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Search DataFrame , found 10 results

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Pandas Data Manipulation

Jeffallan/claude-skills

Pandas Pro orchestrates DataFrame transformations for cleaning, aggregating, merging, resampling, and optimizing large tables using vectorized patterns and validation checks, ideal for analytics pipelines.

Spark Performance Engineer

Jeffallan/claude-skills

Guides building, tuning, and validating production Apache Spark jobs, covering DataFrame/RDD choices, partitioning, broadcast joins, skew handling, caching, and cluster configuration for big data pipelines.

Scaling Pandas with Dask

K-Dense-AI/claude-scientific-skills

Dask is a Python library that scales pandas/NumPy workflows beyond RAM by parallelizing DataFrames, Arrays, Bags, and custom tasks across cores or clusters for ETL, log analysis, and distributed ML.

FlowIO FCS Handler

K-Dense-AI/claude-scientific-skills

FlowIO reads and writes Flow Cytometry Standard (FCS) files, exposing metadata, channels, and NumPy event arrays while allowing CSV/DataFrame conversion, making it ideal for cytometry pipelines and backend preprocessing services.

Polars Fast DataFrame

K-Dense-AI/claude-scientific-skills

Polars is a blazing fast in-memory DataFrame library based on Apache Arrow that replaces pandas for 1-100GB workloads, offering expression-based lazy execution, parallel operators, and rich I/O for ETL pipelines.

Vaex Big Data

K-Dense-AI/claude-scientific-skills

Vaex is a high-performance Python library for lazy out-of-core DataFrames that process billions of rows without RAM. Use it to analyze massive CSV/HDF5/Arrow/Parquet tables, compute fast statistics, visualize big data, and build scikit-learn pipelines.

Scaling Pandas with Dask

K-Dense-AI/scientific-agent-skills

Dask is a Python library that scales pandas/NumPy workflows beyond RAM by parallelizing DataFrames, Arrays, Bags, and custom tasks across cores or clusters for ETL, log analysis, and distributed ML.

FlowIO FCS Handler

K-Dense-AI/scientific-agent-skills

FlowIO reads and writes Flow Cytometry Standard (FCS) files, exposing metadata, channels, and NumPy event arrays while allowing CSV/DataFrame conversion, making it ideal for cytometry pipelines and backend preprocessing services.

Polars Fast DataFrame

K-Dense-AI/scientific-agent-skills

Polars is a blazing fast in-memory DataFrame library based on Apache Arrow that replaces pandas for 1-100GB workloads, offering expression-based lazy execution, parallel operators, and rich I/O for ETL pipelines.

Vaex Big Data

K-Dense-AI/scientific-agent-skills

Vaex is a high-performance Python library for lazy out-of-core DataFrames that process billions of rows without RAM. Use it to analyze massive CSV/HDF5/Arrow/Parquet tables, compute fast statistics, visualize big data, and build scikit-learn pipelines.

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