nalepae/pandarallel

Health v2 · maintenance only

Adoption not part of the score

3799 stars · 213 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

Pandaral·lel is a Python library that parallelizes pandas operations across all available CPU cores by changing only one line of code (e.g., df.apply becomes df.parallel_apply). It supports apply, applymap, map, groupby, rolling, and expanding APIs, and displays progress bars.

Use cases

• speed up pandas apply on large dataframes
• parallelize groupby apply across cores
• use all CPUs for pandas map operations
• add progress bars to long pandas computations
• parallelize rolling window apply functions

When to choose

• you have CPU-bound pandas apply/map/groupby workloads on a multi-core machine
• you want a minimal one-line change instead of rewriting code with multiprocessing or Dask
• you want progress bars for long-running pandas operations

When to avoid

• your operations are I/O-bound or already vectorized, where parallelization overhead outweighs gains
• you need distributed computing across multiple machines (use Dask or Ray instead)
• you need a project with active development, as it is seeking a new maintainer

Facets

library · maturity maintenance

concurrency data-science data-science developer-tools performance python windows cross-platform pandas parallelization multiprocessing dataframe progress-bar linux macos

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