ENH/API: Series.rename and NDFrame.rename_axis

Overloading the APIs of Series.rename and
NDFramed.rename_axis for method chaining.

doc/source/basics.rst

@@ -1170,6 +1170,15 @@ The :meth:`~DataFrame.rename` method also provides an ``inplace`` named
 parameter that is by default ``False`` and copies the underlying data. Pass
 ``inplace=True`` to rename the data in place.
 
+.. versionadded:: 0.18.0
+
+Finally, :meth:`~Series.rename` also accepts a scalar or list-like
+for altering the ``Series.name`` attribute.
+
+.. ipython:: python
+
+   s.rename("scalar-name")
+
 .. _basics.rename_axis:
 
 The Panel class has a related :meth:`~Panel.rename_axis` class which can rename

doc/source/dsintro.rst

@@ -214,6 +214,17 @@ Series can also have a ``name`` attribute:
 The Series ``name`` will be assigned automatically in many cases, in particular
 when taking 1D slices of DataFrame as you will see below.
 
+.. versionadded:: 0.18.0
+
+You can rename a Series with the :meth:`pandas.Series.rename` method.
+
+.. ipython:: python
+
+   s2 = s.rename("different")
+   s2.name
+
+Note that ``s`` and ``s2`` refer to different objects.
+
 .. _basics.dataframe:
 
 DataFrame

doc/source/whatsnew/v0.18.0.txt

@@ -104,6 +104,27 @@ And multiple aggregations
    r.agg({'A' : ['mean','std'],
           'B' : ['mean','std']})
 
+.. _whatsnew_0180.enhancements.rename:
+
+``Series.rename`` and ``NDFrame.rename_axis`` can now take a scalar or list-like
+argument for altering the Series or axis *name*, in addition to their old behaviors of altering labels. (:issue:`9494`, :issue:`11965`)
+
+.. ipython: python
+
+   s = pd.Series(np.random.randn(10))
+   s.rename('newname')
+
+.. ipython: python
+
+   df = pd.DataFrame(np.random.randn(10, 2))
+   (df.rename_axis("indexname")
+      .rename_axis("columns_name", axis="columns"))
+
+The new functionality works well in method chains.
+Previously these methods only accepted functions or dicts mapping a *label* to a new label.
+This continues to work as before for function or dict-like values.
+
+
 .. _whatsnew_0180.enhancements.rangeindex:
 
 Range Index

pandas/core/common.py

@@ -2456,6 +2456,10 @@ def is_list_like(arg):
             not isinstance(arg, compat.string_and_binary_types))
 
 
+def is_dict_like(arg):
+    return hasattr(arg, '__getitem__') and hasattr(arg, 'keys')
+
+
 def is_named_tuple(arg):
     return isinstance(arg, tuple) and hasattr(arg, '_fields')
 

pandas/core/generic.py

@@ -546,13 +546,16 @@ def swaplevel(self, i, j, axis=0):
     _shared_docs['rename'] = """
         Alter axes input function or functions. Function / dict values must be
         unique (1-to-1). Labels not contained in a dict / Series will be left
-        as-is.
+        as-is. Alternatively, change ``Series.name`` with a scalar
+        value (Series only).
 
