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Bodo

Bodo is an open-source Python DataFrame library and SQL engine for large-scale analytics and AI/ML data processing. It offers Pandas-compatible APIs, MPI-based parallel execution and JIT compilation for custom transformations.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Bodo — OpenViST project profile in AI Data & Analytics.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Consider Bodo when a Python analytics or AI/ML pipeline needs to process larger datasets or accelerate supported Pandas-style transformations, and its documented Python and platform requirements fit the intended environment.

Who it is for

  • Python users with Pandas-based data-processing workflows.
  • Data and machine-learning teams preparing large datasets or building feature-engineering pipelines.
  • Developers who want DataFrame and SQL processing in a Python workflow.

Documented uses

  • Adapt a Pandas-based workflow to use Bodo's compatible DataFrame interface.
  • Parallelize supported Python data-processing workloads using MPI-based execution.
  • Use JIT compilation to accelerate custom transformations.
  • Process data through integrated SQL and DataFrame interfaces.
  • Read and write supported formats and platforms, including Parquet, CSV, JSON, Iceberg and Snowflake.
Before you choose

Requirements & limits

The repository identifies Bodo as open source under the Apache-2.0 license and documents pip or Conda installation. It requires Python 3.10 or later and lists Linux x86, Mac x86, Mac ARM and Windows support.

  • The library targets supported Python workloads; the repository describes selective acceleration and fallback to vanilla Pandas for unsupported sections.
  • Bodo uses MPI-based high-performance computing for parallel execution.
  • The documented installation requires Python 3.10 or later; Linux ARM support is described as forthcoming.
  • The repository's sample reports a workload-specific laptop speedup, which should not be read as a general performance guarantee.
  • Its listed data access includes Parquet, CSV, JSON, Iceberg and Snowflake.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install Bodo and switch a Pandas import

Install the library and make the documented import change in a Python data workflow.

Before you start

  • Python 3.10 or later.
  • A documented environment: Linux x86, Mac x86, Mac ARM or Windows.
  • Pip or Conda.

Steps

  1. Install Bodo with the command below, or create a Conda environment and install Bodo from conda-forge using the repository's instructions.

    Documented command
    pip install -U bodo

  2. In a Pandas-based script, replace import pandas as pd with import bodo.pandas as pd.

  3. Use the changed import in a supported section of the workflow; the repository describes fallback to vanilla Pandas for unsupported workloads.

Expected outcome

A Python workflow can use Bodo's Pandas-compatible interface in place of the standard Pandas import.

Guide limitations

  • The repository describes selective acceleration of supported workload sections; the import change alone does not establish that every operation is accelerated.
  • Linux ARM support is described as forthcoming.
  • No general performance result is guaranteed by the repository's example.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
8 October 2026
Original website title
bodo-ai/Bodo · Official homepage title · retained in its source language
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