polars

Polars GPU Skill

Fast DataFrame library with GPU acceleration via RAPIDS cuDF engine. Lazy evaluation, expressions API, and optimized query execution.

Official Sources:

• Polars Documentation
• GPU Support Guide
• cuDF Polars Engine

Installation

# CPU-only Polars
pip install polars

# With GPU support
pip install polars[gpu]

# CUDA 11 (RAPIDS v25.06 and earlier)
pip install polars cudf-polars-cu11

GPU Requirements:

• NVIDIA Volta™ or newer (compute capability 7.0+)
• CUDA 12 (CUDA 11 support ends with RAPIDS v25.06)
• Linux or WSL2

Quick Start

import polars as pl

# DataFrame (eager)
df = pl.DataFrame({"name": ["Alice", "Bob"], "age": [25, 30]})
df.filter(pl.col("age") > 28)

# LazyFrame (deferred)
lf = pl.LazyFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
result = lf.filter(pl.col("a") > 1).collect()      # CPU
result = lf.filter(pl.col("a") > 1).collect(engine="gpu")  # GPU

GPU Execution

# CPU vs GPU
df = lf.collect()                    # CPU
df = lf.collect(engine="gpu")        # GPU

# Config: device, raise_on_fail, verbose
df = lf.collect(engine=pl.GPUEngine(device=1, raise_on_fail=True))

# Peak performance: export POLARS_GPU_ENABLE_CUDA_MANAGED_MEMORY=0

Expressions

# Column selection
pl.col("name")                        # Single
pl.col("a", "b", "c")                 # Multiple
pl.col("^value_.*$")                  # Regex
pl.col(pl.Int64)                      # By dtype

# Arithmetic & comparisons
pl.col("a") + 10
pl.col("age") > 30
pl.col("value").is_between(10, 20)

# Aggregations
pl.col("value").sum()
pl.col("value").mean()
df.group_by("category").agg([
    pl.col("value").sum().alias("total"),
    pl.col("id").count().alias("count")
])

String, temporal, list operations

pl.col("text").str.to_lowercase() pl.col("text").str.contains("pattern") pl.col("date").dt.year() pl.col("timestamp").dt.truncate("1h") pl.col("items").list.sum() pl.col("items").list.explode()

## I/O Operations

```python