Course Python NumPy

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  • Certificate
  • Course Python NumPy : Content

    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.

    Overview NumPy and SciPy

    The course starts with an overview of NumPy and its sister library SciPy and how we can install these libraries.

    NumPy ndarray

    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.

    Matrix Handling

    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.

  • Course Python NumPy : Training

    Audience Python NumPy Course

    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.

    Prerequisites Course Python NumPy

    To participate in this course prior knowledge of Python programming is necessary. Knowledge of numerical methods is beneficial for the understanding.

    Realization Training Python NumPy

    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.

    Certification Python NumPy

    The participants receive an official certificate Numerical Python after succesful completion of the course.

    Course Numerical Python
  • Course Python NumPy : Modules

    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
    Missing values
    Handling NaNs
    nanmean(), nanvar() and nanstd()
    File I/O
    Loading from CSV files
    mean() function
    Value range
    full() and full_like() functions
    Working with Matrices
    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

    Module 4 : Special Routines

    Module 5 : Plotting with MathplotLib

    partition() function
    Complex numbers
    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
    Logarithmic plots
    Scatter plots
    Fill between
    Legend and annotations
    Threedimensional plots
    Contour Plots
  • Course Python NumPy : General

    Course Forms

    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.

    Course times

    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.

  • Course Python NumPy : Reviews

  • Course Python NumPy : Certificate