class_docstr: Helper class for creating an over window. Similar to SQL, over window aggregates compute an
  aggregate for each input row over a range of its neighboring rows.
  
  Over-windows for batch tables are currently not supported.
  
  Example:
  ::
  
      >>> from pyflink.table.expressions import col, UNBOUNDED_RANGE
      >>> Over.partition_by(col("a")) \
      ...     .order_by(col("rowtime")) \
      ...     .preceding(UNBOUNDED_RANGE) \
      ...     .alias("w")
class_docstr_tok: ["Helper","class","for","creating","an","over","window",".","Similar","to","SQL",",","over","window","aggregates","compute","an","aggregate","for","each","input","row","over","a","range","of","its","neighboring","rows",".","Over-windows","for","batch","tables","are","currently","not","supported",".","Example",":",":",":",">",">",">","from","pyflink.table.expressions","import","col",",","UNBOUNDED_RANGE",">",">",">","Over.partition_by","(","col","(","``","a","''",")",")","\\","...",".order_by","(","col","(","``","rowtime","''",")",")","\\","...",".preceding","(","UNBOUNDED_RANGE",")","\\","...",".alias","(","``","w","''",")"]
docstr: Partitions the elements on some partition keys.
  
  Each partition is individually sorted and aggregate functions are applied to each
  partition separately.
  
  :param partition_by: List of field references.
  :return: An over window with defined partitioning.
docstr_tok: ["Partitions","the","elements","on","some","partition","keys",".","Each","partition","is","individually","sorted","and","aggregate","functions","are","applied","to","each","partition","separately",".",":","param","partition_by",":","List","of","field","references",".",":","return",":","An","over","window","with","defined","partitioning","."]
code: def partition_by(
      cls, *partition_by: Expression
  ) -> "OverWindowPartitioned":
      """
      Partitions the elements on some partition keys.
  
      Each partition is individually sorted and aggregate functions are applied to each
      partition separately.
  
      :param partition_by: List of field references.
      :return: An over window with defined partitioning.
      """
      return OverWindowPartitioned(
          get_gateway().jvm.Over.partitionBy(
              to_expression_jarray(partition_by)
          )
      )
  
imports: from py4j.java_gateway import get_method
  from pyflink.java_gateway import get_gateway
  from pyflink.table import Expression
  from pyflink.table.expression import _get_java_expression
  from pyflink.table.utils import to_expression_jarray
label_desc: Use image node_id 2 for calling the Over obj's underlying member method code with example usage: obj.partition_by(cls) and returns: OverWindowPartitioned