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Numerical Calculations with NumPy

Target Audience

The course targets medium level to experienced Python programmers who would like to work effectively with with numerical arrays. It is also appropriate for scientists and engineers who need to write numerical code.

Motivaton

The library NumPy is the defacto standard for the work with arrays and linear algebra. It provides array processing capabilities comparable with MATLAB and offers a high-level tool for efficient and convenient work with numerical data.

Content

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.

Working with Missing Values

Often some values in an array are missing or not valid for certain operations. NumPy offers masked and NA-masked arrays to handle these types of data. The course introduces these data structures and shows how to use them to work with real-life data.

Customizing Error Handling

Numerical errors such as division by zero, over and underflow or invalid floating point operations happen during calculations. NumPy offers a fine-grained approach to handle these types of errors without impacting the performance.

Testing Support

Testing is very important for code quality. NumPy includes helpers to write to test code. The course introduces to testing basics with NumPy.

Course Duration

1 day

Exercises

The participants can follow all steps directly on their computers. There are exercises at the end of each unit providing ample opportunity to apply the freshly learned knowledge.