12 Best Jupyter Notebook Alternatives In 2023
Love Jupyter Notebooks? Here are 12 online Jupyter Notebook alternatives that you will love! Plus, find out which Jupyter alternatives rank first for Data Analysis, Machine Learning, and Business Use.
This article is a comprehensive review and feature comparison of the 12 best Jupyter Notebook alternatives in 2023. See which one ranks first for Data Analysis, Data Science, Python, SQL, Machine Learning, and Enterprise.
What are Jupyter Notebooks?
Jupyter Notebooks are interactive, web-based documents that allow users to write and execute code, as well as include text, equations, and visualizations in a single document. They are widely used in the field of data science and machine learning for tasks such as data analysis, visualization, and prototyping.
Jupyter Notebooks are made up of cells where each cell can be a code cell or a text cell. Text is written in Markdown, which allows users to include elements such as headings, lists, links, and images. For code cells, Jupyter Notebooks supports popular programming languages, including Python, R, Julia, and more.
The key reason behind the popularity of Jupyter Notebooks is that it makes a data project clean, readable, and easily exportable to a variety of formats, such as HTML, PDF, and more for sharing with others or posting online.
Jupyter Notebooks fall short as a serious enterprise solution that businesses can rely on. It lacks some of the essential needs of businesses like real-time collaboration, security, commenting, versioning, publishing, native integrations to other tools, and native SQL
Is Jupyter Notebook outdated?
Not really! Jupyter Notebook is an open source tool which is developed by an online community of highly driven individuals. It is the default choice when working on public data projects.
However, vanilla Jupyter Notebooks fall short as a serious enterprise solution that businesses can rely on. It lacks some of the essential needs of businesses like real-time collaboration, security, commenting, versioning, publishing, native integrations to other tools, and native SQL.
All of these missing features are what business data teams need to efficiently work on data projects, facilitate focused reviews and discussions, and collaborate with their stakeholders.
In addition, for many open source projects that require multiple people to work on the same dataset or code base, multiple versions of the same Jupyter notebooks become extremely difficult to share and manage. In these cases, a collaborative and interactive notebook environment is a must-have.
What can I use instead of Jupyter Notebook?
There are several alternatives that offer additional features and capabilities and fill the gap that Jupyer notebook has for businesses. In this article, we will explore some of the alternatives to Jupyter Notebook that are worth considering. We will also reveal our top pick among the 12 Jupyter Notebook alternatives our top pick.
12 Jupyter Notebook Alternatives
- Noteable (Our #1 pick as a serious notebook environment)
- Google Colab
- Kaggle Notebooks
- Microsoft Azure Notebooks
- CoCalc
- Jetbrains Datalore
- Deepnote
- Hex
- NextJournal
- Paperspace Gradient
- Apache Zeplin
- Count.co
Noteable — Collaborative Data Notebook for Python, SQL and R
Noteable is a cloud-based data notebook platform for data analysis, data science, and machine learning that delivers an interactive and collaborative experience for users. It offers some of the most comprehensive set of features that any user or business may need.
Noteable focuses on collaboration and interactivity allowing users to quickly explore data, perform data analysis, and share notebooks with other users. Users are also able to work on the notebook together in real-time, making it the best data notebook for teams and organizations.
Some of the cool features worth mentioning are:
- Real-time collaboration (like Google Drive)
- Write SQL queries on CSV or JSON files (no need for data tables)
- Effortlessly use Python & SQL in the same notebook on any data frames
- Interactive data visualization (like Tableau or Excel)
- Interactive tables with format helpers (convert date formats, strings, numbers, float etc)
- Select hardware resources between 1 to 15 CPUs (free compute, fair use policy applies)
- Secrets Vault to securely store, use, and share API Keys and Credentials
- Unlimited cloud storage
- Versioning to manage multiple versions of a notebook
- Publish unlimited number of interactive notebooks
- Schedule notebooks to run periodically
- integration with Dagster, Airflow and Papermill for orchestration
With its ability to work with CSV, JSON, Excel files using Python, R, SQL or No-Code data exploration, it brings data practitioners (Data Analysts, Data Scientists, Data Engineers, BI Engineers) and stakeholders on the same platform and ensure effective reviews, action items, feedback via comments, annotations and @mentions.
