| A | Collaboration and Version Control in Jupyter Note… |
Need for enhanced collaboration features, including real-time collaboration, versioning, and integration with documentation and text editors for a better workflow in Jupyter Notebook. |
| 1 | Integration with documentation |
Challenges in integrating Jupyter Notebooks with other documentation formats and tools. |
| 2 | Integration with text editors |
Desire for better integration between Jupyter Notebook and text editors for improved workflow. |
| 3 | Collaboration and version control |
Need for better collaboration and versioning features in Jupyter Notebook. |
| 4 | Live Collaboration |
Desire for real-time collaboration features in Jupyter Notebook. |
| B | Jupyter Notebook Interface and Usability |
Issues with the Jupyter Notebook interface design, usability, and the desire for it to function more like a standalone application with improved file management and code presentation options. |
| 5 | User Interface Issues |
Problems related to the usability and design of the Jupyter Notebook interface. |
| 6 | Usability as an Application |
Desire for Jupyter Notebook to function more like a standalone application, reducing the need for multiple windows. |
| 7 | Code Presentation Options |
Need for options to hide code when presenting work to non-technical audiences. |
| 8 | File Management |
Desire for improved file management features, such as easier file opening and navigation. |
| C | Jupyter Notebook Code Execution and Language Supp… |
Desire for more flexible code execution, better support for multiple programming languages, and compatibility with command line environments in Jupyter Notebook. |
| 9 | Integration of Programming Languages |
Need for better support and integration of different programming languages, such as R, in Jupyter Notebook. |
| 10 | Command Line Compatibility |
Challenges with using the same code in both Jupyter Notebook and command line environments. |
| 11 | Code Execution Flexibility |
Desire for more flexible options in executing code chunks within Jupyter Notebook. |
| D | Jupyter Notebook Performance and Debugging |
Concerns about Jupyter Notebook's performance with large files and the need for improved debugging capabilities for a more efficient coding experience. |
| 12 | Performance Issues |
Concerns about the performance of Jupyter Notebook when handling large files or notebooks. |
| 13 | Debugging Features |
Need for improved debugging capabilities for variables in Jupyter Notebook. |
| E | Jupyter Notebook Setup and Workflow Integration |
Challenges with the installation process, DevOps, kernel management, and integrating Jupyter Notebook into various workflows. |
| 14 | Installation Challenges |
Difficulties encountered during the installation process of Jupyter Notebook. |
| 15 | Workflow integration |
Challenges in switching between different tasks, such as library development and prototyping. |
| 16 | DevOps and setup challenges |
Issues related to accessing preconfigured environments and the complexity of setup. |
| 17 | Kernel management |
Challenges related to adding and managing kernels in Jupyter Notebook. |
| F | Jupyter Notebook Content Management and Sharing |
Need for better methods to organize notebooks, ease of sharing results, and overcoming limitations in nonlinear workflows and PDF conversion. |
| 18 | Notebook organization |
Desire for better methods to organize and reuse notebook cells. |
| 19 | Ease of sharing and understanding results |
Challenges related to sharing results and making notebooks easier to understand for colleagues. |
| 20 | Nonlinear workflow limitations |
Issues with the strict linearity of notebooks and the inability to reference code from other notebooks. |
| 21 | PDF conversion complexity |
Difficulties with converting notebooks to PDF, particularly with complex templates and plugins. |
| G | Jupyter Notebook Preferences and Alternatives |
Preferences and constraints influencing the choice of tools for tasks, including time limitations and the preference for alternatives to Jupyter Notebook. |
| 22 | Usage of IPython |
Some users primarily utilize IPython instead of the Jupyter Notebook interface. |
| 23 | Time constraints |
Limited time availability hinders the regular use of Jupyter Notebook. |
| 24 | Preference for other tools |
Users prefer using other tools or methods over Jupyter Notebook for their tasks. |
| A | Jupyter Notebook Blogging and Documentation Integ… |
Enhancing Jupyter Notebook by integrating with blogging frameworks like Sphinx, Pelican, Nikola, and documentation tools such as nbformat for creating slides and presentations. |
| 1 | Integration with Blogging Frameworks |
