undo and merge

Author: alexander-ponomaroff

pandas/core/algorithms.py

@@ -289,6 +289,11 @@ def unique(values):
       - If the input is a Categorical dtype, the return is a Categorical
       - If the input is a Series/ndarray, the return will be an ndarray
 
+    See Also
+    --------
+    pandas.Index.unique
+    pandas.Series.unique
+
     Examples
     --------
     >>> pd.unique(pd.Series([2, 1, 3, 3]))
@@ -338,11 +343,6 @@ def unique(values):
 
     >>> pd.unique([('a', 'b'), ('b', 'a'), ('a', 'c'), ('b', 'a')])
     array([('a', 'b'), ('b', 'a'), ('a', 'c')], dtype=object)
-
-    See Also
-    --------
-    pandas.Index.unique
-    pandas.Series.unique
     """
 
     values = _ensure_arraylike(values)

pandas/core/arrays/categorical.py

@@ -273,6 +273,16 @@ class Categorical(ExtensionArray, PandasObject):
         If an explicit ``ordered=True`` is given but no `categories` and the
         `values` are not sortable.
 
+    See Also
+    --------
+    pandas.api.types.CategoricalDtype : Type for categorical data.
+    CategoricalIndex : An Index with an underlying ``Categorical``.
+
+    Notes
+    -----
+    See the `user guide
+    <http://pandas.pydata.org/pandas-docs/stable/categorical.html>`_ for more.
+
     Examples
     --------
     >>> pd.Categorical([1, 2, 3, 1, 2, 3])
@@ -293,16 +303,6 @@ class Categorical(ExtensionArray, PandasObject):
     Categories (3, object): [c < b < a]
     >>> c.min()
     'c'
-
-    Notes
-    -----
-    See the `user guide
-    <http://pandas.pydata.org/pandas-docs/stable/categorical.html>`_ for more.
-
-    See Also
-    --------
-    pandas.api.types.CategoricalDtype : Type for categorical data.
-    CategoricalIndex : An Index with an underlying ``Categorical``.
     """
 
     # For comparisons, so that numpy uses our implementation if the compare
@@ -827,11 +827,6 @@ def set_categories(self, new_categories, ordered=None, rename=False,
         dtypes on python3, which does not considers a S1 string equal to a
         single char python string.
 
-        Raises
-        ------
-        ValueError
-            If new_categories does not validate as categories
-
         Parameters
         ----------
         new_categories : Index-like
@@ -850,6 +845,11 @@ def set_categories(self, new_categories, ordered=None, rename=False,
         -------
         cat : Categorical with reordered categories or None if inplace.
 
+        Raises
+        ------
+        ValueError
+            If new_categories does not validate as categories
+
         See Also
         --------
         rename_categories
@@ -882,12 +882,6 @@ def rename_categories(self, new_categories, inplace=False):
         """
         Renames categories.
 
-        Raises
-        ------
-        ValueError
-            If new categories are list-like and do not have the same number of
-            items than the current categories or do not validate as categories
-
         Parameters
         ----------
         new_categories : list-like, dict-like or callable
@@ -922,6 +916,12 @@ def rename_categories(self, new_categories, inplace=False):
            With ``inplace=False``, the new categorical is returned.
            With ``inplace=True``, there is no return value.
 
+        Raises
+        ------
+        ValueError
+            If new categories are list-like and do not have the same number of
+            items than the current categories or do not validate as categories
+
         See Also
         --------
         reorder_categories
@@ -979,12 +979,6 @@ def reorder_categories(self, new_categories, ordered=None, inplace=False):
         `new_categories` need to include all old categories and no new category
         items.
 
-        Raises
-        ------
-        ValueError
-            If the new categories do not contain all old category items or any
-            new ones
-
         Parameters
         ----------
         new_categories : Index-like
@@ -1000,6 +994,12 @@ def reorder_categories(self, new_categories, ordered=None, inplace=False):
         -------
         cat : Categorical with reordered categories or None if inplace.
 
+        Raises
+        ------
+        ValueError
+            If the new categories do not contain all old category items or any
+            new ones
+
         See Also
         --------
         rename_categories
@@ -1022,12 +1022,6 @@ def add_categories(self, new_categories, inplace=False):
         `new_categories` will be included at the last/highest place in the
         categories and will be unused directly after this call.
 
