Question

我试图从MySQL服务器查询数据并使用pandas .to_gbq api将其写入Google BigQuery。

def production_to_gbq(table_name_prod,prefix,table_name_gbq,dataset,project):
    # Extract data from Production

    q = """
        SELECT *
        FROM
            {}
        """.format(table_name_prod)

    df = pd.read_sql(q, con)

    # Write to gbq    
    df.to_gbq(dataset + table_name_gbq, project, chunksize=1000, verbose=True, reauth=False, if_exists='replace', private_key=None)

    return df

我一直收到400错误，表示输入无效。

Load is 100.0% Complete
---------------------------------------------------------------------------
BadRequest                                Traceback (most recent call last)
/usr/local/lib/python3.6/site-packages/pandas_gbq/gbq.py in load_data(self, dataframe, dataset_id, table_id, chunksize, schema)
    569                     self.client, dataframe, dataset_id, table_id,
--> 570                     chunksize=chunksize):
    571                 self._print("\rLoad is {0}% Complete".format(

/usr/local/lib/python3.6/site-packages/pandas_gbq/_load.py in load_chunks(client, dataframe, dataset_id, table_id, chunksize, schema)
     73             destination_table,
---> 74             job_config=job_config).result()

/usr/local/lib/python3.6/site-packages/google/cloud/bigquery/job.py in result(self, timeout)
    527         # TODO: modify PollingFuture so it can pass a retry argument to done().
--> 528         return super(_AsyncJob, self).result(timeout=timeout)
    529 

/usr/local/lib/python3.6/site-packages/google/api_core/future/polling.py in result(self, timeout)
    110             # Pylint doesn't recognize that this is valid in this case.
--> 111             raise self._exception
    112 

BadRequest: 400 Error while reading data, error message: CSV table encountered too many errors, giving up. Rows: 10; errors: 1. Please look into the error stream for more details.

During handling of the above exception, another exception occurred:

GenericGBQException                       Traceback (most recent call last)
<ipython-input-73-ef9c7cec0104> in <module>()
----> 1 departments.to_gbq(dataset + table_name_gbq, project, chunksize=1000, verbose=True, reauth=False, if_exists='replace', private_key=None)
      2 

/usr/local/lib/python3.6/site-packages/pandas/core/frame.py in to_gbq(self, destination_table, project_id, chunksize, verbose, reauth, if_exists, private_key)
   1058         return gbq.to_gbq(self, destination_table, project_id=project_id,
   1059                           chunksize=chunksize, verbose=verbose, reauth=reauth,
-> 1060                           if_exists=if_exists, private_key=private_key)
   1061 
   1062     @classmethod

/usr/local/lib/python3.6/site-packages/pandas/io/gbq.py in to_gbq(dataframe, destination_table, project_id, chunksize, verbose, reauth, if_exists, private_key)
    107                       chunksize=chunksize,
    108                       verbose=verbose, reauth=reauth,
--> 109                       if_exists=if_exists, private_key=private_key)

/usr/local/lib/python3.6/site-packages/pandas_gbq/gbq.py in to_gbq(dataframe, destination_table, project_id, chunksize, verbose, reauth, if_exists, private_key, auth_local_webserver, table_schema)
    980     connector.load_data(
    981         dataframe, dataset_id, table_id, chunksize=chunksize,
--> 982         schema=table_schema)
    983 
    984 

/usr/local/lib/python3.6/site-packages/pandas_gbq/gbq.py in load_data(self, dataframe, dataset_id, table_id, chunksize, schema)
    572                     ((total_rows - remaining_rows) * 100) / total_rows))
    573         except self.http_error as ex:
--> 574             self.process_http_error(ex)
    575 
    576         self._print("\n")

/usr/local/lib/python3.6/site-packages/pandas_gbq/gbq.py in process_http_error(ex)
    453         # <https://cloud.google.com/bigquery/troubleshooting-errors>`__
    454 
--> 455         raise GenericGBQException("Reason: {0}".format(ex))
    456 
    457     def run_query(self, query, **kwargs):

GenericGBQException: Reason: 400 Error while reading data, error message: CSV table encountered too many errors, giving up. Rows: 10; errors: 1. Please look into the error stream for more details.

我已经调查了表格架构，

id  INTEGER NULLABLE    
name    STRING  NULLABLE    
description STRING  NULLABLE    
created_at  INTEGER NULLABLE    
modified_at FLOAT   NULLABLE

，它与数据帧相同：

id                        int64
name                     object
description              object
created_at                int64
modified_at             float64

该表以GBQ创建，但仍为空。

我读了一下但是在pandas.to_gbq api上找不到多少，除了这看似相关但没有回复：

bigquery table is empty when using pandas to_gbq

我找到了一个关于对象数据类型中数字的潜在解决方案，它们被传递到没有引号的GBQ表中，通过将列数据类型设置为字符串来修复。

I use to_gbq on pandas for updating Google BigQuery and get GenericGBQException

我尝试了修复：

for col in df.columns:
    if df[col].dtypes == object:
        df[col] = df[col].fillna('')
        df[col] = df[col].astype(str)

不幸的是我仍然遇到同样的错误。类似地，尝试格式化缺失数据并为int和float设置dtypes也会产生相同的错误。

我有什么遗失的吗？

Answer 1

发现bigquery无法正确处理 \ r （有时 \ n ）有同样的问题，本地化问题，当我用空格替换 \ r 时我真的很惊讶：

for col in list(df.columns):
    df[col] = df[col].apply(lambda x: x.replace(u'\r', u' ') if isinstance(x, str) or isinstance(x, unicode) else x)

Answer 2

当我遇到类似的问题时，我从云存储中的镶木地板文件导入到bigquery中遇到了类似的问题。但是，每次我忘记解决问题的方法时，我希望将我的发现留在这里并不过分违反协议！

我意识到我有全为NULL的列，看起来它们在pandas中具有数据类型，但是如果您使用pyarrow.parquet.read_schema（parquet_file），您将看到数据类型为null 。

删除该列后，上传将起作用！

Answer 3

我在string列中有一些无效字符（在object中的pandas中）。我使用@Echochi方法，并且有效

for col in list(parsed_data.select_dtypes(include='object').columns):
        parsed_data[col] = parsed_data[col].apply(lambda x:re.sub('[^A-Za-z0-9]+','', str(x)))

对可接受的字符有一点限制，所以我使用了更通用的方法，因为与UTF-8 bigquery docs的双向查询兼容性

for col in list(parsed_data.select_dtypes(include='object').columns):
        parsed_data[col] = parsed_data[col].apply(lambda x:re.sub(r"[^\u0900-\u097F]+",,'?', str(x)))

使用r"[^\u0900-\u097F]+"，您将接受所有UTF-8兼容的字符集

400使用pandas to_gbq读取数据时出错，以在Google BigQuery中创建表格

3 个答案: