diff --git a/rules/S7187/metadata.json b/rules/S7187/metadata.json
new file mode 100644
index 0000000000..2c63c08510
--- /dev/null
+++ b/rules/S7187/metadata.json
@@ -0,0 +1,2 @@
+{
+}
diff --git a/rules/S7187/python/metadata.json b/rules/S7187/python/metadata.json
new file mode 100644
index 0000000000..08a4e81262
--- /dev/null
+++ b/rules/S7187/python/metadata.json
@@ -0,0 +1,25 @@
+{
+  "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": "infeasible",
+  "code": {
+    "impacts": {
+      "RELIABILITY": "MEDIUM"
+    },
+    "attribute": "CONVENTIONAL"
+  }
+}
diff --git a/rules/S7187/python/rule.adoc b/rules/S7187/python/rule.adoc
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+This rule raises an issue when a PySpark Pandas DataFrame column name is set to a reserved name.
+
+== Why is this an issue?
+
+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.
+
+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
+
+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]
+----
+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]
+----
+import pyspark.pandas as ps
+
+df = ps.DataFrame({'value': [1, 2, 3]}) # Compliant
+----
+
+
+== 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]
+