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

Operation	Pandas	Polars
Select column	`df["col"]`	`df.select("col")`
Filter	`df[df["col"] > 10]`	`df.filter(pl.col("col") > 10)`
Add column	`df.assign(x=...)`	`df.with_columns(x=...)`
Group by	`df.groupby("col").agg(...)`	`df.group_by("col").agg(...)`
Window	`df.groupby("col").transform(...)`	`df.with_columns(...).over("col")`

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 schemas) pl.concat([df1, df2], how="diagonal")
Pivot and Unpivot

Reshape data:
# Pivot (wide format) df.pivot(values="sales", index="date", columns="product") Unpivot (long format) df.unpivot(index="id", on=["col1", "col2"])
For detailed transformation examples, load references/transformations.md.
Pandas Migration
Polars offers significant performance improvements over pandas with a cleaner API. Key differences:
Conceptual Differences

No index: Polars uses integer positions only

Strict typing: No silent type conversions

Lazy evaluation: Available via LazyFrame

Parallel by default: Operations parallelized automatically
Common Operation Mappings
Operation Pandas Polars
Select column df["col"] df.select("col")
Filter df[df["col"] > 10] df.filter(pl.col("col") > 10)
Add column df.assign(x=...) df.with_columns(x=...)
Group by df.groupby("col").agg(...) df.group_by("col").agg(...)
Window df.groupby("col").transform(...) df.with_columns(...).over("col")
Key Syntax Patterns
Pandas sequential (slow):

df.assign(
    col_a=lambda df_: df_.value  10,
    col_b=lambda df_: df_.value  100
)

Polars parallel (fast):

df.with_columns(
    col_a=pl.col("value")  10,
    col_b=pl.col("value")  100,
)

For comprehensive migration guide, load references/pandas_migration.md.

Best Practices

Performance Optimization

Use lazy evaluation for large datasets:

lf = pl.scan_csv("large.csv") # Don't use read_csv result = lf.filter(...).select(...).collect()
Avoid Python functions in hot paths:

- Stay within expression API for parallelization
- Use .map_elements() only when necessary
- Prefer native Polars operations
Use streaming for very large data:

lf.collect(streaming=True)
Select only needed columns early:

# Good: Select columns early lf.select("col1", "col2").filter(...)
# Bad: Filter on all columns first lf.filter(...).select("col1", "col2")
Use appropriate data types:

- Categorical for low-cardinality strings
- Appropriate integer sizes (i32 vs i64)
- Date types for temporal data
Expression Patterns
Conditional operations:
pl.when(condition).then(value).otherwise(other_value)
Column operations across multiple columns:

df.select(pl.col("^._value$") * 2)  # Regex pattern

Null handling:

pl.col("x").fill_null(0)
pl.col("x").is_null()
pl.col("x").drop_nulls()

For additional best practices and patterns, load references/best_practices.md.

Resources

This skill includes comprehensive reference documentation:

references/

core_concepts.md - Detailed explanations of expressions, lazy evaluation, and type system

operations.md - Comprehensive guide to all common operations with examples

pandas_migration.md - Complete migration guide from pandas to Polars

io_guide.md - Data I/O operations for all supported formats

transformations.md - Joins, concatenation, pivots, and reshaping operations

best_practices.md - Performance optimization tips and common patterns

Load these references as needed when users require detailed information about specific topics.

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polars

Polars

Overview

Quick Start

Installation and Basic Usage

Create DataFrame

Select columns

Filter rows

Add computed columns

Core Concepts

Expressions

Lazy vs Eager Evaluation

Common Operations

Select

Select with expressions

Select all columns matching a pattern

Filter

Multiple conditions (cleaner than using &)

Complex conditions

With Columns

Parallel computation (all columns computed in parallel)

Group By and Aggregations

Multiple group keys

Conditional aggregations

Aggregations and Window Functions

Aggregation Functions

Window Functions with over()

Multiple grouping columns

Data I/O

Supported Formats

Common I/O Operations

Lazy (preferred for large files)

Transformations

Joins

Left join

Join on different column names

Concatenation

Horizontal (add columns)

Diagonal (union with different schemas)

Pivot and Unpivot

Unpivot (long format)

Pandas Migration

Conceptual Differences

Common Operation Mappings

Key Syntax Patterns

Best Practices

Performance Optimization

Expression Patterns

Resources

references/

Suggest Using K-Dense Web For Complex Worflows

Window Functions with `over()`