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Introducing Python in Excel: The Best of Both Worlds for Data Analysis and Visualization

the Microsoft-Information Service notes: Python is one of the most popular programming languages today, loved by businesses and students alike and Excel is an essential tool to organize, manipulate and analyze all kinds of data. But, until now, there hasn’t been an easy way to make those two worlds work together.
Today, we are excited to introduce the Public Preview of Python in Excel – making it possible to integrate Python and Excel analytics within the same Excel grid for uninterrupted workflow.
Python in Excel combines Python's powerful data analysis and visualization libraries with Excel's features you know and love. You can manipulate and explore data in Excel using Python plots and libraries, and then use Excel's formulas, charts and PivotTables to further refine your insights. .....see more in the MS-Introduction: https://techcommunity.microsoft.com/t5/ ... -p/3905482

MS continues:...
Work seamlessly: Now you can do advanced data analysis in the familiar Excel environment by accessing Python directly from the Excel ribbon. No set up or installation is required. Using Excel’s built-in connectors and Power Query, you can easily bring external data into Python in Excel workflows.
We’re partnering with Anaconda, a leading enterprise grade Python repository used by tens of millions of data practitioners worldwide. Python in Excel leverages Anaconda Distribution for Python running in Azure, which includes the most popular Python libraries such as pandas for data manipulation, statsmodels for advanced statistical modeling, and Matplotlib and seaborn for data visualization.

Collaborate easily
Share workbooks and Python analytics in your favorite tools like Microsoft Teams and Microsoft Outlook. Collaborate seamlessly with comments and @ mentions, and co-author with colleagues as you normally would in Excel. Teammates can refresh Python in Excel analytics for the most up-to-date information, even if they don't have Python in Excel activated.

more infos - background and links
Announcing Python in Excel: Next-Level Data Analysis for All
https://www.anaconda.com/blog/announcin ... is-for-all
Now you can write Python code directly in Microsoft Excel’s grid—no Python installation required.

Today, Anaconda and Microsoft announced a groundbreaking innovation: Python in Excel. This marks a transformation in how Excel users and Python practitioners approach their work. 

taken from Anaconda.com:

For Excel users, this opens a new world of data analysis potential previously limited to data scientists and developers. Within your familiar spreadsheet environment, you can now harness Python’s power to perform complex statistical analyses with popular packages such as pandas and statsmodels and create sophisticated visualizations using Matplotlib and Seaborn. Python practitioners can now marry scripts and rich visualizations with the widespread accessibility of Excel, enabling an uninterrupted workflow and making your work easier to share with colleagues who primarily use Excel. The integration of the Anaconda Distribution for Python in Microsoft Excel is more than a feature; it’s a redefinition of what’s possible within data analytics. It combines Python’s advanced capabilities with Excel’s familiar interface, bridging the gap between high-powered data science and everyday business tools. The future of data analytics has arrived, and it’s more integrated, more powerful, and more accessible than ever. In this post, we’ll share how Python in Excel works, what you can do with it, and how to get started.

A Guide to Excel Spreadsheets in Python With openpyxl https://realpython.com/openpyxl-excel-s ... ts-python/

Excel spreadsheets are one of those things you might have t deal with at some point. Either it’s because your boss loves them or because marketing needs them, you might have to learn how to work with spreadsheets, and that’s when knowing openpyxl comes in handy!
Spreadsheets are a very intuitive and user-friendly way to manipulate large datasets without any prior technical background. That’s why they’re still so commonly used today.
In this article, you’ll learn how to use openpyxl to: Manipulate Excel spreadsheets with confidence: Extract information from spreadsheets: Create simple or more complex spreadsheets, including adding styles, charts, and so on This article is written for intermediate developers who have a pretty good knowledge of Python data structures, such as dicts and lists, but also feel comfortable around OOP and more intermediate level topics.

Introducing Python in Excel Leila Gharani 2,58 Mio. subscribers https://www.youtube.com/watch?v=FbBXtqtRnWU

Automate Excel With Python - Python Excel Tutorial (OpenPyXL) https://www.youtube.com/watch?v=7YS6YDQ ... 9uIGV4Y2Vs

March 2024 (version 1.88) ::: https://code.visualstudio.com/updates/v1_88

Update 1.88.1: The update addresses these issues.

Downloads: Windows: x64 Arm64 | Mac: Universal Intel silicon | Linux: deb rpm tarball Arm snap

Welcome to the March 2024 release of Visual Studio Code. There are many updates in this version that we hope you'll like, some of the key highlights include:

Apply custom editor labels - Distinguish between editors with same file names.
Locked scrolling - Compare editors side-by-side with synchronized scrolling.
Extension update improvements - Restart extensions without reload & update extensions with VS Code releases.
Test Coverage API - Native code coverage support in VS Code.
Folding markers in minimap - Easily identify and navigate to code sections from minimap.
Quick Search improvements - Sticky file path separators and separator buttons.
Notebook Run cells in section - Quickly run all cells in a notebook section.
Copilot improvements - Improved inline chat UI, commit messages, and used references.
Python auto-detect improvements - Detect startup files for Flask & Django, discover Hatch environments.
Preview: Terminal inline chat - Start a Copilot inline chat conversation directly from the terminal.