Desire for better integration of Jupyter Notebook with blogging tools like Sphinx, Pelican, and Nikola. |
| 2 | Documentation and Presentation Tools |
Request for improved integration of documentation and presentation tools such as nbformat, Sphinx, and converting notebooks to slides. |
| B | Jupyter Notebook Data Analysis and Visualization … |
Enhancing data analysis and visualization in Jupyter Notebook by integrating with libraries like Pandas, Matplotlib, R, and big data tools like Apache Spark. |
| 3 | Data Visualization Tools |
Need for enhanced integration with data visualization libraries like Pandas and Matplotlib. |
| 4 | Big Data Tools |
Interest in integrating Jupyter Notebook with big data tools like Apache Spark for enhanced data processing capabilities. |
| 5 | Integration with R |
Interest in integrating Jupyter Notebook with the R programming language for data analysis and visualization. |
| 6 | JavaScript libraries |
Libraries like X3DOM, Bokeh, and D3 for enhanced visualization capabilities. |
| C | Jupyter Notebook Development and Environment Mana… |
Streamlining Jupyter Notebook development by integrating with tools like Atom, PyCharm, Conda, Virtualenv, and supporting command line interactions. |
| 7 | Command Line Integration |
Request for better command line support and interaction within Jupyter Notebook. |
| 8 | Development Tools |
Interest in integrating Jupyter Notebook with various development tools like Atom, PyCharm, and graphical profilers. |
| 9 | Environment Management Tools |
Request for better integration with environment management tools like Conda and Virtualenv. |
| D | Jupyter Notebook Editor and Autocompletion Enhanc… |
Improving editing experience in Jupyter Notebook by integrating with text editors like Emacs, Vim, and enhancing autocompletion features. |
| 10 | Autocompletion Features |
Desire for enhanced autocompletion features in Jupyter Notebook similar to those found in PyCharm. |
| 11 | Text Editor Integration |
Interest in integrating Jupyter Notebook with text editors like Emacs. |
| 12 | Bash and Vim Integration |
Request for improved integration with command line tools like Bash and text editors like Vim. |
| E | Jupyter Notebook Machine and Deep Learning Integr… |
Facilitating machine and deep learning in Jupyter Notebook by integrating with libraries like scikit-learn, Theano, and supporting parallel computing. |
| 13 | Machine Learning Libraries |
Interest in integrating machine learning libraries like scikit-learn with Jupyter Notebook. |
| 14 | Deep Learning Frameworks |
Interest in integrating deep learning frameworks like Theano with Jupyter Notebook. |
| F | Jupyter Notebook Cloud Hosting and Web Developmen… |
Enhancing Jupyter Notebook's hosting on cloud platforms like AWS, Google Cloud, Azure, and integration with web application frameworks. |
| 15 | Web application frameworks |
Frameworks that facilitate the creation of web applications using Jupyter Notebook. |
| 16 | Hosting Support |
Desire for improved hosting capabilities on platforms like AWS, Google Cloud, and Azure. |
| G | Jupyter Notebook Data Management and Manipulation… |
Integrating Jupyter Notebook with SQL databases, spreadsheet functionalities, and tools for managing and manipulating data tables. |
| 17 | Integration with Google Tools |
Interest in integrating Jupyter Notebook with Google Spreadsheets and other Google Docs applications. |
| 18 | Spreadsheet integration |
Integration of spreadsheet functionalities for data manipulation. |
| 19 | SQL Integration |
Interest in integrating Jupyter Notebook with SQL databases for data querying and manipulation. |
| 20 | Data manipulation tools |
Tools that allow for direct editing and manipulation of data tables. |
| H | Jupyter Notebook Testing and Debugging Tools |
Integrating Python unit testing frameworks and debugging tools to facilitate code testing and error fixing within Jupyter Notebook. |
| 21 | Debugging tools |
Tools that assist in identifying and fixing code errors. |
| 22 | Testing Frameworks |
Request for better integration with Python unit testing frameworks to facilitate testing within Jupyter Notebook. |
| I | Jupyter Notebook User Interface and Editing Enhan… |
Desire for improved user interface features such as code collapsing, theme selection, interactive elements, and enhanced editing capabilities. |
| 23 | Editing Tools |
Desire for enhanced editing capabilities within Jupyter Notebook, similar to features found in Sublime Text. |
| 24 | User Interface Enhancements |
Desire for improved user interface features such as code collapsing, theme selection, and interactive elements. |
| J | Jupyter Notebook Extended Language and Tool Suppo… |
Integrating additional programming languages, mathematical software like Sagemath, and tools for variable management and citation handling. |
