NumPy Cookbook.pdf

【实例截图】

【核心代码】

Table of Contents
Preface 1
Chapter 1: Winding Along with IPython 5
Introduction 5
Installing IPython 6
Using IPython as a shell 8
Reading manual pages 10
Installing Matplotlib 11
Running a web notebook 12
Exporting a web notebook 14
Importing a web notebook 16
Configuring a notebook server 20
Exploring the SymPy profile 23
Chapter 2: Advanced Indexing and Array Concepts 25
Introduction 25
Installing SciPy 26
Installing PIL 28
Resizing images 29
Creating views and copies 32
Flipping Lena 34
Fancy indexing 36
Indexing with a list of locations 38
Indexing with booleans 40
Stride tricks for Sudoku 42
Broadcasting arrays 45
Chapter 3: Get to Grips with Commonly Used Functions 49
Introduction 50
Summing Fibonacci numbers 50
Finding prime factors 54
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Finding palindromic numbers 56
The steady state vector determination 58
Discovering a power law 64
Trading periodically on dips 67
Simulating trading at random 70
Sieving integers with the Sieve of Erasthothenes 72
Chapter 4: Connecting NumPy with the Rest of the World 75
Introduction 75
Using the buffer protocol 76
Using the array interface 79
Exchanging data with MATLAB and Octave 80
Installing RPy2 82
Interfacing with R 82
Installing JPype 84
Sending a NumPy array to JPype 84
Installing Google App Engine 86
Deploying NumPy code in the Google cloud 88
Running NumPy code in a Python Anywhere web console 90
Setting up PiCloud 92
Chapter 5: Audio and Image Processing 95
Introduction 95
Loading images into memory map 96
Combining images 100
Blurring images 104
Repeating audio fragments 108
Generating sounds 110
Designing an audio filter 114
Edge detection with the Sobel filter 117
Chapter 6: Special Arrays and Universal Functions 121
Introduction 121
Creating a universal function 121
Finding Pythagorean triples 122
Performing string operations with chararray 124
Creating a masked array 125
Ignoring negative and extreme values 128
Creating a scores table with recarray 131
Chapter 7: Profiling and Debugging 135
Introduction 135
Profiling with timeit 135
Profiling with IPython 139
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Installing line_profiler 142
Profiling code with line_profiler 143
Profiling code with the cProfile extension 144
Debugging with IPython 146
Debugging with pudb 148
Chapter 8: Quality Assurance 151
Introduction 151
Installing Pyflakes 151
Performing static analysis with Pyflakes 152
Analyzing code with Pylint 153
Performing static analysis with Pychecker 155
Testing code with docstrings 156
Writing unit tests 158
Testing code with mocks 162
Testing the BDD way 164
Chapter 9: Speed Up Code with Cython 169
Introduction 169
Installing Cython 170
Building a Hello World program 170
Using Cython with NumPy 172
Calling C functions 173
Profiling Cython code 175
Approximating factorials with Cython 178
Chapter 10: Fun with Scikits 183
Introduction 183
Installing scikits-learn 184
Loading an example dataset 184
Clustering Dow Jones stocks with scikits-learn 185
Installing scikits-statsmodels 189
Performing a normality test with scikits-statsmodels 190
Installing scikits-image 191
Detecting corners 191
Detecting edges 193
Installing Pandas 194
Estimating stock returns correlation with Pandas 195
Loading data as pandas objects from statsmodels 198
Resampling time series data 200
Index 205