diff --git a/examples/dataframe/create_write_data.py b/examples/dataframe/create_write_data.py
index a1580323..57c48f03 100644
--- a/examples/dataframe/create_write_data.py
+++ b/examples/dataframe/create_write_data.py
@@ -5,6 +5,10 @@
     import pyarrow as pa  # type: ignore
 except Exception:
     pa = None
+try:
+    import pandas as pd  # type: ignore
+except Exception:
+    pd = None
 from nisystemlink.clients.core import HttpConfiguration
 from nisystemlink.clients.dataframe import DataFrameClient
 from nisystemlink.clients.dataframe.models import (
@@ -40,6 +44,11 @@
     )
 )
 
+
+print(f"Created table with ID: {table_id}")
+
+print("Appending data to table...")
+
 # Append via explicit AppendTableDataRequest (JSON)
 frame_request = DataFrame(
     data=[[str(i), str(random.random()), datetime.now().isoformat()] for i in range(3)]
@@ -55,6 +64,7 @@
 client.append_table_data(table_id, frame_direct)
 
 if pa is not None:
+    print("Appending data to table via Arrow RecordBatches...")
     # Append via single RecordBatch (Arrow)
     batch_single = pa.record_batch(
         [
@@ -79,9 +89,31 @@
     ]
     client.append_table_data(table_id, batch_list)
 
-    # Mark end_of_data for the table
-    # Supply `None` and `end_of_data=True`
-    client.append_table_data(table_id, None, end_of_data=True)
-else:
-    # If pyarrow not installed, flush via JSON path
-    client.append_table_data(table_id, None, end_of_data=True)
+if pa is not None and pd is not None:
+    print("Appending data to table via Pandas DataFrame...")
+    # Append via DataFrame (Pandas)
+    df = pd.DataFrame(
+        {
+            "ix": [11, 12, 13],
+            "Float_Column": [0.6, 0.7, 0.8],
+            "Timestamp_Column": [datetime.now() for _ in range(3)],
+        }
+    )
+
+    # Optional - coerce df types to the dataframe table schema
+    df = df.astype(
+        {
+            "ix": "Int32",
+            "Float_Column": "float32",
+            "Timestamp_Column": "datetime64[ns]",
+        }
+    )
+
+    # convert Pandas DataFrame to Arrow RecordBatch and append
+    record_batch = pa.RecordBatch.from_pandas(df)
+    client.append_table_data(table_id, record_batch)
+
+# Mark end_of_data for the table
+# Supply `None` and `end_of_data=True`
+print("Finished appending data.")
+client.append_table_data(table_id, None, end_of_data=True)