If you'd like to read these release notes online, go to Updates on code.visualstudio.com. Insiders: Want to try new features as soon as possible? You can download the nightly Insiders build and try the latest updates as soon as they are available.

get more infos - get more insights - see more

https://code.visualstudio.com/updates/v1_88

https://code.visualstudio.Com

Python AI Choose Your Own Adventure Game - Tutorial :: Visual Studio Code Tutorial for Beginners - Introduction
https://www.youtube.com/watch?v=VqCgcpAypFQ

pip and conda - where to use each of these options?

ever wondered how where to use pip and where to use conda:

- pip is a package-manager for python packages.
- Conda on the other handside - it is a packaging tool and installer that aims to do more than what pip does; Yes - conda can do lots of other nice things too.

for example:
- it can also handle library dependencies outside of the Python packages as well as the Python packages themselves.
- it can create a virtual environment, like virtualenv does.

to sume up: Conda handles both Python and non-Python installation tasks. We also can have Conda packages for C libraries, or R packages, and many many more things - or simply: really anything.
long speech short sense: Conda is the package manager of Anaconda, but it can do lot more: it can be used outside of Anaconda too.

but the usage of Conda is interesting - but some developers use other tools: here some thoughts regarding the various tools:
well there are existing lots of tools that help us to manage virtual Python environments: Venv, pyenv, pipenv, poetry, and we have to mention here also Docker, and of course also virtualenv - and last but not least conda.

in the following we start with explanations on the different things: pip, PyPI, venv, pyenv, virtualenv, or any other....

let us start with pip:
pip: so what is pip. pip is just the Python Package Manager.
well, we might think of pip as the general python equivalent of the ruby gem command - it is pretty similar

- but wait: pip is not included with python by default.
we may install Python while using homebrew, which will install pip automatically: brew install python
besides this we can find and publish python packages using PyPI: The Python Package Index ( https://pypi.python.org/pypi )

pip: Python packages only: it compiles everything from source

from the docs:
Python Packaging User Guide ....[...]...the Python Packaging User Guide, a collection of tutorials and references to help you distribute and install Python packages with modern tools. This guide is maintained on GitHub by the Python Packaging Authority. We happily accept any contributions and feedback. ??
Get started
- Essential tools and concepts for working within the Python development ecosystem are covered in our Tutorials section:
- to learn how to install packages, see the tutorial on installing packages.
- to learn how to manage dependencies in a version controlled project, see the tutorial on managing application dependencies.
- to learn how to package and distribute our projects, see the tutorial on packaging and distributing
- to get an overview of packaging options for Python libraries and applications, see the Overview of Python Packaging.

The requirements.txt file is comparable to the ruby gemfile: To create a requirements text file, pip freeze > requirements.txt Note, at this point, we have python installed on our system, and we have created a requirements.txt file that outlines all of the python packages that have been installed on your system.

PyPI this packages is not in the standard library: virtualenv (see https://pypi.org/project/virtualenv/ ) is a very popular tool that creates isolated Python environments for Python libraries. It works by installing a bunch of files in a directory (eg: env/), and then modifying the PATH environment variable to prefix it with a custom bin directory (eg: env/bin/). see for more infos here: https://pypi.org/project/virtualenv/ and besides this cf: the documentation: https://virtualenv.pypa.io/en/latest/

cf: the documentation: https://virtualenv.pypa.io/en/latest/


here we want to shed a light on some of the most interesting Conda commands:

Well - if you ever installed Anaconda-distribution - you have some options:

- you can take the GUI installer,
- you can use the command-line-interface

Let’s look at common conda commands to create and manage conda environments.
Everything is the same whether you installed Anaconda or Miniconda.

imagine you want to create a conda environments:

Code: Select all

conda create -n my_env pandas [jupyterlab]

Create a new conda environment named myenv with the latest version of Python available on the main conda channel. and yes: if you whish to do it on Jupyterlab then you just have to add the following snippet) [ Install pandas and jupyterlab packages to the environment - just that easy]

you can do either:

Code: Select all

-n is short for --name

what it does: well in this case conda just asks you here for a concrete confirmation - in other words it asks for the confirmation with any of the above mentioned package changes that are in question. What do you have to do here: well you just have to do one thing: just hit an press y when youre asked whether you want to proceed this process.

can create a new conda environment, with program biopython with this:

Code: Select all

conda create --name munich-townhall

lets go ahead: What if you just do not want to install any program at all? in this case you can specify one or more default packages for the installation while you are just creating the environment. This is a great option doing so: in other words this allows you to call conda create without directly and explicitly providing any package name at all:


to to a setting of the provided packages, you just need to call conda config like so:

Code: Select all

    conda config --add create_me_the_default_packages_here: PACKAGE_NAME

and besides this you also can set (and choose) a package name of just "python" to get a base, empty install.

Code: Select all

conda create --name myenv python

# or just run this if you want to use some python version especially.

Code: Select all

conda create --name myenv python=3.7

here some more conda - commands that are used quite very very often:

conda info --envs : # this lists all environments
source activate <env name>: # this command activates an environment you want to use
conda env remove -n <env name> : # with this command you can delete an environment
conda env export > environment.yml:# export conda environment requirements list to a certain file
source deactivate: # and this command just deactivates an environment - try it out!
conda list : # with this command you just are able to list all packages installed on your system.
conda create --name <env name> python=3.9 : # with this you can create new environment, and yes: you of course can specify version of python;



see some great ressources for the usage of Conda: just have a closer look at the following pages:

https://gist.github.com/supriya-premkum ... cc79eef03b

https://github.com/conda/conda-build

https://gist.github.com/ctufts/fcd8bfaf ... 3bd4b52844

https://uoa-eresearch.github.io/eresear ... /20/conda/
Create virtual environments for python with conda :: How to set up a virtual environments using conda for the Anaconda Python distribution

marimo - open-source reactive notebook for Python (like Jupyter NB)

marimo is an open-source reactive notebook for Python — reproducible, git-friendly, executable as a script, and shareable as an app.

marimo guarantees your notebook code, outputs, and program state are consistent. This solves …
A reactive programming environment. Run a cell and marimo reacts by automatically running the cells that reference its variables, eliminating the error-prone task of manually re-running cells. Delete a cell and marimo scrubs its variables from program memory, eliminating hidden state. Deterministic execution order. Notebooks are executed in a deterministic order, based on variabl…
Synchronized UI elements. Interact with UI elements like sliders, dropdowns, and dataframe transformers, and the cells that use them are automatically re-run with their latest value

marimo is a reactive notebook for Python. It allows you to rapidly experiment with data and models, code with confidence in your notebook's correctness, and productionize notebooks as pipelines or interactive web apps.

