Python
The language nearly all machine learning code is written in
As of 30 August 2026, Python is free to use. Python is an interpreted, dynamically typed language whose numerical libraries are written in C, C++ and CUDA. Softwr lists it under Machine Learning. Python is made by Python Software Foundation, launched in 1991, available on Windows, macOS, Linux, Android.
Overview
What Python does
Python is a general purpose language maintained by the Python Software Foundation, with CPython as the reference implementation. A new version ships each October and receives roughly five years of support, and the pace of that cycle matters more in machine learning than elsewhere because compiled libraries take months to publish wheels for a new release. Almost none of the arithmetic in a machine learning workload actually happens in Python: NumPy, PyTorch, TensorFlow, XGBoost, scikit-learn and the rest are C, C++, Fortran, Rust and CUDA underneath, and Python is the language that describes which of those routines to call and in what order. The distinguishing property is not the language design, it is the C extension interface and the library gravity that followed from it. Every serious numerical library acquired a Python binding, so every new library gets one too, and that is a network effect rather than a technical merit. It is why arguments about Python being slow rarely change any decision: the hot loop is not in Python, and the alternative costs you every dependency, every tutorial, every model card, every vendor SDK and most of the candidates you could hire. Choosing another language for machine learning means paying an integration tax on each of those, repeatedly. Everyone in the field uses it, so the question is what it costs. The global interpreter lock means CPU-bound parallelism inside one process requires either multiprocessing, with its memory duplication and pickling overhead, or a native extension that releases the lock. Packaging is the chronic tax: pip, conda, uv and poetry, plus a matrix of CUDA-linked wheels, routinely produce container images measured in gigabytes and environments that break when any one dependency publishes a major version. And a language that makes prototyping this fast also makes it trivially easy to ship a notebook into production with no types, no tests and a dependency set nobody can reconstruct twelve months later.
What people use it for
- Training and evaluating models, where every mainstream framework offers Python as its primary interface
- Data preparation and analysis with pandas, Polars or PySpark before anything is modelled
- Gluing systems together, where the job is calling several services and libraries rather than computing anything heavy
- Research code that has to be readable by people whose speciality is statistics or a scientific domain rather than software engineering
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The honest half
Where it falls short
Concrete and checkable, so you can decide whether any of them matter to you. This is the half of a review a vendor will not write about Python.
- The global interpreter lock serialises bytecode execution within a process, so CPU-bound parallel work needs multiprocessing with its memory duplication and serialisation costs; the free-threaded build added in 3.13 is opt-in and much of the compiled ecosystem does not yet support it.
- Dependency resolution is the standing cost of the ecosystem: a project pinning a CUDA-linked framework, a NumPy major version and a dozen libraries that constrain both produces multi-gigabyte images and installs that break whenever one of those publishes a new major version.
- Ecosystem-wide binary breaks propagate badly, because a library compiled against an older extension interface fails at import with a low-level error rather than a clear message, and a team with a frozen environment discovers it cannot add one package without rebuilding all of them.
- Dynamic typing pushes whole categories of error to run time, which in machine learning means a shape mismatch or a None surfacing six hours into a training job rather than at a compile step, and type hints are optional, unenforced at run time and applied inconsistently across ML libraries.
- Interpreter start-up and per-call overhead make it a poor host for low-latency serving of small models, where the wrapper can cost more time than the inference itself, which is why serving layers get rewritten in Go, Rust or C++ once traffic justifies the work.
Capabilities
Features
C extension interface
A stable-enough binary interface that lets numerical libraries expose native code to Python
Dynamic typing
No compile step and no type declarations required, with optional annotations checked by external tools
Rich standard library
Covers file handling, networking, concurrency, serialisation and testing without third-party packages
Interactive interpreter and notebooks
Statement-at-a-time execution that suits exploratory data work
Package index
A single public repository of libraries installable with pip, plus conda channels for compiled scientific stacks
Virtual environments
Per-project dependency isolation built into the standard distribution
Cross-platform
Same code runs on Linux, macOS and Windows, though ML wheels are best supported on Linux
Free-threaded build
An opt-in interpreter without the global interpreter lock, introduced experimentally in 3.13
Foreign function interfaces
ctypes, cffi and tools such as Cython and PyO3 for calling or writing native code
Annual release cadence
A predictable October release with about five years of security support per version
Answered, with sources
Questions people ask
Each answer names the page it came from, so you can check it rather than take our word for it.
Which version should I use for machine learning?
Usually one release behind the newest. Compiled ML wheels lag the interpreter by months, and being first to a new version mostly buys you a broken environment.
Is Python too slow for machine learning?
The numerical work is not in Python. It matters for data preprocessing loops written in pure Python and for serving small models at high request rates, and in both cases the answer is to move that specific part into a vectorised library or a compiled extension.
pip or conda?
pip with virtual environments, or uv, is simpler and now covers most cases. Conda still earns its place when you need non-Python system libraries, particular CUDA builds or a scientific stack pinned as a set.
Do I need to know C to work in machine learning?
No, but you need to know that the libraries are C underneath, because that explains why an error message is unreadable, why a wheel will not install and why one line of pandas is a thousand times faster than the loop it replaced.
Is the global interpreter lock being removed?
A free-threaded build exists from 3.13 onward as an opt-in variant. It is not the default, and the compiled libraries that matter for machine learning are still working through support for it.
Behind it
Who makes Python
- Company
- Python Software Foundation
- Based in
- Global Open Source Project
Keep looking
Where to go from Python
- Python alternativesEvery product people weigh against it
- Python pricingPlans, floors and what the tiers exclude
- Python badgesWhich rules it meets, and the figure behind each
- All Machine Learning softwareThe full category
- Best Machine Learning softwareThe shortlist, and how it was cut
Best Machine Learning software for
Compare Python with
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- scikit-learn
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- KNIME
- PyTorch
- ClearML
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- MLflow
- DVC
- H2O.ai
- Hugging Face
- Kubeflow
- Langwatch
- LlamaIndex
- TensorFlow
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Softwr does not host reviews and shows no star rating for Python, because a rating we did not collect is not ours to publish. What is here is the pricing and platform detail from the vendor’s own pages, limitations we could state concretely, and alternatives a reviewer confirmed people weigh against it. Tell us if any of it is wrong.
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