对 Pyspark 数据帧中的多列执行函数

Question

我必须在 Pyspark 数据帧中的多个列上应用某些函数。下面是我的代码：

finaldf=df.withColumn('phone_number',regexp_replace("phone_number","[^0-9]",""))\
    .withColumn('account_id',regexp_replace("account_id","[^0-9]",""))\
    .withColumn('credit_card_limit',regexp_replace("credit_card_limit","[^0-9]",""))\
    .withColumn('credit_card_number',regexp_replace("credit_card_number","[^0-9]",""))\
    .withColumn('full_name',regexp_replace("full_name","[^a-zA-Z ]",""))\
    .withColumn('transaction_code',regexp_replace("transaction_code","[^a-zA-Z]",""))\
    .withColumn('shop',regexp_replace("shop","[^a-zA-Z ]",""))

finaldf=finaldf.filter(finaldf.account_id.isNotNull())\
    .filter(finaldf.phone_number.isNotNull())\
    .filter(finaldf.credit_card_number.isNotNull())\
    .filter(finaldf.credit_card_limit.isNotNull())\
    .filter(finaldf.transaction_code.isNotNull())\
    .filter(finaldf.amount.isNotNull())

从代码中可以看出我写了一些多余的代码，也延长了程序的长度。我还了解到spark UDF效率不高

有没有办法优化这段代码？请告诉我。非常感谢！

Answer 1

对于multiple filters，你应该这样做。

filter_cols= ['account_id','phone_number','credit_card_number','credit_card_limit','transaction_code','amount']
    
final_df.filter(' and '.join([x+' is not null' for x in  filter_cols]))

对 Pyspark 数据帧中的多列执行函数

Performing functions on multiple columns in Pyspark dataframes

apache-spark

apache-spark-sql

pyspark

pyspark-dataframes