Implement cumulative aggregation (dask#433)

Co-authored-by: crusaderky <crusaderky@gmail.com>

README.md

@@ -79,6 +79,10 @@ API Coverage
 - `combine_first`
 - `copy`
 - `count`
+- `cummax`
+- `cummin`
+- `cumprod`
+- `cumsum`
 - `dask`
 - `div`
 - `divide`
@@ -169,6 +173,10 @@ API Coverage
 - `combine_first`
 - `copy`
 - `count`
+- `cummax`
+- `cummin`
+- `cumprod`
+- `cumsum`
 - `dask`
 - `div`
 - `divide`
@@ -204,6 +212,7 @@ API Coverage
 - `partitions`
 - `pow`
 - `prod`
+- `product`
 - `radd`
 - `rdiv`
 - `rename`
@@ -295,6 +304,7 @@ API Coverage
 - `mean`
 - `median`
 - `min`
+- `nunique`
 - `prod`
 - `shift`
 - `size`

dask_expr/_collection.py

@@ -765,6 +765,18 @@ def align(self, other, join="outer", fill_value=None):
     def nunique_approx(self):
         return new_collection(self.expr.nunique_approx())
 
+    def cumsum(self, skipna=True):
+        return new_collection(self.expr.cumsum(skipna=skipna))
+
+    def cumprod(self, skipna=True):
+        return new_collection(self.expr.cumprod(skipna=skipna))
+
+    def cummax(self, skipna=True):
+        return new_collection(self.expr.cummax(skipna=skipna))
+
+    def cummin(self, skipna=True):
+        return new_collection(self.expr.cummin(skipna=skipna))
+
     def memory_usage_per_partition(self, index=True, deep=False):
         return new_collection(self.expr.memory_usage_per_partition(index, deep))
 

dask_expr/_cumulative.py

@@ -0,0 +1,116 @@
+import functools
+
+from dask.dataframe import methods
+from dask.utils import M
+
+from dask_expr._expr import Blockwise, Expr, Projection
+
+
+class CumulativeAggregations(Expr):
+    _parameters = ["frame", "axis", "skipna"]
+    _defaults = {"axis": None}
+
+    chunk_operation = None
+    aggregate_operation = None
+
+    def _divisions(self):
+        return self.frame._divisions()
+
+    @functools.cached_property
+    def _meta(self):
+        return self.frame._meta
+
+    def _lower(self):
+        chunks = CumulativeBlockwise(
+            self.frame, self.axis, self.skipna, self.chunk_operation
+        )
+        chunks_last = TakeLast(chunks, self.skipna)
+        return CumulativeFinalize(chunks, chunks_last, self.aggregate_operation)
+
+    def _simplify_up(self, parent):
+        if isinstance(parent, Projection):
+            return type(self)(self.frame[parent.operand("columns")], *self.operands[1:])
+
+
+class CumulativeBlockwise(Blockwise):
+    _parameters = ["frame", "axis", "skipna", "operation"]
+    _defaults = {"skipna": True, "axis": None}
+    _projection_passthrough = True
+
+    @functools.cached_property
+    def _meta(self):
+        return self.frame._meta
+
+    @functools.cached_property
+    def operation(self):
+        return self.operand("operation")
+
+    @functools.cached_property
+    def _args(self) -> list:
+        return self.operands[:-1]
+
+
+class TakeLast(Blockwise):
+    _parameters = ["frame", "skipna"]
+    _projection_passthrough = True
+
+    @staticmethod
+    def operation(a, skipna=True):
+        if skipna:
+            a = a.bfill()
+        return a.tail(n=1).squeeze()
+
+
+class CumulativeFinalize(Expr):
+    _parameters = ["frame", "previous_partitions", "aggregator"]
+
+    def _divisions(self):
+        return self.frame._divisions()
+
+    @functools.cached_property
+    def _meta(self):
+        return self.frame._meta
+
+    def _layer(self) -> dict:
+        dsk = {}
+        frame, previous_partitions = self.frame, self.previous_partitions
+        dsk[(self._name, 0)] = (frame._name, 0)
+
+        intermediate_name = self._name + "-intermediate"
+        for i in range(1, self.frame.npartitions):
+            if i == 1:
+                dsk[(intermediate_name, i)] = (previous_partitions._name, i - 1)
+            else:
+                # aggregate with previous cumulation results
+                dsk[(intermediate_name, i)] = (
+                    methods._cum_aggregate_apply,
+                    self.aggregator,
+                    (intermediate_name, i - 1),
+                    (previous_partitions._name, i - 1),
+                )
+            dsk[(self._name, i)] = (
+                self.aggregator,
+                (self.frame._name, i),
+                (intermediate_name, i),
+            )
+        return dsk
+
+
+class CumSum(CumulativeAggregations):
+    chunk_operation = M.cumsum
+    aggregate_operation = staticmethod(methods.cumsum_aggregate)
+
+
+class CumProd(CumulativeAggregations):
+    chunk_operation = M.cumprod
+    aggregate_operation = staticmethod(methods.cumprod_aggregate)
+
+
+class CumMax(CumulativeAggregations):
+    chunk_operation = M.cummax
+    aggregate_operation = staticmethod(methods.cummax_aggregate)
+
+
+class CumMin(CumulativeAggregations):
+    chunk_operation = M.cummin
+    aggregate_operation = staticmethod(methods.cummin_aggregate)

dask_expr/_expr.py

@@ -361,6 +361,26 @@ def min(self, skipna=True, numeric_only=False, min_count=0):
     def count(self, numeric_only=False):
         return Count(self, numeric_only)
 
+    def cumsum(self, skipna=True):
+        from dask_expr._cumulative import CumSum
+
+        return CumSum(self, skipna=skipna)
+
+    def cumprod(self, skipna=True):
+        from dask_expr._cumulative import CumProd
+
+        return CumProd(self, skipna=skipna)
+
+    def cummax(self, skipna=True):
+        from dask_expr._cumulative import CumMax
+
+        return CumMax(self, skipna=skipna)
+
+    def cummin(self, skipna=True):
+        from dask_expr._cumulative import CumMin
+
+        return CumMin(self, skipna=skipna)
+
     def abs(self):
         return Abs(self)
 

dask_expr/tests/test_collection.py

@@ -242,6 +242,24 @@ def test_std_kwargs(axis, skipna, ddof):
     )
 
 
+@pytest.mark.parametrize("func", ["cumsum", "cumprod", "cummin", "cummax"])
+def test_cumulative_methods(df, pdf, func):
+    assert_eq(getattr(df, func)(), getattr(pdf, func)(), check_dtype=False)
+    assert_eq(getattr(df.x, func)(), getattr(pdf.x, func)())
+
+    q = getattr(df, func)()["x"]
+    assert q.simplify()._name == getattr(df.x, func)()
+
+    pdf.loc[slice(None, None, 2), "x"] = np.nan
+    df = from_pandas(pdf, npartitions=10)
+    assert_eq(
+        getattr(df, func)(skipna=False),
+        getattr(pdf, func)(skipna=False),
+        check_dtype=False,
+    )
+    assert_eq(getattr(df.x, func)(skipna=False), getattr(pdf.x, func)(skipna=False))
+
+
 @xfail_gpu("nbytes not supported by cudf")
 def test_nbytes(pdf, df):
     with pytest.raises(NotImplementedError, match="nbytes is not implemented"):