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Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Numeric Computing with Python, 2nd edition\n### [Original ebook on Apress](https://www.apress.com/us/book/9781484242452)\n![book-cover](pics/bookcover-2nd-ed.png)\n\n### TODO:\n- Fix FeniCS installation bugs (Chapter 11)\n\n\u003ctable\u003e\n  \u003ctr\u003e\n    \u003cth\u003eChapter\u003c/th\u003e\u003cth\u003eTopics\u003c/th\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003eIntro\u003c/td\u003e\n    \u003ctd\u003ePython, iPython, Jupyter\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003eIntro to NumPy\u003c/td\u003e\n    \u003ctd\u003eArrays: Create, Index, Slice, Reshape, Resize, Vectorized ops, Matrix \u0026 Vector ops\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003eIntro to SymPy\u003c/td\u003e\n    \u003ctd\u003eSymbols, Expressions, Manipulations, Calculus, Equations, Linear Algebra\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003ePlotting \u0026 Visualization\u003cbr/\u003eIntro to Matplotlib\u003c/td\u003e\n    \u003ctd\u003ePlots, Steps, Bargraphs, Histograms, ErrorBars, ScatterPlots, Fill_Between, QuiverPlots, ColorMaps, 3D Plots\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003eEquation Solvers with SciPy\u003c/td\u003e\n    \u003ctd\u003eLinear Equations (square, rectangular), Eigenvalues, NonLinear Equations\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003eOptimization with SciPy\u003c/td\u003e\n    \u003ctd\u003eUnivariate, Multivariate (unconstrained), Nonlinear Least Squares, Constrained\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003eInterpolation with SciPy\u003c/td\u003e\n    \u003ctd\u003ePolynomials, Splines, Multivariate\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003eIntegration with SciPy \u0026 Scikit-monaco\u003c/td\u003e\n    \u003ctd\u003eNumerical Methods, Multiple integration, Symbolic \u0026 arbitrary precision, Integral transforms\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003eOrdinary Differential Equations (ODEs)\u003c/td\u003e\n    \u003ctd\u003eDirection fields, Laplace transforms, Numerical methods\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003eSparse Matrices \u0026 Graphs\u003c/td\u003e\n    \u003ctd\u003eMatrix ops, Linear equation systems incl. Eigenvalues, Graphs/Networks\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003ePartial Differential Equations (PDEs)\u003c/td\u003e\n    \u003ctd\u003eFinite-difference methods, Finite element methods \u0026 libraries, PDE solvers with FEniCS\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003eData Analysis with Pandas \u0026 Seaborn\u003c/td\u003e\n    \u003ctd\u003eSeries, DataFrames, TimeSeries\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003eStatistics with SciPy \u0026 NumPy\u003c/td\u003e\n    \u003ctd\u003eProbability, Random numbers, Distributions, Hypothesis testing, Nonparametric methods\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003eStatistical Modeling with Stasmodels \u0026 Patsy\u003c/td\u003e\n    \u003ctd\u003eModel definitions, Linear regression, Logistic regression, Poisson models, Time series\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003eIntro to Machine Learning with scikit-learn\u003c/td\u003e\n    \u003ctd\u003eConcepts, Regression, Classification, Clustering\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003eBayesian Statistics with pyMC\u003c/td\u003e\n    \u003ctd\u003eConcepts, Sampling posterior distributions, Linear regression\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003eSignal Processing with SciPy, fftpack, signal, wavfile \u0026 io\u003c/td\u003e\n    \u003ctd\u003eSpectral analysis (Fourier transforms, windows, spectrograms), Signal filters (Convolution, FIR/IIR filters)\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003eData I/O\u003c/td\u003e\n    \u003ctd\u003eCSV, HDF5 (h5py files, groups, datasets, attributes, PyTables, HDFStore), JSON, Serialization\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003eCode optimization\u003c/td\u003e\n    \u003ctd\u003eNumba, Cython\u003c/td\u003e\n  \u003c/tr\u003e\n\u003c/table\u003e\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbjpcjp%2Fnumeric-python","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbjpcjp%2Fnumeric-python","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbjpcjp%2Fnumeric-python/lists"}