Add note about how to wrap functions from Pyspark (#440)

See #435

README.md

@@ -107,7 +107,7 @@ val payloadSchema = schema_of_xml(df.select("payload").as[String])
 val parsed = df.withColumn("parsed", from_xml($"payload", payloadSchema))
 ```
 
-- This can converts arrays of strings containing XML to arrays of parsed structs. Use `schema_of_xml_array` instead
+- This can convert arrays of strings containing XML to arrays of parsed structs. Use `schema_of_xml_array` instead
 - `com.databricks.spark.xml.from_xml_string` is an alternative that operates on a String directly instead of a column,
   for use in UDFs
 - If you use `DROPMALFORMED` mode with `from_xml`, then XML values that do not parse correctly will result in a
@@ -116,6 +116,34 @@ val parsed = df.withColumn("parsed", from_xml($"payload", payloadSchema))
   instead default to `DROPMALFORMED`.
   If however you include a column in the schema for `from_xml` that matches the `columnNameOfCorruptRecord`, then
   `PERMISSIVE` mode will still output malformed records to that column in the resulting struct. 
+
+#### Pyspark notes
+
+The functions above are exposed in the Scala API only, at the moment, as there is no separate Python package
+for `spark-xml`. They can be accessed from Pyspark by manually declaring some helper functions that call
+into the JVM-based API from Python. Example:
+
+```python
+from pyspark.sql.column import Column, _to_java_column
+from pyspark.sql.types import _parse_datatype_json_string
+
+def ext_from_xml(xml_column, schema, options={}):
+    java_column = _to_java_column(xml_column.cast('string'))
+    java_schema = spark._jsparkSession.parseDataType(schema.json())
+    scala_map = spark._jvm.org.apache.spark.api.python.PythonUtils.toScalaMap(options)
+    jc = spark._jvm.com.databricks.spark.xml.functions.from_xml(
+        java_column, java_schema, scala_map)
+    return Column(jc)
+
+def ext_schema_of_xml_df(df, options={}):
+    assert len(df.columns) == 1
+
+    scala_options = spark._jvm.PythonUtils.toScalaMap(options)
+    java_xml_module = getattr(getattr(
+        spark._jvm.com.databricks.spark.xml, "package$"), "MODULE$")
+    java_schema = java_xml_module.schema_of_xml_df(df._jdf, scala_options)
+    return _parse_datatype_json_string(java_schema.json())
+```
 
 ## Structure Conversion