pyspark-pandas
Tools and algorithms for pandas Dataframes distributed on pyspark. Please consider the SparklingPandas project before this one
Decision gist · record as of 2026-08-14
No. This package is abandoned (last release 2014-10-14, no commits since), has an unclear license, and explicitly recommends users evaluate alternatives instead. Modern alternatives are far better choices for any current use case.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- No runtime dependencies declared; compatibility with modern PySpark and Pandas versions is unknown given the 2014-10-14 release date.
- High install friction and abandoned maintenance status.
- Last release was 2014-10-14, with no commits since then.
License · maintenance · safety
UNKNOWN (unclear) — License is marked UNKNOWN with no SPDX identifier, creating unclear legal standing for use or redistribution.
last release 2014-10-14 (4322 days) · last repo commit 2014-10-15 · 6 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 895,169 downloads/mo, #4,791 on PyPI
Alternatives
Verify before relying
pip install pyspark-pandas==0.0.7
import pyspark_pandas- Whether this package is compatible with modern versions of PySpark and Pandas
- What Python versions this package actually supports (unspecified in metadata)
- Whether the codebase has any undisclosed security issues despite zero OSV records
What it is and what it does
pyspark-pandas aims to bridge Pandas and Apache Spark by providing utilities to distribute Pandas DataFrames and Series across Spark clusters. It was designed to enable data analysis workflows that combine Pandas' ease-of-use with Spark's distributed processing power for handling large datasets.
However, this project has been abandoned since 2014-10-14 and is no longer maintained. The package itself explicitly directs users to consider alternatives instead, suggesting this codebase may have been superseded. With no runtime dependencies listed and no recent activity, it represents a snapshot from the early Spark-Pandas integration era.
Use it for
- Distributing Pandas DataFrames across Spark clusters for parallel processing of large datasets
- Combining Pandas data manipulation with Spark's distributed computing for legacy workflows
- Prototyping distributed data analysis before migrating to modern solutions
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
No.
This package is abandoned (last release 2014-10-14, no commits since), has an unclear license, and explicitly recommends users evaluate alternatives instead. Modern alternatives are far better choices for any current use case.
Install
pyspark-pandas on PyPI
Before you install
High install friction and abandoned maintenance status. Last release was 2014-10-14, with no commits since then. The project itself recommends evaluating alternatives before adopting this package.
No runtime dependencies declared; compatibility with modern PySpark and Pandas versions is unknown given the 2014-10-14 release date.
License in practice
License is marked UNKNOWN with no SPDX identifier, creating unclear legal standing for use or redistribution.
Quickstart
pip install pyspark-pandas==0.0.7
import pyspark_pandasVerify before relying
- Whether this package is compatible with modern versions of PySpark and Pandas
- What Python versions this package actually supports (unspecified in metadata)
- Whether the codebase has any undisclosed security issues despite zero OSV records
Package facts
| License | UNKNOWN unclear |
| Python support | Not specified |
| Install friction | High. Source build required |
| Runtime dependencies | None |
| Maintenance | Abandoned 4,322 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 895,169 / month, #4,791 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: pyspark-pandas-0.0.7.zip
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “pyspark pandas integration”
- pyspark-pandasProvides tools for distributing Pandas DataFrames and Series across…
- snowpark-connectSnowpark Connect for Spark lets you run Spark workloads directly…
- highcharts-coreGenerates interactive Highcharts data visualizations from Python,…
Give your agent the search over MCP, or paste the wish link into any chat.
More Distributed Computing packages
gRPC Python is an HTTP/2-based RPC framework that enables you to define and call remote procedures across network boundaries using protocol buffers for serialization.
Install it if you need RPC communication in a distributed system or are integrating with existing gRPC services.
execnet lets you spawn and communicate with Python interpreters across local processes, remote hosts, and different platforms, using a simple API for task distribution and inter-process messaging.
However, the aging maintenance status (275 days since last release) means you should verify it meets your concurrency and performance needs before committing to a…
Cloudpickle extends Python's standard pickle module to serialize lambda functions, interactively-defined functions and classes, and other constructs that the default pickle cannot handle, making it suitable for cluster computing and remote code execution.
Install it if you need to serialize lambda functions, interactively-defined code, or non-standard Python constructs for cluster computing or distributed execution.
Provides a unified, open()-compatible Python API for streaming large files from remote storage (S3, GCS, Azure, HDFS, SFTP, HTTP) and local filesystems, with transparent compression support.
Install it if you work with large files on cloud storage or remote systems and want to avoid writing boilerplate around multiple SDKs.
Portalocker provides cross-platform file locking with support for exclusive and shared locks, plus Redis-based distributed locks and process-aware PID file locking.
Install it if you need file or process coordination; the optional extras (pywin32, redis) are only required for specific lock types.
Ray is a distributed computing framework that scales Python applications from a single machine to multi-node clusters, providing abstractions for parallel tasks, stateful actors, and shared objects.
See also koalas · pbspark · pyspark · repartipy · raydp · pandas · spark-sklearn · pyspark-client · graphframes-py · pyspark-extension