By Leo (Liang-Huan) Chin,Tanmay Dutta
- Optimize your Python scripts with strong NumPy modules
- Explore the great possibilities to construct impressive medical/ analytical modules via yourself
- Packed with wealthy examples that can assist you grasp NumPy arrays and common functions
In modern-day international of technological know-how and expertise, it is all approximately pace and adaptability. by way of clinical computing, NumPy tops the checklist. NumPy delivers either the rate and excessive productiveness you need.
This e-book will stroll you thru NumPy utilizing transparent, step by step examples and simply the correct amount of thought. we are going to consultant you thru wider purposes of NumPy in medical computing and should then specialize in the basics of NumPy, together with array gadgets, services, and matrices, every one of them defined with sensible examples.
You will then find out about diversified NumPy modules whereas acting mathematical operations equivalent to calculating the Fourier rework; fixing linear structures of equations, interpolation, extrapolation, regression, and curve becoming; and comparing integrals and derivatives. we are going to additionally introduce you to utilizing Cython with NumPy arrays and writing extension modules for NumPy code utilizing the C API. This ebook provides you with publicity to the large NumPy library and assist you construct effective, high-speed courses utilizing quite a lot of mathematical features.
What you'll learn
- Manipulate the main attributes and common capabilities of NumPy
- Utilize matrix and mathematical computation utilizing linear algebra modules
- Implement regression and curve becoming for models
- Perform time frequency / spectral density research utilizing the Fourier rework modules
- Collate with the distutils and setuptools modules utilized by different Python libraries
- Establish Cython with NumPy arrays
- Write extension modules for NumPy code utilizing the C API
- Build refined information constructions utilizing NumPy array with libraries corresponding to Panda and Scikits
About the Author
Leo (Liang-Huan) Chin is a knowledge engineer with greater than five years of expertise within the box of Python. He works for Gogoro clever scooter, Taiwan, the place his activity includes learning new and engaging cycling styles . His prior paintings event contains ESRI, California, united states, which eager about spatial-temporal info mining. He loves information, analytics, and the tales at the back of facts and analytics. He acquired an MA measure of GIS in geography from nation college of latest York, Buffalo. whilst Leo isn't really glued to a working laptop or computer reveal, he spends time on images, touring, and exploring a few amazing eating places internationally. you could achieve Leo at http://chinleock.github.io/portfolio/.
Tanmay Dutta is a pro programmer with services in programming languages similar to Python, Erlang, C++, Haskell, and F#. He has vast adventure in constructing numerical libraries and frameworks for funding banking companies. He used to be additionally instrumental within the layout and improvement of a possibility framework in Python (pandas, NumPy, and Django) for a wealth fund in Singapore. Tanmay has a master's measure in monetary engineering from Nanyang Technological collage, Singapore, and a certification in computational finance from Tepper company university, Carnegie Mellon University.
Table of Contents
- An creation to NumPy
- The NumPy ndarray Object
- Using NumPy Arrays
- NumPy center and Libs Submodules
- Linear Algebra in NumPy
- Fourier research in NumPy
- Building and allotting NumPy Code
- Speeding Up NumPy with Cython
- Introduction to the NumPy C-API
- Further Reading