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| 📅 Today’s Picks |
Polars v1.35: Native Rolling Rank for Time Series
Problem:
How do you rank values within a rolling window?
For example, you might want to compare today’s sales to the last 3 days to answer: “How does today’s sales compare to the last 3 days?”
Solution:
Polars v1.35 introduces rolling_rank() for native window ranking operations.
How it works:
- Define a window size (e.g., last 3 values)
- Each value gets ranked against others in its window
- Rank 1 = lowest, Rank N = highest
This method is useful for tracking performance over time, detecting anomalies, or alerting when metrics underperform.
Coiled: Run Python in the Cloud with One Decorator (Sponsored)
Problem:
Imagine you need to run data processing on a file that is larger than your laptop’s RAM. What should you do?
Traditional solutions require buying more RAM, renting expensive cloud VMs, or learning Kubernetes. All of these add complexity and cost.
Solution:
Coiled’s serverless functions let you run your Python code on cloud VMs with the memory you need by simply adding a decorator.
Key capabilities:
- Use any data framework: pandas, Polars, DuckDB, Dask, and more
- Process multiple files in parallel with .map()
- Sync local packages to cloud without Docker
- Cut costs with spot instances and auto-fallback
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| ☕️ Weekly Finds |
codon Python Utils
A high-performance, zero-overhead, extensible Python compiler with built-in NumPy support
khoj LLM
Your AI second brain. Self-hostable personal assistant with RAG, semantic search, and support for PDFs, Markdown, Notion, and more
lm-evaluation-harness MLOps
A framework for few-shot evaluation of language models. Powers Hugging Face’s Open LLM Leaderboard
