Merge pull request #5169 from cpcloud/doc-build-fixes

DOC/CLN: A few fixes and cleanup for doc warnings/errors

Author: cpcloud

doc/source/io.rst

@@ -2932,55 +2932,56 @@ if the source datatypes are compatible with BigQuery ones.
 For specifics on the service itself, see `here <https://developers.google.com/bigquery/>`__
 
 As an example, suppose you want to load all data from an existing table
-: `test_dataset.test_table`
-into BigQuery and pull it into a DataFrame.
+``test_dataset.test_table`` into BigQuery and pull it into a ``DataFrame``.
 
-.. code-block:: python
+::
 
    from pandas.io import gbq
    data_frame = gbq.read_gbq('SELECT * FROM test_dataset.test_table')
 
-The user will then be authenticated by the `bq` command line client -
+The user will then be authenticated by the ``bq`` command line client -
 this usually involves the default browser opening to a login page,
 though the process can be done entirely from command line if necessary.
-Datasets and additional parameters can be either configured with `bq`,
-passed in as options to `read_gbq`, or set using Google's gflags (this
-is not officially supported by this module, though care was taken
-to ensure that they should be followed regardless of how you call the
+Datasets and additional parameters can be either configured with ``bq``,
+passed in as options to :func:`~pandas.read_gbq`, or set using Google's
+``gflags`` (this is not officially supported by this module, though care was
+taken to ensure that they should be followed regardless of how you call the
 method).
 
 Additionally, you can define which column to use as an index as well as a preferred column order as follows:
 
-.. code-block:: python
+::
 
    data_frame = gbq.read_gbq('SELECT * FROM test_dataset.test_table',
                              index_col='index_column_name',
                              col_order='[col1, col2, col3,...]')
 
-Finally, if you would like to create a BigQuery table, `my_dataset.my_table`, from the rows of DataFrame, `df`:
+Finally, if you would like to create a BigQuery table, `my_dataset.my_table`,
+from the rows of DataFrame, `df`:
 
-.. code-block:: python
+::
 
-   df = pandas.DataFrame({'string_col_name' : ['hello'],
-         'integer_col_name' : [1],
-         'boolean_col_name' : [True]})
+   df = pandas.DataFrame({'string_col_name': ['hello'],
+                          'integer_col_name': [1],
+                          'boolean_col_name': [True]})
    schema = ['STRING', 'INTEGER', 'BOOLEAN']
-   data_frame = gbq.to_gbq(df, 'my_dataset.my_table',
-                           if_exists='fail', schema = schema)
+   data_frame = gbq.to_gbq(df, 'my_dataset.my_table', if_exists='fail',
+                           schema=schema)
 
 To add more rows to this, simply:
 
-.. code-block:: python
+::
 
-   df2 = pandas.DataFrame({'string_col_name' : ['hello2'],
-         'integer_col_name' : [2],
-         'boolean_col_name' : [False]})
+   df2 = pandas.DataFrame({'string_col_name': ['hello2'],
+                           'integer_col_name': [2],
+                           'boolean_col_name': [False]})
    data_frame = gbq.to_gbq(df2, 'my_dataset.my_table', if_exists='append')
 
 .. note::
 
-   There is a hard cap on BigQuery result sets, at 128MB compressed. Also, the BigQuery SQL query language has some oddities,
-   see `here <https://developers.google.com/bigquery/query-reference>`__
+   There is a hard cap on BigQuery result sets, at 128MB compressed. Also, the
+   BigQuery SQL query language has some oddities, see `here
+   <https://developers.google.com/bigquery/query-reference>`__
 
 .. _io.stata:
 

doc/source/missing_data.rst

@@ -397,8 +397,11 @@ at the new values.
 .. _documentation: http://docs.scipy.org/doc/scipy/reference/interpolate.html#univariate-interpolation
 .. _guide: http://docs.scipy.org/doc/scipy/reference/tutorial/interpolate.html
 
-Like other pandas fill methods, ``interpolate`` accepts a ``limit`` keyword argument.
-Use this to limit the number of consecutive interpolations, keeping ``NaN`` s for interpolations that are too far from the last valid observation:
+
+Like other pandas fill methods, ``interpolate`` accepts a ``limit`` keyword
+argument.  Use this to limit the number of consecutive interpolations, keeping
+``NaN`` values for interpolations that are too far from the last valid
+observation:
 
 .. ipython:: python
 

pandas/core/generic.py

@@ -1982,29 +1982,35 @@ def interpolate(self, method='linear', axis=0, limit=None, inplace=False,
 
