Pyspark: Selecting a value after exploding an array - apache-spark

Question

I am new to pyspark and trying to parse telecom.value if telecom.system = "fax|phone" but getting below error. I understand that filter() would return me a struct and I am selecting a column from that. How do I select the column value after calling filter()?
File "", line 3, in raise_from
pyspark.sql.utils.AnalysisException: Resolved attribute(s) telecom#27,telecom#33 missing from name#3,telecom#5,address#7 in operator !Project [name#3.family AS Practitioner_LastName#23, name#3.suffix AS Practitioner_NameSuffix#24, name#3.given[0] AS Practitioner_FirstName#25, telecom#27.value AS telecom.value#42, telecom#33.value AS telecom.value#43, address#7.city AS PractitionerCity#38, address#7.line[0] AS PractitionerAddress_1#39, address#7.postalCode AS PractitionerZip#40, address#7.state AS PractitionerState#41]. Attribute(s) with the same name appear in the operation: telecom,telecom. Please check if the right attribute(s) are used.
root
|-- resource: struct (nullable = true)
| |-- address: array (nullable = true)
| | |-- element: struct (containsNull = true)
| | | |-- city: string (nullable = true)
| | | |-- country: string (nullable = true)
| | | |-- line: array (nullable = true)
| | | | |-- element: string (containsNull = true)
| | | |-- postalCode: string (nullable = true)
| | | |-- state: string (nullable = true)
| | | |-- use: string (nullable = true)
| |-- id: string (nullable = true)
| |-- identifier: array (nullable = true)
| | |-- element: struct (containsNull = true)
| | | |-- type: struct (nullable = true)
| | | | |-- coding: array (nullable = true)
| | | | | |-- element: struct (containsNull = true)
| | | | | | |-- code: string (nullable = true)
| | | | | | |-- system: string (nullable = true)
| | | |-- use: string (nullable = true)
| | | |-- value: string (nullable = true)
| |-- name: array (nullable = true)
| | |-- element: struct (containsNull = true)
| | | |-- family: string (nullable = true)
| | | |-- given: array (nullable = true)
| | | | |-- element: string (containsNull = true)
| | | |-- suffix: array (nullable = true)
| | | | |-- element: string (containsNull = true)
| | | |-- use: string (nullable = true)
| |-- resourceType: string (nullable = true)
| |-- telecom: array (nullable = true)
| | |-- element: struct (containsNull = true)
| | | |-- system: string (nullable = true)
| | | |-- use: string (nullable = true)
| | | |-- value: string (nullable = true)
| |-- text: struct (nullable = true)
| | |-- div: string (nullable = true)
| | |-- status: string (nullable = true)
import sys
import pyspark
from pyspark.context import SparkContext
from pyspark.sql import SparkSession
import pyspark.sql.functions as f
appName = "PySpark Example - JSON file to Spark Data Frame"
master = "local"
spark = SparkSession.builder.appName(appName).master(master).getOrCreate()
json_file_path = 'C:\\Users\\M\\Documents\\Practitioner.json'
source_df = spark.read.json(json_file_path, multiLine=True)
source_df.printSchema()
output = source_df.select(source_df["resource.name"][0].alias("name"),
source_df["resource.telecom"].alias("telecom"),
source_df["resource.address"][0].alias("address"))
output.printSchema()
practitioner = output.select(
output.name.family.alias("Practitioner_LastName"),
output.name.suffix.alias("Practitioner_NameSuffix"),
output.name.given[0].alias("Practitioner_FirstName"),
output.withColumn("telecom", f.explode(f.col("telecom"))).filter(f.col("telecom.system") == "phone").telecom.value,
output.withColumn("telecom", f.explode(f.col("telecom"))).filter(f.col("telecom.system") == "fax").telecom.value,
output.address.city.alias("PractitionerCity"),
output.address.line[0].alias("PractitionerAddress_1"),
output.address.postalCode.alias("PractitionerZip"),
output.address.state.alias("PractitionerState")
)
practitioner.printSchema()
practitioner.show()
My json is:
{"resource":{"resourceType":"Practitioner","id":"scm-ambqa1821624401190","text":{"status":"generated","div":""},"identifier":[{"use":"official","type":{"coding":[{"system":"http:\/\/hl7.org\/fhir\/v2\/0203","code":"NPI"}]},"value":"1548206097"},{"use":"official","type":{"coding":[{"system":"http:\/\/hl7.org\/fhir\/v2\/0203","code":"DEA"}]},"value":"HB1548206"}],"name":[{"use":"official","family":"BERNSTEIN","given":["HELENE","B"],"suffix":["MD"]}],"telecom":[{"system":"phone","value":"6106547854","use":"work"},{"system":"email","value":"sachin.belhekar#allscripts.com","use":"work"},{"system":"fax","value":"7106547895","use":"work"}],"address":[{"use":"work","line":["West Street 1","West Street 2"],"city":"Michigan","state":"MI","postalCode":"49036","country":"USA"}]}}

Answer 1

The data structure is a bit complex, so I will use a UDF to parse it:
import pyspark.sql.functions as f
import pyspark.sql.types as t
#f.udf(t.StringType())
def phone_parser(row):
for item in row:
if item['system'] == 'phone':
return item['value']
#f.udf(t.StringType())
def fax_parser(row):
for item in row:
if item['system'] == 'fax':
return item['value']
output.select(phone_parser('telecom'), fax_parser('telecom'))