| 25 | Variable management features |
Features that assist in managing and editing variable names efficiently. |
| 26 | Mathematical Software |
Desire for integration with mathematical software like Sagemath for advanced computations. |
| 27 | Citation management tools |
Tools for managing citations and references effectively. |
| 28 | Support for additional programming languages |
Integration of more programming languages for expanded functionality. |
| K | Jupyter Notebook File and Document Management |
Tools for effectively managing, organizing, and converting notebook files and writing formats. |
| 29 | Document conversion tools |
Tools for converting documents and writing formats. |
| 30 | File management tools |
Tools for managing and organizing notebook files effectively. |
| L | Other topics |
Topics which could not be grouped into themes. |
| 31 | Collaboration Platforms |
Interest in integrating Jupyter Notebook with collaboration platforms like GitHub and Coursera. |
| A | Data Analysis and Visualization |
The practice of exploring, analyzing, and visualizing data to uncover patterns, insights, and presenting results in a graphical format. |
| 1 | Data exploration |
The practice of analyzing and visualizing data to uncover patterns and insights. |
| 2 | Data visualization |
The ability to integrate and display graphical representations of data within the workflow. |
| 3 | Tabular Data Handling |
The ability to browse and manipulate data organized in tables. |
| 4 | Preliminary data analysis |
Conducting initial assessments and simple analyses of data. |
| 5 | Data mining |
The practice of discovering patterns and extracting valuable information from large datasets. |
| B | Interactive Development and Presentation |
The process of writing, testing, and refining code interactively, with a focus on rapid prototyping, experimentation, and transitioning to effective presentations. |
| 6 | Experimentation |
Testing and trying out different coding approaches and techniques. |
| 7 | Immediate feedback |
Receiving instant results from code execution without interruptions. |
| 8 | Prototyping |
Quickly developing and sharing initial versions of projects. |
| 9 | Iterative development |
The process of continuously improving and refining code and projects through repeated cycles. |
| 10 | Experimentation to Presentation |
The process of transitioning from testing ideas to effectively presenting results. |
| 11 | Interactive coding |
The ability to write and test code in an interactive manner, facilitating experimentation and rapid development. |
| C | Collaborative Documentation and Organization |
Facilitating teamwork and joint efforts in projects while combining notes and computational work for clarity and organized note-taking. |
| 12 | Collaboration |
Facilitating teamwork and joint efforts in projects. |
| 13 | Documentation |
Combining notes and computational work for clarity. |
| 14 | Note-taking and Organization |
The ability to keep notes for work, business, and tutorials in an organized manner. |
| D | Interactive Data Management |
The ability to perform, manage, and manipulate data operations interactively, including cleaning data in memory and handling database operations. |
| 15 | Database operations |
The ability to perform tasks related to managing and manipulating data stored in databases. |
| 16 | Interactive data cleaning |
The ability to clean and manipulate data interactively while retaining it in memory. |
| E | Educational Content Delivery |
The use of tools in educational settings for teaching, learning, and providing demonstrations and tutorials effectively. |
| 17 | Teaching and Learning |
The use of Jupyter Notebook in educational settings for teaching and lab classes. |
| 18 | Demonstration and Tutorials |
The ability to showcase concepts and provide instructional content effectively. |
| F | Advanced Computational Tools |
The capability to perform complex scientific computations, control simulations, and develop algorithms within the data analysis workflow. |
| 19 | Scientific Computation |
The ability to perform complex calculations and analyses in a scientific context. |
| 20 | Simulation Control |
The capability to manage and control simulations within the data analysis workflow. |
| 21 | Algorithm development |
Creating and refining algorithms for various applications. |
| G | Enhanced Interactive Experience |
Engaging users through dynamic and responsive code execution with advanced visual outputs and immediate feedback for an improved user experience. |
| 22 | Rich displays |
Utilizing advanced visual outputs for better data representation. |
| 23 | Interactivity |
