behavior
+	Addition
-	Subtraction
*	Multiplication
/	Division (skips division by zero)
%	Modulus

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Comparison (Filtering)

Comparison operators act as filters:

Operator	Behavior
>	Keep values greater than
<	Keep values less than
>=	Keep values ≥
<=	Keep values ≤
=	Keep equal values
!=	Keep non-equal values

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Advanced Operations

Operator	Behavior
@	Index access (v1[v2])
&	Truthy filter
!&	Falsy filter
^	Intersection
!^	Difference
.	Startswith check (returns True/False)

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Chaining Pipelines

Use -> to chain operations:

pipeline = expression("(x + 1; r) -> (r + 2; r)")
result = pipeline(x=[1, 2, 3])

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Example: Filtering Even Numbers

from CLEX import expression

is_even = expression("(x % 2; r) -> (x !& r:)")

result = is_even(x=[1, 2, 3, 4, 5])
print(result)  # [2, 4]

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Custom Lambda Functions

CLEX supports user-defined transformations using square-bracket lambda syntax:

expr = expression("(x [lambda x, y: x + 1] 1; r)")

• Note that lambdas are called for each element in an iterable passed as the first parameter with the second being the scalar.

Requirements

• Must accept two parameters
• Must return an iterable
• Should produce results compatible with element-wise execution

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Safety Model

Lambda expressions are executed using Python’s eval, but with strict controls:

• Built-in functions are disabled by default
• Safe functions can be enabled via a whitelist
• Dunder methods are blocked unless explicitly allowed

This ensures controlled execution of dynamic expressions.

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Parser & Engine

CLEX uses a modern parsing system designed for flexibility and performance:

• Built with Lark
• Supports arbitrarily long variable names
• Optimized execution pipeline
• Prefix-based result filtering system

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Design Philosophy

CLEX is built around a few core ideas:

• Clarity over nesting
• Composable transformations
• Minimal syntax, maximum expressiveness
• Safe execution of dynamic logic

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Roadmap

• Port to Java, C#, and JavaScript
• Expand built-in operations
• Improve runtime performance and safety
• Enable deeper integration with Python functions

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Contributing

Contributions, issues, and suggestions are welcome.

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Summary

CLEX provides:

• A clean DSL for functional pipelines
• A readable alternative to nested functional code
• Safe execution of dynamic expressions
• Extensible and composable transformations

It’s designed for developers who want powerful data transformations without sacrificing readability.