         Parameters
         ----------
-        method : {'linear', 'time', 'values', 'index' 'nearest',
-                  'zero', 'slinear', 'quadratic', 'cubic',
-                  'barycentric', 'krogh', 'polynomial', 'spline'
-                  'piecewise_polynomial', 'pchip'}
-            'linear': ignore the index and treat the values as equally spaced. default
-            'time': interpolation works on daily and higher resolution
+        method : {'linear', 'time', 'values', 'index' 'nearest', 'zero',
+                  'slinear', 'quadratic', 'cubic', 'barycentric', 'krogh',
+                  'polynomial', 'spline' 'piecewise_polynomial', 'pchip'}
+
+            * 'linear': ignore the index and treat the values as equally
+               spaced. default
+            * 'time': interpolation works on daily and higher resolution
                 data to interpolate given length of interval
-            'index': use the actual numerical values of the index
-            'nearest', 'zero', 'slinear', 'quadratic', 'cubic', 'barycentric',
-            'polynomial' is passed to `scipy.interpolate.interp1d` with the order given
-                both 'polynomial' and 'spline' requre that you also specify and order (int)
-                e.g. df.interpolate(method='polynomial', order=4)
-            'krogh', 'piecewise_polynomial', 'spline', and 'pchip' are all wrappers
-            around the scipy interpolation methods of similar names. See the
-            scipy documentation for more on their behavior:
-            http://docs.scipy.org/doc/scipy/reference/interpolate.html#univariate-interpolation
-            http://docs.scipy.org/doc/scipy/reference/tutorial/interpolate.html
+            * 'index': use the actual numerical values of the index
+            * 'nearest', 'zero', 'slinear', 'quadratic', 'cubic',
+              'barycentric', 'polynomial' is passed to
+              `scipy.interpolate.interp1d` with the order given both
+              'polynomial' and 'spline' requre that you also specify and order
+              (int) e.g. df.interpolate(method='polynomial', order=4)
+            * 'krogh', 'piecewise_polynomial', 'spline', and 'pchip' are all
+               wrappers around the scipy interpolation methods of similar
+               names. See the scipy documentation for more on their behavior:
+               http://docs.scipy.org/doc/scipy/reference/interpolate.html#univariate-interpolation
+               http://docs.scipy.org/doc/scipy/reference/tutorial/interpolate.html
+
         axis : {0, 1}, default 0
-            0: fill column-by-column
-            1: fill row-by-row
-        limit : int, default None. Maximum number of consecutive NaNs to fill.
+            * 0: fill column-by-column
+            * 1: fill row-by-row
+        limit : int, default None.
+            Maximum number of consecutive NaNs to fill.
         inplace : bool, default False
+            Update the NDFrame in place if possible.
         downcast : optional, 'infer' or None, defaults to 'infer'
+            Downcast dtypes if possible.
 
         Returns
         -------

pandas/core/panel.py

@@ -391,8 +391,8 @@ def to_excel(self, path, na_rep='', engine=None, **kwargs):
             ``io.excel.xlsx.writer``, ``io.excel.xls.writer``, and
             ``io.excel.xlsm.writer``.
 
-        Keyword Arguments
-        -----------------
+        Other Parameters
+        ----------------
         float_format : string, default None
             Format string for floating point numbers
         cols : sequence, optional
@@ -409,6 +409,8 @@ def to_excel(self, path, na_rep='', engine=None, **kwargs):
         startow : upper left cell row to dump data frame
         startcol : upper left cell column to dump data frame
 
+        Notes
+        -----
         Keyword arguments (and na_rep) are passed to the ``to_excel`` method
         for each DataFrame written.
         """

pandas/io/html.py

@@ -782,7 +782,10 @@ def read_html(io, match='.+', flavor=None, header=None, index_col=None,
         latest information on table attributes for the modern web.
 
     parse_dates : bool, optional
-        See :func:`~pandas.read_csv` for details.
+        See :func:`~pandas.io.parsers.read_csv` for more details. In 0.13, this
+        parameter can sometimes interact strangely with ``infer_types``. If you
+        get a large number of ``NaT`` values in your results, consider passing
+        ``infer_types=False`` and manually converting types afterwards.
 
     tupleize_cols : bool, optional
         If ``False`` try to parse multiple header rows into a
@@ -824,12 +827,12 @@ def read_html(io, match='.+', flavor=None, header=None, index_col=None,
 
     See Also
     --------
-    pandas.read_csv
+    pandas.io.parsers.read_csv
     """
     if infer_types is not None:
         warnings.warn("infer_types will have no effect in 0.14", FutureWarning)
     else:
-        infer_types = True  # TODO: remove in 0.14
+        infer_types = True  # TODO: remove effect of this in 0.14
 
     # Type check here. We don't want to parse only to fail because of an
     # invalid value of an integer skiprows.