00:01Hello everyone, and welcome to Python for data analysis

00:05This series does not assume any prior programming knowledge or experience, so it is suitable for complete beginners to programming

00:11Of course, if you already know some other language, but you don't know Python, this would also be appropriate for you, or if you just want to learn more about how to use Python specifically for doing data analysis tasks, this could also be useful, although you might be able to skip ahead to some of the later lessons, because the first ones are just gonna cover the very basics

00:32We'll start by discussing a little bit what Python Is Python? Python is a programming language, which happens to be the most popular one for doing data analysis and predictive modeling right now, so if you're looking to get into data science, it's probably the best language to start learning

00:47That said, Python is a general-purpose language that is popular in other domains outside of data science, so for instance, Python can be used for general software development, and it's also used quite often in web programming and back-end development

01:03Now, since Python started as a general-purpose programming language, the base language doesn't include many of the data structures and functions necessary to do data analysis, but Python has a very robust and extensive selection of add-on packages that give it great flexibility to tackle just about any task you want to

01:26But that means when you're doing data analysis with Python, you'll generally have to be installing or loading some packages in order to get the sorts of data structures and functions you'll need to do what you want to do

01:37Now, I'll talk a bit about getting set up with Python

01:41In this guide, I'm going to be using the Kaggle kernel, or now known as the Kaggle notebook environment, to deliver content, so what you're Seeing here is actually an interactive web programming environment on the website Kaggle.com, and it's run in the cloud, so I can access this and run it from anywhere, and anybody can sign up for a free account and do the same thing

02:05So this guide, if you want to follow along with it and perhaps run some of the code or even fork or copy the notebooks that I'm using, you can feel free to do that

02:14I will leave links to the notebooks in the descriptions of each video, and I will assume that you have Python running in some form, and I will explain how you can get it set up, so the easiest way to get set up right away and be able to Follow along and make sure everything will work with this guide, is to just sign up for a Kaggle account, so you'd go to Kaggle.com, click log in or sign up or whatever the buttons are, and once you do that, you can go to the link I'm gonna provide and just go to the page for this Python for data 1 getting started notebook, and you can click the big blue copy and edit button

02:52It will be in the upper right corner of the page, and by clicking that, you'll just copy over whatever notebook I'm presenting for that lesson, and there will be an edit button then after you copy it, and you'll be thrown into this exact same editor that I'm

03:07Using, so you'll have all the same software at your disposal, and everything should work fine

03:12If you want to follow along with perhaps a local installed Python, that's fine too

03:19Possibly, you might encounter more compatibility issues there, because you might need to install certain packages, or versions could be different

03:24For the most part, it should work, especially with the first several lessons that are just going to be on basics, that will work pretty much on any Python installation

03:33You do want to get a local installation of Python, I would recommend downloading the Anaconda Python distribution from

03:41Continuum Analytics, basically Anaconda, is a distribution of Python that bundles a bunch of useful packages together with it

03:50A lot of the things that you're going to want to use in data science will come already installed, so you won't have to worry about downloading tons and tons of packages after your initial install

04:01So, for the rest of this video, we're actually not going to start learning Python yet, because this is kind of just getting us set up and oriented

04:06We're going to start learning Python in the next video

04:10For the rest of this video, I'm just going to give you a quick tour of this Kaggle notebook

04:13Environment we're gonna be using, so that if you do sign up, or I already have an account and want to follow along with this, you'll know what you're doing here

04:21So basically, a programming notebook is an environment that interleaves cells of text and code and presents it in a nice format, like I have here

04:33So right now, this notebook I'm working with is actually mostly text, but you can put code cells in here as well

04:40So you'll see as I'm moving my cursor around, like this Python setup is a heading cell, and this is a text cell, and if I double click on it, I will actually go into an edit mode where I could add additional text

04:51Or I could do various things like make it bold, italic, etc., and then if you press Ctrl+Enter, it will run whatever cell you're currently highlighted

05:00For a markdown cell like this, or a text cell, it'll just make it revert to that, but if it's a code cell, it will actually, Ctrl+Enter, will run the code in the cell

