The point is that STDF data should be converted to a useable format like pandas ( numpy alone will not do as plenty of data is not numerical) WHILE the data is being generated, preferrably not post-factum and definitely not pre-usage! One can optimize that a bit by using Cython or maybe numba but that is besides the point.

you must still be living in the last century (not to say last millennium 🤪)Ī good parser is written in a higher level language (like Python) and it does an awefull lot of checking (and if needed correcting) and doesn't throw any information away, so as to return reliably with full, meaningfull and correct data! This of course makes it slower. In any case, when you start parsing STDF's at the moment you want to interact with the data, you are, as they say, too little too late. However in real life STDF files are far from perfect, meaning that fast parsers will FAIL to do their intended job! You might tweak them for one or another ATE in your environment, but it will not be a can-do-everything parser! Ok, a fast parser is first of all writen in probably C/ C++, and it has to dispence of a lot of the checking/correcting in order to become realy fast, and probably throwing away information not deemed interesting enough (and later turns out to be vital). If this is what you are after, keep on looking and by all means, hit the wall later on, and at that point you might consider to return! 🤣 Often people are searching for 'the fastest' STDF parser. This library is NOT intended to be the fastest in the world!

Convert Stdf To Csv