scala - Create new column in Spark DataFrame with diff of previous values from another column

Scala - Create new column in Spark DataFrame with diff of previous values from another column

In Spark DataFrame, you can create a new column that contains the difference between the current value and the previous value of another column using the lag function. Here's how you can do it in Scala:

import org.apache.spark.sql.expressions.Window import org.apache.spark.sql.functions._ // Sample DataFrame val df = Seq((1, 10), (2, 15), (3, 25), (4, 20), (5, 30)).toDF("id", "value") // Define a window specification val windowSpec = Window.orderBy("id") // Create a new column with the difference between the current and previous value val result = df.withColumn("diff", col("value") - lag("value", 1).over(windowSpec)) result.show()

Output:

+---+-----+----+ | id|value|diff| +---+-----+----+ | 1| 10|null| | 2| 15| 5| | 3| 25| 10| | 4| 20| -5| | 5| 30| 10| +---+-----+----+

In this example:

  • We use the lag function with a window specification to get the previous value of the "value" column.
  • The Window.orderBy("id") defines a window specification where rows are ordered by the "id" column.
  • We subtract the previous value from the current value to calculate the difference and create a new column named "diff".
  • For the first row, where there is no previous value, the difference is null.
  • The resulting DataFrame result contains the original columns along with the new "diff" column.

Examples

  1. "scala spark dataframe lag column"

    • Description: Users may search for how to create a new column in a Spark DataFrame that contains the difference between consecutive values from another column.
    // Adding a new column with the difference between current and previous values import org.apache.spark.sql.expressions.Window import org.apache.spark.sql.functions._ val windowSpec = Window.orderBy("some_column") val dfWithDiff = df.withColumn("diff_column", col("some_column") - lag("some_column", 1).over(windowSpec))

  2. "scala spark dataframe previous value"

    • Description: This query focuses on retrieving the previous value from a specific column in a Spark DataFrame to compute the difference with the current value.
    // Obtaining the previous value from a column in Spark DataFrame import org.apache.spark.sql.expressions.Window import org.apache.spark.sql.functions._ val windowSpec = Window.orderBy("some_column") val prevValue = lag("some_column", 1).over(windowSpec)

  3. "scala spark dataframe calculate difference"

    • Description: Users may want to calculate the difference between consecutive values in a column of a Spark DataFrame for various analytical purposes.
    // Calculating the difference between consecutive values in a column import org.apache.spark.sql.expressions.Window import org.apache.spark.sql.functions._ val windowSpec = Window.orderBy("some_column") val diffColumn = col("some_column") - lag("some_column", 1).over(windowSpec)

  4. "scala spark dataframe lag function"

    • Description: Users search for information on the lag function in Spark DataFrame API, which is used to access the value of a column in the previous row.
    // Using the lag function to access previous row's value import org.apache.spark.sql.functions._ val prevValue = lag("some_column", 1)

  5. "scala spark dataframe withColumn"

    • Description: Users may want to add a new column to a Spark DataFrame using the withColumn function, particularly for calculating differences between values.
    // Adding a new column to Spark DataFrame import org.apache.spark.sql.functions._ val dfWithNewColumn = df.withColumn("new_column", someTransformationFunction(col("some_column")))

  6. "scala spark dataframe window function"

    • Description: Users may search for information on window functions in Spark DataFrame API, which are used for performing calculations over a sliding window of data.
    // Using window functions in Spark DataFrame import org.apache.spark.sql.expressions.Window import org.apache.spark.sql.functions._ val windowSpec = Window.orderBy("some_column")

  7. "scala spark dataframe difference between rows"

    • Description: Users may seek methods to compute the difference between consecutive rows in a column of a Spark DataFrame, which is useful for various analytical tasks.
    // Computing the difference between consecutive rows in a column import org.apache.spark.sql.expressions.Window import org.apache.spark.sql.functions._ val windowSpec = Window.orderBy("some_column") val diffColumn = col("some_column") - lag("some_column", 1).over(windowSpec)

  8. "scala spark dataframe sliding window"

    • Description: Users may want to implement sliding window operations in Spark DataFrame, which involves calculating aggregates or transformations over a specified window of rows.
    // Implementing sliding window operations in Spark DataFrame import org.apache.spark.sql.expressions.Window import org.apache.spark.sql.functions._ val windowSpec = Window.orderBy("some_column").rowsBetween(-1, 0)

  9. "scala spark dataframe lag difference"

    • Description: Users may search for a combination of the lag function and computing differences to achieve their specific analytical requirements.
    // Using lag function and computing differences in Spark DataFrame import org.apache.spark.sql.expressions.Window import org.apache.spark.sql.functions._ val windowSpec = Window.orderBy("some_column") val diffColumn = col("some_column") - lag("some_column", 1).over(windowSpec)

  10. "scala spark dataframe calculate diff from previous"

    • Description: This query reflects users' intention to directly calculate the difference from the previous value in a column of a Spark DataFrame for analytical purposes.
    // Calculating the difference from the previous value in a column import org.apache.spark.sql.expressions.Window import org.apache.spark.sql.functions._ val windowSpec = Window.orderBy("some_column") val diffColumn = col("some_column") - lag("some_column", 1).over(windowSpec)

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