Newsletter #244: Handle Large Data with Polars Streaming Mode
📅 Today’s Picks
Handle Large Data with Polars Streaming Mode
Problem
In Polars, the .collect() method executes a lazy query and loads the entire dataset into memory. This works well for smaller data, but once the dataset grows beyond your available RAM, it can easily crash your process.
Solution
Add engine=”streaming” to .collect() to process large datasets in small batches without running out of memory.
How it works:
Breaks the dataset into smaller, memory-friendly chunks
Processes one batch at a time while freeing memory as it goes
Combines all partial results into a single DataFrame
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Problem
Python packages turn your code into reusable modules you can share across projects.
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Solution
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☕️ Weekly Finds
whenever
[Python Utils]
– Modern datetime library for Python that ensures correct and type-checked datetime manipulations. It is DST-safe and way faster than standard datetime libraries.
lancedb
[MLOps]
– Developer-friendly, embedded retrieval database for AI/ML applications. The ultimate multimodal data platform designed for fast, scalable, and production-ready vector search.
grip
[Python Utils]
– Preview GitHub README.md files locally before committing them. A command-line server that uses GitHub’s Markdown API to render local readme files.
Looking for a specific tool? Explore 70+ Python tools →
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