Add DataFrame and Series median method

dask/dataframe/core.py

@@ -2160,6 +2160,34 @@ def mean(
                 result.divisions = (self.columns.min(), self.columns.max())
             return handle_out(out, result)
 
+    def median_approximate(
+        self,
+        axis=None,
+        method="default",
+    ):
+        """Return the approximate median of the values over the requested axis.
+
+        Parameters
+        ----------
+        axis : {0, 1, "index", "columns"} (default 0)
+            0 or ``"index"`` for row-wise, 1 or ``"columns"`` for column-wise
+        method : {'default', 'tdigest', 'dask'}, optional
+            What method to use. By default will use Dask's internal custom
+            algorithm (``"dask"``).  If set to ``"tdigest"`` will use tdigest
+            for floats and ints and fallback to the ``"dask"`` otherwise.
+        """
+        return self.quantile(q=0.5, axis=axis, method=method)
+
+    @derived_from(pd.DataFrame)
+    def median(self, axis=None, method="default"):
+        if axis in (1, "columns") or self.npartitions == 1:
+            # Can provide an exact median in these cases
+            return self.quantile(q=0.5, axis=axis, method=method)
+        raise NotImplementedError(
+            "Dask doesn't implement an exact median in all cases as this is hard to do in parallel. "
+            "See the `median_approximate` method instead, which uses an approximate algorithm."
+        )
+
     @_numeric_only
     @derived_from(pd.DataFrame)
     def var(
@@ -3613,6 +3641,28 @@ def quantile(self, q=0.5, method="default"):
         """
         return quantile(self, q, method=method)
 
+    def median_approximate(self, method="default"):
+        """Return the approximate median of the values over the requested axis.
+
+        Parameters
+        ----------
+        method : {'default', 'tdigest', 'dask'}, optional
+            What method to use. By default will use Dask's internal custom
+            algorithm (``"dask"``).  If set to ``"tdigest"`` will use tdigest
+            for floats and ints and fallback to the ``"dask"`` otherwise.
+        """
+        return self.quantile(q=0.5, method=method)
+
+    @derived_from(pd.Series)
+    def median(self, method="default"):
+        if self.npartitions == 1:
+            # Can provide an exact median in these cases
+            return self.quantile(q=0.5, method=method)
+        raise NotImplementedError(
+            "Dask doesn't implement an exact median in all cases as this is hard to do in parallel. "
+            "See the `median_approximate` method instead, which uses an approximate algorithm."
+        )
+
     def _repartition_quantiles(self, npartitions, upsample=1.0):
         """Approximate quantiles of Series used for repartitioning"""
         from dask.dataframe.partitionquantiles import partition_quantiles

dask/dataframe/tests/test_dataframe.py

@@ -40,6 +40,11 @@
 from dask.utils import M, is_dataframe_like, is_series_like, put_lines
 from dask.utils_test import _check_warning, hlg_layer
 
+try:
+    import crick
+except ImportError:
+    crick = None
+
 dsk = {
     ("x", 0): pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}, index=[0, 1, 3]),
     ("x", 1): pd.DataFrame({"a": [4, 5, 6], "b": [3, 2, 1]}, index=[5, 6, 8]),
@@ -391,11 +396,17 @@ def test_rename_series_method_2():
 
 
 @pytest.mark.parametrize(
-    "method,test_values", [("tdigest", (6, 10)), ("dask", (4, 20))]
+    "method,test_values",
+    [
+        pytest.param(
+            "tdigest",
+            (6, 10),
+            marks=pytest.mark.skipif(not crick, reason="Requires crick"),
+        ),
+        ("dask", (4, 20)),
+    ],
 )
 def test_describe_numeric(method, test_values):
-    if method == "tdigest":
-        pytest.importorskip("crick")
     # prepare test case which approx quantiles will be the same as actuals
     s = pd.Series(list(range(test_values[1])) * test_values[0])
     df = pd.DataFrame(
@@ -1337,11 +1348,16 @@ def test_nbytes():
 
 @pytest.mark.parametrize(
     "method,expected",
-    [("tdigest", (0.35, 3.80, 2.5, 6.5, 2.0)), ("dask", (0.0, 4.0, 1.2, 6.2, 2.0))],
+    [
+        pytest.param(
+            "tdigest",
+            (0.35, 3.80, 2.5, 6.5, 2.0),
+            marks=pytest.mark.skipif(not crick, reason="Requires crick"),
+        ),
+        ("dask", (0.0, 4.0, 1.2, 6.2, 2.0)),
+    ],
 )
 def test_quantile(method, expected):
-    if method == "tdigest":
-        pytest.importorskip("crick")
     # series / multiple
     result = d.b.quantile([0.3, 0.7], method=method)
 
@@ -1380,10 +1396,16 @@ def test_quantile(method, expected):
     assert result == expected[4]
 
