groupby#

Dataset.groupby(key: str | List[str] | None, num_partitions: int | None = None) → GroupedData[source]#

Group rows of a Dataset according to a column.

Use this method to transform data based on a categorical variable.

Tip

Grouping hash-shuffles the dataset by key. If the dataset is much larger than the cluster’s aggregate object-store memory, enable disk-based shuffle by setting ray.data.DataContext.get_current().use_disk_based_hash_shuffle = True or the environment variable RAY_DATA_ENABLE_DISK_SHUFFLE=1.

Note

This operation requires all inputs to be materialized in object store for it to execute.

Examples

>>> import pandas as pd
>>> import ray
>>> def normalize_variety(group: pd.DataFrame) -> pd.DataFrame:
...     for feature in group.drop(columns=["variety"]).columns:
...         group[feature] = group[feature] / group[feature].abs().max()
...     return group
>>> ds = (
...     ray.data.read_parquet("s3://anonymous@ray-example-data/iris.parquet")
...     .groupby("variety")
...     .map_groups(normalize_variety, batch_format="pandas")
... )

Time complexity: O(dataset size * log(dataset size / parallelism))

Parameters:

key (str | List[str] | None) – A column name or list of column names. If this is None, place all rows in a single group.
num_partitions (int | None) – Number of partitions data will be partitioned into (only relevant if hash-shuffling strategy is used). When not set defaults to DataContext.min_parallelism.

Returns:

A lazy GroupedData.

Return type:

GroupedData