March 2025
Intermediate to advanced
504 pages
12h 5m
Italian
Content preview from Python Polars: la guida definitiva
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Capitolo 13. Riassumere e aggregare
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Riassumere e aggregare i dati è un passo fondamentale per trasformare i dataset grezzi in approfondimenti significativi. Che tu stia lavorando con i dati di vendita, le informazioni sui clienti o le letture dei sensori, la capacità di raggruppare e aggregare i tuoi dati ti permette di rispondere a domande importanti e di identificare tendenze che altrimenti potrebbero rimanere nascoste.
Ti ritroverai spesso a fare domande come:
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"Qual è il ricavo medio delle vendite per negozio?".
-
"Quanti prodotti unici ha acquistato ogni cliente?".
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"Qual è la spesa totale per categoria di prodotto ogni mese?".
Sono proprio questi i tipi di domande a cui l'aggregazione ti aiuta a rispondere. Raggruppando i tuoi dati in base a una o più colonne e poi eseguendo calcoli - come somme, medie o conteggi - puoi ottenere una comprensione più chiara degli schemi sottostanti al tuo set di dati.
Polars rende questo processo semplice ed efficiente con il metodo df.group_by(), che ti permette di raggruppare il tuo DataFrame per una o più colonne ed espressioni. Una volta raggruppati i dati, puoi applicare una serie di funzioni di aggregazione per riassumere i risultati. Ad esempio, puoi calcolare la somma, la media o la mediana per ogni gruppo, oppure contare il numero di righe in ogni gruppo.
In questo capitolo imparerai a conoscere: ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.