Learning NumPy Array. Supercharge your scientific Python computations by understanding how to use the NumPy library effectively
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- Autor:
- Ivan Idris
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Szczegóły ebooka
- Tytuł oryginału:
- Learning NumPy Array. Supercharge your scientific Python computations by understanding how to use the NumPy library effectively
- ISBN Ebooka:
- 978-17-839-8391-9, 9781783983919
- Data wydania ebooka:
-
2014-06-13
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- Język publikacji:
- angielski
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- 3.9MB
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- 3.7MB
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Spis treści ebooka
- 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
- Support files, eBooks, discount offers, and more
- 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
- The NumPy array object
- 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
- Introducing the Sunspot data
- 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
- Assert functions
- 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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