Python Tutorial: Text Munging with regular expressions

Want to learn more? Take the full course at https://learn.datacamp.com/courses/building-chatbots-in-python at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Regular expressions are a powerful tool for processing text. We will use them for matching messages against known patterns, for extracting key phrases, and for transforming sentences grammatically. These are the core pieces we need to create our ELIZA style bot. Much of the magic of the ELIZA system relied on giving the _impression_ that the bot had understood you, even though the underlying logic was extremely simple. For example, asking ELIZA "do you remember when you ate strawberries in the garden?", she would respond: "How could I forget when I ate strawberries in the garden?". Part of what makes this example so compelling is the subject. We are asking about _memories_, which we associate with our conscious minds and our sense of self. The memory itself, of eating strawberries in the garden, invokes powerful emotions. But if we pick apart how the response is generated, we see that it's actually quite simple. To build an ELIZA-like system you need a few key components. The first is a simple pattern matcher. This consist of a set of rules for matching user messages, like "do you remember x" To match patterns we use a technology called *regular expressions*, to use these in python we `import re`. Regular expressions are a way to define patterns of characters, and then seeing if those patterns occur in a string. In regular expressions, the dot character is special, and matches *any* character. The asterisk means "match 0 or more occurrences of this pattern", so "dot star" is basically a catch-all, it says match any string of characters. We can check whether a message matches a pattern by calling re dot search brackets pattern comma message. This returns a match object. If the string doesn't match the pattern, the match object will be