lidavidm commented on code in PR #13412:
URL: https://github.com/apache/arrow/pull/13412#discussion_r941317519
##########
cpp/src/arrow/compute/api_vector.h:
##########
@@ -246,6 +249,70 @@ class ARROW_EXPORT CumulativeSumOptions : public
FunctionOptions {
bool check_overflow = false;
};
+/// \brief Options for cumulative product function
+class ARROW_EXPORT CumulativeProductOptions : public FunctionOptions {
+ public:
+ explicit CumulativeProductOptions(double start = 1, bool skip_nulls = false,
+ bool check_overflow = false);
+ explicit CumulativeProductOptions(std::shared_ptr<Scalar> start,
+ bool skip_nulls = false, bool
check_overflow = false);
+ static constexpr char const kTypeName[] = "CumulativeProductOptions";
+ static CumulativeProductOptions Defaults() { return
CumulativeProductOptions(); }
+
+ const bool is_minmax = false;
+ const bool is_max = false;
+
+ /// Optional starting value for cumulative product
+ std::shared_ptr<Scalar> start;
+
+ /// If true, nulls in the input are ignored and produce a corresponding null
output.
+ /// When false, the first null encountered is propagated through the
remaining output.
+ bool skip_nulls = false;
+
+ /// When true, returns an Invalid Status when overflow is detected
+ bool check_overflow = false;
+};
+
+/// \brief Options for cumulative min functions
+class ARROW_EXPORT CumulativeMinOptions : public FunctionOptions {
+ public:
+ explicit CumulativeMinOptions(bool skip_nulls = false);
+ explicit CumulativeMinOptions(double start, bool skip_nulls = false);
+ explicit CumulativeMinOptions(std::shared_ptr<Scalar> start, bool skip_nulls
= false);
+ static constexpr char const kTypeName[] = "CumulativeMinOptions";
+ static CumulativeMinOptions Defaults() { return CumulativeMinOptions(); }
+
+ const bool is_minmax = true;
Review Comment:
nit: it might be more idiomatic to have this as `constexpr static bool`,
then below use `if (OptionsType::is_minmax)`
##########
python/pyarrow/tests/test_compute.py:
##########
@@ -2605,6 +2608,204 @@ def test_cumulative_sum(start, skip_nulls):
pc.cumulative_sum([1, 2, 3], start=strt)
[email protected]('start', (1.25, 10.5, -10.5))
[email protected]('skip_nulls', (True, False))
+def test_cumulative_product(start, skip_nulls):
+ # Exact tests (e.g., integral types)
+ start_int = int(start)
+ starts = [start_int, pa.scalar(start_int, type=pa.int8()),
+ pa.scalar(start_int, type=pa.int64())]
+ for strt in starts:
+ arrays = [
+ pa.array([1, 2, 3]),
+ pa.array([1, None, 3, 4]),
+ pa.chunked_array([[1, None], [3, 4]])
+ ]
+ expected_arrays = [
+ pa.array([1, 2, 6]),
+ pa.array([1, None, 3, 12])
+ if skip_nulls else pa.array([1, None, None, None]),
+ pa.chunked_array([[1, None, 3, 12]])
+ if skip_nulls else pa.chunked_array([[1, None, None, None]])
+ ]
+ for i, arr in enumerate(arrays):
+ result = pc.cumulative_product(
+ arr, start=strt, skip_nulls=skip_nulls)
+ # Add `start` offset to expected array before comparing
+ expected = pc.multiply(expected_arrays[i], strt)
+ assert result.equals(expected)
+
+ starts = [start, pa.scalar(start, type=pa.float32()),
+ pa.scalar(start, type=pa.float64())]
+ for strt in starts:
+ arrays = [
+ pa.array([1.125, 2.25, 3.03125]),
+ pa.array([1, np.nan, 2, -3, 4, 5]),
+ pa.array([1, np.nan, None, 3, None, 5])
+ ]
+ expected_arrays = [
+ np.array([1.125, 2.53125, 7.6728515625]),
+ np.array([1, np.nan, np.nan, np.nan, np.nan, np.nan]),
+ np.array([1, np.nan, None, np.nan, None, np.nan])
+ if skip_nulls else np.array([1, np.nan, None, None, None, None])
+ ]
+ for i, arr in enumerate(arrays):
+ result = pc.cumulative_product(
+ arr, start=strt, skip_nulls=skip_nulls)
+ # Add `start` offset to expected array before comparing
+ expected = pc.multiply(expected_arrays[i], strt)
+ np.testing.assert_array_almost_equal(result.to_numpy(
Review Comment:
Are we converting to NumPy here because Arrow doesn't have an approx equals?
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