SNOW-2331792: Support DataFrame.eval for single-level index.

CHANGELOG.md

@@ -44,6 +44,7 @@
 - Added a new option `cacheResult` to `DataFrameReader.xml` that allows users to cache the result of the XML reader to a temporary table after calling `xml`. It helps improve performance when subsequent operations are performed on the same DataFrame.
 
 ### Snowpark pandas API Updates
+- Added support for `DataFrame.eval()` for dataframes with single-level indexes.
 
 #### New Features
 

docs/source/modin/supported/dataframe_supported.rst

@@ -165,7 +165,7 @@ Methods
 +-----------------------------+---------------------------------+----------------------------------+----------------------------------------------------+
 | ``equals``                  | Y                               |                                  |                                                    |
 +-----------------------------+---------------------------------+----------------------------------+----------------------------------------------------+
-| ``eval``                    | N                               |                                  |                                                    |
+| ``eval``                    | P                               |                                  | No support for dataframes with a row MultiIndex.   |
 +-----------------------------+---------------------------------+----------------------------------+----------------------------------------------------+
 | ``ewm``                     | N                               |                                  |                                                    |
 +-----------------------------+---------------------------------+----------------------------------+----------------------------------------------------+

src/snowflake/snowpark/modin/plugin/_internal/utils.py

@@ -119,6 +119,11 @@
 
 _logger = logging.getLogger(__name__)
 
+# Flag guarding certain features available only in newer modin versions.
+# Snowpark pandas supports the newest two released versions of modin; update this flag and remove legacy
+# code as needed when we bump dependency versions.
+MODIN_IS_AT_LEAST_0_36_0 = version.parse(pd.__version__) >= version.parse("0.36.0")
+
 
 # This is the default statement parameters for queries from Snowpark pandas API. It provides the fine grain metric for
 # the server to track all pandas API usage.

src/snowflake/snowpark/modin/plugin/extensions/dataframe_overrides.py

@@ -36,6 +36,7 @@
 from modin.pandas.base import BasePandasDataset
 from modin.pandas.io import from_pandas
 from modin.pandas.utils import is_scalar
+from modin.core.computation.eval import eval as _eval
 from modin.core.storage_formats.pandas.query_compiler_caster import (
     register_function_for_pre_op_switch,
 )
@@ -226,9 +227,63 @@ def dot(self, other):  # noqa: PR01, RT01, D200
     pass  # pragma: no cover
 
 
-@register_dataframe_not_implemented()
+# Override eval because
+# 1) we treat the "engine" parameter differently
+# 2) We have to update the level parameter to reflect this method's place in
+#    the function stack. Modin can't do that for us since it doesn't know our
+#    place in the stack.
+@register_dataframe_accessor("eval")
 def eval(self, expr, inplace=False, **kwargs):  # noqa: PR01, RT01, D200
-    pass  # pragma: no cover
+    """
+    Evaluate a string describing operations on ``DataFrame`` columns.
+    """
+    if self._query_compiler.nlevels() > 1:
+        # If the rows of this dataframe have a multi-index, we store the index
+        # as a native_pd.MultiIndex, and the usual method of getting index
+        # resolvers with _get_index_resolvers() does not work.
+        ErrorMessage.not_implemented("eval() does not support a multi-level index.")
+
+    inplace = validate_bool_kwarg(inplace, "inplace")
+
+    # numexpr engine is useful for chained operations on numpy-backed
+    # arrays. It doesn't support all the syntax that the python engine
+    # does, and the Snowpark backend doesn't store data in numpy, so the
+    # numexpr performance optimizations are not useful. Ignore the "engine"
+    # requirement, and warn the user that if they explicitly select
+    # engine="numexpr", we will not honor their preference.
+    if kwargs.get("engine", None) == "numexpr":
+        WarningMessage.ignored_argument(
+            operation="eval",
+            argument="engine",
+            message="Snowpark pandas always uses the python engine in "
+            + "favor of the numexpr engine, even if the numexpr engine is "
+            + "available",
+        )
+    kwargs["engine"] = "python"
+
+    # eval() lets the user reference variables according to dynamic scope
+    # at `level` stack frames below the stack frame that called eval(). The
+    # eval() implementation is 4 stack frames above the frame where we execute
+    # the _eval() implementation:
+    # 1) query_compiler_caster wrapper dispatches to snowflake implementation
+    # 2) telemetry wrapper 1
+    # 3) telemetry wrapper 2 calls this implementation.
+    # 4) This method implementation calls the _eval() implementation
+    # so we add 4 to the `level` param.
+    kwargs["level"] = kwargs.get("level", 0) + 4
+
+    index_resolvers = self._get_index_resolvers()
+    column_resolvers = self._get_cleaned_column_resolvers()
+    kwargs["resolvers"] = (
+        *kwargs.get("resolvers", ()),
+        index_resolvers,
+        column_resolvers,
+    )
+
+    if "target" not in kwargs:
+        kwargs["target"] = self
+
+    return _eval(expr, inplace=inplace, **kwargs)
 
 
 @register_dataframe_not_implemented()

tests/integ/modin/conftest.py

@@ -20,6 +20,7 @@
 from snowflake.snowpark.modin.plugin._internal.apply_utils import (
     clear_session_udf_and_udtf_caches,
 )
+from snowflake.snowpark.modin.plugin.utils.warning_message import WarningMessage
 from tests.integ.modin.pandas_api_coverage import PandasAPICoverageGenerator
 from tests.integ.utils.sql_counter import (
     SqlCounter,
@@ -857,3 +858,12 @@ def testing_dfs_from_read_snowflake(
         auto_create_table=True,
     )
     return pd.read_snowflake(test_table_name), pandas_df
+
+
+@pytest.fixture(autouse=True)
+def clear_printed_warnings() -> Generator[None, None, None]:
+    # Preserve a copy of the warnings printed before the test.
+    warnings = set(WarningMessage.printed_warnings)
+    WarningMessage.printed_warnings.clear()
+    yield
+    WarningMessage.printed_warnings = warnings