CODEWITHSUNDEEP
pysparkdatabricks
Alessandro
Alessandro

Reputation: 337

Pyspark Data Frame: Access to a Column (TypeError: Column is not iterable)

I am struggling with a PySpark code, in particular, I'd like to call a function on an object col which is not iterable.

from pyspark.sql.functions import col, lower, regexp_replace, split
from googletrans import Translator

def clean_text(c):
  c = lower(c)
  c = regexp_replace(c, r"^rt ", "")
  c = regexp_replace(c, r"(https?\://)\S+", "")
  c = regexp_replace(c, "[^a-zA-Z0-9\\s]", "") #removePunctuation 
  c = regexp_replace(c, r"\n", " ")
  c = regexp_replace(c, r"   ", " ")
  c = regexp_replace(c, r"  ", " ")  
#   c = translator.translate(c, dest='en', src='auto')
  return c

clean_text_df = uncleanedText.select(clean_text(col("unCleanedCol")).alias("sentence"))
clean_text_df.printSchema()
clean_text_df.show(10)

As soon as I run the code within c = translator.translate(c, dest='en', src='auto') the error shown from Spark is TypeError: Column is not iterable.

What I would like to do is a translation word by word:

From:

+--------------------+
|            sentence|
+--------------------+
|ciao team there a...|
|dear itteam i urg...|
|buongiorno segnal...|
|hi team regarding...|
|hello please add ...|
|ciao vorrei effet...|
|buongiorno ho vis...|
+--------------------+

To:

+--------------------+
|            sentence|
+--------------------+
|hello team there ...|
|dear itteam i urg...|
|goodmorning segna...|
|hi team regarding...|
|hello please add ...|
|hello would effet...|
|goodmorning I see...|
+--------------------+

The schema of the DataFrame is:

root
 |-- sentence: string (nullable = true)

Could anyone please help me?

Thank you very much

Upvotes: 2

Views: 1522

Answers (1)

pault
pault

Reputation: 43544

PySpark is just the Python API written to support Apache Spark. If you want to use custom python functions, you will have to define a user defined function (udf).

Keep your clean_text() function as is (with the translate line commented out) and try the following:

from pyspark.sql.functions import udf
from pyspark.sql.Types import StringType

def translate(c):
  return translator.translate(c, dest='en', src='auto')

translateUDF = udf(translate, StringType())

clean_text_df = uncleanedText.select(
  translateUDF(clean_text(col("unCleanedCol"))).alias("sentence")
)

The other functions in your original clean_text (lower and regexp_replace) are built-in spark functions and operate on apyspark.sql.Column.

Be aware that using this udf will bring a performance hit. See: Spark functions vs UDF performance?

Upvotes: 3

Related Questions