[SPARK-48561][PS][CONNECT] Throw PandasNotImplementedError for unsu…

…pported plotting functions

### What changes were proposed in this pull request?
Throw `PandasNotImplementedError` for unsupported plotting functions:
- {Frame, Series}.plot.hist
- {Frame, Series}.plot.kde
- {Frame, Series}.plot.density
- {Frame, Series}.plot(kind="hist", ...)
- {Frame, Series}.plot(kind="hist", ...)
- {Frame, Series}.plot(kind="density", ...)

### Why are the changes needed?
the previous error message is confusing:
```
In [3]: psdf.plot.hist()
/Users/ruifeng.zheng/Dev/spark/python/pyspark/pandas/utils.py:1017: PandasAPIOnSparkAdviceWarning: The config 'spark.sql.ansi.enabled' is set to True. This can cause unexpected behavior from pandas API on Spark since pandas API on Spark follows the behavior of pandas, not SQL.
  warnings.warn(message, PandasAPIOnSparkAdviceWarning)
[*********************************************-----------------------------------] 57.14% Complete (0 Tasks running, 1s, Scanned[*********************************************-----------------------------------] 57.14% Complete (0 Tasks running, 1s, Scanned[*********************************************-----------------------------------] 57.14% Complete (0 Tasks running, 1s, Scanned                                                                                                                                ---------------------------------------------------------------------------
PySparkAttributeError                     Traceback (most recent call last)
Cell In[3], line 1
----> 1 psdf.plot.hist()

File ~/Dev/spark/python/pyspark/pandas/plot/core.py:951, in PandasOnSparkPlotAccessor.hist(self, bins, **kwds)
    903 def hist(self, bins=10, **kwds):
    904     """
    905     Draw one histogram of the DataFrame’s columns.
    906     A `histogram`_ is a representation of the distribution of data.
   (...)
    949         >>> df.plot.hist(bins=12, alpha=0.5)  # doctest: +SKIP
    950     """
--> 951     return self(kind="hist", bins=bins, **kwds)

File ~/Dev/spark/python/pyspark/pandas/plot/core.py:580, in PandasOnSparkPlotAccessor.__call__(self, kind, backend, **kwargs)
    577 kind = {"density": "kde"}.get(kind, kind)
    578 if hasattr(plot_backend, "plot_pandas_on_spark"):
    579     # use if there's pandas-on-Spark specific method.
--> 580     return plot_backend.plot_pandas_on_spark(plot_data, kind=kind, **kwargs)
    581 else:
    582     # fallback to use pandas'
    583     if not PandasOnSparkPlotAccessor.pandas_plot_data_map[kind]:

File ~/Dev/spark/python/pyspark/pandas/plot/plotly.py:41, in plot_pandas_on_spark(data, kind, **kwargs)
     39     return plot_pie(data, **kwargs)
     40 if kind == "hist":
---> 41     return plot_histogram(data, **kwargs)
     42 if kind == "box":
     43     return plot_box(data, **kwargs)

File ~/Dev/spark/python/pyspark/pandas/plot/plotly.py:87, in plot_histogram(data, **kwargs)
     85 psdf, bins = HistogramPlotBase.prepare_hist_data(data, bins)
     86 assert len(bins) > 2, "the number of buckets must be higher than 2."
---> 87 output_series = HistogramPlotBase.compute_hist(psdf, bins)
     88 prev = float("%.9f" % bins[0])  # to make it prettier, truncate.
     89 text_bins = []

File ~/Dev/spark/python/pyspark/pandas/plot/core.py:189, in HistogramPlotBase.compute_hist(psdf, bins)
    183 for group_id, (colname, bucket_name) in enumerate(zip(colnames, bucket_names)):
    184     # creates a Bucketizer to get corresponding bin of each value
    185     bucketizer = Bucketizer(
    186         splits=bins, inputCol=colname, outputCol=bucket_name, handleInvalid="skip"
    187     )
--> 189     bucket_df = bucketizer.transform(sdf)
    191     if output_df is None:
    192         output_df = bucket_df.select(
    193             F.lit(group_id).alias("__group_id"), F.col(bucket_name).alias("__bucket")
    194         )

File ~/Dev/spark/python/pyspark/ml/base.py:260, in Transformer.transform(self, dataset, params)
    258         return self.copy(params)._transform(dataset)
    259     else:
--> 260         return self._transform(dataset)
    261 else:
    262     raise TypeError("Params must be a param map but got %s." % type(params))

File ~/Dev/spark/python/pyspark/ml/wrapper.py:412, in JavaTransformer._transform(self, dataset)
    409 assert self._java_obj is not None
    411 self._transfer_params_to_java()
--> 412 return DataFrame(self._java_obj.transform(dataset._jdf), dataset.sparkSession)

File ~/Dev/spark/python/pyspark/sql/connect/dataframe.py:1696, in DataFrame.__getattr__(self, name)
   1694 def __getattr__(self, name: str) -> "Column":
   1695     if name in ["_jseq", "_jdf", "_jmap", "_jcols", "rdd", "toJSON"]:
-> 1696         raise PySparkAttributeError(
   1697             error_class="JVM_ATTRIBUTE_NOT_SUPPORTED", message_parameters={"attr_name": name}
   1698         )
   1700     if name not in self.columns:
   1701         raise PySparkAttributeError(
   1702             error_class="ATTRIBUTE_NOT_SUPPORTED", message_parameters={"attr_name": name}
   1703         )

PySparkAttributeError: [JVM_ATTRIBUTE_NOT_SUPPORTED] Attribute `_jdf` is not supported in Spark Connect as it depends on the JVM. If you need to use this attribute, do not use Spark Connect when creating your session. Visit https://spark.apache.org/docs/latest/sql-getting-started.html#starting-point-sparksession for creating regular Spark Session in detail.
```

