python-polars

# Python Polars

Polars is a lightning-fast DataFrame library for Python built on Apache Arrow. It provides an expression-based API, lazy evaluation framework, and automatic parallelization for high-performance data processing.

## Quick start

### Installation

```python
uv pip install polars
```

### Basic operations

```python
import polars as pl

# Create DataFrame
df = pl.DataFrame({
    "name": ["Alice", "Bob", "Charlie"],
    "age": [25, 30, 35],
    "city": ["NY", "LA", "SF"]
})

# Select columns
df.select("name", "age")

# Filter rows
df.filter(pl.col("age") > 25)

# Add computed columns
df.with_columns(
    age_plus_10=pl.col("age") + 10
)
```

## Core concepts

### Expressions

Expressions are composable units describing data transformations. Use `pl.col("column_name")` to reference columns and chain methods for complex operations:

```python
df.select(
    pl.col("name"),
    (pl.col("age") * 12).alias("age_in_months")
)
```

Expressions execute within contexts: `select()`, `with_columns()`, `filter()`, `group_by().agg()`.

### Lazy vs eager evaluation

**Eager (DataFrame)**: Operations execute immediately.

```python
df = pl.read_csv("file.csv")  # Reads immediately
result = df.filter(pl.col("age") > 25)
```

**Lazy (LazyFrame)**: Operations build an optimized query plan.

```python
lf = pl.scan_csv("file.csv")  # Doesn't read yet
result = lf.filter(pl.col("age") > 25).select("name", "age")
df = result.collect()  # Executes optimized query
```

Use lazy mode for large datasets, complex pipelines, and when performance is critical. Benefits include automatic query optimization, predicate pushdown, projection pushdown, and parallel execution.

For detailed concepts including data types, type casting, null handling, and parallelization, see `references/core-concepts.md`.

## Common operations

### Select and with_columns

```python
# Select specific columns
df.select("name", "age")

# Select with expressions
df.select(
    pl.col("name"),
    (pl.col("age")