DOC: clean up some doc-build warnings

Author: jreback

doc/source/dsintro.rst

@@ -692,6 +692,7 @@ R package):
 
 .. ipython:: python
    :suppress:
+   :okwarning:
 
    # restore GlobalPrintConfig
    pd.reset_option('^display\.')

doc/source/options.rst

@@ -438,6 +438,7 @@ For instance:
 
 .. ipython:: python
    :suppress:
+   :okwarning:
 
    pd.reset_option('^display\.')
 

doc/source/release.rst

@@ -12,7 +12,7 @@
    import matplotlib.pyplot as plt
    plt.close('all')
 
-   options.display.max_rows=15
+   pd.options.display.max_rows=15
    import pandas.util.testing as tm
 
 *************

doc/source/whatsnew/v0.18.0.txt

@@ -863,11 +863,10 @@ Previous API will work but deprecations
 
       In [7]: r.iloc[0] = 5
       ValueError: .resample() is now a deferred operation
-            use .resample(...).mean() instead of .resample(...)
-            assignment will have no effect as you are working on a copy
+      use .resample(...).mean() instead of .resample(...)
 
    There is a situation where the new API can not perform all the operations when using original code.
-   This code is intending to resample every 2s, take the ``mean`` AND then take the ``min` of those results.
+   This code is intending to resample every 2s, take the ``mean`` AND then take the ``min`` of those results.
 
    .. code-block:: python
 

pandas/core/frame.py

@@ -1627,6 +1627,7 @@ def info(self, verbose=None, buf=None, max_cols=None, memory_usage=None,
             human-readable units (base-2 representation).
         null_counts : boolean, default None
             Whether to show the non-null counts
+
             - If None, then only show if the frame is smaller than
               max_info_rows and max_info_columns.
             - If True, always show counts.
@@ -4932,6 +4933,7 @@ def quantile(self, q=0.5, axis=0, numeric_only=True,
             0 or 'index' for row-wise, 1 or 'columns' for column-wise
         interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'}
             .. versionadded:: 0.18.0
+
             This optional parameter specifies the interpolation method to use,
             when the desired quantile lies between two data points `i` and `j`:
 
@@ -4945,11 +4947,12 @@ def quantile(self, q=0.5, axis=0, numeric_only=True,
         Returns
         -------
         quantiles : Series or DataFrame
-            If ``q`` is an array, a DataFrame will be returned where the
-            index is ``q``, the columns are the columns of self, and the
-            values are the quantiles.
-            If ``q`` is a float, a Series will be returned where the
-            index is the columns of self and the values are the quantiles.
+
+            - If ``q`` is an array, a DataFrame will be returned where the
+              index is ``q``, the columns are the columns of self, and the
+              values are the quantiles.
+            - If ``q`` is a float, a Series will be returned where the
+              index is the columns of self and the values are the quantiles.
 
         Examples
         --------
@@ -4965,6 +4968,7 @@ def quantile(self, q=0.5, axis=0, numeric_only=True,
         0.1  1.3   3.7
         0.5  2.5  55.0
         """
+
         self._check_percentile(q)
         per = np.asarray(q) * 100
 

pandas/core/generic.py

@@ -2041,11 +2041,13 @@ def sort_index(self, axis=0, level=None, ascending=True, inplace=False,
             method to use for filling holes in reindexed DataFrame.
             Please note: this is only  applicable to DataFrames/Series with a
             monotonically increasing/decreasing index.
-              * default: don't fill gaps
-              * pad / ffill: propagate last valid observation forward to next
-                valid
-              * backfill / bfill: use next valid observation to fill gap
-              * nearest: use nearest valid observations to fill gap
+
+            * default: don't fill gaps
+            * pad / ffill: propagate last valid observation forward to next
+              valid
+            * backfill / bfill: use next valid observation to fill gap
+            * nearest: use nearest valid observations to fill gap
+
         copy : boolean, default True
             Return a new object, even if the passed indexes are the same
         level : int or name
@@ -2265,11 +2267,13 @@ def _reindex_multi(self, axes, copy, fill_value):
         axis : %(axes_single_arg)s
         method : {None, 'backfill'/'bfill', 'pad'/'ffill', 'nearest'}, optional
             Method to use for filling holes in reindexed DataFrame:
-              * default: don't fill gaps
-              * pad / ffill: propagate last valid observation forward to next
-                valid
-              * backfill / bfill: use next valid observation to fill gap
-              * nearest: use nearest valid observations to fill gap
+
+            * default: don't fill gaps
+            * pad / ffill: propagate last valid observation forward to next
+              valid
+            * backfill / bfill: use next valid observation to fill gap
+            * nearest: use nearest valid observations to fill gap
+
         copy : boolean, default True
             Return a new object, even if the passed indexes are the same
         level : int or name

pandas/core/series.py

@@ -1289,8 +1289,10 @@ def quantile(self, q=0.5, interpolation='linear'):
             0 <= q <= 1, the quantile(s) to compute
         interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'}
             .. versionadded:: 0.18.0
+
             This optional parameter specifies the interpolation method to use,
             when the desired quantile lies between two data points `i` and `j`:
+
                 * linear: `i + (j - i) * fraction`, where `fraction` is the
                   fractional part of the index surrounded by `i` and `j`.
                 * lower: `i`.
@@ -1306,15 +1308,15 @@ def quantile(self, q=0.5, interpolation='linear'):
 
