SNOW-1371757: Fix repr when max_cols or max_rows is None (#1545)

1. Which Jira issue is this PR addressing? Make sure that there is an
accompanying issue to your PR.

   Fixes SNOW-1371757

2. Fill out the following pre-review checklist:

- [x] I am adding a new automated test(s) to verify correctness of my
new code
   - [ ] I am adding new logging messages
   - [ ] I am adding a new telemetry message
   - [ ] I am adding new credentials
   - [ ] I am adding a new dependency

3. Please describe how your code solves the related issue.

Catch `None` values at the API layer and sanitize in the same way Modin
OSS does:
   

https://github.com/modin-project/modin/blob/06699a892c2a020c92ecf0c54d1aa35bbcd5660b/modin/pandas/dataframe.py#L268-L269

---------

Signed-off-by: Devin Petersohn <devin.petersohn@snowflake.com>

src/snowflake/snowpark/modin/pandas/dataframe.py

@@ -254,9 +254,9 @@ def __repr__(self):
         str
         """
         # TODO: SNOW-1063346: Modin upgrade - modin.pandas.DataFrame functions
-        num_rows = pandas.get_option("display.max_rows") or 10
+        num_rows = pandas.get_option("display.max_rows") or len(self)
         # see _repr_html_ for comment, allow here also all column behavior
-        num_cols = pandas.get_option("display.max_columns")
+        num_cols = pandas.get_option("display.max_columns") or len(self.columns)
 
         (
             row_count,

src/snowflake/snowpark/modin/plugin/PANDAS_CHANGELOG.md

@@ -2,6 +2,7 @@
 
 ### Bug Fixes
 - Fixed bug when creating a DataFrame with a dict of Series objects.
+- Fixed support for `display.max_rows` and `display.max_cols` being set to `None`.
 - Fixed bug when performing multiple DataFrameGroupBy apply/transform operations on the same DataFrame.
 
 ### Improvements

tests/integ/modin/frame/test_repr.py

@@ -152,6 +152,11 @@ def setup_class(self):
         self.num_rows = pd.get_option("display.max_rows")
         self.num_cols = pd.get_option("display.max_columns")
 
+        self.native_df = native_pd.DataFrame(
+            data=np.zeros(shape=(40, 40)), columns=[f"x{i}" for i in range(40)]
+        )
+        self.snow_df = pd.DataFrame(self.native_df)
+
     def teardown_class(self):
         native_pd.set_option("display.max_rows", self.native_num_rows)
         native_pd.set_option("display.max_columns", self.native_num_cols)
@@ -162,19 +167,36 @@ def test_with_max_columns(self):
         native_pd.set_option("display.max_columns", 10)
         pd.set_option("display.max_columns", 10)
 
-        native_df = native_pd.DataFrame(
-            data=np.zeros(shape=(40, 40)), columns=[f"x{i}" for i in range(40)]
-        )
-        snow_df = pd.DataFrame(native_df)
-
         # This test should only issue 6 SQL queries given dataframe creation from large
         # local data: 1) Creating temp table, 2) Setting query tag, 3) Inserting into temp table,
         # 4) Unsetting query tag, 5) Select Columns, 6) Drop temp table.
         # However, an additional 6 queries are issued to eagerly get the row count.
         # for now, track only SELECT count here
         with SqlCounter(select_count=1):
-            snow_str = repr(snow_df)
-        native_str = repr(native_df)
+            snow_str = repr(self.snow_df)
+        native_str = repr(self.native_df)
+
+        assert snow_str == native_str
+        self.teardown_class()
+
+    def test_with_max_rows_none(self):
+        native_pd.set_option("display.max_rows", None)
+        pd.set_option("display.max_rows", None)
+
+        with SqlCounter(select_count=2):
+            snow_str = repr(self.snow_df)
+        native_str = repr(self.native_df)
+
+        assert snow_str == native_str
+        self.teardown_class()
+
+    def test_with_max_columns_none(self):
+        native_pd.set_option("display.max_columns", None)
+        pd.set_option("display.max_columns", None)
+
+        with SqlCounter(select_count=1):
+            snow_str = repr(self.snow_df)
+        native_str = repr(self.native_df)
 
         assert snow_str == native_str