feat(api): support median and quantile on more types

ibis/backends/duckdb/registry.py

@@ -489,8 +489,16 @@ def _array_remove(t, op):
         ops.JSONGetItem: fixed_arity(_json_get_item, 2),
         ops.RowID: lambda *_: sa.literal_column("rowid"),
         ops.StringToTimestamp: _strptime,
-        ops.Quantile: reduction(sa.func.quantile_cont),
-        ops.MultiQuantile: reduction(sa.func.quantile_cont),
+        ops.Quantile: lambda t, op: (
+            reduction(sa.func.quantile_cont)(t, op)
+            if op.arg.dtype.is_numeric()
+            else reduction(sa.func.quantile_disc)(t, op)
+        ),
+        ops.MultiQuantile: lambda t, op: (
+            reduction(sa.func.quantile_cont)(t, op)
+            if op.arg.dtype.is_numeric()
+            else reduction(sa.func.quantile_disc)(t, op)
+        ),
         ops.TypeOf: unary(sa.func.typeof),
         ops.IntervalAdd: fixed_arity(operator.add, 2),
         ops.IntervalSubtract: fixed_arity(operator.sub, 2),

ibis/backends/oracle/registry.py

@@ -81,6 +81,19 @@
     return sa.func.concat(*toolz.interpose(sep, values))
 
 
+def _median(t, op):
+    arg = op.arg
+    if (where := op.where) is not None:
+        arg = ops.IfElse(where, arg, None)
+
+    if arg.dtype.is_numeric():
+        return sa.func.median(t.translate(arg))
+    return sa.cast(
+        sa.func.percentile_disc(0.5).within_group(t.translate(arg)),
+        t.get_sqla_type(op.dtype),
+    )
+
+
 operation_registry.update(
     {
         ops.Log2: unary(lambda arg: sa.func.log(2, arg)),
@@ -96,7 +109,7 @@
         ops.Covariance: _cov,
         ops.Correlation: _corr,
         ops.ApproxMedian: reduction(sa.func.approx_median),
-        ops.Median: reduction(sa.func.median),
+        ops.Median: _median,
         # Temporal
         ops.ExtractSecond: _second,
         # String

ibis/backends/polars/compiler.py

@@ -673,6 +673,7 @@ def struct_column(op, **kw):
     ops.Mean: "mean",
     ops.Median: "median",
     ops.Min: "min",
+    ops.Mode: "mode",
     ops.StandardDev: "std",
     ops.Sum: "sum",
     ops.Variance: "var",
@@ -682,22 +683,36 @@ def struct_column(op, **kw):
 
     @translate.register(reduction)
     def reduction(op, **kw):
-        arg = translate(op.arg, **kw)
+        args = [
+            translate(arg, **kw)
+            for name, arg in zip(op.argnames, op.args)
+            if name not in ("where", "how")
+        ]
+
         agg = _reductions[type(op)]
-        filt = arg.is_not_null()
+
+        predicates = [arg.is_not_null() for arg in args]
         if (where := op.where) is not None:
-            filt &= translate(where, **kw)
-        arg = arg.filter(filt)
-        method = getattr(arg, agg)
-        return method().cast(dtype_to_polars(op.dtype))
+            predicates.append(translate(where, **kw))
 
+        first, *rest = args
+        method = operator.methodcaller(agg, *rest)
+        return method(first.filter(reduce(operator.and_, predicates))).cast(
+            dtype_to_polars(op.dtype)
+        )
 
-@translate.register(ops.Mode)
-def mode(op, **kw):
+
+@translate.register(ops.Quantile)
+def execute_quantile(op, **kw):
     arg = translate(op.arg, **kw)
+    quantile = translate(op.quantile, **kw)
+    filt = arg.is_not_null() & quantile.is_not_null()
     if (where := op.where) is not None:
-        arg = arg.filter(translate(where, **kw))
-    return arg.mode().min()
+        filt &= translate(where, **kw)
+
+    # we can't throw quantile into the _reductions mapping because Polars'
+    # default interpolation of "nearest" doesn't match the rest of our backends
+    return arg.filter(filt).quantile(quantile, interpolation="linear")
 
