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A lazy copy is an implementation of a deep copy. When initially copying an object, a (fast) shallow copy is used. A counter is also used to track how many objects share the data. When the program wants to modify an object, it can determine if the data is shared (by examining the counter) and can do a deep copy if needed.
The pandas package in Python implements this operation as "melt" function which converts a wide table to a narrow one. The process of converting a narrow table to wide table is generally referred to as "pivoting" in the context of data transformations.
A column can be part of a ColumnFamily that resembles at most a relational row, but it may appear in one row and not in the others. Also, the number of columns may change from row to row, and new updates to the data store model may also modify the column number. So, all the work of keeping up with changes relies on the application programmer.
Word2vec is a group of related models that are used to produce word embeddings.These models are shallow, two-layer neural networks that are trained to reconstruct linguistic contexts of words.
The default implementation of Object.clone() performs a shallow copy. When a class desires a deep copy or some other custom behavior, they must implement that in their own clone() method after they obtain the copy from the superclass. The syntax for calling clone in Java is (assuming obj is a variable of a class type that has a public clone ...
Data structure alignment is the way data is arranged and accessed in computer memory.It consists of three separate but related issues: data alignment, data structure padding, and packing.
Zero-copy programming techniques can be used when exchanging data within a user space process (i.e. between two or more threads, etc.) and/or between two or more processes (see also producer–consumer problem) and/or when data has to be accessed / copied / moved inside kernel space or between a user space process and kernel space portions of operating systems (OS).
The Pandas and Polars Python libraries implement the Pearson correlation coefficient calculation as the default option for the methods pandas.DataFrame.corr and polars.corr, respectively. Wolfram Mathematica via the Correlation function, or (with the P value) with CorrelationTest. The Boost C++ library via the correlation_coefficient function.