diff --git a/Export-to-csv.py b/Export-to-csv.py
new file mode 100644
index 0000000..8fbb0b6
--- /dev/null
+++ b/Export-to-csv.py
@@ -0,0 +1,59 @@
+
+import boto3
+import logging 
+from botocore.exceptions import ClientError
+import pandas as pd 
+import io
+import os 
+
+file_name = "records.parquet"
+bucket_name = "webpresence-geocore-geojson-to-parquet-dev"
+
+
+# Change directory 
+# Get the current working directory
+current_directory = os.getcwd()
+print(f"Current Directory: {current_directory}")
+
+# Change the working directory
+new_directory = "C:/TEMP"  # Replace with the directory you want to switch to
+os.chdir(new_directory)
+current_directory = os.getcwd()
+print(f"New Directory: {current_directory}")
+
+
+# Function to open a S3 file from bucket and filename and return the parquet as pandas dataframe
+def open_S3_file_as_df(bucket_name, file_name):
+    """Open a S3 parquet file from bucket and filename and return the parquet as pandas dataframe
+    :param bucket_name: Bucket name
+    :param file_name: Specific file name to open
+    :return: body of the file as a string
+    """
+    try: 
+        s3 = boto3.resource('s3')
+        object = s3.Object(bucket_name, file_name)
+        body = object.get()['Body'].read()
+        df = pd.read_parquet(io.BytesIO(body))
+        print(f'Loading {file_name} from {bucket_name} to pandas dataframe')
+        return df
+    except ClientError as e:
+        logging.error(e)
+        return e
+    
+df = open_S3_file_as_df(bucket_name, file_name)
+print(f'The shape of the raw metadata parquet dataset is {df.shape}')
+
+
+# Add a new column to log the process, and loop through the pandas rows to assign values  
+df['process_log'] = ''
+
+## Loop through the DataFrame and update the new column based on processing condition 'Fail' or 'Success'
+for index, row in df.iterrows():
+    if Transfromed == True:
+        df.at[index, 'process_log'] = 'Success' # or 1 
+    else:
+        df.at[index, 'process_log'] = 'Fail' # or 0
+
+# Save all the records as a CSV to local path 
+save_path = os.path.join(os.getcwd(), 'records.csv')
+df.to_csv(save_path)