Advanced topics with python
- 1. Advanced Topics with Python
- 2. Topics Advanced Git Tips for Python Developers Pure Python vs NumPy vs TensorFlow Performance Comparison Socket Programming in Python Itertools in Python 3 Python Metaclasses What is the Python Global Interpreter Lock (GIL)? Shallow vs Deep Copying of Python Objects
- 3. Advanced Git Tips for Python Developers
- 4. If you’ve done a little work in Git and are starting to understand the basics we covered in Our Introduction to Git, but you want to learn to be more efficient and have more control, then this is the place for you! In this tutorial, we’ll talk about how to address specific commits and entire ranges of commits, using the stash to save temporary work, comparing different commits, changing history, and how to clean up the mess if something doesn’t work out. Python training in Chennai
- 5. Pure Python vs NumPy vs TensorFlow Performance Comparison
- 6. Python has a design philosophy that stresses allowing programmers to express concepts readably and in fewer lines of code. It is technically possible to implement scalar and matrix calculations using Python lists. However, this can be unwieldy, and performance is poor when compared to languages suited for numerical computation, such as MATLAB or Fortran, or even some general purpose languages, such as C or C++. Numpy provides support for large multidimensional arrays and matrices along with a collection of mathematical functions to operate on these elements. TensorFlow is an open-source library for numerical computation originally developed by researchers and engineers working at the Google Brain team. Python training in Bangalore
- 7. Socket Programming in Python
- 8. Sockets and the socket API are used to send messages across a network. They provide a form of Inter process Communication (IPC). This tutorial has three different iterations of building a socket server and client with Python: We’ll start the tutorial by looking at a simple socket server and client. Once you’ve seen the API and how things work in this initial example, we’ll look at an improved version that handles multiple connections simultaneously. Finally, we’ll progress to building an example server and client that functions like a full-fledged socket application, complete with its own custom header and content. Python training in Pune
- 9. Itertools in Python 3
- 10. It has been called a ”gem” and “Pretty much the coolest thing ever,” and if you have not heard of it, then you are missing out on one of the greatest corners of the Python 3 standard library: itertools. A handful of excellent resources exist for learning what functions are available in the itertools module. The docs themselves are a great place to start. So is this post. The thing about itertools, though, is that it is not enough to just know the definitions of the functions it contains. The real power lies in composing these functions to create fast, memory-efficient, and good-looking code. Python training institute in Chennai
- 12. The term metaprogramming refers to the potential for a program to have knowledge of or manipulate itself. Python supports a form of metaprogramming for classes called metaclasses. Metaclasses are an esoteric OOP concept, lurking behind virtually all Python code. You are using them whether you are aware of it or not. For the most part, you don’t need to be aware of it. Most Python programmers rarely, if ever, have to think about metaclasses. Python training institute in Bangalore
- 13. What is the Python Global Interpreter Lock (GIL)?
- 14. The Python Global Interpreter Lock or GIL, in simple words, is a mutex (or a lock) that allows only one thread to hold the control of the Python interpreter. This means that only one thread can be in a state of execution at any point in time. The impact of the GIL isn’t visible to developers who execute single-threaded programs, but it can be a performance bottleneck in CPU-bound and multi-threaded code. Python training institute in Pune
- 15. Shallow vs Deep Copying of Python Objects
- 16. Assignment Statement in python do not create copies of objects, they only bind names to an object. For immutable objects, that usually doesn’t make a difference. But for working with mutable objects or collections of mutable objects, you might be looking for a way to create “real copies” or “clones” of these objects. Essentially, you’ll sometimes want copies that you can modify without automatically modifying the original at the same time. In this article I’m going to give you the rundown on how to copy or “clone” objects in Python 3 and some of the caveats involved. Python training in USA