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Faster pandas

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Overview

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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

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