after this PR:
```
In [3]: psdf.plot.hist()
---------------------------------------------------------------------------
PandasNotImplementedError                 Traceback (most recent call last)
Cell In[3], line 1
----> 1 psdf.plot.hist()

File ~/Dev/spark/python/pyspark/pandas/plot/core.py:957, in PandasOnSparkPlotAccessor.hist(self, bins, **kwds)
    909 """
    910 Draw one histogram of the DataFrame’s columns.
    911 A `histogram`_ is a representation of the distribution of data.
   (...)
    954     >>> df.plot.hist(bins=12, alpha=0.5)  # doctest: +SKIP
    955 """
    956 if is_remote():
--> 957     return unsupported_function(class_name="pd.DataFrame", method_name="hist")()
    959 return self(kind="hist", bins=bins, **kwds)

File ~/Dev/spark/python/pyspark/pandas/missing/__init__.py:23, in unsupported_function.<locals>.unsupported_function(*args, **kwargs)
     22 def unsupported_function(*args, **kwargs):
---> 23     raise PandasNotImplementedError(
     24         class_name=class_name, method_name=method_name, reason=reason
     25     )

PandasNotImplementedError: The method `pd.DataFrame.hist()` is not implemented yet.
```

### Does this PR introduce _any_ user-facing change?
yes, error message improvement

### How was this patch tested?
CI

### Was this patch authored or co-authored using generative AI tooling?
No

Closes apache#46911 from zhengruifeng/ps_plotting_unsupported.

Authored-by: Ruifeng Zheng <[email protected]>
Signed-off-by: Ruifeng Zheng <[email protected]>

dev/sparktestsupport/modules.py

@@ -1102,6 +1102,8 @@ def __hash__(self):
         "python/pyspark/pandas",
     ],
     python_test_goals=[
+        # unittests dedicated for Spark Connect
+        "pyspark.pandas.tests.connect.test_connect_plotting",
         # pandas-on-Spark unittests
         "pyspark.pandas.tests.connect.test_parity_categorical",
         "pyspark.pandas.tests.connect.test_parity_config",

python/pyspark/pandas/plot/core.py

@@ -23,6 +23,7 @@
 from pandas.core.dtypes.inference import is_integer
 
 from pyspark.sql import functions as F
+from pyspark.sql.utils import is_remote
 from pyspark.pandas.missing import unsupported_function
 from pyspark.pandas.config import get_option
 from pyspark.pandas.utils import name_like_string
@@ -571,10 +572,14 @@ def _get_plot_backend(backend=None):
         return module
 
     def __call__(self, kind="line", backend=None, **kwargs):
+        kind = {"density": "kde"}.get(kind, kind)
+
+        if is_remote() and kind in ["hist", "kde"]:
+            return unsupported_function(class_name="pd.DataFrame", method_name=kind)()
+
         plot_backend = PandasOnSparkPlotAccessor._get_plot_backend(backend)
         plot_data = self.data
 
-        kind = {"density": "kde"}.get(kind, kind)
         if hasattr(plot_backend, "plot_pandas_on_spark"):
             # use if there's pandas-on-Spark specific method.
             return plot_backend.plot_pandas_on_spark(plot_data, kind=kind, **kwargs)
@@ -948,6 +953,9 @@ def hist(self, bins=10, **kwds):
             >>> df = ps.from_pandas(df)
             >>> df.plot.hist(bins=12, alpha=0.5)  # doctest: +SKIP
         """
+        if is_remote():
+            return unsupported_function(class_name="pd.DataFrame", method_name="hist")()
+
         return self(kind="hist", bins=bins, **kwds)
 
     def kde(self, bw_method=None, ind=None, **kwargs):
@@ -1023,6 +1031,9 @@ def kde(self, bw_method=None, ind=None, **kwargs):
             ... })
             >>> df.plot.kde(ind=[1, 2, 3, 4, 5, 6], bw_method=0.3)  # doctest: +SKIP
         """
+        if is_remote():
+            return unsupported_function(class_name="pd.DataFrame", method_name="kde")()
+
         return self(kind="kde", bw_method=bw_method, ind=ind, **kwargs)
 
