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Spis treści

Learning NumPy Array. Supercharge your scientific Python computations by understanding how to use the NumPy library effectively eBook -- spis treści

• 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
  • 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