Installation and Setup

0:07<v ->Hello and welcome.</v>

0:09My name is Jonathan Barrios

0:10and I'm excited to be your trainer

0:12for the Python and Pandas Data Manipulation course.

0:15In this set of videos,

0:16we're going to talk about pandas, Jupyter notebook,

0:20Anaconda, and Conda,

0:22with an aim of setting up our development environment

0:24for the rest of the course.

0:26Let's take a look at each one of these one-by-one.

0:31So this is pandas, and this is a pandas documentation.

0:34It actually has really great documentation.

0:36And this is what pandas sort of looks like.

0:38Here are some examples.

0:40And it's not like Excel

0:43that uses a graphical user interface.

0:45It's a programmatic approach to data analysis.

0:48So this is pandas,

0:49but you can't really download it right away

0:52using an installer or something like that.

0:54So let's take a look at the IDE,

0:56or the integrated development environment we're gonna use,

0:59Jupyter Notebook.

1:02This is Jupyter Notebook.

1:04And so what is Jupyter Notebook?

1:06Well, I said that it was an IDE,

1:09which is an integrated development environment.

1:12It's also called a interactive notebook.

1:16And Jupyter notebook, according to the website,

1:18is the original web application

1:21for creating and sharing computational documents.

1:24It's simple, streamlined, and document-centric.

1:28So what does that mean?

1:29Well, here you can see that you have a text cell

1:32with some text, some different formatting.

1:34And this is using markdown.

1:36You can also use LaTech, or LaTeX to write formulas.

1:42And this is the code cell where you would write your code,

1:45and it would generate an output

1:47of either a series or a data frame,

1:50which is basically like a spreadsheet

1:52or some sort of numbers,

1:54or in this case a visualization.

1:57And Anaconda is the Python distribution

2:00that we're gonna use to get you up and running

2:02with the packages and libraries that you'll need.

2:05And we'll use Conda to create environments,

2:09or also known as Conda environments.

2:12This might be a little bit tricky to understand

2:14without actually doing it.

2:15So my explanation is pretty straightforward.

2:18When you're creating a project,

2:19you're gonna need packages and dependencies,

2:21and they may be different versions

2:23from another project, and another project, and so on.

2:26You don't want to have all of your projects

2:27using multiple versions,

2:29and have all of them mixed together,

2:31'cause that'll cause conflicts.

2:33And it's also not very shareable.

2:34So Conda is included with Anaconda,

2:37and easily allows us to create Conda environments

2:40to make our packet

2:41or keep our projects separated and isolated.

2:45And this is a best practice

2:46for whenever you're doing data analysis

2:48and for sharing your work.

2:50You can also create a YAML file,

2:52or environment.YAML file,

2:54that you can easily send to somebody,

2:56and they can spin up the same exact environment

2:59that you're using so that they have the same experience.

3:03I hope you're excited. I know that I am.

3:05And I hope this has been informative

3:06and I'd like to thank you for viewing.

0:07<v ->Hello and welcome back.</v>

0:09In this video, we're gonna talk about "What is Pandas?"

0:12So let's take a look.

0:14So you already know that Pandas is a fast, powerful,

0:18and flexible and easy-to-use open source data analysis

0:22and manipulation tool.

0:23And you already know that it's built

0:25on top of the Python programming language.

0:28But I also think it's important

0:30to talk about the documentation

0:32before we dive into the details about Pandas.

0:35For example, this getting started section is pretty cool

0:39because it shows you that if you're coming from SQL

0:42and you already have some familiarity,

0:43you can see the Select, Group By and Join,

0:46have equivalents in Pandas.

0:48And also that Excel basically has a lot of similarities

0:52and the concepts are transferable even though

0:55that the user interface is quite different.

0:59And here is the user guide.

1:01It's really helpful and has,

1:04I guess, there's 10 Minutes to Pandas,

1:06which is really great, but anytime that you're working

1:09with some sort of a method or attribute,

1:12or you have some sort of questions about the parameters,

1:15you can just type it in here.

1:18So let's say I'm working with a series,

1:19which is basically a column,

1:22you can just find that here

1:23and you can scroll through there.

1:25I think...

1:26Okay, so the first one, that's what I'm looking for.

1:29You'll be more familiar with these sort of dot notation,

1:32but that's the actual series class.

1:35So let's take a look at that.

1:38And this is great.

1:39If you ever want to get rid of these highlights,

1:40just take away the the fragment of URL

1:43after the question mark or the question mark and after,

1:46and then hit enter,

1:47and you'll just see the same documentation

1:49without all the highlighting.

1:51And then it gives you some examples that are really useful.

1:54And then we'll dive into the two major objects,

1:57which is basically a series and a data frame.

2:01So what is a series and a data frame?

2:04A series is basically a column and a data frame

2:07is two or more series put together or multiple columns.

