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Nov 9, 2018
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remove old docs #233

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17 changes: 0 additions & 17 deletions docs/source/intro.rst
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Expand Up @@ -9,23 +9,6 @@ Pandas supports all these `BigQuery data types <https://cloud.google.com/bigquer
``TIMESTAMP`` (microsecond precision). Data types ``BYTES`` and ``RECORD``
are not supported.

Integer and boolean ``NA`` handling
+++++++++++++++++++++++++++++++++++

Since all columns in BigQuery queries are nullable, and NumPy lacks of ``NA``
support for integer and boolean types, this module will store ``INTEGER`` or
``BOOLEAN`` columns with at least one ``NULL`` value as ``dtype=object``.
Otherwise those columns will be stored as ``dtype=int64`` or ``dtype=bool``
respectively.

This is opposite to default pandas behaviour which will promote integer
type to float in order to store NAs.
`See here for how this works in pandas <https://pandas.pydata.org/pandas-docs/stable/gotchas.html#nan-integer-na-values-and-na-type-promotions>`__

While this trade-off works well for most cases, it breaks down for storing
values greater than 2**53. Such values in BigQuery can represent identifiers
and unnoticed precision lost for identifier is what we want to avoid.

Logging
+++++++

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3 changes: 2 additions & 1 deletion docs/source/reading.rst
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Expand Up @@ -50,6 +50,7 @@ your job. For more information about query configuration parameters see `here
.. note::

The ``dialect`` argument can be used to indicate whether to use BigQuery's ``'legacy'`` SQL
or BigQuery's ``'standard'`` SQL (beta). The default value is ``'legacy'``. For more information
or BigQuery's ``'standard'`` SQL (beta). The default value is ``'legacy'``, though this will change
in a subsequent release to ``'standard'``. For more information
on BigQuery's standard SQL, see `BigQuery SQL Reference
<https://cloud.google.com/bigquery/sql-reference/>`__
6 changes: 0 additions & 6 deletions docs/source/tables.rst
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Expand Up @@ -14,9 +14,3 @@ Creating Tables
{'name': 'my_int64', 'type': 'INTEGER'},
{'name': 'my_string', 'type': 'STRING'}]}

.. note::

If you delete and re-create a BigQuery table with the same name, but different table schema,
you must wait 2 minutes before streaming data into the table. As a workaround, consider creating
the new table with a different name. Refer to
`Google BigQuery issue 191 <https://code.google.com/p/google-bigquery/issues/detail?id=191>`__.
5 changes: 0 additions & 5 deletions docs/source/writing.rst
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Expand Up @@ -49,11 +49,6 @@ a ``TableCreationError`` if the destination table already exists.
If an error occurs while streaming data to BigQuery, see
`Troubleshooting BigQuery Errors <https://cloud.google.com/bigquery/troubleshooting-errors>`__.

.. note::

The BigQuery SQL query language has some oddities, see the
`BigQuery Query Reference Documentation <https://cloud.google.com/bigquery/query-reference>`__.

.. note::

While BigQuery uses SQL-like syntax, it has some important differences
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