Python 'ascii' codec can't encode character u'\u2013' in position 151: ordinal not in range(128) AWS Glue

I have a Python script that is running in AWS Glue. Up until last month is was working fine and I ran into the following error.

I've seen several different nasty hacks suggested on SO and am having issues trying to figure out how to implement correct answers in my script: UnicodeDecodeError: 'ascii' codec can't decode byte 0xe2 in position 13: ordinal not in range(128)

'ascii' codec can't encode character u'\u2013' in position 151: ordinal not in range(128). It looks like this character is an em dash - more than likely was user entered and this script did not take that into account when I wrote it. I've looked up other issues with this on SO but a lot of them involve nasty hacks that I'd rather not implement. My Python is very sparse (glue generates most of the boilerplate). How can I modify my script to negate this encoding error?

import boto3 import sys from datetime import datetime from awsglue.transforms import * from awsglue.utils import getResolvedOptions from pyspark.context import SparkContext from pyspark.sql.types import * from awsglue.context import GlueContext from awsglue.job import Job # @params: [JOB_NAME, database, path, company_id, user_id] args = getResolvedOptions( sys.argv, ['JOB_NAME', 'database', 'path', 'company_id', 'user_id']) sc = SparkContext() glueContext = GlueContext(sc) spark = glueContext.spark_session job = Job(glueContext) job.init(args['JOB_NAME'], args) datasource = glueContext.create_dynamic_frame.from_catalog( database=args['database'], table_name="employee_data", transformation_ctx="datasource0") filtered_data = Filter.apply( frame=datasource, f=lambda x: x["company_id"] == args['company_id']) filtered_data = Filter.apply( frame=filtered_data, f=lambda x: x["email"] == args['user_id']) def get_diffs(rec): diff_length = 0 try: diff_length = len(rec["diffs"]) except: pass if diff_length > 0: for key, value in rec["diffs"].items(): if len(value) > 0: rec[key] = {} rec[key]["old"] = value[0] rec[key]["new"] = value[1] rec[key]["timestamp"] = rec["timestamp"] rec[key]["action"] = rec["action"] rec[key]["source"] = rec["source"] rec[key]["requester"] = rec["requester"] if rec["requester"] != None else "" rec[key]["employee"] = rec["email"] rec[key]["field"] = key del rec["diffs"] else: rec["0"] = {} rec["0"]["old"] = "" rec["0"]["new"] = "" rec["0"]["timestamp"] = rec["timestamp"] rec["0"]["action"] = rec["action"] rec["0"]["source"] = rec["source"] rec["0"]["requester"] = rec["requester"] if rec["requester"] != None else "" rec["0"]["employee"] = rec["email"] rec["0"]["field"] = "" del rec["payload"] del rec["partition"] del rec["timestamp"] del rec["source"] del rec["action"] del rec["requester"] del rec["email"] del rec["company_id"] return rec filtered_data = Map.apply( frame=filtered_data, f=get_diffs) new_data_rdd = filtered_data.toDF().rdd new_data = new_data_rdd.flatMap( lambda row: ( ( row ) ) ) new_data = new_data.filter(lambda x: x) schema = StructType([StructField('action', StringType(), False), StructField('field', StringType(), False), StructField('old', StringType(), False), StructField('employee', StringType( ), False), StructField('source', StringType(), False), StructField('timestamp', StringType(), False), StructField('requester', StringType(), False), StructField('new', StringType(), False)]) datasource0 = glueContext.create_dynamic_frame.from_rdd( new_data, name='unpivoted', schema=schema) applymapping1 = ApplyMapping.apply(frame=datasource0, mappings=[("timestamp", "string", "date", "string"), ("employee", "string", "employee", "string"), ("action", "string", "action", "string"), ("field", "string", "employee_field_changed", "string"), ( "old", "string", "previous_value", "string"), ("new", "string", "new_value", "string"), ("source", "string", "source", "string"), ("requester", "string", "changed_by", "string")], transformation_ctx="applymapping1") repartition = applymapping1.repartition(1) file_path = "s3://"+args["path"]+"/audit_log/" + \ args["company_id"]+"/"+args["user_id"] datasink2 = glueContext.write_dynamic_frame.from_options(frame=repartition, connection_type="s3", connection_options={ "path": file_path}, format="csv", transformation_ctx="datasink2") client = boto3.client('s3') prefix = "audit_log/"+args["company_id"]+"/"+args["user_id"] audit_key = prefix+"/audit.csv" client.delete_object(Bucket=args["path"], Key=audit_key) response = client.list_objects(Bucket=args["path"], Prefix=prefix) name = response["Contents"][0]["Key"] client.copy_object(Bucket=args["path"], CopySource=args["path"] + "/" + name, Key=audit_key) client.delete_object(Bucket=args["path"], Key=name) job.commit() 

Here is the traceback

Log Contents: Traceback (most recent call last): File "runscript.py", line 63, in <module> default_error_msg = " {} : {} ".format(e_type.__name__, str(e_value).split("\n")[0]) UnicodeEncodeError: 'ascii' codec can't encode character u'\u2013' in position 151: ordinal not in range(128)