2:11So just think rows and columns, right?

2:14Let me kinda explain that using the spreadsheet,

2:16I think this is probably the best way to visualize it

2:20and it should make quite a bit of sense.

2:22And if it doesn't now, it will when we start

2:24to get into Pandas.

2:27So this would be considered series.

2:30So this is a column.

2:31And if you have two or more of these series,

2:34well, that would represent something called a data frame.

2:37So it's very similar to a spreadsheet,

2:40except that we're not using a graphical user interface

2:43like a spreadsheet.

2:45But first, let's talk a little bit about

2:47the history of spreadsheet programs.

2:50This is not a new concept and I like to start here

2:53because a lot of people think that

2:55Excel was, sort of, the first tool, but actually it wasn't.

2:59It was something called VisiCalc.

3:00Let's take a look.

3:02So VisiCalc actually kind of looks like Pandas.

3:06So this is a programmatic version of a spreadsheet, right?

3:10It's not like Excel that we just looked at or Google Sheets.

3:13And here you can see that we have these columns

3:16and they're kind of glued together,

3:18and I would consider that a data frame.

3:20And then right here, you have the, sort of, the index.

3:23So this, to me, is already, kind of like, like Pandas,

3:27but this started in 1979

3:31and it was originally for the Apple II computer.

3:33So it's been around for a while.

3:35However, if you scroll down here,

3:37you'll see that the first program,

3:41spreadsheet programs were actually in 1961.

3:45So Pandas, in my opinion, the concept has been around

3:49for a a long time

3:51and there's always been a need for business

3:53to use spreadsheets.

3:54And so it's not Excel, the graphical user interface,

3:57that was the first one.

3:59That's just the de facto tool for businesses

4:03because it's visual and it doesn't require a lot of training

4:06and it became very, very popular.

4:08But I just wanted to show you that

4:10this is not a new concept.

4:12It comes from 1961.

4:14So let's dive into a little bit more about Pandas.

4:19So this is Pandas inside of Jupyter Notebook.

4:22So I just wanted to give you a little bit of a tour

4:24of the final project, which is "Visualizing Cryptocurrency."

4:28It's like a cryptocurrency portfolio,

4:31and I think this will give you a better idea

4:33of what Pandas is.

4:34Let's take a look.

4:36So in this very top section,

4:39we'll go over this in the course,

4:40but I just wanna show you that first you import

4:42your libraries and here, without having to do

4:45any web scraping at all,

4:47you're pulling information from the Internet,

4:49and in this case, what we're doing is going to Yahoo Finance

4:54and grabbing Bitcoin prices in real time.

4:58And if you look, it's just one line of code.

5:02If you know how to web scrape,

5:03you know that this is pretty awesome

5:05because you need to know HTML with web scraping

5:09and you have to write, sometimes, some functions,

5:11and you know, this is a lot more, sort of, work involved.

5:16With Pandas, you can use this data reader method here

5:21and it pulls that information.

5:22So I think it's really cool.

5:24So really something to look forward to

5:26at the very, very end.

5:27But let's keep on taking a look at this notebook

5:30to see what Pandas is like.

5:31So on top of that, well here is the data frame

5:34that I was talking about in the series.

5:37So this is our index, right?

5:39So instead of having numbers, we're using dates

5:41because we're working with cryptocurrencies.

5:44Same thing if you were working with stocks.

5:46And again, you can actually change the stock ticker here

5:49to something like, you know, Microsoft or something

5:52and it would work the same.

5:53So it's pretty cool.

5:54You can do cryptocurrencies or stocks.

5:58So this is a series right here and there's another series.

6:02And as you can see, we have a bunch of series

6:05and that collection of series represents a data frame.

6:08And so this is much like Excel or Google Sheets,

6:12but it's inside of Jupyter Notebook being powered by Pandas.

6:16You can also use Matplotlib, which we'll talk about,

6:19to visualize data.

6:20And so let's take a look at some of the visualizations,

6:23and at the very end, I'm gonna show you a moving average

6:26and kind of explain a little bit about that.

6:29So we have different ways of visualizing

6:31and they're called plots.

6:32We're plotting here,

6:33and these are the opening, the high

6:36and opening and closing prices.

6:38This is the opening, closing, and adjusted closing price.

6:42And you're plotting more than one column in this case.

6:45Here, you're plotting the volume.

6:50And here, we have the moving average.

6:53So what's happening here?

6:54So we're taking 10, 20, and 50-day periods

6:59and smoothing out that line.

7:01So what does that mean?

7:02Well, let's take a look at this output.

7:05This is our data frame with all of the real time information

7:08from Yahoo Finance.

7:09And this is a visualization.

7:11And what it's doing is that normally,

7:13you would see a lot of this up and down, sort of, behavior

7:16and, sort of, stock ticker, right?

