static-frame
mercury
static-frame | mercury | |
---|---|---|
8 | 77 | |
406 | 3,779 | |
1.0% | 1.0% | |
9.9 | 8.5 | |
1 day ago | 20 days ago | |
Python | Python | |
GNU General Public License v3.0 or later | GNU Affero General Public License v3.0 |
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.
static-frame
- Static-frame: Immutable/statically-typed DataFrames with runtime type validation
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Type-Hinting DataFrames for Static Analysis and Runtime Validation
This is inadequate, as it ignores the types contained within the container. A DataFrame might have string column labels and three columns of integer, string, and floating-point values; these characteristics define the type. A function argument with such type hints provides developers, static analyzers, and runtime checkers with all the information needed to understand the expectations of the interface. StaticFrame 2 now permits this:
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Memoizing DataFrame Functions: Using Hashable DataFrames and Message Digests to Optimize Repeated Calculations
StaticFrame is an alternative DataFrame library that offers efficient solutions to this problem, both for in-memory and disk-based memoization.
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The Performance Advantage of No-Copy DataFrame Operations
A NumPy array is a Python object that stores data in a contiguous C-array buffer. The excellent performance of these arrays comes not only from this compact representation, but also from the ability of arrays to share "views" of that buffer among many arrays. NumPy makes frequent use of "no-copy" array operations, producing derived arrays without copying underling data buffers. By taking full advantage of NumPy's efficiency, the StaticFrame DataFrame library offers orders-of-magnitude better performance than Pandas for many common operations.
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Which not so well known Python packages do you like to use on a regular basis and why?
static-frame. An immutable alternative to pandas.
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One Fill Value Is Not Enough: Preserving Columnar Types When Reindexing DataFrames
StaticFrame is an immutable DataFrame library that offers solutions to such problems. In StaticFrame, alternative fill value representations can be used to preserve columnar types in reindexing, shifting, and many other operations that require fill_value arguments. For operations on heterogeneously typed columnar data, one fill value is simply not enough.
- static-frame: Immutable and grow-only Pandas-like DataFrames with a more explicit and consistent interface.
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Bug Sur 11.4 stuttering issues on RX 6800
For me, one example of high cpu usage is when i visit links like this one (https://github.com/InvestmentSystems/static-frame/blob/master/static_frame/performance/core.py) on GitHub. Safari is extremely laggy when i do nothing more than just scrolling around. Do you have sth like this?
mercury
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Ask HN: What's the best charting library for customer-facing dashboards?
I'm build dashboards in Jupyter Lab. My plotting libraries are Altair, matplotlib, seaborn, Plotly - all work well in notebook.
My favorite is Altair. It provides interactivity for charts, so you can move/zoom your plots and have tooltips. It is much lighter than Plotly after saving the notebook to ipynb file. Altair charts looks much better than in matplotlib. One drawback, that exporting to PDF doesn't work. To serve notebook as dashboard with code hidden, I use Mercury framework, you can check example https://runmercury.com/tutorials/vega-altair-dashboard/
disclaimer: I'm author of Mercury framework https://github.com/mljar/mercury
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mercury VS solara - a user suggested alternative
2 projects | 13 Oct 2023
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Show HN: Web App with GUI for AutoML on Tabular Data
Web App is using two open-source packages that I've created:
- MLJAR AutoML - Python package for AutoML on tabular data https://github.com/mljar/mljar-supervised
- Mercury - framework for converting Jupyter Notebooks into Web App https://github.com/mljar/mercury
You can run Web App locally. What is more, you can adjust notebook's code for your needs. For example, you can set different validation strategies or evalutaion metrics or longer training times. The notebooks in the repo are good starting point for you to develop more advanced apps.
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streamlit VS mercury - a user suggested alternative
2 projects | 8 Jul 2023
- GitHub - mljar/mercury: Convert Jupyter Notebooks to Web Apps
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[P] Opinionated Web Framework for Converting Jupyter Notebooks to Web Apps
The GitHub repository https://github.com/mljar/mercury
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Show HN: Opinionated Web Framework for Converting Jupyter Notebooks to Web Apps
We are working on open-source web framework Mercury that converts Python notebooks to Web Apps.
It is very opinionated:
- it has no callbacks - we automatically re-execute cells below updated widget
- it has no layout widgets, all input widgets are always in the left sidebar
Thanks to above decisions you don't need to change notebook's code to have web app and fit to the framework.
The simplicity of the framework is very important to us. We also care about deployment simplicity. That's why we created a shared hosting service called Mercury Cloud. You can deploy notebook by uploading a file.
The GitHub repository https://github.com/mljar/mercury
Documentation https://RunMercury.com/docs/
Mercury Cloud https://cloud.runmercury.com
- Show HN: Build Web Apps in Jupyter Notebook with Python Only
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[OC] Analyzing 15,963 Job Listings to Uncover the Top Skills for Data Analysts (update)
Analysis was done in Jupyter Notebook with Python 3.10, Pandas, Matplotlib, wordcloud and Mercury framework.
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[OC] Data Analyst Skills in need based on 15,963 job listings
Analysis was done in Jupyter Notebook with Python 3.10 kernel, Pandas, Matplotlib, wordcloud and Mercury framework to share notebook as a web application with widgets and code hidden. Gif created in Canva.
What are some alternatives?
pandas-ta - Technical Analysis Indicators - Pandas TA is an easy to use Python 3 Pandas Extension with 150+ Indicators
streamlit - Streamlit — A faster way to build and share data apps.
pandastable - Table analysis in Tkinter using pandas DataFrames.
voila - Voilà turns Jupyter notebooks into standalone web applications
python-lenses - A python lens library for manipulating deeply nested immutable structures
papermill - 📚 Parameterize, execute, and analyze notebooks
bidict - The bidirectional mapping library for Python.
voila-gridstack - Dashboard template for Voilà based on GridStackJS
bambi - BAyesian Model-Building Interface (Bambi) in Python.
jupytext - Jupyter Notebooks as Markdown Documents, Julia, Python or R scripts
rubygems - Library packaging and distribution for Ruby.
awesome-streamlit - The purpose of this project is to share knowledge on how awesome Streamlit is and can be