How to install Panthera and set up Python dependencies for Clojure?

First, follow libpython-clj's documentation to install and initialize Python in your Clojure environment. Then, add Panthera as a dependency via Clojars. Ensure pandas and numpy are installed in Python for full functionality.

Is Panthera stable enough for production data analysis workflows?

No, Panthera is in alpha with an incomplete and changing API, as per the disclaimer. It's best for experimentation or development, not for critical production systems where stability is key.

Can I use Panthera without installing Python on my system?

No, Panthera relies on libpython-clj, which requires a Python runtime. Without Python, the interop won't work, so it's unsuitable for environments where Python is not available or desired.

Panthera vs tech.ml.dataset for Clojure dataframes, which should I choose?

Panthera leverages Python's pandas for data manipulation with Clojure syntax, while tech.ml.dataset is a native Clojure library. Choose Panthera if you need pandas features and accept Python dependencies; opt for tech.ml.dataset for a pure, stable Clojure solution.

What are some real-world examples of using Panthera for data science?

Check the examples folder in the GitHub repo, which includes Jupyter notebooks on basic concepts and panthera intro, showing how to read CSV files, subset columns, and apply statistical functions like median in a Clojure REPL.

How can I contribute or report issues with Panthera?

The README encourages contributions via GitHub issues, PRs, or direct messages on Clojurians Slack/Zulip. Since it's alpha, feedback is vital for improving documentation, testing, and API stability.

Panthera — Clojure Pandas & Numpy Library

What is Panthera?

Panthera is a Clojure library that provides data-frames and arrays by interfacing with Python's pandas and numpy. It allows Clojure developers to perform data manipulation and numerical computing using familiar pandas operations within a Clojure environment, bridging the gap between Clojure and Python's data science ecosystem.

Target Audience

Clojure developers who need data analysis and manipulation capabilities, especially those familiar with or wanting to leverage pandas and numpy without leaving the Clojure REPL.

Value Proposition

Panthera offers a seamless way to use pandas and numpy from Clojure, combining Clojure's functional programming strengths with Python's mature data science libraries, all through a clean API and libpython-clj interop.

Data-frames & arrays on Clojure

Use Cases

Best For

Performing data analysis in Clojure using pandas operations
Manipulating CSV data within a Clojure application
Bridging Clojure and Python for numerical computing tasks
Extending Clojure with vectorized functions for Python objects
Wrapping other Python libraries in Clojure for interoperability
Exploring data science workflows in a Clojure REPL

Not Ideal For

Projects requiring stable, production-ready data science libraries with long-term support
Environments where Python runtime is unavailable or must be avoided for dependency reasons
Teams that rely on comprehensive documentation and mature ecosystems for rapid development
Applications needing pure Clojure solutions without external interop complexities

Pros & Cons

Pros

Seamless Python Interop

Leverages libpython-clj to directly access pandas and numpy, allowing Clojure code to use Python's robust data science libraries with minimal overhead, as demonstrated in the read-csv and median examples.

Functional Data Pipelines

Integrates pandas operations into Clojure's functional style, enabling clean and expressive data manipulation chains, such as the example with subset-cols and median in the README.

Extensible Design

Can be extended to wrap other Python libraries beyond pandas and numpy, shown in the general wrapper example, providing flexibility for future integrations.

Vectorized Performance

Offers vectorized versions of Clojure functions like mod for Python objects, enhancing performance for numerical computations by leveraging numpy's efficiency.

Cons

Alpha Stability Issues

The README explicitly states it's alpha with frequent breaking changes and an unsettled API, making it unreliable for production use or long-term projects.

Incomplete Documentation

Lacks comprehensive documentation, relying on examples which may not cover all use cases, as admitted in the disclaimer about being mostly undocumented.

Complex Setup Dependencies

Requires setting up libpython-clj and a Python environment, adding setup overhead and potential dependency conflicts compared to pure Clojure alternatives.

Frequently Asked Questions

What is Panthera?

Target Audience

Clojure developers who need data analysis and manipulation capabilities, especially those familiar with or wanting to leverage pandas and numpy without leaving the Clojure REPL.

Value Proposition

Use Cases

Best For

Performing data analysis in Clojure using pandas operations
Manipulating CSV data within a Clojure application
Bridging Clojure and Python for numerical computing tasks
Extending Clojure with vectorized functions for Python objects
Wrapping other Python libraries in Clojure for interoperability
Exploring data science workflows in a Clojure REPL

Not Ideal For

Projects requiring stable, production-ready data science libraries with long-term support
Environments where Python runtime is unavailable or must be avoided for dependency reasons
Teams that rely on comprehensive documentation and mature ecosystems for rapid development
Applications needing pure Clojure solutions without external interop complexities

Pros & Cons

Pros

Seamless Python Interop

Functional Data Pipelines

Integrates pandas operations into Clojure's functional style, enabling clean and expressive data manipulation chains, such as the example with subset-cols and median in the README.

Extensible Design

Can be extended to wrap other Python libraries beyond pandas and numpy, shown in the general wrapper example, providing flexibility for future integrations.

Vectorized Performance

Offers vectorized versions of Clojure functions like mod for Python objects, enhancing performance for numerical computations by leveraging numpy's efficiency.

Cons

Alpha Stability Issues

The README explicitly states it's alpha with frequent breaking changes and an unsettled API, making it unreliable for production use or long-term projects.

Incomplete Documentation

Lacks comprehensive documentation, relying on examples which may not cover all use cases, as admitted in the disclaimer about being mostly undocumented.

Complex Setup Dependencies

Requires setting up libpython-clj and a Python environment, adding setup overhead and potential dependency conflicts compared to pure Clojure alternatives.

Frequently Asked Questions

Panthera

What is Panthera?

Overview

Use Cases

Best For

Not Ideal For

Pros & Cons

Pros

Cons

Frequently Asked Questions

Related Projects

Found a gem we're missing?

Panthera

What is Panthera?

Overview

Use Cases

Best For

Not Ideal For

Pros & Cons

Pros

Cons

Frequently Asked Questions

Related Projects

Found a gem we're missing?