updates to README and small bug fix

CHANGE_LOG.txt

@@ -182,3 +182,9 @@ Version 0.0.34
 Version 0.0.35
 --------------
 - added functions.to_utc_timestamp extension
+
+Version 0.0.36
+--------------
+- fixing issue with the `%%sql` magics that was causing a double execution
+- update README.md for PMML
+- update for the `extras/wheel_loader` thanks Karol Tarcak

README.md

@@ -2,7 +2,10 @@
 
 Snowpark by itself is a powerful library, but still some utility functions can always help.
 
-**NOTE: we have working to integrate some of the snowpark extensions directly into the snowpark-python library.
+
+BTW what about `Java`/ `Scala`/ `SQL` ? There is an [additional repo](https://github.com/Snowflake-Labs/snowpark-extensions "Snowpark Extensions for Java, Scala and SQL") where you will have also utility functions and extensions for those technologies.
+
+**NOTE: we have been working to integrate some of the snowpark extensions directly into the snowpark-python library.
 In most cases the APIs will be exactly the same, so there should no changes needed in your code.
 However there might be breaking changes, so consider that before updating.
 If any of these breaking changes are affecting you, please enter an issue so we can address it.**
@@ -59,7 +62,6 @@ import snowpark_extensions
 new_session = Session.builder.env().appName("app1").create()
 ```
 
-
 > NOTE: since 1.8.0 the [python connector was updated](https://docs.snowflake.com/en/release-notes/clients-drivers/python-connector-2023#version-3-1-0-july-31-2023) and we provide support for an unified configuration storage for `snowflake-python-connector` and `snowflake-snowpark-python` with this approach.
 >
 > You can use this connections leveraging `Session.builder.getOrCreate()` or `Session.builder.create()`
@@ -98,8 +100,6 @@ new_session = Session.builder.env().appName("app1").create()
 >
 > ```
 
-
-
 The `appName` can use to setup a query_tag like `APPNAME=tag;execution_id=guid` which can then be used to track job actions with a query like
 
 You can then use a query like:
@@ -392,7 +392,7 @@ sf_df = session.createDataFrame([(1, ["foo", "bar"], {"x": 1.0}), (2, [], {}), (
 ```
 
 ```
-#  +---+----------+----------+                                       
+#  +---+----------+----------+                                     
 # | id|  an_array|     a_map|
 # +---+----------+----------+
 # |  1|[foo, bar]|{x -> 1.0}|

extras/pmml/README.md

@@ -4,10 +4,10 @@ Predictive Model Markup Language ([PMML](https://en.wikipedia.org/wiki/Predictiv
 
 In order to easily use these models in SnowPark a simple helper has been provided.
 
-
 NOTE: this helper requires some JAR in order to perform the PMML loading and scoring.
 
 These libraries can be downloaded from maven and they should be uploaded into an stage:
+
 ```
  https://mvnrepository.com/artifact/org.pmml4s/pmml4s
  https://repo1.maven.org/maven2/org/pmml4s/pmml4s_2.12/1.0.1/pmml4s_2.12-1.0.1.jar
@@ -20,6 +20,7 @@ These libraries can be downloaded from maven and they should be uploaded into an
 ```
 
 And to use it in your code you can use an snippet like:
+
 ```
 from pmml_builder import ScoreModelBuilder
 scorer = ScoreModelBuilder() \
@@ -30,4 +31,22 @@ scorer = ScoreModelBuilder() \
 
 scorer.transform(df).show()
 
+```
+
+# NOTE:
+
+In some scenarios the number for input parameters or the number of output parameters can vary, in order to simplify the usage in those situations we provide a [helper UDF ](https://github.com/Snowflake-Labs/snowpark-extensions-py/blob/main/extras/pmml/PMML_SCORER.sql)
+
+To use that helper you will need to previously upload the:
+
+* SCALA_LIB for example `scala-library-2.12.17.jar` into an stage, you can [check maven](https://mvnrepository.com/artifact/org.scala-lang/scala-library)
+* PMMLS_LIB for example `pmml4s_2.12-1.0.1.jar` into an stage, you can [check maven](https://mvnrepository.com/artifact/org.pmml4s/pmml4s)
+* SPRAY_LIB for example `spray-json_2.12-1.3.6.jar` into an stage, you can [check maven](https://mvnrepository.com/artifact/io.spray/spray-json)
+
+To use this in python it will be easy to do:
+
+```python
+table_with_data
+.select(sql_expr("object_construct(*)").alias("INPUT_DATA"))
+.join_table_function("PMML_SCORER",lit("@STAGE/path/to/model"),col("INPUT_DATA"))
 ```

setup.py

@@ -5,7 +5,7 @@
 this_directory = Path(__file__).parent
 long_description = (this_directory / "README.md").read_text()
 
-VERSION = '0.0.35'
+VERSION = '0.0.36'
 
 setup(name='snowpark_extensions',
       version=VERSION,

snowpark_extensions/__init__.py

@@ -15,6 +15,7 @@ def register_sql_magic():
         from IPython.core.magic import register_cell_magic
         def sql(line, cell):
             import IPython
+            import re
             user_ns = IPython.get_ipython().user_ns
             if "session" in user_ns:
                 session = user_ns['session']
@@ -24,7 +25,13 @@ def sql(line, cell):
                 name = None
                 if line and line.strip():
                     name = line.strip().split(" ")[0]
-                df = session.sql(res)
+                # If there are several statements only last will be returned
+                # also we will remove all ; at the end to avoid issues with empty statements
+                res = re.sub(r';+$', '', res)
+                for cursor in session.connection.execute_string(res):
+                    df = session.sql(f"SELECT * FROM TABLE(RESULT_SCAN('{cursor.sfqid}'))")
+                    # to avoid needed to do a count on display
+                    setattr(df,"_cached_rowcount",cursor.rowcount)
                 if name:
                     user_ns[name] = df
                 else:

snowpark_extensions/dataframe_extensions.py

@@ -47,7 +47,8 @@ def _repr_html_(self):
         else:
             from IPython.display import display
         try:
-            count = self.count()
+            count = self._cached_rowcount if hasattr(self,"_cached_rowcount") else self.count()
+            self.count()
             if count == 0:
                 return "No rows to display"
             elif count == 1: