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Interactive Data Analysis (23)
Data Transformations (23)
Connect to Multiple Data Sources (23)
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- Easy to use
- Easy to understand
- User Interface can be improved
- More features in online Jupyter lab can be added
- Good & free documentation
- Coding and error correction line by line
- Simple and Effectiveness
- Easy to use for visualisation and presentation of code
- Could be used at any place any time without hassle
- could have some standard python libraries imported already
- compiled images and results should be alterable in size
- compiler runtime
- Great interface.
- Segments for codes.
- Markdown for code explanation.
- Sometimes I have to restart it when I import any new python library.
- Should be available for more languages.
- Giving presentation to a client or explaining your code to a colleague
- Developing code snippets for big or small projects
- Easy to share your work with others
- Highly recommended data analysis presentation
- Need more Hotkeys for creating a beautiful notebook. Sometimes we need to download other plugins which messes [with] its default settings.
- Not as powerful as IDE, which sometimes makes [the] job difficult and allows duplicate code as it get confusing when the number of lines increases. Need a feature where [an] error comes if duplicate code is found or [if a] developer tries the same function name.
- We can use it as a notebook and share the slide and also publish it online through GitHub.
- Attractive programming environment.
- Easy navigation platform.
- Should work on Configuration setup, it takes a lot of time.
- Work on code styling correction, sometimes it makes a major difference.
- There is no IDE integration.
- Should include more programming language.
- Code presentation- with Jupyter Notebook you can deploy codes and markdowns which makes the code easy to read and understand.
- User interface- the user interface of the Jupyter Notebook is very smooth, there are a lot of easy shortcuts as well the icons to make our work easier
- Server hosting- with Jupyter Notebook server hosting is very easy which adds to the security feature
- no code style correction- it doesn't have any functionality to auto correct the code style such as spaces.
- sometimes while executing the script the system gets frozen.
- no third party app integration
- Sharing/showcasing work in a step by step manner
- Exploratory data analysis/viewing code in-line
- Data exploration/visualization
- Switch between different coding languages
- No IDE integration/linting
- No testing integrations
- Difficult to view changes in GitHub
- Notebook harder to productionize than scripts
- Visually intuitive organization of code.
- Static (but changeable) display of function outputs.
- Easy replication of notebooks or into new notebooks, or into PDFs.
- I'd like to see bookmarks made available for easier scrolling through long notebooks.
- A dark mode option would be helpful, too.
- I wish that the display of graphics would be a little bit more customizable as a native function of Jupyter.
- User-friendly UI.
- Easy to debug at each code line.
- Great support for Python Math libraries.
- Advanced data visualization capabilities.
- Notebook sharing feature.
- Intellisense not up to the mark.
- Limited collaboration scope.
- No IDE integration supported.
- Can become sluggish at times when datasets are huge.
- Simple and elegant code writing ability. Easier to understand the code that way.
- The ability to see the output after each step.
- The ability to use ton of library functions in Python.
- Easy-user friendly interface.
- The ability to use night mode background.
- The ability to select multiple lines of code to run and see the output.
- The ability to open .py extension file as well along with .ipynb
- More languages support
- Enhanced GPU support
- Multi-environment support
- Easy to use and handle as it takes less hardware requirements.
- Helps [in] creating the data visualization.
- Provide support for difficult language.
- Needs more code styling.
- The markdown is limited.
- For the new user, there [should be] some intro videos or tutorial for that.
- Its one by one cell execution.
- It is user friendly and easy to work on.
- Only installing Anaconda can bring us to use this Jupyter Notebook easily.
- Freely available.
- Sometimes we face issues in executing the code because Jupyter Notebook hangs.
- Auto save option is not there which sometimes creates issues in saving your work.
- Markdown for comments/explanations.
- Interactive programming.
- Easy to use and share notebooks.
- Doesn't have some features that competitors have.
- Difficult to do direct collaboration on the same notebook.
- Doesn't provide great code style support/corrections.
- Easy to learn and use
- Data [modeling]
- Data analysis and reporting
- Predefined visualizations models
- Not much troubleshooting support available
- No support for IDE
- Deployment is complex for cloud environments
- Great visualizations
- Easy to edit and track data flows
- Colaborative working
- Literate programming
- Interactive programming
- Server side deployment supporting many users to code via web browser.
- Lacks some features like autoindent code.
- Too many different instructions for deployment.
- Could have better environment isolation.
- Data Analytics
- easy visualization
- ML possibilities
- slows down device sometimes
- can't identify syntax errors
- no collaboration
- Easy and interactive Python environment.
- Latex markdown for explanations.
- Terminal access through cell itself.
- Fast Intellisense.
- Documentation access through cell commands.
- Intuitive Key Bindings.
- Creating and installing a virtual environment can be tricky.
- Conda environment can be granular to work with.
- Data plotting
- Easy to switch settings and see the changes right away in the plots.
- Easy user interface, and commands.
- None that I can think of.
- Data Analysis
- Limited Markdown Styling
- Complex to handle multiple kernels
- Difficult python code styling
It is less appropriate for any kind of python development as I have mainly used it for documentation, data handling, cleaning or visualizations.
- Markdown presentation
- Easy to learn
- Good organization
- Can be more visually appealing to users
- Easier ways to link projects to websites like Wix
- Ability to work collaboratively on the platform
Not so appropriate if you want to make codes that are constantly running.
- Big data analysis on cloud desktop.
- Exploratory analysis.
- Common machine learning models.
- Nicer output format for explanatory analysis.
- Easy update on packages.
- Better compatibility with AWS tools.
1) analyze big data above millions of records
2) develop machine learning codes that can be deployed in production system
1) quick and easy statistical analysis
2) entry level users
- Saves the results until next time the notebook is reopened
- Provides so many libraries
- Allows to code on multiple platforms
- Automatically creates checkpoints
- Really lightweight
- Would like to see syntax errors highlighted while coding
- PDF integration should get better to print notebook results