The goal of data analysis is to write code that manipulates data to give us answers. Ideally, we could translate our questions into code as quickly as we could think! Dplython is an open source Python library (inspired by R's "dplyr") that improves productivity by constraining analysis to a core set of the most common data manipulation operations. By mapping the way we think about typical tasks to functions, dplython moves data analysis closer to "speed-of-thought." In this talk, I'll describe the core ideas behind dplython, present a tutorial on using it for data analysis, and give a technical peek at Pythonic lazy evaluations created with operator overloading.