SNOW-1805842: Add plotly integ tests and Interoperability doc (#2725)

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   Fixes SNOW-1805842

2. Fill out the following pre-review checklist:

- [x] I am adding a new automated test(s) to verify correctness of my
new code
- [ ] If this test skips Local Testing mode, I'm requesting review from
@snowflakedb/local-testing
   - [ ] I am adding new logging messages
   - [ ] I am adding a new telemetry message
   - [ ] I am adding new credentials
   - [ ] I am adding a new dependency
- [ ] If this is a new feature/behavior, I'm adding the Local Testing
parity changes.
- [x] I acknowledge that I have ensured my changes to be thread-safe.
Follow the link for more information: [Thread-safe Developer
Guidelines](https://github.com/snowflakedb/snowpark-python/blob/main/CONTRIBUTING.md#thread-safe-development)

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

Adding Plotly express interoperability tests and a docs page for
guaranteed interoperable APIs.

---------

Signed-off-by: Labanya Mukhopadhyay <[email protected]>
Co-authored-by: Mahesh Vashishtha <[email protected]>
Co-authored-by: Hazem Elmeleegy <[email protected]>

docs/source/modin/index.rst

@@ -19,5 +19,6 @@ For your convenience, here is all the :doc:`Supported APIs <supported/index>`
     window
     groupby
     resampling
+    interoperability
     numpy
     performance

docs/source/modin/interoperability.rst

@@ -0,0 +1,58 @@
+Interoperability with third party libraries
+=============================================
+
+Many third party libraries are interoperable with pandas, for example by accepting pandas dataframes objects as function
+inputs. Here we have a non-exhaustive list of third party library use cases with pandas and note whether each method
+works in Snowpark pandas as well.
+
+Snowpark pandas supports the `dataframe interchange protocol <https://data-apis.org/dataframe-protocol/latest/>`_, which
+some libraries use to interoperate with Snowpark pandas to the same level of support as pandas.
+
+The following table is structured as follows: The first column contains a method name.
+The second column is a flag for whether or not interoperability is guaranteed with Snowpark pandas. For each of these
+methods, we validate that passing in a Snowpark pandas dataframe as the dataframe input parameter behaves equivalently
+to passing in a pandas dataframe.
+
+.. note::
+    ``Y`` stands for yes, i.e., interoperability is guaranteed with this method, and ``N`` stands for no.
+
+Plotly.express module methods
+
+.. note::
+    Currently only plotly versions <6.0.0 are supported through the dataframe interchange protocol.
+
++-------------------------+---------------------------------------------+--------------------------------------------+
+| Method name             | Interoperable with Snowpark pandas? (Y/N)   | Notes for current implementation           |
++-------------------------+---------------------------------------------+--------------------------------------------+
+| ``scatter``             | Y                                           |                                            |
++-------------------------+---------------------------------------------+--------------------------------------------+
+| ``line``                | Y                                           |                                            |
++-------------------------+---------------------------------------------+--------------------------------------------+
+| ``area``                | Y                                           |                                            |
++-------------------------+---------------------------------------------+--------------------------------------------+
+| ``timeline``            | Y                                           |                                            |
++-------------------------+---------------------------------------------+--------------------------------------------+
+| ``violin``              | Y                                           |                                            |
++-------------------------+---------------------------------------------+--------------------------------------------+
+| ``bar``                 | Y                                           |                                            |
++-------------------------+---------------------------------------------+--------------------------------------------+
+| ``histogram``           | Y                                           |                                            |
++-------------------------+---------------------------------------------+--------------------------------------------+
+| ``pie``                 | Y                                           |                                            |
++-------------------------+---------------------------------------------+--------------------------------------------+
+| ``treemap``             | Y                                           |                                            |
++-------------------------+---------------------------------------------+--------------------------------------------+
+| ``sunburst``            | Y                                           |                                            |
++-------------------------+---------------------------------------------+--------------------------------------------+
+| ``icicle``              | Y                                           |                                            |
++-------------------------+---------------------------------------------+--------------------------------------------+
+| ``scatter_matrix``      | Y                                           |                                            |
++-------------------------+---------------------------------------------+--------------------------------------------+
+| ``funnel``              | Y                                           |                                            |
++-------------------------+---------------------------------------------+--------------------------------------------+
+| ``density_heatmap``     | Y                                           |                                            |
++-------------------------+---------------------------------------------+--------------------------------------------+
+| ``boxplot``             | Y                                           |                                            |
++-------------------------+---------------------------------------------+--------------------------------------------+
+| ``imshow``              | Y                                           |                                            |
++-------------------------+---------------------------------------------+--------------------------------------------+

