Do you have the need to import quantities from #CSV files in order to process them with #Python and you'd like to use #pandas and #pint for that purpose?
I run quite frequently into this situation and created an importer for it, which I just added to the #QuanTables repo:
I’ve just deposited some Python code in a public codeberg repo 🚀
The modules are designed to simplify GUI creation with PySide6 (Qt), specifically tailored for handling quantities (with uncertainties). Perfect for anyone working on scientific or engineering applications where precision matters.
🔗 https://codeberg.org/Cs137/QuanTables
Feel free to explore, fork, and contribute! 🛠️✨
#Python #PySide6 #Qt #pint #uncertainties #UnPaAc #OpenSource #codeberg #QuanTables
Since I'd like to display a #pandas DataFrame containing Pint Uncertainty Series in a GUI using `QtTableView` widgets, I drafted a module that provides a suitable `QAbstractTableModel` derivation.
The output is customisable: `deconvolute` toggles the way #uncertainties are displayed, `significant_digits` should be self-explanatory, and the `unit_separator` is placed between the measure and the #unit in the header.
I played a bit with integrating *Pint Uncertainty Series* containing DFs into `QtTableView` widgets to display the data in a GUI. Here is the resulting module, in case someone has a similar need (the `MainWindow` class serves only as an example). The output can be adjusted via the `deconvolute`, ...
Last week I reported about some #pandas accesors I released (#UnPaAc, #NuPaAc). Over the weekend, I thought about a generic approach to making class methods and properties available via series accessors, and wrote a prototype. I stuck to my naming pattern and called it "Dynamic Pandas Accessors" #DynPaAc.
I was curious what other users thought of it, so I published it on #PyPI. It should be useful in #DataScience and for #python #developers.
🔗 https://codeberg.org/Cs137/DynPaAc
Feedback appreciated 🙂
The #UnPaAc package is now available on #PyPI. One advantage compared to #pint and #uncertainties (including their #pandas integrations) is, that it allows to store and restore DataFrames with Series containing #units and uncertainties to/from csv files. An example workflow of this procedure is described in the package's readme file: https://codeberg.org/Cs137/UnPaAc#save-a-dataframe-to-csv-and-restore-dataframe-from-csv
I created accessors which simplify working with #uncertainties and #units in #pandas objects. Since the weekend was to short, the package is not ready for a #pypi deposition yet, but available via the #codeberg repo:
https://codeberg.org/Cs137/UnPaAc
Feedback appreciated 🙂
Schreini
