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Reactive Python for data science : push-based data analysis with RxPy /
"Reactive programming is shaping the future of how we model data. With reactive, not only can you concisely wrangle and analyze static data, you can effectively work with data as a real-time infinite feed. Reactive Extensions (Rx) first gained traction in 2009 and has been ported to over a doze...
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | |
| Formato: | Electrónico Video |
| Idioma: | Inglés |
| Publicado: |
[Place of publication not identified] :
O'Reilly,
[2016]
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 245 | 1 | 0 | |a Reactive Python for data science : |b push-based data analysis with RxPy / |c with Thomas Nield. |
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| 511 | 0 | |a Presenter, Thomas Nield. | |
| 500 | |a Title from title screen (viewed February 1, 2017). | ||
| 520 | |a "Reactive programming is shaping the future of how we model data. With reactive, not only can you concisely wrangle and analyze static data, you can effectively work with data as a real-time infinite feed. Reactive Extensions (Rx) first gained traction in 2009 and has been ported to over a dozen major languages and platforms. In this course, you'll learn to use RxPy, a lightweight Rx library, in Python data analysis workflows. It's designed for basic Python users who want to move beyond ad hoc data analysis and make their code geared toward a production environment, as well as for programmers familiar with Scala, Java 8, C#, Swift, and Kotlin who are interested in using the modern higher-order functional chain patterns from those languages."--Resource description page. | ||
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| 650 | 6 | |a Exploration de données (Informatique) | |
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