Engaging users through dynamic and responsive code execution. |
| 24 | Integration of Code and Visualization |
The ability to combine coding, mathematics, and data visualization in a cohesive manner. |
| H | User Experience Optimization |
Enhancing the overall experience for users by focusing on creating interfaces that are accessible and simplifying the process of managing work. |
| 25 | User-Friendly Interface |
The importance of having an accessible and easy-to-use interface for users. |
| 26 | Ease of use |
Simplifying the process of saving and managing work. |
| I | Other topics |
Topics which could not be grouped into themes. |
| 27 | Remote Execution |
The ability to run code on remote servers and visualize the output effectively. |
| 28 | Reproducibility |
Ensuring that results can be consistently replicated. |
| A | User Interface and Experience Enhancements |
The need for improvements in user interface customization, including hiding code for non-technical users, better document formatting, and user-friendly data input methods. |
| 1 | User Interface Customization |
The need for more options to customize the user interface, such as hiding cells or organizing content. |
| 2 | User-Friendly Data Input |
The need for a more intuitive way to input data, similar to spreadsheet applications. |
| 3 | Code Hiding for Non-Technical Users |
The need for features that allow hiding code boxes to facilitate sharing with non-Python users. |
| 4 | Document Formatting |
The requirement for improved capabilities to create well-structured documents from code and outputs. |
| B | Collaboration and Security Features |
The importance of features that support collaboration, such as access control and secure sharing, while ensuring data provenance and security. |
| 5 | Data Provenance |
The importance of tracking the origin and history of data used in notebooks. |
| 6 | Collaboration and Accessibility |
The importance of enabling students to easily access and edit notebooks for collaborative learning. |
| 7 | Granular Access Control |
The need for detailed permissions and access settings for notebooks to enhance security and collaboration. |
| C | Integration and Compatibility Improvements |
The necessity for better integration with other tools, support for additional programming languages, and the ability to edit notebooks externally. |
| 8 | External Editing Capabilities |
Need for better support for editing notebooks externally, particularly with JavaScript and browser restrictions. |
| 9 | Support for Additional Programming Languages |
Need for better integration and support for languages other than Python, such as Javascript. |
| 10 | Integration with Other Tools |
The necessity for Jupyter Notebook to better integrate with other programming environments and tools. |
| D | Development and Code Management Tools |
The need for advanced tools for code versioning, quality control, complex function development, and IDE-like features for efficient code development. |
| 11 | Code Versioning |
The need for better tools and features to manage different versions of code within Jupyter Notebook. |
| 12 | Code Quality and Development Tools |
Need for better tools focused on code quality control and application development. |
| 13 | Complex Function Development |
Need for tools that support the development of more complex functions and objects. |
| 14 | Integrated Development Environment (IDE) Features |
Need for IDE-like features to support the development of small to large code projects. |
| E | Data Handling and Analysis Features |
The requirement for enhanced tools for data management, exploration, visualization, and the ability to perform SQL query analysis. |
| 15 | Data Visualization Tools |
Desire for enhanced tools for visualizing data, particularly with rich graphics and integration with existing libraries. |
| 16 | Data Exploration Tools |
The need for more robust tools for data exploration, such as widgets and sliders. |
| 17 | Data Management |
The need for better data management and portability features. |
| F | Computing and Execution Capabilities |
The need for improved support for high performance computing, multiple kernel support, asynchronous code execution, and a robust debugger. |
| 18 | Asynchronous Code Execution |
Desire for support in executing asynchronous code and commands without additional setup. |
| 19 | High Performance Computing |
The need for better support and integration for high performance computing simulations. |
| 20 | Debugger |
Need for a more robust debugging tool within Jupyter Notebook. |
| 21 | Multiple Kernel Support |
The necessity for Jupyter Notebook to run multiple kernels, whether in the same or different programming languages. |
| G | Documentation and Reporting Enhancements |
