Groupby
-
class
nvtabular.ops.
Groupby
(groupby_cols=None, sort_cols=None, aggs='list', name_sep='_')[source] Bases:
nvtabular.ops.operator.Operator
Groupby Transformation
Locally transform each partition of a Dataset with one or more groupby aggregations.
WARNING: This transformation does NOT move data between partitions. Please make sure that the target Dataset object is already shuffled by
groupby_cols
, otherwise the output may be incorrect. See:Dataset.shuffle_by_keys
.Example usage:
groupby_features = [ 'user_id', 'session_id', 'month', 'prod_id', ] >> ops.Groupby( groupby_cols=['user_id', 'session_id'], sort_cols=['month'], aggs={ 'prod_id': 'list', 'month': ['first', 'last'], }, ) processor = nvtabular.Workflow(groupby_features)
- Parameters
groupby_cols (str or list of str) – The column names to be used as groupby keys.
sort_cols (str or list of str) – Columns to be used to sort each partition before groupby aggregation is performed. If this argument is not specified, the results will not be sorted.
aggs (dict, list or str) – Groupby aggregations to perform. Supported list-based aggregations include “list”, “first” & “last”. Most conventional aggregations supported by Pandas/cuDF are also allowed (e.g. “sum”, “count”, “max”, “mean”, etc.).
name_sep (str) – String separator to use for new column names.
-
transform
(col_selector: nvtabular.columns.selector.ColumnSelector, df: pandas.core.frame.DataFrame) → pandas.core.frame.DataFrame[source] Transform the dataframe by applying this operator to the set of input columns
- Parameters
columns (list of str or list of list of str) – The columns to apply this operator to
df (Dataframe) – A pandas or cudf dataframe that this operator will work on
- Returns
Returns a transformed dataframe for this operator
- Return type
DataFrame
-
compute_output_schema
(input_schema: nvtabular.columns.schema.Schema, col_selector: nvtabular.columns.selector.ColumnSelector) → nvtabular.columns.schema.Schema[source]