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The data retention policies within an organization are a set of guidelines that describes which data will be archived, how long it will be kept, what happens to the data at the end of the retention period (archive or destroy) and other factors concerning the retention of the data. [6]
By default, a Pandas index is a series of integers ascending from 0, similar to the indices of Python arrays. However, indices can use any NumPy data type, including floating point, timestamps, or strings. [4]: 112 Pandas' syntax for mapping index values to relevant data is the same syntax Python uses to map dictionary keys to values.
Data must remain available in the long term. At present, a growing problem is the time taken to reproduce an archive, for instance following a hardware or system upgrade. Since the sheer volume of archive data continues to grow, new hardware is always required to maintain the archive and so regular migration of data to a new system must be ...
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In computing, a persistent data structure or not ephemeral data structure is a data structure that always preserves the previous version of itself when it is modified. Such data structures are effectively immutable , as their operations do not (visibly) update the structure in-place, but instead always yield a new updated structure.
In contrast, data holdings are collections of gathered data that are informally kept, and not necessarily prepared for long-term preservation. For example, a collection or back-up of personal files. Data holdings are generally the storage methods used in the past when data has been lost due to environmental and other historical disasters. [4]
Python uses the * operator for duplicating a string a specified number of times. The @ infix operator is intended to be used by libraries such as NumPy for matrix multiplication. [104] [105] The syntax :=, called the "walrus operator", was introduced in Python 3.8. It assigns values to variables as part of a larger expression. [106]
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