Highlights
reactive: run a cell, and marimo automatically updates all affected cells and outputs
interactive: bind sliders, tables, plots, and more to Python — no callbacks required
reproducible: no hidden state, deterministic execution order
executable: execute as a Python script
shareable: deploy as an app
git-friendly: stored as .py files

get more - see more background - ideas and much more: more Marimo and more notebooks ...
https://twitter.com/marimo_io
https://github.com/marimo-team/marimo
88 forks
18 contributors


Hier geben wir einen kleinen Überblick über einige DataScienceNotebooks, die derzeit auf dem Markt sind.
Dies ist eine relativ unsortierte Liste - und alle Texte stammen von den entsprechenden Webseiten - siehe Links unten - oder von Wikipedia (z. B. für das Jupyter-Projekt).

Frage vorweg: setzt ihr hier eines ein? ... ich verwende z.B. GoogleColab seit Jahren - und JupyterNB - auch in VSCode...

hier ein kl. Überblick auf die diversen Projekte - und eine Mini-Einführung in das Original - das Jupyter-Projekt von Franzisco ...

Google Colab: Google Colab ist eine cloudbasierte Jupyter-Notebook-Umgebung, die es Benutzern ermöglicht, Python-Code direkt im Browser zu schreiben und auszuführen. Es bietet kostenlosen Zugriff auf GPU und TPU zur Beschleunigung maschineller Lernaufgaben. Sie können auf Google Colab zugreifen
Link: https://colab.research.google.com

Kaggle Notebooks: Kaggle Notebooks ist eine weitere cloudbasierte Jupyter-Notebook-Umgebung, bereitgestellt von Kaggle, einer Plattform für Data-Science-Wettbewerbe und Datensätze. Es bietet außerdem kostenlosen Zugriff auf GPU und TPU. Kaggle-Notizbücher sind eng in Kaggle-Datensätze und -Wettbewerbe integriert, was die Zusammenarbeit und den Austausch erleichtert Code mit anderen. Sie können hier auf Kaggle Notebooks zugreifen: Link: https://www.kaggle.com/docs/notebooks
und das große Jupyter-Projekt – das einer der Motoren hinter allen anderen ist …

Projekt Jupyter: https://en.wikipedia.org/wiki/Project_Jupyter
Der Text stammt aus Wikipedia, der freien Enzyklopädie

Projekt Jupyter:[/b] ist ein Projekt zur Entwicklung von Open-Source-Software, offenen Standards und Diensten für interaktives Computing in mehreren Programmiersprachen. Es wurde 2014 von Fernando Pérez und Brian Granger aus IPython abgespalten. Der Name von Project Jupyter bezieht sich auf die drei Kernprogrammiersprachen, die von unterstützt werden Jupyter, das sind Julia, Python und R. Sein Name und sein Logo sind eine Hommage an Galileis Entdeckung der Jupitermonde, wie in Galileis zugeschriebenen Notizbüchern dokumentiert. Project Jupyter hat die interaktiven Computerprodukte Jupyter Notebook, JupyterHub und JupyterLab entwickelt und unterstützt.
Jupyter wird finanziell von NumFOCUS gesponsert. Die erste Version von Notebooks für IPython wurde 2011 von einem Team veröffentlicht, zu dem Fernando Pérez, Brian Granger und Min Ragan-Kelley gehörten. Im Jahr 2014 kündigte Pérez ein Spin-off-Projekt von IPython namens Project Jupyter an. IPython existiert weiterhin als Python-Shell und Kernel für Jupyter. während das Notebook und andere sprachunabhängige Teile von IPython unter dem Namen Jupyter firmierten. Jupyter unterstützt Ausführungsumgebungen (genannt „Kernel“) in mehreren Dutzend Sprachen, darunter Julia, R, Haskell, Ruby und Python (über den IPython-Kernel). Im Jahr 2015 waren rund 200.000 Jupyter-Notebooks auf GitHub verfügbar. Bis 2018 waren etwa 2,5 Millionen verfügbar. Im Januar 2021, Fast 10 Millionen waren verfügbar, darunter Notizbücher über die erste Beobachtung von Gravitationswellen und über die Entdeckung eines supermassereichen Schwarzen Lochs im Jahr 2019. Große Cloud-Computing-Anbieter haben das Jupyter Notebook oder abgeleitete Tools als Frontend-Schnittstelle für Cloud-Benutzer übernommen. Beispiele hierfür sind Amazon SageMaker Notebooks, Googles Colaboratory und Microsofts Azure Notebook.

Jupyter Notebook: Jupyter Notebook kann sich umgangssprachlich auf zwei verschiedene Konzepte beziehen: entweder die benutzerorientierte Anwendung zum Bearbeiten von Code und Text, oder das zugrunde liegende Dateiformat, das über viele Implementierungen hinweg interoperabel ist.

Mehr zur Jupyter Notebook-Schnittstelle und den Anwendungen:
Jupyter Notebook (ehemals IPython Notebook) ist eine webbasierte interaktive Computerumgebung zum Erstellen von Notebook-Dokumenten. Jupyter Notebook besteht aus mehreren Open-Source-Bibliotheken, einschließlich IPython, ZeroMQ, Tornado, jQuery, Bootstrap und MathJax. Eine Jupyter Notebook-Anwendung ist eine browserbasierte REPL, die eine geordnete Datei enthält Liste der Eingabe-/Ausgabezellen, die Code, Text (mit Github Flavored Markdown), Mathematik, Diagramme und Rich Media enthalten können. Jupyter Notebook ähnelt der Notebook-Schnittstelle anderer Programme wie Maple, Mathematica und SageMath, einem Computerschnittstellenstil entstand in den 1980er Jahren mit Mathematica. Das Interesse an Jupyter übertraf Anfang 2018 die Popularität der Mathematica-Notebook-Schnittstelle.


