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Introduzione a BigQuery DataFrames
BigQuery DataFrames è un insieme di librerie Python open source che ti consentono
di sfruttare l'elaborazione dei dati BigQuery utilizzando API Python
familiari. BigQuery DataFrames fornisce un DataFrame Pythonic basato
sul motore BigQuery e implementa le API pandas e
scikit-learn eseguendo l'elaborazione in BigQuery
tramite la conversione SQL. In questo modo puoi utilizzare BigQuery per esplorare
ed elaborare terabyte di dati, nonché addestrare modelli di machine learning (ML),
il tutto con le API Python.
Il seguente diagramma descrive il flusso di lavoro di BigQuery DataFrames:
Vantaggi di BigQuery DataFrames
BigQuery DataFrames esegue le seguenti operazioni:
Offre più di 750 API pandas e scikit-learn implementate tramite
la conversione SQL trasparente in BigQuery e
le API BigQuery ML.
Rimanda l'esecuzione delle query per migliorare le prestazioni.
Estende le trasformazioni dei dati con funzioni Python definite dall'utente per consentirti di elaborare i dati in Google Cloud. Queste funzioni vengono
implementate automaticamente come
funzioni remote di BigQuery.
Si integra con Vertex AI per consentirti di utilizzare i modelli Gemini
per la generazione di testo.
Per maggiori dettagli, consulta la directory
third_party/bigframes_vendored
nel repository GitHub di BigQuery DataFrames.
Quote e limiti
Le quote di BigQuery si applicano a
BigQuery DataFrames, inclusi componenti hardware, software e di rete.
È supportato un sottoinsieme di API pandas e scikit-learn. Per ulteriori
informazioni, consulta
API pandas supportate.
Devi eseguire esplicitamente la pulizia di tutte le funzioni Cloud Run create automaticamente
nell'ambito della pulizia della sessione. Per maggiori informazioni, vedi
API pandas supportate.
Prezzi
BigQuery DataFrames è un insieme di librerie Python open source
disponibili per il download senza costi aggiuntivi.
BigQuery DataFrames utilizza BigQuery,
Cloud Run Functions, Vertex AI e altri
Google Cloud servizi, che comportano costi propri.
Durante l'utilizzo regolare, BigQuery DataFrames archivia i dati temporanei,
come i risultati intermedi, nelle tabelle BigQuery. Queste
tabelle vengono conservate per sette giorni per impostazione predefinita e ti vengono addebitati i dati
memorizzati al loro interno. Le tabelle vengono create nel set di dati _anonymous_
nel progetto Google Cloud che specifichi nell'opzione
bf.options.bigquery.project.
[[["Facile da capire","easyToUnderstand","thumb-up"],["Il problema è stato risolto","solvedMyProblem","thumb-up"],["Altra","otherUp","thumb-up"]],[["Difficile da capire","hardToUnderstand","thumb-down"],["Informazioni o codice di esempio errati","incorrectInformationOrSampleCode","thumb-down"],["Mancano le informazioni o gli esempi di cui ho bisogno","missingTheInformationSamplesINeed","thumb-down"],["Problema di traduzione","translationIssue","thumb-down"],["Altra","otherDown","thumb-down"]],["Ultimo aggiornamento 2025-09-04 UTC."],[[["\u003cp\u003eBigQuery DataFrames are open-source Python libraries that enable users to leverage BigQuery's data processing power through familiar Python APIs.\u003c/p\u003e\n"],["\u003cp\u003eIt offers over 750 implemented pandas and scikit-learn APIs by converting them transparently into SQL for BigQuery and BigQuery ML API processing.\u003c/p\u003e\n"],["\u003cp\u003eBigQuery DataFrames enhances performance by deferring query execution and allowing user-defined Python functions for data transformation, which are automatically deployed as BigQuery remote functions.\u003c/p\u003e\n"],["\u003cp\u003eThe libraries integrate with Vertex AI for text generation with Gemini models, alongside other external packages like Ibis, pandas, and scikit-learn, and is distributed under the Apache-2.0 license.\u003c/p\u003e\n"],["\u003cp\u003eUsers should be aware of BigQuery quotas, the subset of supported pandas and scikit-learn APIs, and that the usage of BigQuery, Cloud Run functions, and Vertex AI may incur additional costs.