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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.
Dask is an open-source Python library for parallel computing.Dask [1] scales Python code from multi-core local machines to large distributed clusters in the cloud. Dask provides a familiar user interface by mirroring the APIs of other libraries in the PyData ecosystem including: Pandas, scikit-learn and NumPy.
Data loading, or simply loading, is a part of data processing where data is moved between two systems so that it ends up in a staging area on the target system. With the traditional extract, transform and load (ETL) method, the load job is the last step, and the data that is loaded has already been transformed.
In practice, if the main database is being frequently searched, the aux-aux index and much of the aux index may reside in a disk cache, so they would not incur a disk read. The B-tree remains the standard index implementation in almost all relational databases , and many nonrelational databases use them too.
Extract, transform, load (ETL) is a three-phase computing process where data is extracted from an input source, transformed (including cleaning), and loaded into an output data container. The data can be collected from one or more sources and it can also be output to one or more destinations.
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Index is a full index so data file does not have to be ordered; Pros and cons versatile data structure – sequential as well as random access; access is fast; supports exact, range, part key and pattern matches efficiently. volatile files are handled efficiently because index is dynamic – expands and contracts as table grows and shrinks
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