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High-performance DataFrame library for Python ETL, analytics, and pandas migration. Use for expression-based data manipulation with lazy query optimization, parallel execution, streaming out-of-core processing, Arrow interoperability, and optional GPU execution.
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High-performance DataFrame library for Python ETL, analytics, and pandas migration. Use for expression-based data manipulation with lazy query optimization, parallel execution, streaming out-of-core processing, Arrow interoperability, and optional GPU execution. 📦 Ships with scientific-agent-skills
</> SKILL.md
polars.SKILL.md
---
name : polars
description : High-performance DataFrame library for Python ETL, analytics, and pandas migration. Use for expression-based data manipulation with lazy query optimization, parallel execution, streaming out-of-core processing, Arrow interoperability, and optional GPU execution.
license : https://github.com/pola-rs/polars/blob/main/LICENSE
allowed-tools: Read
compatibility : Requires Python 3.10+ for polars 1.41.x. Install with uv pip install; optional extras enable Excel, database, cloud, pandas/NumPy, and GPU integrations.
metadata : {"version": "1.1", "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 the current stable Polars release verified during this refresh:
```bash
uv pip install "polars==1.41.2"
```
Install optional integrations only when needed:
```bash
uv pip install "polars[excel,database,fsspec,pandas,numpy]==1.41.2"
```
Basic DataFrame creation and operations:
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```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(on="product", values="sales", index="date")
# 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
1. **Use lazy evaluation for large datasets:**
```python
lf = pl.scan_csv("large.csv") # Don't use read_csv
result = lf.filter(...).select(...).collect()
```
2. **Avoid Python functions in hot paths:**
- Stay within expression API for parallelization
- Use `.map_elements()` only when necessary
- Prefer native Polars operations
3. **Use streaming for very large data:**
```python
lf.collect(engine="streaming")
```
4. **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")
```
5. **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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