On May 28, 2014 Phillip Cloud, core contributor for the Pandas data analytics Python library, spoke at a joint meetup of the New York Quantitative Python User’s Group (NY QPUG) and the NY Finance PUG. Enthought hosted and about 60 people joined us to listen to Phillip present some of the less-well-known, but really useful features that have come out since Pandas version 0.11 and some that are coming soon. We all learned more about how to take full advantage of the Pandas Python library, and got a better sense of how excited Phillip was to discover Pandas during his graduate work.
After a fairly comprehensive overview of Pandas, Phillip got into the new features. In version 0.11 he covered:
- indexers loc/at, iloc/iat,
- dtypes,
- using numexpr to evaluate arithmetic expressions for large objects, focusing mainly on numexpr. Then in version 0.12 he went into some depth on read_html. In the process he read data from a website and re-created a plot from the website. His examples are valuable as a way to see how an expert uses the Pandas package. He also goes over read_json and others new features as well, again with some really interesting examples.
Phillip covered some experimental features in version 0.13 including query/eval, msgpack IO and Google BigQuery IO. He then wrapped up with a sneak peak at some version 0.14 (soon to be released) features including MultiIndex slicing. His MultiIndex slicing example comes from his work on neuroscience (his cool data collection system is in the figure below).
You can watch his presentation below, and you can get his iPython Notebooks from the talk as well.
The Latest and Greatest Pandas Features (since v 0.11) from NYQPUG.
Related Content
ChatGPT on Software Engineering
Recently, I’ve been working on a new course offering in Enthought Academy titled Software Engineering for Scientists and Engineers course. I’ve focused on distilling the…
What’s in a __name__?
if __name__ == “__main__”: When I was new to Python, I ran into a mysterious block of code that looked something like: def main(): Â …
Why Python?
Why Python? Of all of the questions that I have been asked as the instructor of an Enthought Python course, this has been one of…
Accelerating Science: the Classical Mechanics Perspective
When thinking about enhancing R&D processes, Newton’s second law of motion provides the perfect framework. Classical mechanics teaches us that putting a body into motion…
Retuning the Heavens: Machine Learning and Ancient Astronomy
What can we learn about machine learning from ancient astronomy? When thinking about Machine Learning it is easy to be model-centric and get caught up…
Announcing Enthought Academy
Dear Students and Friends of Enthought, I am pleased to announce Enthought Academy—the culmination of over twenty years of teaching Scientific Python. Since our founding…
Extracting Target Labels from Deep Learning Classification Models
In the blog post Configuring a Neural Network Output Layer we highlighted how to correctly set up an output layer for deep learning models. Here,…
Exploring Python Objects
Introduction When we teach our foundational Python class, one of the things we do is make sure that our students know how to explore Python…
Choosing the Right Number of Clusters
Introduction When I first started my machine learning journey, K-means clustering was one of the first algorithms I was introduced to – and it is…
Prospecting for Data on the Web
Introduction At Enthought we teach a lot of scientists and engineers about using Python and the ecosystem of scientific Python packages for processing, analyzing, and…