March 2025
Intermediate to advanced
504 pages
12h 5m
Italian
Content preview from Python Polars: la guida definitiva
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Capitolo 4. Strutture dati e tipi di dati
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Ora che sei stato presentato correttamente, è il momento di concentrarci sul funzionamento di Polars.
I dati sono di molte forme e dimensioni e per poterli utilizzare è necessario memorizzarli in strutture adeguate. Per poter gestire tutti i dati con cui lavorerai, Polars implementa la specifica di memoria Arrow, che offre una vasta gamma di tipi di dati.
In questo capitolo imparerai a conoscere:
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Le strutture che Polars utilizza per memorizzare i dati
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I diversi tipi di dati disponibili
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Alcuni tipi di dati che non sono così immediati
Iniziamo il bellissimo viaggio di conoscenza dei polacchi.
Le istruzioni per ottenere i file di cui potresti aver bisogno sono riportate nel Capitolo 2. Partiamo dal presupposto che i file si trovino nella sottodirectory dei dati.
Serie, DataFrames e LazyFrames
Polars memorizza tutti i suoi dati in una Serie o in un DataFrame.
Una Serie è una struttura di dati unidimensionale che contiene una sequenza di valori. Tutti i valori di una Serie hanno lo stesso tipo di dati, come gli interi, i float o le stringhe. Le Serie possono esistere da sole, ma sono più comunemente utilizzate come colonne in un DataFrame.
Un esempio di serie è il seguente:
sales_series=pl.Series("sales",[150.00,300.00,250.00])sales_series
shape: (3,) Series: 'sales' [f64] [ 150.0 300.0 250.0 ...
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