This article introduces AIO Sandbox, an all‑in‑one Docker container that unifies browser, shell, VSCode, and Jupyter environments, provides agent‑oriented APIs and multi‑language SDKs, and demonstrates a quick‑start workflow and a Python example converting web pages to Markdown.
The article introduces the Jupyter MCP server, an extension that implements the Model Context Protocol to enable seamless, secure integration of large language models with Jupyter’s kernels, file system, and terminals, and details its architecture, core features, deployment steps, API mechanisms, and security considerations.
Discover ten lesser‑known Jupyter Notebook features—from magic commands that list variables and benchmark code to shortcuts, export utilities, and interactive help—that turn a simple notebook into a highly efficient, production‑ready data‑science workspace.
This article walks through ten hidden Jupyter Notebook features—including magic commands, terminal integration, shortcut keys, markdown styling, collapsible headings, nbconvert exporting, and tab completion—showing how each can streamline debugging, documentation, and reporting to save roughly an hour of work per day.
This article explains why Chinese characters appear as garbled text in Jupyter notebooks and VS Code, demonstrates the issue with screenshots, evaluates several Python code fixes, and ultimately provides a reliable solution by setting the console code page to UTF‑8.
This article introduces the GitHub repository “Homemade Machine Learning,” which provides pure‑Python implementations of popular supervised and unsupervised algorithms—including linear and logistic regression, K‑means clustering, anomaly detection, and multilayer perceptrons—accompanied by mathematical explanations, code samples, and interactive Jupyter Notebook demonstrations.
This article introduces the newly released JupyterLab visual debugger, explains why debugging is needed in Jupyter, provides step‑by‑step installation commands for the frontend extension and xeus‑python kernel, and highlights key UI features and related VS Code visual debugging tools.
This article provides a complete, illustrated walkthrough for downloading and installing Anaconda on Windows, configuring the environment, launching Jupyter Notebook, and troubleshooting common issues such as missing environment variables and permission errors.
In this post a Python enthusiast shares a real‑world troubleshooting session where a pyecharts chart fails to display in Jupyter, discusses common causes such as missing git or offline resources, and provides a concrete fix by setting the online host URL via CurrentConfig, along with practical tips for future queries.
The article provides a detailed technical analysis of Microsoft’s preview‑only Python in Excel feature, outlining its current capabilities, major limitations such as lack of VBA replacement, restricted package usage, cloud‑dependency, and workflow friction, while suggesting improvements and alternative approaches for data‑centric users.
This article introduces the GitHub "Homemade Machine Learning" project, which provides pure‑Python implementations of common supervised and unsupervised machine‑learning algorithms, complete with mathematical explanations, Jupyter‑Notebook demos, installation instructions, and links to datasets for hands‑on learning.
This article explores the origins, key innovations, and lasting impact of Jupyter notebooks, highlighting how their multi‑language support, interactive computing, reproducibility, and extensibility have transformed data exploration, collaboration, education, and research in modern data science and machine learning.
This article introduces four Python packages—pivottablejs, PyGWalker, Qgrid, and Itables—that transform Pandas DataFrames into interactive, visual tables within notebooks, providing code examples and screenshots to demonstrate how to create, explore, and edit data interactively.
This article introduces four Python packages—Pivottablejs, PyGWalker, Qgrid, and Itables—that transform Pandas DataFrames into interactive tables within Jupyter notebooks, providing code examples, visual demonstrations, and guidance on when to choose each tool for data analysis and visualization.
The article surveys the most important data‑science tools—including Jupyter Notebooks, notebook lab platforms, RStudio, Sweave/Knitr, IDEs, domain‑specific solutions, hardware, and data sources—explaining how they support modern, real‑time analytics and help organizations turn raw data into actionable insights.
This article introduces four practical techniques for measuring Python code performance, including the built‑in time module, Jupyter’s %%time magic, line_profiler for per‑line timing, and memory_profiler for detailed memory consumption, complete with example code and interpretation of results.
This article introduces the GoPUP Python library, explains how to install it, demonstrates retrieving Weibo index data with code examples, shows how to visualize the results using Pandas, Jupyter and Matplotlib, and lists the wide range of public data APIs the library supports.
