org.apache.spark.sql

DataFrameNaFunctions

final class DataFrameNaFunctions extends AnyRef

:: Experimental :: Functionality for working with missing data in DataFrames.

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@Experimental()
Source
DataFrameNaFunctions.scala
Since

1.3.1

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  1. final def !=(arg0: AnyRef): Boolean

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  2. final def !=(arg0: Any): Boolean

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  3. final def ##(): Int

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  4. final def ==(arg0: AnyRef): Boolean

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  5. final def ==(arg0: Any): Boolean

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  6. final def asInstanceOf[T0]: T0

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  7. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
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    @throws( ... )

  8. 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

  9. 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

  10. 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

  11. 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

  12. 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

  13. 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

  14. 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

  15. 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

  16. 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

  17. final def eq(arg0: AnyRef): Boolean

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  18. def equals(arg0: Any): Boolean

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  19. 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.

    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

  20. 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.

    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

  21. 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

  22. 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

  23. 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

  24. 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

  25. 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

  26. 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

  27. def finalize(): Unit

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  28. final def getClass(): Class[_]

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  29. def hashCode(): Int

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  30. final def isInstanceOf[T0]: Boolean

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  31. final def ne(arg0: AnyRef): Boolean

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  32. final def notify(): Unit

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  33. final def notifyAll(): Unit

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

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

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

    list of columns to apply the value replacement

    replacement

    value replacement map, as explained above

    Since

    1.3.1

  35. 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. Key and value of replacement map must have the same type, and can only be doubles, strings or booleans. If col is "*", then the replacement is applied on all string columns , numeric columns or boolean columns.

    // Replaces all occurrences of 1.0 with 2.0 in column "height".
df.replace("height", Map(1.0 -> 2.0))

// Replaces all occurrences of "UNKNOWN" with "unnamed" in column "name".
df.replace("name", Map("UNKNOWN" -> "unnamed")

// Replaces all occurrences of "UNKNOWN" with "unnamed" in all string columns.
df.replace("*", Map("UNKNOWN" -> "unnamed")
    col

    name of the column to apply the value replacement

    replacement

    value replacement map, as explained above

    Since

    1.3.1

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

    import com.google.common.collect.ImmutableMap;

// Replaces all occurrences of 1.0 with 2.0 in column "height" and "weight".
df.replace(new String[] {"height", "weight"}, ImmutableMap.of(1.0, 2.0));

// Replaces all occurrences of "UNKNOWN" with "unnamed" in column "firstname" and "lastname".
df.replace(new String[] {"firstname", "lastname"}, ImmutableMap.of("UNKNOWN", "unnamed"));
    cols

    list of columns to apply the value replacement

    replacement

    value replacement map, as explained above

    Since

    1.3.1

  37. 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. Key and value of replacement map must have the same type, and can only be doubles, strings or booleans. If col is "*", then the replacement is applied on all string columns or numeric columns.

    import com.google.common.collect.ImmutableMap;

// Replaces all occurrences of 1.0 with 2.0 in column "height".
df.replace("height", ImmutableMap.of(1.0, 2.0));

// Replaces all occurrences of "UNKNOWN" with "unnamed" in column "name".
df.replace("name", ImmutableMap.of("UNKNOWN", "unnamed"));

// Replaces all occurrences of "UNKNOWN" with "unnamed" in all string columns.
df.replace("*", ImmutableMap.of("UNKNOWN", "unnamed"));
    col

    name of the column to apply the value replacement

    replacement

    value replacement map, as explained above

    Since

    1.3.1

  38. final def synchronized[T0](arg0: ⇒ T0): T0

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  39. def toString(): String

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  40. final def wait(): Unit

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  41. final def wait(arg0: Long, arg1: Int): Unit

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  42. final def wait(arg0: Long): Unit

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