Деталі електронної книги

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

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

Ivan Idris

Eлектронна книга
  • 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
  • Назва: Learning NumPy Array. Supercharge your scientific Python computations by understanding how to use the NumPy library effectively
  • Автор: Ivan Idris
  • Оригінальна назва: Learning NumPy Array. Supercharge your scientific Python computations by understanding how to use the NumPy library effectively
  • ISBN: 9781783983919, 9781783983919
  • Дата видання: 2014-06-13
  • Формат: Eлектронна книга
  • Ідентифікатор видання: e_3cwx
  • Видавець: Packt Publishing