This article introduces seven lesser‑known but highly useful Jupyter Notebook extensions—voila, nbdime, RISE, bokeh, nbgrader, jupytext, and jupyterlab‑git—detailing their features, GitHub popularity, and how they can transform notebooks into interactive web apps, version‑controlled documents, slides, visualizations, grading tools, and more.
This article explains why Chinese characters appear as garbled symbols in Jupyter notebooks, reviews several ineffective Python‑based fixes, and presents a simple Windows console command (chcp 65001) that reliably resolves the encoding issue.
Microsoft has recently divided its VS Code Python extension, releasing three independent extensions—Black for code formatting, isort for import sorting, and Jupyter Powertoys for experimental notebook features—each offering LSP‑based support, version checks, and marketplace installation for Python developers.
This guide walks through setting up Docker on a Windows or Linux host, enabling Nvidia GPU support via the Container Toolkit, pulling a TensorFlow GPU image, launching a Jupyter Notebook server inside the container, and connecting it to Google Colab for deep‑learning training.
This article introduces Altair, a simple yet powerful Python library built on the Vega‑Lite JSON specification, explains its declarative syntax, showcases basic and advanced visualizations with code examples, and highlights its advantages for efficient data analysis and presentation.
Altair is a declarative Python visualization library built on Vega‑Lite that lets you create elegant, interactive statistical charts with minimal code, supporting data aggregation, transformation, and export to PNG/SVG or HTML, and integrates seamlessly with Jupyter environments for exploratory data analysis.
This guide introduces Uber's open‑source Kepler.gl library for geospatial visualization in Jupyter notebooks, covering installation via pip, a simple map example, adding CSV or DataFrame data, customizing through the UI, retrieving configurations, and exporting the map to an HTML file.
This guide introduces Kepler.gl, Uber’s open‑source geospatial visualization library, shows how to install it in a Jupyter notebook, demonstrates creating maps from CSV, GeoJSON or DataFrame sources, customizing charts, extracting configurations, and exporting the result to an HTML file for easy sharing.
This tutorial introduces Plotly for Python, showing how to install it, configure offline mode in Jupyter, and create line, scatter, and bar charts with complete code examples and explanations of each visualization type.
This tutorial introduces Kepler.gl, an open‑source geospatial visualization library from Uber, showing how to install it in a Jupyter notebook, create maps, load CSV or DataFrame data, customize charts via the GUI, retrieve configurations, and export the interactive map to HTML.
EMR Studio is a one‑stop, open‑source‑compatible big data development platform that integrates Zeppelin, Jupyter, Airflow and a custom Cluster Manager to streamline job creation, scheduling, monitoring, and cluster switching, thereby addressing common usability challenges in Spark, Flink, Hive, and Presto workflows.
Jupytext lets you convert Jupyter Notebooks to plain‑text formats, enabling seamless version control and IDE editing while preserving the visual advantages of notebooks, making data‑science projects more manageable and collaborative.
handcalcs is an open‑source Python library that automatically transforms calculation code into LaTeX‑styled formulas, offering Jupyter cell magic and decorator interfaces, customizable display options, and support for symbolic, short, and long representations, while noting limitations with collection types and variable reuse.
This article explains what PEP8 is, outlines key Python coding conventions such as indentation, line length, spacing, comments, imports, and naming, and shows how to automatically apply these standards in Jupyter Notebook using the Autopep8 formatter.
This article presents a collection of practical techniques—including Pandas Profiling, Cufflinks/Plotly visualizations, IPython magic commands, Jupyter formatting, keyboard shortcuts, multiple-output cells, and RISE slide creation—to streamline and speed up everyday data analysis workflows in Jupyter notebooks.
This article introduces IPython, covering its installation, core components, key interactive features such as tab completion, introspection, magic commands, system shell integration, and how to use it within Jupyter Notebook, providing practical command examples and shortcuts for efficient Python development.
This article explains how IPython and Jupyter notebooks support multi‑language execution, integrate Fortran via F2PY, and enable parallel and distributed computing with ipyparallel, illustrating practical magic commands, cluster setup, and performance considerations for scientific Python workflows.
Discover five powerful Python tools—including Python Tutor, IPython, Jupyter Notebook, Anaconda, and Skulpt—that simplify coding, visualization, package management, and online execution, helping developers work more efficiently and deepen their understanding of Python programs.
