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Learning NumPy Array

By : Ivan Idris
Book Image

Learning NumPy Array

By: Ivan Idris

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Learning NumPy Array
Ivan Idris
Jun, 2014 | 164 pages
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Table of Contents (14 chapters)
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
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

Installing NumPy, Matplotlib, and SciPy on Mac OS X

You can install NumPy, Matplotlib, and SciPy on the Mac with a graphical installer or from the command line with a port manager, such as MacPorts or Fink, depending on your preference.

Note

We can get a NumPy installer from the SourceForge website at http://sourceforge.net/projects/numpy/files/. Similar files exist for Matplotlib and SciPy. Just change numpy in the previous URL to scipy or matplotlib. IPython didn't have a GUI installer at the time of writing. Download the appropriate DMG file as shown in the following screenshot; usually the latest one is the best. Another alternative is the SciPy Superpack (https://github.com/fonnesbeck/ScipySuperpack). Whichever option you choose, it is important to make sure that updates which impact the system Python library don't negatively influence the already installed software by not building against the Python library provided by Apple.

We will install NumPy with a GUI installer using the following steps:

  1. Open the DMG file as shown in the following screenshot (in this example, numpy-1.8.0-py2.7-python.org-macosx10.6.dmg):

  2. Double-click on the icon of the opened box, that is, the one having a subscript that ends with .mpkg. We will be presented with the welcome screen of the installer.

  3. Click on the Continue button to go to the Read Me screen, where we will be presented with a short description of NumPy, as shown in the following screenshot:

  4. Click on the Continue button to go to the License screen.

  5. Read the license, click on the Continue button, and then on the Accept button, when prompted to accept the license. Continue through the next screens and click on the Finish button at the end.

Alternatively, we can install NumPy, SciPy, Matplotlib, and IPython through the MacPorts route or with Fink. The following installation steps install all these packages. We only need NumPy for the tutorials in this book, so please omit the packages you are not interested in.

  • To install with MacPorts, type the following command:

    sudo port install py-numpy py-scipy py-matplotlib py-ipython

  • Fink also has packages for NumPy: scipy-core-py24, scipy-core-py25, and scipy-core-py26. The SciPy packages are: scipy-py24, scipy-py25, and scipy-py26. We can install NumPy and the other recommended packages that we will be using in this book for Python 2.6 with the following command:

    fink install scipy-core-py26 scipy-py26 matplotlib-py26
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