setup.py

@@ -200,6 +200,8 @@ def run(self):
             "scipy",  # Snowpark pandas 3rd party library testing
             "statsmodels",  # Snowpark pandas 3rd party library testing
             "scikit-learn==1.5.2",  # Snowpark pandas scikit-learn tests
+            # plotly version restricted due to foreseen change in query counts in version 6.0.0+
+            "plotly<6.0.0",  # Snowpark pandas 3rd party library testing
         ],
         "localtest": [
             "pandas",

tests/integ/modin/interoperability/plotly/test_plotly.py

@@ -0,0 +1,211 @@
+#
+# Copyright (c) 2012-2024 Snowflake Computing Inc. All rights reserved.
+#
+
+import modin.pandas as pd
+import numpy as np
+import plotly.express as px
+import pytest
+import pandas as native_pd
+
+import snowflake.snowpark.modin.plugin  # noqa: F401
+from tests.integ.utils.sql_counter import sql_count_checker
+from tests.integ.modin.utils import eval_snowpark_pandas_result
+
+# Integration tests for plotly.express module (https://plotly.com/python-api-reference/plotly.express.html).
+# To add tests for additional APIs,
+#     - Call the method with Snowpark pandas and native pandas df input and get the JSON representation with
+#     `to_plotly_json()`.
+#     - Assert correctness of the plot produced using `assert_plotly_equal` function defined below.
+
+
+def assert_plotly_equal(expect, got):
+    # referenced from cudf plotly integration test
+    # https://github.com/rapidsai/cudf/blob/main/python/cudf/cudf_pandas_tests/third_party_integration_tests/tests/test_plotly.py#L10
+
+    assert type(expect) == type(got)
+    if isinstance(expect, dict):
+        assert expect.keys() == got.keys()
+        for k in expect.keys():
+            assert_plotly_equal(expect[k], got[k])
+    elif isinstance(got, list):
+        assert len(expect) == len(got)
+        for i in range(len(expect)):
+            assert_plotly_equal(expect[i], got[i])
+    elif isinstance(expect, np.ndarray):
+        if isinstance(expect[0], float):
+            np.testing.assert_allclose(expect, got)
+        else:
+            assert (expect == got).all()
+    else:
+        assert expect == got
+
+
+@pytest.fixture()
+def test_dfs():
+    nsamps = 50
+    rng = np.random.default_rng(seed=42)
+    data = {
+        "x": rng.random(nsamps),
+        "y": rng.random(nsamps),
+        "category": rng.integers(0, 5, nsamps),
+        "category2": rng.integers(0, 5, nsamps),
+    }
+    snow_df = pd.DataFrame(data)
+    native_df = native_pd.DataFrame(data)
+    return snow_df, native_df
+
+
+@sql_count_checker(query_count=1)
+def test_scatter(test_dfs):
+    eval_snowpark_pandas_result(
+        *test_dfs,
+        lambda df: px.scatter(df, x="x", y="y").to_plotly_json(),
+        comparator=assert_plotly_equal
+    )
+
+
+@sql_count_checker(query_count=1)
+def test_line(test_dfs):
+    eval_snowpark_pandas_result(
+        *test_dfs,
+        lambda df: px.line(df, x="category", y="y").to_plotly_json(),
+        comparator=assert_plotly_equal
+    )
+
+
+@sql_count_checker(query_count=1)
+def test_area(test_dfs):
+    eval_snowpark_pandas_result(
+        *test_dfs,
+        lambda df: px.area(df, x="category", y="y").to_plotly_json(),
+        comparator=assert_plotly_equal
+    )
+
+
+@sql_count_checker(query_count=1)
+def test_timeline():
+    native_df = native_pd.DataFrame(
+        [
+            dict(Task="Job A", Start="2009-01-01", Finish="2009-02-28"),
