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:

python

uv pip install polars

Basic DataFrame creation and 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 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:

python

# 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

python

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

python

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:

python

# 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:

python

# 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:

python

# 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:

python

# 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:

python

# 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:

python

# 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):

python

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

JSON:

python

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:

python

# 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:

python

# 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:

python

# 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):

python

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

Polars parallel (fast):

python

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:

python

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:
python
```
lf.collect(streaming=True)
```

Select only needed columns early:

python

# 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:

python

pl.when(condition).then(value).otherwise(other_value)

Column operations across multiple columns:

python

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

Null handling:

python

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

NPX Install

Tags

SKILL.md Content

Polars

Overview

Quick Start

Installation and Basic Usage

Core Concepts

Expressions

Lazy vs Eager Evaluation

Common Operations

Select

Filter

With Columns

Group By and Aggregations

Aggregations and Window Functions

Aggregation Functions

Window Functions with
`over()`

Data I/O

Supported Formats

Common I/O Operations

Transformations

Joins

Concatenation

Pivot and Unpivot

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