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Now we've got a little bit of an overview of what pandas is.

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Let's start to code in so to begin we're going to load up a terminal or command prompt.

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Now this is a workflow we've been through a couple of times now but we're going to keep going through

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with each new concept to make sure we kind of get it down pat because that's what we're good doing right.

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We're just practicing and practicing and practicing.

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So if we try to access Jupiter notebooks which is our workspace where we want to use pandas we run this

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code elsewhere in the base environment.

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It's not going to work.

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So we need to activate the environment that we've created.

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So if I go Conda and list this will show me my environments and in my case I want to go to this sample

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project folder that we created and beginning because that environment has all the tools such as map

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plot lib pandas Jupiter num pi etc..

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We're focused on using Jupiter in pandas in this section so to activate that we're going to type in

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Conda activate then I could copy this here and put it down there but we're going to practice typing

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out commands in the command line because that's what we want to get proficient and used to typing these

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sort of things.

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The normal course sample project slash M will hit enter.

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So now that is activated this and you can see we've changed from base to having this environment activated.

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So if I load up a Jupiter notebook now by typing to put a notebook it's going to open up my browser.

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Beautiful now because I'm in my home directory I need to look into my desktop and then email course

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sample project.

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There we are.

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So now we're running this sample project folder within the Jupiter dashboard.

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Now this is a project that we made in before and previous session where we created the example notebook

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so to begin exploring pandas we're going to go new Python 3 notebook we'll give this a name we'll call

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it introduction to pandas There we go into it so to begin with pandas the first step is to import pandas

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as PD we've seen this a few times already but this abbreviation payday is just a short version of pandas

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it saves us from having to type pandas continually so we'll hit shift and enter and this is just going

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to tell python as a running Python 3 Hey Python go and get the pandas tool for us because we want to

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use it and now the first thing to know about pandas is there's two main data types riding that little

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hash this is a comment and I'll hit Enter the first one I'm going to look at is series so we go series

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equals PD series and now remember where the style we're gonna we're gonna go through these concepts

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is by looking at them first before discussing them because remember if in doubt run the code now if

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I make a series here the command PD dot series takes a python list so I've got a few car brands in there

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so we could really call this car brands if we want we'll leave it a series so let's hit shift and enter

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and to view our series in Jupiter notebook we'll type in series hit shift enter so now we can see we've

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got like a single one dimensional column of information that's the main thing you need to know about

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series through series equals one dimensional so one column let's practice making another one hit escape

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and create a new cell.

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So what about this one we do some colors colors equals PDT not series Red we'll go blue and white excellent

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so if we go underneath here and we'll put colors underneath so it displays it shift into beautiful.

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So now we've got two one day series one dimensional.

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When I say one day by the way one of car brands and one of different colors.

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So now the difference between a series and a data frame now a data frame is two dimensional and data

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frame is far more color common.

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I'm saying column because data frames have columns a data frame is far more common than a series because

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oftentimes with your data you'll have more than just one single column.

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So let's create a data frame.

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We'll call this one car data maybe car data equals PD dot data frame and now data frame is a little

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bit different because it takes a Python dictionary.

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So micro car make.

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And then the beautiful thing about data frames is that we can create a data frame out of series.

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So we've got series here.

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So car make.

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This is the key.

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This is the value will go color and we'll give it our colors series so down here we'll go get car data

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beautiful.

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That is our first data frame.

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Now this is so exciting right.

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Because I love data frames and it's a beautiful thing to start seeing your first ever data frame being

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made.

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And now if you have a look here making a data frame from scratch can kind of be a bit tedious.

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So what you usually do is rather than creating a data frame from some series you're going to import

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data.

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So we'll type that down and import data.

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And now where would you get data say for example we wanted some car data.

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Where could we find that.

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Well if we go up to this beautifully created premade spreadsheet and by the way all the resources as

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to what we're working on we'll be in the resources section we can see that this is a spreadsheet called

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car sales.

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And now how do you think we might get this into a panda's data frame.

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That's right.

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If you don't know the answer we haven't been through it yet but this is what panders is great at doing

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right.

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You'll have some structured data maybe in a spreadsheet or something which could be anything right.

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This is car sales but you could have transactions you could have patient records something like that

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but we want to rather than manipulate the data in a spreadsheet we want to start using code specifically

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Panda's code.

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So the first thing is we have to import it.

