
Koalas
Koalas implements the pandas DataFrame API on Apache Spark and is deprecated; for Apache Spark 3.2 and above, use PySpark directly.

About Koalas
Koalas implements the pandas DataFrame API on Apache Spark. It supports a single codebase for pandas tests or smaller datasets and for Spark jobs that process distributed datasets, letting DataFrame operations carry over between those settings.
Pricing is not clearly stated.
Key features
- Converts a pandas DataFrame into a Koalas DataFrame with ks.from_pandas
- Installs with Conda from conda-forge or with pip
- Pre-installed in Databricks Runtime 7.1 and above
- Deprecated and in maintenance mode
- Supports Apache Spark 3.1 and below only
Koalas at a glance
- Website
- github.com/databricks/koalas
- Category
- Development › Developer Tools
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Details verified
- Oct 22, 2026 · Official source
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
Guides for Developer Tools
Koalas alternatives
Tools and sites you could use instead, ranked by how closely they match.
DataComPy compares Pandas, Polars, Spark, and Snowpark dataframes, with output that includes comparison statistics.
The Geni project provides a Clojure dataframe library that runs on Apache Spark.
AWS Data Wrangler connects Pandas with AWS data services and databases, including Athena, Glue, Redshift, and S3.
Python developers can use Dash to build data apps and dashboards without requiring JavaScript.
Machine learning pipelines can use whylogs, an open-source library for logging data quality and model performance.
For Python data preparation, DataPrep offers an open-source low-code library to collect, clean, and visualize data.
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