NumPy Beginner's Guide (Python)

✍ Scribed by Ivan Idris

• Publisher: Packt Pub Limited
• Year: 2013
• Tongue: English
• Leaves: 310
• Category: Library

✦ Synopsis

The book is written in beginner's guide style with each aspect of NumPy demonstrated with real world examples and required screenshots.If you are a programmer, scientist, or engineer who has basic Python knowledge and would like to be able to do numerical computations with Python, this book is for you. No prior knowledge of NumPy is required.

✦ Table of Contents

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
Windows
Time for action – installing NumPy, Matplotlib, SciPy, and IPython on Windows
Linux
Time for action – installing NumPy, Matplotlib, SciPy, and IPython on Linux
Mac OS X
Time for action – installing NumPy, Matplotlib, and SciPy 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
dtype constructors
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
Stacking
Time for action – stacking arrays
Splitting
Time for action – splitting arrays
Array attributes
Time for action – converting arrays
Summary
Chapter 3: Get in Terms with Commonly Used Functions
File I/O
Time for action – reading and writing files
CSV 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
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
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
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 function
Universal function methods
Time for action – applying the ufunc methods on add
Arithmetic functions
Time for action – dividing arrays
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
Summary
Chapter 6: Move 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
Pseudoinverse
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
Summary
Chapter 7: Peeking into Special Routines
Sorting
Time for action – sorting lexically
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 scatter plot
Fill between
Time for action – shading plot regions based on a condition
Legend and annotations
Time for action – using legend and annotations
Three dimensional plots
Time for action – plotting in three dimensions
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
Index