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Pandas is built around data structures called Series and DataFrames. Data for these collections can be imported from various file formats such as comma-separated values, JSON, Parquet, SQL database tables or queries, and Microsoft Excel. [8] A Series is a 1-dimensional data structure built on top of NumPy's array.
Bitemporal modeling is a specific case of temporal database information modeling technique designed to handle historical data along two different timelines. [1] This makes it possible to rewind the information to "as it actually was" in combination with "as it was recorded" at some point in time.
Later it was used to refer to a subset of Structured Query Language (SQL) for declaring tables, columns, data types and constraints. SQL-92 introduced a schema manipulation language and schema information tables to query schemas. [2] These information tables were specified as SQL/Schemata in SQL:2003.
With the development of SQL and its attendant use in real-life applications, database users realized that when they added date columns to key fields, some issues arose. For example, if a table has a primary key and some attributes, adding a date to the primary key to track historical changes can lead to creation of more rows than intended.
A view is a relational table, and the relational model defines a table as a set of rows. Since sets are not ordered — by definition — neither are the rows of a view. Therefore, an ORDER BY clause in the view definition is meaningless; the SQL standard ( SQL:2003 ) does not allow an ORDER BY clause in the subquery of a CREATE VIEW command ...
{{Death-date and age}} displays a person's date of death and age at that date. Besides calculating the age at death, the benefit of using this template is to allow for the inclusion of hidden microformat dates, which may be indexed or searched by software tools.
This table is in 4NF, but the Supplier ID is equal to the join of its projections: {{Supplier ID, Title}, {Title, Franchisee ID}, {Franchisee ID, Supplier ID}}. No component of that join dependency is a superkey (the sole superkey being the entire heading), so the table does not satisfy the ETNF and can be further decomposed: [12]
This template returns a person's date of death and age at that date. Template parameters [Edit template data] Parameter Description Type Status Year of death 1 The year in which the person died Number required Month of death 2 The month (number) in which the person died Number required Day of death 3 The day (number) in which the person died Number required Year of birth 4 The year in which ...