Tag: pandas
All the articles with the tag "pandas".
Python vs JavaScript DataFrames in the Browser — Live Benchmarks with No Backend
Posted on:March 22, 2026 at 10:00 AMBoth Python (pandas via Pyodide WebAssembly) and JavaScript (arquero) can process DataFrames entirely in the browser. This post runs the same groupby, filter, and pivot benchmarks in both — live, client-side, no server needed — and measures the real tradeoffs.
Vectorization in Python — NumPy vs Pandas vs Polars vs Numba
Posted on:April 14, 2025 at 10:00 AMSystematic benchmarks comparing four vectorization approaches across different dataset sizes and operation types. When to use NumPy directly, when Polars wins, and when Numba's JIT compilation is the only answer.
Polars vs Pandas — A Benchmark That Changed How I Process Data
Posted on:October 14, 2024 at 10:00 AMComprehensive benchmarks comparing Polars and pandas across groupby, join, filter, and window operations on datasets from 1M to 100M rows. Polars wins by 5-20x in most scenarios — here's what that means for your data pipelines.
Python Time Series at Scale — Lessons from Processing 400M Financial Records
Posted on:July 22, 2024 at 10:00 AMReal-world lessons from building a time series pipeline that processes 400 million financial data points daily. Covers memory layout, chunked processing, dtype optimization, and the specific pandas/NumPy patterns that keep memory under control at scale.
Pandas Performance — Stop Using .iterrows() (with Benchmarks)
Posted on:March 14, 2024 at 10:00 AMBenchmarking five approaches to row-level operations in pandas — from the naive .iterrows() to fully vectorized NumPy operations — with real timing numbers. Shows 100-1000x speedups using vectorization and explains why Python's object model makes .iterrows() so slow.