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Due to Python’s Global Interpreter Lock, local threads provide parallelism only when the computation is primarily non-Python code, which is the case for Pandas DataFrame, Numpy arrays or other Python/C/C++ based projects. Local process A multiprocessing scheduler leverages Python’s concurrent.futures.ProcessPoolExecutor to execute computations.
In languages which support first-class functions and currying, map may be partially applied to lift a function that works on only one value to an element-wise equivalent that works on an entire container; for example, map square is a Haskell function which squares each element of a 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.
When eager evaluation is desirable (primarily when the sequence is finite, as otherwise evaluation will never terminate), one can either convert to a list, or use a parallel construction that creates a list instead of a generator. For example, in Python a generator g can be evaluated to a list l via l = list(g), while in F# the sequence ...
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.
But Dash also works for R, and most recently supports Julia, and while still described a Python framework, Python isn't used for the other languages, "describing Dash as a Python framework misses a key feature of its design: the Python side (the back end/server) of Dash was built to be lightweight and stateless [allowing] multiple back-end ...
The core library is written in C, and provides an application programming interface (API) for C, C++ and two APIs for Fortran applications, one for Fortran 77, and one for Fortran 90. An independent implementation, also developed and maintained by Unidata, is written in 100% Java, which extends the core data model and adds additional functionality.
java.util.Collection class and interface hierarchy Java's java.util.Map class and interface hierarchy. The Java collections framework is a set of classes and interfaces that implement commonly reusable collection data structures. [1] Although referred to as a framework, it works in a manner of a library. The collections framework provides both ...