- NumPy Cookbook
- Table of Contents
- NumPy Cookbook
- Credits
- About the Author
- About the Reviewers
- www.PacktPub.com
- Preface
- 1. Winding Along with IPython
- 2. Advanced Indexing and Array Concepts
- 3. Get to Grips with Commonly Used Functions
- 4. Connecting NumPy with the Rest of the World
- Introduction
- Using the buffer protocol
- Using the array interface
- Exchanging data with MATLAB and Octave
- Installing RPy2
- Interfacing with R
- Installing JPype
- Sending a NumPy array to JPype
- Installing Google App Engine
- Deploying NumPy code in the Google cloud
- Running NumPy code in a Python Anywhere web console
- Setting up PiCloud
- 5. Audio and Image Processing
- 6. Special Arrays and Universal Functions
- 7. Profiling and Debugging
- 8. Quality Assurance
- 9. Speed Up Code with Cython
- 10. Fun with Scikits
- Introduction
- Installing scikits-learn
- Loading an example dataset
- Clustering Dow Jones stocks with scikits-learn
- Installing scikits-statsmodels
- Performing a normality test with scikits-statsmodels
- Installing scikits-image
- Detecting corners
- Detecting edges
- Installing Pandas
- Estimating stock returns correlation with Pandas
- Loading data as pandas objects from statsmodels
- Resampling time series data
- Index
Jython is the default interoperability solution for Python and Java. However, Jython runs on the Java Virtual Machine, and therefore, cannot access NumPy modules because they are mostly written in C. JPype is an open-source project that tries to solve this problem. The interfacing occurs on the native level between the Python and Java virtual machines. Let's install JPype.
Follow the ensuing steps for installing JPype:
- Download JPype.
Download JPype from http://sourceforge.net/projects/jpype/files/.
- Build JPype.
Unpack JPype and run the following command:
python setup.py install
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