Noteable is also library agnostic which means you can use pretty much any library you would like with your Python code, including TensorFlow and PyTorch. This makes it a convenient platform for the varying needs of data analysts, data scientists and machine learning practitioners. Try Noteable today →
Here is a quick overview of Noteable:
Google Colab
A popular alternative to Jupyter Notebooks is Google Colab (or google colaboratory) , which is a free, cloud-based Jupyter Notebook environment provided by Google. It allows users to write and execute code in a variety of programming languages, including Python, R, and Julia.
One of the main advantages of Google Colab is that it provides access to powerful hardware resources, including GPUs and TPUs, for running machine learning models and deep learning models. This can be particularly useful for users who do not have access to high-performance computing resources on their local machines.
In addition to its hardware resources, Google Colab also offers a number of other useful features, such as the ability to import and export data from Google Drive, integration with Google Cloud Storage, and support for version control with Git. It also has a built-in code editor and a rich set of libraries and frameworks, including TensorFlow and PyTorch, making it a convenient platform for data scientists and machine learning practitioners.
One of the main disadvantages of Google Colab is that it can be resource-intensive, particularly when working with large datasets or running computationally-intensive tasks. This can lead to slow performance and potentially long wait times for computations to complete.
Another potential disadvantage of Google Colab is that it is primarily geared towards users of Google products, such as Google Drive and Google Cloud Storage. This can be inconvenient for users who do not use these products and may prefer to use other storage solutions.
It is important for users to carefully consider their needs and preferences when deciding whether Google Colab is the right choice for them.
Kaggle Notebooks (a notebook environment for Kaggle Datasets)
Another popular alternative to Jupyter Notebooks is Kaggle Notebooks, which is a cloud-based platform for data science and machine learning provided by Kaggle. Like Google Colab, Kaggle Notebooks offers access to powerful hardware resources, including GPUs and TPUs, for running machine learning and deep learning models. It also offers a number of other useful features, such as integration with the Kaggle API, support for version control with Git, and the ability to easily share and collaborate on notebooks with other users.
The main disadvantages of Kaggle Notebooks is that it is specifically geared towards data science and machine learning datasets hosted on Kaggle for the various competitions that Kaggle hosts. For a hobbyist, it is a good tool.
Microsoft Azure Notebooks
Microsoft Azure Notebooks is another cloud-based platform for data science and machine learning that is similar to Google Colab and Kaggle Notebooks. It provides access to powerful hardware resources, including GPUs and TPUs, for running machine learning and deep learning models, as well as a number of other useful features, such as integration with Microsoft Azure Storage, support for version control with Git, and the ability to easily share and collaborate on notebooks with other users.
One of the main advantages of Microsoft Azure Notebooks is that it offers a wide variety of programming languages and libraries, including Python, R, F#, and Julia, making it a versatile platform for data scientists and analysts who use multiple languages. It also has a built-in code editor and a rich set of libraries and frameworks, including TensorFlow and PyTorch, making it a convenient platform for machine learning practitioners.
CoCalc
CoCalc (formerly known as SageMathCloud) is a cloud-based platform for collaborative computing that offers many of the same features as Jupyter Notebooks, as well as a number of additional capabilities. It supports a wide variety of programming languages, including Python, R, and Julia, and provides access to powerful hardware resources, including GPUs. In addition to its core features, CoCalc also offers support for LaTeX and computer algebra systems, making it a good choice for users who need these capabilities.
