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I am loading the table from google bigquery in python, the code as follow def read_bq(project_id, dataset_id, table_id, bigquery_client, size): query = f""" SELECT * FROM {project_id}.{dataset_id}.{table_id} WHERE extract_id < {size} """ a = time() query_job = bigquery_client.query(query) b = time() # Convert the result into a Pandas DataFrame c = time() df = query_job.to_dataframe() d = time() #df = pandas_gbq.read_gbq(query, credentials=set_credential(), dialect='standard', use_bqstorage_api=True)...

If your NumPy-based code is too slow, you can sometimes use Numba to speed it up. Numba is a compiled language that uses the same syntax as Python, and it compiles at runtime, so it’s very easy to write. And because it re-implements a large part of the NumPy APIs, it can also easily be used with existing NumPy-based...