@@ -801,7 +801,7 @@ def to_gbq(self, destination_table, project_id=None, chunksize=10000,
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into a default chunk size of 10,000. Failures return the complete error
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response which can be quite long depending on the size of the insert.
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There are several important limitations of the Google streaming API
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- which are `here <https://developers.google.com/bigquery/streaming-data-into-bigquery>`_
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+ which are `here <https://developers.google.com/bigquery/streaming-data-into-bigquery>`__
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Parameters
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----------
@@ -1488,7 +1488,7 @@ def to_latex(self, buf=None, columns=None, col_space=None, colSpace=None,
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Make the row labels bold in the output
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column_format : str, default None
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The columns format as specified in `LaTeX table format
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- <https://en.wikibooks.org/wiki/LaTeX/Tables>`_ e.g 'rcl' for 3 columns
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+ <https://en.wikibooks.org/wiki/LaTeX/Tables>`__ e.g 'rcl' for 3 columns
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longtable : boolean, default False
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Use a longtable environment instead of tabular. Requires adding
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a \\ usepackage{longtable} to your LaTeX preamble.
@@ -2113,7 +2113,7 @@ def select_dtypes(self, include=None, exclude=None):
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* To select strings you must use the ``object`` dtype, but note that
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this will return *all* object dtype columns
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* See the `numpy dtype hierarchy
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- <http://docs.scipy.org/doc/numpy/reference/arrays.scalars.html>`_
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+ <http://docs.scipy.org/doc/numpy/reference/arrays.scalars.html>`__
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* To select Pandas categorical dtypes, use 'category'
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Examples
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