Alternatives
Products that do what Xpandas – running Pandas-style computation directly in pure C++ does
Hi HN, I’ve been exploring whether pandas can be used as a computation description, rather than a runtime. The idea is to write data logic in pandas / NumPy, then freeze that logic into a static compute graph and execute it in pure C++, without embedding Python. This is not about reimplementing pandas or speeding up Python. It’s about situations where pandas-style logic is useful, but Python itself becomes a liability (latency, embedding, deployment). The project is still small and experimental, but it already works for a restricted subset of pandas-like operations and runs…
- 1TY
Hey, guys. I've just made a plugin which turns your pandas dataframe into a tableau-style component. It allows you to explore the dataframe with easy drag-and-drop UI. You can use PyGWalker in Jupyter, Google Colab, or even Kaggle Notebook to easily explore your data and generate interactive visualizations. PyGWalker (pronounced like "Pig Walker", just for fun) is named as an abbreviation of "Python binding of Graphic Walker". Here are some links to check it out: The Github Repo: https://github.com/Kanaries/pygwalker Use PyGWalker in Kaggle:…
2023 · github.com
- 2IB
Hi everyone, I built PyXL — a hardware processor that executes a custom assembly generated from Python programs, without using a traditional interpreter or virtual machine. It compiles Python -> CPython Bytecode -> Instruction set designed for direct hardware execution. I’m sharing an early benchmark: a GPIO test where PyXL achieves a 480ns round-trip toggle — compared to 14-25 micro seconds on a MicroPython Pyboard - even though PyXL runs at a lower clock (100MHz vs. 168MHz). The design is stack-based, fully pipelined, and preserves Python's dynamic typing without static type restrictions.…
2025 · runpyxl.com
- 3SA
Hey HN! I’m excited to share sketch: a tool to help anyone who uses python and pandas quickly iterate and get to answers for their data questions. Sketch installs as a pandas extension that offers utility functions that operate on natural language prompts. Using the `ask` interface you can get answers in natural language. Using the `howto` interface you can get get python and pandas code directly. The primary benefit of this over copilot and chatGPT is that this adds data-content based context so that the generated answers are much more accurate and relevant to the data problem at hand.…
2023 · github.com
- 4PS
Probly was built to reduce context-switching between spreadsheet applications, Python notebooks, and AI tools. It’s a simple spreadsheet that lets you talk to your data. Need pandas analysis? Just ask in plain English, and the code runs right in your browser. Want a chart? Just ask. While there are tools available in this space like TheBricks, Probly is a minimalist, open-source solution built with React, TypeScript, Next.js, Handsontable, Hyperformula, Apache Echarts, OpenAI, and Pyodide. It's still a work in progress, but it's already useful for my daily tasks.
2025 · github.com
- 5IB
Hi HN, Over the past few months, I've been building `dsc`, a tensor library from scratch in C++/CUDA. My main focus has been on getting the basics right, prioritizing a clean API, simplicity, and clear observability for running small LLMs locally. The key features are: - C++ core with CUDA support written from scratch. - A familiar, PyTorch-like Python API. - Runs real models: it's complete enough to load a model like Qwen from HuggingFace and run inference on both CUDA and CPU with a single line change[1]. - Simple, built-in observability for both Python and C++. Next on the roadmap is…
2025 · github.com
- 6MA
Hi HN! We’re excited to share marimo, an open-source reactive notebook for Python [1]. marimo aims to solve well-known problems with traditional notebooks [2]: marimo notebooks are reproducible (no hidden state), git-friendly (stored as Python files), executable as Python scripts, and deployable as web apps. GitHub repo: https://github.com/marimo-team/marimo In marimo, a notebook’s code, outputs, and program state are always consistent. Run a cell and marimo reacts by automatically running the cells that reference its declared variables. Delete a cell and marimo scrubs…
2024 · github.com
- 7WA
Hey HN I’ve been building *W++*, a scripting language that looks like Python but runs on the .NET runtime. It started as a fun side project, but it evolved into something surprisingly powerful — and potentially useful: Key Features: - Python-style syntax with semicolon-based simplicity - Compiles to .NET IL with experimental JIT support - Can run interpreted or compiled - Built-in CLI for managing projects, running, and building - Supports importing NuGet packages and converts them to .ingot modules automatically - MIT licensed and fully open-source You can even do things like: wpp import…
2025 · github.com
- 8BA
2019 · bamboolib.com
- 9
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- 11SP
Hi HN, Over the past 6 months I've been working on a technical book focused on helping aspiring data scientists to get hands-on experience with cloud computing environments using the Python ecosystem. The book is targeted at readers already familiar with libraries such as Pandas and scikit-learn that are looking to build out a portfolio of applied projects. To author the book, I used the Leanpub platform to provide drafts of the text as I completed each chapter. To typeset the book, I used the R bookdown package by Yihui Xie to translate my markdown into a PDF format. I also used Google docs…