Desire for improved documentation features, including writing tool integration, enhanced code editing, and automated reporting. |
| 22 | Enhanced Code Editing |
Desire for better code editing experiences, comparing favorably to other editors like Atom. |
| 23 | Automated Reporting |
The requirement for features that facilitate automated reporting processes. |
| 24 | Writing Tool Integration |
Desire for better integration with familiar writing tools for documentation purposes. |
| 25 | Enhanced Documentation Features |
Desire for improved tools for documentation, including drag-and-drop functionality and citation management. |
| H | Versioning, Deployment, and File Management |
Need for improved versioning and deployment tools with Git integration, along with better file management, editing, and remote backup solutions. |
| 26 | File Management and Editing |
Need for improved file handling, editing capabilities, and remote backup solutions. |
| 27 | Versioning and Deployment |
Need for improved versioning and deployment tools, particularly with Git integration. |
| I | Interactive Learning and Workflow Tools |
Desire for interactive learning tools, text editing features like spell check, and workflow continuity to enhance user engagement and productivity. |
| 28 | Text Editing Features |
Request for features like a spell checker. |
| 29 | Interactive Learning Tools |
Desire for features that enhance user engagement, such as quizzes and interactive graphics. |
| 30 | Workflow Continuity |
Desire for features that allow users to easily continue their work from where they left off. |
| J | Publishing, Sharing, and Terminal Management |
Challenges in publishing notebooks as documents and managing terminal output for reliability and accuracy. |
| 31 | Terminal Output Management |
Concerns regarding the reliability and accuracy of terminal output. |
| 32 | Publishing and Sharing |
Challenges in publishing notebooks as documents. |
| K | Automated Testing and Analysis Repetition |
Need for automated unit testing capabilities and features that allow for easy repetition of the same analysis. |
| 33 | Analysis Repetition |
Desire for features that allow for easy repetition of the same analysis. |
| 34 | Automated Testing |
Need for automated unit testing capabilities. |
| A | User Experience Design |
The design elements and usability features that enhance user interaction, including the use of inline graphics, Markdown, and documentation integration. |
| 1 | User Interface |
The design and usability aspects that enhance the user experience. |
| 2 | Inline Graphics and Markdown |
The use of inline graphics and Markdown enhances documentation and visualization within the notebook. |
| 3 | Documentation Integration |
The inclusion of documentation alongside code aids in understanding and usability. |
| B | Development Workflow Enhancements |
Features that improve workflow efficiency and flexibility, such as command history, cell management, and rapid prototyping capabilities. |
| 4 | Command History |
Ability to replay previous commands improves workflow continuity. |
| 5 | Cell Management |
The ability to add, rearrange, and run specific cells enhances workflow flexibility. |
| 6 | Rapid Prototyping |
Facilitates quick development and testing of ideas through immediate feedback. |
| C | Real-time Interaction and Feedback |
The ability to interact with code and data in real-time, providing immediate feedback and live editing features. |
| 7 | Live Editing Features |
The ability to edit text and code in real-time, enhancing interactivity. |
| 8 | Interactivity |
The ability to interact with code and data in real-time. |
| 9 | Immediate feedback |
The ability to execute code immediately and see results in real-time. |
| D | Multilingual and Tool Integration |
Support for multiple programming languages, Bash commands, and access to JavaScript libraries, enhancing the tool's versatility. |
| 10 | Bash integration |
Support for executing Bash commands within Jupyter Notebook. |
| 11 | Multi-language Support |
The capability to work with multiple programming languages within the same environment. |
| E | Code Assistance and Efficiency |
Features that assist in writing code more efficiently, such as tab completion, inline help, and keyboard shortcuts. |
| 12 | Keyboard Shortcuts |
The efficiency and speed provided by keyboard shortcuts in Jupyter Notebook. |
| 13 | Tab Completion and Inline Help |
Features that assist users in writing code more efficiently through suggestions and immediate help. |
| F | Content Management and Sharing |
Capabilities that facilitate the organization, export, and sharing of content, maintaining formatting and enhancing collaboration. |
| 14 | Shareability |
