Apache Spark ML Pipeline: filter empty rows in dataset











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In my Spark ML Pipeline (Spark 2.3.0) I use RegexTokenizer like this:



val regexTokenizer = new RegexTokenizer()

      .setInputCol("text")

      .setOutputCol("words")

      .setMinTokenLength(3)


It transforms DataFrame to the one with arrays of words, for example:



text      | words

-------------------------

a the     | [the]

a of to   | 

big small | [big,small]


How to filter rows with empty arrays?
Should I create custom transformer and pass it to pipeline?






scala apache-spark-mllib apache-spark-ml







share|improve this question


























    up vote
    0
    down vote

    favorite












    In my Spark ML Pipeline (Spark 2.3.0) I use RegexTokenizer like this:



    val regexTokenizer = new RegexTokenizer()
    
      .setInputCol("text")
    
      .setOutputCol("words")
    
      .setMinTokenLength(3)


    It transforms DataFrame to the one with arrays of words, for example:



    text      | words
    
-------------------------
    
a the     | [the]
    
a of to   | 
    
big small | [big,small]


    How to filter rows with empty arrays?
    Should I create custom transformer and pass it to pipeline?






    scala apache-spark-mllib apache-spark-ml







    share|improve this question
























      up vote
      0
      down vote

      favorite









      up vote
      0
      down vote

      favorite











      In my Spark ML Pipeline (Spark 2.3.0) I use RegexTokenizer like this:



      val regexTokenizer = new RegexTokenizer()
      
      .setInputCol("text")
      
      .setOutputCol("words")
      
      .setMinTokenLength(3)


      It transforms DataFrame to the one with arrays of words, for example:



      text      | words
      
-------------------------
      
a the     | [the]
      
a of to   | 
      
big small | [big,small]


      How to filter rows with empty arrays?
      Should I create custom transformer and pass it to pipeline?






      scala apache-spark-mllib apache-spark-ml







      share|improve this question













      In my Spark ML Pipeline (Spark 2.3.0) I use RegexTokenizer like this:



      val regexTokenizer = new RegexTokenizer()
      
      .setInputCol("text")
      
      .setOutputCol("words")
      
      .setMinTokenLength(3)


      It transforms DataFrame to the one with arrays of words, for example:



      text      | words
      
-------------------------
      
a the     | [the]
      
a of to   | 
      
big small | [big,small]


      How to filter rows with empty arrays?
      Should I create custom transformer and pass it to pipeline?






      scala apache-spark-mllib apache-spark-ml




      scala apache-spark-mllib apache-spark-ml






      share|improve this question













      share|improve this question











      share|improve this question




      share|improve this question










      asked 22 hours ago









      Igorock

      93841229




      93841229
























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          df
          
  .select($text, $words)
          
  .where(size($words) > 0)





          share|improve this answer

















          • 1




            I cannot do this because I use pipeline. I can create custom transformer to wrap this code but I want to know if there are another options
            – Igorock
            22 hours ago










          • While this code may answer the question, providing additional context regarding how and/or why it solves the problem would improve the answer's long-term value.
            – Nic3500
            13 hours ago











          Your Answer






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          up vote
          0
          down vote













          df
          
  .select($text, $words)
          
  .where(size($words) > 0)





          share|improve this answer

















          • 1




            I cannot do this because I use pipeline. I can create custom transformer to wrap this code but I want to know if there are another options
            – Igorock
            22 hours ago










          • While this code may answer the question, providing additional context regarding how and/or why it solves the problem would improve the answer's long-term value.
            – Nic3500
            13 hours ago















          up vote
          0
          down vote













          df
          
  .select($text, $words)
          
  .where(size($words) > 0)





          share|improve this answer

















          • 1




            I cannot do this because I use pipeline. I can create custom transformer to wrap this code but I want to know if there are another options
            – Igorock
            22 hours ago










          • While this code may answer the question, providing additional context regarding how and/or why it solves the problem would improve the answer's long-term value.
            – Nic3500
            13 hours ago













          up vote
          0
          down vote










          up vote
          0
          down vote









          df
          
  .select($text, $words)
          
  .where(size($words) > 0)





          share|improve this answer












          df
          
  .select($text, $words)
          
  .where(size($words) > 0)






          share|improve this answer












          share|improve this answer



          share|improve this answer










          answered 22 hours ago









          Terry Dactyl

          1,091412




          1,091412








          • 1




            I cannot do this because I use pipeline. I can create custom transformer to wrap this code but I want to know if there are another options
            – Igorock
            22 hours ago










          • While this code may answer the question, providing additional context regarding how and/or why it solves the problem would improve the answer's long-term value.
            – Nic3500
            13 hours ago














          • 1




            I cannot do this because I use pipeline. I can create custom transformer to wrap this code but I want to know if there are another options
            – Igorock
            22 hours ago










          • While this code may answer the question, providing additional context regarding how and/or why it solves the problem would improve the answer's long-term value.
            – Nic3500
            13 hours ago








          1




          1




          I cannot do this because I use pipeline. I can create custom transformer to wrap this code but I want to know if there are another options
          – Igorock
          22 hours ago




          I cannot do this because I use pipeline. I can create custom transformer to wrap this code but I want to know if there are another options
          – Igorock
          22 hours ago












          While this code may answer the question, providing additional context regarding how and/or why it solves the problem would improve the answer's long-term value.
          – Nic3500
          13 hours ago




          While this code may answer the question, providing additional context regarding how and/or why it solves the problem would improve the answer's long-term value.
          – Nic3500
          13 hours ago


















           

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