Learning Numpy Array

Description

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. 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