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Python is a high-level, general-purpose programming language. Its design philosophy emphasizes code readability with the use of significant indentation. [33] Python is dynamically type-checked and garbage-collected. It supports multiple programming paradigms, including structured (particularly procedural), object-oriented and functional ...
With languages that feature dynamic dispatch (i.e. Java or C++), [5] first-class functions (i.e. Python or Racket), or function pointers (i.e. C), computing a static call graph precisely requires alias analysis results. Conversely, computing precise aliasing requires a call graph.
Data mining is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. [4]
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.
Data-flow analysis is a technique for gathering information about the possible set of values calculated at various points in a computer program.A program's control-flow graph (CFG) is used to determine those parts of a program to which a particular value assigned to a variable might propagate.
Exploratory data analysis is an analysis technique to analyze and investigate the data set and summarize the main characteristics of the dataset. Main advantage of EDA is providing the data visualization of data after conducting the analysis.
It supports many binary instrument data formats and has its own vectorized programming language. IGOR Pro, a software package with emphasis on time series, image analysis, and curve fitting. It comes with its own programming language and can be used interactively. LabPlot is a data analysis and visualization application built on the KDE Platform.
Functional data analysis (FDA) is a branch of statistics that analyses data providing information about curves, surfaces or anything else varying over a continuum. In its most general form, under an FDA framework, each sample element of functional data is considered to be a random function.