Pandas is easily the most used library when it comes to analyzing and cleaning the data. However, it is not the preferred choice when dealing with large datasets, due to its single-core processing.
However, its processing can be boosted by dividing the task among multiple CPU cores. This is achieved using a Pandas plugin called Pandarallel (Pandas+Parallel). Nice name, isnāt it?
Performing complex operations on Pandas DataFrame can be done using Pandarallelās parallel_apply function just like Pandas standard apply function. Parallel
uses standard multiprocessing and assigns sub-processes to each CPU to work separately to reduce total processing time.
Note that Pandarallel wonāt be the right choice when dealing with Big Data. There are other frameworks for that such as Dask and Vaex (more on these later).
Check out the time differences between standard Pandas apply and Pandarallel apply below!