This is the course material for the course: Scientific computing in Python with SciPy,
given on Feb 1st, 2019 by Dr. Robert Cimrman

Tentative programme:

9:00-9:30	Introduction: installation and basic IPython
9:30-10:30	Quick overview of Python language and introduction to NumPy
10:30-11:00	Coffee break at Subway (ground floor)
11:00-12:15	Practice session: intro to Jupyter Notebook, with basic NumPy and Matplotlib
12:15-13:00	Lunch at the University mensa (table is reserved)
13:00-14:00	More NumPy and Matplotlib
14:00-15:00	Practice session: NumPy and Matplotlib
15:00-16:30	SciPy and some examples
16:30-17:00	Concluding remarks

Installing Scientific Python

Two major “distributions” contain NumPy, SciPy, Matplotlib, Pandas and many other Python libraries:

• Anaconda distribution
• Enthought Canopy

SciPy Lecture Notes

• SciPy Lecture notes in HTML
• SciPy Lecture notes in PDF

Cheat sheets

• Python 3 basics
• NumPy and NumPy vs Matlab
• Matplotlib
• SciPy
• Pandas

Practice sessions

Here are the Jupyter notebooks for all practice sessions:

• practiceJupyterNumpyMatplotlib.ipynb
• practiceNumpy.ipynb
• practiceMatplotlib.ipynb
• practiceSciPy.ipynb
• practicePandasDataAnalysis.ipynb with Excel data course_data.xls

Examples

Here are some further examples:

• belousov-zhabotinsky.ipynb
• game-of-life.ipynb
• ode_spring_mass2.py