SNOW-1885815: Add support for seed argument in DataFrame.stat.sample_by

CHANGELOG.md

@@ -43,6 +43,7 @@
   - `try_to_binary`
 - Added support for specifying a schema string (including implicit struct syntax) when calling `DataFrame.create_dataframe`.
 - Added support for `DataFrameWriter.insert_into/insertInto`. This method also supports local testing mode.
+- Added support for `seed` argument in `DataFrame.stat.sample_by`. Note that it only supports a `Table` object, and will be ignored for a `DataFrame` object.
 
 #### Experimental Features
 

src/snowflake/snowpark/dataframe_stat_functions.py

@@ -17,7 +17,7 @@
 from snowflake.snowpark._internal.error_message import SnowparkClientExceptionMessages
 from snowflake.snowpark._internal.telemetry import adjust_api_subcalls
 from snowflake.snowpark._internal.type_utils import ColumnOrName, LiteralType
-from snowflake.snowpark._internal.utils import publicapi
+from snowflake.snowpark._internal.utils import publicapi, warning
 from snowflake.snowpark.functions import (
     _to_col_if_str,
     approx_percentile_accumulate,
@@ -374,6 +374,7 @@ def sample_by(
         self,
         col: ColumnOrName,
         fractions: Dict[LiteralType, float],
+        seed: Optional[int] = None,
         _emit_ast: bool = True,
     ) -> "snowflake.snowpark.DataFrame":
         """Returns a DataFrame containing a stratified sample without replacement, based on a ``dict`` that specifies the fraction for each stratum.
@@ -388,6 +389,9 @@ def sample_by(
             col: The name of the column that defines the strata.
             fractions: A ``dict`` that specifies the fraction to use for the sample for each stratum.
                 If a stratum is not specified in the ``dict``, the method uses 0 as the fraction.
+            seed: Specifies a seed value to make the sampling deterministic. Can be any integer between 0 and 2147483647 inclusive.
+                Default value is ``None``. This parameter is only supported for :class:`Table`, and it will be ignored
+                if it is specified for :class`DataFrame`.
         """
 
         stmt = None
@@ -416,15 +420,42 @@ def sample_by(
             return res_df
 
         col = _to_col_if_str(col, "sample_by")
-        res_df = reduce(
-            lambda x, y: x.union_all(y, _emit_ast=False),
-            [
-                self._dataframe.filter(col == k, _emit_ast=False).sample(
-                    v, _emit_ast=False
+        if seed is not None and isinstance(self._dataframe, snowflake.snowpark.Table):
+
+            def equal_condition_str(k: LiteralType) -> str:
+                return self._dataframe._session._analyzer.binary_operator_extractor(
+                    (col == k)._expression,
+                    df_aliased_col_name_to_real_col_name=self._dataframe._plan.df_aliased_col_name_to_real_col_name,
                 )
-                for k, v in fractions.items()
-            ],
-        )
+
+            # Similar to how `Table.sample` is implemented, because SAMPLE clause does not support subqueries,
+            # we just use session.sql to compile a flat query
+            res_df = reduce(
+                lambda x, y: x.union_all(y, _emit_ast=False),
+                [
+                    self._dataframe._session.sql(
+                        f"SELECT * FROM {self._dataframe.table_name} SAMPLE ({v * 100.0}) SEED ({seed}) WHERE {equal_condition_str(k)}",
+                        _emit_ast=False,
+                    )
+                    for k, v in fractions.items()
+                ],
+            )
+        else:
+            if seed is not None:
+                warning(
+                    "stat.sample_by",
+                    "`seed` argument is ignored on `DataFrame` object. Save this DataFrame to a temporary table "
+                    "to get a `Table` object and specify a seed.",
+                )
+            res_df = reduce(
+                lambda x, y: x.union_all(y, _emit_ast=False),
+                [
+                    self._dataframe.filter(col == k, _emit_ast=False).sample(
+                        v, _emit_ast=False
+                    )
+                    for k, v in fractions.items()
+                ],
+            )
         adjust_api_subcalls(
             res_df,
             "DataFrameStatFunctions.sample_by",

tests/integ/scala/test_dataframe_suite.py

@@ -2,6 +2,7 @@
 # Copyright (c) 2012-2025 Snowflake Computing Inc. All rights reserved.
 #
 
+import logging
 import copy
 import math
 import os
@@ -837,6 +838,34 @@ def test_df_stat_sampleBy(session):
     assert len(sample_by_3.collect()) == 0
 
 
+@pytest.mark.skipif(
+    "config.getoption('local_testing_mode', default=False)",
+    reason="session.sql is not supported in local testing",
+)
+def test_df_stat_sampleBy_seed(session, caplog):
+    temp_table_name = Utils.random_name_for_temp_object(TempObjectType.TABLE)
+    TestData.monthly_sales(session).write.save_as_table(
+        temp_table_name, table_type="temp", mode="overwrite"
+    )
+    df = session.table(temp_table_name)
+
+    # with seed, the result is deterministic and should be the same
+    sample_by_action = (
+        lambda df: df.stat.sample_by(col("empid"), {1: 0.5, 2: 0.5}, seed=1)
+        .sort(df.columns)
+        .collect()
+    )
+    result = sample_by_action(df)
+    for _ in range(3):
+        Utils.check_answer(sample_by_action(df), result)
+
+    # DataFrame doesn't work with seed
+    caplog.clear()
+    with caplog.at_level(logging.WARNING):
+        sample_by_action(TestData.monthly_sales(session))
+    assert "`seed` argument is ignored on `DataFrame` object" in caplog.text
+
+
 @pytest.mark.skipif(
     "config.getoption('local_testing_mode', default=False)",
     reason="FEAT: RelationalGroupedDataFrame.Pivot not supported",