The ability to easily share notebooks with others, improving collaboration. |
| 15 | Export Functionality |
The ease of exporting content and maintaining formatting across different platforms. |
| 16 | Modular structure |
The ability to organize content in a modular way for better clarity and management. |
| G | Visualization and Presentation |
Built-in tools for data visualization, rich media outputs, and support for mathematical formatting to enhance data presentation. |
| 17 | Integrated visualization |
Built-in tools for visualizing data within the notebook environment. |
| 18 | Rich Output |
The ability to generate and display rich media outputs, enhancing the presentation of data. |
| 19 | Mathematical Formatting |
The support for MathJax and inline figures for better representation of mathematical content. |
| H | System Architecture and Accessibility |
The server-client architecture and web interface that enhance system usability, performance, and remote data visualization. |
| 20 | Remote Visualization |
The capability to visualize data on a remote server, enhancing accessibility and usability. |
| 21 | Web Interface |
The user-friendly web-based interface that facilitates access and interaction. |
| 22 | Server-Client Architecture |
The structure that allows interaction between the server and client, enhancing usability and performance. |
| I | Other topics |
Topics which could not be grouped into themes. |
| 23 | Python Support |
Strong support for Python enhances user experience and workflow efficiency. |
| 24 | Reliability |
The stability and low crash rate of Jupyter Notebook during use. |
| 25 | Efficiency |
Overall speed and practicality of the tool contribute to a smoother user experience. |
| A | Setup, Configuration, and Environment Management |
Problems with initial setup, configuration, managing dependencies, virtual environments, and ensuring consistent reproducibility across different setups. |
| 1 | Reproducibility concerns |
Challenges in ensuring consistent results across different environments and setups. |
| 2 | Environment management |
Challenges related to managing dependencies and virtual environments in Jupyter Notebook. |
| 3 | Setup and configuration issues |
Problems related to the initial setup and configuration of Jupyter Notebook, including security and dependency management. |
| 4 | Environment window absence |
Lack of a dedicated environment window for managing variables and dependencies. |
| B | Version Control and File Format Challenges |
Difficulties in managing notebook versions, integrating with systems like Git, and issues with notebook file formats that hinder version control. |
| 5 | File format compatibility |
Issues with notebook file formats that are not friendly for version control systems like Git. |
| 6 | Version control challenges |
Difficulties related to managing versions of notebooks and integrating with version control systems like Git. |
| C | User Interface and Experience Limitations |
Challenges with the user interface and overall experience, including drag-and-drop, scrolling, responsiveness, navigation, and interaction with notebook elements. |
| 7 | User experience issues |
Problems related to the overall user experience, including navigation and interaction with notebook elements. |
| 8 | User interface limitations |
Limitations in the user interface, such as lack of drag-and-drop functionality and strange scrolling behavior. |
| 9 | Performance problems |
Issues with the responsiveness and efficiency of the notebook interface. |
| 10 | Browser environment limitations |
Challenges related to the performance and compatibility of Jupyter Notebook in different browser environments. |
| D | Collaboration and Multi-user Support Challenges |
Issues related to collaborative work in Jupyter Notebook, including streaming output, awkward collaboration, and limited support for multiple users on Windows. |
| 11 | Multi-user support limitations |
Lack of support for multiple users working simultaneously in Jupyter Notebook on Windows. |
| 12 | Collaboration challenges |
Difficulties in working together with others in Jupyter Notebook, including issues with streaming output and awkward collaboration. |
| E | Documentation and Onboarding Challenges |
Insufficient or unclear documentation complicating Jupyter Notebook use and difficulties faced by beginners when first learning to use it. |
| 13 | Documentation gaps |
Insufficient or unclear documentation that complicates the use of Jupyter Notebook. |
| 14 | Onboarding difficulties |
Challenges faced by beginners when first using Jupyter Notebooks. |
| F | Data Handling and Display Issues |
Challenges with managing, processing, and displaying large datasets within Jupyter Notebook, including inline data display complexities. |