         Examples
         --------
-
         >>> s = Series([1, 2, 3, 4])
         >>> s.quantile(.5)
-            2.5
+        2.5
         >>> s.quantile([.25, .5, .75])
         0.25    1.75
         0.50    2.50
         0.75    3.25
         dtype: float64
+
         """
 
         self._check_percentile(q)

pandas/io/parsers.py

@@ -121,13 +121,15 @@ class ParserWarning(Warning):
     If True, skip over blank lines rather than interpreting as NaN values
 parse_dates : boolean or list of ints or names or list of lists or dict, \
 default False
+
     * boolean. If True -> try parsing the index.
     * list of ints or names. e.g. If [1, 2, 3] -> try parsing columns 1, 2, 3
       each as a separate date column.
     * list of lists. e.g.  If [[1, 3]] -> combine columns 1 and 3 and parse as
         a single date column.
     * dict, e.g. {'foo' : [1, 3]} -> parse columns 1, 3 as date and call result
       'foo'
+
     Note: A fast-path exists for iso8601-formatted dates.
 infer_datetime_format : boolean, default False
     If True and parse_dates is enabled for a column, attempt to infer

pandas/tseries/tools.py

@@ -190,6 +190,7 @@ def to_datetime(arg, errors='raise', dayfirst=False, yearfirst=False,
     ----------
     arg : string, datetime, list, tuple, 1-d array, or Series
     errors : {'ignore', 'raise', 'coerce'}, default 'raise'
+
         - If 'raise', then invalid parsing will raise an exception
         - If 'coerce', then invalid parsing will be set as NaT
         - If 'ignore', then invalid parsing will return the input
@@ -201,10 +202,12 @@ def to_datetime(arg, errors='raise', dayfirst=False, yearfirst=False,
         with day first (this is a known bug, based on dateutil behavior).
     yearfirst : boolean, default False
         Specify a date parse order if `arg` is str or its list-likes.
+
         - If True parses dates with the year first, eg 10/11/12 is parsed as
           2010-11-12.
         - If both dayfirst and yearfirst are True, yearfirst is preceded (same
           as dateutil).
+
         Warning: yearfirst=True is not strict, but will prefer to parse
         with year first (this is a known bug, based on dateutil beahavior).
 
@@ -214,14 +217,17 @@ def to_datetime(arg, errors='raise', dayfirst=False, yearfirst=False,
         Return UTC DatetimeIndex if True (converting any tz-aware
         datetime.datetime objects as well).
     box : boolean, default True
+
         - If True returns a DatetimeIndex
         - If False returns ndarray of values.
     format : string, default None
         strftime to parse time, eg "%d/%m/%Y", note that "%f" will parse
         all the way up to nanoseconds.
     exact : boolean, True by default
+
         - If True, require an exact format match.
         - If False, allow the format to match anywhere in the target string.
+
     unit : unit of the arg (D,s,ms,us,ns) denote the unit in epoch
         (e.g. a unix timestamp), which is an integer/float number.
     infer_datetime_format : boolean, default False
@@ -273,6 +279,7 @@ def to_datetime(arg, errors='raise', dayfirst=False, yearfirst=False,
     datetime.datetime(1300, 1, 1, 0, 0)
     >>> pd.to_datetime('13000101', format='%Y%m%d', errors='coerce')
     NaT
+
     """
     return _to_datetime(arg, errors=errors, dayfirst=dayfirst,
                         yearfirst=yearfirst,

pandas/util/nosetester.py

@@ -123,7 +123,8 @@ def _get_custom_doctester(self):
         return None
 
     def _test_argv(self, label, verbose, extra_argv):
-        ''' Generate argv for nosetest command
+        """
+        Generate argv for nosetest command
 
         Parameters
         ----------
@@ -138,7 +139,8 @@ def _test_argv(self, label, verbose, extra_argv):
         -------
         argv : list
             command line arguments that will be passed to nose
-        '''
+        """
+
         argv = [__file__, self.package_path]
         if label and label != 'full':
             if not isinstance(label, string_types):
@@ -170,13 +172,15 @@ def test(self, label='fast', verbose=1, extra_argv=None,
             Identifies the tests to run. This can be a string to pass to
             the nosetests executable with the '-A' option, or one of several
             special values.  Special values are:
+
             * 'fast' - the default - which corresponds to the ``nosetests -A``
               option of 'not slow'.
             * 'full' - fast (as above) and slow tests as in the
               'no -A' option to nosetests - this is the same as ''.
             * None or '' - run all tests.
             * attribute_identifier - string passed directly to nosetests
               as '-A'.
+
         verbose : int, optional
             Verbosity value for test outputs, in the range 1-10. Default is 1.
         extra_argv : list, optional
@@ -191,14 +195,15 @@ def test(self, label='fast', verbose=1, extra_argv=None,
             This specifies which warnings to configure as 'raise' instead
             of 'warn' during the test execution.  Valid strings are:
 
-            - "develop" : equals ``(DeprecationWarning, RuntimeWarning)``
-            - "release" : equals ``()``, don't raise on any warnings.
+            - 'develop' : equals ``(DeprecationWarning, RuntimeWarning)``
+            - 'release' : equals ``()``, don't raise on any warnings.
 
         Returns
         -------
         result : object
             Returns the result of running the tests as a
             ``nose.result.TextTestResult`` object.
+
         """
 
         # cap verbosity at 3 because nose becomes *very* verbose beyond that