refactor to use more logic from google-cloud-bigquery

[tswast]

The backend made some improvements to the performance of time-to-first-byte for query results. I'm working on implementing these changes here: googleapis/python-bigquery#362

pandas-gbq will not be able to take advantage of many of these performance improvements unless it uses the return value from QueryJob.result() or even QueryJob.to_dataframe() directly.

• Use QueryJob.to_dataframe() (remove any manual list_rows() on destination table calls if still present).
• Use Client.load_table_from_dataframe() (Done in feat: to_gbq uses Parquet by default, use api_method="load_csv" for old behavior #413)

Related: #149

[tswast]

Even if we're not using the jobs.query method, I think it still makes sense to re-use much of the code which has been recently added.

read_gbq:: Using QueryJob.to_dataframe() directly should be possible for all except when max_results is set now that progress bar support has been added in googleapis/python-bigquery#343 After googleapis/python-bigquery#296 is implemented, it can even be used in that case.

to_gbq:: Using Client.load_table_from_dataframe() (and with streaming support Client.insert_rows_from_dataframe #300) should now be possible, because google-cloud-bigquery added CSV serialization as an option in googleapis/python-bigquery#383

[tswast]

Closing as a duplicate of #118