7:18And cryptocurrencies, especially, do stuff like this, right?

7:22But what we're doing here with the moving average

7:25is smoothing that line out.

7:26And that's why we have these different lines.

7:29And you can see here we have a legend that shows you

7:31the 10, 20, and 50 days.

7:34And this is helpful to know when the prices are going up

7:37and when the prices are going down.

7:39Clearly it's going up and then it's going down.

7:42You can see here this is a low point

7:44and this is very useful when you're trying to manage

7:47a stock portfolio and trying to decide

7:51about purchasing and so on.

7:53So I just wanted to give you a little bit preview

7:54and I think this is a nice explanation

7:57about what Pandas is.

8:01In the next video, we'll dive into Jupyter Notebook

8:03because we really can't use Pandas

8:05without some sort of a integrated development environment.

8:09You could use Visual Studio Code,

8:11but interactive notebooks such as Jupyter Notebook,

8:14which is a, or IDE,

8:16is really, probably, the most popular tool

8:19and that's what we were looking at just now.

8:21So without further ado, I will see you in the next video

8:25and I hope this has been informative

8:27and I'd like to thank you for viewing.

0:07<v ->In this video, we'll talk about Jupyter Notebook.</v>

0:10So we already talked about pandas and what pandas is,

0:13but how do you use pandas?

0:14Well, you would use

0:15some sort of a interactive development environment.

0:19But before we talk about Jupyter Notebook,

0:20I want to show you Visual Studio Code,

0:23which is another option for using pandas,

0:25and it's probably the most popular IDE out there.

0:28Let's take a look.

0:30This is Visual Studio Code.

0:32Again, probably one of the most popular IDEs

0:34out there right now.

0:36And it's popular for, mostly for programmers, I would say,

0:39compared to data analysts and data scientists.

0:42But if you're really working more on writing functions

0:47and classes and things like that,

0:49this is just as good as Jupyter Notebook in many ways,

0:53but here's the main difference.

0:55What makes this an integrated development environment

0:57is that you have a section here with extensions

1:00and this bottom part is a terminal

1:02that allows you to access,

1:04well, it's the terminal command line,

1:06and you can run the script from the terminal

1:09and you can also install packages.

1:11But what's different

1:12is that you write a bunch of lines of code,

1:17then you write something like app.py,

1:20if that's the name of your file,

1:22and when you execute that,

1:24then you get your output down here.

1:26And the output is not gonna look that great

1:28and you can't really visualize things

1:30'cause what it will do is it'll create a popup

1:33showing you that visualization, and that's okay.

1:36Again, it really just depends on what your needs are.

1:41If you're just getting started

1:43and you're coming from Excel or from SQL,

1:46I think Jupyter Notebook is the way to go.

1:49However, you can definitely use Visual Studio Code.

1:52So let's check out Jupyter Notebook next.

1:57Okay, so this is Jupyter Notebook,

2:00and what's the difference?

2:01Well, the main difference here

2:03is it's an iterative development environment, right?

2:06So that's not the same as IDE,

2:10which is integrated development environment,

2:11but let me explain what I mean by iterative.

2:14So you would write some code

2:17and then you execute that code

2:19and immediately you get the output.

2:21You write some more code,

2:22and immediately you get that output, right?

2:24So it's all self-contained in this one notebook, right?

2:28So that's why people call it an interactive notebook.

2:32Also, what's really great about Visual Studio Code

2:35is that you have over 40 languages that you can use.

2:38It's super easy to share,

2:40and if someone doesn't understand Python,

2:42well, they can look at this

2:44and kind of get an idea of what's going on,

2:46depending on what project you're sharing.

2:49For example, we looked at the stock prices

2:53or Bitcoin prices from Yahoo Finance,

2:56and that was pretty straightforward in that visualization.

2:59Reading the code, if you have comments,

3:01makes it a lot more accessible

3:03for people who don't know how to write code

3:06or Python, right?

3:07So that's kind of the main difference

3:09between those two integrated development environments.

3:13You can also use LaTeX and HTML

3:17and images and even videos.

3:19So it's an interactive output and it's great for big data.

3:23So there's integration with,

3:26here it says pandas, scikit-learn, and TensorFlow

3:30and Apache Spark, Python, R, Scala.

3:34So it's really pretty amazing,

3:36and no wonder it's famous, right?

3:39It's got a lot going on.

3:43And this is what Jupyter Notebook actually looks like

3:45when you get it up and running.

3:47And if you wanted to look at something,

3:48well, we're looking at the cryptocurrency,

3:51this is our project at the very, very end.

3:53We could also look at different charts.

3:56So here again, we have importing of libraries

4:00that we're gonna be using.

4:01Here, you can see this is how you use pandas.

4:04You import pandas

4:06and the data reader that pulls things from Yahoo Finance,

4:09that's how you use that.

