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So you may recall any previous skill we talked about how important YAML is in

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the world of

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network automation it is utilized say for example by Ansible and again this is

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one of the reasons

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why Ansible is so popular is because of its reliance on YAML because YAML is

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very very human

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readable. So as such many of our configuration files are going to be stored in

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the YAML format

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similarly if we want to perhaps create our own inventory and read it into

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Python very often

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the fill format we will be using is actually the YAML format so we want to have
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the fill format we will be using is actually the YAML format so we want to have

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a way to read

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in YAML files directly and natively within Python. Now there actually is a

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particular way in which

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we can do this and this is by using a specialized library known as PyYAML. Now

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in order to actually

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get our hands on PyYAML it doesn't actually come with Python directly so we're

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going to have to do

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some additional installation. Now before we actually do this let me just take a

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brief word to talk

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to about what is known as a virtual environment. So really all a virtual

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environment is as a way whereby
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environment is as a way whereby

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you can safely install particular libraries into your environment and it's not

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going to have any

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effect on your main system installations basically they're going to be isolated

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and separated

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and quite honestly it's just a very good practice to do so. So let me just show

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you how we can create

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a virtual environment within our directory here. Now because I want to be into

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what I will say is

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pseudo-opt install and what I will say is PyYAML 3-VenV if I hit Enter here and

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then type in my super

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user password and hit Enter game. What's going to happen is we're going to

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download the VenV package.
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download the VenV package.

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This is going to allow me to create virtual environments. So now if I just say

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PyYAML 3-M for the module

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and then say VenV what I will now do is say dot VenV pretty much I'm saying pay

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PyYAML use the VenV

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module to create a virtual environment in this directory called dot VenV now

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dot just makes it

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hidden environment or rather hidden directory. So when I do it irregular

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listing you're not going

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to see this particular folder but if I do CD to change into that directory we

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actually see it does

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exist. Now inside this folder we're going to have some particular files namely
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exist. Now inside this folder we're going to have some particular files namely

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this one here the bin

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file rather the bin directory. If I just say source bin/activate on the left

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hand side here we're

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actually seeing that the virtual environment has now been activated so let me

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just go back

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to the directory and here we are right here but the thing is now I am working

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within a virtual

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environment meaning that anything that happened to install is not installed

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system-wide. So basically

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I've got a little bit more freedom I don't have to worry about this particular

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installation breaking

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my machine. So the way we can actually install things in PyThon is by using the
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my machine. So the way we can actually install things in PyThon is by using the

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pip package manager.

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So what I was first say is Sido apt install PyThon3-Pip and to enter. Now as it

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transpires I have already

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got pip installed so what I can now do is they pip3 install and I'm going to

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install PyYaml. So now

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we can see here I have PyYaml and what this now means is that I can import this

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library into my

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scripts and make use of it. So let me just create a new file let's just call

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this about me.Yaml
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this about me.Yaml

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okay really great simple so if I open this up right here I'm going to basically

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describe myself

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utilizing Yaml. I'll say my name is John McGovern. I'll say my occupation as a

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trainer. So my age is

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38 and I'll say my hobbies and I'm going to have more than one hobby so what I

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will do is use this

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particular syntax with these hyphens. So I'll say I like to do programming, I

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like to do networking,

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I like to golf, I also enjoy movies and you know that'll do that kind of sums

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me up okay
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me up okay

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now right now the data here is fairly trivial it doesn't matter what it is this

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could be network

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configurations this could be device information to connect to. The point is is

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that the information

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is in the YAML format and we want to be able to get access to this information

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utilizing Python.