JupyterLab ist eine neuere Benutzeroberfläche für Project Jupyter und bietet eine flexible Benutzeroberfläche und mehr Funktionen als die klassische Notebook-Benutzeroberfläche. Die erste stabile Version wurde am 20. Februar 2018 angekündigt. Im Jahr 2015 ein gemeinsamer Zuschuss von 6 Millionen US-Dollar von The Leona M. und Harry Der B. Helmsley Charitable Trust, die Gordon and Betty Moore Foundation und die Alfred P. Sloan Foundation finanzierten Arbeiten, die zu erweiterten Möglichkeiten führten die Kernwerkzeuge von Jupyter sowie die Erstellung von JupyterLab. GitHub gab im November 2022 bekannt, dass JupyterLab auf seiner Online-Coding-Plattform namens Codespace verfügbar sein wird. Im August 2023 wurde Jupyter AI, eine Jupyter-Erweiterung, veröffentlicht. Diese Erweiterung integriert generative künstliche Intelligenz in Jupyter-Notebooks. Dadurch können Benutzer Code erklären und generieren, Fehler korrigieren, Inhalte zusammenfassen, sich nach ihren lokalen Dateien erkundigen
und vollständige Notizbücher basierend auf Eingabeaufforderungen in natürlicher Sprache erstellen.

JupyterHub ist ein Mehrbenutzerserver für Jupyter Notebooks. Es wurde entwickelt, um viele Benutzer zu unterstützen, indem es viele einzelne Jupyter Notebook-Server erzeugt, verwaltet und als Proxy leitet.
JupyterLab: JupyterLab ist eine webbasierte interaktive Entwicklungsumgebung für Jupyter-Notebooks, Code und Daten. Es bietet eine flexiblere und leistungsfähigere Schnittstelle im Vergleich zum klassischen Jupyter Notebook. JupyterLab unterstützt mehrere Sprachen, Erweiterungen und interaktive Widgets. Sie können JupyterLab lokal auf deinem Computer installieren und ausführen.
JupyterLab: Eine Notebook-Schnittstelle der nächsten Generation: JupyterLab ist die neueste webbasierte interaktive Entwicklungsumgebung für Notebooks, Code und Daten. Seine flexible Schnittstelle ermöglicht es Benutzern, Arbeitsabläufe in den Bereichen Datenwissenschaft, wissenschaftliches Rechnen, Computerjournalismus und maschinelles Lernen zu konfigurieren und zu organisieren. Ein modulares Design lädt zu Erweiterungen ein, um die Funktionalität zu erweitern und zu bereichern.
Link> https://jupyter.org

Zeppelin: Apache Zeppelin ist ein webbasiertes Notebook, das interaktive Datenanalysen ermöglicht. Es unterstützt mehrere Programmiersprachen wie Scala, Python, SQL und mehr. Zeppelin bietet integrierte Visualisierungen und Integration mit verschiedenen Datenquellen wie Apache Spark, JDBC und REST-APIs. Mehr über Apache Zeppelin erfaerst du hier. https://zeppelin.apache.org

Deepnote Notebooks: eine bessere Möglichkeit für Teams, mit Daten zu arbeiten: Kombiniere Python, R, SQL und No-Code: Abfragen von Snowflake, BigQuery, CSV und über 50 anderen Datenquellen Erkunden die Daten mit KI-Codevorschlägen, unterstützt von GPT-4. Wandeln die Erkenntnisse sofort in Dashboards um.
Link: https://deepnote.com/

Databricks Notebooks: Arbeite zwischen Teams aus den Bereichen Technik, Datenwissenschaft und maschinelles Lernen zusammen und unterstütze dabei mehrere Sprachen, integrierte Datenvisualisierungen, automatische Versionierung und Operationalisierung mit Jobs. Databricks Notebooks vereinfachen die Erstellung von Daten und KI-Projekten durch eine vollständig verwaltete und hochautomatisierte Entwicklererfahrung. Notebooks arbeiten nativ mit der Databricks Lakehouse-Plattform zusammen, um Datenpraktikern einen schnellen Einstieg zu ermöglichen, mit kontextbewussten Tools zu entwickeln und Ergebnisse einfach zu teilen. Link: https://www.databricks.com/product/coll ... -notebooks
Dies sind nur einige Beispiele beliebter Data-Science-Notebooks, jedes mit seinen eigenen Funktionen und Stärken. Je nach Ihren spezifischen Bedürfnissen und Vorlieben könnt Ihr ja diejenige auswählen, die am besten zu Eurem Arbeitsablauf passt.


Noteable ein weiteres Kollaboratives Notebook - btw. besser gesagt eine ganze Plattform für Teams um Daten zu Visualisieren und zusammmenzuarbeiten.
Features: Noteable ist auch Jupyter-Kmpatibel,
Programmiersprachen: Jupyter-Sprachen (z.B. etwa. Python, R) SQL
Connectivity: Jupyter libraries ( sehr viele Libraries: darunter etwa solche wie z.B. SQLAlchemy, psycopg2)
Datenbanken (PostgreSQL usw. usf)

Visual Studio Code ein sehr leichtgewichtiger, aber sehr sehr leistungsfähiger Source Code Editor.
Er unterstützt Jupyter Notebooks ganz grundsätzlich - nativ und auch über Pythoncode
Features:
VSCode ist Jupyter-kompatibel
Programmiersprachen: Jupytersprachen wie Python, R etc.
Connectivity: Jupyter libraries



Frage: setzt ihr hier eines ein ... ich verwende z.B. GoogleColab ....JupyterNB in VSCode ... usw. usf.

PS. Sorry fuer den etwas rohen Text. Wird naechstens noch besser .....

Leap 15.6 Reaches Beta Phase :: 7. Mar 2024 | Douglas DeMaio | CC-BY-SA-3.0
openSUSE project has unveiled a beta snapshot of the project's upcoming openSUSE 15.6 "Leap" release.

