Array-Construction and Array-Properties

There are different ways to construct arrays with numpy. Using examples the most useful way a certain purpose is demonstrated. The properties if of array objects are explained.

Data Types

In contrast to Python data types that are determined dynamically at run time, data types of numpy arrays have to be explicitly specified. This is one requirement to achieve the speed advantages of numpy compared to pure Python. There are considerably more data types in numpy than in Python. The course covers the usage of those data types and especially the correspondence with C data types.

Slicing and Broadcasting

The technique of slicing allows read and write access to arbitrary parts of arrays. Since it works with multidimensional arrays it often allows for short and elegant programs without loops. Experience shows that the first steps with slicing need getting used to it. Therefore, numerous exercises are included in the course to cover different types of applications.

The so called broadcasting is applied in numpy if arrays with different shapes are used in computations. Missing parts of arrays are filled in if possible. A good understanding of this mechanism is a basic requirement for an effective work with numpy.

Universal Functions

numpy allows to apply many operations on whole arrays independent from their dimensions. Examples are use to demonstrate the usage of these universal functions.

Numerical Algebra

numpy provides basic functionality for solving problems in numerical algebra. Examples are used to show its usage.

ASCII Files

The simplest way to store data is the use of ASCII files. It is shown how ASCII files can be read and written efficiently. Techniques for conversion of column based data in Python data structures are given.

Excel Files

Many data are stored in Excel files. Python offers several ways of reading and writing data in Excel files. Access via Microsoft’s COM interface are applied as well as direct binary reading and writing of files as an platform independent solution.

NetCDF Files

The file format NetCDF can be used to store large amounts of array data up to several terabytes. Python offers an interface for NetCDF. Its handling will be cover in course.

HDF Files

The HDF-Format is used for very similar purposes as the NetCDF-Format. There are plans to unite both formats with future releases. HDF stand for hierarchical data format and offers better opportunities to organized data. The module pyTables is an mature and comfortable interface to HDF. In course example for it are given.

Databases

Frequently, data are stored in databases. Python offers a uniform API for accessing all major databases. The use of this API is taught in the course.

Pickling and Shelving

Python offers ways to conveniently store Python objects with the built-in modules pickle and shelve. Complex data structures can be store on disk for later usage without the need to come up with a file format for storage. Application as well as advantages and disadvantages are presented in the course.

matplotlib

The library matplotlib provides many different types of diagrams from within Python with only few lines of code. Examples are used to exercise the use of this library.

MayaVi

Three-dimensional visualization and animation can be easily achieved with MayaVi. The standalone usage is taught in the course as well as the automation from within a Python program.