Learning NumPy电子书

Learning NumPy Array

Table of Contents

Learning NumPy Array

Credits

About the Author

About the Reviewers

www.PacktPub.com

Support files, eBooks, discount offers, and more

Why subscribe?

Free access for Packt account holders

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Downloading the example code

Errata

Piracy

Questions

1. Getting Started with NumPy

Python

Installing NumPy, Matplotlib, SciPy, and IPython on Windows

Installing NumPy, Matplotlib, SciPy, and IPython on Linux

Installing NumPy, Matplotlib, and SciPy on Mac OS X

Building from source

NumPy arrays

Adding arrays

Online resources and help

Summary

2. NumPy Basics

The NumPy array object

The advantages of using NumPy arrays

Creating a multidimensional array

Selecting array elements

NumPy numerical types

Data type objects

Character codes

dtype constructors

dtype attributes

Creating a record data type

One-dimensional slicing and indexing

Manipulating array shapes

Stacking arrays

Splitting arrays

Array attributes

Converting arrays

Creating views and copies

Fancy indexing

Indexing with a list of locations

Indexing arrays with Booleans

Stride tricks for Sudoku

Broadcasting arrays

Summary

3. Basic Data Analysis with NumPy

Introducing the dataset

Determining the daily temperature range

Looking for evidence of global warming

Comparing solar radiation versus temperature

Analyzing wind direction

Analyzing wind speed

Analyzing precipitation and sunshine duration

Analyzing monthly precipitation in De Bilt

Analyzing atmospheric pressure in De Bilt

Analyzing atmospheric humidity in De Bilt

Summary

4. Simple Predictive Analytics with NumPy

Examining autocorrelation of average temperature with pandas

Describing data with pandas DataFrames

Correlating weather and stocks with pandas

Predicting temperature

Autoregressive model with lag 1

Autoregressive model with lag 2

Analyzing intra-year daily average temperatures

Introducing the day-of-the-year temperature model

Modeling temperature with the SciPy leastsq function

Day-of-year temperature take two

Moving-average temperature model with lag 1

The Autoregressive Moving Average temperature model

The time-dependent temperature mean adjusted autoregressive model

Outliers analysis of average De Bilt temperature

Using more robust statistics

Summary

5. Signal Processing Techniques

Introducing the Sunspot data

Sifting continued

Moving averages

Smoothing functions

Forecasting with an ARMA model

Filtering a signal

Designing the filter

Demonstrating cointegration

Summary

6. Profiling, Debugging, and Testing

Assert functions

The assert_almost_equal function

Approximately equal arrays

The assert_array_almost_equal function

Profiling a program with IPython

Debugging with IPython

Performing Unit tests

Nose tests decorators

Summary

7. The Scientific Python Ecosystem

Numerical integration

Interpolation

Using Cython with NumPy

Clustering stocks with scikit-learn

Detecting corners

Comparing NumPy to Blaze

Summary

Index