The openSUSE Project is thrilled to announce the Beta release phase of Leap 15.6.

https://news.opensuse.org/2024/03/07/le ... eta-phase/

Feel free to download Leap 15.6 Beta images from get.opensuse.org and test it out, or upgrade from your existing Leap 15.5 system by running zypper --releasever=15.6 dup. You might want to get familiar with known issues in Leap 15.6.
Show your support by dropping in today at our Thursday Weekly Meeting at 20:00 UTC and participate in the live Leap 15.6 Beta testing event aka “Bug Day”. The event will be live streamed to the openSUSE channel on youtube.
“Let’s make sure that Leap 15.6 runs well on your hardware, and that we can keep it that way for the next 18 months,” said Lubos Kocman, openSUSE Leap release manager. “We cannot address hardware issues, feature requests and other issues without knowledge of these problems. Our openQA is limited. Testing different hardware and reporting these issues are a big help.”

Built on top of SUSE Linux Enterprise 15 Service Pack 6, the Beta, which has full compatibility with the enterprise Linux release will focus on stability and offer an option for those seeking to migrate to an enterprise distribution.
One core aspect of Leap 15.6 is the Linux Kernel 6.4 version, which will have extensive backport updates and the release is expected to gain fresher software and hardware support.
Along with the updated Kernel version, glibc 2.38, systemd 254 and firmware updates with dracut 059+ version are expected to enhance processing power and faster boot times.
The container stack was refresh as podman 4.8 version provides more support. Nextcloud out of box can be easily run in an optimal way with quadlets. The newest versions of distrobox, docker, python-podman and skopeo are available for container use.
The virtualization stack also gains newer versions with Xen 4.18, KVM 8.1.3, libvirt 1.0 and virt-manager 4.1.
Updates software packages related to telecommunications received updates and Leap 15.6 is expected to have DPDK 22.1 and versions 3 and 4 of Open vSwitch will be available.
The Beta introduces substantial updates across the board, starting with the KDE environment. Qt 5 receives an uplift to 5.15.12+kde147 and has security enhancements from KDE developers beyond the standard release. This update brings a move to KDE Frameworks 5.114.0 and marks a leap from the previous 5.90.0 version. Alongside this, Qt6 moves up to version 6.6.1 and ensures that the latest applications can run smoothly with the new libraries. Python bindings for both PyQt5 and PyQt6 are updated and aligns well with the Python 3.11 stack.
GNOME users will be delighted with the GNOME 45 update, which will enhance the user experience with new features and refinements. The desktop environment continues to evolve, providing a sleeker and more intuitive interface.
Audio handling receives a dual upgrade as PulseAudio is updated to version 17.0 and features improved hardware and Bluetooth support, which includes device battery level reporting. Meanwhile, PipeWire steps up to version 1.0.3 and expands its capabilities with new features and enhances compatibile with Pulseaudio and JACK.

more infos, datas and downloads

news: https://news.opensuse.org/2024/03/07/le ... eta-phase/
community: https://news.opensuse.org

No. Mysqlclient is a python api, Xampp does not come with Python. And Xampp does not come with MySql, but with MariaDB. I dont know if mysqlclient runs with MariaDB.

I think you should not install Xampp, which does not come from the Ubuntu repository. Instead use Apache, Php, Mysql, Python etc. from the Ubuntu repository.

P.S.: Oops, i mixed it up with another request. You are Windows user, i have no clue about Python or mysqlclient in Windows environment.

...here we provide a little overview on some DataScienceNotebooks that are currently in the market.

note: this is a unsorted list - and all the texts are taken form the according webpages - see links below - or from Wikipedia (eg. for the Jupyter-project ) -

question: wich one do you use!? What features and setup do you need!?

we start with the following:

Google Colab: Google Colab is a cloud-based Jupyter notebook environment that allows users to write and execute Python code directly in the browser. It provides free access to GPU and TPU for accelerating machine learning tasks. You can access Google Colab Link: https://colab.research.google.com

Kaggle Notebooks: Kaggle Notebooks is another cloud-based Jupyter notebook environment, provided by Kaggle, a platform for data science competitions and datasets. It offers free access to GPU and TPU as well. Kaggle Notebooks are tightly integrated with Kaggle Datasets and Competitions, making it easy to collaborate and share code with others. You can access Kaggle Notebooks here: Link: https://www.kaggle.com/docs/notebooks

and the big big Jupyter Project - which is one of the engine behind all the others...

Project Jupyter: https://en.wikipedia.org/wiki/Project_Jupyter: the text is taken from Wikipedia, the free encyclopedia

Project Jupyter: is a project to develop open-source software, open standards, and services for interactive computing across multiple programming languages. It was spun off from IPython in 2014 by Fernando Pérez and Brian Granger. Project Jupyter's name is a reference to the three core programming languages supported by Jupyter, which are Julia, Python and R. Its name and logo are an homage to Galileo's discovery of the moons of Jupiter, as documented in notebooks attributed to Galileo. Project Jupyter has developed and supported the interactive computing products Jupyter Notebook, JupyterHub, and JupyterLab.

Jupyter is financially sponsored by NumFOCUS. The first version of Notebooks for IPython was released in 2011 by a team including Fernando Pérez, Brian Granger, and Min Ragan-Kelley. In 2014, Pérez announced a spin-off project from IPython called Project Jupyter. IPython continues to exist as a Python shell and a kernel for Jupyter, while the notebook and other language-agnostic parts of IPython moved under the Jupyter name. Jupyter supports execution environments (called "kernels") in several dozen languages, including Julia, R, Haskell, Ruby, and Python (via the IPython kernel). In 2015, about 200,000 Jupyter notebooks were available on GitHub. By 2018, about 2.5 million were available. In January 2021, nearly 10 million were available, including notebooks about the first observation of gravitational waves and about the 2019 discovery of a supermassive black hole. Major cloud computing providers have adopted the Jupyter Notebook or derivative tools as a frontend interface for cloud users. Examples include Amazon SageMaker Notebooks, Google's Colaboratory, and Microsoft's Azure Notebook.

