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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]
Orange – A visual programming tool featuring interactive data visualization and methods for statistical data analysis, data mining, and machine learning. Pandas – Python library for data analysis. PAW – FORTRAN/C data analysis framework developed at CERN. R – A programming language and software environment for statistical computing and ...
Mathematica – provides built in tools for text alignment, pattern matching, clustering and semantic analysis. See Wolfram Language, the programming language of Mathematica. MATLAB offers Text Analytics Toolbox for importing text data, converting it to numeric form for use in machine and deep learning, sentiment analysis and classification ...
This is a list of statistical procedures which can be used for the analysis of categorical data, also known as data on the nominal scale and as categorical variables.
In compilers, live variable analysis (or simply liveness analysis) is a classic data-flow analysis to calculate the variables that are live at each point in the program. A variable is live at some point if it holds a value that may be needed in the future, or equivalently if its value may be read before the next time the variable is written to.
Tab-separated values (TSV) is a simple, text-based file format for storing tabular data. [3] Records are separated by newlines, and values within a record are separated by tab characters. The TSV format is thus a delimiter-separated values format, similar to comma-separated values.
A canonical example of a data-flow analysis is reaching definitions. A simple way to perform data-flow analysis of programs is to set up data-flow equations for each node of the control-flow graph and solve them by repeatedly calculating the output from the input locally at each node until the whole system stabilizes, i.e., it reaches a fixpoint.
doc2vec, generates distributed representations of variable-length pieces of texts, such as sentences, paragraphs, or entire documents. [ 14 ] [ 15 ] doc2vec has been implemented in the C , Python and Java / Scala tools (see below), with the Java and Python versions also supporting inference of document embeddings on new, unseen documents.