‫Welcome back, everyone.
‫And now I'm so excited for this series of videos as well because we're going to talk about pandas.
‫I wish I could give this guy a high five in real life.
‫How beautiful of an animal is this panda?
‫Now, the thing about pandas is they've usually got black ears with white face and some of them have
‫black around their eyes.
‫But in rare cases, you'll get different patterns of black.
‫No, I'm kidding.
‫We're not talking about the animal pandas, but that would be pretty cool.
‫We're talking about the data analysis library pandas.
‫Now, if you're doing machine learning or data science in Python, you're going to be using pandas.
‫And it's used to explore data, analyze data, manipulate data, get it ready for machine learning.
‫So that's what we're going to be focused on.
‫So we revisit our framework.
‫We've got this data modelling section here.
‫We've been through these steps.
‫Now we're going to really start to get into some of the tools that we can use.
‫So let's change this up a little.
‫We've still got our framework up here, but now we're starting to dive into these tools.
‫We've covered Jupyter notebooks, we've looked at Anaconda and Mini Condor.
‫We've set up an environment using those.
‫Now we're going to start diving in to doing data analysis first with things like pandas, map, plot,
‫lib and numpy, and then later on we'll look at some machine learning.
‫But for now, we're focused on pandas, which is part of usually part of these three steps here, the
‫data analysis section.
‫Now.
‫Why pandas?
‫Well, first of all, it's simple to use.
‫It's integrated with many other data science and machine learning python tools because that's what we're
‫focused on.
‫Right.
‫We want to find machine learning and data science tools in Python and use our knowledge of Python to
‫use those tools to gain insights out of data and build machine learning models.
‫And oftentimes, you'll get your data in a format that isn't quite ready for machine learning.
‫The data is there, but fairly often you have to go through a few steps to make your data in a format
‫that can be imported into a machine learning algorithm.
‫And so that's where pandas comes in.
‫It helps us take a raw table of data.
‫Raw data means data that you've just gathered and then manipulate it.
‫Ready?
‫So you can use it with machine learning algorithms.
‫So what are we going to cover in this section?
‫Well, because PANDAS is so vast, it would take quite a long time to look at the entire library.
‫So we're going to be focused on the most useful functions, things that you'll use basically in every
‫data analysis that you do.
‫We'll look at the different types of PANDAS data types.
‫There's two main ones series and DataFrame, but we'll look at that in a second.
‫Look at how to import and export data using pandas.
‫Different ways you can describe your data.
‫So if you just imported some of your data, how can you describe that statistically?
‫Like what is the average value of a certain column, etc.?
‫Pandas is so beautiful in that it allows you to view and select your data in almost any way that you
‫can imagine.
‫So if you can think of it, you can probably do it in pandas.
‫And then finally, once we've described our data, once we've seen it, once we know how to select it,
‫we'll look at how we can change it up and manipulate it.
‫We can massage it into a format that's ready for our machine learning algorithms.
‫So I think it's important before we actually start to jump into doing the actual code is to remember
‫where you can get help.
‫As we go through these videos, follow along with the code in a Jupyter notebook if you can.
‫If you can't while the video is going.
‫Don't worry, just watch the video.
‫Watch it as I go through it and then go through it on your own.
‫After the video's done.
‫It's very important to try and just write the code yourself.
‫If in doubt, just run your code and don't worry if an error pops up, there's different places you
‫can look such as StackOverflow or Pandas documentation to figure out what's gone wrong.
‫The most important is to try for yourself and then to try it again because this is what will help you
‫learn.
‫So if in doubt, run the code and then if you're still stuck, don't forget, it's always okay to ask
‫a question.
‫There's never a silly question.
‫That's an overview of what we're going to cover, what pandas is, where to get help.
‫Let's dive in and start coding.