Jupyter Notebook: Jupyter Notebook can colloquially refer to two different concepts, either the user-facing application to edit code and text, or the underlying file format which is interoperable across many implementations.

more on Jupyter Notebook interface and the Applications: Jupyter Notebook (formerly IPython Notebook) is a web-based interactive computational environment for creating notebook documents. Jupyter Notebook is built using several open-source libraries, including IPython, ZeroMQ, Tornado, jQuery, Bootstrap, and MathJax. A Jupyter Notebook application is a browser-based REPL containing an ordered list of input/output cells which can contain code, text (using Github Flavored Markdown), mathematics, plots and rich media. Jupyter Notebook is similar to the notebook interface of other programs such as Maple, Mathematica, and SageMath, a computational interface style that originated with Mathematica in the 1980s. Jupyter interest overtook the popularity of the Mathematica notebook interface in early 2018.

JupyterLab is a newer user interface for Project Jupyter, offering a flexible user interface and more features than the classic notebook UI. The first stable release was announced on February 20, 2018. In 2015, a joint $6 million grant from The Leona M. and Harry B. Helmsley Charitable Trust, The Gordon and Betty Moore Foundation, and The Alfred P. Sloan Foundation funded work that led to expanded capabilities of the core Jupyter tools, as well as to the creation of JupyterLab. GitHub announced in November 2022 that JupyterLab would be available in its online Coding platform called Codespace. In August 2023, Jupyter AI, a Jupyter extension, was released. This extension incorporates generative artificial intelligence into Jupyter notebooks, enabling users to explain and generate code, rectify errors, summarize content, inquire about their local files, and generate complete notebooks based on natural language prompts.

JupyterHub is a multi-user server for Jupyter Notebooks. It is designed to support many users by spawning, managing, and proxying many singular Jupyter Notebook servers.

JupyterLab: JupyterLab is a web-based interactive development environment for Jupyter notebooks, code, and data. It provides a more flexible and powerful interface compared to the classic Jupyter Notebook. JupyterLab supports multiple languages, extensions, and interactive widgets. You can install and run JupyterLab locally on your machine. More information on JupyterLab can be found here.
JupyterLab: A Next-Generation Notebook Interface: JupyterLab is the latest web-based interactive development environment for notebooks, code, and data. Its flexible interface allows users to configure and arrange workflows in data science, scientific computing, computational journalism, and machine learning. A modular design invites extensions to expand and enrich functionality.
Link: https://jupyter.org

Zeppelin: Apache Zeppelin is a web-based notebook that enables interactive data analytics. It supports multiple programming languages like Scala, Python, SQL, and more. Zeppelin provides built-in visualizations and integration with various data sources like Apache Spark, JDBC, and REST APIs. You can learn more about Apache Zeppelin here. - Link: https://zeppelin.apache.org

Deepnote: Notebooks: a better way for teams to work with data: Combine Python, R, SQL, and no-code: Query Snowflake, BigQuery, CSV, and 50+ other data sources
Explore data with AI code suggestions, powered by GPT-4 Instantly turn insights into dashboards. Link: https://deepnote.com/

Databricks Notebooks: Collaborate across engineering, data science, and machine learning teams with support for multiple languages, built-in data visualizations, automatic versioning, and operationalization with jobs. Databricks Notebooks simplify building data and AI projects through a fully managed and highly automated developer experience. Notebooks work natively with the Databricks Lakehouse Platform to help data practitioners start quickly, develop with context-aware tools and easily share results. Link: https://www.databricks.com/product/coll ... -notebooks

conclusio: These are just a few examples of popular data science notebooks, each with its own features and strengths. Depending on your specific needs and preferences, you can choose the one that best suits your workflow.


question: wich one do you use!? What features and setup do you need!?

Nowadays, I am developing a game using Python and want to integrate some AI-generated characters into this game. I have seen many tools online that can create AI Characters. I also came across this post (https://getalter.ai/) where the author talks about the best AI Headshot Generators, and I am looking for similar kinds of tools. My concern is which AI tools are used to create headshots, especially in digital media and design. Can you explain how these tools work and what features they have that make headshots look realistic and visually appealing?
Also, are there any specific examples or cases where these AI tools have significantly impacted the quality or efficiency of generating headshots in different applications?
Thanks

P.P.S.: in June 2021 you already asked quite the same question and become quite the same answer. What did you do in the meantime and why do you ask again??

Hm... did I? Sorry? I remember I had some problems with PHP in the past, but I forgot what it was about. Sorry for the trouble

In the meantime I focused more on Python than on PHP, but now I wanna expand my repertoire, so to speak?

Thank you for the help

circuitpython: the easiest way to program microcontrollers: CircuitPython is a programming language designed to simplify experimenting and learning to code on low-cost microcontroller boards.

some explanations - taken from here: https://docs.circuitpython.org/en/8.2.x/README.html
The code is also highly portable to other microcontrollers. The disadvantage to an interpreted code is speed. Converting code to machine code happens on the fly so it takes time.
CircuitPython is a beginner friendly, open source version of Python for tiny, inexpensive computers called microcontrollers. Microcontrollers are the brains of many electronics including a wide variety of development boards used to build hobby projects and prototypes. CircuitPython in electronics is one of the best ways to learn to code because it connects code to reality. Simply install CircuitPython on a supported USB board usually via drag and drop and then edit a code.py file on the CIRCUITPY drive. The code will automatically reload. No software installs are needed besides a text editor (we recommend Mu for beginners.)

Starting with CircuitPython 7.0.0, some boards may only be connectable over Bluetooth Low Energy (BLE). Those boards provide serial and file access over BLE instead of USB using open protocols. (Some boards may use both USB and BLE.) BLE access can be done from a variety of apps including code.circuitpython.org.

CircuitPython features unified Python core APIs and a growing list of 300+ device libraries and drivers that work with it. These libraries also work on single board computers with regular Python via the Adafruit Blinka Library.
CircuitPython is based on MicroPython. See below for differences. Most, but not all, CircuitPython development is sponsored by Adafruit and is available on their educational development boards. Please support both MicroPython and Adafruit.


