TypeError:“列"对象无法使用WithColumn调用 [英] TypeError: 'Column' object is not callable using WithColumn

问题描述

我想在函数get_distance的数据框"df"上添加新列:

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

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"]))
)

但是，我明白了:

TypeError: 'Column' object is not callable

我认为这是因为x和y是Column对象，我需要转换为String才能在查询中使用.我对吗?如果是这样，我该怎么办?

I think it happens because x and y are Column objects and I need to be converted to String to use in my query. Am I right? If so, how can I do this?

推荐答案

Spark应该知道您使用的功能不是普通功能，而是UDF.

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

因此，有两种方法可以在数据帧上使用UDF.

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

方法1:具有@udf批注

@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"]))
)

方法2:使用pyspark.sql.functions.udf替代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"]))
)

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