polars
Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.
84
81%
Does it follow best practices?
Impact
86%
1.01xAverage score across 3 eval scenarios
Passed
No known issues
Evaluation results
87%
2%Log Data ETL Pipeline
Lazy evaluation and query optimization
Lazy CSV reading
100%
100%
Calls collect()
100%
100%
Filter before aggregation
100%
100%
Categorical for low-cardinality columns
0%
0%
Parquet output
100%
100%
Expression-based column references
100%
100%
Multiple filter args
66%
100%
No row iteration
100%
100%
No in-place assignment
100%
100%
Chained operations
50%
50%
group_by syntax
100%
100%
Pipeline log file
100%
100%
100%
Migrate Analytics Script from Pandas to Polars
Pandas to Polars migration patterns
group_by spelling
100%
100%
No reset_index
100%
100%
when/then/otherwise
100%
100%
with_columns for new columns
100%
100%
No Python apply/lambda
100%
100%
Expression-based filter
100%
100%
No row iteration
100%
100%
Polars import only
100%
100%
pl.col() references
100%
100%
Produces output CSV
100%
100%
73%
3%Sales Performance Ranking Dashboard
Window functions and parallel column computation
over() for group stats
100%
100%
over() for ranking
100%
100%
Parallel with_columns
50%
50%
rechunk on concat
0%
0%
Categorical for region
0%
0%
No map_elements/apply
100%
100%
No row iteration
100%
100%
Parquet output
100%
100%
Expression-based
100%
100%
Functional chaining
50%
80%
- Repository
- K-Dense-AI/claude-scientific-skills
- Commit
b58ad7e
- Evaluated
- Agent
- Claude Code
- Model
- Claude Sonnet 4.6
Table of Contents
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