NumPy: Beginner's Guide : Build Efficient, High-speed Programs Using the High-performance NumPy Mathematical Library

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 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. 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. 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. 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.