DataScienceNotebooks: Jupyter-project & many others more

[unleash_it]

...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!?