-        Raises
-        ------
-        ValueError
-            If the new categories include old categories or do not validate as
-            categories
-
         Parameters
         ----------
         new_categories : category or list-like of category
@@ -1040,6 +1034,12 @@ def add_categories(self, new_categories, inplace=False):
         -------
         cat : Categorical with new categories added or None if inplace.
 
+        Raises
+        ------
+        ValueError
+            If the new categories include old categories or do not validate as
+            categories
+
         See Also
         --------
         rename_categories
@@ -1072,11 +1072,6 @@ def remove_categories(self, removals, inplace=False):
         `removals` must be included in the old categories. Values which were in
         the removed categories will be set to NaN
 
-        Raises
-        ------
-        ValueError
-            If the removals are not contained in the categories
-
         Parameters
         ----------
         removals : category or list of categories
@@ -1089,6 +1084,11 @@ def remove_categories(self, removals, inplace=False):
         -------
         cat : Categorical with removed categories or None if inplace.
 
+        Raises
+        ------
+        ValueError
+            If the removals are not contained in the categories
+
         See Also
         --------
         rename_categories

pandas/core/arrays/datetimes.py

@@ -892,6 +892,11 @@ def to_period(self, freq=None):
             When converting a DatetimeArray/Index with non-regular values,
             so that a frequency cannot be inferred.
 
+        See Also
+        --------
+        PeriodIndex: Immutable ndarray holding ordinal values.
+        DatetimeIndex.to_pydatetime: Return DatetimeIndex as object.
+
         Examples
         --------
         >>> df = pd.DataFrame({"y": [1,2,3]},
@@ -908,11 +913,6 @@ def to_period(self, freq=None):
         >>> idx.to_period()
         PeriodIndex(['2017-01-01', '2017-01-02'],
                     dtype='period[D]', freq='D')
-
-        See Also
-        --------
-        PeriodIndex: Immutable ndarray holding ordinal values.
-        DatetimeIndex.to_pydatetime: Return DatetimeIndex as object.
         """
         from pandas.core.arrays import PeriodArray
 
@@ -1087,17 +1087,17 @@ def date(self):
     by 6. This method is available on both Series with datetime
     values (using the `dt` accessor) or DatetimeIndex.
 
+    Returns
+    -------
+    Series or Index
+        Containing integers indicating the day number.
+
     See Also
     --------
     Series.dt.dayofweek : Alias.
     Series.dt.weekday : Alias.
     Series.dt.day_name : Returns the name of the day of the week.
 
-    Returns
-    -------
-    Series or Index
-        Containing integers indicating the day number.
-
     Examples
     --------
     >>> s = pd.date_range('2016-12-31', '2017-01-08', freq='D').to_series()

pandas/core/arrays/interval.py

@@ -84,21 +84,21 @@
 set_closed
 %(extra_methods)s\
 
-%(examples)s\
-
-Notes
-------
-See the `user guide
-<http://pandas.pydata.org/pandas-docs/stable/advanced.html#intervalindex>`_
-for more.
-
 See Also
 --------
 Index : The base pandas Index type.
 Interval : A bounded slice-like interval; the elements of an %(klass)s.
 interval_range : Function to create a fixed frequency IntervalIndex.
 cut : Bin values into discrete Intervals.
 qcut : Bin values into equal-sized Intervals based on rank or sample quantiles.
+
+Notes
+------
+See the `user guide
+<http://pandas.pydata.org/pandas-docs/stable/advanced.html#intervalindex>`_
+for more.
+
+%(examples)s\
 """
 
 
@@ -236,18 +236,18 @@ def _from_factorized(cls, values, original):
 
         .. versionadded:: 0.23.0
 
+    See Also
+    --------
+    interval_range : Function to create a fixed frequency IntervalIndex.
+    %(klass)s.from_arrays : Construct from a left and right array.
+    %(klass)s.from_tuples : Construct from a sequence of tuples.
+
     Examples
     --------
     >>> pd.%(klass)s.from_breaks([0, 1, 2, 3])
     %(klass)s([(0, 1], (1, 2], (2, 3]]
                   closed='right',
                   dtype='interval[int64]')
-
-    See Also
-    --------
-    interval_range : Function to create a fixed frequency IntervalIndex.
-    %(klass)s.from_arrays : Construct from a left and right array.
-    %(klass)s.from_tuples : Construct from a sequence of tuples.
     """
 
