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 (Update)
  • Table Of Contents Toc
  • Feedback & Rating feedback
Numpy Beginner's Guide (Update)

Numpy Beginner's Guide (Update)

By : Ivan Idris
2 (1)
Buy this Book
close
close
Numpy Beginner's Guide (Update)

Numpy Beginner's Guide (Update)

2 (1)
By: Ivan Idris
Buy this Book

Overview of this book

This book is for the scientists, engineers, programmers, or analysts looking for a high-quality, open source mathematical library. Knowledge of Python is assumed. Also, some affinity, or at least interest, in mathematics and statistics is required. However, I have provided brief explanations and pointers to learning resources.
Table of Contents (16 chapters)
close
close
close
Preface
Icon
Preface
Icon
What is NumPy?
Icon
History
Icon
Why use NumPy?
Icon
Limitations of NumPy
Icon
What this book covers
Icon
What you need for this book
Icon
Who this book is for
Icon
Sections
Icon
Time for action – heading
Icon
Conventions
Icon
Reader feedback
Icon
Customer support
Lock Free Chapter
1
1. NumPy Quick Start
chevron up
Icon
1. NumPy Quick Start
Icon
Python
Icon
Time for action – installing Python on different operating systems
Icon
The Python help system
Icon
Time for action – using the Python help system
Icon
Basic arithmetic and variable assignment
Icon
Time for action – using Python as a calculator
Icon
Time for action – assigning values to variables
Icon
The print() function
Icon
Time for action – printing with the print() function
Icon
Code comments
Icon
Time for action – commenting code
Icon
The if statement
Icon
Time for action – deciding with the if statement
Icon
The for loop
Icon
Time for action – repeating instructions with loops
Icon
Python functions
Icon
Time for action – defining functions
Icon
Python modules
Icon
Time for action – importing modules
Icon
NumPy on Windows
Icon
Time for action – installing NumPy, matplotlib, SciPy, and IPython on Windows
Icon
NumPy on Linux
Icon
Time for action – installing NumPy, matplotlib, SciPy, and IPython on Linux
Icon
NumPy 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
2. Beginning with NumPy Fundamentals
Icon
2. 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
3. Getting Familiar with Commonly Used Functions
Icon
3. Getting Familiar with Commonly Used Functions
Icon
File I/O
Icon
Time for action – reading and writing files
Icon
Comma-seperated value 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 – performing simple statistics
Icon
Stock returns
Icon
Time for action – analyzing stock returns
Icon
Dates
Icon
Time for action – dealing with dates
Icon
Time for action – using the datetime64 data type
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
Missing values and Jackknife resampling
Icon
Time for action – handling NaNs with the nanmean(), nanvar(), and nanstd() functions
Icon
Summary
4
4. Convenience Functions for Your Convenience
Icon
4. 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
Initialization
Icon
Time for action – creating value initialized arrays with the full() and full_like() functions
Icon
Summary
5
5. Working with Matrices and ufuncs
Icon
5. 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 functions
Icon
Universal function methods
Icon
Time for action – applying the ufunc methods to the add function
Icon
Arithmetic functions
Icon
Time for action – dividing arrays
Icon
Modulo operation
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
Fancy indexing
Icon
Time for action – fancy indexing in-place for ufuncs with the at() method
Icon
Summary
6
6. Moving Further with NumPy Modules
Icon
6. Moving 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
Pseudo inverse
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
Bootstrapping in statistics
Icon
Time for action – sampling with numpy.random.choice()
Icon
Summary
7
7. Peeking into Special Routines
Icon
7. Peeking into Special Routines
Icon
Sorting
Icon
Time for action – sorting lexically
Icon
Time for action – partial sorting via selection for a fast median with the partition() function
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 the 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
8. Assuring Quality with Testing
Icon
8. Assuring 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
Object 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 test decorators
Icon
Time for action – decorating tests
Icon
Docstrings
Icon
Time for action – executing doctests
Icon
Summary
9
9. Plotting with matplotlib
Icon
9. 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 derivatives
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 a 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 a 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
10. When NumPy Is Not Enough – SciPy and Beyond
Icon
10. 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
Sample 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
11. Playing with Pygame
Icon
11. 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
12
A. Pop Quiz Answers
Icon
Chapter 1, NumPy Quick Start
Icon
Chapter 2, Beginning with NumPy Fundamentals
Icon
Chapter 3, Getting Familiar 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, Assuring Quality with Testing
Icon
Chapter 9, Plotting with matplotlib
Icon
Chapter 10, When NumPy Is Not Enough –Scipy and Beyond
13
B. Additional Online Resources
Icon
B. Additional Online Resources
Icon
Python
Icon
Mathematics and statistics
14
C. NumPy Functions' References
Icon
C. NumPy Functions' References
15
Index
Icon
Index

Time for action – installing Python on different operating systems

NumPy has binary installers for Windows, various Linux distributions, and Mac OS X at http://sourceforge.net/projects/numpy/files/. There is also a source distribution, if you prefer that. You need to have Python 2.4.x or above installed on your system. We will go through the various steps required to install Python on the following operating systems:

  • Debian and Ubuntu: Python might already be installed on Debian and Ubuntu, but the development headers are usually not. On Debian and Ubuntu, install the python and python-dev packages with the following commands:
    $ [sudo] apt-get install python
$ [sudo] apt-get install python-dev
  • Windows: The Windows Python installer is available at https://www.python.org/downloads/. On this website, we can also find installers for Mac OS X and source archives for Linux, UNIX, and Mac OS X.
  • Mac: Python comes preinstalled on Mac OS X. We can also get Python through MacPorts, Fink, Homebrew, or similar projects.

    Install, for instance, the Python 2.7 port by running the following command:

    $ [sudo] port install python27

    Linear Algebra PACKage (LAPACK) does not need to be present but, if it is, NumPy will detect it and use it during the installation phase. It is recommended that you install LAPACK for serious numerical analysis as it has useful numerical linear algebra functionality.

What just happened?

We installed Python on Debian, Ubuntu, Windows, and the Mac OS X.

Note

You can download the example code files for all the Packt books you have purchased from your account at https://www.packtpub.com/. If you purchased this book elsewhere, you can visit https://www.packtpub.com/books/content/support and register to have the files e-mailed directly to you.

Visually different images
CONTINUE READING
83
Tech Concepts
36
Programming languages
73
Tech Tools
Icon Unlimited access to the largest independent learning library in tech of over 8,000 expert-authored tech books and videos.
Icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Icon 50+ new titles added per month and exclusive early access to books as they are being written.
Numpy Beginner's Guide (Update)
End of Section 1
Next Section
notes
bookmark Notes and Bookmarks search Search in title playlist Add to playlist font-size Font size

Change the font size

margin-width Margin width

Change margin width

day-mode Day/Sepia/Night Modes

Change background colour

Close icon Search
Country selected
Close icon Your notes and bookmarks

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

Submit Your Feedback

Modal Close icon
Modal Close icon
Modal Close icon