Python Polars - Pipe-Style Data Manipulation

Purpose

Enforce Polars as the default for all Python data manipulation. Polars provides a functional, pipe-style API similar to dplyr in R—code reads as a clear series of composable transformations rather than imperative steps. This produces more readable, maintainable, and performant data pipelines.

CRITICAL: Polars-First Mandate

YOU MUST use Polars for ALL data manipulation in Python. No exceptions for new code.

IMMEDIATELY upon loading this skill:

1. Announce: "Using python-polars - enforcing pipe-style data manipulation with Polars"
2. Reject any pandas code without explicit justification
3. Convert all data operations to Polars expressions and method chains

Authority: Why Polars is Mandatory

• Pipe-style syntax: Method chains read as clear transformation steps (like dplyr's %>%)
• Immutable data: No hidden state mutations, predictable transformations
• Expression-based: Operations are explicit and composable
• Automatic optimization: Query planner optimizes your pipeline
• Modern standard: Polars is the dplyr equivalent for Python

Commitment: Exception Protocol

If you MUST use pandas (ML ecosystem compatibility only):

1. Announce: "FALLING BACK TO PANDAS: [specific reason]"
2. Keep pandas usage at boundary (convert: df_polars.to_pandas())
3. Return to Polars immediately after boundary operation

Social Proof: Industry Standard

Polars represents the modern functional approach to data manipulation—like dplyr in R, it emphasizes readable pipelines over imperative code. Legacy pandas patterns (in-place mutation, index manipulation, row iteration) are technical debt.

Core Principles: The Pipe-Style Philosophy

The Readable Pipeline Pattern

GOOD - Clear pipe-style chain:

import polars as pl

result = (
    pl.scan_csv("sales.csv")                    # 1. Start with data source
    .filter(pl.col("date") >= "2024-01-01")    # 2. Filter to relevant rows
    .with_columns(                              # 3. Add computed columns
        revenue=pl.col("units") * pl.col("price"),
        is_weekend=pl.col("date").dt.weekday().is_in([6, 7]),
    )
    .group_by(["region", "is_weekend"])         # 4. Group for aggregation
    .agg(                                       # 5. Calculate metrics
        total_revenue=pl.col("revenue").sum(),
        order_count=pl.col("order_id").count(),
        avg_order=pl.col("revenue").mean(),
    )
    .filter(pl.col("order_count") > 10)         # 6. Filter aggregated results
    .sort(["region", "total_revenue"], descending=[False, True])
    .collect()                                  # 7. Execute pipeline
)

BAD - Broken into imperative steps:

import pandas as pd  # WRONG - using pandas

df = pd.read_csv("sales.csv")  # Eager load
df = df[df["date"] >= "2024-01-01"]  # Filter
df["revenue"] = df["units"] * df["price"]  # Mutate
df["is_weekend"] = df["date"].dt.weekday.isin([6, 7])  # More mutation
result = df.groupby(["region", "is_weekend"]).agg({  # Group and aggregate
    "revenue": ["sum", "mean", "count"]
})

YOU MUST

• Use Polars for all data manipulation - pandas is legacy tech debt
• Write pipe-style chains - continuous method chains, not broken steps
• Start with lazy scanning - pl.scan_csv(), pl.scan_parquet() for large data
• Use explicit column references - pl.col("name") over string indexing
• Compute multiple columns in single context - parallel execution within .with_columns()
• Name each transformation clearly - code should read like data flows
• Query Context7 for Polars APIs - context7_query-docs(libraryId="/pola-rs/polars", query="...")

NEVER

• Use pandas for new data manipulation code - convert to Polars immediately
• Break pipelines into sequential steps - use single chain with comments
• Iterate rows with iter_rows() - use vectorized expressions
• Apply Python functions with apply() - use native Polars expressions
• Mutate DataFrames in place - Polars is immutable, embrace it
• Use index-based operations - Polars has no index (and that's good)
• Mix pandas and Polars - choose one per pipeline, convert at boundaries only

Quick Reference: Pandas vs Polars

The Expression is Everything

Master these patterns for pipe-style code:

# Column reference and arithmetic
pl.col("revenue") / pl.col("units")

# Conditional logic (CASE WHEN equivalent)
pl.when(pl.col("age") >= 18).then("adult").otherwise("minor")

# String operations
pl.col("name").str.to_uppercase()
pl.col("email").str.contains("@")

# Date/time operations
pl.col("timestamp").dt.year()
pl.col("date").dt.truncate("1d")

# Aggregations (use in .agg() context)
pl.col("value").sum()
pl.col("value").mean()
pl.col("id").n_unique()

# Window functions (use .over() for group transforms)
pl.col("value").sum().over("category")  # Category total per row
pl.col("value").rank().over("category")   # Rank within category

References (Load on Demand)

• references/migration-patterns.md - Load when converting existing pandas code or need side-by-side operation comparisons
• references/anti-patterns.md - Load when reviewing code quality or debugging performance issues
• references/pipe-style-guide.md - Load when designing complex multi-step pipelines or need formatting guidance
• references/expression-patterns.md - Load when building reusable expression functions or need advanced composition patterns

Context7 Integration

YOU MUST query Context7 for Polars APIs before writing implementation code.

# Resolve library ID first
context7_resolve-library-id(libraryName="polars", query="Polars DataFrame library")

# Then query for specific operations
context7_query-docs(libraryId="/pola-rs/polars", query="lazy scan filter example")

Context7 provides current, accurate API documentation. Pre-trained knowledge of Polars APIs will be stale and cause bugs.