| 15 | Data display issues |
Problems related to the inline display of large datasets within Jupyter Notebook. |
| 16 | Data handling complexities |
Issues with managing and processing large datasets within Jupyter Notebook, requiring complex setups. |
| G | Code Editing and Navigation Difficulties |
Challenges related to coding in the notebook, navigating code, and organizing code cells, including limitations of the text editor and code folding. |
| 17 | Code navigation difficulties |
Issues with navigating and understanding code within the notebook environment. |
| 18 | Text editor limitations |
Concerns regarding the quality and customization options of the text editor used in Jupyter Notebook. |
| 19 | Cell organization challenges |
Difficulties in managing and organizing code cells within Jupyter Notebook, especially in larger notebooks. |
| 20 | Code editing experience |
Challenges related to coding directly in the notebook instead of a preferred text editor. |
| H | Output and Variable Management Issues |
Problems with controlling notebook output, managing variables, and the persistence of variables in deleted or modified cells. |
| 21 | Output management |
Issues with handling and controlling output, especially when dealing with large amounts of printed data. |
| 22 | Variable persistence issues |
Problems with the hidden persistence of variables created in deleted or modified cells. |
| I | Interactive Elements and Debugging Limitations |
Problems with the functionality of interactive features and concerns regarding the lack of debugging features in Jupyter Notebook. |
| 23 | Interactive elements challenges |
Problems with the functionality and usability of interactive features in Jupyter Notebook. |
| 24 | Debugging limitations |
Concerns regarding the lack of debugging features and the complexity of keyboard commands in Jupyter Notebook. |
| J | Workflow and Tool Integration Limitations |
Issues related to the constraints of a web-based workflow, integration with other tools, and limitations when editing on mobile devices. |
| 25 | Integration with other tools |
Challenges related to the compatibility and integration of Jupyter Notebook with other software and tools. |
| 26 | Mobile editing limitations |
Issues related to the reduced functionality and features available for editing notebooks on mobile devices. |
| 27 | Web-based workflow limitations |
Issues related to the constraints of a web-based workflow, such as copy-pasting code cells. |
| K | File and Local Module Management |
Problems with accessing and managing files, storing notebooks on local disks, and challenges when reimporting local modules without restarting the kernel. |
| 28 | File management issues |
Problems related to the ease of accessing and managing files in the Jupyter Notebook interface. |
| 29 | Local storage issues |
Challenges related to storing notebooks on local disks, affecting accessibility and collaboration. |
| 30 | Local module reimport issues |
Challenges faced when trying to reimport local modules after modifications without restarting the kernel. |
| L | Browser Dependency and Single Kernel Limitations |
Requirement of a web browser to run notebooks and difficulties associated with using a single kernel per notebook. |
| 31 | Single kernel usage |
Difficulties associated with using a single kernel per notebook, impacting workflow and functionality. |
| 32 | Browser dependency |
Requirement of a web browser to run notebooks, limiting their use as standalone Python command-line tools. |
| M | Searchability and Language Limitations |
Difficulties in finding up-to-date information and resources related to Jupyter Notebook and restrictions related to the use of only Python. |
| 33 | Searchability issues |
Difficulties in finding up-to-date information and resources related to Jupyter Notebook. |
| 34 | Language limitations |
Restrictions related to the use of only Python in Jupyter Notebook. |
| N | Other topics |
Topics which could not be grouped into themes. |
| 35 | Navigation and organization |
Issues with navigating and organizing content within Jupyter Notebooks, including the need for better display options. |
| A | Notebook Interface Customization |
Enhancements to Jupyter Notebook's user interface for improved visual layout, usability, text formatting, and screen space utilization. |
| 1 | Screen Utilization |
Enhancements for better use of screen space within the notebook. |
| 2 | Text Formatting Control |
Options to control text formatting, such as line width for better readability. |
| 3 | User Interface Enhancements |
Improvements to the visual layout and usability of Jupyter Notebook, including file system exploration and customizable settings. |
