IbisIbis

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IbisIbis is an open-source Python dataframe library for efficient data manipulation. It offers a consistent API to work across various data systems and integrates with visualization tools.

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At a glance

Pricing

open-source

Free tier

Yes

API

Yes

Skill level

Technical

About

What is IbisIbis?

IbisIbis is an open-source Python dataframe library designed for efficient data manipulation across diverse data systems. It provides a unified API, enabling users to write data manipulation code once and execute it seamlessly against 20 different backends. This consistency simplifies data workflows and enhances productivity for data professionals. The library also integrates with popular visualization libraries such as Altair and Plotly, facilitating data exploration and presentation directly from the manipulated dataframes.

Best used for

Efficiently manipulating and analyzing data across various data systems using a consistent Python API.

Common actions

manipulate data

query data

integrate data sources

analyze data

visualize data

PythonAPIopen-sourcedatabasesanalystsDevelopersdata scientistsduckdbHigh PerformanceData Manipulation+ 3 more

Capabilities

Key features

Open-source Python library
Efficient data manipulation
Consistent API
Supports 20 backends
Visualization integration

Target Audience

Data ScientistsData EngineersPython DevelopersAnalysts

Integrations

Not yet documented

Pricing & Plans

open-source

Not publicly disclosed. Check ibis.chat for current pricing.

FAQs

Given Ibis is open-source, are there any hidden costs or enterprise-specific features I should be aware of?

No, Ibis is genuinely open-source under the Apache 2.0 License. All features, including its extensive backend support and API, are freely available without any hidden costs or enterprise-only tiers.

Which specific data backends does Ibis support, and how does its performance compare to native connectors for those backends?

Ibis supports over 20 backends, including popular ones like DuckDB, PostgreSQL, BigQuery, Snowflake, and PySpark. While it adds an abstraction layer, Ibis is designed for efficiency, often compiling operations into optimized queries that perform comparably to native connectors, especially for complex analytical tasks.

Can Ibis replace my existing Pandas workflows, and what are the main benefits of switching for large datasets?

Ibis offers a Pandas-like API but is designed for out-of-core computation and distributed systems, making it ideal for datasets that don't fit into memory. It allows you to scale your data manipulation logic from local files to large databases or data warehouses without rewriting code.

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