ENH: Add set_index to Series

Author: h-vetinari

doc/source/whatsnew/v0.24.0.txt

@@ -181,6 +181,7 @@ Other Enhancements
   The default compression for ``to_csv``, ``to_json``, and ``to_pickle`` methods has been updated to ``'infer'`` (:issue:`22004`).
 - :func:`to_timedelta` now supports iso-formated timedelta strings (:issue:`21877`)
 - :class:`Series` and :class:`DataFrame` now support :class:`Iterable` in constructor (:issue:`2193`)
+- :class:`Series` has gained the method :meth:`Series.set_index`, which works like its :class:`DataFrame` counterpart :meth:`DataFrame.set_index` (:issue:`21684`)
 - :class:`DatetimeIndex` gained :attr:`DatetimeIndex.timetz` attribute. Returns local time with timezone information. (:issue:`21358`)
 - :class:`Resampler` now is iterable like :class:`GroupBy` (:issue:`15314`).
 - :meth:`Series.resample` and :meth:`DataFrame.resample` have gained the :meth:`Resampler.quantile` (:issue:`15023`).

pandas/core/frame.py

@@ -3826,43 +3826,54 @@ def shift(self, periods=1, freq=None, axis=0):
     def set_index(self, keys, drop=True, append=False, inplace=False,
                   verify_integrity=False):
         """
-        Set the DataFrame index (row labels) using one or more existing
-        columns. By default yields a new object.
+        Set the DataFrame index (row labels) using one or more columns.
 
         Parameters
         ----------
         keys : column label or list of column labels / arrays
+            Either a column label, Series, Index, MultiIndex, list,
+            np.ndarray or a list containing only column labels, Series, Index,
+            MultiIndex, list, np.ndarray.
         drop : boolean, default True
-            Delete columns to be used as the new index
+            Delete columns to be used as the new index.
         append : boolean, default False
-            Whether to append columns to existing index
+            Whether to append columns to existing index.
         inplace : boolean, default False
-            Modify the DataFrame in place (do not create a new object)
+            Modify the DataFrame in place (do not create a new object).
         verify_integrity : boolean, default False
             Check the new index for duplicates. Otherwise defer the check until
             necessary. Setting to False will improve the performance of this
-            method
+            method.
+
+        Returns
+        -------
+        reindexed : DataFrame if inplace is False, else None
+
+        See Also
+        --------
+        Series.set_index: Corresponding method for Series
 
         Examples
         --------
         >>> df = pd.DataFrame({'month': [1, 4, 7, 10],
         ...                    'year': [2012, 2014, 2013, 2014],
-        ...                    'sale':[55, 40, 84, 31]})
-           month  sale  year
-        0  1      55    2012
-        1  4      40    2014
-        2  7      84    2013
-        3  10     31    2014
+        ...                    'sale': [55, 40, 84, 31]})
+        >>> df
+           month  year  sale
+        0      1  2012    55
+        1      4  2014    40
+        2      7  2013    84
+        3     10  2014    31
 
         Set the index to become the 'month' column:
 
         >>> df.set_index('month')
-               sale  year
+               year  sale
         month
-        1      55    2012
-        4      40    2014
-        7      84    2013
-        10     31    2014
+        1      2012    55
+        4      2014    40
+        7      2013    84
+        10     2014    31
 
         Create a multi-index using columns 'year' and 'month':
 
@@ -3883,73 +3894,14 @@ def set_index(self, keys, drop=True, append=False, inplace=False,
         2  2014  4      40
         3  2013  7      84
         4  2014  10     31
-
-        Returns
-        -------
-        dataframe : DataFrame
         """
-        inplace = validate_bool_kwarg(inplace, 'inplace')
         if not isinstance(keys, list):
             keys = [keys]
 
-        if inplace:
-            frame = self
-        else:
-            frame = self.copy()
-
-        arrays = []
-        names = []
-        if append:
-            names = [x for x in self.index.names]
-            if isinstance(self.index, MultiIndex):
-                for i in range(self.index.nlevels):
-                    arrays.append(self.index._get_level_values(i))
-            else:
-                arrays.append(self.index)
-
-        to_remove = []
-        for col in keys:
-            if isinstance(col, MultiIndex):
-                # append all but the last column so we don't have to modify
-                # the end of this loop
-                for n in range(col.nlevels - 1):
-                    arrays.append(col._get_level_values(n))
-
-                level = col._get_level_values(col.nlevels - 1)
-                names.extend(col.names)
-            elif isinstance(col, Series):
-                level = col._values
-                names.append(col.name)
-            elif isinstance(col, Index):
-                level = col
-                names.append(col.name)
-            elif isinstance(col, (list, np.ndarray, Index)):
-                level = col
-                names.append(None)
-            else:
-                level = frame[col]._values
-                names.append(col)
-                if drop:
-                    to_remove.append(col)
-            arrays.append(level)
-
-        index = ensure_index_from_sequences(arrays, names)
-
-        if verify_integrity and not index.is_unique:
-            duplicates = index[index.duplicated()].unique()
-            raise ValueError('Index has duplicate keys: {dup}'.format(
-                dup=duplicates))
-
-        for c in to_remove:
-            del frame[c]
-
-        # clear up memory usage
-        index._cleanup()
-
-        frame.index = index
-
-        if not inplace:
-            return frame
+        vi = verify_integrity
+        return super(DataFrame, self).set_index(keys=keys, drop=drop,
+                                                append=append, inplace=inplace,
+                                                verify_integrity=vi)
 
