NumPy: Beginner's Guide - Third Edition

Time for action -- using the datetime64 data typeWeekly summary; Time for action -- summarizing data; Average True Range; Time for action -- calculating the average true range; Simple Moving Average; Time for action -- computing the simple moving average; Exponential Moving Average; Time for action -- calculating the exponential moving average; Bollinger Bands; Time for action -- enveloping with Bollinger bands; Linear model; Time for action -- predicting price with a linear model; Trend lines; Time for action -- drawing trend lines; Methods of ndarray.;Time for action -- clipping and compressing arrays.;Time for action -- deciding with the if statementThe for loop; Time for action -- repeating instructions with loops; Python functions; Time for action -- defining functions; Python modules; Time for action -- importing modules; NumPy on Windows; Time for action -- installing NumPy, matplotlib, SciPy, and IPython on Windows; NumPy on Linux; Time for action -- installing NumPy, matplotlib, SciPy, and IPython on Linux; NumPy on Mac OS X; Time for action -- installing NumPy, SciPy, matplotlib, and IPython with MacPorts or Fink; Building from source; Arrays; Time for action -- adding vectors.;IPython -- an interactive shellOnline resources and help; Summary; Chapter 2: Beginning with NumPy Fundamentals; NumPy array object; Time for action -- creating a multidimensional array; Selecting elements; NumPy numerical types; Data type objects; Character codes; The dtype constructors; The dtype attributes; Time for action -- creating a record data type; One-dimensional slicing and indexing; Time for action -- slicing and indexing multidimensional arrays; Time for action -- manipulating array shapes; Time for action -- stacking arrays; Time for action -- splitting arrays.;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;Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: NumPy Quick Start; Python; Time for action -- installing Python on different operating systems; The Python help system; Time for action -- using the Python help system; Basic arithmetic and variable assignment; Time for action -- using Python as a calculator; Time for action -- assigning values to variables; The print() function; Time for action -- printing with the print() function; Code comments; Time for action -- commenting code; The if statement.;Time for action -- converting arraysSummary; Chapter 3: Getting Familiar with Commonly Used Functions; File I/O; Time for action -- reading and writing files; Comma Separated Values files; Time for action -- loading from CSV files; Volume Weighted Average Price; Time for action -- calculating volume weighted average price; The mean() function; Time-weighted average price; Value range; Time for action -- finding highest and lowest values; Statistics; Time for action -- doing simple statistics; Stock returns; Time for action -- analyzing stock returns; Dates; Time for action -- dealing with dates. Cover Copyright Credits About the Author About the Reviewers www.PacktPub.com Table of Contents Preface Chapter 1: NumPy Quick Start Python Time for action – installing Python on different operating systems The Python help system Time for action – using the Python help system Basic arithmetic and variable assignment Time for action – using Python as a calculator Time for action – assigning values to variables The print() function Time for action – printing with the print() function Code comments Time for action – commenting code The if statement Time for action – deciding with the if statement The for loop Time for action – repeating instructions with loops Python functions Time for action – defining functions Python modules Time for action – importing modules NumPy on Windows Time for action – installing NumPy, matplotlib, SciPy, and IPython on Windows NumPy on Linux Time for action – installing NumPy, matplotlib, SciPy, and IPython on Linux NumPy on Mac OS X Time for action – installing NumPy, SciPy, matplotlib, and IPython with MacPorts or Fink Building from source Arrays Time for action – adding vectors IPython – an interactive shell Online resources and help Summary Chapter 2: Beginning with NumPy Fundamentals NumPy array object Time for action – creating a multidimensional array Selecting elements NumPy numerical types Data type objects Character codes The dtype constructors The dtype attributes Time for action – creating a record data type One-dimensional slicing and indexing Time for action – slicing and indexing multidimensional arrays Time for action – manipulating array shapes Time for action – stacking arrays Time for action – splitting arrays Time for action – converting arrays Summary Chapter 3: Getting Familiar with Commonly Used Functions File I/O Time for action – reading and writing files Comma Separated Values files Time for action – loading from CSV files Volume Weighted Average Price Time for action – calculating volume weighted average price The mean() function Time-weighted average price Value range Time for action – finding highest and lowest values Statistics Time for action – doing simple statistics Stock returns Time for action – analyzing stock returns Dates Time for action – dealing with dates Time for action – using the datetime64 data type Weekly summary Time for action – summarizing data Average True Range Time for action – calculating the average true range Simple Moving Average Time for action – computing the simple moving average Exponential Moving Average Time for action – calculating the exponential moving average Bollinger Bands Time for action – enveloping with Bollinger bands Linear model Time for action – predicting price with a linear model Trend lines Time for action – drawing trend lines Methods of ndarray Time for action – clipping and compressing arrays Factorial Time for action – calculating the factorial Missing values and Jackknife resampling Time for action – handling NaNs with the nanmean(), nanvar(), and nanstd() functions Summary Chapter 4: Convenience Functions for Your Convenience Correlation Time for action – trading correlated pairs Polynomials Time for action – fitting to polynomials On-balance Volume Time for action – balancing volume Simulation Time for action – avoiding loops with vectorize() Smoothing Time for action – smoothing with the hanning() function Initialization Time for action – creating value initialized arrays with the full() and full_like() functions Summary Chapter 5: Working with Matrices and ufuncs Matrices Time for action – creating matrices Creating a matrix from other matrices