10 Jupyter Notebook Frameworks in 10 Days (Kaggle, etc)

Compare traditional applications with notebook workflows, and learn Jupyter Notebook basics, including basic cell editing, split and merge cells, storytelling, and using VS Code extensions.

Clone the notebook frameworks repo, set up a Python virtual environment, install faker, pandas, and matplotlib, and build interactive Streamlit apps with data frames and histograms.

Explore Jupyter Lite, a lightweight notebook environment, showing how to create code and markdown cells, run cells with Python kernels, visualize with matplotlib, and manage kernels.

Master basic cell editing in Jupyter notebooks, including code and markdown cells, inserting, duplicating, merging and splitting cells, moving cells, running kernels, and using shortcuts to streamline editing.

Install jupyter notebook locally with pip install notebook and launch a local interface. Explore notebook and lab flavors, and ipykernel powered interactive python.

Explore how VS Code runs Jupyter notebooks with ipynb support, the Jupyter server, and interactive Python via IPython, including cells, outputs, and nbconvert exports.

Discover how GitHub Codespaces delivers cloud-based, VSCode-like environments for Jupyter notebooks, enabling uploading notebooks, configuring a Python runtime, and managing code spaces with stop and delete options.

Master IPython magic commands in Jupyter Notebook, using % for single-line and %% for multi-line inputs. Preview how to run Python, shell, and other languages within a kernel.

Review the pros and cons of Jupyter notebooks, highlighting Markdown support, interactive multi-language cells, a single kernel per notebook, and challenges with performance and version control.

Discover how Project Jupyter expands the Jupyter Notebook ecosystem with Jupyter Lab interfaces, widgets, Voila dashboards, and JupyterHub backends, highlighting front-end and back-end architectures for shareable notebooks and dashboards.

Install Jupiter Lab locally and explore its modern front-end interface, starting a local web app at localhost 8888 and comparing it with the classic Jupyter Notebook at localhost 8088.

Explore ipywidgets in jupyter to build interactive dashboards, using sliders to update histograms in real time, with callback events and layout containers for rich controls and output.

Voila transforms a Jupyter notebook into a standalone, interactive dashboard, enabling easy sharing of notebooks as apps via ipywidgets and templates, without exposing code.

Explore the JupyterHub back end and subprojects, including Jupyter server, rest APIs, notebook lab, Voila, kernel gateway, and BinderHub, plus JupyterHub distributions for Kubernetes and single-VM setups.

Assess the pros and cons of the Jupyter ecosystem’s supporting applications, from notebook to lab, Voila, and JupyterHub. Weigh open‑source maintenance, deployment challenges, and IDE alternatives like Colab or Hex.

Learn how the conda package manager serves as an alternative to pip and handles non-python packages, and explore Anaconda cloud's free access and notebook debugging tools.

Explore Anaconda's conda package manager and its two distributions, Anaconda and Miniconda, for data science package installation. Learn how Anaconda cloud and Jupyter Lab enable cloud notebooks and learning resources.

Master conda package management and virtual environments in this hands-on guide, switching between conda and pip, creating and managing environments, installing packages, and configuring Jupyter kernels in VSCode.

Sign in to the Anaconda cloud to access free Jupyter notebooks, manage conda environments, and use extensions and an AI assistant to run code and explore data sets.

Assess Anaconda's pros and cons, including Anaconda Cloud, conda environments, AI-assisted package management, and notebook-based courses with interactive dashboards, versus a bloated, slow, and dated UI.

Discover Amazon SageMaker Studio Lab, its free access, setup process, and limitations, plus GPU acceleration for PyTorch, TensorFlow, and LLMs via a Jupyter Lab interface.

Explore Studio Lab, a free limited version of Amazon SageMaker, a 100% JupyterLab cloud environment for data science with CPU and GPU runtimes.

Request a free studio lab account to access a hosted Jupyter Lab with CPU and GPU runtimes, note daily quotas, potential delays, and non-production data-sharing limitations.

Explore exploratory data analysis in Studio Lab using Jupyter notebooks, building and switching to the EDA environment with conda, and visualizing with matplotlib and seaborn.

Explore the pros and cons of AWS SageMaker Studio Lab, a free, non-commercial Jupyter environment with GPUs and CPUs, but with limited compute, registrations, verification, and quotas.

Explore Google Colab and Studio Lab, compare GPU and TPU options, Google Drive integration, widgets and forms in notebooks, Colab xterm terminal, and a TensorFlow exam-inspired hands-on problem workflow.

Explore Google Colab as a freemium, cloud-based Jupyter notebook platform by Google Research, offering hosted notebooks with no setup, free CPUs, GPUs, and TPUs.

Discover free Google Colab access, sign in to run notebooks with GPU and TPU runtimes, connected to Drive or GitHub, with notes on plan limits.

Explore Google Colab's features, including the user interface, markdown editor, runtime options, Google Drive and GitHub integration, secrets, and GPU or TPU support for Python, R, and Julia.

