DataFrame에서 데이터 로드

Pandas DataFrame의 콘텐츠를 테이블에 로드합니다.

코드 샘플

Python

이 샘플을 사용해 보기 전에 BigQuery 빠른 시작: 클라이언트 라이브러리 사용의 Python 설정 안내를 따르세요. 자세한 내용은 BigQuery Python API 참고 문서를 확인하세요.

BigQuery에 인증하려면 애플리케이션 기본 사용자 인증 정보를 설정합니다. 자세한 내용은 클라이언트 라이브러리의 인증 설정을 참조하세요.

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
    )
)

다음 단계

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