Implement Series.aggregate and agg (#816)

Like pandas Series.aggregate (https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.aggregate.html)

I implemented aggregate function for series.

Example:

```python
>>> s = ks.Series([1, 2, 3, 4])
>>> s
0    1
1    2
2    3
3    4
Name: 0, dtype: int64

>>> s.agg('min')
1

>>> s.agg(['min', 'max'])
max    4
min    1
Name: 0, dtype: int64
```

(above example is using pandas one)

Author: itholic

databricks/koalas/frame.py

@@ -871,7 +871,6 @@ def applymap(self, func):
                                        column_index=[c._internal.column_index[0] for c in applied])
         return DataFrame(internal)
 
-    # TODO: Series support is not implemented yet.
     # TODO: not all arguments are implemented comparing to Pandas' for now.
     def aggregate(self, func: Union[List[str], Dict[str, List[str]]]):
         """Aggregate using one or more operations over the specified axis.

databricks/koalas/missing/series.py

@@ -48,8 +48,6 @@ class _MissingPandasLikeSeries(object):
     flags = unsupported_property('flags', deprecated=True)
 
     # Functions
-    agg = unsupported_function('agg')
-    aggregate = unsupported_function('aggregate')
     align = unsupported_function('align')
     argsort = unsupported_function('argsort')
     asfreq = unsupported_function('asfreq')

databricks/koalas/series.py

@@ -2294,6 +2294,51 @@ def apply(self, func, args=(), **kwds):
         wrapped = ks.pandas_wraps(return_col=return_sig)(apply_each)
         return wrapped(self, *args, **kwds).rename(self.name)
 
+    # TODO: not all arguments are implemented comparing to Pandas' for now.
+    def aggregate(self, func: Union[str, List[str]]):
+        """Aggregate using one or more operations over the specified axis.
+
+        Parameters
+        ----------
+        func : str or a list of str
+            function name(s) as string apply to series.
+
+        Returns
+        -------
+        scalar, Series
+            The return can be:
+            - scalar : when Series.agg is called with single function
+            - Series : when Series.agg is called with several functions
+
+        Notes
+        -----
+        `agg` is an alias for `aggregate`. Use the alias.
+
+        See Also
+        --------
+        databricks.koalas.Series.apply
+        databricks.koalas.Series.transform
+
+        Examples
+        --------
+        >>> s = ks.Series([1, 2, 3, 4])
+        >>> s.agg('min')
+        1
+
+        >>> s.agg(['min', 'max'])
+        max    4
+        min    1
+        Name: 0, dtype: int64
+        """
+        if isinstance(func, list):
+            return self.to_frame().agg(func)[self.name]
+        elif isinstance(func, str):
+            return getattr(self, func)()
+        else:
+            raise ValueError("func must be a string or list of strings")
+
+    agg = aggregate
+
     def transpose(self, *args, **kwargs):
         """
         Return the transpose, which is by definition self.

databricks/koalas/tests/test_series.py

@@ -647,3 +647,14 @@ def test_astype(self):
         kser = koalas.Series(pser)
         with self.assertRaisesRegex(ValueError, 'Type int63 not understood'):
             kser.astype('int63')
+
+    def test_aggregate(self):
+        pser = pd.Series([10, 20, 15, 30, 45], name='x')
+        kser = koalas.Series(pser)
+        msg = 'func must be a string or list of strings'
+        with self.assertRaisesRegex(ValueError, msg):
+            kser.aggregate({'x': ['min', 'max']})
+        msg = ('If the given function is a list, it '
+               'should only contains function names as strings.')
+        with self.assertRaisesRegex(ValueError, msg):
+            kser.aggregate(['min', max])

docs/source/reference/series.rst

@@ -85,6 +85,8 @@ Function application, GroupBy & Window
    :toctree: api/
 
    Series.apply
+   Series.agg
+   Series.aggregate
    Series.map
    Series.groupby
    Series.pipe