Create rule S7187: PySpark Pandas DataFrame columns should not use a

reserved name

rules/S7187/python/metadata.json

@@ -1,24 +1,24 @@
 {
-  "title": "FIXME",
+  "title": "PySpark Pandas DataFrame columns should not use a reserved name",
   "type": "CODE_SMELL",
   "status": "ready",
   "remediation": {
     "func": "Constant\/Issue",
     "constantCost": "5min"
   },
   "tags": [
+    "data-science",
+    "pyspark"
   ],
   "defaultSeverity": "Major",
   "ruleSpecification": "RSPEC-7187",
   "sqKey": "S7187",
   "scope": "All",
   "defaultQualityProfiles": ["Sonar way"],
-  "quickfix": "unknown",
+  "quickfix": "infeasible",
   "code": {
     "impacts": {
-      "MAINTAINABILITY": "HIGH",
-      "RELIABILITY": "MEDIUM",
-      "SECURITY": "LOW"
+      "RELIABILITY": "MEDIUM"
     },
     "attribute": "CONVENTIONAL"
   }

rules/S7187/python/rule.adoc

@@ -1,44 +1,42 @@
-FIXME: add a description
-
-// If you want to factorize the description uncomment the following line and create the file.
-//include::../description.adoc[]
+This rule raises an issue when a PySpark Pandas DataFrame column name is set to a reserved name.
 
 == Why is this an issue?
 
-FIXME: remove the unused optional headers (that are commented out)
+PySpark offers powerful APIs to work with Pandas DataFrames in a distributed environment. 
+While the integration between PySpark and Pandas is seamless, there are some caveats that should be taken into account.
 
-//=== What is the potential impact?
+Spark Pandas API uses some special column names for internal purposes. 
+These column names contain leading `++__++` and trailing `++__++`.
+Therefore, when using PySpark with Pandas and naming or renaming columns,
+it is discouraged to use such reserved column names as they are not guaranteed to yield the expected results.
 
 == How to fix it
-//== How to fix it in FRAMEWORK NAME
+
+To fix this issue provide a column name without leading and trailing `++__++`.
 
 === Code examples
 
 ==== Noncompliant code example
 
 [source,python,diff-id=1,diff-type=noncompliant]
 ----
-FIXME
+import pyspark.pandas as ps
+
+df = ps.DataFrame({'__value__': [1, 2, 3]}) # Noncompliant: __value__ is a reserved column name
 ----
 
 ==== Compliant solution
 
 [source,python,diff-id=1,diff-type=compliant]
 ----
-FIXME
-----
+import pyspark.pandas as ps
 
-//=== How does this work?
+df = ps.DataFrame({'value': [1, 2, 3]}) # Compliant
+----
 
-//=== Pitfalls
 
-//=== Going the extra mile
+== Resources
+=== Documentation
 
+* PySpark Documentation - https://spark.apache.org/docs/latest/api/python/user_guide/pandas_on_spark/best_practices.html#avoid-reserved-column-names[Best Practices]
 
-//== Resources
-//=== Documentation
-//=== Articles & blog posts
-//=== Conference presentations
-//=== Standards
-//=== External coding guidelines
-//=== Benchmarks