NumPy: Beginner's Guide

Book Image

NumPy: Beginner's Guide

By : Ivan Idris

Book Image

NumPy: Beginner's Guide

By: Ivan Idris

Overview of this book

NumPy Beginner's Guide Third Edition

NumPy Beginner's Guide Third Edition

Credits

About the Author

About the Author

About the Reviewers

About the Reviewers

www.PacktPub.com

www.PacktPub.com

Preface

Free Chapter

NumPy Quick Start

NumPy Quick Start

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

Time for action – commenting code

The if statement

Time for action – deciding with the if statement

Time for action – repeating instructions with loops

Python functions

Time for action – defining functions

Time for action – importing modules

NumPy on Windows

Time for action – installing NumPy, matplotlib, SciPy, and IPython on Windows

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

Time for action – adding vectors

IPython – an interactive shell

Online resources and help

Beginning with NumPy Fundamentals

Beginning with NumPy Fundamentals

NumPy array object

Time for action – creating a multidimensional array

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 arrays

Getting Familiar with Commonly Used Functions

Getting Familiar with Commonly Used Functions

Time for action – reading and writing files

Comma-seperated value files

Time for action – loading from CSV files

Volume Weighted Average Price

Time for action – calculating Volume Weighted Average Price

Time for action – finding highest and lowest values

Time for action – performing simple statistics

Time for action – analyzing stock returns

Time for action – dealing with dates

Time for action – using the datetime64 data type

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

Time for action – predicting price with a linear model

Time for action – drawing trend lines

Methods of ndarray

Time for action – clipping and compressing arrays

Time for action – calculating the factorial

Missing values and Jackknife resampling

Time for action – handling NaNs with the nanmean(), nanvar(), and nanstd() functions

Convenience Functions for Your Convenience

Convenience Functions for Your Convenience

Time for action – trading correlated pairs

Time for action – fitting to polynomials

On-balance volume

Time for action – balancing volume

Time for action – avoiding loops with vectorize()

Time for action – smoothing with the hanning() function

Time for action – creating value initialized arrays with the full() and full_like() functions

Working with Matrices and ufuncs

Working with Matrices and ufuncs

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 functions

Universal function methods

Time for action – applying the ufunc methods to the add function

Arithmetic functions

Time for action – dividing arrays

Modulo operation

Time for action – computing the modulo

Fibonacci numbers

Time for action – computing Fibonacci numbers

Lissajous curves

Time for action – drawing Lissajous curves

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

Time for action – fancy indexing in-place for ufuncs with the at() method

Moving Further with NumPy Modules

Moving Further with NumPy Modules

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

Time for action – computing the pseudo inverse of a matrix

Time for action – calculating the determinant of a matrix

Fast Fourier transform

Time for action – calculating the Fourier transform

Time for action – shifting frequencies

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

Bootstrapping in statistics

Time for action – sampling with numpy.random.choice()

Peeking into Special Routines

Peeking into Special Routines

Time for action – sorting lexically

Time for action – partial sorting via selection for a fast median with the partition() function

Complex numbers

Time for action – sorting complex numbers

Time for action – using searchsorted

Array elements extraction

Time for action – extracting elements from an array

Financial functions

Time for action – determining the future 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

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

Time for action – plotting the Hamming window

Time for action – plotting the Kaiser window

Special mathematical functions

Time for action – plotting the modified Bessel function

Time for action – plotting the sinc function

Assuring Quality with Testing

Assuring 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

Time for action – comparing arrays

Ordering arrays

Time for action – checking the array order

Object 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

Time for action – writing a unit test

Nose test decorators

Time for action – decorating tests

Time for action – executing doctests

Plotting with matplotlib

Plotting with matplotlib

Time for action – plotting a polynomial function

Plot format string

Time for action – plotting a polynomial and its derivatives

Time for action – plotting a polynomial and its derivatives

Time for action – plotting a year's worth of stock quotes

Time for action – charting stock price distributions

Logarithmic plots

Time for action – plotting stock volume

Time for action – plotting price and volume returns with a scatter plot

Time for action – shading plot regions based on a condition

Legend and annotations

Time for action – using a legend and annotations

Three-dimensional plots

Time for action – plotting in three dimensions

Time for action – drawing a filled contour plot

Time for action – animating plots

When NumPy Is Not Enough – SciPy and Beyond

When NumPy Is Not Enough – SciPy and Beyond

MATLAB and Octave

Time for action – saving and loading a .mat file

Time for action – analyzing random values

Sample 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

Time for action – interpolating in one dimension

Image processing

Time for action – manipulating Lena

Audio processing

Time for action – replaying audio clips

Playing with Pygame

Playing with Pygame

Time for action – installing Pygame

Time for action – creating a simple game

Time for action – animating objects with NumPy and Pygame

Time for Action – using matplotlib in Pygame

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

Pop Quiz Answers

Pop Quiz Answers

Chapter 1, NumPy Quick Start

Chapter 2, Beginning with NumPy Fundamentals

Chapter 3, Getting Familiar with Commonly Used Functions

Chapter 4, Convenience Functions for Your Convenience

Chapter 5, Working with Matrices and ufuncs

Chapter 6, Move Further with NumPy Modules

Chapter 7, Peeking into Special Routines

Chapter 8, Assuring Quality with Testing

Chapter 9, Plotting with matplotlib

Chapter 10, When NumPy Is Not Enough –Scipy and Beyond

Additional Online Resources

Additional Online Resources

Mathematics and statistics

NumPy Functions' References

NumPy Functions' References

Index

Customer Reviews

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Time for action – plotting the Kaiser window

Let's plot the Kaiser window:

Call the NumPy kaiser() function:
```
window = np.kaiser(42, 14)
```
Plot the window with matplotlib:
```
plt.plot(window)
plt.show()
```
The Kaiser window appears as follows:

What just happened?

We plotted the Kaiser window with the NumPy kaiser() function.