PySpark 程序抛出错误“TypeError：无效参数，不是字符串或列”

Question

我有一个名为“new_emp_final_1”的数据框。当我尝试从 CookTime 和 prepTime 派生列“难度”时，通过从 udf 调用函数难度，它给了我错误。

new_emp_final_1.dtypes 如下 -

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

new_emp_final_1.schema 的结果是 -

StructType(List(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)))

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.

我期望现有数据帧 new_emp_final_1 中有一个名为“difficulty”的列，其值为“Hard”、“Medium”、“Easy”或“Unknown”。

Answer 1

我在使用 Python\xe2\x80\x99s 时遇到了这个问题sum，因为与 Spark\xe2\x80\x99s SQL \xe2\x80\x94 存在冲突，这是sum为什么这个\xe2\x80\xaf 的现实说明：

\n

from pyspark.sql.functions import *\n

\n

不好。​

\n

不言而喻，解决方案是要么限制导入所需的函数，要么导入pyspark.sql.functions所需的函数并为其添加前缀。

\n

Answer 2

通过研究 udf（难度），我看到了两件事：

• 您正在尝试对 udf 中的 2 个字符串（cookTime 和 prepTime）求和
• udf 应该返回 StringType()

这个例子对我有用：

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)