\u003c/p\u003e\n"]]],[],null,["# Introduction to BigQuery DataFrames\n===================================\n\nBigQuery DataFrames is a set of open source Python libraries that let\nyou take advantage of BigQuery data processing by using familiar\nPython APIs. BigQuery DataFrames provides a Pythonic DataFrame powered\nby the BigQuery engine, and it implements the pandas and\nscikit-learn APIs by pushing the processing down to BigQuery\nthrough SQL conversion. This lets you use BigQuery to explore\nand process terabytes of data, and also train machine learning (ML) models,\nall with Python APIs.\n\nThe following diagram describes the workflow of BigQuery DataFrames:\n\n| **Note:** There are breaking changes to some default parameters in BigQuery DataFrames version 2.0. To learn about these changes and how to migrate to version 2.0, see [Migrate to BigQuery DataFrames\n| 2.0](/bigquery/docs/use-bigquery-dataframes#version-2).\n\nBigQuery DataFrames benefits\n----------------------------\n\nBigQuery DataFrames does the following:\n\n- Offers more than 750 pandas and scikit-learn APIs implemented through transparent SQL conversion to BigQuery and BigQuery ML APIs.\n- Defers the execution of queries for enhanced performance.\n- Extends data transformations with user-defined Python functions to let you process data in Google Cloud. These functions are automatically deployed as BigQuery [remote functions](/bigquery/docs/remote-functions).\n- Integrates with Vertex AI to let you use Gemini models for text generation.\n\nLicensing\n---------\n\nBigQuery DataFrames is distributed with the\n[Apache-2.0 license](https://github.com/googleapis/python-bigquery-dataframes/blob/main/LICENSE).\n\nBigQuery DataFrames also contains code derived from the following\nthird-party packages:\n\n- [Ibis](https://ibis-project.org/)\n- [pandas](https://pandas.pydata.org/)\n- [Python](https://www.python.org/)\n- [scikit-learn](https://scikit-learn.org/)\n- [XGBoost](https://xgboost.readthedocs.io/en/stable/)\n\nFor details, see the\n[`third_party/bigframes_vendored`](https://github.com/googleapis/python-bigquery-dataframes/tree/main/third_party/bigframes_vendored)\ndirectory in the BigQuery DataFrames GitHub repository.\n\nQuotas and limits\n-----------------\n\n- [BigQuery quotas](/bigquery/quotas) apply to BigQuery DataFrames, including hardware, software, and network components.\n- A subset of pandas and scikit-learn APIs are supported. For more information, see [Supported pandas APIs](/python/docs/reference/bigframes/latest/supported_pandas_apis).\n- You must explicitly clean up any automatically created Cloud Run functions functions as part of session cleanup. For more information, see [Supported pandas APIs](/python/docs/reference/bigframes/latest/supported_pandas_apis).\n\nPricing\n-------\n\n- BigQuery DataFrames is a set of open source Python libraries available for download at no extra cost.\n- BigQuery DataFrames uses BigQuery, Cloud Run functions, Vertex AI, and other Google Cloud services, which incur their own costs.\n- During regular usage, BigQuery DataFrames stores temporary data, such as intermediate results, in BigQuery tables. These tables persist for seven days by default, and you are charged for the data stored in them. The tables are created in the `_anonymous_` dataset in the Google Cloud project you specify in the [`bf.options.bigquery.project` option](/python/docs/reference/bigframes/latest/bigframes._config.bigquery_options.BigQueryOptions).\n\nWhat's next\n-----------\n\n- Try the [BigQuery DataFrames quickstart](/bigquery/docs/dataframes-quickstart).\n- Learn how to [use BigQuery DataFrames](/bigquery/docs/use-bigquery-dataframes).\n- Learn how to [visualize graphs using BigQuery DataFrames](/bigquery/docs/dataframes-visualizations).\n- Learn how to [use the `dbt-bigquery` adapter](/bigquery/docs/dataframes-dbt)."]]
