Pypsark:如何有条件地将函数应用于Spark DataFrame列并填充空值

2024-06-02 07:41:58 发布

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我有一个spark数据框,其中有一列特别是location_string,我基本上只想将其分解为3列,分别称为countryregioncity。然后我想将这些列与已经存在的countryregioncity列合并,以确保填充空值。或者换句话说,我想将我的函数应用于cityregioncountry为空的行,以尝试使用location_string填充这些值

示例数据集:

+--------------------+-----------------+------+-------+
|     location_string|             city|region|country|
+--------------------+-----------------+------+-------+
|Jonesboro, AR, US...|             NULL|    AR|   NULL|
|Lake Village, AR,...|     Lake Village|    AR|    USA|
|Little Rock, AR, ...|      Little Rock|    AR|    USA|
|Little Rock, AR, ...|      Little Rock|    AR|    USA|
|Malvern, AR, US, ...|          Malvern|  NULL|    USA|
|Malvern, AR, US, ...|          Malvern|    AR|    USA|
|Morrilton, AR, US...|        Morrilton|    AR|    USA|
|Morrilton, AR, US...|        Morrilton|    AR|    USA|
|N. Little Rock, A...|North Little Rock|    AR|    USA|
|N. Little Rock, A...|North Little Rock|    AR|    USA|
|Ozark, AR, US, 72949|            Ozark|    AR|    USA|
|Ozark, AR, US, 72949|            Ozark|    AR|    USA|
|Palestine, AR, US...|             NULL|    AR|    USA|
|Pine Bluff, AR, U...|       Pine Bluff|    AR|   NULL|
|Pine Bluff, AR, U...|       Pine Bluff|    AR|    USA|
|Prescott, AR, US,...|         Prescott|    AR|    USA|
|Prescott, AR, US,...|         Prescott|    AR|    USA|
|Searcy, AR, US, 7...|           Searcy|    AR|    USA|
|Searcy, AR, US, 7...|           Searcy|    AR|    USA|
|West Memphis, AR,...|     West Memphis|  NULL|    USA|
+--------------------+-----------------+------+-------+

分解位置字符串的示例函数:

def geocoder_decompose_location(location_string):
    if not location_string:
        return {'country': None, 'state': None, 'city': None}
    GOOGLE_GEOCODE_API_KEY = "<API KEY HERE>"
    result = geocoder.google(location_string, key=GOOGLE_GEOCODE_API_KEY)
    return {'country': result.country, 'state': result.state, 'city': result.city}

Tags: citystringlocationcountrynullregionarus
1条回答
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1楼 · 发布于 2024-06-02 07:41:58

scala伪码

首先,我们需要从df中删除所有重复项(这将减少对google服务的API调用)

import spark.implicits._
    case class Data(location_string:String,city: String,region: String,country: String)
    val cleaner = ((location_string: String) => {
      try{
        GOOGLE_GEOCODE_API_KEY = "<API KEY HERE>"
        val result = geocoder.google(location_string, key=GOOGLE_GEOCODE_API_KEY)
        Some(result)
      } catch {
        case error: Exception => println(error); None;
      }
    })
    output.as[Data].dropDuplicates("location_string").map(x => {
       val toCheck = (x.city ==null || x.country == null || x.region == null) // can also add blank check with StringUtils.isBlank
       if(toCheck){
         val result = cleaner(x.location_string)
         val city = if(nullcheck on result.city value) result.city else x.city
         val country = if(nullcheck on result.country value) result.country else x.country
         val region = if(nullcheck on result.state value) result.state else x.region
         Data(x.location_string , city,country,region)
       }else x
    })

我们还可以在删除重复项之前执行orderBy(desc(“city”)、desc(“country”)、desc(“state”)),以便在存在重复项时(将删除具有空值的项)

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