Carica dati da DataFrame

Caricare i contenuti di un DataFrame Pandas in una tabella.

Esempio di codice

Python

Prima di provare questo esempio, segui le istruzioni di configurazione di Python nella guida rapida di BigQuery per l'utilizzo delle librerie client. Per saperne di più, consulta la documentazione di riferimento dell'API BigQuery Python.

Per eseguire l'autenticazione in BigQuery, configura le Credenziali predefinite dell'applicazione. Per saperne di più, vedi Configurare l'autenticazione per le librerie client.

import datetime

from google.cloud import bigquery
import pandas
import pytz

# Construct a BigQuery client object.
client = bigquery.Client()

# TODO(developer): Set table_id to the ID of the table to create.
# table_id = "your-project.your_dataset.your_table_name"

records = [
    {
        "title": "The Meaning of Life",
        "release_year": 1983,
        "length_minutes": 112.5,
        "release_date": pytz.timezone("Europe/Paris")
        .localize(datetime.datetime(1983, 5, 9, 13, 0, 0))
        .astimezone(pytz.utc),
        # Assume UTC timezone when a datetime object contains no timezone.
        "dvd_release": datetime.datetime(2002, 1, 22, 7, 0, 0),
    },
    {
        "title": "Monty Python and the Holy Grail",
        "release_year": 1975,
        "length_minutes": 91.5,
        "release_date": pytz.timezone("Europe/London")
        .localize(datetime.datetime(1975, 4, 9, 23, 59, 2))
        .astimezone(pytz.utc),
        "dvd_release": datetime.datetime(2002, 7, 16, 9, 0, 0),
    },
    {
        "title": "Life of Brian",
        "release_year": 1979,
        "length_minutes": 94.25,
        "release_date": pytz.timezone("America/New_York")
        .localize(datetime.datetime(1979, 8, 17, 23, 59, 5))
        .astimezone(pytz.utc),
        "dvd_release": datetime.datetime(2008, 1, 14, 8, 0, 0),
    },
    {
        "title": "And Now for Something Completely Different",
        "release_year": 1971,
        "length_minutes": 88.0,
        "release_date": pytz.timezone("Europe/London")
        .localize(datetime.datetime(1971, 9, 28, 23, 59, 7))
        .astimezone(pytz.utc),
        "dvd_release": datetime.datetime(2003, 10, 22, 10, 0, 0),
    },
]
dataframe = pandas.DataFrame(
    records,
    # In the loaded table, the column order reflects the order of the
    # columns in the DataFrame.
    columns=[
        "title",
        "release_year",
        "length_minutes",
        "release_date",
        "dvd_release",
    ],
    # Optionally, set a named index, which can also be written to the
    # BigQuery table.
    index=pandas.Index(
        ["Q24980", "Q25043", "Q24953", "Q16403"], name="wikidata_id"
    ),
)
job_config = bigquery.LoadJobConfig(
    # Specify a (partial) schema. All columns are always written to the
    # table. The schema is used to assist in data type definitions.
    schema=[
        # Specify the type of columns whose type cannot be auto-detected. For
        # example the "title" column uses pandas dtype "object", so its
        # data type is ambiguous.
        bigquery.SchemaField("title", bigquery.enums.SqlTypeNames.STRING),
        # Indexes are written if included in the schema by name.
        bigquery.SchemaField("wikidata_id", bigquery.enums.SqlTypeNames.STRING),
    ],
    # Optionally, set the write disposition. BigQuery appends loaded rows
    # to an existing table by default, but with WRITE_TRUNCATE write
    # disposition it replaces the table with the loaded data.
    write_disposition="WRITE_TRUNCATE",
)

job = client.load_table_from_dataframe(
    dataframe, table_id, job_config=job_config
)  # Make an API request.
job.result()  # Wait for the job to complete.

table = client.get_table(table_id)  # Make an API request.
print(
    "Loaded {} rows and {} columns to {}".format(
        table.num_rows, len(table.schema), table_id
    )
)

Passaggi successivi

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