JetBrains Datalore
JetBrains Datalore is a cloud-based data science platform that offers many of the same features as Jupyter Notebooks, as well as a number of additional capabilities. It supports a wide variety of programming languages, including Python, R, and SQL, and provides access to powerful hardware resources, including GPUs. One of the main advantages of JetBrains Datalore is its integration with the JetBrains ecosystem of tools, which includes IDEs such as PyCharm and IntelliJ.
It is worth mentioning that it is primarily geared towards users of the JetBrains ecosystem of tools, such as PyCharm and IntelliJ. This can be inconvenient for users who do not use these tools and may prefer to use other IDEs or code editors.
Deepnote
Deepnote is a cloud-based data science platform that is similar to Jupyter Notebooks, but with a focus on collaboration and real-time editing. It allows users to write and execute code in a variety of programming languages, as well as include text, equations, and visualizations in a single document. Deepnote also has a built-in code editor and supports a wide range of libraries and frameworks, including TensorFlow and PyTorch.
Hex
Hex is a cloud-based platform for data science and machine learning that offers many of the same features as Jupyter Notebooks, as well as a number of additional capabilities. It supports a wide variety of programming languages, including Python, R, and Julia, and provides access to powerful hardware resources, including GPUs. Hex also has a built-in code editor and supports a wide range of libraries.
Nextjournal
Nextjournal is a cloud-based platform for scientific computing and data science that offers many of the same features as Jupyter Notebooks, as well as a number of additional capabilities. It supports a wide variety of programming languages, including Python, R, and Julia, and provides access to powerful hardware resources, including GPUs.
One of the main advantages of Nextjournal is its focus on reproducibility, which allows users to easily share and reproduce their work.
Paperspace Gradient
Paperspace Gradient is a cloud-based platform for data science and machine learning that offers many of the same features as Jupyter Notebooks, as well as a number of additional capabilities. It supports a wide variety of programming languages, including Python, R, and Julia, and provides access to powerful hardware resources, including GPUs.
Paperspace Gradient also has a built-in code editor and supports a wide range of libraries and frameworks, including TensorFlow and PyTorch.
Apache Zeppelin
Apache Zeppelin is an open-source platform for data science and analytics that is similar to Jupyter Notebooks. It allows users to write and execute code in a variety of programming languages, as well as include text, equations, and visualizations in a single document. Apache Zeppelin also has a built-in code editor and supports a wide range of libraries and frameworks, including TensorFlow and PyTorch.
Count.co
Count.co is a cloud-based platform for data science and machine learning that offers many of the same features as Jupyter Notebooks, as well as a number of additional capabilities. It supports a wide variety of programming languages, including Python, R, and Julia, and provides access to powerful hardware resources, including GPUs.
Count.co also has a built-in code editor and supports a wide range of libraries and frameworks, including TensorFlow and PyTorch. One of the main advantages of Count.co is its focus on collaboration, which allows users to easily share and collaborate on notebooks with other users.
Features Comparison of Jupyter notebooks alternatives
A full feature comparison of these 12 tools is available in our blog post.
In the blog post, you will also find the ranking notebooks for:
1. Best Jupyter Notebook Alternative for Data Analysis
2. Best Jupyter Notebook Alternative for Data Science
3. Best Jupyter Notebook Alternative for Machine Learning
4. Best Jupyter Notebook Alternative for SQL
5. Best Jupyter Notebook Alternative for Python
6. Best Jupyter Notebook Alternative for Businesses
Overall, What is the best Jupyter Notebook Alternative?
Noteable is the best alternative to Jupyter Notebooks. It has the most comprehensive set of features that any data science team or data team will ever need. In addition, Noteable has the security features and integrations that are a must-have requirements for enterprise.
Moreover, the interactive visualization and collaboration features of Noteable bridge the gap between data practitioners and stakeholders bringing them together to work on the same platform. This saves time, frustration and ensures that data teams don’t have to hop between multiple SQL, Python IDE, BI and Slideshow tools to deliver a project.
If you are looking for a powerful Jupyter alternative built to support the needs of modern day data teams, we recommend Noteable.
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