Compute evolution
This Compute evolution steps allows you to calculate the change in values over time, both in absolute terms and as a percentage. It's particularly useful for tracking trends and growth rates in your data.
Step parameters
Date column
column(string)*: the date column (must be of typedate
) that will be used as a reference for the computationValue column
column(string)*: the value column that you want to compute the evolution ofCompute evolution vs
string["last year", "last month", "last week", "last day"]*: whether you want to compute evolution versus last year, last month, last week or last dayCompute evolution in
string["absolute value", "percentage"]column(string)*: Chooseabsolute value
for absolute difference orpercentage
for percentage change.Group by
(optional) column(array): Use this option if you need to perform the evolution computation by group of rows. You should do so to make sure every date is unique inside each and every group. See examples 2 and 3 for a concrete illustration.New column name
string (optional): Use this option if you want to give a custom name to the output column. By default, it will be your original column name suffixed with either_EVOL_ABS
or_EVOL_PCT
depending on the kind of evolution that you chose.
Example
Input

Configuration
{
"date_col": "date",
"value_col": "sales",
"evolution_type": "last year",
"evolution_format": "abs",
"index_columns": ["region", "product"]
"new_column_name": ""
}
Output

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