coverpage
Learning NumPy Array
Credits
About the Author
About the Reviewers
www.PacktPub.com
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Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
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
Online resources and help
Summary
Chapter 2. NumPy Basics
The NumPy array object
Creating a multidimensional array
Selecting array elements
NumPy numerical types
Creating a record data type
One-dimensional slicing and indexing
Manipulating array shapes
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
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
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
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
Chapter 5. Signal Processing Techniques
Introducing the Sunspot data
Moving averages
Smoothing functions
Forecasting with an ARMA model
Filtering a signal
Demonstrating cointegration
Summary
Chapter 6. Profiling Debugging and Testing
Assert functions
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
更新时间：2021-04-02 10:02:55
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