+            dict(Task="Job B", Start="2009-03-05", Finish="2009-04-15"),
+            dict(Task="Job C", Start="2009-02-20", Finish="2009-05-30"),
+        ]
+    )
+    snow_df = pd.DataFrame(native_df)
+    eval_snowpark_pandas_result(
+        snow_df,
+        native_df,
+        lambda df: px.timeline(
+            df, x_start="Start", x_end="Finish", y="Task"
+        ).to_plotly_json(),
+        comparator=assert_plotly_equal,
+    )
+
+
+@sql_count_checker(query_count=1)
+def test_violin(test_dfs):
+    eval_snowpark_pandas_result(
+        *test_dfs,
+        lambda df: px.violin(df, y="y").to_plotly_json(),
+        comparator=assert_plotly_equal
+    )
+
+
+@sql_count_checker(query_count=1)
+def test_bar(test_dfs):
+    eval_snowpark_pandas_result(
+        *test_dfs,
+        lambda df: px.bar(df, x="category", y="y").to_plotly_json(),
+        comparator=assert_plotly_equal
+    )
+
+
+@sql_count_checker(query_count=1)
+def test_histogram(test_dfs):
+    eval_snowpark_pandas_result(
+        *test_dfs,
+        lambda df: px.histogram(df, x="category").to_plotly_json(),
+        comparator=assert_plotly_equal
+    )
+
+
+@sql_count_checker(query_count=1)
+def test_pie(test_dfs):
+    eval_snowpark_pandas_result(
+        *test_dfs,
+        lambda df: px.pie(df, values="category", names="category2").to_plotly_json(),
+        comparator=assert_plotly_equal
+    )
+
+
+@sql_count_checker(query_count=1)
+def test_treemap(test_dfs):
+    eval_snowpark_pandas_result(
+        *test_dfs,
+        lambda df: px.treemap(df, names="category", values="y").to_plotly_json(),
+        comparator=assert_plotly_equal
+    )
+
+
+@sql_count_checker(query_count=1)
+def test_sunburst(test_dfs):
+    eval_snowpark_pandas_result(
+        *test_dfs,
+        lambda df: px.sunburst(df, names="category", values="y").to_plotly_json(),
+        comparator=assert_plotly_equal
+    )
+
+
+@sql_count_checker(query_count=1)
+def test_icicle(test_dfs):
+    eval_snowpark_pandas_result(
+        *test_dfs,
+        lambda df: px.icicle(df, names="category", values="y").to_plotly_json(),
+        comparator=assert_plotly_equal
+    )
+
+
+@sql_count_checker(query_count=1)
+def test_scatter_matrix(test_dfs):
+    eval_snowpark_pandas_result(
+        *test_dfs,
+        lambda df: px.scatter_matrix(df, dimensions=["category"]).to_plotly_json(),
+        comparator=assert_plotly_equal
+    )
+
+
+@sql_count_checker(query_count=1)
+def test_funnel(test_dfs):
+    eval_snowpark_pandas_result(
+        *test_dfs,
+        lambda df: px.funnel(df, x="x", y="y").to_plotly_json(),
+        comparator=assert_plotly_equal
+    )
+
+
+@sql_count_checker(query_count=1)
+def test_density_heatmap(test_dfs):
+    eval_snowpark_pandas_result(
+        *test_dfs,
+        lambda df: px.density_heatmap(df, x="x", y="y").to_plotly_json(),
+        comparator=assert_plotly_equal
+    )
+
+
+@sql_count_checker(query_count=1)
+def test_box(test_dfs):
+    eval_snowpark_pandas_result(
+        *test_dfs,
+        lambda df: px.box(df, x="category", y="y").to_plotly_json(),
+        comparator=assert_plotly_equal
+    )
+
+
+@sql_count_checker(query_count=4)
+def test_imshow(test_dfs):
+    eval_snowpark_pandas_result(
+        *test_dfs,
+        lambda df: px.imshow(df, x=df.columns, y=df.index).to_plotly_json(),
+        comparator=assert_plotly_equal
+    )