Python is everywhere, if you want advanced scripting features spend your time learning this! Python is a general-purpose, interpreted high-level programming language whose design philosophy emphasizes code readability. Like other dynamic languages, Python is often used as a scripting language. Python supports multiple programming paradigms, including object-oriented (like C++), imperative and functional programming styles. Using third-party tools, Python code can be packaged into standalone executable programs. Python interpreters are available for many operating systems.
While Python is a scripting language, it has plenty of facilities for high performance computing. This is why you will find python both in sections on scripting and also in sections about programming.
Recommended
Introduction to Python - Part 1
Novices (and people looking for an argument) often ask, "What's the best programming language?" The answer depends on what we want to do. If we want to squeeze the last ounce of performance out of our hardware, then compiled languages like C++, C#, and Fortran are still good options, but if we want to write small programs quickly, and be able to manage the complexity of larger ones, then dynamic languages like Python, Ruby, R, and MATLAB optimize development time, which is often the biggest bottleneck for researchers. Learning how to structure a program as reusable pieces, each of which is small enough to fit into a programmer's working memory, is the key to building large programs efficiently.
To view the material please click on the link in the box below:
Introduction to Python - Part 1 |
Credit: Software Carpentry
(to open their website click here)
Added to our repository: 2015-03-27 by Super User
Introduction to Python - Part 2
Novices (and people looking for an argument) often ask, "What's the best programming language?" The answer depends on what we want to do. If we want to squeeze the last ounce of performance out of our hardware, then compiled languages like C++, C#, and Fortran are still good options, but if we want to write small programs quickly, and be able to manage the complexity of larger ones, then dynamic languages like Python, Ruby, R, and MATLAB optimize development time, which is often the biggest bottleneck for researchers. Learning how to structure a program as reusable pieces, each of which is small enough to fit into a programmer's working memory, is the key to building large programs efficiently.
To view the material please click on the link in the box below:
Introduction to Python - Part 2 |
Credit: Software Carpentry
(to open their website click here)
Added to our repository: -0001-11-30 by Super User
Python - Unit Testing
It's pretty obvious that if we want to be sure our programs are right, we need to put in some effort. What isn't so obvious is that focusing on quality is also the best way—in fact, the only way—to improve productivity as well. Getting something wrong and then fixing it almost always takes longer than getting it right in the first place. Designing testable code, practicing defensive programming, writing and running tests, and thinking about what the right answer is supposed to be all help get us answers faster, as well as ones that are more likely to be correct.
To view the material please click on the link in the box below:
Python - Unit Testing |
Credit: Software Carpentry
(to open their website click here)
Added to our repository: -0001-11-30 by Super User
Python for High Performance
While Python is a scripting language, it has plenty of facilities for high performance computing.
To view the material please click on the link in the box below:
Python for High Performance |
Credit: Cornell Virtual Workshop
(to open their website click here)
Added to our repository: 2014-03-26 by Stelios
Python for HPC (2021 Lecture)
This lecture gives a description of Python for HPC.
To view the material please click on the link in the box below:
Python for HPC - Presentation |
Python for HPC - Notebook |
Python for HPC - Video 1 |
Python for HPC - Video 2 |
Python for HPC - Video 3 |
Python for HPC - Video 4 |
Credit: EuroCC - Cyprus National Competence Center
Captured
Introduction to Python - Part 1
Novices (and people looking for an argument) often ask, "What's the best programming language?" The answer depends on what we want to do. If we want to squeeze the last ounce of performance out of our hardware, then compiled languages like C++, C#, and Fortran are still good options, but if we want to write small programs quickly, and be able to manage the complexity of larger ones, then dynamic languages like Python, Ruby, R, and MATLAB optimize development time, which is often the biggest bottleneck for researchers. Learning how to structure a program as reusable pieces, each of which is small enough to fit into a programmer's working memory, is the key to building large programs efficiently.
To view the material please click on the link in the box below:
Introduction to Python - Part 1 |
Credit: Software Carpentry
(to open their website click here)
Added to our repository: 2015-03-27 by Super User
Introduction to Python - Part 2
Novices (and people looking for an argument) often ask, "What's the best programming language?" The answer depends on what we want to do. If we want to squeeze the last ounce of performance out of our hardware, then compiled languages like C++, C#, and Fortran are still good options, but if we want to write small programs quickly, and be able to manage the complexity of larger ones, then dynamic languages like Python, Ruby, R, and MATLAB optimize development time, which is often the biggest bottleneck for researchers. Learning how to structure a program as reusable pieces, each of which is small enough to fit into a programmer's working memory, is the key to building large programs efficiently.
To view the material please click on the link in the box below:
Introduction to Python - Part 2 |
Credit: Software Carpentry
(to open their website click here)
Added to our repository: -0001-11-30 by Super User
Python - Unit Testing
It's pretty obvious that if we want to be sure our programs are right, we need to put in some effort. What isn't so obvious is that focusing on quality is also the best way—in fact, the only way—to improve productivity as well. Getting something wrong and then fixing it almost always takes longer than getting it right in the first place. Designing testable code, practicing defensive programming, writing and running tests, and thinking about what the right answer is supposed to be all help get us answers faster, as well as ones that are more likely to be correct.
To view the material please click on the link in the box below:
Python - Unit Testing |
Credit: Software Carpentry
(to open their website click here)
Added to our repository: -0001-11-30 by Super User
Python for HPC (2021 Lecture)
This lecture gives a description of Python for HPC.
To view the material please click on the link in the box below:
Python for HPC - Presentation |
Python for HPC - Notebook |
Python for HPC - Video 1 |
Python for HPC - Video 2 |
Python for HPC - Video 3 |
Python for HPC - Video 4 |
Credit: EuroCC - Cyprus National Competence Center