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  • Book Overview & Buying Learning NumPy Array
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Learning NumPy Array

Learning NumPy Array

By : Ivan Idris
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Learning NumPy Array

Learning NumPy Array

4 (9)
By: Ivan Idris
Buy this Book

Overview of this book

Table of Contents (14 chapters)
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Learning NumPy Array
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Learning NumPy Array
Credits
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Credits
About the Author
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About the Author
About the Reviewers
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About the Reviewers
www.PacktPub.com
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www.PacktPub.com
Preface
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Preface
Lock Free Chapter
1
Getting Started with NumPy
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Getting Started with NumPy
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Python
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Installing NumPy, Matplotlib, SciPy, and IPython on Windows
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Installing NumPy, Matplotlib, SciPy, and IPython on Linux
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Installing NumPy, Matplotlib, and SciPy on Mac OS X
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Building from source
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NumPy arrays
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Online resources and help
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Summary
2
NumPy Basics
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NumPy Basics
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The NumPy array object
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Creating a multidimensional array
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Selecting array elements
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NumPy numerical types
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Creating a record data type
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One-dimensional slicing and indexing
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Manipulating array shapes
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Creating views and copies
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Fancy indexing
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Indexing with a list of locations
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Indexing arrays with Booleans
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Stride tricks for Sudoku
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Broadcasting arrays
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Summary
3
Basic Data Analysis with NumPy
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Basic Data Analysis with NumPy
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Introducing the dataset
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Determining the daily temperature range
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Looking for evidence of global warming
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Comparing solar radiation versus temperature
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Analyzing wind direction
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Analyzing wind speed
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Analyzing precipitation and sunshine duration
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Analyzing monthly precipitation in De Bilt
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Analyzing atmospheric pressure in De Bilt
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Analyzing atmospheric humidity in De Bilt
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Summary
4
Simple Predictive Analytics with NumPy
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Simple Predictive Analytics with NumPy
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Examining autocorrelation of average temperature with pandas
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Describing data with pandas DataFrames
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Correlating weather and stocks with pandas
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Predicting temperature
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Analyzing intra-year daily average temperatures
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Introducing the day-of-the-year temperature model
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Modeling temperature with the SciPy leastsq function
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Day-of-year temperature take two
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Moving-average temperature model with lag 1
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The Autoregressive Moving Average temperature model
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The time-dependent temperature mean adjusted autoregressive model
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Outliers analysis of average De Bilt temperature
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Using more robust statistics
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Summary
5
Signal Processing Techniques
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Signal Processing Techniques
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Introducing the Sunspot data
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Moving averages
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Smoothing functions
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Forecasting with an ARMA model
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Filtering a signal
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Demonstrating cointegration
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Summary
6
Profiling, Debugging, and Testing
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Profiling, Debugging, and Testing
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Assert functions
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Profiling a program with IPython
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Debugging with IPython
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Performing Unit tests
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Nose tests decorators
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Summary
7
The Scientific Python Ecosystem
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The Scientific Python Ecosystem
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Numerical integration
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Interpolation
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Using Cython with NumPy
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Clustering stocks with scikit-learn
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Detecting corners
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Comparing NumPy to Blaze
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Summary
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Index
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Index

Chapter 7. The Scientific Python Ecosystem

SciPy is built on top of NumPy. It adds functionality such as numerical integration, optimization, statistics, and special functions. Historically, NumPy was part of SciPy but was then separated in order to be used by other Python libraries. These, when combined, define the common stack for scientific and numerical analysis. Of course, the stack itself is not set in stone; however, everybody agrees on NumPy being at the center of it all. The examples in this chapter should give you some idea about the power of the scientific Python ecosystem.

In this chapter, we will cover the following topics:

  • Numerical integration

  • Interpolation

  • Using Cython with NumPy

  • Clustering with scikit-learn

  • Detecting corners

  • Comparing NumPy to Blaze

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