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.
Fetches polars from davila7/claude-code-templates and configures it for Cursor.
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Confirm successful installation by checking the skill directory location:
.cursor/skills/polars
Restart Cursor to activate polars. Access via /polars in your agent's command palette.
⚠
Security Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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.
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)
For detailed concepts, load references/core_concepts.md.
Common Operations
Select
Select and manipulate columns:
# Select specific columnsdf.select("name","age")# Select with expressionsdf.select( pl.col("name"),(pl.col("age")*2).alias("double_age"))# Select all columns matching a patterndf.select(pl.col("^.*_id$"))
Filter
Filter rows by conditions:
# Single conditiondf.filter(pl.col("age")>25)# Multiple conditions (cleaner than using &)df.filter( pl.col("age")>25, pl.col("city")=="NY")# Complex conditionsdf.filter((pl.col("age")>25)|(pl.col("city")=="LA"))
With Columns
Add or modify columns while preserving existing ones:
# Add new columnsdf.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,)
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 rowdf.with_columns( avg_age_by_city=pl.col("age").mean().over("city"), rank_in_city=pl.col("salary").rank().over("city"))# Multiple grouping columnsdf.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:
# Eagerdf = pl.read_csv("file.csv")df.write_csv("output.csv")# Lazy (preferred for large files)lf = pl.scan_csv("file.csv"
✓
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
›Access to product documentation and roadmap tools (Jira, Notion, etc.)
›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Steps
1Install product management skill
2Start with user story generation for known feature
3Progress to competitive analysis: research 2-3 competitors
4Use for roadmap prioritization: apply RICE/ICE scoring
5Draft stakeholder communications and refine based on feedback
6Build template library for recurring PM tasks
7Share effective prompts with product team
Common Pitfalls
⚠Not validating competitive research—verify facts before sharing
⚠Accepting user stories without involving engineering team
⚠Over-relying on frameworks without qualitative judgment
⚠Not customizing outputs to company culture and communication style
⚠Skipping stakeholder validation of generated requirements
Best Practices
✓ Do
+Validate research and competitive analysis with real data
+Collaborate with engineering when generating technical requirements
+Customize frameworks and templates to your company context
+Use skill for first drafts, refine with stakeholder input
+Document successful prompt patterns for PM tasks
+Combine AI efficiency with human judgment and intuition
✗ Don't
−Don't publish competitive analysis without fact-checking
−Don't finalize user stories without engineering review
−Don't make prioritization decisions solely on AI scoring
−Don't skip customer validation of generated requirements
−Don't ignore company-specific context and culture
💡 Pro Tips
★Provide context: company goals, constraints, customer feedback
★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
★Use skill for 70% generation + 30% customization to company needs
When to Use This
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path
1Basic: user stories, feature specs, status updates
