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The library NumPy can be used for manipulating arrays, SciPy for scientific and mathematical analysis, Pandas for analyzing table data, Scikit-learn for various machine learning tasks, NLTK and spaCy for natural language processing, OpenCV for computer vision, and Matplotlib for data visualization. [3]
Matplotlib (portmanteau of MATLAB, plot, and library [3]) is a plotting library for the Python programming language and its numerical mathematics extension NumPy.It provides an object-oriented API for embedding plots into applications using general-purpose GUI toolkits like Tkinter, wxPython, Qt, or GTK.
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]
Numerical computation and simulation with extended 2D/3D visualization. Emphasis on vectorised processing. Maxima: MIT Project MAC and Bill Schelter et al. 1967 1982 5.47.0 31 May 2023: Free GPL: Mainly a computer algebra system: MLAB: Civilized Software, Inc. 1970 (in SAIL), 1985 (in C) 1972 (on DEC-10), 1988 (on PCs), 1993 (on MACs) 2015 2015
Depends on NumPy and Matplotlib: OpendTect [20] Geoscience interpretation and visualization dGB Earth Sciences GPL or custom Cross-platform: C++: Interfaces with GMT Modelgeo [21] General 3D mathematics with modelling and visualization of geoscience data ModelGeo AS Free for non-profit use Windows C++, TCL
scikit-learn (formerly scikits.learn and also known as sklearn) is a free and open-source machine learning library for the Python programming language. [3] It features various classification, regression and clustering algorithms including support-vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific ...
The Mandelbrot set, one of the most famous examples of mathematical visualization. Mathematical phenomena can be understood and explored via visualization. Classically, this consisted of two-dimensional drawings or building three-dimensional models (particularly plaster models in the 19th and early 20th century).
It is also known as Principal Coordinates Analysis (PCoA), Torgerson Scaling or Torgerson–Gower scaling. It takes an input matrix giving dissimilarities between pairs of items and outputs a coordinate matrix whose configuration minimizes a loss function called strain, [2] which is given by (,,...,) = (, (),) /, where denote vectors in N-dimensional space, denotes the scalar product between ...