         Parameters
         ----------
-        %(axes)s : dict-like or function, optional
-            Transformation to apply to that axis values
-
+        %(axes)s : scalar, list-like, dict-like or function, optional
+            Scalar or list-like will alter the ``Series.name`` attribute,
+            and raise on DataFrame or Panel.
+            dict-like or functions are transformations to apply to
+            that axis' values
         copy : boolean, default True
             Also copy underlying data
         inplace : boolean, default False
@@ -562,6 +565,43 @@ def swaplevel(self, i, j, axis=0):
         Returns
         -------
         renamed : %(klass)s (new object)
+
+        See Also
+        --------
+        pandas.NDFrame.rename_axis
+
+        Examples
+        --------
+        >>> s = pd.Series([1, 2, 3])
+        >>> s
+        0    1
+        1    2
+        2    3
+        dtype: int64
+        >>> s.rename("my_name") # scalar, changes Series.name
+        0    1
+        1    2
+        2    3
+        Name: my_name, dtype: int64
+        >>> s.rename(lambda x: x ** 2)  # function, changes labels
+        0    1
+        1    2
+        4    3
+        dtype: int64
+        >>> s.rename({1: 3, 2: 5})  # mapping, changes labels
+        0    1
+        3    2
+        5    3
+        dtype: int64
+        >>> df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
+        >>> df.rename(2)
+        ...
+        TypeError: 'int' object is not callable
+        >>> df.rename(index=str, columns={"A": "a", "B": "c"})
+           a  c
+        0  1  4
+        1  2  5
+        2  3  6
         """
 
     @Appender(_shared_docs['rename'] % dict(axes='axes keywords for this'
@@ -617,12 +657,15 @@ def f(x):
     def rename_axis(self, mapper, axis=0, copy=True, inplace=False):
         """
         Alter index and / or columns using input function or functions.
+        A scaler or list-like for ``mapper`` will alter the ``Index.name``
+        or ``MultiIndex.names`` attribute.
+        A function or dict for ``mapper`` will alter the labels.
         Function / dict values must be unique (1-to-1). Labels not contained in
         a dict / Series will be left as-is.
 
         Parameters
         ----------
-        mapper : dict-like or function, optional
+        mapper : scalar, list-like, dict-like or function, optional
         axis : int or string, default 0
         copy : boolean, default True
             Also copy underlying data
@@ -631,11 +674,88 @@ def rename_axis(self, mapper, axis=0, copy=True, inplace=False):
         Returns
         -------
         renamed : type of caller
+
+        See Also
+        --------
+        pandas.NDFrame.rename
+        pandas.Index.rename
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
+        >>> df.rename_axis("foo")  # scalar, alters df.index.name
+             A  B
+        foo
+        0    1  4
+        1    2  5
+        2    3  6
+        >>> df.rename_axis(lambda x: 2 * x)  # function: alters labels
+           A  B
+        0  1  4
+        2  2  5
+        4  3  6
+        >>> df.rename_axis({"A": "ehh", "C": "see"}, axis="columns")  # mapping
+           ehh  B
+        0    1  4
+        1    2  5
+        2    3  6
+        """
+        is_scalar_or_list = (
+            (not com.is_sequence(mapper) and not callable(mapper)) or
+            (com.is_list_like(mapper) and not com.is_dict_like(mapper))
+        )
+
+        if is_scalar_or_list:
+            return self._set_axis_name(mapper, axis=axis)
+        else:
+            axis = self._get_axis_name(axis)
+            d = {'copy': copy, 'inplace': inplace}
+            d[axis] = mapper
+            return self.rename(**d)
+
+    def _set_axis_name(self, name, axis=0):
         """
-        axis = self._get_axis_name(axis)
-        d = {'copy': copy, 'inplace': inplace}
-        d[axis] = mapper
-        return self.rename(**d)
+        Alter the name or names of the axis, returning self.
+
+        Parameters
+        ----------
+        name : str or list of str
+            Name for the Index, or list of names for the MultiIndex
+        axis : int or str
+           0 or 'index' for the index; 1 or 'columns' for the columns
+
+        Returns
+        -------
+        renamed : type of caller
+
+        See Also
+        --------
+        pandas.DataFrame.rename
+        pandas.Series.rename
+        pandas.Index.rename
+
+        Examples
+        --------
+        >>> df._set_axis_name("foo")
+             A
+        foo
+        0    1
+        1    2
+        2    3
+        >>> df.index = pd.MultiIndex.from_product([['A'], ['a', 'b', 'c']])
+        >>> df._set_axis_name(["bar", "baz"])
+                 A
+        bar baz
+        A   a    1
+            b    2
+            c    3
+        """
+        axis = self._get_axis_number(axis)
+        idx = self._get_axis(axis).set_names(name)
+
+        renamed = self.copy(deep=True)
+        renamed.set_axis(axis, idx)
+        return renamed
 