2020
- 12ML
Just wanted to share with HN a simple/minimal open source Python library that generates SVG files visualizing two dimensional data and distributions, in case others find it useful or interesting. I wrote it as a fun project, mostly because I found that the standard libraries in Python generated unnecessarily large SVG files. One nice property is that I can configure the visuals through CSS, which allows me to support dark/light mode browser settings. The graphs are specified as JSON files (the repository includes a few examples). It supports scatterplots, line plots, histograms,…
Mar 2026 · github.com
- 13HR
Hello HN, I'm releasing Hyperdiv (https://hyperdiv.io), a framework for rapidly developing reactive browser UIs in Python, with immediate-mode syntax and using Shoelace (https://shoelace.style) as its built-in component system. This short coding video will give you a good idea of what it is: https://www.youtube.com/watch?v=4XJKfxaqvGE I wrote a brief article about the motivation and approach: https://hyperdiv.io/intro.html Hyperdiv doesn't aim to compete with serious full-stack frameworks. The core aim was to make it easy and fast to…
2024 · github.com
- 14PL
2022 · github.com
- 15IB
Hi HN! Korean high school senior here, about to start CS in college. I built a browser engine from scratch in C++ to understand how browsers work. First time using C++, 8 weeks of development, lots of debugging—but it works! Features: - HTML parsing with error correction - CSS cascade and inheritance - Block/inline layout engine - Async image loading + caching - Link navigation + history Hardest parts: - String parsing(html, css) - Rendering - Image Caching & Layout Reflowing What I learned (beyond code): - Systematic debugging is crucial - Ship with known bugs rather than chase…
Jan 2026 · github.com
- 16MO
Hi HN! Last month, we shared marimo [1][2], an open-source reactive notebook for Python. For those who missed it, marimo notebooks are reproducible, stored as Python files, executable as Python scripts, and deployable as web apps. It’s now possible to run marimo notebooks entirely in the browser via WebAssembly (WASM). - A marimo tutorial as a WASM notebook: https://marimo.app/l/c7h6pz - Training a neural network with Karpathy’s micrograd: https://marimo.app/l/xpd4te - Visualizing attractors, as a read-only app:…
2024 · marimo.app
- 17AL
Hey HN! I built a local Python prototyping tool that is finally the Python development environment I've always wanted. It has a Jupyter notebook for data crunching, a database of your choice (Python or MongoDB), and a Streamlit app for building a frontend visualization. You can edit the Streamlit backend via an embedded VSCode editor, or locally on your own IDE. The best part for me is that the database connectors within Jupyter and Streamlit are configured out-of-the-box, so you don't need to spend time thinking about how to tie all that together - you can just pick the database you want to…
2023 · github.com
- 18HP
I built a Rust-powered Wavelet Matrix library for Python. There were surprisingly few practical Wavelet Matrix implementations available for Python, so I implemented one with a focus on performance, usability, and typed APIs. It supports fast rank/select, top-k, quantile, range queries, and even dynamic updates. Feedback welcome!
Dec 2025 · pypi.org
- 19BP
What? Another flow based programming library for Python? Yes. All the FBP libraries out there for Python need to be run as a self contained application. They are not components that could be integrated into your existing data workflows. Barfi, on the other hand can be integrated. At the moment it has a Streamlit component that you can use in your Streamlit apps. Currently, I am working on a Jupyter notebook widget.
2022 · github.com
- 20OP
Hey HN, I just launched this online Python compiler which lets you use popular Python libraries like requests, Matplotlib, Plotly, Pandas, NumPy etc. online. It uses Pyodide to execute Python in the browser using WebAssembly.
2025 · cliprun.com
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- 22RA
2023 · github.com
- 23SD
Hey all! We’ve built a Pandas-like interface to make it easy to work with streaming data using what we call ‘Streaming DataFrames’. For example, suppose that you want to convert speed measurement units from meters per second to kilometers per hour With static data in Pandas, you’d do this: df["speed_km_h"] = df["speed_m_s"] * 3.6 With Streaming DataFrames, it’s pretty much the same thing… sdf["speed_km_h"] = sdf["speed_m_s"] * 3.6 …except it’s being done continuously and the updated records can be sent to an output topic in Kafka with almost no delay after they’ve been processed. You can…
2024 · github.com
- 24ET
We (me and @aarondia) built a tool to help you turn psuedo-software Excel files into real-software Python. Ideally, Pyoneer helps you automate your manual Excel processes. You can try it today here: https://pyoneer.ai. How it works: 1. You upload an Excel file 2. We statically parse the Excel file and build a dependency graph of all the cells, tables, formulas, and pivots. 3. We do a graph traversal, and translate nodes as we hit them. We use OpenAI APIs to translate formulas. There’s a bunch of extra work here — because even with the best prompt engineering a fella like me can do,…
2024 · pyoneer.ai
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