Explore ipywidgets in Google Colab to build interactive forms with buttons, sliders, dropdowns, and markdown fields, and learn how to synchronize values and display outputs.

Master input and output in Google Colab with file upload, download controls, Google Drive mounting, and saving notebooks. Explore graphviz, py dot, and bash commands in a Linux environment.

Explore how to use Google Colab to tackle a TensorFlow certification exam with GPU-accelerated notebooks, featuring five problems and a hurricane damage binary classification using CNNs in TensorFlow Keras.

Learn how to open a terminal in Google Colab on the free plan using Colab Xterm, install it with pip, and run bash commands inside a notebook cell.

Google Colab's pros and cons highlight free GPU and TPU access, real-time collaboration, and seamless integration with Google Drive, Google Workspace, and GitHub, while noting free-plan resource limits.

Discover Kaggle as a free, gpu-capable data science platform for experiments, competitions, and public projects; set up your account and showcase work to boost your resume.

Explore Kaggle as a social network for data scientists, featuring hosted Jupyter notebooks, shared datasets and models, and active participation in competitions, discussions, and job-search showcases.

Create a free Kaggle account using Google, sign in options, and explore notebooks, datasets, and competitions while understanding quotas, GPU hours, and public sharing.

Explore Kaggle notebooks to perform exploratory data analysis with pandas, load datasets from Kaggle input, profile data, handle missing values, and create correlation heatmaps.

Explore how Kaggle competitions work, from hosting and joining ongoing challenges to submitting results via kernels or the api, with prizes, teamwork, and leaderboard insights.

Kaggle's free, no paid plans, Google-backed platform offers datasets and a social network for data scientists to share notebooks, build portfolios, and gain visibility, with quotas and limited support.

Explore hex, a commercial application with a modern web UI and a free trial edition. Review its application builder and try exploratory data analysis with machine learning anomaly detection experiments.

Explore hex, a next-generation Jupyter notebook framework with a polished React-based UI, offering a data workspace for teams, and analysis in SQL and Python notebooks with interactive data sharing.

Explore hex pricing and features, from a free community edition to a 14-day team trial, and learn how notebooks, workflows, and integrations like snowflake and dbt power data analysis.

Learn to build apps inside a jupyter notebook workflow with hex, using yaml projects, uploading notebooks, configuring environments, and publishing dashboards via the app builder with no-code controls.

Explore exploratory data analysis in Hex with a Snowflake connection, Hex project exported to YAML, and no-code visualizations to inspect data, features, and outliers using pandas or Snowpark data frames.

Explore anomaly detection in hex using a CSV dataset, reading with pandas, building an isolation forest and a one-class SVM, and visualizing outliers with Altair, seaborn, and matplotlib.

Evaluate hex's pros and cons, highlighting a polished web UI, easy ad hoc analysis in code, and an app-building experience with limited free GPU access.

Explore Deepnote, a notebook platform linked with Snowflake partners, by examining its free 14-day trial, system architecture, and key features, while weighing pros and cons through practical experiments.

Explore Deepnote: an AI-powered data workspace using GPT-4 to generate and fix SQL, and build data apps from notebooks with Python, SQL, and R and 50 data sources.

Create a free Deepnote team trial, explore private workspaces, projects, notebooks, and integrations, and compare free and team editions with AI blocks and Snowflake support.

Explore the system architecture of a Deepnote notebook, connecting to a Snowflake trial, loading a weather parquet dataset, running SQL and Python blocks, and creating interactive charts and visuals.

Explore Deepnote features for workspaces, projects, notebooks, data, and apps with Python, SQL, and Markdown blocks. Use AI-assisted code, variable substitution, charts, and real-time collaboration with version history.

Explore the pros and cons of deep note, including a good free plan, real-time team collaboration, auto-save, and integrations with GitHub and S3, plus pre-configured virtual machines and plan-based resources.

Explore JetBrains Datalore, a minimalistic notebook platform with an easy report builder and interactive dashboards, integrated with other JetBrains tools, plus a 14-day trial of the paid edition.

Discover JetBrains Datalore, a cloud-based Jupyter notebook platform for data analysis and machine learning in Python, with Kotlin, Scala, and R support, no-code widgets, and collaborative cloud or on-premises deployment.

Sign up for a 14-day free trial of JetBrains Datalore to create notebooks in a workspace, connect databases, and build interactive dashboards with widgets and markdown within cloud free limits.

Explore the Datalore report builder by importing notebooks, installing packages like Faker, and building interactive dashboards with no-code widgets, then publish and share static or interactive reports.

Explore Datalore notebook features, from multi-language environments (Python, R, SQL, Kotlin, Scala) and conda/pip setups to reactive mode, scheduling, interactive controls, and AI assistant tools.

Explore JetBrains Datalore cloud free edition for quick prototyping, with 100% free sharing, multi-language notebooks, cloud storage, integrated tools, and an interactive Python package manager.