dtale
Probabilistic-Programming-and-Bayesian-Methods-for-Hackers
dtale | Probabilistic-Programming-and-Bayesian-Methods-for-Hackers | |
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46 | 30 | |
4,550 | 26,362 | |
0.8% | - | |
8.1 | 0.0 | |
6 days ago | 5 months ago | |
TypeScript | Jupyter Notebook | |
GNU Lesser General Public License v3.0 only | MIT License |
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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
Probabilistic-Programming-and-Bayesian-Methods-for-Hackers
- Probabilistic Programming and Bayesian Methods for Hackers (2013)
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[Q] Bayesian statistics!
Also this is quite nice practical introduction which might help with finding answers to your questions: https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers
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How many of you have used algebra, calculus, geometry, etc in your business careers/the real world?
This is a good intro to probabilistic programming.
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Suggestions for some best books on computer vision
Probabilistic programming is a nice technique to have up your sleeve.
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Bayes examples and study help
+1 for Statistical Rethinking. I’m also partial to Bayesian Methods for Hackers.
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✨ 10 Free Books for Machine Learning & Data Science 📚
🔗 https://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/
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Predicting the distribution of a variable rather than a point estimate
You’re welcome! I would recommend Bayesian Methods for Hackers
- Bayesian Methods for Hackers
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A collaborative book on DeFi
All content is open-source: everyone is free to read, but also to contribute to the book using github. I know of one other book that followed this open-source 'publishing' model and became quite successful eventually through community efforts. I contemplated for a bit to create a book DAO but I think it's going to be overkill :).
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[R] Analysis of Russian vaccine trial outcomes suggests they are lazily faked. Distribution of efficacies across age groups is quite improbable
Jake Vanderplas's Statistics for Hackers presentation is a perfect place to start. Bayesian Methods for Hackers is also very good.
What are some alternatives?
PandasGUI - A GUI for Pandas DataFrames
NLP-Model-for-Corpus-Similarity - A NLP algorithm I developed to determine the similarity or relation between two documents/Wikipedia articles. Inspired by the cosine similarity algorithm and built from WordNet.
ydata-profiling - 1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.
JLee_LinearOptimizationBook
jupyterlab-autoplot - Magical Plotting in JupyterLab
clojure-style-guide - A community coding style guide for the Clojure programming language
pandastable - Table analysis in Tkinter using pandas DataFrames.
paip-lisp - Lisp code for the textbook "Paradigms of Artificial Intelligence Programming"
sqliteviz - Instant offline SQL-powered data visualisation in your browser
Scala school - Lessons in the Fundamentals of Scala
best-of-ml-python - 🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.
guide.elm-lang.org - My book introducing you to Elm!