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Pandas is built around data structures called Series and DataFrames. Data for these collections can be imported from various file formats such as comma-separated values, JSON, Parquet, SQL database tables or queries, and Microsoft Excel. [8] A Series is a 1-dimensional data structure built on top of NumPy's array.
Dataframe may refer to: A tabular data structure common to many data processing libraries: pandas (software) § DataFrames; The Dataframe API in Apache Spark; Data frames in the R programming language; Frame (networking)
OptimJ is a mathematical Java-based modeling language for describing and solving high-complexity problems for large-scale optimization. Perl Data Language, [22] [23] also known as PDL, an array extension to Perl ver.5, used for data manipulation, statistics, numerical simulation and visualization.
Function rank is an important concept to array programming languages in general, by analogy to tensor rank in mathematics: functions that operate on data may be classified by the number of dimensions they act on. Ordinary multiplication, for example, is a scalar ranked function because it operates on zero-dimensional data (individual numbers).
NumPy (pronounced / ˈ n ʌ m p aɪ / NUM-py) is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays. [3]
Stack-based algorithms manipulate data by popping data from and pushing data to the stack. Operators govern how the stack manipulates data. To emphasize the effect of a statement, a comment is often used showing the top of the stack before and after the statement; this is known as the stack effect diagram.
Programming languages and libraries suited to work with tabular data contain functions that allow the creation and manipulation of pivot tables. Python data analysis toolkit pandas has the function pivot_table [ 16 ] and the xs method useful to obtain sections of pivot tables.
Data-driven programming is similar to event-driven programming, in that both are structured as pattern matching and resulting processing, and are usually implemented by a main loop, though they are typically applied to different domains.