/polars

name: polars
description: Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.
license: https://github.com/pola-rs/polars/blob/main/LICENSE
metadata:
    skill-author: K-Dense Inc.

Polars

Overview

Polars is a lightning-fast DataFrame library for Python and Rust built on Apache Arrow. Work with Polars' expression-based API, lazy evaluation framework, and high-performance data manipulation capabilities for efficient data processing, pandas migration, and data pipeline optimization.

Quick Start

Installation and Basic Usage

Install Polars:

uv pip install polars

Basic DataFrame creation and operations:

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 the fundamental building blocks of Polars operations. They describe transformations on data and can be composed, reused, and optimized.

**Key principles:**

• Use `pl.col("column_name")` to reference columns
• Chain methods to build complex transformations
• Expressions are lazy and only execute within contexts (select, with_columns, filter, group_by)

**Example:**

# Expression-based computation
df.select(
    pl.col("name"),
    (pl.col("age") * 12).alias("age_in_months")
)

Lazy vs Eager Evaluation

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

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

**Lazy (LazyFrame):** Operations build a query plan, optimized before execution

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

**When to use lazy:**

• Working with large datasets
• Complex query pipelines
• When only some columns/rows are needed
• Performance is critical

**Benefits of lazy evaluation:**

• Automatic query optimization
• Predicate pushdown
• Projection pushdown
• Parallel execution

For detailed concepts, load `references/core_concepts.md`.

Common Operations

Select

Select and manipulate columns:

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

# Select with expressions
df.select(
    pl.col("name"),
    (pl.col("age") * 2).alias("double_age")
)

# Select all columns matching a pattern
df.select(pl.col("^.*_id$"))

Filter

Filter rows by conditions:

# Single condition
df.filter(pl.col("age") > 25)

# Multiple conditions (cleaner than using &)
df.filter(
    pl.col("age") > 25,
    pl.col("city") == "NY"
)

# Complex conditions
df.filter(
    (pl.col("age") > 25) | (pl.col("city") == "LA")
)

With Columns

Add or modify columns while preserving existing ones:

# Add new columns
df.with_columns(
    age_plus_10=pl.col("age") + 10,
    name_upper=pl.col("name").str.to_uppercase()
)

# Parallel computation (all columns computed in parallel)
df.with_columns(
    pl.col("value") * 10,
    pl.col("value") * 100,
)

Group By and Aggregations

Group data and compute aggregations:

# Basic grouping
df.group_by("city").agg(
    pl.col("age").mean().alias("avg_age"),
    pl.len().alias("count")
)

# Multiple group keys
df.group_by("city", "department").agg(
    pl.col("salary").sum()
)

# Conditional aggregations
df.group_by("city").agg(
    (pl.col("age") > 30).sum().alias("over_30")
)

For detailed operation patterns, load `references/operations.md`.

Aggregations and Window Functions

Aggregation Functions

Common aggregations within `group_by` context:

• `pl.len()` - count rows
• `pl.col("x").sum()` - sum values
• `pl.col("x").mean()` - average
• `pl.col("x").min()` / `pl.col("x").max()` - extremes
• `pl.first()` / `pl.last()` - first/last values

Window Functions with `over()`

Apply aggregations while preserving row count:

# Add group statistics to each row
df.with_columns(
    avg_age_by_city=pl.col("age").mean().over("city"),
    rank_in_city=pl.col("salary").rank().over("city")
)

# Multiple grouping columns
df.with_columns(
    group_avg=pl.col("value").mean().over("category", "region")
)

**Mapping strategies:**

• `group_to_rows` (default): Preserves original row order
• `explode`: Faster but groups rows together
• `join`: Creates list columns

Data I/O

Supported Formats

Polars supports reading and writing:

• CSV, Parquet, JSON, Excel
• Databases (via connectors)
• Cloud storage (S3, Azure, GCS)
• Google BigQuery
• Multiple/partitioned files

Common I/O Operations

**CSV:**

# Eager
df = pl.read_csv("file.csv")
df.write_csv("output.csv")

# Lazy (preferred for large files)
lf = pl.scan_csv("file.csv")
result = lf.filter(...).select(...).collect()

**Parquet (recommended for performance):**

df = pl.read_parquet("file.parquet")
df.write_parquet("output.parquet")

**JSON:**

df = pl.read_json("file.json")
df.write_json("output.json")

For comprehensive I/O documentation, load `references/io_guide.md`.

Transformations

Joins

Combine DataFrames:

# Inner join
df1.join(df2, on="id", how="inner")

# Left join
df1.join(df2, on="id", how="left")

# Join on different column names
df1.join(df2, left_on="user_id", right_on="id")

Concatenation

Stack DataFrames:

# Vertical (stack rows)
pl.concat([df1, df2], how="vertical")

# Horizontal (add columns)
pl.concat([df1, df2], how="horizontal")

# Diagonal (union with different schema