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Learning NumPy Array : supercharge your scientific Python computations by understanding how to use the NumPy library effectively /
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.
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham, England :
Packt Publishing,
2014.
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| Colección: | Community experience distilled.
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Idris, Ivan, |e author. | |
| 245 | 1 | 0 | |a Learning NumPy Array : |b supercharge your scientific Python computations by understanding how to use the NumPy library effectively / |c Ivan Idris ; Duraid Fatouhi, cover image. |
| 264 | 1 | |a Birmingham, England : |b Packt Publishing, |c 2014. | |
| 264 | 4 | |c ©2014 | |
| 300 | |a 1 online resource (164 pages) : |b illustrations (some color). | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a text file | ||
| 490 | 1 | |a Community experience distilled | |
| 588 | 0 | |a Online resource; title from PDF title page (ebrary, viewed July 3, 2014). | |
| 505 | 0 | |a 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 | |
| 505 | 8 | |a Data type objectsCharacter 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 | |
| 505 | 8 | |a Analyzing wind directionAnalyzing 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 | |
| 505 | 8 | |a Introducing the day-of-the-year temperature modelModeling 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 | |
| 505 | 8 | |a Demonstrating cointegrationSummary; 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 | |
| 520 | |a 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. | ||
| 546 | |a English. | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Python (Computer program language) | |
| 650 | 0 | |a Numerical analysis |x Data processing. | |
| 650 | 6 | |a Python (Langage de programmation) | |
| 650 | 6 | |a Analyse numérique |x Informatique. | |
| 650 | 7 | |a COMPUTERS |x Databases |x General. |2 bisacsh | |
| 650 | 7 | |a COMPUTERS |x Programming Languages |x SQL. |2 bisacsh | |
| 650 | 7 | |a Numerical analysis |x Data processing |2 fast | |
| 650 | 7 | |a Python (Computer program language) |2 fast | |
| 700 | 1 | |a Fatouhi, Duraid, |e cover designer. | |
| 776 | 0 | 8 | |i Print version: |a Idris, Ivan. |t Learning NumPy Array : supercharge your scientific Python computations by understanding how to use the NumPy library effectively. |d Birmingham, England : Packt Publishing, ©2014 |h 145 pages |z 9781783983902 |
| 830 | 0 | |a Community experience distilled. | |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9781783983902/?ar |z Texto completo (Requiere registro previo con correo institucional) |
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