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  1. Spark
  2. SPARK-30082

Zeros are being treated as NaNs

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    Details

    • Type: Bug
    • Status: Resolved
    • Priority: Major
    • Resolution: Fixed
    • Affects Version/s: 2.0.2, 2.1.3, 2.2.3, 2.3.4, 2.4.4
    • Fix Version/s: 2.4.5, 3.0.0
    • Component/s: SQL
    • Labels:

      Description

      If you attempt to run

      df = df.replace(float('nan'), somethingToReplaceWith)
      

      It will replace all 0 s in columns of type Integer

      Example code snippet to repro this:

      from pyspark.sql import SQLContext
      spark = SQLContext(sc).sparkSession
      df = spark.createDataFrame([(1, 0), (2, 3), (3, 0)], ("index", "value"))
      df.show()
      df = df.replace(float('nan'), 5)
      df.show()
      

      Here's the output I get when I run this code:

      Welcome to
            ____              __
           / __/__  ___ _____/ /__
          _\ \/ _ \/ _ `/ __/  '_/
         /__ / .__/\_,_/_/ /_/\_\   version 2.4.4
            /_/
      
      Using Python version 3.7.5 (default, Nov  1 2019 02:16:32)
      SparkSession available as 'spark'.
      >>> from pyspark.sql import SQLContext
      >>> spark = SQLContext(sc).sparkSession
      >>> df = spark.createDataFrame([(1, 0), (2, 3), (3, 0)], ("index", "value"))
      >>> df.show()
      +-----+-----+
      |index|value|
      +-----+-----+
      |    1|    0|
      |    2|    3|
      |    3|    0|
      +-----+-----+
      
      >>> df = df.replace(float('nan'), 5)
      >>> df.show()
      +-----+-----+
      |index|value|
      +-----+-----+
      |    1|    5|
      |    2|    3|
      |    3|    5|
      +-----+-----+
      
      >>>
      

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            • Assignee:
              jayad John Ayad
              Reporter:
              jayad John Ayad
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              Dates

              • Created:
                Updated:
                Resolved: