TypeError: 'Column' object is not callable using WithColumn

Question

I would like append a new column on dataframe \"df\" from function get_distance:

def get_distance(x, y):
         


        

Answer 1

• You cannot use Python function on a Column objects directly, unless it is intended to operate on Column objects / expressions. You need udf for that:

  @udf
  def get_distance(x, y):
      ...
  

• But you cannot use SQLContext in udf (or mapper in general).

• Just join:

  tab = hiveContext.table("tab").groupBy("column1", "column2").agg(first("column3"))
  df.join(tab, ["column1", "column2"])
  

Answer 2

Spark should know the function that you are using is not ordinary function but the UDF.

So, there are 2 ways by which we can use the UDF on dataframes.

Method-1: With @udf annotation

@udf
def get_distance(x, y):
    dfDistPerc = hiveContext.sql("select column3 as column3, \
                                  from tab \
                                  where column1 = '" + x + "' \
                                  and column2 = " + y + " \
                                  limit 1")

    result = dfDistPerc.select("column3").take(1)
    return result

df = df.withColumn(
    "distance",
    lit(get_distance(df["column1"], df["column2"]))
)

Method-2: Regestering udf with pyspark.sql.functions.udf

def get_distance(x, y):
    dfDistPerc = hiveContext.sql("select column3 as column3, \
                                  from tab \
                                  where column1 = '" + x + "' \
                                  and column2 = " + y + " \
                                  limit 1")

    result = dfDistPerc.select("column3").take(1)
    return result

calculate_distance_udf = udf(get_distance, IntegerType())

df = df.withColumn(
    "distance",
    lit(calculate_distance_udf(df["column1"], df["column2"]))
)