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Hi, welcome back to a new section in this section, you will learn to use regular expressions.

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So in this lecture, I'll explain you what regular expressions or first thing you should know is that

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regular expressions are use when you are dealing with text and when you're dealing with text.

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A good tool to use is a Jupyter notebook style environment, such as Jupiter Lab, which is a local

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solution or cloud based solution such as deep notes or Google.

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Call that I'm using deep notes in my case.

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So feel free to use any Jupiter notebook, or you can also use your own ideas, such as visual studio

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codes or by charm or Apple.

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So any idea with do, but I suggest you use Jupiter notebooks such as the wonks I'm using on deep notes.

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So if you want to use deep nodes, go to Duplo.

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That's com creates a free account and then go to new projects and you create a new Jupiter notebook.

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Then I'll give a name to my new notebook.

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I'll change it from ontitled pilot project to regular expressions.

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Press answers to change the name.

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And so now we have this notebook.

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You can simply run codes here.

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So just this is a sale.

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This is where we write codes.

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So print hello, execute with control, enter or command.

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Enter.

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And you get the offers.

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You're going to add some more codes here.

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Print.

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Hello again.

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Press command answer and on.

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Now back to regular expressions, what are regular expressions?

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Well, regular expressions basically are a set of characters, special characters that we write in Python.

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For example, a.

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Dash, that means any lowercase letter between A and set dot means any possible character zero to nine

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means any possible number and so on.

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So by giving these special characters, you are searching in a string for text patterns.

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Let me give you an example.

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Let's suppose we have some text file in our current directory here, and we want to process those files.

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As you know from the previous section or on file and folder operations, you could get a list of file

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names using the path dot ITR to methods.

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So that should give you something like file names, a list basically, which contains all the file names,

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which could be something like Nov 12 dot

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November 14 Dot Sixty.

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Perhaps Oct. four, October 17, that you could have Nov 22, the TSA unsworn, so we could have a thousand

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files now what if your task was to extract only the files that have a particular name pattern?

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For example, you only warned the files from the 1st of November to the 20th of November.

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So in that case, in our small example here, you would have to extract this file name that but not

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these two.

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So you want to automate the way to extract only those files that have the file name related to 1st of

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November, up to the 20th of November.

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In that case, you have to use regular expressions.

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You have to create a pattern using your regular expressions, so a set of special characters.

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So by creating that pattern, you are instructing Python.

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You are giving instructions to extract only those file names that have a pattern, such as, for example,

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that we want to see start with no, then have a dash and then have a number from one to 20.

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So that's the idea.

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That's what we want to do in our minds, but we want to translate that into this set of characters and

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then filter those out.

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So that's what we're going to do in the next video.

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So that is a list of strings, but you could also have just one single string.

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For example, it could be just some simple text, such as, Hi, there you hear.

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And then you have perhaps some email addresses such as example, the dot example, dot com, some more

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text here and there.

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And then perhaps you have another email address.

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Another ATS example?

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Dot the E!

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And so on.

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So in this other example, your task could be to extract the email addresses only from these big piece

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of text.

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Again, you'd have to write a regular expression where you would see half any number of characters van,

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followed by ET.

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So that's the distinct part of an email address or email addresses have this at symbol and then followed

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by another set of characters.

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Any number of characters, followed by a dot and followed by another set of characters which could become

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the E etc. So the final output would be a list of all the email addresses.

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So those are two typical examples of using regular expressions.

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You have probably also encountered regular expressions in programs such as what processors say, Microsoft

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Office, where there is a built in tool that allows you to write a regular expression.

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So a regular expression is not exclusive to Python or regular expressions are basically a language.

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You can avoid those expressions in other programs as well.

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Microsoft Office as an example also Python.

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However, writing regular expressions inside Python takes you to another level because you can do everything

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with Python.

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You can combine the search with another operation off of that.

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So, for example, here you want to extract this email addresses and then send an email to all those

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email addresses.

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So Python allows you to do that because you have libraries for everything.

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So you have libraries for sending out emails, and perhaps you want to see these into a seasoned file.

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You have pandas, the pandas library for events as well.

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So Microsoft Word is just a tiny, tiny dot compared to Python towards Python can do.

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So I hope that gives you some insights on what regular expressions are, but you will see them in practice,

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certainly in the next video.

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We will be working on this email example from the email addresses, from this piece of text.

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So see you in the next video.