Aggregate¶
group by
Group and aggregate a dataset using any of a number of predefined functions.
After optionally sorting the dataset, it is grouped by the unique values (or combinations of unique values) in one or more columns. Each group's rows are then aggregated using one or more predefined functions. A new dataset is thus created containing one column per selected aggregation function, and one row for each unique group.
Usage¶
The following are the step's expected inputs and outputs and their specific types.
aggregate(ds_in: dataset, {"param": value}) -> (ds_out: dataset)
where the object {"param": value}
is optional in most cases and if present may contain any of the parameters described in the
corresponding section below.
Example¶
Given an online retail dataset products
, where rows represent items with id product_id
, and which have
been added to a shopping basket at time time_added
, we can aggregate these items into a new dataset baskets
containing one row per basket. The following configuration calculates this aggregation, creating a new dataset
with three columns:
products
: a list of all items in a given basket, preserving the order they were addedsize
: the number of items in the baskettotal
: the total value of the basket
aggregate(products, {
"by": "order_id",
"presort": {
"columns": "time_added"
},
"aggregations": {
"product_id": {
"products": {"func": "list"},
"size": {"func": "count"}
},
"item_total": {
"total": {"func": "sum"}}
}
}) -> (baskets)
Inputs¶
ds_in: dataset
A dataset to group and aggregate.
Outputs¶
ds_out: dataset
The result of the aggregation.
Parameters¶
by: string | array[string]
Grouping column(s). The name(s) of column(s) whose unique values define the groups to aggregate.
Example parameter values:
"order_id"
["weekday", "hour"]
presort: object
Pre-aggregation row sorting. Sort the dataset rows before aggregating, e.g. when in a particular aggregation function (such as list
) the
encountered order is important.
Items in presort
columns: null | string | array[string]
The sort column name(s). These column(s) will be used to sort the dataset before aggregating (if multiple, in specified order).
Example parameter values:
"date_added"
["lastname", "firstname"]
ascending: boolean = True
Whether to sort in ascending order (or in descending order if false).
Example parameter values:
-
With a single column for sorting:
"presort": { "columns": "date_added", "ascending": true }
aggregations: object
Definition of desired aggregations. A dictionary mapping original columns to new aggregated columns, specifying an aggregation function for each.
Aggregations are functions that reduce all the values in a particular column of a single group to a single summary value of that group.
E.g. a sum
aggregation of column A calculates a single total by adding up all the values in A belonging to each group.
Possible aggregations functions accepted as func
parameters are:
n
,size
orcount
: calculate number of rows in groupsum
: sum total of valuesmean
: take mean of valuesmax
: take max of valuesmin
: take min of valuesfirst
: take first item foundlast
: take last item foundunique
: collect a list of unique valuesn_unique
: count the number of unique valueslist
: collect a list of all valuesconcatenate
: convert all values to text and concatenate them into one long textconcat_lists
: concatenate lists in all rows into a single larger listcount_where
: number of rows in which the column matches a value, needs parametervalue
with the value that you want to countpercent_where
: percentage of the column where the column matches a value, needs parametervalue
with the value that you want to count
Note that in the case of count_where
and percent_where
an additional value
parameter is required.
Items in aggregations
input_aggregations: object
One item per input column. Each key should be the name of an input column, and each value an object defining one or more aggregations for that column. An individual aggregation consists of the name of a desired output column, mapped to a specific aggregation function. For example:
{
"input_col": {
"output_col": {"func": "sum"}
}
}
Items in input_aggregations
aggregation_func: object
Object defining how to aggregate a single output column. Needs at least the "func"
parameter. If the aggregation function accepts further arguments,
like the "value"
parameter in case of count_where
and percent_where
, these need to be provided also.
For example:
{
"output_col": {"func": "count_where", "value": 2}
}
Items in aggregation_func
func: string
Aggregation function.
Must be one of:
"n"
,
"size"
,
"count"
,
"sum"
,
"mean"
,
"n_unique"
,
"count_where"
,
"percent_where"
,
"concatenate"
,
"max"
,
"min"
,
"first"
,
"last"
,
"concat_lists"
,
"unique"
,
"list"
Example parameter values:
-
Including an aggregation function with additional parameters:
{ "product_id": { "products": {"func": "list"}, "size": {"func": "count"} }, "item_total": { "total": {"func": "sum"}, }, "item_category": { "num_food_items": {"func": "count_where", "value": "food"} } }
drop_nan: boolean = False
Whether to ignore missing values (NaNs) in group columns. If false
(default), missing values (NaNs) will be grouped together in their own group. Otherwise, rows
containing NaNs in the group column will be ignored.
sort_groups: boolean = False
Whether to sort groups by values in the grouping columns. This doesn't affect sorting of rows within groups, which is always maintained (and may depend on the
presort
parameter), but only the ordering amongst groups. If the order of groups is not important,
leaving this off will usually result in faster execution (false
by default) .
force_pandas: boolean = False
Enforce use of Pandas aggregation. Normally, depending on dataset size, the step will automatically switch between Pandas and Dask aggregation, preferring whichever represents a better trade-off between execution-time and memory usage. For very large datasets, Dask is the only viable method, but Dask has limitations when it comes to sorting. For intermediate dataset sizes, and if you need to sort the dataset before aggregation on more than a single column, you can try enforcing the use of Pandas if otherwise you see warning or errors related to sorting.