     # ----------------------------------------------------------------------
     # Comparisons

pandas/core/series.py

@@ -8,6 +8,7 @@
 
 import types
 import warnings
+from collections import MutableMapping
 
 from numpy import nan, ndarray
 import numpy as np
@@ -1109,6 +1110,20 @@ def to_sparse(self, kind='block', fill_value=None):
         return SparseSeries(self, kind=kind,
                             fill_value=fill_value).__finalize__(self)
 
+    def _set_name(self, name, inplace=False):
+        '''
+        Set the Series name.
+
+        Parameters
+        ----------
+        name : str
+        inplace : bool
+            whether to modify `self` directly or return a copy
+        '''
+        ser = self if inplace else self.copy()
+        ser.name = name
+        return ser
+
     # ----------------------------------------------------------------------
     # Statistics, overridden ndarray methods
 
@@ -2313,6 +2328,12 @@ def align(self, other, join='outer', axis=None, level=None, copy=True,
 
     @Appender(generic._shared_docs['rename'] % _shared_doc_kwargs)
     def rename(self, index=None, **kwargs):
+        is_scalar_or_list = (
+            (not com.is_sequence(index) and not callable(index)) or
+            (com.is_list_like(index) and not isinstance(index, MutableMapping))
+        )
+        if is_scalar_or_list:
+            return self._set_name(index, inplace=kwargs.get('inplace'))
         return super(Series, self).rename(index=index, **kwargs)
 
     @Appender(generic._shared_docs['reindex'] % _shared_doc_kwargs)

pandas/tests/series/test_alter_axes.py

@@ -55,6 +55,29 @@ def test_rename(self):
         renamed = renamer.rename({})
         self.assertEqual(renamed.index.name, renamer.index.name)
 
+    def test_rename_set_name(self):
+        s = Series(range(4), index=list('abcd'))
+        for name in ['foo', ['foo'], ('foo',)]:
+            result = s.rename(name)
+            self.assertEqual(result.name, name)
+            self.assert_numpy_array_equal(result.index.values, s.index.values)
+            self.assertTrue(s.name is None)
+
+    def test_rename_set_name_inplace(self):
+        s = Series(range(3), index=list('abc'))
+        for name in ['foo', ['foo'], ('foo',)]:
+            s.rename(name, inplace=True)
+            self.assertEqual(s.name, name)
+            self.assert_numpy_array_equal(s.index.values,
+                                          np.array(['a', 'b', 'c']))
+
+    def test_set_name(self):
+        s = Series([1, 2, 3])
+        s2 = s._set_name('foo')
+        self.assertEqual(s2.name, 'foo')
+        self.assertTrue(s.name is None)
+        self.assertTrue(s is not s2)
+
     def test_rename_inplace(self):
         renamer = lambda x: x.strftime('%Y%m%d')
         expected = renamer(self.ts.index[0])

pandas/tests/test_common.py

@@ -631,6 +631,17 @@ def test_is_list_like():
         assert not com.is_list_like(f)
 
 
+def test_is_dict_like():
+    passes = [{}, {'A': 1}, pd.Series([1])]
+    fails = ['1', 1, [1, 2], (1, 2), range(2), pd.Index([1])]
+
+    for p in passes:
+        assert com.is_dict_like(p)
+
+    for f in fails:
+        assert not com.is_dict_like(f)
+
+
 def test_is_named_tuple():
     passes = (collections.namedtuple('Test', list('abc'))(1, 2, 3), )
     fails = ((1, 2, 3), 'a', Series({'pi': 3.14}))