從 DataFrame 載入資料

將 pandas DataFrame 的內容載入資料表。

程式碼範例

Python

在試用這個範例之前，請先按照「使用用戶端程式庫的 BigQuery 快速入門導覽課程」中的 Python 設定說明操作。詳情請參閱 BigQuery Python API 參考說明文件。

如要向 BigQuery 進行驗證，請設定應用程式預設憑證。詳情請參閱「設定用戶端程式庫的驗證作業」。

import datetime
from zoneinfo import ZoneInfo

import bigframes.pandas as bpd
import pandas as pd
import pandas_gbq

# Set partial ordering mode for BigQuery DataFrames.
bpd.options.bigquery.ordering_mode = "partial"


def load_table_dataframe_bigframes(
    table_id: str = "your-project.your_dataset.your_table_name",
) -> None:
    """Loads a pandas DataFrame into a BigQuery table using BigQuery DataFrames."""
    records = [
        {
            "title": "The Meaning of Life",
            "release_year": 1983,
            "length_minutes": 112.5,
            "release_date": datetime.datetime(
                1983, 5, 9, 13, 0, 0, tzinfo=ZoneInfo("Europe/Paris")
            ).astimezone(datetime.timezone.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": datetime.datetime(
                1975, 4, 9, 23, 59, 2, tzinfo=ZoneInfo("Europe/London")
            ).astimezone(datetime.timezone.utc),
            "dvd_release": datetime.datetime(2002, 7, 16, 9, 0, 0),
        },
        {
            "title": "Life of Brian",
            "release_year": 1979,
            "length_minutes": 94.25,
            "release_date": datetime.datetime(
                1979, 8, 17, 23, 59, 5, tzinfo=ZoneInfo("America/New_York")
            ).astimezone(datetime.timezone.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": datetime.datetime(
                1971, 9, 28, 23, 59, 7, tzinfo=ZoneInfo("Europe/London")
            ).astimezone(datetime.timezone.utc),
            "dvd_release": datetime.datetime(2003, 10, 22, 10, 0, 0),
        },
    ]
    dataframe = pd.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=pd.Index(["Q24980", "Q25043", "Q24953", "Q16403"], name="wikidata_id"),
    )

    bq_df = bpd.read_pandas(dataframe)
    bq_df.to_gbq(table_id, if_exists="replace", index=True)
    print(f"Loaded DataFrame to {table_id} using BigQuery DataFrames.")


def load_table_dataframe_pandas_gbq(
    table_id: str = "your-project.your_dataset.your_table_name",
) -> None:
    """Loads a pandas DataFrame into a BigQuery table using pandas-gbq."""
    records = [
        {
            "title": "The Meaning of Life",
            "release_year": 1983,
            "length_minutes": 112.5,
            "release_date": datetime.datetime(
                1983, 5, 9, 13, 0, 0, tzinfo=ZoneInfo("Europe/Paris")
            ).astimezone(datetime.timezone.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": datetime.datetime(
                1975, 4, 9, 23, 59, 2, tzinfo=ZoneInfo("Europe/London")
            ).astimezone(datetime.timezone.utc),
            "dvd_release": datetime.datetime(2002, 7, 16, 9, 0, 0),
        },
        {
            "title": "Life of Brian",
            "release_year": 1979,
            "length_minutes": 94.25,
            "release_date": datetime.datetime(
                1979, 8, 17, 23, 59, 5, tzinfo=ZoneInfo("America/New_York")
            ).astimezone(datetime.timezone.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": datetime.datetime(
                1971, 9, 28, 23, 59, 7, tzinfo=ZoneInfo("Europe/London")
            ).astimezone(datetime.timezone.utc),
            "dvd_release": datetime.datetime(2003, 10, 22, 10, 0, 0),
        },
    ]
    dataframe = pd.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=pd.Index(["Q24980", "Q25043", "Q24953", "Q16403"], name="wikidata_id"),
    )

    pandas_gbq.to_gbq(dataframe, table_id, if_exists="replace")
    print(f"Loaded DataFrame to {table_id} using pandas-gbq.")


# [Preferred] Run using BigQuery DataFrames:
# load_table_dataframe_bigframes("your-project.your_dataset.your_table_name")

# Alternatively, run using pandas-gbq:
# load_table_dataframe_pandas_gbq("your-project.your_dataset.your_table_name")

後續步驟

如要搜尋及篩選其他 Google Cloud 產品的程式碼範例，請參閱Google Cloud 範例瀏覽工具。