Regular Expressions in Python
Learn about string manipulation and become a master at using regular expressions.
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Course Description
As a data scientist, you will encounter many situations where you will need to extract key information from huge corpora of text, clean messy data containing strings, or detect and match patterns to find useful words. All of these situations are part of text mining and are an important step before applying machine learning algorithms. This course will take you through understanding compelling concepts about string manipulation and regular expressions. You will learn how to split strings, join them back together, interpolate them, as well as detect, extract, replace, and match strings using regular expressions. On the journey to master these skills, you will work with datasets containing movie reviews or streamed tweets that can be used to determine opinion, as well as with raw text scraped from the web.
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Get your team access to the full DataCamp platform, including all the features.In the following Tracks
Associate Python Developer
Go To TrackPython Programming Toolbox
Go To Track- 1
Basic Concepts of String Manipulation
FreeStart your journey into the regular expression world! From slicing and concatenating, adjusting the case, removing spaces, to finding and replacing strings. You will learn how to master basic operation for string manipulation using a movie review dataset.
- 2
Formatting Strings
Following your journey, you will learn the main approaches that can be used to format or interpolate strings in python using a dataset containing information scraped from the web. You will explore the advantages and disadvantages of using positional formatting, embedding expressing inside string constants, and using the Template class.
- 3
Regular Expressions for Pattern Matching
Time to discover the fundamental concepts of regular expressions! In this key chapter, you will learn to understand the basic concepts of regular expression syntax. Using a real dataset with tweets meant for sentiment analysis, you will learn how to apply pattern matching using normal and special characters, and greedy and lazy quantifiers.
Introduction to regular expressions50 xpAre they bots?100 xpFind the numbers100 xpMatch and split100 xpRepetitions50 xpEverything clean100 xpSome time ago100 xpGetting tokens100 xpRegex metacharacters50 xpFinding files100 xpGive me your email100 xpInvalid password100 xpGreedy vs. non-greedy matching50 xpUnderstanding the difference100 xpGreedy matching100 xpLazy approach100 xp - 4
Advanced Regular Expression Concepts
In the last step of your journey, you will learn more complex methods of pattern matching using parentheses to group strings together or to match the same text as matched previously. Also, you will get an idea of how you can look around expressions.
Capturing groups50 xpTry another name100 xpFlying home100 xpAlternation and non-capturing groups50 xpLove it!100 xpUgh! Not for me!100 xpBackreferences50 xpParsing PDF files100 xpClose the tag, please!100 xpReeepeated characters100 xpLookaround50 xpSurrounding words100 xpFiltering phone numbers100 xpFinishing line50 xp
Training 2 or more people?
Get your team access to the full DataCamp platform, including all the features.In the following Tracks
Associate Python Developer
Go To TrackPython Programming Toolbox
Go To Trackcollaborators
prerequisites
Intermediate Python
Maria Eugenia InzaugaratData Scientist and Artificial Intelligence Consultant
Eugenia is a passionate, dedicated, and proactive data scientist and Artificial Intelligence Consultant that enjoys not only doing machine learning projects but also telling stories with data. She obtained a Ph.D. from the University of Buenos Aires. She has taught university courses in mathematics and biology as well as online courses on Data Science. Having transitioned from an academic background into data science, Eugenia loves teaching concepts related to python programming, data science, and machine learning to help others also gain knowledge about these fields.
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