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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.
[4]: 114 A DataFrame is a 2-dimensional data structure of rows and columns, similar to a spreadsheet, and analogous to a Python dictionary mapping column names (keys) to Series (values), with each Series sharing an index. [4]: 115 DataFrames can be concatenated together or "merged" on columns or indices in a manner similar to joins in SQL.
Pivot tables are not created automatically. For example, in Microsoft Excel one must first select the entire data in the original table and then go to the Insert tab and select "Pivot Table" (or "Pivot Chart"). The user then has the option of either inserting the pivot table into an existing sheet or creating a new sheet to house the pivot table.
A wide-column store (or extensible record store) is a type of NoSQL database. [1] It uses tables, rows, and columns, but unlike a relational database, the names and format of the columns can vary from row to row in the same table. A wide-column store can be interpreted as a two-dimensional key–value store. [1]
Fact_Sales is the fact table and there are three dimension tables Dim_Date, Dim_Store and Dim_Product. Each dimension table has a primary key on its Id column, relating to one of the columns (viewed as rows in the example schema) of the Fact_Sales table's three-column (compound) primary key (Date_Id, Store_Id, Product_Id).
DAX expressions allow a user to create calculated columns and measures to summarize and aggregate large quantities of data. Queries in the model are reduced to xmSQL, a pseudo-SQL language in the storage engines that drive the data model. [11] A companion feature to Power Pivot named Power Query may be used to perform ETL processes prior to ...
Many data description languages use a declarative syntax to define columns and data types. Structured Query Language (SQL), however, uses a collection of imperative verbs whose effect is to modify the schema of the database by adding, changing, or deleting definitions of tables or other elements.
Then is called a pivotal quantity (or simply a pivot). Pivotal quantities are commonly used for normalization to allow data from different data sets to be compared. It is relatively easy to construct pivots for location and scale parameters: for the former we form differences so that location cancels, for the latter ratios so that scale cancels.