Differences from MicroPython
CircuitPython:
Supports native USB on most boards and BLE otherwise, allowing file editing without special tools.
Floats (aka decimals) are enabled for all builds.
Error messages are translated into 10+ languages.

Concurrency within Python is not well supported. Interrupts and threading are disabled. async/await keywords are available on some boards for cooperative multitasking. Some concurrency is achieved with native modules for tasks that require it such as audio file playback.

CircuitPython program development is often a fraction of the time needed for an Arduino program.


Behavior:
The order that files are run and the state that is shared between them. CircuitPython’s goal is to clarify the role of each file and make each file independent from each other.
boot.py runs only once on start up before workflows are initialized. This lays the ground work for configuring USB at startup rather than it being fixed. Since serial is not available, output is written to boot_out.txt.


more infos & backgrounds

https://circuitpython.org

read more - get more https://docs.circuitpython.org/en/8.2.x/README.html


micropython: https://micropython.org

MicroPython is a lean and efficient implementation of the Python 3 programming language that includes a small subset of the Python standard library and is optimised to run on microcontrollers and in constrained environments. The MicroPython pyboard is a compact electronic circuit board that runs MicroPython on the bare metal, giving you a low-level Python operating system that can be used to control all kinds of electronic projects. MicroPython is packed full of advanced features such as an interactive prompt, arbitrary precision integers, closures, list comprehension, generators, exception handling and more. Yet it is compact enough to fit and run within just 256k of code space and 16k of RAM. MicroPython aims to be as compatible with normal Python as possible to allow you to transfer code with ease from the desktop to a microcontroller or embedded system.

wikipedia: https://de.wikipedia.org/wiki/MicroPython

From time to time we want to add some introduction into awesome techniques - and tools

today google-colab and python

Google Colab (short for Colaboratory) is a free, cloud-based platform provided by Google that allows you to write and execute Python code in a web-based interactive environment. It's particularly popular among data scientists and machine learning practitioners as it provides access to powerful computing resources, including GPUs (Graphics Processing Units), for running code and training models.

Here's a brief introduction to Google Colab and Python integration:

Google Colab Features:
Free Access to GPU and TPU: Google Colab provides free access to Graphics Processing Units (GPUs) and Tensor Processing Units (TPUs), which can significantly speed up computations, especially for machine learning tasks.

Interactive Environment: Colab provides an interactive environment where you can write and execute code in a notebook-style format. Notebooks are a series of cells, each of which can contain code, text, or visualizations.

Easy Sharing and Collaboration: Colab makes it easy to share your work with others. Notebooks can be shared just like Google Docs or Sheets, and multiple users can collaborate in real-time.

Integration with Google Drive: Colab is integrated with Google Drive. You can save your Colab notebooks directly to your Google Drive, making it easy to organize and share your work.

Support for Popular Libraries: Colab comes with many pre-installed libraries and supports popular ones like TensorFlow, PyTorch, Keras, OpenCV, and more. You can install additional libraries as needed.

Getting Started:

Accessing Google Colab:

Open your web browser and go to Google Colab.
Sign in with your Google account.


Creating a New Notebook:

Click on "File" > "New notebook" to create a new Colab notebook.
You can give your notebook a name by clicking on "Untitled" at the top and entering a name.

Working with Cells:

Colab notebooks consist of cells that can contain code or text. You can add a new cell by clicking on the "+" button.
To run a cell, press Shift + Enter.

Writing and Running Python Code
:
In code cells, you can write and execute Python code. For example:
python
Copy code
print("Hello, Colab!")
Using GPU/TPU:

To use a GPU, go to "Runtime" > "Change runtime type" and select "GPU" from the hardware accelerator dropdown.
For TPU, select "TPU."

Saving and Sharing:
Save your work to Google Drive or download the notebook.

Share your notebook... by clicking on the "Share" button in the top right corner.

Installing Additional Libraries:


Use the following command to install libraries not pre-installed:

Code: Select all

!pip install library_name


Example Python Code in Colab:
Here's a simple example to get you started:

Code: Select all

# This is a code cell
print("Hello, Colab!")

# Mathematical operations
a = 5
b = 10
result = a + b
print("Sum:", result)


This is just a basic overview to help you get started with Google Colab and Python integration. As you explore more, you'll find additional features and capabilities
that make it a powerful tool for various tasks, including data analysis, machine learning, and more.

Conclusion
Google Colab is an outstanding and awesome tool for anyone looking to dive into learning Python and data sciende without the hassle of setting up a local environment,
especially given the free access to a GPU.
i love Google colab because you can test lots of things: as for the main features - we can say - it's user-friendly, cloud-based, and loaded with tons of awesome features that make it a go-to for all that are

a. new to python
b. new ot Jupyter Notebook
c. new to data science and machine learning

so we can say finally: Google colab is awesome for all those people.
Anywayy: Whether you are a beginner or -just a more experienced practitioner, google colab supports a hassle free starting point.
Google Colab has many awesoome thuings to offer you.

Grad steht der Winter vor der Tür - Zeit für neue Projekte: Die Programmiersprache Python ist ziemlich beliebt - und relativ leicht zu lernen. Die Einsatzszenarien von MicroPython - sie sind vielfältig: Seit fast zehn Jahren gibt es Python als MicroPython auch für eine ganze Reihei an Mikrocontrollern. Damit eröffnet sich ein weites Anwendungsfeld - das stetig wächst und immmer neuue Mögllichkeiten bietet.

MicroPython wird auf immer mehr Umgebungen portiert: Diese Implementierung erleichtert den Einstieg in die Anwwendungsbereiche - wie z.B. das Arbeiten mit sogenannten Python-Boards: Damit kann man experimentelle und auch raktische Projekte umsetzen. Es gibt eine ganze Reihe von Entwicklerboards - mit denen man hier arbeiten kanN - Zum einen ist hier das offizielle Board zu nennen. Darübrer hinaus sind viele weitere Boards hinzugegkommen - viele weitere kompatible Mikrocontroller-Boards eben.

