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Regular Expressions in Python

Learn about string manipulation and become a master at using regular expressions.

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4 Stunden15 Videos54 Übungen41.440 LernendeTrophyLeistungsnachweis

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Kursbeschreibung

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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In den folgenden Tracks

Assoziierter Python-Entwickler

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Python Programming Toolbox

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  1. 1

    Basic Concepts of String Manipulation

    Kostenlos

    Start 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.

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    Introduction to string manipulation
    50 xp
    First day!
    100 xp
    Artificial reviews
    100 xp
    Palindromes
    100 xp
    String operations
    50 xp
    Normalizing reviews
    100 xp
    Time to join!
    100 xp
    Split lines or split the line?
    100 xp
    Finding and replacing
    50 xp
    Finding a substring
    100 xp
    Where's the word?
    100 xp
    Replacing negations
    100 xp
  2. 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.

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    Positional formatting
    50 xp
    Put it in order!
    100 xp
    Calling by its name
    100 xp
    What day is today?
    100 xp
    Formatted string literal
    50 xp
    Literally formatting
    100 xp
    Make this function
    100 xp
    On time
    100 xp
    Template method
    50 xp
    Preparing a report
    100 xp
    Identifying prices
    100 xp
    Playing safe
    100 xp
  3. 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.

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    Introduction to regular expressions
    50 xp
    Are they bots?
    100 xp
    Find the numbers
    100 xp
    Match and split
    100 xp
    Repetitions
    50 xp
    Everything clean
    100 xp
    Some time ago
    100 xp
    Getting tokens
    100 xp
    Regex metacharacters
    50 xp
    Finding files
    100 xp
    Give me your email
    100 xp
    Invalid password
    100 xp
    Greedy vs. non-greedy matching
    50 xp
    Understanding the difference
    100 xp
    Greedy matching
    100 xp
    Lazy approach
    100 xp
  4. 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.

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    Capturing groups
    50 xp
    Try another name
    100 xp
    Flying home
    100 xp
    Alternation and non-capturing groups
    50 xp
    Love it!
    100 xp
    Ugh! Not for me!
    100 xp
    Backreferences
    50 xp
    Parsing PDF files
    100 xp
    Close the tag, please!
    100 xp
    Reeepeated characters
    100 xp
    Lookaround
    50 xp
    Surrounding words
    100 xp
    Filtering phone numbers
    100 xp
    Finishing line
    50 xp
Für Unternehmen

Trainierst du 2 oder mehr?

Verschaffen Sie Ihrem Team Zugriff auf die vollständige DataCamp-Plattform, einschließlich aller Funktionen.

In den folgenden Tracks

Assoziierter Python-Entwickler

Gehe zu Track

Python Programming Toolbox

Gehe zu Track

Datensätze

Movie ReviewsWikipedia Web PageSentiment140

Mitwirkende

Collaborator's avatar
Hillary Green-Lerman
Collaborator's avatar
Sara Billen

Voraussetzungen

Intermediate Python
Maria Eugenia Inzaugarat HeadshotMaria Eugenia Inzaugarat

Data Scientist and Artificial Intelligence Consultant

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