class_docstr: Represents a grouped dataset created by calling ``Dataset.groupby()``.
  
  The actual groupby is deferred until an aggregation is applied.
class_docstr_tok: ["Represents","a","grouped","dataset","created","by","calling","``","Dataset.groupby","(",")","``",".","The","actual","groupby","is","deferred","until","an","aggregation","is","applied","."]
docstr: Compute count aggregation.
  
  Examples:
      >>> import ray
      >>> ray.data.from_items([ # doctest: +SKIP
      ...     {"A": x % 3, "B": x} for x in range(100)]).groupby( # doctest: +SKIP
      ...     "A").count() # doctest: +SKIP
  
  Returns:
      A dataset of ``[k, v]`` columns where ``k`` is the groupby key and
      ``v`` is the number of rows with that key.
      If groupby key is ``None`` then the key part of return is omitted.
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code: def count(self) -> Dataset:
      """Compute count aggregation.
  
      Examples:
          >>> import ray
          >>> ray.data.from_items([ # doctest: +SKIP
          ...     {"A": x % 3, "B": x} for x in range(100)]).groupby( # doctest: +SKIP
          ...     "A").count() # doctest: +SKIP
  
      Returns:
          A dataset of ``[k, v]`` columns where ``k`` is the groupby key and
          ``v`` is the number of rows with that key.
          If groupby key is ``None`` then the key part of return is omitted.
      """
      return self.aggregate(Count())
  
imports: from typing import Any, Dict, Iterable, List, Optional, Tuple, Union
  from ._internal.table_block import TableBlockAccessor
  from ray.data._internal import sort
  from ray.data._internal.compute import ComputeStrategy
  from ray.data._internal.delegating_block_builder import DelegatingBlockBuilder
  from ray.data._internal.execution.interfaces import TaskContext
  from ray.data._internal.logical.interfaces import LogicalPlan
  from ray.data._internal.logical.operators.all_to_all_operator import Aggregate
  from ray.data._internal.plan import AllToAllStage
  from ray.data._internal.push_based_shuffle import PushBasedShufflePlan
  from ray.data._internal.shuffle import ShuffleOp, SimpleShufflePlan
  from ray.data._internal.sort import SortKey
  from ray.data.aggregate import AggregateFn, Count, Max, Mean, Min, Std, Sum
  from ray.data.aggregate._aggregate import _AggregateOnKeyBase
  from ray.data.block import Block, BlockAccessor, BlockExecStats, BlockMetadata, KeyType, UserDefinedFunction
  from ray.data.context import DataContext
  from ray.data.dataset import DataBatch, Dataset
  from ray.util.annotations import PublicAPI
label_desc: Use image node_id 6 for calling the GroupedData obj's underlying member method code with example usage: obj.count() and returns: self