| title | Resources |
|---|
- Composing Programs from MIT. Chapter 1 is Functional Programming, which we will cover.
- LearnPython collection of topic-specific tutorials. The book Learn Python is one of the best Python books, but too long.
- Real Python is a collection of tutorials, including advanced topics. Some parts are paid access, others are free.
- Invent With Python has several books. Online versions are free.
- Think Python or PDF textbook from Programming 1.
- Python Books on Github
www.pythontutor.com visualizes what Python code is doing to memory.
These are highly recommended by multiple sources, but I have not tried them myself.
- Intro to Python at DataCamp.
- Python for Everyone basic and intermediate Python
- Jupyter Notebooks for Interactive Data Science, material on Github.
- Introduction to Interactive Programming in Python, Part 1 and Part 2 on Coursera, for beginners. Teaches interactive and graphical programming.
In the 2nd half of the course you will use numpy, scipy, pandas, and Jupyter notebooks. You can add these packages to your current Python 3 installation, or install everything at once (including Python) using the "Anaconda" or "Miniconda" bundles.
Intel has a high-performance implementation of many Python libraries, that are much faster than the standard Python libraries. If you plan to do compute-intensive work in Python on an Intel CPU, it is worth trying. For just learning Python there is no benefit.
You can download Intel Python as a separate distribution.
Or, add a "channel" to your existing Anaconda distribution.
To add a channel to Anaconda:
conda update conda
conda config --add channels intel
# Now add various packages.
# Some Conda packages will be replaced or downgraded with Intel verions.
conda install numpy scipy matplotlib sympy mkl