05:10So I'll show how to do that

05:13When I'm hovering here, you can see there's a little button that says "plus code" or "plus markdown." If I click one of those, it'll add a new cell

05:19So if I click plus code, now we have a new cell down here with these little brackets, this is a code

05:26So, if I enter code here and then do Ctrl+Enter, it will

05:29Actually run the code in Python and then print out whatever output it is, so let's try something there

05:36Let's say 2+2, and then if I hit control enter, it will run the cell, and we can see the output is 4, so it successfully added 2 together

05:44Now, there are various other ways of running cells and interacting with cells

05:49You can see up here we have some menus, so under file, we can do things like upload or download notebooks, share kernels, check version histories, and whatnot, but we can also under this run thing, we can run the current cell, we can run the entire notebook, run things before, after occur and cell, and do other Things like restarting the session, since I don't really want to keep this cell forever, we can delete the cell

06:16So you can see these buttons here over on the right

06:19These buttons will move the cell up or down in the order of cells you have

06:23This one will delete it, and there's some other options here, but in this case, I'm going to delete it

06:30So the important thing to keep in mind is that there's two modes within the notebook

06:33There's edit mode, which is when you're actually in a cell editing it like this, and there's command mode, where you're editing things on the entire notebook level instead of the cell level

06:44To enter the command mode, you press escape, so I do that

06:46We'll pull out of this cell

06:48You can see it changed because we've pulled back to command mode, and now in command mode, we can do things on the cell level

06:54There are a lot of keyboard shortcuts that you can use to do things

06:58You can navigate with the up and down arrows, like I'm doing here

07:02You can change cells from one type to the other, so you can see this little cell here that says turn this cell into a code cell

07:08Right now, it is a markdown cell, which means we can't actually run that command 5 + 10

07:13If I try to do that, it just doesn't do anything because it's text right now

07:17But if I press the Y key, which is the key that converts something into a code cell, now you can see it looks different

07:25It's actually a code, so now, and now when I press control enter, it will actually run it and give us the output

07:31See, I enter back into command mode, and I press M, that will convert it back into a markdown cell

07:34So there are a whole bunch of different keyboard shortcuts

07:38I don't want to get too much into the weeds of what those all are, but I will show how you can look them up and just show a few of the basic, most useful ones

07:45So to check the keyboard shortcuts, we can go into command mode with escape, we'll press the

07:50P key, this brings up a searchable list of all the different commands we can do, and we'll just search for "show keyboard shortcuts." That's one of the commands you can do, so we've pulled up a list here of the various keyboard shortcuts you can do

08:04So, if you ever want to check those, this is a way to do it

08:08You don't have to worry too much about all these for this guide, but I'm just gonna go through a couple of these, they're useful

08:14So, in command mode, some of the more useful ones are just creating new cells, so you press A, will make a new cell above the current selection

08:21So here, I'm just adding cells, and B will add a cell below if

08:23You press D twice, it deletes the cell, and then all the other ones shift up, so I'm deleting cells, and another useful one is Z

08:34So if you get rid of a cell, Z will bring it back, it's like an undo at the cell level

08:40So like if I delete this cell with all this text, I go, I might not want to have done that, so you press Z and it will come back

08:46So that's all we'll really need to know about interacting with the notebook environment here

08:51Now if anything comes up as we go along, I'll just explain it at that time

08:53They all say one other thing, in the notebook window here, you'll notice this gray console bar at the bottom, you can actually

09:02Pull that up, and if you do that, you can just run code directly from Python without having to put it in a cell first, so that can be a nice way to just do some, perhaps, quick and dirty calculations and things where you're not actually wanting to type things down and save what you're typing, you just want to do some things

09:19You'll notice it also does show the output of previous things we've run, so that's all we really are going to need to know to get started

09:25In the next lesson, we will jump in and start actually learning Python itself with a lesson on Python arithmetic, so essentially using Python as a very powerful

09:36calculator, so thanks for watching and I'll see you next time.