8 out of 10 IT Professionals are aware of the Open Source Programming languages and most of them would prefer to work with R or Python for obvious reasons. Python Data Science Course can help to prepare you for the next stage of AI, machine learning and Cloud Computing projects.
To give a push to the ongoing Python surge, we have some exciting news for all developers and Data Science pros. Python just announced their latest version. In its beta version, Python 3.8.0 is undergoing development and the release of 3.8.0b1 is intended to provide Open Source community with better features and faster bug fixes.
Why this version is important?
Well, as per Python’s latest documentation, we will see three more beta version developments. However, there would be no new features added to the Python module. Python Organization has called out the open source developers to come and work with Python Data Science courses to test with V3.8 and get it ready for productive phase in the coming weeks.
Key Features you should know from Python 3.8.0b1
You would be curious to know how this version is different from its predecessors! Isn’t it?
So, here is a quick snapshot of the Python 3.8.0.b1 (v3.8 highlights).
- Version 3.8 was created in January 2018, but released in beta only recently in June 2019.
- 8 will receive a dynamic bug fix update every 1-3 months for a year or more.
- Once the next version 3.9.0 final arrives, version 3.8.0 would also be released for bug fix.
- Security updates for Python 3.8.0 would continue to get security updates for the next 5 years, until October 2024.
Added Features: Features for Beta Python 3.8
Some of the notable features of Python 3.8 include:
- PEP 570, Positional-only arguments
- PEP 572, Assignment Expressions
- PEP 574, Pickle protocol 5 with out-of-band data
- PEP 578, Runtime audit hooks
- PEP 587, Python Initialization Configuration
- PEP 590, Vectorcall: a fast calling protocol for CPython
- Typing-related: PEP 591 (Final qualifier), PEP 586 (Literal types), and PEP 589 (TypedDict)
- Parallel filesystem cache for compiled bytecode
- Debug builds share ABI as release builds
- f-strings support a handy = specifier for debugging
- continue is now legal in finally: blocks
- on Windows, the default asyncio event loop is now ProactorEventLoop
- on macOS, the spawn start method is now used by default in multiprocessing
- multiprocessing can now use shared memory segments to avoid pickling costs between processes
- typed_ast is merged back to CPython
- LOAD_GLOBAL is now 40% faster
- pickle now uses Protocol 4 by default, improving performance
All these features are available in the Public Domain for a generic consumption and training purposes.
To report bugs, you can use this page to understand the current status with the type of behavior and title of the debugging process.
Here is a sample statistical representation of the Open Issues reported from Java and Python.