NumPy Cookbook

Table of Contents

• Preface

• Chapter 1: Winding Along with IPython
• Introduction
• Installing IPython
• Using IPython as a shell
• Reading manual pages
• Installing Matplotlib
• Running a web notebook
• Exporting a web 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: Get to Grips with Commonly Used Functions
• Introduction
• Summing Fibonacci numbers
• Finding prime factors
• Finding palindromic numbers
• The steady state vector determination
• Discovering a power law
• Trading periodically on dips
• Simulating trading at random
• Sieving integers with the Sieve of Erasthothenes

• 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 NumPy code in the Google cloud
• Running NumPy code in a Python Anywhere web console
• Setting up PiCloud

• Chapter 5: Audio and Image Processing
• Introduction
• Loading images into memory map
• 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 recarray

• 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: Speed Up Code with Cython
• Introduction
• Installing Cython
• Building a Hello World program
• Using Cython with NumPy
• Calling C functions
• Profiling Cython code
• Approximating factorials with Cython

• Chapter 10: Fun with Scikits
• Introduction
• Installing scikits-learn
• Loading an example dataset
• Clustering Dow Jones stocks with scikits-learn
• Installing scikits-statsmodels
• Performing a normality test with scikits-statsmodels
• Installing scikits-image
• Detecting corners
• Detecting edges
• Installing Pandas
• Estimating stock returns correlation with Pandas
• Loading data as pandas objects from statsmodels
• Resampling time series data

• Index