Big Data

Choosing Pandas, Polars, or PyArrow? Sometimes the File Format Matters Even More

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Choosing Pandas, Polars, or PyArrow: Why CSV vs Parquet vs Apache Arrow Matters for Python Data Engineering

Introduction When developers discuss high-performance data processing in Python, the conversation usually centers around Pandas, Polars, or PyArrow. While selecting the right DataFrame library is important, my recent benchmark reminded me of a more fundamental truth: Performance problems often begin before your processing code runs. If your data is stored inefficiently, even the fastest DataFrame … Read more

CSV vs Parquet vs Apache Arrow: A PyArrow Benchmark on 500K–1M Rows

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CSV vs Parquet vs Apache Arrow (Feather) PyArrow benchmark in Python by Akhilesh Singh Shrinet comparing storage size, read/write performance, and analytics efficiency.

Discover how CSV, Apache Parquet, and Apache Arrow (Feather) compare in real-world Python benchmarks. Learn which file format delivers the best storage efficiency, fastest read/write performance, and why columnar formats are the preferred choice for modern data engineering and analytics workloads.