 
-@pytest.mark.parametrize("method", ["tdigest", "dask"])
+@pytest.mark.parametrize(
+    "method",
+    [
+        pytest.param(
+            "tdigest", marks=pytest.mark.skipif(not crick, reason="Requires crick")
+        ),
+        "dask",
+    ],
+)
 def test_quantile_missing(method):
-    if method == "tdigest":
-        pytest.importorskip("crick")
     df = pd.DataFrame({"A": [0, np.nan, 2]})
     # TODO: Test npartitions=2
     # (see https://github.com/dask/dask/issues/9227)
@@ -1397,10 +1419,16 @@ def test_quantile_missing(method):
     assert_eq(result, expected)
 
 
-@pytest.mark.parametrize("method", ["tdigest", "dask"])
+@pytest.mark.parametrize(
+    "method",
+    [
+        pytest.param(
+            "tdigest", marks=pytest.mark.skipif(not crick, reason="Requires crick")
+        ),
+        "dask",
+    ],
+)
 def test_empty_quantile(method):
-    if method == "tdigest":
-        pytest.importorskip("crick")
     result = d.b.quantile([], method=method)
     exp = full.b.quantile([])
     assert result.divisions == (None, None)
@@ -1417,7 +1445,7 @@ def test_empty_quantile(method):
 @pytest.mark.parametrize(
     "method,expected",
     [
-        (
+        pytest.param(
             "tdigest",
             (
                 pd.Series([9.5, 29.5, 19.5], index=["A", "X", "B"]),
@@ -1427,6 +1455,7 @@ def test_empty_quantile(method):
                     columns=["A", "X", "B"],
                 ),
             ),
+            marks=pytest.mark.skipif(not crick, reason="Requires crick"),
         ),
         (
             "dask",
@@ -1442,8 +1471,6 @@ def test_empty_quantile(method):
     ],
 )
 def test_dataframe_quantile(method, expected):
-    if method == "tdigest":
-        pytest.importorskip("crick")
     # column X is for test column order and result division
     df = pd.DataFrame(
         {
@@ -4307,6 +4334,51 @@ def test_dataframe_mode():
     assert_eq(ddf.mode(), df.mode(), check_index=False)
 
 
+def test_median():
+    df = pd.DataFrame({"x": [1, 2, 3, 4, 5], "y": [1.1, 2.2, 3.3, 4.4, 5.5]})
+    ddf = dd.from_pandas(df, npartitions=3)
+
+    # Exact medians work when `axis=1` or when there's only have a single partition
+    # TODO: shouldn't need to specify `check_names=False` below, but names currently don't match
+    assert_eq(ddf.median(axis=1), df.median(axis=1), check_names=False)
+    ddf_single = dd.from_pandas(df, npartitions=1)
+    assert_eq(ddf_single.median(axis=1), df.median(axis=1), check_names=False)
+    assert_eq(ddf_single.x.median(), df.x.median(), check_names=False)
+
+    # Ensure `median` redirects to `median_approximate` appropriately
+    for axis in [None, 0, "rows"]:
+        with pytest.raises(
+            NotImplementedError, match="See the `median_approximate` method instead"
+        ):
+            ddf.median(axis=axis)
+
+    with pytest.raises(
+        NotImplementedError, match="See the `median_approximate` method instead"
+    ):
+        ddf.x.median()
+
+
+@pytest.mark.parametrize(
+    "method",
+    [
+        "dask",
+        pytest.param(
+            "tdigest", marks=pytest.mark.skipif(not crick, reason="Requires crick")
+        ),
+    ],
+)
+def test_median_approximate(method):
+    df = pd.DataFrame({"x": range(100), "y": range(100, 200)})
+    ddf = dd.from_pandas(df, npartitions=10)
+    # TODO: shouldn't need to specify `check_names=False` below, but names currently don't match
+    assert_eq(
+        ddf.median_approximate(method=method),
+        df.median(),
+        check_names=False,
+        rtol=1,
+    )
+
+
 def test_datetime_loc_open_slicing():
     dtRange = pd.date_range("01.01.2015", "05.05.2015")
     df = pd.DataFrame(np.random.random((len(dtRange), 2)), index=dtRange)

docs/source/dataframe-api.rst

@@ -73,6 +73,8 @@ Dataframe
     DataFrame.mask
     DataFrame.max
     DataFrame.mean
+    DataFrame.median
+    DataFrame.median_approximate
     DataFrame.melt
     DataFrame.memory_usage
     DataFrame.memory_usage_per_partition
@@ -203,6 +205,8 @@ Series
    Series.mask
    Series.max
    Series.mean
+   Series.median
+   Series.median_approximate
    Series.memory_usage
    Series.memory_usage_per_partition
    Series.min