Easy parameterized data extraction from BigQuery
Very often, your enterprise data warehouse contains the sources for you to build your own training data, and simple SQL query commands would meet your requirements for row and column selection and feature transformation. So let's take a look at a convenient, flexible, and fast way of selecting and manipulating original data through SQL queries, where the result of the query is a pandas DataFrame. We have already seen how to use the %%bigquery
interpreter to execute a query and return the result as a pandas DataFrame. We now will look at how to pass in query parameters so users may explore and select data suitable for model training. The following example uses one of the public datasets, covid19_juh_csse
, and its summary
table.
This table has the following structure:
In the JupyterLab provided by any of the three methods discussed earlier, you...