 
 @translate.register(ops.Correlation)

ibis/backends/postgres/registry.py

@@ -457,17 +457,23 @@ def _quantile(t, op):
     arg = op.arg
     if (where := op.where) is not None:
         arg = ops.IfElse(where, arg, None)
-    return sa.func.percentile_cont(t.translate(op.quantile)).within_group(
-        t.translate(arg)
-    )
+    if arg.dtype.is_numeric():
+        func = sa.func.percentile_cont
+    else:
+        func = sa.func.percentile_disc
+    return func(t.translate(op.quantile)).within_group(t.translate(arg))
 
 
 def _median(t, op):
     arg = op.arg
     if (where := op.where) is not None:
         arg = ops.IfElse(where, arg, None)
 
-    return sa.func.percentile_cont(0.5).within_group(t.translate(arg))
+    if arg.dtype.is_numeric():
+        func = sa.func.percentile_cont
+    else:
+        func = sa.func.percentile_disc
+    return func(0.5).within_group(t.translate(arg))
 
 
 def _binary_variance_reduction(func):

ibis/backends/tests/test_aggregation.py

@@ -1,5 +1,8 @@
 from __future__ import annotations
 
+from datetime import date
+from operator import methodcaller
+
 import numpy as np
 import pandas as pd
 import pytest
@@ -23,11 +26,6 @@
 except ImportError:
     GoogleBadRequest = None
 
-try:
-    from polars.exceptions import ComputeError
-except ImportError:
-    ComputeError = None
-
 try:
     from clickhouse_connect.driver.exceptions import (
         DatabaseError as ClickhouseDatabaseError,
@@ -40,12 +38,16 @@
 except ImportError:
     Py4JError = None
 
-
 try:
     from pyexasol.exceptions import ExaQueryError
 except ImportError:
     ExaQueryError = None
 
+try:
+    from polars.exceptions import InvalidOperationError as PolarsInvalidOperationError
+except ImportError:
+    PolarsInvalidOperationError = None
+
 
 @reduction(input_type=[dt.double], output_type=dt.double)
 def mean_udf(s):
@@ -899,7 +901,6 @@ def test_count_distinct_star(alltypes, df, ibis_cond, pandas_cond):
                         "impala",
                         "mssql",
                         "mysql",
-                        "polars",
                         "sqlite",
                         "druid",
                         "oracle",
@@ -1210,6 +1211,102 @@ def test_median(alltypes, df):
     assert result == expected
 
