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posted by Fnord666 on Thursday September 12 2019, @07:16AM   Printer-friendly
from the switching-gears dept.

MATLAB and Python are both rather popular languages. Real Python has an overview of the two with an eye towards encouraging use of Python. There is a lot to say when comparing languages, so this is a long read.

MATLABĀ® is widely known as a high-quality environment for any work that involves arrays, matrices, or linear algebra. Python is newer to this arena but is becoming increasingly popular for similar tasks. As you’ll see in this article, Python has all of the computational power of MATLAB for science tasks and makes it fast and easy to develop robust applications. However, there are some important differences when comparing MATLAB vs Python that you’ll need to learn about to effectively switch over.

In this article, you’ll learn how to:

  • Evaluate the differences of using MATLAB vs Python
  • Set up an environment for Python that duplicates the majority of MATLAB functions
  • Convert scripts from MATLAB to Python
  • Avoid common issues you might have when switching from MATLAB to Python
  • Write code that looks and feels like Python

Earlier on SN:
Python's Guido van Rossum Steps Down (2018)
What's Today's Top Language? Python... no, Wait, Java... no, C (2017)
GNU Octave - Open Source Answer to Matlab - Hits 4.0.0 (2015)
You Want MatLab on Your Resume to Get a Job at Google (2014)
Why Python is Slow: Looking Under the Hood (2014)


Original Submission

 
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  • (Score: 1, Informative) by Anonymous Coward on Thursday September 12 2019, @10:53AM

    by Anonymous Coward on Thursday September 12 2019, @10:53AM (#893107)

    Certain reductions in numpy discards singleton dimensions when they are needed further down the line. For example, batched inner products might be needed for calculating certain quantities to be matrix-multiplied further down the line, so you would use `sum' across some axis e.g. to get a column vector [n,1]. This can't be done unless you also pass keepdims=True to the reduction, or reshape. In octave, you'd get the correct dimension directly.

    Painful lesson is never use shapes of type (n,) in numpy and always keep singleton dimensions unless you know explicitly that you will never need them.

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