Ivan Idris [Ivan Idris] - NumPy: Beginner’s Guide - Third Edition
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Build efficient, high-speed programs using the high-performance NumPy mathematical library
About This Book- Written as a step-by-step guide, this book aims to give you a strong foundation in NumPy and breaks down its complex library features into simple tasks
- Perform high performance calculations with clean and efficient NumPy code
- Analyze large datasets with statistical functions and execute complex linear algebra and mathematical computations
This book is for the scientists, engineers, programmers, or analysts looking for a high-quality, open source mathematical library. Knowledge of Python is assumed. Also, some affinity, or at least interest, in mathematics and statistics is required. However, I have provided brief explanations and pointers to learning resources.
What You Will Learn- Install NumPy, matplotlib, SciPy, and IPython on various operating systems
- Use NumPy array objects to perform array operations
- Familiarize yourself with commonly used NumPy functions
- Use NumPy matrices for matrix algebra
- Work with the NumPy modules to perform various algebraic operations
- Test NumPy code with the numpy.testing module
- Plot simple plots, subplots, histograms, and more with matplotlib
In todays world of science and technology, its all about speed and flexibility. When it comes to scientific computing, NumPy tops the list. NumPy will give you both speed and high productivity. This book will walk you through NumPy with clear, step-by-step examples and just the right amount of theory. The book focuses on the fundamentals of NumPy, including array objects, functions, and matrices, each of them explained with practical examples. You will then learn about different NumPy modules while performing mathematical operations such as calculating the Fourier transform, finding the inverse of a matrix, and determining eigenvalues, among many others. This book is a one-stop solution to knowing the ins and outs of the vast NumPy library, empowering you to use its wide range of mathematical features to build efficient, high-speed programs.
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About the AuthorIvan Idris
Ivan Idris has an MSc in experimental physics. His graduation thesis had a strong emphasis on applied computer science. After graduating, he worked for several companies as a Java developer, data warehouse developer, and QA Analyst. His main professional interests are business intelligence, big data, and cloud computing. Ivan enjoys writing clean, testable code and interesting technical articles. He is the author of NumPy Beginners Guide, NumPy Cookbook, Learning NumPy Array, and Python Data Analysis. You can find more information about him and a blog with a few examples of NumPy at http://ivanidris.net/wordpress/.
Ivan Idris [Ivan Idris]: author's other books
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What does | It creates a NumPy array with values 0-4 The created NumPy array has values 0, 1, 2, 3, and 4 |
How is the shape of an | It is stored in a tuple |
Which function returns the weighted average of an array? |
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Which function returns the covariance of two arrays? |
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What is the row delimiter in a string accepted by the | Semicolon |
Which function can create matrices? |
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Which NumPy module deals with random numbers? |
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Which parameter of the |
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What does the | It does neither 1, 2, or 3 |
Which function loads |
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This appendix contains links to the relevant websites.
- Learn Python the Hard Way (for Python 2) at http://learnpythonthehardway.org/
- Dive Into Python 3 (for Python 3) at http://www.diveintopython3.net/
- Beginner's Guide to Python at https://wiki.python.org/moin/BeginnersGuide
- Non-programmers Tutorial for Python 3 can be found at http://en.wikibooks.org/wiki/Non-Programmer%27s_Tutorial_for_Python_3
- A Byte of Python is available at http://www.swaroopch.com/notes/python/
- An Introduction to Interactive Programming in Python can be found at https://www.coursera.org/course/interactivepython1
- Learn Python online by Code Mentor at https://www.codementor.io/learn-python-online
- Learn Python by visualizing code execution at http://pythontutor.com/
- Find Codecademy Python exercises at http://www.codecademy.com/tracks/python
- Google's Python class is available at https://developers.google.com/edu/python/
- A Python style guide from Google can be found at https://google-styleguide.googlecode.com/svn/trunk/pyguide.html
- The IPython website can be found at http://ipython.org/
- matplotlib a Python plotting library at http://matplotlib.org/
- NumPy and SciPy documentation can be accessed at http://docs.scipy.org/doc/
- NumPy and SciPy mailing lists can be found at http://www.scipy.org/scipylib/mailing-lists.html
- Linear algebra tutorials are available from Khan Academy at https://www.khanacademy.org/math/linear-algebra
- Pre-calculus tutorials from Khan Academy are available at https://www.khanacademy.org/math/precalculus
- Probability and statistics tutorials from Khan Academy can be found at https://www.khanacademy.org/math/probability
- Trigonometry tutorials from Khan Academy can be found at https://www.khanacademy.org/math/trigonometry
- Access Alcumus by ArtofProblemSolving ( AoPS ) at http://www.artofproblemsolving.com/alcumus
- Find the Pre-Calculus Coursera course at https://www.coursera.org/course/precalculus
- The Coursera course on linear algebra, which uses Python, can be found at https://www.coursera.org/course/matrix
- An introduction to probability by HarvardUniversity can be accessed at https://itunes.apple.com/us/course/statistics-110-probability/id502492375
- The statistics wikibook is available at https://en.wikibooks.org/wiki/Statistics
- The Electronic Statistics Textbook. Tulsa, OK: StatSoft. WEB can be found at: http://www.statsoft.com/Textbook
This appendix contains a list of useful NumPy functions and their descriptions.
numpy.apply_along_axis(func1d, axis, arr, *args): Applies the functionfunc1dalong an axis on 1D slices ofarr.numpy.arange([start,] stop[, step,], dtype=None): Creates a NumPy array with evenly spaced values within a specified range.numpy.argsort(a, axis=-1, kind='quicksort', order=None): Returns the indices that would sort the input array.numpy.argmax(a, axis=None): Returns the indices of the maximum values along an axis.numpy.argmin(a, axis=None): Returns the indices of the minimum values along an axis.numpy.argwhere(a): Finds the indices of non-zero elements.numpy.array(object, dtype=None, copy=True, order=None, subok=False, ndmin=0): Creates a NumPy array from an array-like sequence, such as a Python list.numpy.testing.assert_allclose((actual, desired, rtol=1e-07, atol=0, err_msg='', verbose=True): Raises an error if two objects are unequal up to a specified precision.numpy.testing.assert_almost_equal(): Raises an exception if two numbers are not equal up to a specified precision.numpy.testing.assert_approx_equal(): Raises an exception if two numbers are not equal up to a certain significance.numpy.testing.assert_array_almost_equal(): Raises an exception if two arrays are not equal up to a specified precision.numpy.testing.assert_array_almost_equal_nulp(x, y, nulp=1): Compares arrays to their unitofleastprecision ( ULP ).numpy.testing.assert_array_equal(): Raises an exception if two arrays are not equal.
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