     def reset_index(self, level=None, drop=False, inplace=False, col_level=0,
                     col_fill=''):

pandas/core/generic.py

@@ -32,11 +32,13 @@
 from pandas.core.dtypes.cast import maybe_promote, maybe_upcast_putmask
 from pandas.core.dtypes.inference import is_hashable
 from pandas.core.dtypes.missing import isna, notna
-from pandas.core.dtypes.generic import ABCSeries, ABCPanel, ABCDataFrame
+from pandas.core.dtypes.generic import (ABCIndexClass, ABCMultiIndex, ABCPanel,
+                                        ABCSeries, ABCDataFrame)
 
 from pandas.core.base import PandasObject, SelectionMixin
-from pandas.core.index import (Index, MultiIndex, ensure_index,
-                               InvalidIndexError, RangeIndex)
+from pandas.core.index import (Index, MultiIndex,
+                               InvalidIndexError, RangeIndex,
+                               ensure_index, ensure_index_from_sequences)
 import pandas.core.indexing as indexing
 from pandas.core.indexes.datetimes import DatetimeIndex
 from pandas.core.indexes.period import PeriodIndex, Period
@@ -663,6 +665,137 @@ def _set_axis(self, axis, labels):
         y : same as input
         """
 
+    def set_index(self, keys, drop=True, append=False, inplace=False,
+                  verify_integrity=False):
+        """
+        Set the Series/DataFrame index (row labels) using one or more given
+        arrays (or column labels in case of DataFrame).
+        By default yields a new object.
+
+        Parameters
+        ----------
+        keys : column label or list of column labels / arrays. For Series case,
+            only array or list of arrays is allowed.
+        drop : boolean, default True
+            Delete columns to be used as the new index (only for DataFrame).
+        append : boolean, default False
+            Whether to append columns to existing index
+        inplace : boolean, default False
+            Modify the Series/DataFrame in place (do not create a new object)
+        verify_integrity : boolean, default False
+            Check the new index for duplicates. Otherwise defer the check until
+            necessary. Setting to False will improve the performance of this
+            method
+
+        Returns
+        -------
+        reindexed : Series/DataFrame if inplace is False, else None
+
+        See Also
+        --------
+        DataFrame.set_index: method adapted for DataFrame
+        Series.set_index: method adapted for Series
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'month': [1, 4, 7, 10],
+        ...                    'year': [2012, 2014, 2013, 2014],
+        ...                    'sale': [55, 40, 84, 31]})
+        >>> df
+           month  year  sale
+        0      1  2012    55
+        1      4  2014    40
+        2      7  2013    84
+        3     10  2014    31
+
+        Set the index to become the 'month' column:
+
+        >>> df.set_index('month')
+               year  sale
+        month
+        1      2012    55
+        4      2014    40
+        7      2013    84
+        10     2014    31
+
+        Create a multi-index using columns 'year' and 'month':
+
+        >>> df.set_index(['year', 'month'])
+                    sale
+        year  month
+        2012  1     55
+        2014  4     40
+        2013  7     84
+        2014  10    31
+
+        Create a multi-index using a set of values and a column:
+
+        >>> df.set_index([[1, 2, 3, 4], 'year'])
+                 month  sale
+           year
+        1  2012  1      55
+        2  2014  4      40
+        3  2013  7      84
+        4  2014  10     31
+        """
+        inplace = validate_bool_kwarg(inplace, 'inplace')
+        if inplace:
+            obj = self
+        else:
+            obj = self.copy()
+
+        arrays = []
+        names = []
+        if append:
+            names = [x for x in self.index.names]
+            if isinstance(self.index, ABCMultiIndex):
+                for i in range(self.index.nlevels):
+                    arrays.append(self.index._get_level_values(i))
+            else:
+                arrays.append(self.index)
+
+        to_remove = []
+        for col in keys:
+            if isinstance(col, ABCMultiIndex):
+                for n in range(col.nlevels):
+                    arrays.append(col._get_level_values(n))
+                names.extend(col.names)
+            elif isinstance(col, ABCIndexClass):
+                # Index but not MultiIndex (treated above)
+                arrays.append(col)
+                names.append(col.name)
+            elif isinstance(col, ABCSeries):
+                arrays.append(col._values)
+                names.append(col.name)
+            elif isinstance(col, (list, np.ndarray)):
+                arrays.append(col)
+                names.append(None)
+            # from here, col can only be a column label (and obj a DataFrame);
+            # see checks in Series.set_index and DataFrame.set_index
+            else:
+                arrays.append(obj[col]._values)
+                names.append(col)
+                if drop:
+                    to_remove.append(col)
+
+        index = ensure_index_from_sequences(arrays, names)
+
+        if verify_integrity and not index.is_unique:
+            duplicates = list(index[index.duplicated()])
+            raise ValueError('Index has duplicate keys: {dup}'.format(
+                dup=duplicates))
+
+        for c in to_remove:
+            del obj[c]
+
+        # clear up memory usage
+        index._cleanup()
+
+        obj.index = index
+
+        if not inplace:
+            return obj
+
     @Appender(_shared_docs['transpose'] % _shared_doc_kwargs)
     def transpose(self, *args, **kwargs):