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Learn how to create and use Polars LazyFrames for efficient data processing. Discover lazy evaluation, predicate and projection pushdown, and how to handle large datasets.
The article explores 11 essential tips for leveraging the full potential of the Pandas library to boost productivity and streamline workflows in handling and analyzing complex datasets. It uses a real-world dataset from Kaggle's Airbnb listings to illustrate techniques such as chunked processing and parallel execution.
Mastering specific Pandas functions can enhance data manipulation skills for data scientists using Python, focusing on less explored methods for data transformation and analysis.
Turn your Pandas data frame into a knowledge graph using LLMs. Learn how to build your own LLM graph-builder, implement LLMGraphTransformer by LangChain, and perform QA on your knowledge graph.
Reset a pandas DataFrame index
Use the MICE algorithm
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