Python itself does not include vectors, matrices, or dataframes as fundamental data types. As Python became an increasingly popular language, however, it was quickly realized that this was a major short-coming, and new libraries were created that added these data-types (and did so in a very, very high performance manner) to Python.
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The original library that added vectors and matrices to Python was called numpy. But numpy, while very powerful, was a no-frills library. You couldn’t do things like mix data-types, label your columns, etc.. To remedy this shortcoming a new library was created – built on top of numpy – that added all the nice features we’ve come to expect from modern languages: pandas.
Advice¶
Many things about pandas will seem familiar to users of other languages like Matlab, R, or Stata, and most differences are relatively obvious. However, there are two things I think most tutorials under-emphasize I want to alert you to:
- Indices: Most languages (like R, Matlab, and Stata) organize data based on it’s order. In R, for example, if you cbind two vectors, they attached to one another based on the order of rows. In pandas, every row of a DataFrame has a name (an index label), and most things in pandas are designed to keep careful track of those index labels, and where possible to make sure that when different objects are combined, they are always aligned according to those indices.
- Changes: As of late 2015, pandas was in version 0.18. Until Version 1, pandas developers are likely to feel relatively free to make changes to some core functions in pandas to improve the language. For example, in mid-2015, the sort function was changed to help make the behavior more intuitive. So you’re aware of changes, I strongly recommend subscribing to the pydata Google Group Mailing List so when updates to pandas come out you’ll be alerted and get a summary of changes.
Tutorials¶
3 Pandas 3
Format: Text / iPython Notebooks
Summary: A really nice, text-based tutorial. Very good for basics.
Format: Video, with linked iPython notebooks
Summary: One of two very good video introductions to pandas (the other is the Jonathan Rocher video below). I suggest watching a little of both of these and picking the one that suits your learning style. This tutorial is a little more “let’s learn the principles of Pandas in the abstract then apply them”, while the Rocher tutorial is a little more “let’s learn as we do”, but mix principles and examples well depending on your learning style.
3 Pandas At Night
Format: Video, with linked iPython notebooks
3 Pandas 2 Night
Summary: The second very good video introductions to pandas. Again, I suggest watching a little of both of these and picking the one that suits your learning style.
3 Pandas 4
Format: Text / iPython Notebooks
3 Pandas At Night
Summary: Nice. A little dense, and a little more focused on getting users going than teaching basic organizing principles, but if you want to get to useable code fast, a good option.