PySpark程序出现错误“TypeError: 无效的参数，不是字符串或列”

Question

我有一个名为“new_emp_final_1”的数据帧。当我试图从cookTime和prepTime派生一列'difficity'时，通过从一个udf调用函数difficity，它给了我错误。在

新的_emp_final_1.d类型如下-

[('name', 'string'), ('ingredients', 'string'), ('url', 'string'), ('image', 'string'), ('cookTime', 'string'), ('recipeYield', 'string'), ('datePublished', 'strin
g'), ('prepTime', 'string'), ('description', 'string')]

新的_emp_final_1.schema的结果是-

^{pr2}$

代码：

def difficulty(cookTime, prepTime):   
    if not cookTime or not prepTime:
        return "Unkown"

    total_duration = cookTime + prepTime
    if total_duration > 3600:
        return "Hard"
    elif total_duration > 1800 and total_duration < 3600:
        return "Medium"
    elif total_duration < 1800:
        return "Easy" 
    else: 
        return "Unkown"

func_udf = udf(difficulty, IntegerType())
new_emp_final_1 = new_emp_final_1.withColumn("difficulty", func_udf(new_emp_final_1.cookTime, new_emp_final_1.prepTime))
new_emp_final_1.show(20,False)

错误是-

File "/home/raghavcomp32915/mypycode.py", line 56, in <module> func_udf = udf(difficulty, IntegerType()) File "/usr/hdp/current/spark2-client/python/lib/pyspark.zip/pyspark/sql/udf.py", line 186, in wrapper File "/usr/hdp/current/spark2-client/python/lib/pyspark.zip/pyspark/sql/udf.py", line 166, in __call__ File "/usr/hdp/current/spark2-client/python/lib/pyspark.zip/pyspark/sql/column.py", line 66, in _to_seq File "/usr/hdp/current/spark2-client/python/lib/pyspark.zip/pyspark/sql/column.py", line 54, in _to_java_column TypeError: Invalid argument, not a string or column: <function difficulty at 0x7f707e9750c8> of type <type 'function'>. For column literals, use 'lit', 'array', 's truct' or 'create_map' function.

我期望在现有的数据帧new emp_final_1中有一个名为“困难”的列，其值为Hard、Medium、Easy或Unknown。在

Answer 1

纵观udf（困难），我看到了两件事：

• 您正在尝试对udf中的两个字符串求和（cookTime和prepTime）
• udf应返回StringType（）

这个例子对我很有用：

from pyspark.sql.types import StringType, StructType, StructField, IntegerType
import pandas as pd

schema = StructType([StructField("name", StringType(), True), 
                 StructField('ingredients',StringType(),True), 
                 StructField('url',StringType(),True), 
                 StructField('image',StringType(),True), 
                 StructField('cookTime',StringType(),True), 
                 StructField('recipeYield',StringType(),True), 
                 StructField('datePublished',StringType(),True), 
                 StructField('prepTime',StringType(),True), 
                 StructField('description',StringType(),True)])


data = {
    "name": ['meal1', 'meal2'],
    "ingredients": ['ingredient11, ingredient12','ingredient21, ingredient22'],
    "url": ['URL1', 'URL2'],
    "image": ['Image1', 'Image2'],
    "cookTime": ['60', '3601'],
    "recipeYield": ['recipeYield1', 'recipeYield2'],
    "prepTime": ['0','3000'],
    "description": ['desc1','desc2']
    }

new_emp_final_1_pd = pd.DataFrame(data=data)
new_emp_final_1 = spark.createDataFrame(new_emp_final_1_pd)

def difficulty(cookTime, prepTime):   
    if not cookTime or not prepTime:
        return "Unkown"

    total_duration = int(cookTime) + int(prepTime)
    if total_duration > 3600:
        return "Hard"
    elif total_duration > 1800 and total_duration < 3600:
        return "Medium"
    elif total_duration < 1800:
        return "Easy" 
    else: 
        return "Unkown"

func_udf = udf(difficulty, StringType())
new_emp_final_1 = new_emp_final_1.withColumn("difficulty", 
func_udf(new_emp_final_1.cookTime, new_emp_final_1.prepTime))
new_emp_final_1.show(20,False)

Answer 2

您是否尝试过像这样发送cookTime和prepTime的文字值：

new_emp_final_1 = new_emp_final_1.withColumn("difficulty", func_udf(new_emp_final_1.lit(cookTime), new_emp_final_1.lit(prepTime)))