Xpandas
running Pandas-style computation directly in pure C++
Details
- External ID
- 47215037
- Source
- HN
- Company
- —
- Product
- —
- Website domain
- —
- Launched
- March 2, 2026
- Cohort
- —
- Upvotes
- 6
- Upvotes percentile
- 0.2853628536285363
- Tags
- —
- Fetched at
- Sept. 7, 2026, 9:26 p.m.
- Updated at
- Sept. 7, 2026, 9:26 p.m.
Description
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 deterministically in C++.Repo: https://github.com/CVPaul/xpandasI’d love feedback on whether this direction makes sense, and where people think it would break down.
Enrichment
- Theme
- database infrastructure and developer tools
- Vertical
- Horizontal
- Function
- Data infrastructure
- Audience
- Developer
- AI stance
- Not AI
- Project type
- Commercial product
- Normalized one-liner
- fast pandas-style computation in c++
- Manually corrected
- False
Could you build this?
No Translating Pandas/NumPy execution into a statically compiled, standalone C++ compute graph without embedding Python requires deep compiler, AST parsing, and systems programming expertise.
What it would actually take: Building this requires designing an IR (Intermediate Representation) or compute graph generator that captures dynamic Python/Pandas operations via bytecode or AST analysis, followed by an optimizing C++ code generator or execution engine that links against optimized linear algebra kernels (like BLAS/LAPACK or custom tensor backends). This necessitates deep compiler theory, C++ template metaprogramming, and deep familiarity with NumPy/Pandas internal memory models.
Discussion
No comments on this launch.
Competitors
Other products that read as similar to this one — 81 launches clear the similarity bar, closest 8 shown.
Attention rank: #62 of 82 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).
Launched 114 days after the earliest competitor.
- Skillancy Compilers · ph · 2026-09-30 · 1 upvotes · similarity 0.47
- In-browser Python/Pandas/Git practice with animated Git simulator · hn · 2026-06-17 · 5 upvotes · similarity 0.45
- Typol · hn · 2026-06-07 · 5 upvotes · similarity 0.44
- Axiom · hn · 2026-02-02 · 5 upvotes · similarity 0.43
- Minimalist library to generate SVG views of scientific data · hn · 2026-03-23 · 47 upvotes · similarity 0.41
- I got tired of print(x.shape) so I built runtime type hints for Python · hn · 2026-03-18 · 7 upvotes · similarity 0.41
- Lythonic · hn · 2026-04-13 · 5 upvotes · similarity 0.40
- Misata · hn · 2025-12-16 · 24 upvotes · similarity 0.40
Other launches for this product
- No other launches for this product.
Same idea, different domain
Nobody's really built a data infrastructure tool for Media & entertainment yet.