Using the `xl` function, you can load connected data into a Pandas DataFrame, seamlessly combining Excel’s familiar interface with Python’s advanced analytical tools.

In the world of data cleaning, two approaches often stand out: Python, the go-to for coding enthusiasts, and Excel’s Power Query, a code-free, user-friendly alternative.

It’s also possible to interface with Python code by way of the PyCall library, and even share data between Python and Julia. Julia supports metaprogramming.

As a general-purpose language, Python has an advantage over R, Khalusova said. Python is more production-friendly, and it's easier to learn -- both for beginners and those who switch to it from other ...

Data handling for energy system optimisation models is a necessary but tedious task. Depending on the workflow, user skill level, and model implementation, the data interfacing requirements can be ...