DOC: some rst fixes

doc/source/groupby.rst

@@ -1200,14 +1200,14 @@ Regroup columns of a DataFrame according to their sum, and sum the aggregated on
    df
    df.groupby(df.sum(), axis=1).sum()
 
-.. _groupby.multicolumn_factorization
+.. _groupby.multicolumn_factorization:
 
 Multi-column factorization
 ~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 By using ``.ngroup()``, we can extract information about the groups in
 a way similar to :func:`factorize` (as described further in the
-:ref:`reshaping API <reshaping.factorization>`) but which applies
+:ref:`reshaping API <reshaping.factorize>`) but which applies
 naturally to multiple columns of mixed type and different
 sources. This can be useful as an intermediate categorical-like step
 in processing, when the relationships between the group rows are more

pandas/core/generic.py

@@ -1292,7 +1292,7 @@ def to_hdf(self, path_or_buf, key, **kwargs):
             As of v0.20.2 these additional compressors for Blosc are supported
             (default if no compressor specified: 'blosc:blosclz'):
             {'blosc:blosclz', 'blosc:lz4', 'blosc:lz4hc', 'blosc:snappy',
-             'blosc:zlib', 'blosc:zstd'}.
+            'blosc:zlib', 'blosc:zstd'}.
             Specifying a compression library which is not available issues
             a ValueError.
         fletcher32 : bool, default False

pandas/core/groupby.py

@@ -1212,8 +1212,6 @@ def ohlc(self):
             lambda x: x._cython_agg_general('ohlc'))
 
     @Appender(DataFrame.describe.__doc__)
-    @Substitution(name='groupby')
-    @Appender(_doc_template)
     def describe(self, **kwargs):
         self._set_group_selection()
         result = self.apply(lambda x: x.describe(**kwargs))

pandas/core/series.py

@@ -1847,7 +1847,8 @@ def argsort(self, axis=0, kind='quicksort', order=None):
                 dtype='int64').__finalize__(self)
 
     def nlargest(self, n=5, keep='first'):
-        """Return the largest `n` elements.
+        """
+        Return the largest `n` elements.
 
         Parameters
         ----------
@@ -1893,7 +1894,8 @@ def nlargest(self, n=5, keep='first'):
         return algorithms.SelectNSeries(self, n=n, keep=keep).nlargest()
 
     def nsmallest(self, n=5, keep='first'):
-        """Return the smallest `n` elements.
+        """
+        Return the smallest `n` elements.
 
         Parameters
         ----------

pandas/io/parsers.py

@@ -152,7 +152,7 @@
     Additional strings to recognize as NA/NaN. If dict passed, specific
     per-column NA values.  By default the following values are interpreted as
     NaN: '""" + fill("', '".join(sorted(_NA_VALUES)),
-                     70, subsequent_indent="    ") + """'`.
+                     70, subsequent_indent="    ") + """'.
 keep_default_na : bool, default True
     If na_values are specified and keep_default_na is False the default NaN
     values are overridden, otherwise they're appended to.
@@ -181,22 +181,23 @@
 
     Note: A fast-path exists for iso8601-formatted dates.
 infer_datetime_format : boolean, default False
-    If True and parse_dates is enabled, pandas will attempt to infer the format
-    of the datetime strings in the columns, and if it can be inferred, switch
-    to a faster method of parsing them. In some cases this can increase the
-    parsing speed by 5-10x.
+    If True and `parse_dates` is enabled, pandas will attempt to infer the
+    format of the datetime strings in the columns, and if it can be inferred,
+    switch to a faster method of parsing them. In some cases this can increase
+    the parsing speed by 5-10x.
 keep_date_col : boolean, default False
-    If True and parse_dates specifies combining multiple columns then
+    If True and `parse_dates` specifies combining multiple columns then
     keep the original columns.
 date_parser : function, default None
     Function to use for converting a sequence of string columns to an array of
     datetime instances. The default uses ``dateutil.parser.parser`` to do the
-    conversion. Pandas will try to call date_parser in three different ways,
+    conversion. Pandas will try to call `date_parser` in three different ways,
     advancing to the next if an exception occurs: 1) Pass one or more arrays
-    (as defined by parse_dates) as arguments; 2) concatenate (row-wise) the
-    string values from the columns defined by parse_dates into a single array
-    and pass that; and 3) call date_parser once for each row using one or more
-    strings (corresponding to the columns defined by parse_dates) as arguments.
+    (as defined by `parse_dates`) as arguments; 2) concatenate (row-wise) the
+    string values from the columns defined by `parse_dates` into a single array
+    and pass that; and 3) call `date_parser` once for each row using one or
+    more strings (corresponding to the columns defined by `parse_dates`) as
+    arguments.
 dayfirst : boolean, default False
     DD/MM format dates, international and European format
 iterator : boolean, default False