我正在使用pandas_gbq模块尝试将 Dataframe 附加到Google BigQuery中的表中。
我一直收到这个错误：
ArrowTypeError：应为字节数，但获得了“int”对象。
我可以确认dataframe的数据类型与BQ表的模式匹配。
我发现这篇文章关于 parquet 文件不能有混合数据类型：Pandas to parquet file
在我收到的错误消息中，我看到有一个对Parquet文件的引用，所以我假设df.to_gbq()调用正在创建一个Parquet文件，并且我有一个混合数据类型列，这导致了错误。错误消息没有指定。
我认为我的挑战是我无法找到哪个列具有混合数据类型-我尝试将它们全部转换为字符串，然后指定表模式参数，但这也不起作用。
这是完整的错误追溯：

In [76]: df.to_gbq('Pricecrawler.Daily_Crawl_Data', project_id=project_id, if_exists='append')
ArrowTypeError                            Traceback (most recent call last)
<ipython-input-76-74cec633c5d0> in <module>
----> 1 df.to_gbq('Pricecrawler.Daily_Crawl_Data', project_id=project_id, if_exists='append')

~\Anaconda3\lib\site-packages\pandas\core\frame.py in to_gbq(self, destination_table, 
project_id, chunksize, reauth, if_exists, auth_local_webserver, table_schema, location, 
progress_bar, credentials)
   1708         from pandas.io import gbq
   1709
-> 1710         gbq.to_gbq(
   1711             self,
   1712             destination_table,

~\Anaconda3\lib\site-packages\pandas\io\gbq.py in to_gbq(dataframe, destination_table, project_id, chunksize, reauth, if_exists, auth_local_webserver, table_schema, location, progress_bar, credentials)
    209 ) -> None:
    210     pandas_gbq = _try_import()
--> 211     pandas_gbq.to_gbq(
    212         dataframe,
    213         destination_table,

~\Anaconda3\lib\site-packages\pandas_gbq\gbq.py in to_gbq(dataframe, destination_table, project_id, chunksize, reauth, if_exists, auth_local_webserver, table_schema, location, progress_bar, credentials, api_method, verbose, private_key)
   1191         return
   1192
-> 1193     connector.load_data(
   1194         dataframe,
   1195         destination_table_ref,

~\Anaconda3\lib\site-packages\pandas_gbq\gbq.py in load_data(self, dataframe, destination_table_ref, chunksize, schema, progress_bar, api_method, billing_project)
    584
    585         try:
--> 586             chunks = load.load_chunks(
    587                 self.client,
    588                 dataframe,

~\Anaconda3\lib\site-packages\pandas_gbq\load.py in load_chunks(client, dataframe, destination_table_ref, chunksize, schema, location, api_method, billing_project)
    235 ):
    236     if api_method == "load_parquet":
--> 237         load_parquet(
    238             client,
    239             dataframe,

~\Anaconda3\lib\site-packages\pandas_gbq\load.py in load_parquet(client, dataframe, destination_table_ref, location, schema, billing_project)
    127
    128     try:
--> 129         client.load_table_from_dataframe(
    130             dataframe,
    131             destination_table_ref,

~\Anaconda3\lib\site-packages\google\cloud\bigquery\client.py in load_table_from_dataframe(self, dataframe, destination, num_retries, job_id, job_id_prefix, location, project, job_config, parquet_compression, timeout)
   2669                         parquet_compression = parquet_compression.upper()
   2670
-> 2671                     _pandas_helpers.dataframe_to_parquet(
   2672                         dataframe,
   2673                         job_config.schema,

~\Anaconda3\lib\site-packages\google\cloud\bigquery\_pandas_helpers.py in dataframe_to_parquet(dataframe, bq_schema, filepath, parquet_compression, parquet_use_compliant_nested_type)
    584
    585     bq_schema = schema._to_schema_fields(bq_schema)
--> 586     arrow_table = dataframe_to_arrow(dataframe, bq_schema)
    587     pyarrow.parquet.write_table(
    588         arrow_table, filepath, compression=parquet_compression, **kwargs,

~\Anaconda3\lib\site-packages\google\cloud\bigquery\_pandas_helpers.py in dataframe_to_arrow(dataframe, bq_schema)
    527         arrow_names.append(bq_field.name)
    528         arrow_arrays.append(
--> 529             bq_to_arrow_array(get_column_or_index(dataframe, bq_field.name), bq_field)
    530         )
    531         arrow_fields.append(bq_to_arrow_field(bq_field, arrow_arrays[-1].type))

~\Anaconda3\lib\site-packages\google\cloud\bigquery\_pandas_helpers.py in bq_to_arrow_array(series, bq_field)
    288     if field_type_upper in schema._STRUCT_TYPES:
    289         return pyarrow.StructArray.from_pandas(series, type=arrow_type)
--> 290     return pyarrow.Array.from_pandas(series, type=arrow_type)
    291
    292

~\Anaconda3\lib\site-packages\pyarrow\array.pxi in pyarrow.lib.Array.from_pandas()

~\Anaconda3\lib\site-packages\pyarrow\array.pxi in pyarrow.lib.array()

~\Anaconda3\lib\site-packages\pyarrow\array.pxi in pyarrow.lib._ndarray_to_array()

~\Anaconda3\lib\site-packages\pyarrow\error.pxi in pyarrow.lib.check_status()

ArrowTypeError: Expected bytes, got a 'int' object