| B | Notebook Collaboration and Version Control |
Integration of version control systems and collaborative editing features to manage changes, track versions, and allow real-time multi-user editing in Jupyter Notebook. |
| 4 | Notebook Sharing |
Features that facilitate easy sharing of notebooks to cloud platforms like Git or Gist. |
| 5 | Collaborative editing |
Features that allow multiple users to edit notebooks in real-time. |
| 6 | Version Control Features |
Integration of version control systems to manage changes and track versions effectively. |
| 7 | Version control and collaboration |
Better integration of version control and collaborative features. |
| C | Notebook Content Management |
Features for managing notebook content including cell management, find and replace functionality, and exporting options. |
| 8 | Cell Management Features |
Options for selecting, moving, hiding, or disabling cells to enhance workflow organization. |
| 9 | Find and Replace Functionality |
Ability to find and replace text across all cells for improved editing efficiency. |
| 10 | Export Options |
Features that allow exporting specific cells to formats like PDF. |
| D | Notebook Extensibility and Interactive Features |
Options to extend Jupyter Notebook functionality with interactive visualizations, customizable settings, and integration with external tools. |
| 11 | Interactive Visualizations |
Enhancements for creating and managing interactive plots and widgets. |
| 12 | Tool integration |
Integration with external tools for enhanced data analysis. |
| 13 | Extensibility and Customization |
Desire for more options to extend functionality and customize the user experience in Jupyter Notebook. |
| E | Notebook Workflow Enhancements |
Improvements to Jupyter Notebook's workflow with features for live reloading, navigation, environment management, and task execution. |
| 14 | Environment management |
Features for creating and managing virtual environments within Jupyter Notebook. |
| 15 | Navigation enhancements |
Features to improve navigation within long notebooks. |
| 16 | Task management |
Enhancements in managing code dependencies and task execution. |
| 17 | Live Reloading |
Functionality that allows for real-time updates and changes without needing to restart the notebook. |
| F | Notebook Code Development |
Enhancements to the code development process in Jupyter Notebook, including code completion, execution feedback, editor improvements, and debugging tools. |
| 18 | Code editor improvements |
Upgrades to the code editing experience for better usability. |
| 19 | Debugging enhancements |
Improvements for integrating debugging tools and functionalities. |
| 20 | Code Completion Improvements |
Enhanced features for code completion to assist in writing code more efficiently. |
| 21 | Function Importing |
Improvements in importing functions from IPython. |
| 22 | Code execution feedback |
Indicators for when cell outputs are outdated due to changes in data. |
| G | Notebook File and State Management |
Features for managing files and the state of notebooks, including file type filtering, state saving, and kernel state management. |
| 23 | File management |
Features for managing files more effectively within Jupyter Notebook. |
| 24 | Notebook state management |
Improvements for saving and managing the state of notebooks, including freezing kernel states and modularization. |
| H | Notebook Documentation and Annotations |
Incorporation of markdown enhancements and annotation capabilities to improve documentation within Jupyter Notebook. |
| 25 | Markdown Enhancements |
Incorporation of spellcheck functionality in markdown cells. |
| 26 | Annotations |
The ability to add notes or comments directly to cells for better documentation. |
| I | Notebook Installation and Web Integration |
Simplified installation processes for Jupyter Notebook and built-in functions for web content integration. |
| 27 | Web integration |
Built-in functions for retrieving and displaying web content. |
| 28 | Installation Ease |
Simplified installation processes for various environments, including Windows and Jupyter Hub. |
| J | Notebook Application Development |
Capabilities for creating standalone web applications and exploring the workspace environment within Jupyter Notebook. |
| 29 | Workspace explorer |
A feature that provides an overview of the workspace, including variables and their states. |
| 30 | Standalone web applications |
Creating web applications using widgets that can function independently. |
| K | Other topics |
Topics which could not be grouped into themes. |
| 31 | Dataframe editing |
Improvements in editing pandas dataframes similar to spreadsheets. |