08 Strings And Regular Expressions Ipynb Colab

There was an error while loading. Please reload this page. You can order print and ebook versions of Think Python 3e from Bookshop.org and Amazon. Strings are not like integers, floats, and booleans. A string is a sequence, which means it contains multiple values in a particular order. In this chapter we’ll see how to access the values that make up a string, and we’ll use functions that process strings.

We’ll also use regular expressions, which are a powerful tool for finding patterns in a string and performing operations like search and replace. As an exercise, you’ll have a chance to apply these tools to a word game called Wordle. A string is a sequence of characters. A character can be a letter (in almost any alphabet), a digit, a punctuation mark, or white space. The text is released under the CC-BY-NC-ND license, and code is released under the MIT license. If you find this content useful, please consider supporting the work by buying the book!

< Pivot Tables | Contents | Working with Time Series > One strength of Python is its relative ease in handling and manipulating string data. Pandas builds on this and provides a comprehensive set of vectorized string operations that become an essential piece of the type of munging required when working with (read: cleaning up) real-world data. In this section, we'll walk through some of the Pandas string operations, and then take a look at using them to partially clean up a very messy dataset of recipes collected from the Internet. We saw in previous sections how tools like NumPy and Pandas generalize arithmetic operations so that we can easily and quickly perform the same operation on many array elements. For example:

This vectorization of operations simplifies the syntax of operating on arrays of data: we no longer have to worry about the size or shape of the array, but just about what operation we want... For arrays of strings, NumPy does not provide such simple access, and thus you're stuck using a more verbose loop syntax: There was an error while loading. Please reload this page. This notebook comes from A Whirlwind Tour of Python by Jake VanderPlas (OReilly Media, 2016). This content is licensed CC0.

The full notebook listing is available at https://github.com/jakevdp/WhirlwindTourOfPython. < Modules and Packages | Contents | A Preview of Data Science Tools > One place where the Python language really shines is in the manipulation of strings. This section will cover some of Python's built-in string methods and formatting operations, before moving on to a quick guide to the extremely useful subject of regular expressions. Such string manipulation patterns come up often in the context of data science work, and is one big perk of Python in this context. Strings in Python can be defined using either single or double quotations (they are functionally equivalent):

In addition, it is possible to define multi-line strings using a triple-quote syntax: There was an error while loading. Please reload this page.

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