python - Split string in a spark dataframe column by regular expressions capturing groups

Python - Split string in a spark dataframe column by regular expressions capturing groups

To split a string in a Spark DataFrame column by regular expressions capturing groups, you can use the split function along with regular expressions. Here's an example:

from pyspark.sql import SparkSession from pyspark.sql.functions import split # Create a Spark session spark = SparkSession.builder.appName("example").getOrCreate() # Sample data data = [("John-Doe",), ("Jane-Smith",), ("Alice-Wonderland",)] columns = ["name"] # Create a DataFrame df = spark.createDataFrame(data, columns) # Split the string using regular expressions and capturing groups split_expr = r'(-)' df_split = df.withColumn("name_split", split(df["name"], split_expr)) # Show the result df_split.show(truncate=False)

In this example, we use the regular expression r'(-)' to split the strings based on the hyphen (-) character, and the capturing group is used to include the delimiter in the result. Adjust the regular expression based on your specific use case.

The result will look like:

+------------------+-------------+ |name |name_split | +------------------+-------------+ |John-Doe |[John, Doe] | |Jane-Smith |[Jane, Smith]| |Alice-Wonderland |[Alice, Wonderland]| +------------------+-------------+

You can access individual elements of the resulting array using indexing, for example, df_split["name_split"][0] to get the first element of the array.

Examples

  1. "PySpark split string by regex capturing groups"

    • Code Implementation:
      from pyspark.sql import SparkSession from pyspark.sql.functions import split spark = SparkSession.builder.appName("example").getOrCreate() data = [("John-Doe",), ("Jane-Smith",)] columns = ["name"] df = spark.createDataFrame(data, columns) # Split the "name" column using a regex with capturing groups df_split = df.withColumn("name_parts", split(df["name"], r"[-]")) df_split.show()
    • Description: This code splits the "name" column in a PySpark DataFrame using the regex pattern r"[-]" to capture groups separated by a hyphen.

  2. "PySpark DataFrame split string with regex groups"

    • Code Implementation:
      from pyspark.sql import SparkSession from pyspark.sql.functions import split spark = SparkSession.builder.appName("example").getOrCreate() data = [("John-Doe",), ("Jane-Smith",)] columns = ["name"] df = spark.createDataFrame(data, columns) # Split the "name" column using a regex with capturing groups df_split = df.withColumn("name_parts", split(df["name"], r"[-]")) df_split.show()
    • Description: This code demonstrates splitting a PySpark DataFrame column named "name" using a regex pattern with capturing groups (e.g., r"[-]").

  3. "PySpark regex split column with capturing groups"

    • Code Implementation:
      from pyspark.sql import SparkSession from pyspark.sql.functions import split spark = SparkSession.builder.appName("example").getOrCreate() data = [("John-Doe",), ("Jane-Smith",)] columns = ["name"] df = spark.createDataFrame(data, columns) # Split the "name" column using a regex with capturing groups df_split = df.withColumn("name_parts", split(df["name"], r"[-]")) df_split.show()
    • Description: Use this code to split a PySpark DataFrame column ("name" in this case) using a regex pattern with capturing groups.

  4. "Split string in PySpark DataFrame with regex groups"

    • Code Implementation:
      from pyspark.sql import SparkSession from pyspark.sql.functions import split spark = SparkSession.builder.appName("example").getOrCreate() data = [("John-Doe",), ("Jane-Smith",)] columns = ["name"] df = spark.createDataFrame(data, columns) # Split the "name" column using a regex with capturing groups df_split = df.withColumn("name_parts", split(df["name"], r"[-]")) df_split.show()
    • Description: This code snippet illustrates how to split a PySpark DataFrame column ("name") using a regex pattern with capturing groups.

  5. "PySpark split column by regex with groups"

    • Code Implementation:
      from pyspark.sql import SparkSession from pyspark.sql.functions import split spark = SparkSession.builder.appName("example").getOrCreate() data = [("John-Doe",), ("Jane-Smith",)] columns = ["name"] df = spark.createDataFrame(data, columns) # Split the "name" column using a regex with capturing groups df_split = df.withColumn("name_parts", split(df["name"], r"[-]")) df_split.show()
    • Description: Use this code to split a PySpark DataFrame column ("name" in this case) by a regex pattern with capturing groups.

  6. "PySpark split string with regex and capture groups"

    • Code Implementation:
      from pyspark.sql import SparkSession from pyspark.sql.functions import split spark = SparkSession.builder.appName("example").getOrCreate() data = [("John-Doe",), ("Jane-Smith",)] columns = ["name"] df = spark.createDataFrame(data, columns) # Split the "name" column using a regex with capturing groups df_split = df.withColumn("name_parts", split(df["name"], r"[-]")) df_split.show()
    • Description: This code provides an example of splitting a PySpark DataFrame column ("name") using a regex pattern with capturing groups.

  7. "PySpark regex split string column capture groups"

    • Code Implementation:
      from pyspark.sql import SparkSession from pyspark.sql.functions import split spark = SparkSession.builder.appName("example").getOrCreate() data = [("John-Doe",), ("Jane-Smith",)] columns = ["name"] df = spark.createDataFrame(data, columns) # Split the "name" column using a regex with capturing groups df_split = df.withColumn("name_parts", split(df["name"], r"[-]")) df_split.show()
    • Description: Use this code to split a PySpark DataFrame column ("name") by a regex pattern with capturing groups.

  8. "PySpark split string column by regex with capture groups"

    • Code Implementation:
      from pyspark.sql import SparkSession from pyspark.sql.functions import split spark = SparkSession.builder.appName("example").getOrCreate() data = [("John-Doe",), ("Jane-Smith",)] columns = ["name"] df = spark.createDataFrame(data, columns) # Split the "name" column using a regex with capturing groups df_split = df.withColumn("name_parts", split(df["name"], r"[-]")) df_split.show()
    • Description: This code demonstrates splitting a PySpark DataFrame column ("name") using a regex pattern with capturing groups.

  9. "PySpark DataFrame split string regex groups"

    • Code Implementation:
      from pyspark.sql import SparkSession from pyspark.sql.functions import split spark = SparkSession.builder.appName("example").getOrCreate() data = [("John-Doe",), ("Jane-Smith",)] columns = ["name"] df = spark.createDataFrame(data, columns) # Split the "name" column using a regex with capturing groups df_split = df.withColumn("name_parts", split(df["name"], r"[-]")) df_split.show()
    • Description: Use this code to split a PySpark DataFrame column ("name") by a regex pattern with capturing groups.

  10. "PySpark DataFrame split string regex with groups"

    • Code Implementation:
      from pyspark.sql import SparkSession from pyspark.sql.functions import split spark = SparkSession.builder.appName("example").getOrCreate() data = [("John-Doe",), ("Jane-Smith",)] columns = ["name"] df = spark.createDataFrame(data, columns) # Split the "name" column using a regex with capturing groups df_split = df.withColumn("name_parts", split(df["name"], r"[-]")) df_split.show()
    • Description: This code showcases how to split a PySpark DataFrame column ("name") using a regex pattern with capturing groups.

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