Learning by doing
Trainers with practical experience
Detailed course material
Clear content description
Tailormade content possible
Training that proceeds
The course starts with an overview of NumPy and its sister library SciPy and how we can install these libraries.
Next the NumPy's ndarray object and its methods are discussed. Attention is paid to many different array manipulation techniques. These methods process large data sets very efficiently.
Next matrix handling with Numpy is treated and attention is paid to special routines for ordening, searching and comparing data in matrices.
Finally the MatplotLib library is discussed. This library is closely integrated with NumPy and SciPy and this makes it a very powerful tool to create and plot complex figures. The course uses real world examples to visualize of one- and two dimensional data.
The course Python NumPy is intended for scientists and Big Data analysts who want to use Python with NumPy and MatPlotLib for data analysis and data processing.
To participate in this course prior knowledge of Python programming is necessary. Knowledge of numerical methods is beneficial for the understanding.
The theory is dealt with on the basis of presentation slides. The concepts are illustrated with demos. The theory is interspersed with exercises. The course times are from 9.30 to 16.30.
The participants receive an official certificate Numerical Python after succesful completion of the course.
Module 1 : Numpy Intro
Module 2 : Common Functions
Module 3 : Matrices
|What is NumPy?
What is SciPy?
NumPy array object
NumPy numerical types
Data type objects
Onedimensional slicing and indexing
Multidimensional slicing and indexing
any(),all(), slicing, reshape()
Manipulating array shapes
Stacking and Splitting arrays
|Methods of ndarray
Views versus copies
nanmean(), nanvar() and nanstd()
Loading from CSV files
full() and full_like() functions
|Working with Matrices
Singular value decomposition
Module 4 : Special Routines
Module 5 : Plotting with MathplotLib
Array elements extraction
Almost equal arrays
Floating point comparisons
Plot format string
Legend and annotations