Numpy Cookbook (Python)

Description

Cover Copyright Credits About the Author About the Reviewers www.PacktPub.com Table of Contents Preface Chapter 1: Winding Along with IPython Introduction Installing IPython Using IPython as a shell Reading manual pages Installing matplotlib Running an IPython notebook Exporting an IPython notebook Importing a web notebook Configuring a notebook server Exploring the SymPy profile Chapter 2 : Advanced Indexing and Array Concepts Introduction Installing SciPy Installing PIL Resizing images Creating views and copies Flipping Lena Fancy indexing Indexing with a list of locations Indexing with Booleans Stride tricks for Sudoku Broadcasting arrays Chapter 3 : Getting to Grips with Commonly Used Functions Introduction Summing Fibonacci numbers Finding prime factors Finding palindromic numbers The steady state vector Discovering a power law Trading periodically on dips Simulating trading at random Sieving integers with the Sieve of Eratosthenes Chapter 4 : Connecting NumPy with the Rest of the World Introduction Using the buffer protocol Using the array interface Exchanging data with MATLAB and Octave Installing RPy2 Interfacing with R Installing JPype Sending a NumPy array to JPype Installing Google App Engine Deploying the NumPy code on the Google Cloud Running the NumPy code in a PythonAnywhere web console Chapter 5 : Audio and Image Processing Introduction Loading images into memory maps Combining images Blurring images Repeating audio fragments Generating sounds Designing an audio filter Edge detection with the Sobel filter Chapter 6 : Special Arrays and Universal Functions Introduction Creating a universal function Finding Pythagorean triples Performing string operations with chararray Creating a masked array Ignoring negative and extreme values Creating a scores table with a recarray function Chapter 7 : Profiling and Debugging Introduction Profiling with timeit Profiling with IPython Installing line_profiler Profiling code with line_profiler Profiling code with the cProfile extension Debugging with IPython Debugging with PuDB Chapter 8 : Quality Assurance Introduction Installing Pyflakes Performing static analysis with Pyflakes Analyzing code with Pylint Performing static analysis with Pychecker Testing code with docstrings Writing unit tests Testing code with mocks Testing the BDD way Chapter 9 : Speeding Up Code with Cython Introduction Installing Cython Building a Hello World program Using Cython with NumPy Calling C functions Profiling the Cython code Approximating factorials with Cython Chapter 10 : Fun with Scikits Introduction Installing scikit-learn Loading an example dataset Clustering Dow Jones stocks with scikits-learn Installing statsmodels Performing a normality test with statsmodels Installing scikit-image Detecting corners Detecting edges Installing pandas Estimating correlation of stock returns with pandas Loading data as pandas objects from statsmodels Resampling time series data Chapter 11 : Latest and Greatest NumPy Introduction Fancy indexing in place for ufuncs with the at() method Partial sorting via selection for fast median with the partition() function Skipping NaNs with the nanmean(), nanvar(), and nanstd() functions Creating value initialized arrays with the full() and full_like() functions Random sampling with numpy.random.choice() Using the datetime64 type and related API Chapter 12 : Exploratory and Predictive Data Analysis with NumPy Introduction Exploring atmospheric pressure Exploring the day-to-day pressure range Studying annual atmospheric pressure averages Analyzing maximum visibility Predicting pressure with an autoregressive model Predicting pressure with a moving average model Studying intrayear average pressure Studying extreme values of atmospheric pressure Index