I just read “Python Creator Guido van Rossum Asks: Is ‘Worse is Better’ Still True for Programming Languages?” and I wonder what can we Eiffellers learn from it.
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Codon: high-performance Python compiler
Codon: high-performance Python compiler
Codon is a high-performance Python implementation that compiles to native machine code without any runtime overhead. Typical speedups over vanilla Python are on the order of 10-100x or more, on a single thread. Codon’s performance is typically on par with (and sometimes better than) that of C/C++. Unlike Python, Codon supports native multithreading, which can lead to speedups many times higher still.
Think of Codon as Python reimagined for static, ahead-of-time compilation, built from the ground up with best possible performance in mind.
How to connect Python to WordPress
pyper – Concurrent Python made simple
Python Shared Memory in Multiprocessing
Python Shared Memory in Multiprocessing
np_array's size=220.0MB
With SharedMemory: ...
Current memory usage 0.11283MB; Peak: 0.156706MB
Time elapsed: 0.99s
No SharedMemory: ...
Current memory usage 0.026587MB; Peak: 467.558995MB
Time elapsed: 5.48s
I think I shall go for shared memory! My Amiga formation years requires it!
Huge chances?
I know I know the Observer Pattern. I just wanted to be sure what was the best practice in Python. I found this:
Risky Implementations: If the pattern is not implemented carefully, there are huge chances that you will end up with large complexity code.
From https://www.geeksforgeeks.org/observer-method-python-design-patterns/
With all the complex code out there having “risky implementations” the observer pattern is one of the mildest.
Did You Know — Adding = After an Expression in a Python f-string
Did You Know — Adding = After an Expression in a Python f-string | by Liu Zuo Lin
# eg. f’{x=}’ returns ‘x=’ n = 5 print(f'{n+1=}') # n+1=6n = 5 print(f'{n+10=}') # n+10=15
Textual
Textual is a TUI framework for Python, inspired by modern web development.
Textual is a Rapid Application Development framework for Python, built by Textualize.io.
Build sophisticated user interfaces with a simple Python API. Run your apps in the terminal or a web browser!
Well, I just wish I could have it in EIffel….
Using CSV File as data storage and access
Thanks adamchainz!
You can use “CSV” table storage in (at least) these database backends:
- SQLite: https://www.sqlite.org/csv.html 42
- MySQL/MariaDB via the “CSV” storage engine: https://dev.mysql.com/doc/refman/8.0/en/csv-storage-engine.html 15 . Or MariaDB via the CONNECT storage engine: https://mariadb.com/kb/en/connect/ 1
However these will be slow though. Changes to CSV based tables require a lot of parsing and re-saving.
It’s probably better to import the CSV into a table with an optimized storage engine, and export it again later. django-import-export can help with that: https://pypi.org/project/django-import-export/
Source: Using CSV File as data storage and access
Noob or “just” oldish?
Young Jedi Anmol Tomar, your pythoninc knowledge lacks historical viewpoint. List Comprehensions were the Python Enhancement Proposals( PEP) 202.
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