🚀How to scale up Pandas with the Pandarallel library
Every Data Scientist knows that the Pandas Python library is quite slow and doesn't allocate large amounts of data. However, every Data Scientist uses it. 🤷‍♀️ To make Pandas faster, you can include Pandarallel in your project, a simple and convenient tool for parallelizing Pandas operations on all available processors.
Pandas only uses a single CPU profit, while Pandarallel allows you to profit from a multi-core computer. Pandarallel also offers progress bars for programs available on the laptop and terminal to get a rough idea of ​​the remaining results of the calculations that are needed to collect data.
The library can be used on any computer running Linux and macOS, and on Windows there are small quirks: due to the multiprocessor system, the function that is offered in Pandarallel must be standalone and must not be excluded from external sources.
https://nalepae.github.io/pandarallel/