fix: handle list when filtering rows with dates (#4700)

## 📝 Summary

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#123).
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Filtering lists of dates would previously throw an error as they were
not converted to dates.

## 🔍 Description of Changes

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problem addressed and how it was resolved. If applicable, provide before
and after comparisons, screenshots, or any relevant details to help
reviewers understand the changes easily.
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## 📋 Checklist

- [X] I have read the [contributor
guidelines](https://github.com/marimo-team/marimo/blob/main/CONTRIBUTING.md).
- [ ] For large changes, or changes that affect the public API: this
change was discussed or approved through an issue, on
[Discord](https://marimo.io/discord?ref=pr), or the community
[discussions](https://github.com/marimo-team/marimo/discussions) (Please
provide a link if applicable).
- [X] I have added tests for the changes made.
- [X] I have run the code and verified that it works as expected.

## 📜 Reviewers

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@akshayka OR @mscolnick

Author: Light2Dark

marimo/_plugins/ui/_impl/dataframes/transforms/handlers.py

@@ -3,7 +3,7 @@
 
 import datetime
 from collections.abc import Sequence
-from typing import TYPE_CHECKING, Any, Optional, cast
+from typing import TYPE_CHECKING, Any, Callable, Optional, cast
 
 from marimo._plugins.ui._impl.dataframes.transforms.print_code import (
     python_print_ibis,
@@ -268,6 +268,12 @@ def handle_filter_rows(
         # Start with no filter (all rows included)
         filter_expr: Optional[pl.Expr] = None
 
+        # Convert a value whether it's a list or single value
+        def convert_value(v: Any, converter: Callable[[str], Any]) -> Any:
+            if isinstance(v, (tuple, list)):
+                return [converter(str(item)) for item in v]
+            return converter(str(v))
+
         # Iterate over all conditions and build the filter expression
         for condition in transform.where:
             column = col(str(condition.column_id))
@@ -276,12 +282,12 @@ def handle_filter_rows(
             value_str = str(value)
 
             # If columns type is a Datetime, we need to convert the value to a datetime
-            if dtype == pl.Datetime and isinstance(value, str):
-                value = datetime.datetime.fromisoformat(value)
-            elif dtype == pl.Date and isinstance(value, str):
-                value = datetime.date.fromisoformat(value)
-            elif dtype == pl.Time and isinstance(value, str):
-                value = datetime.time.fromisoformat(value)
+            if dtype == pl.Datetime:
+                value = convert_value(value, datetime.datetime.fromisoformat)
+            elif dtype == pl.Date:
+                value = convert_value(value, datetime.date.fromisoformat)
+            elif dtype == pl.Time:
+                value = convert_value(value, datetime.time.fromisoformat)
 
             # If columns type is a Categorical, we need to cast the value to a string
             if dtype == pl.Categorical:

tests/_plugins/ui/_impl/dataframes/test_handlers.py

@@ -1,6 +1,7 @@
 # Copyright 2024 Marimo. All rights reserved.
 from __future__ import annotations
 
+from datetime import date, datetime
 from typing import Any, cast
 from unittest.mock import Mock
 
@@ -443,6 +444,39 @@ def test_handle_filter_rows_6(
         result = apply(df, transform)
         assert_frame_equal(result, expected)
 
+    @staticmethod
+    @pytest.mark.parametrize(
+        ("df", "expected"),
+        [
+            (
+                pd.DataFrame({"date": [date(2001, 1, 1), date(2001, 1, 2)]}),
+                pd.DataFrame({"date": [date(2001, 1, 1)]}),
+            ),
+            (
+                pl.DataFrame({"date": [date(2001, 1, 1), date(2001, 1, 2)]}),
+                pl.DataFrame({"date": [date(2001, 1, 1)]}),
+            ),
+            (
+                ibis.memtable({"date": [date(2001, 1, 1), date(2001, 1, 2)]}),
+                ibis.memtable({"date": [date(2001, 1, 1)]}),
+            ),
+        ],
+    )
+    def test_handle_filter_rows_date(
+        df: DataFrameType, expected: DataFrameType
+    ) -> None:
+        transform = FilterRowsTransform(
+            type=TransformType.FILTER_ROWS,
+            operation="keep_rows",
+            where=[
+                Condition(
+                    column_id="date", operator="==", value=date(2001, 1, 1)
+                )
+            ],
+        )
+        result = apply(df, transform)
+        assert_frame_equal(result, expected)
+
     @staticmethod
     @pytest.mark.parametrize(
         ("df", "expected"),
@@ -472,6 +506,58 @@ def test_filter_rows_in_operator(
         result = apply(df, transform)
         assert_frame_equal(result, expected)
 
+    @staticmethod
+    @pytest.mark.parametrize(
+        ("df", "expected", "column"),
+        [
+            # TODO: Pandas treats date objects as strings
+            # (
+            #     pd.DataFrame({"date": [date(2001, 1, 1), date(2001, 1, 2)]}),
+            #     pd.DataFrame({"date": [date(2001, 1, 1)]}),
+            # ),
+            (
+                pl.DataFrame({"date": [date(2001, 1, 1), date(2001, 1, 2)]}),
+                pl.DataFrame({"date": [date(2001, 1, 1)]}),
+                "date",
+            ),
+            (
+                pl.DataFrame(
+                    {"datetime": [datetime(2001, 1, 1), datetime(2001, 1, 2)]}
+                ),
+                pl.DataFrame({"datetime": [datetime(2001, 1, 1)]}),
+                "datetime",
+            ),
+            (
+                ibis.memtable({"date": [date(2001, 1, 1), date(2001, 1, 2)]}),
+                ibis.memtable({"date": [date(2001, 1, 1)]}),
+                "date",
+            ),
+            (
+                ibis.memtable(
+                    {"datetime": [datetime(2001, 1, 1), datetime(2001, 1, 2)]}
+                ),
+                ibis.memtable({"datetime": [datetime(2001, 1, 1)]}),
+                "datetime",
+            ),
+        ],
+    )
+    def test_filter_rows_in_dates(
+        df: DataFrameType, expected: DataFrameType, column: str
+    ) -> None:
+        transform = FilterRowsTransform(
+            type=TransformType.FILTER_ROWS,
+            operation="keep_rows",
+            where=[
+                Condition(
+                    column_id=column,
+                    operator="in",
+                    value=["2001-01-01"],  # Backend will receive as string
+                ),
+            ],
+        )
+        result = apply(df, transform)
+        assert_frame_equal(result, expected)
+
     @staticmethod
     @pytest.mark.parametrize(
         ("df", "expected"),