JupyterLab now offers a visual debugger plugin and the xeus‑python kernel, allowing users to set breakpoints, inspect variables, and navigate execution directly in notebooks, with simple installation commands and future enhancements such as richer variable rendering and conditional breakpoints.
Discover five essential, free Python tools—including Python Tutor, IPython, Jupyter Notebook, Anaconda, and Skulpt—that boost productivity, simplify learning, and streamline development for anyone seeking powerful, community‑driven solutions.
This article compiles a collection of practical Python and Jupyter Notebook tips—including pandas profiling, interactive plotting with Cufflinks, useful magic commands, debugging shortcuts, and output handling techniques—to help data analysts work faster, produce richer visualizations, and avoid common pitfalls.
This guide walks researchers through selecting a Tencent Cloud GN7 GPU instance, installing NVIDIA drivers, CUDA 10.2, cuDNN, setting up PyTorch and Jupyter, and configuring remote development with PyCharm, enabling efficient, cost‑effective AI development on a Tesla T4 GPU server.
This article introduces Python’s built‑in interpreter, popular text editors such as Geany and Sublime Text, powerful IDEs like PyCharm and Jupyter Notebook, demonstrates code refactoring techniques, and explains how to integrate these tools with public cloud services such as Huawei ModelArts for seamless local‑cloud development.
Meituan Homestay created a platform‑wide Jupyter service built on JupyterHub and Kubernetes that integrates Spark, scheduling, documentation and storage, providing seamless, reproducible notebooks with custom extensions, magics and container isolation to unify data analysis, model training and production workflows.
Python’s ecosystem—Jupyter notebooks, Pandas for data manipulation, scikit-learn for machine learning, and matplotlib/Basemap for powerful visualizations—offers a streamlined, scriptable environment that outperforms traditional Java or PHP workflows, enabling researchers to write, run, and document code seamlessly in a single web interface.
This guide explores advanced Jupyter Notebook techniques, including using shell commands, line and cell magic commands, autosave configuration, timing execution, logging customization, running external scripts, integrating Seaborn for enhanced visualizations, managing databases with ipython-sql, and extending functionality with plugins.
This article introduces Python’s core data‑analysis stack—NumPy for fast multidimensional arrays, pandas for labeled DataFrames, and Matplotlib for interactive plotting—while showing how to set up a Jupyter/VS Code environment, perform basic indexing, slicing, and visualisation, and clean log files with pandas.
This tutorial guides programmers through installing required Python libraries, extracting keywords from One Piece song lyrics using jieba, and generating a Chopper‑shaped word cloud with the wordcloud package in a Jupyter notebook, complete with code examples and visual results.
This article describes Getui's end‑to‑end machine‑learning data platform, covering business use cases, the full ML workflow from data ingestion and feature engineering to model training, deployment, monitoring, and the practical tools and solutions adopted to address common challenges in large‑scale AI projects.
This article explains why Python has become the preferred language for data analysis, highlighting the power of Jupyter notebooks, Pandas data frames, scikit-learn machine‑learning tools, and Matplotlib/Basemap visualization libraries, and compares them with Java and PHP alternatives.
This guide walks you through installing Jupyter via Anaconda, configuring IPython profiles, customizing Matplotlib for inline and Chinese font support, optimizing Retina displays, tweaking notebook CSS, and adding useful extensions like nbextensions and dashboards to create a polished, production‑ready environment.
This guide walks you through installing Anaconda, launching Jupyter Notebook locally, configuring its settings, setting up Nginx reverse proxy with WebSocket support, and securing the server with Let’s Encrypt HTTPS without needing port 80.
This tutorial guides readers with basic programming experience through using Python 3 in Jupyter to extract keywords from One Piece lyrics with jieba, and then generate a Chopper‑shaped word cloud using matplotlib and the wordcloud library, covering required dependencies and step‑by‑step code.
This step‑by‑step guide shows how to compile and install Python 2.7 and IPython, manage multiple Python versions with pyenv, set up Jupyter Notebook, and configure PyCharm on Windows to remotely develop on a Linux server, covering all required packages and commands.
This guide explains why Docker simplifies data‑science environment setup, walks through installing Docker, pulling ready‑made images, running a container with Jupyter Notebook, managing files, installing additional packages, and cleaning up, providing step‑by‑step commands for Windows, macOS, and Linux users.