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Now for this car sales data the way we could do it is to export it.

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Now there's a little download tab here into commerce separated values dot CSC.

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Now this is a very common data storage file type and panders works beautifully with it.

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So if we were to export this spreadsheet data as comma separated values let's do that that's going to

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save down here.

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Now I've already imported this into my sample project folder as car sales dot CSC.

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So let's see how we would import that in pandas.

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So car sales equals PD don't read CSC and then we're going to type in the file name as a string.

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So car sales dot CSC.

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Now the beautiful thing about pandas or Jupiter notebooks in general actually is that you can use tab

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auto complete.

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So let's try that out.

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So we're gonna press tab.

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There we go.

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And we've got some options here that's going to pre fill that.

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So we don't want the missing data one we'll have a look at that in a future video but let's click that.

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There we go.

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Car sales don't CSP.

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So all this is doing is saying hey pandas with PDA read this CSB file and save it to car sales.

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So will it shift into now let's have a look at that data from beautiful.

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So now we can see this is just the exact same information that's here in a panda's data frame.

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Now the beautiful thing about this is that because it's now in a panda's data frame we can take advantage

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of all the functions that pandas has to offer and manipulating viewing and changing this data.

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All right let's have a quick overlook of what the anatomy of this data frame is because different places

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refer to different things.

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So we go to this slide the anatomy of a data frame so we'll break it down on the left hand side is the

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index column.

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Now pandas data frames like Python the list start at zero.

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So if you had five rows in your data frame it would only go up to four member it starts at zero.

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Now this here is a row.

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So these all these values here including the index is called a row and these ones here are columns and

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a row is referred to as access zero and a column is referred to as Axis equals 1.

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So if you ever see a panda's function in the future which has a little parameter called access if it's

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saying axis equals zero its meaning a row.

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Now if it's saying axis equals one it means a column.

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I got confused by this in the beginning I was just like just tell me if it's a row or column.

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But some functions use axis zero or axis equals one.

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Now these individual values here are data you can have different kinds of data types we'll see those

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in a future video and the headings up here in bold are column names.

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So these are the main things you need to know about a panders data frame.

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You can take a screenshot of this or something like that.

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It'll be in the resources section as well but if ever you hear me referring to different terms around

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here you can always have a look at Europe the anatomy of the data frame and know what what's being talked

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about.

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Let's go back to our notebook.

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And so once you have imported data from what you might want to do is make some changes here and then

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export it.

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So let's see how we would do that exporting a data frame.

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Now of course we haven't made any changes to ours just yet but we're going to export it anyway to see

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an example of how to do it to export a data frame.

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We take the variable of the data frame car sales dot to CSC so to import it was read CSB.

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Now we're gonna call this function car sales dot to CSC and then pass it a string name of what we want

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to call it import export and car sales dot CSC.

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Now you could also do car sales dot to excel if you wanted to export it to an excel file.

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But since we're only really working with CSR vs which is kind of a universal data type you might not

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have excel on your computer CSR vs you can run almost anywhere.

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We're going to export to CSC.

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So if we hit shift and enter what do you think that's gonna go let's go back up here.

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We're looking here export and car sales don't CSP a few seconds ago.

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Beautiful.

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So now if we wanted to create a new variable exported car sales just to demonstrate PD don't read CSB

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car sales now we want to export it car sales to all we're doing now is just reinforcing this file here

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to see what it looks like and then we'll go export it car sales fix up this one as well.

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Shifting into beautiful we import it back in but there's some unnamed column here.

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Now this is a little trick of what to see as V does because as this index column here.

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If you don't put in an index I think it's equals false.

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If you don't put in this it'll export it with the index as a column.

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So let's see what happens if we go that index equals false.

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Now we're going to all that's done is re export of the file and we're going to re important.

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There we go.

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It's going to index it as it gets imported.

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So that little parameter there index equals false is go.

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Export this data frame.

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And don't worry about throwing the numbers there but I think that's enough for now.

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We've looked at the main takeaways from this is that pandas have two main data types series which is

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a one dimensional column so there's only one column here data frame which is the far more common type

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which is two columns or two dimensional sorry.

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So you've got rows and columns.

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But the more common way of importing data into pandas is to read something like a CSA v file and then

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once you've made changes on this or manipulated in some way you can export it as a CSP file.

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All right I'll see you in the next video.