Coursera Plus Annual is 40% Off — Ends July 13.

Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

LinkedIn Learning

Faster pandas

via LinkedIn Learning

Write review

No Longer Offered

Noble Desktop Ad

Live Online Classes in Design, Coding & AI — Small Classes, Free Retakes

Learn More → Coursera Ad

AI, Data Science & Cloud Certificates from Google, IBM & Meta

Learn More →

Overview

Google, IBM & Meta Certificates – 40% Off
One plan covers every Professional Certificate on Coursera.
Unlock All Certificates
Learn how to make your pandas code quicker and more efficient. This course covers vectorization, common mistakes, pandas performance, saving memory, Numba, Cython, and more.

Syllabus

Introduction
  • pandas and performance
  • What you should know
  • Working with the files on GitHub
1. Overview
  • Why performance matters
  • Setting goals
  • Measuring performance
  • Profiling
  • Challenge: Identify bottleneck
  • Solution: Identify bottleneck
2. Vectorization
  • What is vectorization?
  • Boolean indexing
  • Understanding ufuncs
  • Challenge: Selecting and manipulating data
  • Solution: Selecting and manipulating data
3. Common Mistakes
  • The limitations of appending
  • The limitations of object dtype
  • The limitations of row iteration
  • Understanding the isin function
  • Parsing time once
  • Challenge: Query a DataFrame
  • Solution: Query a DataFrame
4. pandas Performance
  • Using built-in functions
  • Understanding eval and query
  • Understanding the join function
  • Challenge: Join and query
  • Solution: Join and query
5. Saving Memory
  • Why memory is important?
  • Measuring memory
  • Loading parts of data
  • Categorical data
  • Challenge: Reducing memory
  • Solution: Reducing memory
6. Fast Serialization
  • Various formats and why not CSV
  • Optimizing with SQL
  • Optimizing with HDF5
  • Challenge: Bike ride duration
  • Solution: Bike ride duration
7. Numba and Cython
  • What is Numba?
  • Using Numba
  • What's Cython?
  • Writing Cython code
  • Compiling Cython
  • %%cython magic
  • Challenge: Cython speedup
  • Solution: Cython speedup
8. Alternative DataFrames
  • Overview of alternative DataFrames
  • Using Dask
  • Using Vaex
  • Challenge: Vaex vs. pandas
  • Solution: Vaex vs. pandas
Conclusion
  • Next steps

Taught by

Miki Tebeka

Reviews

4.5 rating at LinkedIn Learning based on 95 ratings

Start your review of Faster pandas

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Sign up for free
Someone learning on their laptop while sitting on the floor.