F4500 Python for physicists

Faculty of Science
Spring 2026
Extent and Intensity
1/2/0. 3 credit(s) (plus extra credits for completion). Type of Completion: k (colloquium).
In-person direct teaching
Teacher(s)
Mgr. Filip Hroch, Ph.D. (lecturer)
Mgr. Petr Klenovský, Ph.D. (lecturer)
Mgr. Filip Münz, PhD. (lecturer)
Filip Novotný, M.Sc. (lecturer)
Mgr. Tomáš Plšek, Ph.D. (lecturer)
Mgr. Petr Zikán, Ph.D. (lecturer)
Guaranteed by
Mgr. Filip Hroch, Ph.D.
Department of Theoretical Physics and Astrophysics – Physics Section – Faculty of Science
Contact Person: Mgr. Petr Zikán, Ph.D.
Supplier department: Department of Condensed Matter Physics – Physics Section – Faculty of Science (33,00 %), Department of Plasma Physics and Technology – Physics Section – Faculty of Science (33,00 %), Department of Theoretical Physics and Astrophysics – Physics Section – Faculty of Science (34,00 %)
Timetable
Mon 16. 2. to Fri 22. 5. Mon 17:00–19:50 F1 6/1014
Prerequisites
One is recommended for students of physics and related fields.
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Abstract
This course introduces Python programming language as a practical tool for physicists. Key ideas of the Python usage are demonstrated starting from simple problems up to more complex tasks. All examples are described on common physical tasks. Lectures are prepared as workshops - lectures, demonstrations, and exercises following each other. Main aim of this course is to provide the ability to prepare a wide spectrum of reports (laboratory exercises, bachelor or diploma theses) in Python including graphs, pictures and tables.
Learning outcomes
Python computer language is introduced including its applications in physics on everyday base.
We hope students will get the following skills:
To be use and install Python run-time environment,
to create programs with basic data types, data structures and flow control statements,
to apply advanced structures for data processing,
plotting scientific graphs and
to use of advanced mathematics libraries.
Key topics
  • 1. Introduction (demonstration, disputation, introduction to Python).
  • 2. ... continuation of introduction ....
  • 3. ... continuation of introduction ....
  • 4. Interpretation of oscilloscope data by using of basic containers.
  • 5. Introduction to objects
  • 6. Numpy + matplotlib
  • 7. Processing of spectroscopic data - advanced numpy.
  • 8. Regression
  • 9. Particle motion in electromagnetic fields.
  • 10. Accessing data on Internet via Python, databases.
  • 11. Introduction to machine learning
  • 12. Telescope control
Study resources and literature
    recommended literature
  • MCKINNEY, Wes. Python for data analysis : [agile tools for real world data]. 1st ed. Sebastopol, Calif.: O'Reilly, 2013, xiii, 452. ISBN 9781449319793. info
Approaches, practices, and methods used in teaching
Lectures, presentations by professionals, demonstrations and exercises.
Method of verifying learning outcomes and course completion requirements
Homework project: report in Python.
Language of instruction
Czech
Further Comments
Study Materials
The course is taught annually.
The course is also listed under the following terms Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025, Spring 2027.
  • Enrolment Statistics (recent)
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