October 2024
Intermediate to advanced
226 pages
6h 1m
Spanish
Content preview from Escalando Python con Dask
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Capítulo 5. Las colecciones de Dask
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Hasta ahora has visto los fundamentos de cómo se construye Dask, así como la forma en que Dask utiliza estos bloques de construcción para apoyar la ciencia de datos con DataFrames. Este capítulo explora dónde son más apropiadas las interfaces bag y array de Dask, que a menudo se pasan por alto, en relación con los DataFrames. Como se mencionó en "Hola Mundos", las bolsas de Dask implementan API funcionales comunes, y las matrices de Dask implementan un subconjunto de matrices de NumPy.
Consejo
Entender la partición es importante para entender las colecciones. Si te saltaste "Particionar/unir colecciones", ahora es un buen momento para volver atrás y echar un vistazo.
Matrices Dask
Las matrices Dask implementan un subconjunto de la interfaz ndarray de NumPy, lo que las hace ideales para portar código que utiliza NumPy para ejecutarlo en Dask. Gran parte de tus conocimientos del capítulo anterior sobre DataFrames se trasladan a las matrices Dask, así como gran parte de tus conocimientos sobre ndarrays.
Casos de uso habituales
Algunos casos habituales de uso de las matrices de Dask son:
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Imágenes a gran escala y datos astronómicos
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Datos meteorológicos
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Datos multidimensionales
Al igual que ocurre con los DataFrames y los pandas de Dask, si no utilizarías un nparray para el problema a menor escala, puede que un array de ...
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I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
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.