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Last update: Mgr. Hana Kudrnová (21.04.2021)
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Last update: Mgr. Michal Belda, Ph.D. (20.04.2021)
Learn the basics of programming in the Python language with a focus on mathematical and physical applications, mainly data processing and visualization. |
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Last update: Mgr. Michal Belda, Ph.D. (20.04.2021)
For credit, a student may either submit three shorter Python programs over the course of the semester. Alternatively, one longer program may be submitted at the end of the semester. |
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Last update: Mgr. Michal Belda, Ph.D. (20.04.2021)
Python Software Foundation: Python Documentation. https://www.python.org/doc/ Pilgrim, M.: Dive into Python 3. https://diveintopython3.problemsolving.io/ |
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Last update: Mgr. Michal Belda, Ph.D. (20.04.2021)
The course is realized as a lecture and practical exercises (bring your own laptop if you can). |
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Last update: Mgr. Michal Belda, Ph.D. (20.04.2021)
Introduction to Python: language basics, history and versions (2 and 3), comparison to other languages; Python philosophy (short readable code, batteries included)
IPython console, Jupyter notebooks; integrated development environments and Python distributions; short simple single-purpose scripts
Python building blocks: syntax, variables, data types, builtins; procedural programming basics - loops, conditions, functions; syntactic sugar - do more with less code
Libraries: builtin libraries and modules, extensions.
Scientific computing: NumPy and SciPy libraries for processing vector and matrix data, statistics; processing tabular data with pandas
Input/Output: formatting, file formats, reading and writing files; specialized libraries for data used in math and physics
Visualization: creating graphs using matplotlib, seaborn and pandas
Object-oriented programming: classes, objects, attributes, methods, encapsulation, inheritance; error handling
Code optimization: NumPy, cython, parallelization
Graphical User Interface: basics of GUI using builtin libraries |