David Cournapeau & Simon Jagoe - Narrowing the Gap Between Array Computing and Databases
640 views · Published 13 April 2014 · 1:13:42 · Indexed 10 October 2026
Channel: PyData · 2014 · People & Blogs
Numpy and pandas are the corner stones of data analysis in python. They allow for efficient data access and manipulation. Yet, they are not always appropriate for more heterogeneous data usage, when access patterns are hard to predict, or when you need to support write parallelism. This is an area where traditional databases systems still shine compared to the traditional data scientist toolset. The goal of this tutorial is to give you an idea of how databases can help you dealing with data which are not just numerical, with minimal effort or knowledge. We will focus on Postgresql, an open source database that have powerful extensions to deal with heterogeneous (aka 'schemaless') data, while being simple to use from python. 00:00 Welcome! 00:10 Help us add time stamps or captions to this video! See the description for details. Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps
More from this channel
-
31:23
Kostas Perifanos - Authorship Attribution Using Python
-
31:08
Prash Majmudar - Measuring the Digital Economy Using Big Data
-
35:01
Martin Goodson - Most Winning A/B Test Results are Illusory
-
41:59
Dirk Gorissen - Measuring and Predicting Departures from Routine in Human Mobility
-
44:17
Jonny Edwards - Recommenders in Python
-
54:39
Gael Varoquaux - Building a cutting-edge data processing environment on a budget
-
1:09:55
Eytan Bakshy - Designing and Deploying Online Experiments with PlanOut
-
41:31
Jacob Barhak - The Reference Model for Disease Progression