Time for action – creating a matrix from other matrices Universal functions Time for action – creating universal functions Universal function methods Time for action – applying the ufunc methods on the add function Arithmetic functions Time for action – dividing arrays Modulo operation Time for action – computing the modulo Fibonacci numbers Time for action – computing Fibonacci numbers Lissajous curves Time for action – drawing Lissajous curves Square waves Time for action – drawing a square wave Sawtooth and triangle waves Time for action – drawing sawtooth and triangle waves Bitwise and comparison functions Time for action – twiddling bits Fancy indexing Time for action – fancy indexing in-place for ufuncs with the at() method Summary Chapter 6: Moving Further with NumPy Modules Linear algebra Time for action – inverting matrices Solving linear systems Time for action – solving a linear system Finding eigenvalues and eigenvectors Time for action – determining eigenvalues and eigenvectors Singular value decomposition Time for action – decomposing a matrix Pseudo inverse Time for action – computing the pseudo inverse of a matrix Determinants Time for action – calculating the determinant of a matrix Fast Fourier transform Time for action – calculating the Fourier transform Shifting Time for action – shifting frequencies Random numbers Time for action – gambling with the binomial Hypergeometric distribution Time for action – simulating a game show Continuous distributions Time for action – drawing a normal distribution Lognormal distribution Time for action – drawing the lognormal distribution Bootstrapping in statistics Time for action – sampling with numpy.random.choice() Summary Chapter 7: Peeking Into Special Routines Sorting Time for action – sorting lexically Time for action – partial sorting via selection for a fast median with the partition() function Complex numbers Time for action – sorting complex numbers Searching Time for action – using searchsorted Array elements extraction Time for action – extracting elements from an array Financial functions Time for action – determining future value Present value Time for action – getting the present value Net present value Time for action – calculating the net present value Internal rate of return Time for action – determining the internal rate of return Periodic payments Time for action – calculating the periodic payments Number of payments Time for action – determining the number of periodic payments Interest rate Time for action – figuring out the rate Window functions Time for action – plotting the Bartlett window Blackman window Time for action – smoothing stock prices with the Blackman window Hamming window Time for action – plotting the Hamming window Kaiser window Time for action – plotting the Kaiser window Special mathematical functions Time for action – plotting the modified Bessel function Sinc Time for action – plotting the sinc function Summary Chapter 8: Assure Quality with Testing Assert functions Time for action – asserting almost equal Approximately equal arrays Time for action – asserting approximately equal Almost equal arrays Time for action – asserting arrays almost equal Equal arrays Time for action – comparing arrays Ordering arrays Time for action – checking the array order Objects comparison Time for action – comparing objects String comparison Time for action – comparing strings Floating-point comparisons Time for action – comparing with assert_array_almost_equal_nulp Comparison of floats with more ULPs Time for action – comparing using maxulp of 2 Unit tests Time for action – writing a unit test Nose tests decorators Time for action – decorating tests Docstrings Time for action – executing doctests Summary Chapter 9: Plotting with matplotlib Simple plots Time for action – plotting a polynomial function Plot format string Time for action – plotting a polynomial and its derivative Subplots Time for action – plotting a polynomial and its derivatives Finance Time for action – plotting a year's worth of stock quotes Histograms Time for action – charting stock price distributions Logarithmic plots Time for action – plotting stock volume Scatter plots Time for action – plotting price and volume returns with a scatter plot Fill between Time for action – shading plot regions based on a condition Legend and annotations Time for action – using a legend and annotations Three-dimensional plots Time for action – plotting in three dimension Contour plots Time for action – drawing a filled contour plot Animation Time for action – animating plots Summary Chapter 10: When NumPy is Not Enough – SciPy and Beyond MATLAB and Octave Time for action – saving and loading a .mat file Statistics Time for action – analyzing random values Samples comparison and SciKits Time for action – comparing stock log returns Signal processing Time for action – detecting a trend in QQQ Fourier analysis Time for action – filtering a detrended signal Mathematical optimization Time for action – fitting to a sine Numerical integration Time for action – calculating the Gaussian integral Interpolation Time for action – interpolating in one dimension Image processing Time for action – manipulating Lena Audio processing Time for action – replaying audio clips Summary Chapter 11: Playing with Pygame Pygame Time for action – installing Pygame Hello World Time for action – creating a simple game Animation Time for action – animating objects with NumPy and Pygame matplotlib Time for Action – using matplotlib in Pygame Surface pixels Time for Action – accessing surface pixel data with NumPy Artificial Intelligence Time for Action – clustering points OpenGL and Pygame Time for Action – drawing the Sierpinski gasket Simulation Game with Pygame Time for Action – simulating life Summary Appendix A: Pop Quiz Answers Appendix B: Additional Online Resources Python Mathematics and statistics Appendix C: NumPy Functions' References Index Key FeaturesBook DescriptionWhat you will learnInstall NumPy, matplotlib, SciPy, and IPython on various operating systemsUse NumPy array objects to perform array operationsFamiliarize yourself with commonly used NumPy functionsUse NumPy matrices for matrix algebraWork with the NumPy modules to perform various algebraic operationsTest NumPy code with the numpy.testing modulePlot simple plots, subplots, histograms, and more with matplotlibWho this book is forThis 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. 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 Who This Book Is For 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.