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org.apache.spark.sql

DataFrameNaFunctions

final class DataFrameNaFunctions extends AnyRef

Functionality for working with missing data in DataFrames.

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@Stable()
Since

1.3.1

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  1. final def !=(arg0: Any): Boolean
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  2. final def ##(): Int
    Definition Classes
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  3. final def ==(arg0: Any): Boolean
    Definition Classes
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  4. final def asInstanceOf[T0]: T0
    Definition Classes
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  5. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
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    Annotations
    @throws( ... ) @native() @HotSpotIntrinsicCandidate()
  6. def drop(minNonNulls: Int, cols: Seq[String]): DataFrame

    (Scala-specific) Returns a new DataFrame that drops rows containing less than minNonNulls non-null and non-NaN values in the specified columns.

    (Scala-specific) Returns a new DataFrame that drops rows containing less than minNonNulls non-null and non-NaN values in the specified columns.

    Since

    1.3.1

  7. def drop(minNonNulls: Int, cols: Array[String]): DataFrame

    Returns a new DataFrame that drops rows containing less than minNonNulls non-null and non-NaN values in the specified columns.

    Returns a new DataFrame that drops rows containing less than minNonNulls non-null and non-NaN values in the specified columns.

    Since

    1.3.1

  8. def drop(minNonNulls: Int): DataFrame

    Returns a new DataFrame that drops rows containing less than minNonNulls non-null and non-NaN values.

    Returns a new DataFrame that drops rows containing less than minNonNulls non-null and non-NaN values.

    Since

    1.3.1

  9. def drop(how: String, cols: Seq[String]): DataFrame

    (Scala-specific) Returns a new DataFrame that drops rows containing null or NaN values in the specified columns.

    (Scala-specific) Returns a new DataFrame that drops rows containing null or NaN values in the specified columns.

    If how is "any", then drop rows containing any null or NaN values in the specified columns. If how is "all", then drop rows only if every specified column is null or NaN for that row.

    Since

    1.3.1

  10. def drop(how: String, cols: Array[String]): DataFrame

    Returns a new DataFrame that drops rows containing null or NaN values in the specified columns.

    Returns a new DataFrame that drops rows containing null or NaN values in the specified columns.

    If how is "any", then drop rows containing any null or NaN values in the specified columns. If how is "all", then drop rows only if every specified column is null or NaN for that row.

    Since

    1.3.1

  11. def drop(cols: Seq[String]): DataFrame

    (Scala-specific) Returns a new DataFrame that drops rows containing any null or NaN values in the specified columns.

    (Scala-specific) Returns a new DataFrame that drops rows containing any null or NaN values in the specified columns.

    Since

    1.3.1

  12. def drop(cols: Array[String]): DataFrame

    Returns a new DataFrame that drops rows containing any null or NaN values in the specified columns.

    Returns a new DataFrame that drops rows containing any null or NaN values in the specified columns.

    Since

    1.3.1

  13. def drop(how: String): DataFrame

    Returns a new DataFrame that drops rows containing null or NaN values.

    Returns a new DataFrame that drops rows containing null or NaN values.

    If how is "any", then drop rows containing any null or NaN values. If how is "all", then drop rows only if every column is null or NaN for that row.

    Since

    1.3.1

  14. def drop(): DataFrame

    Returns a new DataFrame that drops rows containing any null or NaN values.

    Returns a new DataFrame that drops rows containing any null or NaN values.

    Since

    1.3.1

  15. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  16. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  17. def fill(valueMap: Map[String, Any]): DataFrame

    (Scala-specific) Returns a new DataFrame that replaces null values.

    (Scala-specific) Returns a new DataFrame that replaces null values.

    The key of the map is the column name, and the value of the map is the replacement value. The value must be of the following type: Int, Long, Float, Double, String, Boolean. Replacement values are cast to the column data type.

    For example, the following replaces null values in column "A" with string "unknown", and null values in column "B" with numeric value 1.0.

    df.na.fill(Map(
      "A" -> "unknown",
      "B" -> 1.0
    ))
    Since

    1.3.1

  18. def fill(valueMap: Map[String, Any]): DataFrame

    Returns a new DataFrame that replaces null values.

    Returns a new DataFrame that replaces null values.

    The key of the map is the column name, and the value of the map is the replacement value. The value must be of the following type: Integer, Long, Float, Double, String, Boolean. Replacement values are cast to the column data type.

