下面是将DataFrame加载到BQ的代码片段：

import pandas as pd
from google.cloud import bigquery

# Example data
df = pd.DataFrame({'a': [1,2,4], 'b': ['123', '456', '000']})

# Load client
client = bigquery.Client(project='your-project-id')

# Define table name, in format dataset.table_name
table = 'your-dataset.your-table'

# Load data to BQ
job = client.load_table_from_dataframe(df, table)

如果只想指定架构的子集，但仍导入所有列，则可以使用切换最后一行

# Define a job config object, with a subset of the schema
job_config = bigquery.LoadJobConfig(schema=[bigquery.SchemaField('b', 'STRING')])

# Load data to BQ
job = client.load_table_from_dataframe(df, table, job_config=job_config)

下面是工作代码：

from google.cloud import bigquery
import pandas as pd

bigqueryClient = bigquery.Client()
tableRef = bigqueryClient.dataset("dataset-name").table("table-name")

dataFrame = pd.read_csv("file-name")

bigqueryJob = bigqueryClient.load_table_from_dataframe(dataFrame, tableRef)
bigqueryJob.result()

现在只需安装pandas-gbq==0.18.1，然后

df.to_gbq(
        destination_table="my_project_id.my_dataset.my_table",
        project_id="my_project_id",
        credentials=service_account.Credentials.from_service_account_info(
            my_service_account_info     # there are several ways to authenticate
        ),
    )

文件：https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_gbq.html
有关身份验证说明，请参见How to authenticate with Google BigQuery guide。