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Learning NumPy Array

Learning NumPy Array

By : Ivan Idris
4 (9)
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Learning NumPy Array

Learning NumPy Array

4 (9)
By: Ivan Idris

Overview of this book

Table of Contents (14 chapters)
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Learning NumPy Array
Learning NumPy Array
Credits
Credits
About the Author
About the Author
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Getting Started with NumPy
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
2
NumPy Basics
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
3
Basic Data Analysis with NumPy
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
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
5
Signal Processing Techniques
Signal Processing Techniques
Introducing the Sunspot data
Moving averages
Smoothing functions
Forecasting with an ARMA model
Filtering a signal
Demonstrating cointegration
Summary
6
Profiling, Debugging, and Testing
Profiling, Debugging, and Testing
Assert functions
Profiling a program with IPython
Debugging with IPython
Performing Unit tests
Nose tests decorators
Summary
7
The Scientific Python Ecosystem
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The Scientific Python Ecosystem
Numerical integration
Interpolation
Using Cython with NumPy
Clustering stocks with scikit-learn
Detecting corners
Comparing NumPy to Blaze
Summary
Index
Index

Summary

In this chapter, we only scratched the surface of what is possible with the scientific Python ecosystem. We used some of the libraries that are considered, if not part of the common stack, then at least fundamental. We used interpolation and numerical integration provided by SciPy. Two of the dozens of algorithms in scikit-learn were demonstrated. We also saw Cython in action, which is technically a programming language in its own right. Finally, we had a look at Blaze, a library supposed to generalize and extend the principles of NumPy. This is in light of recent developments such as Big Data and Cloud Computing. Blaze and related projects are still in the incubation phase, but we can expect stable software to be produced in the near future. You can refer to http://continuum.io/developer-resources for some of these projects.

Unfortunately, we have come to the end of this book. Because of this book's format, that is the number of pages, you should have essential NumPy knowledge and...

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