Browse Library
Sign In Start Free Trial
Close icon Search icon
Account
Browse Library Advanced Search Sign In Start Free Trial

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Book Overview & Buying NumPy Beginner's Guide
  • Table Of Contents Toc
  • Feedback & Rating feedback
NumPy Beginner's Guide

NumPy Beginner's Guide

By : Ivan Idris
4.2 (14)
Buy this Book
close
close
NumPy Beginner's Guide

NumPy Beginner's Guide

4.2 (14)
By: Ivan Idris
Buy this Book

Overview of this book

NumPy is an extension to, and the fundamental package for scientific computing with Python. In today's world of science and technology, it is all about speed and flexibility. When it comes to scientific computing, NumPy is on the top of the list. NumPy Beginner's Guide will teach you about NumPy, a leading scientific computing library. NumPy replaces a lot of the functionality of Matlab and Mathematica, but in contrast to those products, is free and open source. Write readable, efficient, and fast code, which is as close to the language of mathematics as is currently possible with the cutting edge open source NumPy software library. Learn all the ins and outs of NumPy that requires you to know basic Python only. Save thousands of dollars on expensive software, while keeping all the flexibility and power of your favourite programming language.You will learn about installing and using NumPy and related concepts. At the end of the book we will explore some related scientific computing projects. This book will give you a solid foundation in NumPy arrays and universal functions. Through examples, you will also learn about plotting with Matplotlib and the related SciPy project. NumPy Beginner's Guide will help you be productive with NumPy and have you writing clean and fast code in no time at all.
Table of Contents (19 chapters)
close
close
close
Numpy Beginner's Guide Second Edition
Icon
Numpy Beginner's Guide Second Edition
Credits
Icon
Credits
About the Author
Icon
About the Author
About the Reviewers
Icon
About the Reviewers
www.PacktPub.com
Icon
www.PacktPub.com
Preface
Icon
Preface
Free Chapter
1
NumPy Quick Start
Icon
NumPy Quick Start
Icon
Python
Icon
Time for action – installing Python on different operating systems
Icon
Windows
Icon
Time for action – installing NumPy, Matplotlib, SciPy, and IPython on Windows
Icon
Linux
Icon
Time for action – installing NumPy, Matplotlib, SciPy, and IPython on Linux
Icon
Mac OS X
Icon
Time for action – installing NumPy, Matplotlib, and SciPy on Mac OS X
Icon
Time for action – installing NumPy, SciPy, Matplotlib, and IPython with MacPorts or Fink
Icon
Building from source
Icon
Arrays
Icon
Time for action – adding vectors
Icon
IPython—an interactive shell
Icon
Online resources and help
Icon
Summary
2
Beginning with NumPy Fundamentals
Icon
Beginning with NumPy Fundamentals
Icon
NumPy array object
Icon
Time for action – creating a multidimensional array
Icon
Time for action – creating a record data type
Icon
One-dimensional slicing and indexing
Icon
Time for action – slicing and indexing multidimensional arrays
Icon
Time for action – manipulating array shapes
Icon
Time for action – stacking arrays
Icon
Time for action – splitting arrays
Icon
Time for action – converting arrays
Icon
Summary
3
Get in Terms with Commonly Used Functions
chevron up
Icon
Get in Terms with Commonly Used Functions
Icon
File I/O
Icon
Time for action – reading and writing files
Icon
CSV files
Icon
Time for action – loading from CSV files
Icon
Volume-weighted average price
Icon
Time for action – calculating volume-weighted average price
Icon
Value range
Icon
Time for action – finding highest and lowest values
Icon
Statistics
Icon
Time for action – doing simple statistics
Icon
Stock returns
Icon
Time for action – analyzing stock returns
Icon
Dates
Icon
Time for action – dealing with dates
Icon
Weekly summary
Icon
Time for action – summarizing data
Icon
Average true range
Icon
Time for action – calculating the average true range
Icon
Simple moving average
Icon
Time for action – computing the simple moving average
Icon
Exponential moving average
Icon
Time for action – calculating the exponential moving average
Icon
Bollinger bands
Icon
Time for action – enveloping with Bollinger bands
Icon
Linear model
Icon
Time for action – predicting price with a linear model
Icon
Trend lines
Icon
Time for action – drawing trend lines
Icon
Methods of ndarray
Icon
Time for action – clipping and compressing arrays
Icon
Factorial
Icon
Time for action – calculating the factorial
Icon
Summary
4
Convenience Functions for Your Convenience
Icon
Convenience Functions for Your Convenience
Icon
Correlation
Icon
Time for action – trading correlated pairs
Icon
Polynomials
Icon
Time for action – fitting to polynomials
Icon
On-balance volume
Icon
Time for action – balancing volume
Icon
Simulation
Icon
Time for action – avoiding loops with vectorize
Icon
Smoothing
Icon
Time for action – smoothing with the hanning function
Icon
Summary
5
Working with Matrices and ufuncs
Icon
Working with Matrices and ufuncs
Icon
Matrices
Icon
Time for action – creating matrices
Icon
Creating a matrix from other matrices
Icon
Time for action – creating a matrix from other matrices
Icon
Universal functions
Icon
Time for action – creating universal function
Icon
Universal function methods
Icon
Time for action – applying the ufunc methods on add
Icon
Arithmetic functions
Icon
Time for action – dividing arrays
Icon
Time for action – computing the modulo
Icon
Fibonacci numbers
Icon
Time for action – computing Fibonacci numbers
Icon
Lissajous curves
Icon
Time for action – drawing Lissajous curves
Icon
Square waves
Icon
Time for action – drawing a square wave
Icon
Sawtooth and triangle waves
Icon
Time for action – drawing sawtooth and triangle waves
Icon
Bitwise and comparison functions
Icon
Time for action – twiddling bits
Icon
Summary
6
Move Further with NumPy Modules
Icon
Move Further with NumPy Modules
Icon
Linear algebra
Icon
Time for action – inverting matrices
Icon
Solving linear systems
Icon
Time for action – solving a linear system
Icon
