Why pandas cannot be used in TorchScript — and how xpandas fixes it

Xianqiu Li — Mon, 02 Mar 2026 07:06:34 +0000

The problem I kept running into

I often deploy PyTorch models using TorchScript and LibTorch (C++) for inference.

The model itself is not the hard part.

The problem is data processing.

In many real pipelines — especially in quantitative finance, feature engineering, or low-latency systems — you still need to do things like:

groupby + aggregation
rolling window operations
column-wise transformations

In Python, this is trivial with pandas.

But the moment you try to move the whole pipeline into TorchScript or C++ inference, everything breaks.

Why pandas (and friends) don’t work in TorchScript

This is not a pandas problem. It’s a runtime boundary problem.

1. pandas is Python-runtime dependent

TorchScript explicitly removes the Python runtime:

no Python objects
no dynamic typing
no CPython extensions

pandas relies heavily on:

Python objects
NumPy internals
CPython C-API

So torch.jit.script() simply cannot compile pandas code.

2. Polars / Arrow don’t solve this either

You might think:

“What about Polars? It’s fast and written in Rust.”

Still no.

Polars is not TorchScript-aware
Arrow execution does not integrate with PyTorch JIT graphs
You cannot inline Polars logic inside a TorchScript model

They are great libraries — just solving a different problem.

3. Python preprocessing + C++ inference is often unacceptable

The usual workaround is:

Python (pandas) → Tensor → TorchScript → C++

This fails when you need:

a single deployable artifact
low-latency inference
no Python dependency in production

At that point, you either:

re-implement everything manually in C++
or give up on pandas-like logic altogether

That’s the gap I kept hitting.

The core idea: pandas-like ops as Torch custom operators

Instead of trying to make pandas work in TorchScript, I flipped the approach:

What if pandas-like operations were implemented directly as Torch custom ops?

That means:

inputs are torch::Tensor
logic lives in C++ (LibTorch)
everything is TorchScript-compatible
the entire pipeline can be exported and run in C++

This is what xpandas is.

What xpandas is (and what it is not)

What it is

A small, opinionated subset of pandas-like DataFrame operations
Implemented as Torch C++ custom operators
Fully compatible with torch.jit.script
Designed for inference pipelines, not exploratory analysis

What it is not

A full pandas replacement
A dataframe library for interactive data science
A competitor to Polars or Arrow

Repository:

https://github.com/CVPaul/xpandas

DEV Community: Xianqiu Li