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1 online resource (194 pages) |
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computer c rdamedia |
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online resource cr rdacarrier |
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Intro -- 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 |
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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 |
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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 |
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Description based on publisher supplied metadata and other sources |
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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2020. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries |
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Print version: Idris, Ivan Learning NumPy Array
Olton : Packt Publishing, Limited,c2014 9781783983902
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| Subject |
Python (Computer program language);Numerical analysis -- Data processing
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Electronic books
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