Finding eigenvalues and eigenvectors
Icon
Time for action – determining eigenvalues and eigenvectors
Icon
Singular value decomposition
Icon
Time for action – decomposing a matrix
Icon
Pseudoinverse
Icon
Time for action – computing the pseudo inverse of a matrix
Icon
Determinants
Icon
Time for action – calculating the determinant of a matrix
Icon
Fast Fourier transform
Icon
Time for action – calculating the Fourier transform
Icon
Shifting
Icon
Time for action – shifting frequencies
Icon
Random numbers
Icon
Time for action – gambling with the binomial
Icon
Hypergeometric distribution
Icon
Time for action – simulating a game show
Icon
Continuous distributions
Icon
Time for action – drawing a normal distribution
Icon
Lognormal distribution
Icon
Time for action – drawing the lognormal distribution
Icon
Summary
7
Peeking into Special Routines
Icon
Peeking into Special Routines
Icon
Sorting
Icon
Time for action – sorting lexically
Icon
Complex numbers
Icon
Time for action – sorting complex numbers
Icon
Searching
Icon
Time for action – using searchsorted
Icon
Array elements' extraction
Icon
Time for action – extracting elements from an array
Icon
Financial functions
Icon
Time for action – determining future value
Icon
Present value
Icon
Time for action – getting the present value
Icon
Net present value
Icon
Time for action – calculating the net present value
Icon
Internal rate of return
Icon
Time for action – determining the internal rate of return
Icon
Periodic payments
Icon
Time for action – calculating the periodic payments
Icon
Number of payments
Icon
Time for action – determining the number of periodic payments
Icon
Interest rate
Icon
Time for action – figuring out the rate
Icon
Window functions
Icon
Time for action – plotting the Bartlett window
Icon
Blackman window
Icon
Time for action – smoothing stock prices with the Blackman window
Icon
Hamming window
Icon
Time for action – plotting the Hamming window
Icon
Kaiser window
Icon
Time for action – plotting the Kaiser window
Icon
Special mathematical functions
Icon
Time for action – plotting the modified Bessel function
Icon
sinc
Icon
Time for action – plotting the sinc function
Icon
Summary
8
Assure Quality with Testing
Icon
Assure Quality with Testing
Icon
Assert functions
Icon
Time for action – asserting almost equal
Icon
Approximately equal arrays
Icon
Time for action – asserting approximately equal
Icon
Almost equal arrays
Icon
Time for action – asserting arrays almost equal
Icon
Equal arrays
Icon
Time for action – comparing arrays
Icon
Ordering arrays
Icon
Time for action – checking the array order
Icon
Objects comparison
Icon
Time for action – comparing objects
Icon
String comparison
Icon
Time for action – comparing strings
Icon
Floating point comparisons
Icon
Time for action – comparing with assert_array_almost_equal_nulp
Icon
Comparison of floats with more ULPs
Icon
Time for action – comparing using maxulp of 2
Icon
Unit tests
Icon
Time for action – writing a unit test
Icon
Nose tests decorators
Icon
Time for action – decorating tests
Icon
Docstrings
Icon
Time for action – executing doctests
Icon
Summary
9
Plotting with Matplotlib
Icon
Plotting with Matplotlib
Icon
Simple plots
Icon
Time for action – plotting a polynomial function
Icon
Plot format string
Icon
Time for action – plotting a polynomial and its derivative
Icon
Subplots
Icon
Time for action – plotting a polynomial and its derivatives
Icon
Finance
Icon
Time for action – plotting a year’s worth of stock quotes
Icon
Histograms
Icon
Time for action – charting stock price distributions
Icon
Logarithmic plots
Icon
Time for action – plotting stock volume
Icon
Scatter plots
Icon
Time for action – plotting price and volume returns with scatter plot
Icon
Fill between
Icon
Time for action – shading plot regions based on a condition
Icon
Legend and annotations
Icon
Time for action – using legend and annotations
Icon
Three dimensional plots
Icon
Time for action – plotting in three dimensions
Icon
Contour plots
Icon
Time for action – drawing a filled contour plot
Icon
Animation
Icon
Time for action – animating plots
Icon
Summary
10
When NumPy is Not Enough – SciPy and Beyond
Icon
When NumPy is Not Enough – SciPy and Beyond
Icon
MATLAB and Octave
Icon
Time for action – saving and loading a .mat file
Icon
Statistics
Icon
Time for action – analyzing random values
Icon
Samples’ comparison and SciKits
Icon
Time for action – comparing stock log returns
Icon
Signal processing
Icon
Time for action – detecting a trend in QQQ
Icon
Fourier analysis
Icon
Time for action – filtering a detrended signal
Icon
Mathematical optimization
Icon
Time for action – fitting to a sine
Icon
Numerical integration
Icon
Time for action – calculating the Gaussian integral
Icon
Interpolation
Icon
Time for action – interpolating in one dimension
Icon
Image processing
Icon
Time for action – manipulating Lena
Icon
Audio processing
Icon
Time for action – replaying audio clips
Icon
Summary
11
Playing with Pygame
Icon
Playing with Pygame
Icon
Pygame
Icon
Time for action – installing Pygame
Icon
Hello World
Icon
Time for action – creating a simple game
Icon
Animation
Icon
Time for action – animating objects with NumPy and Pygame
Icon
Matplotlib
Icon
Time for action – using Matplotlib in Pygame
Icon
Surface pixels
Icon
Time for action – accessing surface pixel data with NumPy
Icon
Artificial intelligence
Icon
Time for action – clustering points
Icon
OpenGL and Pygame
Icon
Time for action – drawing the Sierpinski gasket
Icon
Simulation game with PyGame
Icon
Time for action – simulating life
Icon
Summary
Pop Quiz Answers
Icon
Pop Quiz Answers
Icon
Chapter 1, NumPy Quick Start
Icon
Chapter 2, Beginning with NumPy Fundamentals
Icon
Chapter 3, Get into Terms with Commonly Used Functions
Icon
Chapter 4, Convenience functions for your convenience
Icon
Chapter 5, Working with Matrices and ufuncs
Icon
Chapter 6, Move further with NumPy modules
Icon
Chapter 7, Peeking into special routines
Icon
Chapter 8, Assure Quality with Testing
Icon
Chapter 9, Plotting with Matplotlib
Icon
Chapter 10, When NumPy is not enough Scipy and beyond
Index
Icon
Index

