The problem While building a webapp for text analysis and NLP things, I noticed that the project was getting bigger than expected, and having everything run in a single monolithic dash instance was too messy. Therefore, I started looking into how I could combine several dash instances, while still working on a single project and single api server. Needless to say, it was a bit tricky at times, and Dash requires you to do some silly things if you want to decouple and organize your project into several modules.

Table of Contents Rendere le operazioni in Pandas più veloci con multiprocessing Gestire operazioni con concurrent.futures Pandarallel Pandarallel su un’operazione lunga: Rendere le operazioni in Pandas più veloci con multiprocessing Quante volte sarà capitato di avere un nuovo dataset sotto le mani, appena scaricato o appena finito di mettere insieme con dati presi dal web, e voler subito provare qualche funzione sul dataframe per pulirlo, filtrarlo o aggiungere flags e fare df.

Unsupervised Vowels The difference between supervised and unsupervised learning is that the first requires human help to learn to categorize data, while the latter does not need anything more than the data itself to learn and propose its own classification groups. In the future A.I. will conquer the world with supercyborgs, at least that's what I got from tabloids, so it would be better for these cyborg to learn how to distinguish between a u and a i, otherwise there might be the chance that instead of "fire!

There are many ways to represent words, texts, or even speech. One thing they have in common is that, excluding idioms and aphorisms, it's impossible for two texts or speeches to be the same, because there is a lot of variation in the words and phrases they contain. An exception to this, however, are poems and songs. Poems and songs rely on repetition not only of melodies but also of words and phrases.