By Robert Johansson
Leverage the numerical and mathematical modules in Python and its commonplace Library in addition to renowned open resource numerical Python applications like NumPy, SciPy, SymPy, Matplotlib, Pandas, and extra to numerically compute recommendations and mathematically version functions in a couple of components like significant info, cloud computing, monetary engineering, enterprise administration and extra.
After studying and utilizing Numerical Python, you've noticeable examples and case experiences from many parts of computing, and received familiarity with uncomplicated computing thoughts reminiscent of array-based and symbolic computing, all-around functional abilities resembling visualisation and numerical dossier I/O, basic computational equipment akin to equation fixing, optimization, interpolation and integration, and domain-specific computational difficulties, corresponding to differential equation fixing, information research, statistical modeling and computer studying.
Python has received common attractiveness as a computing language: it really is these days hired for computing by way of practitioners in such diversified fields as for instance clinical study, engineering, finance, and knowledge analytics. One cause of the recognition of Python is its high-level and easy-to-work-with syntax, which permits the fast improvement and exploratory computing that's required in smooth computational work.
What youll learn
- How to paintings with vectors and matrices utilizing NumPy
- How to paintings with symbolic computing utilizing SymPy
- How to plan and visualize info with Matplotlib
- How to unravel linear and nonlinear equations with SymPy and SciPy
- How to resolve remedy optimization, interpolation, and integration difficulties utilizing SciPy
- How to resolve traditional and partial differential equations with SciPy and FEniCS
- How to accomplish facts research initiatives and remedy statistical issues of Pandas and SciPy
- How to paintings with statistical modeling and computing device studying with statsmodels and scikit-learn
- How to address dossier I/O utilizing HDF5 and different universal dossier codecs for numerical data
- How to optimize Python code utilizing Numba and Cython
Who this ebook is for
Read or Download Numerical Python: A Practical Techniques Approach for Industry PDF
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Numerical Python: A Practical Techniques Approach for Industry by Robert Johansson