diff --git a/eland/field_mappings.py b/eland/field_mappings.py
index 18c6484..67b9855 100644
--- a/eland/field_mappings.py
+++ b/eland/field_mappings.py
@@ -91,15 +91,19 @@ class Field(NamedTuple):
         return np.dtype(self.pd_dtype)
 
     def is_es_agg_compatible(self, es_agg) -> bool:
-        # Unpack the actual aggregation if this is 'extended_stats'
+        # Unpack the actual aggregation if this is 'extended_stats/percentiles'
         if isinstance(es_agg, tuple):
-            es_agg = es_agg[1]
+            if es_agg[0] == "extended_stats":
+                es_agg = es_agg[1]
+            elif es_agg[0] == "percentiles":
+                es_agg = "percentiles"
+
         # Cardinality works for all types
         # Numerics and bools work for all aggs
         if es_agg == "cardinality" or self.is_numeric or self.is_bool:
             return True
         # Timestamps also work for 'min', 'max' and 'avg'
-        if es_agg in {"min", "max", "avg"} and self.is_timestamp:
+        if es_agg in {"min", "max", "avg", "percentiles"} and self.is_timestamp:
             return True
         return False
 
diff --git a/eland/series.py b/eland/series.py
index 011bd0c..9623a54 100644
--- a/eland/series.py
+++ b/eland/series.py
@@ -1255,7 +1255,7 @@ class Series(NDFrame):
         Returns
         -------
         float
-            max value
+            mean value
 
         See Also
         --------
@@ -1270,6 +1270,30 @@ class Series(NDFrame):
         results = super().mean(numeric_only=numeric_only)
         return results.squeeze()
 
+    def median(self, numeric_only=None):
+        """
+        Return the median of the Series values
+
+        TODO - implement remainder of pandas arguments, currently non-numerics are not supported
+
+        Returns
+        -------
+        float
+            median value
+
+        See Also
+        --------
+        :pandas_api_docs:`pandas.Series.median`
+
+        Examples
+        --------
+        >>> s = ed.DataFrame('localhost', 'flights')['AvgTicketPrice']
+        >>> int(s.median())
+        640
+        """
+        results = super().median(numeric_only=numeric_only)
+        return results.squeeze()
+
     def min(self, numeric_only=None):
         """
         Return the minimum of the Series values
@@ -1279,7 +1303,7 @@ class Series(NDFrame):
         Returns
         -------
         float
-            max value
+            min value
 
         See Also
         --------
@@ -1303,7 +1327,7 @@ class Series(NDFrame):
         Returns
         -------
         float
-            max value
+            sum of all values
 
         See Also
         --------
diff --git a/eland/tests/dataframe/test_metrics_pytest.py b/eland/tests/dataframe/test_metrics_pytest.py
index 702e9f4..4a1c826 100644
--- a/eland/tests/dataframe/test_metrics_pytest.py
+++ b/eland/tests/dataframe/test_metrics_pytest.py
@@ -167,19 +167,20 @@ class TestDataFrameMetrics(TestData):
                 "min": pd.Timestamp("2018-01-01 00:00:00"),
                 "mean": pd.Timestamp("2018-01-21 19:20:45.564438232"),
                 "max": pd.Timestamp("2018-02-11 23:50:12"),
+                "nunique": 12236,
                 "mad": pd.NaT,
-                "median": pd.NaT,
                 "std": pd.NaT,
                 "sum": pd.NaT,
                 "var": pd.NaT,
-                "nunique": 12236,
             }
         }
 
         ed_metrics = ed_timestamps.agg(self.funcs + self.extended_funcs + ["nunique"])
-        assert ed_metrics.to_dict() == expected_values
+        ed_metrics_dict = ed_metrics.to_dict()
+        ed_metrics_dict["timestamp"].pop("median")  # Median is tested below.
+        assert ed_metrics_dict == expected_values
 
-    @pytest.mark.parametrize("agg", ["mean", "min", "max"])
+    @pytest.mark.parametrize("agg", ["mean", "min", "max", "nunique"])
     def test_flights_datetime_metrics_single_agg(self, agg):
         ed_timestamps = self.ed_flights()[["timestamp"]]
         expected_values = {
@@ -190,7 +191,10 @@ class TestDataFrameMetrics(TestData):
         }
         ed_metric = ed_timestamps.agg([agg])
 
-        assert ed_metric.dtypes["timestamp"] == np.dtype("datetime64[ns]")
+        if agg == "nunique":
+            assert ed_metric.dtypes["timestamp"] == np.int64
+        else:
+            assert ed_metric.dtypes["timestamp"] == np.dtype("datetime64[ns]")
         assert ed_metric["timestamp"][0] == expected_values[agg]
 
     @pytest.mark.parametrize("agg", ["mean", "min", "max"])
@@ -205,3 +209,22 @@ class TestDataFrameMetrics(TestData):
 
         assert ed_metric.dtype == np.dtype("datetime64[ns]")
         assert ed_metric[0] == expected_values[agg]
+
+    def test_flights_datetime_metrics_median(self):
+        ed_df = self.ed_flights_small()[["timestamp"]]
+
+        median = ed_df.median(numeric_only=False)[0]
+        assert isinstance(median, pd.Timestamp)
+        assert (
+            pd.to_datetime("2018-01-01 10:00:00.000")
+            <= median
+            <= pd.to_datetime("2018-01-01 12:00:00.000")
+        )
+
+        median = ed_df.agg(["mean"])["timestamp"][0]
+        assert isinstance(median, pd.Timestamp)
+        assert (
+            pd.to_datetime("2018-01-01 10:00:00.000")
+            <= median
+            <= pd.to_datetime("2018-01-01 12:00:00.000")
+        )
diff --git a/eland/tests/series/test_metrics_pytest.py b/eland/tests/series/test_metrics_pytest.py
index d24a796..86a9557 100644
--- a/eland/tests/series/test_metrics_pytest.py
+++ b/eland/tests/series/test_metrics_pytest.py
@@ -100,3 +100,14 @@ class TestSeriesMetrics(TestData):
         ed_metric = getattr(ed_timestamps, agg)()
 
         assert ed_metric == expected_values[agg]
+
+    def test_flights_datetime_median_metric(self):
+        ed_series = self.ed_flights_small()["timestamp"]
+
+        median = ed_series.median()
+        assert isinstance(median, pd.Timestamp)
+        assert (
+            pd.to_datetime("2018-01-01 10:00:00.000")
+            <= median
+            <= pd.to_datetime("2018-01-01 12:00:00.000")
+        )