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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. It is free software released under the three-clause BSD license. [2]
JSON or JavaScript Object Notation, is an open standard format that uses human-readable text to transmit data objects. JSON has been popularized by web services developed utilizing REST principles. Databases such as MongoDB and Couchbase store data natively in JSON format, leveraging the pros of semi-structured data architecture.
The nested set model is a technique for representing nested set collections (also known as trees or hierarchies) in relational databases. It is based on Nested Intervals, that "are immune to hierarchy reorganization problem, and allow answering ancestor path hierarchical queries algorithmically — without accessing the stored hierarchy relation".
Publicly available dynamic nested sampling software packages include: dynesty - a Python implementation of dynamic nested sampling which can be downloaded from GitHub. [15] dyPolyChord: a software package which can be used with Python, C++ and Fortran likelihood and prior distributions. [16] dyPolyChord is available on GitHub.
For complex layouts, rowspan and colspan may be used, but again it is sometimes simpler and more maintainable to use nested tables. Nested tables must start on a new line. In the following example, five different tables are shown nested inside the cells of a sixth, main table. None has any header cells.
algorithm nested_loop_join is for each tuple r in R do for each tuple s in S do if r and s satisfy the join condition then yield tuple <r,s> This algorithm will involve n r *b s + b r block transfers and n r +b r seeks, where b r and b s are number of blocks in relations R and S respectively, and n r is the number of tuples in relation R.
Nested functions can be used for unstructured control flow, by using the return statement for general unstructured control flow.This can be used for finer-grained control than is possible with other built-in features of the language – for example, it can allow early termination of a for loop if break is not available, or early termination of a nested for loop if a multi-level break or ...
Latin hypercube sampling (LHS) is a statistical method for generating a near-random sample of parameter values from a multidimensional distribution.The sampling method is often used to construct computer experiments or for Monte Carlo integration.