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Standard Time (SDT) and Daylight Saving Time (DST) offsets from UTC in hours and minutes. For zones in which Daylight Saving is not observed, the DST offset shown in this table is a simple duplication of the SDT offset.
Pandas (styled as pandas) is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series .
ISO 8601 is an international standard covering the worldwide exchange and communication of date and time-related data.It is maintained by the International Organization for Standardization (ISO) and was first published in 1988, with updates in 1991, 2000, 2004, and 2019, and an amendment in 2022. [1]
Hierarchical Data Format (HDF) is a set of file formats (HDF4, HDF5) designed to store and organize large amounts of data.Originally developed at the U.S. National Center for Supercomputing Applications, it is supported by The HDF Group, a non-profit corporation whose mission is to ensure continued development of HDF5 technologies and the continued accessibility of data stored in HDF.
The leap year problem (also known as the leap year bug or the leap day bug) is a problem for both digital (computer-related) and non-digital documentation and data storage situations which results from errors in the calculation of which years are leap years, or from manipulating dates without regard to the difference between leap years and common years.
SciPy (pronounced / ˈ s aɪ p aɪ / "sigh pie" [2]) is a free and open-source Python library used for scientific computing and technical computing. [3]SciPy contains modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers and other tasks common in science and engineering.
IronPython is an implementation of the Python programming language targeting the .NET and Mono frameworks. The project is currently maintained by a group of volunteers at GitHub.
An entity–attribute–value model (EAV) is a data model optimized for the space-efficient storage of sparse—or ad-hoc—property or data values, intended for situations where runtime usage patterns are arbitrary, subject to user variation, or otherwise unforeseeable using a fixed design.