NumPy Cookbook - Second Edition

NumPy Cookbook - Second Edition

By : Ivan Idris

Buy this Book

NumPy Cookbook - Second Edition

By: Ivan Idris

Buy this Book

Overview of this book

<p>NumPy has the ability to give you speed and high productivity. High performance calculations can be done easily with clean and efficient code, and it allows you to execute complex algebraic and mathematical computations in no time.</p> <p>This book will give you a solid foundation in NumPy arrays and universal functions. Starting with the installation and configuration of IPython, you'll learn about advanced indexing and array concepts along with commonly used yet effective functions. You will then cover practical concepts such as image processing, special arrays, and universal functions. You will also learn about plotting with Matplotlib and the related SciPy project with the help of examples. At the end of the book, you will study how to explore atmospheric pressure and its related techniques. By the time you finish this book, you'll be able to write clean and fast code with NumPy.</p>

NumPy Cookbook Second Edition

Credits

About the Author

About the Reviewers

www.PacktPub.com

Preface

Free Chapter

Winding Along with IPython

Introduction

Installing IPython

Using IPython as a shell

Reading manual pages

Installing matplotlib

Running an IPython notebook

Exporting an IPython notebook

Importing a web notebook

Configuring a notebook server

Exploring the SymPy profile

Advanced Indexing and Array Concepts

Creating views and copies

Flipping Lena

Fancy indexing

Indexing with a list of locations

Indexing with Booleans

Stride tricks for Sudoku

Broadcasting arrays

Getting to Grips with Commonly Used Functions

Introduction

Summing Fibonacci numbers

Finding prime factors

Finding palindromic numbers

The steady state vector

Discovering a power law

Trading periodically on dips

Simulating trading at random

Sieving integers with the Sieve of Eratosthenes

Connecting NumPy with the Rest of the World

Introduction

Using the buffer protocol

Using the array interface

Exchanging data with MATLAB and Octave

Installing RPy2

Interfacing with R

Installing JPype

Sending a NumPy array to JPype

Installing Google App Engine

Deploying the NumPy code on the Google Cloud

Running the NumPy code in a PythonAnywhere web console

Audio and Image Processing

Introduction

Loading images into memory maps

Combining images

Blurring images

Repeating audio fragments

Generating sounds

Designing an audio filter

Edge detection with the Sobel filter

Special Arrays and Universal Functions

Introduction

Creating a universal function

Finding Pythagorean triples

Performing string operations with chararray

Creating a masked array

Ignoring negative and extreme values

Creating a scores table with a recarray function

Profiling and Debugging

Introduction

Profiling with timeit

Profiling with IPython

Installing line_profiler

Profiling code with line_profiler

Profiling code with the cProfile extension

Debugging with IPython

Debugging with PuDB

Quality Assurance

Introduction

Installing Pyflakes

Performing static analysis with Pyflakes

Analyzing code with Pylint

Performing static analysis with Pychecker

Testing code with docstrings

Writing unit tests

Testing code with mocks

Testing the BDD way

Speeding Up Code with Cython

Introduction

Installing Cython

Building a Hello World program

Using Cython with NumPy

Calling C functions

Profiling the Cython code

Approximating factorials with Cython

Fun with Scikits

Introduction

Installing scikit-learn

Loading an example dataset

Clustering Dow Jones stocks with scikits-learn

Installing statsmodels

Performing a normality test with statsmodels

Installing scikit-image

Detecting corners

Detecting edges

Installing pandas

Estimating correlation of stock returns with pandas

Loading data as pandas objects from statsmodels

Resampling time series data

Latest and Greatest NumPy

Introduction

Fancy indexing in place for ufuncs with the at() method

Partial sorting via selection for fast median with the partition() function

Skipping NaNs with the nanmean(), nanvar(), and nanstd() functions

Creating value initialized arrays with the full() and full_like() functions

Random sampling with numpy.random.choice()

Using the datetime64 type and related API

Exploratory and Predictive Data Analysis with NumPy

Introduction

Exploring atmospheric pressure

Exploring the day-to-day pressure range

Studying annual atmospheric pressure averages

Analyzing maximum visibility

Predicting pressure with an autoregressive model

Predicting pressure with a moving average model

Studying intrayear average pressure

Studying extreme values of atmospheric pressure

Index

Customer Reviews

5 star

4 star

3 star

2 star

1 star

Clustering Dow Jones stocks with scikits-learn

Clustering is a type of machine learning algorithm that aims to group items based on similarities. In this example, we will use the log returns of stocks in the Dow Jones Industrial Average (DJI or DJIA) index to cluster. Most of the steps of this recipe have already passed the review in previous chapters.

How to do it...

First, we will download the EOD price data for those stocks from Yahoo! Finance. Then, we will calculate a square affinity matrix. Finally, we will cluster the stocks with the AffinityPropagation class:

Download price data for 2011 using the stock symbols of the DJI Index. In this example, we are only interested in the close price:

# 2011 to 2012
start = datetime.datetime(2011, 01, 01)
end = datetime.datetime(2012, 01, 01)

#Dow Jones symbols
symbols = ["AA", "AXP", "BA", "BAC", "CAT",
   "CSCO", "CVX", "DD", "DIS", "GE", "HD",
   "HPQ", "IBM", "INTC", "JNJ", "JPM", 
   "KO", "MCD", "MMM", "MRK", "MSFT", "PFE",
   "PG", "T",...

NumPy Cookbook - Second Edition

By : Ivan Idris

NumPy Cookbook - Second Edition

By: Ivan Idris

Overview of this book

Related Content you might be interested in

Current Title:

NumPy Cookbook - Second Edition

Clustering Dow Jones stocks with scikits-learn

How to do it...