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Dask Bag [15] is an unordered collection of repeated objects, a hybrid between a set and a list. Dask Bag is used to parallelize computation of semi-structured or unstructured data, such as JSON records, text data, log files or user-defined Python objects using operations such as filter, fold, map and groupby.
Map functions can be and often are defined in terms of a fold such as foldr, which means one can do a map-fold fusion: foldr f z . map g is equivalent to foldr (f . g) z . The implementation of map above on singly linked lists is not tail-recursive , so it may build up a lot of frames on the stack when called with a large list.
[4]: 114 A DataFrame is a 2-dimensional data structure of rows and columns, similar to a spreadsheet, and analogous to a Python dictionary mapping column names (keys) to Series (values), with each Series sharing an index. [4]: 115 DataFrames can be concatenated together or "merged" on columns or indices in a manner similar to joins in SQL.
Spark Core is the foundation of the overall project. It provides distributed task dispatching, scheduling, and basic I/O functionalities, exposed through an application programming interface (for Java, Python, Scala, .NET [16] and R) centered on the RDD abstraction (the Java API is available for other JVM languages, but is also usable for some other non-JVM languages that can connect to the ...
Apache OpenJPA, open-source for Java; DataNucleus, open-source JDO and JPA implementation (formerly known as JPOX) Ebean, open-source ORM framework; EclipseLink, Eclipse persistence platform; Enterprise JavaBeans (EJB) Enterprise Objects Framework, Mac OS X/Java, part of Apple WebObjects; Hibernate, open-source ORM framework, widely used; Java ...
This is the case for tree-based implementations, one representative being the <map> container of C++. [16] The order of enumeration is key-independent and is instead based on the order of insertion. This is the case for the "ordered dictionary" in .NET Framework, the LinkedHashMap of Java and Python. [17] [18] [19] The latter is more common.
In Python, a generator can be thought of as an iterator that contains a frozen stack frame. Whenever next() is called on the iterator, Python resumes the frozen frame, which executes normally until the next yield statement is reached. The generator's frame is then frozen again, and the yielded value is returned to the caller.
Each node of a ternary search tree stores a single character, an object (or a pointer to an object depending on implementation), and pointers to its three children conventionally named equal kid, lo kid and hi kid, which can also be referred respectively as middle (child), lower (child) and higher (child). [1]