     @classmethod
@@ -281,14 +281,6 @@ def from_breaks(cls, breaks, closed='right', copy=False, dtype=None):
         -------
         %(klass)s
 
-        Notes
-        -----
-        Each element of `left` must be less than or equal to the `right`
-        element at the same position. If an element is missing, it must be
-        missing in both `left` and `right`. A TypeError is raised when
-        using an unsupported type for `left` or `right`. At the moment,
-        'category', 'object', and 'string' subtypes are not supported.
-
         Raises
         ------
         ValueError
@@ -304,6 +296,13 @@ def from_breaks(cls, breaks, closed='right', copy=False, dtype=None):
         %(klass)s.from_tuples : Construct an %(klass)s from an
             array-like of tuples.
 
+        Notes
+        -----
+        Each element of `left` must be less than or equal to the `right`
+        element at the same position. If an element is missing, it must be
+        missing in both `left` and `right`. A TypeError is raised when
+        using an unsupported type for `left` or `right`. At the moment,
+        'category', 'object', and 'string' subtypes are not supported.
 
         Examples
         --------
@@ -339,6 +338,16 @@ def from_arrays(cls, left, right, closed='right', copy=False, dtype=None):
 
         ..versionadded:: 0.23.0
 
+    See Also
+    --------
+    interval_range : Function to create a fixed frequency IntervalIndex.
+    %(klass)s.from_arrays : Construct an %(klass)s from a left and
+                                right array.
+    %(klass)s.from_breaks : Construct an %(klass)s from an array of
+                                splits.
+    %(klass)s.from_tuples : Construct an %(klass)s from an
+                                array-like of tuples.
+
     Examples
     --------
     >>> pd.%(klass)s.from_intervals([pd.Interval(0, 1),
@@ -352,16 +361,6 @@ def from_arrays(cls, left, right, closed='right', copy=False, dtype=None):
     >>> pd.Index([pd.Interval(0, 1), pd.Interval(1, 2)])
     %(klass)s([(0, 1], (1, 2]]
                   closed='right', dtype='interval[int64]')
-
-    See Also
-    --------
-    interval_range : Function to create a fixed frequency IntervalIndex.
-    %(klass)s.from_arrays : Construct an %(klass)s from a left and
-                                right array.
-    %(klass)s.from_breaks : Construct an %(klass)s from an array of
-                                splits.
-    %(klass)s.from_tuples : Construct an %(klass)s from an
-                                array-like of tuples.
     """
 
     _interval_shared_docs['from_tuples'] = """
@@ -381,20 +380,19 @@ def from_arrays(cls, left, right, closed='right', copy=False, dtype=None):
 
         ..versionadded:: 0.23.0
 
-
-    Examples
-    --------
-    >>>  pd.%(klass)s.from_tuples([(0, 1), (1, 2)])
-    %(klass)s([(0, 1], (1, 2]],
-                closed='right', dtype='interval[int64]')
-
     See Also
     --------
     interval_range : Function to create a fixed frequency IntervalIndex.
     %(klass)s.from_arrays : Construct an %(klass)s from a left and
                                 right array.
     %(klass)s.from_breaks : Construct an %(klass)s from an array of
                                 splits.
+
+    Examples
+    --------
+    >>>  pd.%(klass)s.from_tuples([(0, 1), (1, 2)])
+    %(klass)s([(0, 1], (1, 2]],
+                closed='right', dtype='interval[int64]')
     """
 
     @classmethod
@@ -1052,6 +1050,10 @@ def repeat(self, repeats, **kwargs):
         ndarray
             Boolean array positionally indicating where an overlap occurs.
 
+        See Also
+        --------
+        Interval.overlaps : Check whether two Interval objects overlap.
+
         Examples
         --------
         >>> intervals = %(constructor)s.from_tuples([(0, 1), (1, 3), (2, 4)])
@@ -1071,10 +1073,6 @@ def repeat(self, repeats, **kwargs):
 
         >>> intervals.overlaps(pd.Interval(1, 2, closed='right'))
         array([False,  True, False])
-
-        See Also
-        --------
-        Interval.overlaps : Check whether two Interval objects overlap.
     """
 
     @Appender(_interval_shared_docs['overlaps'] % _shared_docs_kwargs)