String Cleaning with pandas 2.0

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Seth Berry

1:02:24

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01 - Introduction

01 - Cleaning strings with pandas.mp4

00:49

02 - What you need to know.mp4

01:24

02 - 1. Foundations for String Cleaning

01 - Using the PyArrow backend.mp4

02:38

02 - Testing speed.mp4

04:32

03 - Removing special characters.mp4

03:50

04 - Matching patterns.mp4

02:46

05 - Extracting patterns.mp4

03:07

06 - Counting strings.mp4

02:29

07 - Splitting strings.mp4

02:53

08 - Creating new columns from text.mp4

03:02

03 - 2. Taking Action in pandas

01 - Finding and replacing patterns.mp4

03:55

02 - Using quantifiers.mp4

03:59

03 - Reordering strings with backreferences.mp4

04:34

04 - Naming backreference groups.mp4

04:59

05 - Extracting strings with lookarounds.mp4

04:26

06 - Using boundaries and sets.mp4

03:57

07 - Chaining pandas string methods.mp4

03:14

08 - Regular expression golf.mp4

04:38

thumbs.zip

04 - Conclusion

01 - Advancing regular expressions.mp4

01:12

Description

Cleaning strings is no easy task, but it’s important, necessary work. From basic find-and-replace operations to more complex regular expressions, this course provides an overview of how to clean strings with pandas 2.0, the latest release of the open-source library for the Python programming language.

Gather insights from University of Notre Dame professor Seth Berry as he explores the fundamentals of string cleaning using pandas expressions, functions, operations, and methods, including PyArrow backends, regular expressions to find and replace text for complex patterns, speed tests, text extractions, match and split functions, quantifiers, backreferences, lookarounds, boundaries, chains, and more. By the end of this course, you’ll be ready to leverage a wide variety of pandas solutions to start cleaning strings on your own.

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Python

Pandas

Seth Berry

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