 
+@pytest.mark.notimpl(
+    ["bigquery", "druid", "sqlite"], raises=com.OperationNotDefinedError
+)
+@pytest.mark.notyet(
+    ["impala", "mysql", "mssql", "trino", "exasol", "flink"],
+    raises=com.OperationNotDefinedError,
+)
+@pytest.mark.notyet(
+    ["clickhouse"],
+    raises=ClickhouseDatabaseError,
+    reason="doesn't support median of strings",
+)
+@pytest.mark.notyet(
+    ["oracle"], raises=sa.exc.DatabaseError, reason="doesn't support median of strings"
+)
+@pytest.mark.broken(
+    ["pyspark"], raises=AssertionError, reason="pyspark returns null for string median"
+)
+@pytest.mark.notimpl(["dask"], raises=(AssertionError, NotImplementedError, TypeError))
+@pytest.mark.notyet(
+    ["snowflake"],
+    raises=sa.exc.ProgrammingError,
+    reason="doesn't support median of strings",
+)
+@pytest.mark.notyet(["polars"], raises=PolarsInvalidOperationError)
+@pytest.mark.notyet(["datafusion"], raises=Exception, reason="not supported upstream")
+@pytest.mark.notimpl(
+    ["pandas"], raises=TypeError, reason="results aren't correctly typed"
+)
+@pytest.mark.parametrize(
+    "func",
+    [
+        param(
+            methodcaller("quantile", 0.5),
+            id="quantile",
+            marks=[
+                pytest.mark.notimpl(["oracle"], raises=com.OperationNotDefinedError)
+            ],
+        ),
+        param(
+            methodcaller("median"),
+            id="median",
+            marks=[
+                pytest.mark.notimpl(["pyspark"], raises=com.OperationNotDefinedError)
+            ],
+        ),
+    ],
+)
+def test_string_quantile(alltypes, func):
+    expr = func(alltypes.select(col=ibis.literal("a")).limit(5).col)
+    result = expr.execute()
+    assert result == "a"
+
+
+@pytest.mark.notimpl(["bigquery", "sqlite"], raises=com.OperationNotDefinedError)
+@pytest.mark.notyet(
+    ["impala", "mysql", "mssql", "trino", "exasol", "flink"],
+    raises=com.OperationNotDefinedError,
+)
+@pytest.mark.broken(["druid"], raises=AttributeError)
+@pytest.mark.notyet(
+    ["snowflake"],
+    raises=sa.exc.ProgrammingError,
+    reason="doesn't support median of dates",
+)
+@pytest.mark.notimpl(["dask"], raises=(AssertionError, NotImplementedError, TypeError))
+@pytest.mark.notyet(["polars"], raises=PolarsInvalidOperationError)
+@pytest.mark.notyet(["datafusion"], raises=Exception, reason="not supported upstream")
+@pytest.mark.broken(
+    ["pandas"], raises=AssertionError, reason="possibly incorrect results"
+)
+@pytest.mark.parametrize(
+    "func",
+    [
+        param(
+            methodcaller("quantile", 0.5),
+            id="quantile",
+            marks=[
+                pytest.mark.notimpl(["oracle"], raises=com.OperationNotDefinedError)
+            ],
+        ),
+        param(
+            methodcaller("median"),
+            id="median",
+            marks=[
+                pytest.mark.notimpl(["pyspark"], raises=com.OperationNotDefinedError)
+            ],
+        ),
+    ],
+)
+def test_date_quantile(alltypes, func):
+    expr = func(alltypes.timestamp_col.date())
+    result = expr.execute()
+    assert result == date(2009, 12, 31)
+
+
 @pytest.mark.parametrize(
     ("result_fn", "expected_fn"),
     [

ibis/expr/operations/reductions.py

@@ -187,29 +187,38 @@ def dtype(self):
             return dt.higher_precedence(dtype, dt.float64)
 
 
-@public
-class Median(Filterable, Reduction):
-    arg: Column[dt.Numeric | dt.Boolean]
+class QuantileBase(Filterable, Reduction):
+    arg: Column
 
     @attribute
     def dtype(self):
-        return dt.higher_precedence(self.arg.dtype, dt.float64)
+        dtype = self.arg.dtype
+        if dtype.is_numeric():
+            dtype = dt.higher_precedence(dtype, dt.float64)
+        return dtype
 
 
 @public
-class Quantile(Filterable, Reduction):
-    arg: Value
-    quantile: Value[dt.Numeric]
+class Median(QuantileBase):
+    pass
 
-    dtype = dt.float64
+
+@public
+class Quantile(QuantileBase):
+    quantile: Value[dt.Numeric]
 
 
 @public
 class MultiQuantile(Filterable, Reduction):
-    arg: Value
-    quantile: Value[dt.Array[dt.Float64]]
+    arg: Column
+    quantile: Value[dt.Array[dt.Numeric]]
 
-    dtype = dt.Array(dt.float64)
+    @attribute
+    def dtype(self):
+        dtype = self.arg.dtype
+        if dtype.is_numeric():
+            dtype = dt.higher_precedence(dtype, dt.float64)
+        return dt.Array(dtype)
 
 
 @public