1000x faster data manipulation: vectorizing with Pandas and Numpy with Nathan

1000x faster data manipulation: vectorizing with Pandas and Numpy with Nathan Cheever

The data transformation code you’re writing is correct, but potentially 1000x slower than it needs to be! In this talk, we will go over multiple ways to enhance a data transformation workflow with Pandas and Numpy by showing how to replace slower, perhaps more familiar, ways of operating on Pandas data frames with faster-vectorized solutions to common use cases like:

• if-else logic in applied row-wise functions
• dictionary lookups with conditional logic
• Date comparisons and calculations
• Regex and string column manipulation

and others! …without needing a beefier computer, writing Cython, or other libraries outside the Pandas ecosystem.

A $5 voluntary donation is requested. Students are excluded from donating.

Location logistics:

INSCC Auditorium Room 110

Enter on through the south-west corner doors

https://map.utah.edu/?buildingnumber=19
NOTE Visitor parking on campus will require a fee.

Either use a pay lot or pay in advance for a permit at

https://commuterservices.utah.edu/campus-parking/visitors.php

If you go to https://commuterservices.utah.edu/ and log in as a visitor, you can buy an evening pass for $4. That will let you park in the parking structure that is just north of the INSCC building (in the A, E, and U sections).

Parking is enforced until 8pm. The map below indicates the visitor lots which are easiest to find a spot in

You may try parking in the nearby neighborhood, you risk a city parking ticket; however, with it being so late it may not be heavily enforced.
FYI there is good public transportation to the University.