Now, with all that said, I am in fact a Python developer as well. Performance isn't entirely lost in that language, especially if you're running the Pypy interpreter (instead of the standard Python interpreter, which is much slower). There are many abstractions the language does well, including immutable objects, which make for serious performance advantages. The thing I like about Python is that you can come to understand what's going on under the hood, and make wise performance decisions as a result.
In my opinion, C, C++, and Python all have their places in responsible development, and I wouldn't regard any of them as only good for "toys". You just need to know when to use which, and how to get the most out of them.
Of course, horses for courses. I'm working on scientific, high performance computing both as a researcher and system administrator.
Python has improved a lot in ML, DL areas and very performant with underlying platform native libraries and PyPy, you're true on that regard. Also it's joyful to write Python, because it really does some tasks well with few lines and with great felxibility. It's a very productive language.
However, I generally work on very big matrices filled with double precision floating point numbers and perform operations on these, and C++ is much faster in these cases, at least in my domain.
My relationship with Python is much better now, but if I'm coding something serious I'd use Python for prototyping. If it's a small utility or something, I'd directly implement it in Python. However I'm very resource concious while developing applications, so I'm still not very comfortable to leave an interpreted language 7/24 as a service at the background.
Also, I'd like acknowledge that modern C++ isn't the easiest tool to work with. It's like a double edge blade with no handle. :D
P.S.: Yes, Calling Python a language for "programming toys" was a wrong and childish. Maybe I should be clearer on that part, sorry.
Edit: Writing comments fast while being tired is not a good idea. Corrected all the half sentences, polished the structure. IOW refactored the post completely.
Great clarification. I think we're very much on the same page regarding C++ and Python, and their respective uses. I've been the lead on a game engine project for a while, and we use C++ for that, not Python. We need the additional control.
However, GUI is almost always a lot friendlier in Python than in C++, so I tend towards that for standalone application development.
Incidentally, C++14 and C++17 have added a number of "handles" to that double-edged blade. For example, std::unique_ptr and std::shared_ptr handle a lot of memory safety (and deallocation) challenges quite deftly.
P.S. You'd probably find some stuff interesting in PawLIB, especially the development version (v1.1). It attempts to merge performance with some sweeter syntax.
Thanks, it seems so. GUI was never a strong part of C++ programming, but I intend to learn Qt both on Python and C++. I've some small projects in my mind. Also, I really want to be able to use ncurses on C++.
I have big C++11 code base I'm working on (sorry, can't give many details about it but it's a scientific code), and I may migrate to the new pointers, but I must do extensive testing on performance. Some parts of the code is very very sensitive and very hot, so fiddling with these parts affects just about everything.
Thanks for the pointer to the PawLIB. It really looks interesting, however it doesn't address any of my bottlenecks for now. If I find anything limiting, will give its data structures a try. I'm using Eigen for matrices and algebra, Catch for testing, and Easylogging++ for logging. They addressed what I need so far.
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Ha! Very true.
Now, with all that said, I am in fact a Python developer as well. Performance isn't entirely lost in that language, especially if you're running the Pypy interpreter (instead of the standard Python interpreter, which is much slower). There are many abstractions the language does well, including immutable objects, which make for serious performance advantages. The thing I like about Python is that you can come to understand what's going on under the hood, and make wise performance decisions as a result.
In my opinion, C, C++, and Python all have their places in responsible development, and I wouldn't regard any of them as only good for "toys". You just need to know when to use which, and how to get the most out of them.
Of course, horses for courses. I'm working on scientific, high performance computing both as a researcher and system administrator.
Python has improved a lot in ML, DL areas and very performant with underlying platform native libraries and PyPy, you're true on that regard. Also it's joyful to write Python, because it really does some tasks well with few lines and with great felxibility. It's a very productive language.
However, I generally work on very big matrices filled with double precision floating point numbers and perform operations on these, and C++ is much faster in these cases, at least in my domain.
My relationship with Python is much better now, but if I'm coding something serious I'd use Python for prototyping. If it's a small utility or something, I'd directly implement it in Python. However I'm very resource concious while developing applications, so I'm still not very comfortable to leave an interpreted language 7/24 as a service at the background.
Also, I'd like acknowledge that modern C++ isn't the easiest tool to work with. It's like a double edge blade with no handle. :D
P.S.: Yes, Calling Python a language for "programming toys" was a wrong and childish. Maybe I should be clearer on that part, sorry.
Edit: Writing comments fast while being tired is not a good idea. Corrected all the half sentences, polished the structure. IOW refactored the post completely.
Great clarification. I think we're very much on the same page regarding C++ and Python, and their respective uses. I've been the lead on a game engine project for a while, and we use C++ for that, not Python. We need the additional control.
However, GUI is almost always a lot friendlier in Python than in C++, so I tend towards that for standalone application development.
Incidentally, C++14 and C++17 have added a number of "handles" to that double-edged blade. For example,
std::unique_ptrandstd::shared_ptrhandle a lot of memory safety (and deallocation) challenges quite deftly.P.S. You'd probably find some stuff interesting in PawLIB, especially the development version (v1.1). It attempts to merge performance with some sweeter syntax.
Thanks, it seems so. GUI was never a strong part of C++ programming, but I intend to learn Qt both on Python and C++. I've some small projects in my mind. Also, I really want to be able to use ncurses on C++.
I have big C++11 code base I'm working on (sorry, can't give many details about it but it's a scientific code), and I may migrate to the new pointers, but I must do extensive testing on performance. Some parts of the code is very very sensitive and very hot, so fiddling with these parts affects just about everything.
Thanks for the pointer to the PawLIB. It really looks interesting, however it doesn't address any of my bottlenecks for now. If I find anything limiting, will give its data structures a try. I'm using Eigen for matrices and algebra, Catch for testing, and Easylogging++ for logging. They addressed what I need so far.