We're sorry but this page doesn't work properly without JavaScript enabled. Please enable it to continue.
Feedback

How to use pandas the wrong way

Formal Metadata

Title
How to use pandas the wrong way
Title of Series
Number of Parts
160
Author
License
CC Attribution - NonCommercial - ShareAlike 3.0 Unported:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor and the work or content is shared also in adapted form only under the conditions of this
Identifiers
Publisher
Release Date
Language

Content Metadata

Subject Area
Genre
Abstract
How to use pandas the wrong way [EuroPython 2017 - Talk - 2017-07-12 - Anfiteatro 1] [Rimini, Italy] UPDATE: slides and materials can be found at http://pietrobattiston.it/python:pycon#europython_rimini_july_2017 The pandas library represents a very efficient and convenient tool for data manipulation, but sometimes hides unexpected pitfalls which can arise in various and sometimes unintelligible ways. By briefly referring to some aspects of the implementation, I will review specific situations in which a change of approach can make code based on pandas more robust, or more performant. Some examples: inefficient indexing multiple dtypes and efficiency implicit type casting HDF5 storage overhead GroupBy.apply()... when you don't actually need i