From Mastering Large Datasets by JT Wolohan
This article explores using the map function creatively in a data project.
From Classic Computer Science Problems in Python by David Kopec
A large number of problems which computational tools solve can be broadly categorized as constraint-satisfaction problems (CSPs). CSPs are composed of variables with possible values which fall into ranges known as domains. Constraints between the variables must be satisfied in order for constraint-satisfaction problems to be solved. Those three core concepts—variables, domains, and constraints—are simple to understand, and their generality underlies the wide applicabilit
With Naomi Ceder, author of The Quick Python Book, Third Edition.
Naomi Ceder earned a Ph.D in Classics, but, since 2001, has been learning, teaching, and using Python. An elected fellow of the Python Software Foundation, Naomi currently serves as chair of its board of directors. She also speaks internationally about the Python community, and on inclusion and diversity in technology in general. By day she leads a team of Python programmers for Dick Blick Art Materials, and in her spare time she enjoys sketching, knitting, and deep philosophical conversations with her dog.
From Data Science at Scale with Python and Dask by Jesse C. Daniel
This article discusses Dask, how it compares to Apache Spark, and how to create and understand directed acyclic graphs using the example of the delicious Italian pasta dish bucatini all’Amatriciana.