aggregate_list_items

This is essentially aggregate after “exploding” a column of lists such that each list item has its own row. By default the step produces one row per unique list item, and two columns: the count of how many times each list item was encountered, and a column rows recording the row numbers of the lists in which the element was found ([1,3,7] would mean an item was present in the lists of rows 1, 3 and 7). In addition, predefined functions can be used to add further aggregations of the grouped input dataset.

For example, if a dataset contains texts already separated into lists of individual words, this step will create a new dataset containing one row per word, a column containing each word’s frequency (count) across all texts, and another column of lists indicating in which rows the word was found.

Optionally, if a grouping column is specified using the "by" parameter, otherwise identical items belonging to different groups will be counted separately. If the dataset contains texts in different languages, for example, one may not want to group all occurences of the same word together, irrespective of language. The word “angel” in German signifies a fishing rod, for example, “any” in Catalan means “year”, and the Italian word “burro” means “butter” while in Spanish it refers to “donkey”. Using language as the grouping column would preserve the word in each language as a separate group.

split_column

string

required

Name of column containing the lists to be split and grouped.

[string, null]

Optional grouping column to use for item counting and aggregation.

unique_rows

boolean

Count unique occurences only. Whether to collect in the output column “rows” only the unique rows each item appeared in, or all rows (duplicate row IDs if item appeared more than once in a single row).

rows_as_str

boolean

Row IDs as strings. Output occurrence of items in rows as lists of strings (categorical) rather than lists of row numbers.

aggregations

object

Definition of additional 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 or count: calculate number of rows in group
sum: sum total of values
mean: take mean of values
max: take max of values
min: take min of values
first: take first item found
last: take last item found
unique: collect a list of unique values
n_unique: count the number of unique values
list: collect a list of all values
concatenate: convert all values to text and concatenate them into one long text
concat_lists: concatenate lists in all rows into a single larger list
count_where: number of rows in which the column matches a value, needs parameter value with the value that you want to count
percent_where: percentage of the column where the column matches a value, needs parameter value 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.

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"}
}
}

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}
}

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"}
}
}

Prepare

Analyse

Report