Numpy.NET brings the awesome Python package NumPy to the .NET world. NumPy is THE fundamental library for scientific computing, machine learning and AI in Python. Numpy.NET empowers .NET developers to leverage NumPy's extensive functionality including multi-dimensional arrays and matrices, linear algebra, FFT and many more via a compatible strong typed API.
import numpy as np
a1=np.arange(60000).reshape(300, 200)
a2=np.arange(80000).reshape(200, 400)
result=np.matmul(a1, a2)using Numpy;
// ...
var a1 = np.arange(60000).reshape(300, 200);
var a2 = np.arange(80000).reshape(200, 400);
var result = np.matmul(a1, a2);Numpy.NET uses Python for .NET to call into the Python module numpy. However, this does not mean that it depends on a local Python installation! Numpy.NET.dll uses Python.Included which packages embedded Python 3.7 and automatically deploys it in the user's home directory upon first execution. On subsequent runs, it will find Python already deployed and therefor doesn't install it again. Numpy.NET also packages the NumPy wheel and installs it into the embedded Python installation when not yet installed.
Long story short: as a .NET Developer you don't need to worry about Python at all. You just reference Numpy.NET, use it and it will just work, no matter if you have local Python installations or not.
You might ask how calling into Python affects performance. As always, it depends on your usage. Don't forget that numpy's number crunching algorithms are written in C so the thin pythonnet and Python layers on top won't have a significant impact if you are working with larger amounds of data.
In my experience, calling numpy from C# is about 4 times slower than calling it directly in Python while the execution time of the called operation is of course equal. So if you have an algorithm that needs to call into numpy in a nested loop, Numpy.NET may not be for you due to the call overhead.
All of numpy is centered around the ndarray class which allows you to pass a huge chunk of data into the C routines and let them execute all kinds of operations on the elements efficiently without the need for looping over the data. So if you are manipulating arrays or matrices with thousands or hundreds of thousands of elements, the call overhead will be neglegible.
The most performance sensitive aspect is creating an NDarray from a C# array, since the data has to be moved from the CLR into the Python interpreter. pythonnet does not optimize for passing large arrays from C# to Python but we still found a way to do that very efficiently. When creating an array with np.array( ... ) we internally use Marshal.Copy to copy the entiry C#-array's memory into the numpy-array's storage. And to efficiently retrieve computation results from numpy there is a method called GetData<T> which will copy the data back to C# in the same way.
The SciSharp team is also developing a pure C# port of NumPy called NumSharp which is quite popular albeit incomplete. To help you decide which one to use we compare the advantages and disadvantages of both libraries here:
| Aspect | Numpy.NET | NumSharp |
|---|---|---|
| Dependencies | CPython / NumPy | C++ dlls for certain operations |
| Setup | Reference Nuget-Package | Reference Nuget-Package |
| Completeness | Large parts are wrapped | A small subset of most important functions is ported |
| Development | Fast, due to automated API generation | Slow, due to lack of manpower |
| Correctness | Same results as in Python guaranteed | There are subtle differences |
| Actuality | Can easily keep up with numpy dev |
Will trail behind, due to lack of manpower |
| GPU support | None | Using a GPU backend for calculatons possible, per design |
| Performance | TODO: measure | TODO: measure |
The vast majority of Numpy.NET's code is generated using CodeMinion by parsing the documentation at docs.scipy.org/doc/numpy/. This allowed us to wrap most of the numpy-API in just two weeks. The rest of the API can be completed in a few more weeks, especially if there is popular demand.
TODO: information about completed API categories
Currently, Numpy.NET is targeting .NET Standard (on Windows) and packages the following binaries:
- Python 3.7: (python-3.7.3-embed-amd64.zip)
- NumPy 1.16 (numpy-1.16.3-cp37-cp37m-win_amd64.whl)
To make Numpy.NET support Linux a separate version of Python.Included packaging linux binaries of Python needs to be made and a version of Numpy.NET that packages a linux-compatible NumPy wheel.
Numpy.NET packages and distributes Python, pythonnet as well as numpy. All these dependencies imprint their license conditions upon Numpy.NET. The C# wrapper itself is MIT License.
- Python: PSF License
- Python for .NET (pythonnet): MIT License
- NumPy: BSD License
- Numpy.NET: MIT License