Don’t miss talks from the Anaconda team at PyData NYC! Learn more about Accelerating and Scaling GeoPandas with Cython and Dask with Matt Rocklin on Monday, November 27, at 2:45PM in Track 1, and be sure to check out Money for Nothing: Introducing Pennies, an Open-Source Pythonic Pricing Package by Casey Clements on Tuesday, November 28, at 2:25PM in Track 4.
TALK: Accelerating and Scaling GeoPandas with Cython and Dask, Matt Rocklin, @mrocklin
Geospatial data is used in city planning, real estate, agriculture, and any other field in which location has an impact. GeoPandas is the standard analytical tool for tabular geospatial data in Python. It has a well loved API, and integrates cleanly with the rest of the geospatial ecosystem, but can be very very slow.
This talk describes two recent modifications to GeoPandas that both accelerate it’s performance and enable it to handle very large datasets.
1. We Cythonize the core of GeoPandas, bringing it down to C-level speeds
2. We use Dask to parallelize GeoPandas, allowing it to use both multi-core processors and distributed memory clusters
This talk will include a brief overview of geospatial data and the GeoPandas project using examples from open datasets. It will then describe the use of Cython and Dask to accelerate and scale the project to handle larger datasets more quickly. We will end with benchmarking information and plans for the future.
TALK: Money for Nothing: Introducing Pennies, an Open-Source Pythonic Pricing Package, Casey Clements, @cscruff
Python has become the standard language of the financial community. Despite, this, there are but a few open-source packages available to us. In this talk, we present a new library that aims to provide a solid foundation for those whose work involves pricing and risk management of financial derivatives.
This library in turn builds on the foundation of the Python scientific stack, thus providing a familiar look and feel that can easily be extended. The goal is an intuitive, rigorous, framework for practitioners, not an introductory, or academic, learning tool. We will first motivate the importance of, and then develop, the fundamental building blocks – yield and forward curves. We will describe specification, calibration, and risk, and then show how these curves can be plugged into your models to provide accurate sensitivities to interest rates and spreads. Finally, we will provide a motivating example of how models can be plugged into the library.