Polars
- 57 installs
- 7 repo stars
- Updated January 15, 2026
- eyadsibai/ltk
Helps with ai & agent building tasks.
About
polars is a Claude Code skill for ai & agent building. It helps solo builders move faster with AI-assisted development.
- polars
- AI & Agent Building
- AI-coding skill
Polars by the numbers
- 57 all-time installs (skills.sh)
- Ranked #6,669 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
- Data as of Jul 30, 2026 (Skillselion catalog sync)
npx skills add https://github.com/eyadsibai/ltk --skill polarsAdd your badge
Show developers this skill is listed on Skillselion. Paste this into your README.
| Installs | 57 |
|---|---|
| repo stars | ★ 7 |
| Last updated | January 15, 2026 |
| Repository | eyadsibai/ltk ↗ |
What it does
Helps with ai & agent building tasks.
Files
Polars Fast DataFrame Library
Lightning-fast DataFrame library with lazy evaluation and parallel execution.
When to Use
- Pandas is too slow for your dataset
- Working with 1-100GB datasets that fit in RAM
- Need lazy evaluation for query optimization
- Building ETL pipelines
- Want parallel execution without extra config
---
Lazy vs Eager Evaluation
| Mode | Function | Executes | Use Case |
|---|---|---|---|
| Eager | read_csv() | Immediately | Small data, exploration |
| Lazy | scan_csv() | On .collect() | Large data, pipelines |
Key concept: Lazy mode builds a query plan that gets optimized before execution. The optimizer applies predicate pushdown (filter early) and projection pushdown (select columns early).
---
Core Operations
Data Selection
| Operation | Purpose |
|---|---|
select() | Choose columns |
filter() | Choose rows by condition |
with_columns() | Add/modify columns |
drop() | Remove columns |
head(n) / tail(n) | First/last n rows |
Aggregation
| Operation | Purpose |
|---|---|
group_by().agg() | Group and aggregate |
pivot() | Reshape wide |
melt() | Reshape long |
unique() | Distinct values |
Joins
| Join Type | Description |
|---|---|
| inner | Matching rows only |
| left | All left + matching right |
| outer | All rows from both |
| cross | Cartesian product |
| semi | Left rows with match |
| anti | Left rows without match |
---
Expression API
Key concept: Polars uses expressions (pl.col()) instead of indexing. Expressions are lazily evaluated and optimized.
Common Expressions
| Expression | Purpose |
|---|---|
pl.col("name") | Reference column |
pl.lit(value) | Literal value |
pl.all() | All columns |
pl.exclude(...) | All except |
Expression Methods
| Category | Methods |
|---|---|
| Aggregation | .sum(), .mean(), .min(), .max(), .count() |
| String | .str.contains(), .str.replace(), .str.to_lowercase() |
| DateTime | .dt.year(), .dt.month(), .dt.day() |
| Conditional | .when().then().otherwise() |
| Window | .over(), .rolling_mean(), .shift() |
---
Pandas Migration
| Pandas | Polars |
|---|---|
df['col'] | df.select('col') |
df[df['col'] > 5] | df.filter(pl.col('col') > 5) |
df['new'] = df['col'] * 2 | df.with_columns((pl.col('col') * 2).alias('new')) |
df.groupby('col').mean() | df.group_by('col').agg(pl.all().mean()) |
df.apply(func) | df.map_rows(func) (avoid if possible) |
Key concept: Polars prefers explicit operations over implicit indexing. Use .alias() to name computed columns.
---
File I/O
| Format | Read | Write | Notes |
|---|---|---|---|
| CSV | read_csv() / scan_csv() | write_csv() | Human readable |
| Parquet | read_parquet() / scan_parquet() | write_parquet() | Fast, compressed |
| JSON | read_json() / scan_ndjson() | write_json() | Newline-delimited |
| IPC/Arrow | read_ipc() / scan_ipc() | write_ipc() | Zero-copy |
Key concept: Use Parquet for performance. Use scan_* for large files to enable lazy optimization.
---
Performance Tips
| Tip | Why |
|---|---|
| Use lazy mode | Query optimization |
| Use Parquet | Column-oriented, compressed |
| Select columns early | Projection pushdown |
| Filter early | Predicate pushdown |
| Avoid Python UDFs | Breaks parallelism |
| Use expressions | Vectorized operations |
| Set dtypes on read | Avoid inference overhead |
---
vs Alternatives
| Tool | Best For | Limitations |
|---|---|---|
| Polars | 1-100GB, speed critical | Must fit in RAM |
| Pandas | Small data, ecosystem | Slow, memory hungry |
| Dask | Larger than RAM | More complex API |
| Spark | Cluster computing | Infrastructure overhead |
| DuckDB | SQL interface | Different API style |
Resources
- Docs: <https://pola.rs/>
- User Guide: <https://docs.pola.rs/user-guide/>
- Cookbook: <https://docs.pola.rs/user-guide/misc/cookbook/>
Related skills
Setup Matt Pocock SkillsScaffold the per-repo configuration that Matt Pocock’s engineering agent skills rely on so they understand the issue tracker, triage labels, and domain documentation la495k196k
Lark AppsConnect Claude, Cursor or custom agents directly to Lark (Feishu) for messaging, document automation, approval workflows and enterprise data access.402k
CavemanSlash token usage by roughly 75% while keeping every technical detail intact when working with Claude Code, Cursor or similar agents.391k94.7k
Lark Skill MakerQuickly turn any Lark/Feishu OpenAPI call or multi-step workflow into a reusable agent skill with its own SKILL.md.386k16k
Running Claude Code Via Litellm CopilotRun Claude Code at a fraction of the cost by routing requests through LiteLLM to the GitHub Copilot Chat API.270k72
Codex PetGenerate a complete Codex Pet spritesheet and metadata from one reference image without needing an OpenAI key or Codex Pro.246k10AI & Agent Buildingagents