Python is now the preferred industry language for scientific computing, data science and machine learning.
Python is a high-level script programming language. It is a good alternative language for MATLAB but is free. It has a large community that allows developers to learn, collaborate and knowledge share. This surging popularity comes from its readability, simplicity and superb array of open source scientific packaged programming tools.
Python is the first language for many data scientists. It can be used to develop both small and large-scale projects. Typical applications include scientific research, data processing, signal acquisition, websites and IoT.
Adding value to your projects
Python is fast to program, which means the development period is shorter than using other software languages such as Java or C++, therefore more can be achieved in shorter timescales. Its seed of programming is on par with MATLAB or LabVIEW. It runs on multiple operating systems, from Windows, OS X to Linux plus a wide range of hardware setups. Its data processing speed is fast and as an added bonus, it is free to use so there are no ongoing costs or license fees when using it.
For anything involving data, Python is an obvious choice thanks to its ecosystem of tools and libraries including the highly optimised numpy/sciphy libraries for scientific work making data processing fast. There is also the Scikit-Learn library which contains a huge array of off-the-shelf machine learning tools and interactive jupyter notebooks. All of these tools are free to use so there are no ongoing costs or license fees. We recommend the Anaconda distribution released by Continuum Analytics, and we use this distribution in-house.
Our experience with Python
Our sister company, Green Running, has used Python to develop their advanced home energy monitor, and all coding is done on a single-board computer. The functions include data acquisition, file logging, wireless communication and scientific data processing. We have also deployed it on Raspberry Pi for cost-effective data acquisition solutions.
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