对pysparkDataframe中的多列执行函数

rlcwz9us  于 2021-05-27  发布在  Spark
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我必须对pysparkDataframe中的多列应用某些函数。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())

从代码中你可以看到有多余的代码我已经写了扩展程序的长度也。我还了解到spark-udf效率不高。
有没有办法优化这个代码?请告诉我。谢谢!

ergxz8rk

ergxz8rk1#

为了 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]))

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