08 Strings And Regular Expressions Ipynb Github

There was an error while loading. Please reload this page. There was an error while loading. Please reload this page. 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 are an important part of the type of munging required when working with (read: cleaning up) real-world data.

In this chapter, 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 chapters 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: This is perhaps sufficient to work with some data, but it will break if there are any missing values, so this approach requires putting in extra checks:

This kind of manual approach is not only verbose and inconvenient, it can be error-prone. There was an error while loading. Please reload this page. Instantly share code, notes, and snippets. The text and code are released under the CC0 license; see also the companion project, the Python Data Science Handbook. < 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:

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