diff --git a/doc/source/tutorials.rst b/doc/source/tutorials.rst index db9385519bff2..0398e2892cef5 100644 --- a/doc/source/tutorials.rst +++ b/doc/source/tutorials.rst @@ -26,32 +26,34 @@ repository `_. To run the examples in th clone the GitHub repository and get IPython Notebook running. See `How to use this cookbook `_. -- `A quick tour of the IPython Notebook: `_ +- `A quick tour of the IPython Notebook: `_ Shows off IPython's awesome tab completion and magic functions. -- `Chapter 1: `_ +- `Chapter 1: `_ Reading your data into pandas is pretty much the easiest thing. Even when the encoding is wrong! -- `Chapter 2: `_ +- `Chapter 2: `_ It's not totally obvious how to select data from a pandas dataframe. Here we explain the basics (how to take slices and get columns) -- `Chapter 3: `_ +- `Chapter 3: `_ Here we get into serious slicing and dicing and learn how to filter dataframes in complicated ways, really fast. -- `Chapter 4: `_ +- `Chapter 4: `_ Groupby/aggregate is seriously my favorite thing about pandas and I use it all the time. You should probably read this. -- `Chapter 5: `_ +- `Chapter 5: `_ Here you get to find out if it's cold in Montreal in the winter (spoiler: yes). Web scraping with pandas is fun! Here we combine dataframes. -- `Chapter 6: `_ +- `Chapter 6: `_ Strings with pandas are great. It has all these vectorized string operations and they're the best. We will turn a bunch of strings containing "Snow" into vectors of numbers in a trice. -- `Chapter 7: `_ +- `Chapter 7: `_ Cleaning up messy data is never a joy, but with pandas it's easier. -- `Chapter 8: `_ +- `Chapter 8: `_ Parsing Unix timestamps is confusing at first but it turns out to be really easy. +- `Chapter 9: `_ + Reading data from SQL databases. Lessons for new pandas users