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So what I will do here is just say example 2.py let's open this up and let's

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see how we can get

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access to this particular data. So here's the first thing I'm going to do I'm

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going to import

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YAML and this is the name of the library that I just installed. Now very very
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YAML and this is the name of the library that I just installed. Now very very

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similar to what we

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saw before I'm going to use my context manager so you remember how we do this

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we say with and then

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I'll use the open function and this time though what I'm going to do is specify

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the name of the YAML

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file that I want to open so in this case here it's just called about dashme.yam

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l now what I'm

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going to be doing I'm going to be opening then read mode so I'll say comma then

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R and then I will

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use a variable name so I gain let's just stick with the same table format let's

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just say target file

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okay now this is where things get a little bit different let's just say I want
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okay now this is where things get a little bit different let's just say I want

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to create a variable

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called my data to store the actual information when we read it what I don't

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want to do is simply say

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you know targetfile.read in this case we don't want to do that let me show you

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why if I say print

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my data here and I save this and I run the script well it looks like we got

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access to the data

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but we really didn't this here is just once again regular string data in fact I

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can just prove this

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here just by printing the type so let's just save this try it again as we can

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see it's just a regular
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see it's just a regular

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string and the problem here is that the reason why we've actually used a

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particular file format

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such as YAML is because YAML actually gives us access to structured data

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meaning that we can

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work with the data programmatically now as it stands right now if we just read

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it as regular text

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we cannot do this we want to have this ability to associate things like keys

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with particular values

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and have the concept of things like list objects okay now right now as it

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stands all of this is just

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being associated or rather interpreted as one big string not all of these
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being associated or rather interpreted as one big string not all of these

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individual objects we

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don't have a difference between this here being a string and this here being an

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integer for example

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so we want to make sure that we load it and not print it as a string but load

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it and print it as a

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YAML document so the way we're going to do this is by making use of this

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particular import

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right here YAML account so what I will now say is I will take the YAML library

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and I'm going to use

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what is known as the safe load functions is going to allow me to safely load my

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YAML file

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and what I'm going to be loading I'm going to be loading the target file that I
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and what I'm going to be loading I'm going to be loading the target file that I

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've just opened

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so now we have used this particular library to load the information in this

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file and save it as

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this particular object right here my data so if I now print this save you are a

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up try again

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notice the way this prints out a little bit differently now the reason why is

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that if we say for

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example print the type of the data right here what's what happens okay it's not

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going to be a string

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we're actually getting a Python dictionary because what we actually have here

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are all of these
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are all of these

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Python dictionary keys so the cool thing about this therefore means it can

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still access this data

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programmatically by referencing the key so say for example I just want to very

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particularly target

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the age information within this document like do it as opposed to printing out

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the entire document

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within my square brackets I can just give the name of the key that I want to

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get say for example the

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age if I save this run the script look at this right there I can now very

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precisely target and

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parse out very particular details of this data which as you can imagine is very
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parse out very particular details of this data which as you can imagine is very

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very important

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when we want to target and send very specific pieces of our configuration this

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is why we would

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like to have it in this structure format in fact if I do things such as you

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know age is equal to

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my data and grab the age key if I print the type of the age save this run it

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again but actually

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seeing this as an integer value so what that means therefore is that we could

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run mathematical

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operations I can say if the age is greater than 18 for example what I can do is
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operations I can say if the age is greater than 18 for example what I can do is

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print statement

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this person can go to bars save this run the script as we can see here this

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person can go to bars

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whereas if we change the value let's put the person as 8 for example as opposed

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to 38 if I

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rerun the script as we can see it does not print out the statement we can go to

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bars because we

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didn't pass the condition so really whilst this particular example is kind of

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frivolous the general

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point is very important having this data stored in YAML allows us to store it
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point is very important having this data stored in YAML allows us to store it

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in a programmatic

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fashion which means that when we access the data within our Python scripts we

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can access it as

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programmatic objects not just as a general string like just reading a text file

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we can read things

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like dictionaries we can have access to list of objects we can have access to

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strings within that

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as well as integers so if we want to perform mathematical operations all of

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this is available to us

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by using the correct libraries which is the type of thing we need when we want
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by using the correct libraries which is the type of thing we need when we want

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to say for example

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send real configurations to real devices or perform network configuration tests

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against real

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devices so now we've covered some of the core fundamentals of reading files and

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reading and

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potential configuration files let's now have a little bit more fun and begin

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sending actual commands

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to your devices utilizing Python.