     density = kde

python/pyspark/pandas/tests/connect/plot/test_parity_series_plot_matplotlib.py

@@ -24,6 +24,10 @@
 class SeriesPlotMatplotlibParityTests(
     SeriesPlotMatplotlibTestsMixin, PandasOnSparkTestUtils, TestUtils, ReusedConnectTestCase
 ):
+    @unittest.skip("Test depends on Spark ML which is not supported from Spark Connect.")
+    def test_empty_hist(self):
+        super().test_empty_hist()
+
     @unittest.skip("Test depends on Spark ML which is not supported from Spark Connect.")
     def test_hist(self):
         super().test_hist()

python/pyspark/pandas/tests/connect/test_connect_plotting.py

@@ -0,0 +1,124 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+import unittest
+
+import pandas as pd
+
+from pyspark import pandas as ps
+from pyspark.pandas.exceptions import PandasNotImplementedError
+from pyspark.testing.connectutils import ReusedConnectTestCase
+from pyspark.testing.pandasutils import PandasOnSparkTestUtils, TestUtils
+
+
+class ConnectPlottingTests(PandasOnSparkTestUtils, TestUtils, ReusedConnectTestCase):
+    @property
+    def pdf1(self):
+        return pd.DataFrame(
+            [[1, 2], [4, 5], [7, 8]],
+            index=["cobra", "viper", None],
+            columns=["max_speed", "shield"],
+        )
+
+    @property
+    def psdf1(self):
+        return ps.from_pandas(self.pdf1)
+
+    def test_unsupported_functions(self):
+        with self.assertRaises(PandasNotImplementedError):
+            self.psdf1.plot.hist()
+
+        with self.assertRaises(PandasNotImplementedError):
+            self.psdf1.plot.hist(bins=3)
+
+        with self.assertRaises(PandasNotImplementedError):
+            self.psdf1.plot.kde()
+
+        with self.assertRaises(PandasNotImplementedError):
+            self.psdf1.plot.kde(bw_method=3)
+
+        with self.assertRaises(PandasNotImplementedError):
+            self.psdf1.plot.density()
+
+        with self.assertRaises(PandasNotImplementedError):
+            self.psdf1.plot.density(bw_method=3)
+
+        with self.assertRaises(PandasNotImplementedError):
+            self.psdf1.shield.plot.hist()
+
+        with self.assertRaises(PandasNotImplementedError):
+            self.psdf1.shield.plot.hist(bins=3)
+
+        with self.assertRaises(PandasNotImplementedError):
+            self.psdf1.shield.plot.kde()
+
+        with self.assertRaises(PandasNotImplementedError):
+            self.psdf1.shield.plot.kde(bw_method=3)
+
+        with self.assertRaises(PandasNotImplementedError):
+            self.psdf1.shield.plot.density()
+
+        with self.assertRaises(PandasNotImplementedError):
+            self.psdf1.shield.plot.density(bw_method=3)
+
+    def test_unsupported_kinds(self):
+        with self.assertRaises(PandasNotImplementedError):
+            self.psdf1.plot(kind="hist")
+
+        with self.assertRaises(PandasNotImplementedError):
+            self.psdf1.plot(kind="hist", bins=3)
+
+        with self.assertRaises(PandasNotImplementedError):
+            self.psdf1.plot(kind="kde")
+
+        with self.assertRaises(PandasNotImplementedError):
+            self.psdf1.plot(kind="kde", bw_method=3)
+
+        with self.assertRaises(PandasNotImplementedError):
+            self.psdf1.plot(kind="density")
+
+        with self.assertRaises(PandasNotImplementedError):
+            self.psdf1.plot(kind="density", bw_method=3)
+
+        with self.assertRaises(PandasNotImplementedError):
+            self.psdf1.shield.plot(kind="hist")
+
+        with self.assertRaises(PandasNotImplementedError):
+            self.psdf1.shield.plot(kind="hist", bins=3)
+
+        with self.assertRaises(PandasNotImplementedError):
+            self.psdf1.shield.plot(kind="kde")
+
+        with self.assertRaises(PandasNotImplementedError):
+            self.psdf1.shield.plot(kind="kde", bw_method=3)
+
+        with self.assertRaises(PandasNotImplementedError):
+            self.psdf1.shield.plot(kind="density")
+
+        with self.assertRaises(PandasNotImplementedError):
+            self.psdf1.shield.plot(kind="density", bw_method=3)
+
+
+if __name__ == "__main__":
+    from pyspark.pandas.tests.connect.test_connect_plotting import *  # noqa: F401
+
+    try:
+        import xmlrunner  # type: ignore[import]
+
+        testRunner = xmlrunner.XMLTestRunner(output="target/test-reports", verbosity=2)
+    except ImportError:
+        testRunner = None
+    unittest.main(testRunner=testRunner, verbosity=2)