This is the fifth post in a series on indexing and selecting in pandas. If you are jumping in the middle and want to get caught up, here's what has been discussed so far: Basic indexing, selecting by label and locationSlicing in pandasSelecting by boolean indexingSelecting by callable Once the basics were covered in the … Continue reading Selecting in Pandas using where and mask

In pandas, you can use callables where indexers are accepted. It turns out that can be handy for a pretty common use case.

Slicing data in pandas This is second in the series on indexing and selecting data in pandas. If you haven't read it yet, see the first post that covers the basics of selecting based on index or relative numerical indexing. In this post, I'm going to review slicing, which is a core Python topic, but has … Continue reading Indexing and Selecting in Pandas – slicing

The topic of indexing and selecting data in pandas is core to using pandas, but it can be quite confusing. One reason for that is because over the years pandas has grown organically based on user requests so there are multiple way to select data out of a pandas DataFrame or Series. Reading through the documentation can be … Continue reading Indexing and Selecting in Pandas (part 1)

Pandas has a lot of functionality, but before you can explore or use it, you'll most likely want to access some data from an external source. You'll also likely want to store results for use later or be able to export results to other tools or to share with others. Pandas has a lot of … Continue reading Overview of I/O tools in Pandas

It can be very common when dealing with time series data to end up with duplicate data. This can happen for a variety of reasons, and I've encountered it more than one time when and tried different approaches to eliminate the duplicate values. There's a gem of a solution on Stack Overflow and I thought … Continue reading Removing duplicate data in Pandas