Introduction to Python

This is a collection of introductory slides on Python that were used in a course given at the School of Mathematics, Statistics & Physics of Newcastle University in 2017. Please direct any queries to philipp.edelmann@newcastle.ac.uk.

How to install it

• Conda is a dedicated package manager for Python that works on Linux, macOS and Windows. Packages can be installed using the conda command.
  • It comes in two flavours:
  • Anaconda one large package with most scientific components preinstalled
  • Miniconda minimal version
• distribution packages on Linux
  • Debian/Ubuntu: python3, python3-numpy, python3-matplotlib, ipython, python3-notebook, …
• official Python package manager pip
  • Works well if Python is already present on your system and you just want to get some missing packages. If you don’t have root access, you can also install things to your home directory using pip install --user.

Should I get Python 2 or Python 3?

If you don’t have any legacy Python 2 code to maintain, you should go for Python 3. The course uses Python 3 but it will highlight the difference to Python 2 when they come up. With some care it is possible to write code that runs unchanged in Python 2 and Python 3.

Lectures

All slides from the introductory sessions are publicly available in a repository on Github. They are published under a free license, so reuse them if you like. Corrections and Contributions are appreciated.

Overview

• basic overview of Python and its capabilities

The presentation is generated from a Jupyter notebook. You can use the raw notebook file with your local install of Jupyter or directly view it in slide or HTML notebook form.

You can start an instance of Jupyter using the command jupyter notebook.

Basics

• introduction to the basic language syntax
• important packages in the standard library
• Python 2 vs. Python 3

raw notebook file slides HTML notebook

Classes

• introduction to object-oriented programming

raw notebook file slides HTML notebook

Numpy

• handle multidimensional arrays using numpy
• reading data files using numpy

raw notebook file slides HTML notebook

Matplotlib

• plotting with matplotlib
• manipulating axes and ticks
• pseudo colour plots
• colour maps
• multiple panels
• 3D plots
• object-oriented interface
• animations

raw notebook file slides HTML notebook

Scipy

• scipy: a collection of functions for scientific computing that integrates tightly with numpy
• special functions
• physical constants
• interpolation
• root finding
• fitting
• integration (integrals and ODEs)
• binary I/O from Fortran, Matlab, and IDL
• sparse matrices

raw notebook file slides HTML notebook

Links and resources

• Quick tutorial on Python for scientists