Pyspark Unsupported literal type class java.util.ArrayList [duplicate]

I am using python3 on Spark(2.2.0). I want to apply my UDF to a specified list of strings.

df = ['Apps A','Chrome', 'BBM', 'Apps B', 'Skype']
def calc_app(app, app_list): browser_list = ['Chrome', 'Firefox', 'Opera'] chat_list = ['WhatsApp', 'BBM', 'Skype'] sum = 0 for data in app: name = data['name'] if name in app_list: sum += 1 return sum
calc_appUDF = udf(calc_app)
df = df.withColumn('app_browser', calc_appUDF(df['apps'], browser_list))
df = df.withColumn('app_chat', calc_appUDF(df['apps'], chat_list))

But it failed and returns : 'Unsupported literal type class java.util.ArrayList'

1 Answer

If I understood your requirement correctly then you should try this

from pyspark.sql.functions import udf, col
#sample data
df_list = ['Apps A','Chrome', 'BBM', 'Apps B', 'Skype']
df = sqlContext.createDataFrame([(l,) for l in df_list], ['apps'])
df.show()
#some lists definition
browser_list = ['Chrome', 'Firefox', 'Opera']
chat_list = ['WhatsApp', 'BBM', 'Skype']
#udf definition
def calc_app(app, app_list): if app in app_list: return 1 else: return 0
def calc_appUDF(app_list): return udf(lambda l: calc_app(l, app_list))
#add new columns
df = df.withColumn('app_browser', calc_appUDF(browser_list)(col('apps')))
df = df.withColumn('app_chat', calc_appUDF(chat_list)(col('apps')))
df.show()

Sample input:

+------+
| apps|
+------+
|Apps A|
|Chrome|
| BBM|
|Apps B|
| Skype|
+------+

Output is:

+------+-----------+--------+
| apps|app_browser|app_chat|
+------+-----------+--------+
|Apps A| 0| 0|
|Chrome| 1| 0|
| BBM| 0| 1|
|Apps B| 0| 0|
| Skype| 0| 1|
+------+-----------+--------+