regular-table
dtale
regular-table | dtale | |
---|---|---|
6 | 46 | |
326 | 4,573 | |
2.5% | 1.3% | |
5.6 | 8.1 | |
20 days ago | 14 days ago | |
JavaScript | TypeScript | |
Apache License 2.0 | GNU Lesser General Public License v3.0 only |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
regular-table
- Memray is a memory profiler for Python by Bloomberg
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Show HN: Datagridxl2.js – No-nonsense fast Excel-like data table library
No reason to sacrifice accessibility and styling in the name of performance: https://github.com/jpmorganchase/regular-table
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Quadrillion Rows Example
I'm the author of regular-table, one of the alternatives mentioned in the accompanying blog. We went through a similar set of requirements to OP (and did at one point use fin-hypergrid as well, and also the excellent phosphor-datagrid which is now lumino-datagrid), and came to a slightly different conclusion regarding rendering. We ultimately decided to revert to (albeit bespoke) virtual DOM rendering for many of the same reasons mentioned by other commenters, namely:
- Ask HN: Why are developers so stingy with “rows per page”?
dtale
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The free pandas visualizer, D-Tale, has now been integrated with ArcticDB which will allow users to load huge datasets and easily navigate their databases
[D-Tale](https://github.com/man-group/dtale) has recently released version 3.2.0 on pypi & conda-forge: ``` pip install -U dtale conda install dtale -c conda-forge ``` But if you want to take it one step further you can now integrate it with [ArcticDB](https://github.com/man-group/ArcticDB): ``` pip install -U dtale[arcticdb] ``` This allows you the ability to navigate your libraries of datasets saved to your ArcticDB database! But the best part is that all the reads are occuring directly against ArcticDB so some of the memory constraints you may have been hit with before are now a thing of the past. Here's a full write up how to use this functionality along with a quick demo: https://github.com/man-group/dtale/blob/master/docs/arcticdb/ARCTICDB\_INTEGRATION.md Hope this helps & please support open-source by throwing your star on the [repo](https://github.com/man-group/dtale). Thanks! 🙏
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Data Scientists using neovim: how do you explore dataframes?
I've looked into external tooling, libs such as dtale, which feel overly complicated for my use case (but I'm open to alternatives). What I would like to have instead is something akin to Spyder's variable viewer, which allows sorting by column. VSCode goes a step further and also provides the ability to filter the dataframe.
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I need help lol
D-Tale: A Python library that provides an interactive web-based interface for data exploration and analysis.
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Something better than pandas? with interactive graphical UI?
Try this: https://github.com/man-group/dtale
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Mito – Excel-like interface for Pandas dataframes in Jupyter notebook
https://github.com/man-group/dtale
I find that I'm actually a lot faster using basic Pandas methods to get the data I want in exactly the form I want it.
If I really want to show everything, I just use:
'''
- Memray is a memory profiler for Python by Bloomberg
- Show HN: D-Tale, easy to use pandas GUI
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Added visualizations of statsmodels time series analysis functions to the free pandas visualizer, D-Tale
Just added "Time Series Analysis" in v1.60.1 of D-Tale on pypi & conda-forge: pip install -U dtale conda install dtale -c conda-forge This feature provides a quick and easy way to visualize the usage of the following time series analysis function in statsmodels:
- Show HN: Open-source pandas dataframe visualizer
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For all the python/pandas users out there I just released a bunch of UI updates to the free visualizer, D-Tale
Your data is stored in memory so the size of your dataframe is limited to the memory of your machine. That being said we’ve allowed users to swap out the machanism which stores the data so you can use something like Redis or Shelve to allieviate memory. Here’s some documentation: https://github.com/man-group/dtale/blob/master/docs/GLOBAL_STATE.md
What are some alternatives?
react-virtualized - React components for efficiently rendering large lists and tabular data
PandasGUI - A GUI for Pandas DataFrames
magic-trace - magic-trace collects and displays high-resolution traces of what a process is doing
ydata-profiling - 1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.
memray - Memray is a memory profiler for Python
jupyterlab-autoplot - Magical Plotting in JupyterLab
Glide Data Grid - 🚀 Glide Data Grid is a no compromise, outrageously react fast data grid with rich rendering, first class accessibility, and full TypeScript support.
pandastable - Table analysis in Tkinter using pandas DataFrames.
canvas-datagrid - Canvas based data grid web component. Capable of displaying millions of contiguous hierarchical rows and columns without paging or loading, on a single canvas element.
sqliteviz - Instant offline SQL-powered data visualisation in your browser
reactabular - A framework for building the React table you need (MIT)
best-of-ml-python - 🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.