python - Pyspark: how to duplicate a row n time in dataframe?

Python - Pyspark: how to duplicate a row n time in dataframe?

In PySpark, you can duplicate a row n times in a DataFrame using the union method in combination with the range function. Here's an example:

from pyspark.sql import SparkSession from pyspark.sql.functions import expr # Create a Spark session spark = SparkSession.builder.appName("DuplicateRows").getOrCreate() # Sample DataFrame data = [("Alice", 25), ("Bob", 30), ("Charlie", 22)] columns = ["Name", "Age"] df = spark.createDataFrame(data, columns) # Number of times to duplicate each row n = 3 # Duplicate rows n times using union and range duplicated_df = df.withColumn("duplicate", expr(f"explode(array_repeat(1, {n}))")).drop("duplicate") # Display the original and duplicated DataFrames print("Original DataFrame:") df.show() print(f"\nDataFrame after duplicating each row {n} times:") duplicated_df.show() # Stop the Spark session spark.stop()

In this example:

  1. We use the array_repeat function from the pyspark.sql.functions module to create an array of size n filled with 1s.
  2. We use the explode function to create n copies of each row by exploding the array.
  3. The resulting DataFrame duplicated_df contains each original row duplicated n times.

Adjust the n variable according to the number of times you want to duplicate each row. The array_repeat function and explode function are key to achieving this duplication.

Keep in mind that duplicating rows in this way can lead to a substantial increase in the size of your DataFrame, so use it judiciously based on your specific requirements.

Examples

  1. Pyspark DataFrame duplicate row n times:

    from pyspark.sql import SparkSession from pyspark.sql.functions import explode spark = SparkSession.builder.appName("DuplicateRows").getOrCreate() df = spark.createDataFrame([(1, "data")], ["col1", "col2"]) n = 3 duplicated_df = df.withColumn("dummy", explode(array([lit(1)] * n))).drop("dummy")

    Description: Uses the explode function to duplicate a row n times by creating an array of n ones and then exploding it.

  2. Pyspark DataFrame replicate row multiple times:

    from pyspark.sql import SparkSession from pyspark.sql.functions import monotonically_increasing_id spark = SparkSession.builder.appName("DuplicateRows").getOrCreate() df = spark.createDataFrame([(1, "data")], ["col1", "col2"]) n = 3 duplicated_df = df.crossJoin(range(n)).withColumnRenamed("col1", "new_col1").drop("id")

    Description: Uses crossJoin to replicate a row multiple times by creating a DataFrame with n duplicates.

  3. Pyspark duplicate rows in DataFrame:

    from pyspark.sql import SparkSession spark = SparkSession.builder.appName("DuplicateRows").getOrCreate() df = spark.createDataFrame([(1, "data")], ["col1", "col2"]) n = 3 duplicated_df = df.union(*([df] * (n - 1)))

    Description: Uses the union operation to duplicate rows by concatenating the DataFrame with itself n-1 times.

  4. Pyspark replicate row n times with RDD:

    from pyspark.sql import SparkSession spark = SparkSession.builder.appName("DuplicateRows").getOrCreate() df = spark.createDataFrame([(1, "data")], ["col1", "col2"]) n = 3 rdd = df.rdd.flatMap(lambda x: [x] * n) duplicated_df = spark.createDataFrame(rdd, df.schema)

    Description: Uses RDD to replicate a row n times by flatMapping each row to a list of n identical rows.

  5. Pyspark DataFrame duplicate rows using crossJoin:

    from pyspark.sql import SparkSession spark = SparkSession.builder.appName("DuplicateRows").getOrCreate() df = spark.createDataFrame([(1, "data")], ["col1", "col2"]) n = 3 duplicated_df = df.crossJoin(range(n)).drop("id")

    Description: Utilizes crossJoin with a range to replicate rows n times.

  6. Pyspark DataFrame duplicate row with explode:

    from pyspark.sql import SparkSession from pyspark.sql.functions import explode spark = SparkSession.builder.appName("DuplicateRows").getOrCreate() df = spark.createDataFrame([(1, "data")], ["col1", "col2"]) n = 3 duplicated_df = df.withColumn("dummy", explode(array([lit(1)] * n))).drop("dummy")

    Description: Similar to query 1, uses explode to duplicate a row n times.

  7. Pyspark DataFrame repeat row n times:

    from pyspark.sql import SparkSession spark = SparkSession.builder.appName("DuplicateRows").getOrCreate() df = spark.createDataFrame([(1, "data")], ["col1", "col2"]) n = 3 duplicated_df = df.crossJoin(range(n))

    Description: Uses crossJoin with a range to replicate rows n times.

  8. Pyspark DataFrame duplicate row with array_repeat:

    from pyspark.sql import SparkSession from pyspark.sql.functions import array_repeat spark = SparkSession.builder.appName("DuplicateRows").getOrCreate() df = spark.createDataFrame([(1, "data")], ["col1", "col2"]) n = 3 duplicated_df = df.withColumn("dummy", array_repeat(df["col1"], n)).drop("dummy")

    Description: Utilizes array_repeat to duplicate a column n times and create a new DataFrame.

  9. Pyspark replicate row with broadcast join:

    from pyspark.sql import SparkSession from pyspark.sql.functions import broadcast spark = SparkSession.builder.appName("DuplicateRows").getOrCreate() df = spark.createDataFrame([(1, "data")], ["col1", "col2"]) n = 3 duplicated_df = df.join(broadcast(spark.range(n)), how="cross").drop("id")

    Description: Uses a broadcast join to replicate rows n times.

  10. Pyspark DataFrame duplicate rows with selectExpr:

    from pyspark.sql import SparkSession spark = SparkSession.builder.appName("DuplicateRows").getOrCreate() df = spark.createDataFrame([(1, "data")], ["col1", "col2"]) n = 3 duplicated_df = df.selectExpr(f"stack({n}, {', '.join([f'col1 as new_col1, col2 as new_col2']*n)})")

    Description: Uses selectExpr with the stack function to duplicate rows n times.

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