A step-by-step guide, packed with examples of practical numerical analysis that will give you a comprehensive, but concise overview of NumPy. This book is for programmers, scientists, or engineers, who have basic Python knowledge and would like to be able to do numerical computations with Python.
Author(s): Ivan Idris
Publisher: Packt Pub Limited
Year: 2014
Language: English
Pages: 164
Cover
Copyright
Credits
About the Author
About the Reviewers
www.PacktPub.com
Table of Contents
Preface
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Analysing 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
Chapter 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
Chapter 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
Chapter 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