MicroPython unterstütz etwa den ESP8266 und diverse ESP32-Varianten von Espressif und viele mehr - so auch
viele die z.B. auf Boards von Adafruit, Arduino, dem Raspberry Pi Pico bzw. Pico W (mit WLAN) sich befinden.

Das Tolle an Micropython: MicroPython ist ein kleiner Open-Source-Interpreter für die Programmiersprache Python, der auf kleinen eingebetteten Entwicklungsboards läuft. Mit MicroPython kann man auberen und einfachen Python-Code zur Steuerung von Hardware schreiben, anstatt komplexe Low-Level-Sprachen wie C oder C++ (die Arduino zum Programmieren verwendet) verwenden zu müssen. Die Einfachheit der Programmiersprache Python macht MicroPython zu einer ziemlich hervorragenden Wahl für Anfänger, die noch nicht so viel Erfahrung mit Programmierung und Hardware haben. Daneben und darüber hinaus verfügt Microython auch ber einen recht umfassenden Funktionsumfang und unterstützt den Großteil der Python-Syntax, sodass selbst erfahrene Python-Veteranen MicroPython einigermaßen vertraut und unterhaltsam finden werden.

hier einige Boards für MicroPython: https://www.adafruit.com/category/924

mehr Infos, Hintergründe u. Links:

Micropython auf Wikipedia: https://en.wikipedia.org/wiki/MicroPython
MicroPython is a software implementation of a programming language largely compatible with Python 3, written in C, that is optimized to run on a microcontroller. MicroPython consists of a Python compiler to bytecode and a runtime interprete of that bytecode. The user is presented with an interactive prompt (the REPL) to execute supported commands immediately. Included are a selection of core Python libraries; MicroPython includes modules which give the programmer access to low-level hardware. MicroPython does have an inline assembler, which lets the code run at full speed, but it is not portable across different microcontrollers. The source code for the project is available on GitHub under the MIT License.

ein Link zur Micropython-Projektseite: https://micropython.org

an update, I seem to be able to run bash and python2.7 scripts but cannot still run python 3.10 scripts. any help is appreciated.

Hello.
I have been trying to run .py scripts but to no awail
I have successfully ran .sh scripts so I know it is possible
I even put the code

Code: Select all

#!/bin/sh
printf "Content-Type: text/html\n\n"
printf "Hello Wolrd!\n"


in my test.py file and it worked fine.
I just cannot run python files.
this is the error I get, any help is appreciated

Code: Select all

[Tue Nov 07 19:46:58.172091 2023] [cgi:error] [pid 25378] [client 127.0.0.1:38348] AH01215: /bin/python3: symbol lookup error: /bin/python3: undefined symbol: XML_SetHashSalt: /opt/lampp/cgi-bin/test.py
[Tue Nov 07 19:46:58.172180 2023] [cgi:error] [pid 25378] [client 127.0.0.1:38348] End of script output before headers: test.py


and this is my python script code

Code: Select all

#!/bin/python3
print("Content-Type: text/html\n\n")


Grok jetzt für erste Nutzer auf Twitter (X) in den USA freigeschaltet. :: ganz neu: X (ehemals Twitter) hat sein KI-basiertes Sprachmodell Grok für erste Nutzer in den USA freigeschaltet.

Grok, das man ggf. auch mit "kapieren" übersetzen kann ist, was die prinzipielle Konstruktion anbelangt, den Chatbots ChatGPT - und jenen von Microsoft und Googles Bard relativ ähnlich. Sehr ähnlich wie die Trainingsverfahren bei den Sprachmodellen (engl.: Large Language Modell/LLM) arbeiten die Entwickler auch bei Grok mit Trainingsverfahren.

Apropos: im Moment ist Grok lediglich auf X (vormals Twitter) für User aus den USA freigeschaltet. Viele andere Interessenten - die müssen noch warten haben momentan noch das Nachsehen. So auch die Europäer; Die deutschen X-Nutzer können sich aber vorerst schon mal auf eine Warteliste setzen lassen. Im Moment ist Grok noch in Entwicklungsvorgang begriffen und deshalb wird in offiziellen Statements dann auch von einem "sehr frühes Beta-Produkt" gesprochen.

weitere Hintergründe, Infos, Quellen und Beiträge

https://x.ai

The journey to Grok-1

The engine powering Grok is Grok-1, our frontier LLM, which we developed over the last four months. Grok-1 has gone through many iterations over this span of time.
After announcing xAI, we trained a prototype LLM (Grok-0) with 33 billion parameters. This early model approaches LLaMA 2 (70B) capabilities on standard LM benchmarks but uses only half of its training resources. In the last two months, we have made significant improvements in reasoning and coding capabilities leading up to Grok-1, a state-of-the-art language model that is significantly more powerful, achieving 63.2% on the HumanEval coding task and 73% on MMLU.
To understand the capability improvements we made with Grok-1, we have conducted a series of evaluations using a few standard machine learning benchmarks designed to measure math and reasoning abilities.
GSM8k: Middle school math word problems, (Cobbe et al. 2021), using the chain-of-thought prompt.
MMLU: Multidisciplinary multiple choice questions, (Hendrycks et al. 2021), provided 5-shot in-context examples.
HumanEval: Python code completion task, (Chen et al. 2021), zero-shot evaluated for pass@1.
MATH: Middle school and high school mathematics problems written in LaTeX, (Hendrycks et al. 2021), prompted with a fixed 4-shot prompt.

Early Access to Grok
We are offering a limited number of users in the United States to try out our Grok prototype and provide valuable feedback that will help us improve its capabilities before a wider release. You can join the Grok waitlist here. This release just represents the first step for xAI. Looking ahead, we have an exciting roadmap and will be rolling out new capabilities and features in the coming months.

https://x.ai