Great resources for learning how to program in Python

In this post I am going to list a couple of great resources for learning how to write code in Python. I start with a couple of iPython notebooks. If you are not familliar with iPython (called jupyter these days):

"Notebook documents (or “notebooks”, all lower case) are documents produced by the Jupyter Notebook App which contain both computer code (e.g. python) and rich text elements (paragraph, equations, figures, links, etc...)."

• Intro to Python, iPython Notebook, Numpy, Matplotlib (Homework 0) (solution)

• Poll aggregation, web scraping, plotting, model evaluation, and forecasting (Homework 1) (solutions)

• Data prediction, manipulation, and evaluation (Homework 2) (solutions)

• Predictive modeling, model calibration, sentiment analysis (Homework 3) (solutions)

• Recommendation engines, Using mapreduce (Homework 4) (solutions)

• Network visualization and analysis (Homework 5) (solutions)

I follow up these with more notebooks and they are more on general Python learning;
I end this post with this 3+ hours long video on data analysis with Pandas. This is a tutorial that introduces you to manipulating and analyzing large and small structured data sets.  Hope these resources are enough for now! I may update the list when I find more!
The first jupyter/iPython notebooks come from a Harvard course.

It contains homeworks, and solutions to these homeworks. By doing the homeworks you will be guided through a number of data analysis, mining, scraping, manipulation problems with Python and iPython/Jupyter notebook!

• Introduction to Programming (using Python), an entire introductory Python course. This is super nice and helpful if you want to learn Python. 
• Python for Developers, a complete book on Python programming. Completely Free Python book?! Oh yes, the Python community is so great!
• Exploratory Computing with Python. Contains 15 notebooks (I know right!) that cover exploratory computing, data analysis, and visualization. No prior programming knowledge required. What is extra awesome is that they also contain a number of exercises (with solutions) that should take less than 4 hours to complete. 
• Understanding evolutionary strategies and covariance matrix adaptation