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The process of converting a narrow table to wide table is generally referred to as "pivoting" in the context of data transformations. The "pandas" python package provides a "pivot" method which provides for a narrow to wide transformation.
A pivot table is a table of values which are aggregations of groups of individual values from a more extensive table (such as from a database, spreadsheet, or business intelligence program) within one or more discrete categories. The aggregations or summaries of the groups of the individual terms might include sums, averages, counts, or other ...
This can result in the accumulation of a large number of records in a fact table over time. Fact tables are defined as one of three types: Transaction fact tables record facts about a specific event (e.g., sales events) Snapshot fact tables record facts at a given point in time (e.g., account details at month end) Accumulating snapshot tables ...
You don't need to reprocess the fact table if there is a change in the dimension table (e.g. adding additional fields retrospectively which change the time slices, or if one makes a mistake in the dates on the dimension table one can correct them easily). You can introduce bi-temporal dates in the dimension table.
In their book Pivot Table Data Crunching, authors Bill Jelen and Mike Alexander call Pito Salas the "father of pivot tables" and credit the pivot table concept with allowing an analyst to replace fifteen minutes of complicated data table and database functions with "just seconds" of dragging fields into place.
OLAP clients include many spreadsheet programs like Excel, web application, SQL, dashboard tools, etc. Many clients support interactive data exploration where users select dimensions and measures of interest. Some dimensions are used as filters (for slicing and dicing the data) while others are selected as the axes of a pivot table or pivot chart.
It’s not something I can say [I’m proud of]. It’s hard to correct someone when say I was only married four times instead of six. It’s just a little less bad.
SPSS datasets have a two-dimensional table structure, where the rows typically represent cases (such as individuals or households) and the columns represent measurements (such as age, sex, or household income). Only two data types are defined: numeric and text (or "string"). All data processing occurs sequentially case-by-case through the file ...