    For example, the following replaces null values in column "A" with string "unknown", and null values in column "B" with numeric value 1.0.

    import com.google.common.collect.ImmutableMap;
    df.na.fill(ImmutableMap.of("A", "unknown", "B", 1.0));
    Since

    1.3.1

  19. def fill(value: Boolean, cols: Array[String]): DataFrame

    Returns a new DataFrame that replaces null values in specified boolean columns.

    Returns a new DataFrame that replaces null values in specified boolean columns. If a specified column is not a boolean column, it is ignored.

    Since

    2.3.0

  20. def fill(value: Boolean, cols: Seq[String]): DataFrame

    (Scala-specific) Returns a new DataFrame that replaces null values in specified boolean columns.

    (Scala-specific) Returns a new DataFrame that replaces null values in specified boolean columns. If a specified column is not a boolean column, it is ignored.

    Since

    2.3.0

  21. def fill(value: Boolean): DataFrame

    Returns a new DataFrame that replaces null values in boolean columns with value.

    Returns a new DataFrame that replaces null values in boolean columns with value.

    Since

    2.3.0

  22. def fill(value: String, cols: Seq[String]): DataFrame

    (Scala-specific) Returns a new DataFrame that replaces null values in specified string columns.

    (Scala-specific) Returns a new DataFrame that replaces null values in specified string columns. If a specified column is not a string column, it is ignored.

    Since

    1.3.1

  23. def fill(value: String, cols: Array[String]): DataFrame

    Returns a new DataFrame that replaces null values in specified string columns.

    Returns a new DataFrame that replaces null values in specified string columns. If a specified column is not a string column, it is ignored.

    Since

    1.3.1

  24. def fill(value: Double, cols: Seq[String]): DataFrame

    (Scala-specific) Returns a new DataFrame that replaces null or NaN values in specified numeric columns.

    (Scala-specific) Returns a new DataFrame that replaces null or NaN values in specified numeric columns. If a specified column is not a numeric column, it is ignored.

    Since

    1.3.1

  25. def fill(value: Long, cols: Seq[String]): DataFrame

    (Scala-specific) Returns a new DataFrame that replaces null or NaN values in specified numeric columns.

    (Scala-specific) Returns a new DataFrame that replaces null or NaN values in specified numeric columns. If a specified column is not a numeric column, it is ignored.

    Since

    2.2.0

  26. def fill(value: Double, cols: Array[String]): DataFrame

    Returns a new DataFrame that replaces null or NaN values in specified numeric columns.

    Returns a new DataFrame that replaces null or NaN values in specified numeric columns. If a specified column is not a numeric column, it is ignored.

    Since

    1.3.1

  27. def fill(value: Long, cols: Array[String]): DataFrame

    Returns a new DataFrame that replaces null or NaN values in specified numeric columns.

    Returns a new DataFrame that replaces null or NaN values in specified numeric columns. If a specified column is not a numeric column, it is ignored.

    Since

    2.2.0

  28. def fill(value: String): DataFrame

    Returns a new DataFrame that replaces null values in string columns with value.

    Returns a new DataFrame that replaces null values in string columns with value.

    Since

    1.3.1

  29. def fill(value: Double): DataFrame

    Returns a new DataFrame that replaces null or NaN values in numeric columns with value.

    Returns a new DataFrame that replaces null or NaN values in numeric columns with value.

    Since

    1.3.1

  30. def fill(value: Long): DataFrame

    Returns a new DataFrame that replaces null or NaN values in numeric columns with value.

    Returns a new DataFrame that replaces null or NaN values in numeric columns with value.

    Since

    2.2.0

  31. final def getClass(): Class[_]
    Definition Classes
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    @native() @HotSpotIntrinsicCandidate()
  32. def hashCode(): Int
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    @native() @HotSpotIntrinsicCandidate()
  33. final def isInstanceOf[T0]: Boolean
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  34. final def ne(arg0: AnyRef): Boolean
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  35. final def notify(): Unit
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    @native() @HotSpotIntrinsicCandidate()
  36. final def notifyAll(): Unit
    Definition Classes
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    @native() @HotSpotIntrinsicCandidate()
  37. def replace[T](cols: Seq[String], replacement: Map[T, T]): DataFrame

    (Scala-specific) Replaces values matching keys in replacement map.