Time for action – summarizing data

The data we will summarize will be for a whole business week from Monday to Friday. During the period covered by the data, there was one holiday on February 21st, President's Day. This happened to be a Monday and the US stock exchanges were closed on this day. As a consequence, there is no entry for this day, in the sample. The first day in the sample is a Friday, which is inconvenient. Use the following instructions to summarize data:

  1. To simplify, we will just have a look at the first three weeks in the sample—you can later have a go at improving this.

    close = close[:16]
dates = dates[:16]

    We will build on the code from the Time for action – dealing with dates tutorial.

  2. Commencing, we will find the first Monday in our sample data. Recall that Mondays have the code 0 in Python. This is what we will put in the condition of a where function. Then, we will need to extract the first element that has index 0. The result would be a multidimensional array. Flatten...

Unlock full access

Continue reading for free

A Packt free trial gives you instant online access to our library of over 7000 practical eBooks and videos, constantly updated with the latest in tech
Start a 7-day FREE trial
End of Section 17
Next Section

Create a Note

Modal Close icon
You need to login to use this feature.
notes
bookmark search playlist font-size

Change the font size

margin-width

Change margin width

day-mode

Change background colour

Close icon Search
Country selected
Close icon Your notes and bookmarks

Delete Bookmark

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete

Delete Note

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete

Edit Note

Modal Close icon
Write a note (max 255 characters)
Cancel
Update Note

Confirmation

Modal Close icon
claim successful
Order Page Read Now

Buy this book with your credits?

Modal Close icon
Are you sure you want to buy this book with one of your credits?
Close
YES, BUY