Polynx

String-powered Polars expression engine with extended DataFrame and LazyFrame utilities.

---

What is Polynx?

Polynx bridges the gap between the intuitive, concise expression syntax of Pandas/SQL and the ultra-fast execution engine of Polars.

With Polynx, you can write filtering criteria, multi-statement mutations, conditional branching (case_when, select, where), and group aggregations using simple string expressions without verbose nested method calls—all while retaining the speed, thread safety, and query planning of Polars.

Relationship to Polars

Polynx is built on top of Polars. It wraps native Polars objects while inheriting all built-in methods.

This documentation focuses exclusively on Polynx-specific extensions. For native Polars functions, methods, expressions, and algorithms, refer directly to the Official Polars Python API Reference.

---

Key Highlights

• String-Powered Queries: Query DataFrames with Pandas-like syntax: df.query("@min_val <= A < @max_val & B in @cats").
• Conditional Branching (case_when): Cleanly handle multi-branch logic (case_when([cond1, cond2], [res1, res2], default)) without nested .when().then().otherwise() structures.
• Multi-Statement Assignments (wc): Mutate and create multiple columns in a single call with semicolon-separated assignments: df.wc("A_sum = A.sum().over('B'); E_mean = E.mean()").
• First-Class LazyFrames: Every string operation, aggregation, and filter runs seamlessly in both eager DataFrame and lazy LazyFrame contexts.
• Extended DataFrame Utilities: Shorthand helpers for deduplication (dd), sorting (dsort/asort), pivoting (unstack), counting (vcnt/ucnt), and summary statistics (describe(group_keys=...)).
• High-Performance Caching: Built-in LRU and hash expression caching with automatic cache-bypassing when dynamic loop variables are detected.
• 100% Polars Interoperability: Transparent wrappers around Polars types; all Polars functions and methods are inherited and available out of the box.

---

Quick Example

import polynx as plx

# Create a DataFrame
df = plx.DataFrame({
    'A': [1, 2, 3, 4],
    'B': ['abc', 'bc', 'aaa', None],
    'C': ['2023-01-01', '2021-01-01', '2009-11-01', '2000-11-11'],
    'E': [1.1, 2.1, 3.5, 0.0]
}).wc("C = C.str.to_date('%Y-%m-%d')")

# Filter rows using string expressions with variable substitution
threshold = 2
result = df.query("A >= @threshold & B.str.contains('a|b')")

# Add conditional logic and multi-statement assignments
enhanced = result.wc(
    "Tier = case_when([A < 3, B in ['bc']], ['Bronze', 'Silver'], 'Gold');"
    "Score = A * 10 + E"
)

Ready to dive in? Check out the Getting Started guide!