    (Scala-specific) Replaces values matching keys in replacement map.

    // Replaces all occurrences of 1.0 with 2.0 in column "height" and "weight".
    df.na.replace("height" :: "weight" :: Nil, Map(1.0 -> 2.0));
    
    // Replaces all occurrences of "UNKNOWN" with "unnamed" in column "firstname" and "lastname".
    df.na.replace("firstname" :: "lastname" :: Nil, Map("UNKNOWN" -> "unnamed"));
    cols

    list of columns to apply the value replacement. If col is "*", replacement is applied on all string, numeric or boolean columns.

    replacement

    value replacement map. Key and value of replacement map must have the same type, and can only be doubles, strings or booleans. The map value can have nulls.

    Since

    1.3.1

  38. def replace[T](col: String, replacement: Map[T, T]): DataFrame

    (Scala-specific) Replaces values matching keys in replacement map.

    (Scala-specific) Replaces values matching keys in replacement map.

    // Replaces all occurrences of 1.0 with 2.0 in column "height".
    df.na.replace("height", Map(1.0 -> 2.0));
    
    // Replaces all occurrences of "UNKNOWN" with "unnamed" in column "name".
    df.na.replace("name", Map("UNKNOWN" -> "unnamed"));
    
    // Replaces all occurrences of "UNKNOWN" with "unnamed" in all string columns.
    df.na.replace("*", Map("UNKNOWN" -> "unnamed"));
    col

    name of the column to apply the value replacement. If col is "*", replacement is applied on all string, numeric or boolean columns.

    replacement

    value replacement map. Key and value of replacement map must have the same type, and can only be doubles, strings or booleans. The map value can have nulls.

    Since

    1.3.1

  39. def replace[T](cols: Array[String], replacement: Map[T, T]): DataFrame

    Replaces values matching keys in replacement map with the corresponding values.

    Replaces values matching keys in replacement map with the corresponding values.

    import com.google.common.collect.ImmutableMap;
    
    // Replaces all occurrences of 1.0 with 2.0 in column "height" and "weight".
    df.na.replace(new String[] {"height", "weight"}, ImmutableMap.of(1.0, 2.0));
    
    // Replaces all occurrences of "UNKNOWN" with "unnamed" in column "firstname" and "lastname".
    df.na.replace(new String[] {"firstname", "lastname"}, ImmutableMap.of("UNKNOWN", "unnamed"));
    cols

    list of columns to apply the value replacement. If col is "*", replacement is applied on all string, numeric or boolean columns.

    replacement

    value replacement map. Key and value of replacement map must have the same type, and can only be doubles, strings or booleans. The map value can have nulls.

    Since

    1.3.1

  40. def replace[T](col: String, replacement: Map[T, T]): DataFrame

    Replaces values matching keys in replacement map with the corresponding values.

    Replaces values matching keys in replacement map with the corresponding values.

    import com.google.common.collect.ImmutableMap;
    
    // Replaces all occurrences of 1.0 with 2.0 in column "height".
    df.na.replace("height", ImmutableMap.of(1.0, 2.0));
    
    // Replaces all occurrences of "UNKNOWN" with "unnamed" in column "name".
    df.na.replace("name", ImmutableMap.of("UNKNOWN", "unnamed"));
    
    // Replaces all occurrences of "UNKNOWN" with "unnamed" in all string columns.
    df.na.replace("*", ImmutableMap.of("UNKNOWN", "unnamed"));
    col

    name of the column to apply the value replacement. If col is "*", replacement is applied on all string, numeric or boolean columns.

    replacement

    value replacement map. Key and value of replacement map must have the same type, and can only be doubles, strings or booleans. The map value can have nulls.

    Since

    1.3.1

  41. final def synchronized[T0](arg0: ⇒ T0): T0
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  42. def toString(): String
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  43. final def wait(arg0: Long, arg1: Int): Unit
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    @throws( ... )
  44. final def wait(arg0: Long): Unit
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    @throws( ... ) @native()
  45. final def wait(): Unit
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    @throws( ... )

Deprecated Value Members

  1. def finalize(): Unit
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    @throws( classOf[java.lang.Throwable] ) @Deprecated @deprecated
    Deprecated

    (Since version ) see corresponding Javadoc for more information.

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