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Learning by doing
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Trainers with practical experience
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Classroom training
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Detailed course material
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Clear content description
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Tailormade content possible
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Training that proceeds
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Small groups
In the course Python NumPy the Python packages NumPy en MatplotLib are discussed. These Python add-on libraries are very useful for the creation of data analysis and data processing applications.
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? Installing NumPy NumPy array object Selecting elements NumPy numerical types Data type objects dtype constructors dtype attributes Onedimensional slicing and indexing Multidimensional slicing and indexing Array comparisons any(),all(), slicing, reshape() Manipulating array shapes Stacking and Splitting arrays Converting arrays |
Methods of ndarray Clipping arrays Compressing arrays Views versus copies ravel(),flatten(),transpose() Missing values Handling NaNs nanmean(), nanvar() and nanstd() File I/O Loading from CSV files mean() function Value range Dates Correlation Smoothing full() and full_like() functions |
Working with Matrices ufuncs Creating matrices Universal functions Arithmetic functions Modulo operation Fibonacci numbers Bitwise functions Comparison functions Fancy indexing at() method Inverting matrices Finding eigenvalues Singular value decomposition Pseudo inverse Determinants |
Module 4 : Special Routines |
Module 5 : Plotting with MathplotLib |
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| Sorting partition() function Complex numbers Searching Array elements extraction Assert functions Almost equal arrays Equal arrays Ordering arrays Object comparison String comparison Floating point comparisons Unit tests |
Simple plots Plot format string Subplots Histograms Logarithmic plots Scatter plots Fill between Legend and annotations Threedimensional plots Contour Plots Transformations Animation Projections |
All our courses are classroom courses in which the students are guided through the material on the basis of an experienced trainer with in-depth material knowledge. Theory is always interspersed with exercises.
We also do custom classes and then adjust the course content to your wishes. On request we will also discuss your practical cases.
The course times are from 9.30 to 16.30. But we are flexible in this. Sometimes people have to bring children to the daycare and other times are more convenient for them. In good consultation we can then agree on different course times.
We take care of the computers on which the course can be held. The software required for the course has already been installed on these computers. You do not have to bring a laptop to participate in the course. If you prefer to work on your own laptop, you can take it with you if you wish. The required software is then installed at the start of the course.
Our courses are generally given with Open Source software such as Eclipse, IntelliJ, Tomcat, Pycharm, Anaconda and Netbeans. You will receive the digital course material to take home after the course.
The course includes lunch that we use in a restaurant within walking distance of the course room.
The courses are planned at various places in the country. A course takes place at a location if at least 3 people register for that location. If there are registrations for different locations, the course will take place at our main location, Houten which is just below Utrecht. A course at our main location also takes place with 2 registrations and regularly with 1 registration. And we also do courses at the customer’s location if they appreciate that.
At the end of each course, participants are requested to evaluate the course in terms of course content, course material, trainer and location. The evaluation form can be found at https://www.klantenvertellen.nl/reviews/1039545/spiraltrain?lang=en. The evaluations of previous participants and previous courses can also be found there.
The intellectual property rights of the published course content, also referred to as an information sheet, belong to SpiralTrain. It is not allowed to publish the course information, the information sheet, in written or digital form without the explicit permission of SpiralTrain. The course content is to be understood as the description of the course content in sentences as well as the division of the course into modules and topics in the modules.
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