Python is a widely used general-purpose, high-level programming language. Python supports multiple programming paradigms, including object-oriented, imperative and functional programming or procedural styles. It features a dynamic type system and automatic memory management and has a large and comprehensive standard library. The official repository of third-party software for Python contains more than 38,000 packages covering a wide range of functionality. Click here: https://www.besanttechnologies.com/training-courses/python-training-institute-in-pune

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.
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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.
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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.
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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.
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11. Python Metaclasses

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.
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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.
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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.
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17. Thank you
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