4:10This is the library that we use for visualization

4:14and we'll talk about this little line of code later.

4:18So here what we're doing is basically scraping the Yahoo

4:21to get some Bitcoin prices in real time.

4:25And if we scroll down, you can see

4:28that whenever you execute that code, this is the output.

4:32So here, this is the iterative part

4:35that I was talking about.

4:36You write the code and then you get an output, right?

4:39If we keep going, you write the code.

4:43In this case, two code cells, and then you get this output.

4:46And same thing here, it keeps going,

4:48and I think this makes a lot of sense.

4:50You write your code here

4:51and when you execute that, you get this, right?

4:54And so on and so forth.

4:55So here are some different forms of visualizations.

4:58Not a fan of pie charts,

4:59but that's how you would create a pie chart.

5:02And again, you can't just install Jupyter Notebook

5:05using an installer.

5:07Well, I mean you can if it's the Anaconda installer.

5:10And that's what we're gonna use to install Anaconda.

5:14So in the next video, we'll talk about Anaconda,

5:17and then in the subsequent video,

5:18we'll talk about conda environments.

5:21I hope this has been informative,

5:22and I'd like to thank you for viewing.

0:07<v ->Alright, so we're finally gonna get to install our</v>

0:09development environment, and that's gonna be with Anaconda.

0:14So let's take a look at the Anaconda website

0:16and install this.

0:18I'm on a Mac, so I'm gonna show you how

0:19to install this on a Mac,

0:20but I'm also gonna point out how

0:22to install this on a Windows operating system

0:25and some of the things that you need to look out for so

0:27that we're actually using the same tools

0:30and then using the same syntax.

0:32So let's get started.

0:35Okay, so this is the anaconda.com website

0:39and we're going to download here.

0:41I could just click on this and be done with it

0:43because I'm on a Mac, but I do wanna show you the

0:45additional installers.

0:46So let's take a look at those.

0:50So here we have a Windows option.

0:53And what I want to point out here is

0:55that you wanna make sure that you're using the right version

0:57or the the correct bit.

0:59So it's more common for you to have a 64 bit version

1:03of Windows installed than a 32 bit.

1:06But if you're using a 32 bit version of Windows,

1:09then please use that because

1:10otherwise you're gonna get a lot of errors.

1:12So that's the first thing to keep in mind.

1:14After we install this, I'm gonna point out

1:17how you can use the Anaconda prompt.

1:19So that's the only other difference.

1:21So let's jump into the installation process now.

1:26Okay, so we're going to use the graphical installer.

1:30And so for Windows,

1:31that's the only one that you have available.

1:33So you could use the command line installer,

1:37but the reason that I'm, I'm not gonna do that,

1:40is I think it's a lot easier if you're just starting out

1:43to use the installer.

1:45It's the same thing using the installer versus the command

1:48line, but I just wanted to point that out.

1:51Okay, so let's go ahead and click on the graphical installer

1:55and we're gonna go ahead and just download that.

1:59I'll close this,

2:01and this is pretty for me, it doesn't take very long

2:04to get this up and running,

2:06but we're gonna run this, I already have this installed,

2:09so I'm not gonna go through every prompt,

2:12but I will guide you through this process just

2:14to make sure that you have it.

2:16So it opened up in another screen for me.

2:18So let me drag, drag this over. There we go.

2:21Okay, so you'll be guided through the steps necessary

2:23to install the software continue.

2:25And what's it's talking about here is the modified path

2:29options, and it's talking about how it's changing that path

2:32and, and also it's talking about everything

2:35that is gonna install.

2:37So what is it installing?

2:38Well, you can see

2:39that it's installing a lot of different packages.

2:41You saw Anaconda, let's see,

2:44we even babble if you're familiar with that.

2:46Let's see what else we have here.

2:47We should definitely have, this is good for a

2:49optimization for speed.

2:53It'll have Conda and that's what we're gonna use

2:55for the conda environments.

2:58And let's see what else we should have.

3:00Pandas, Flask is for web development

3:08And ipi I, Python is similar to Jupyter Notebook,

3:13but we should have a Jupyter Notebook

3:17or maybe it's called Notebook, I think here.

3:19So let's look for that. Map

3:22Plot Lib is used for visualization and notebook.

3:26There we go. This is actually Jupyter Notebook, so it's,

3:30that's, it's just a little bit different syntax there.

3:33And then we have Pandas somewhere. There's Pandas.

3:36So basically it's a lot of stuff we can keep going.

3:40We might find Seaborne, which is another visualization tool.

3:44Yep. And there's Seaborne.

3:45So you see it really just installs a lot of stuff

3:48for you out of the box.

3:50Otherwise you'd have to be sort of installing the package

3:54and doing it for each and every one.

3:56So this just makes it a lot easier to get started.

4:00Okay, so what we're gonna do is just click continue

4:03and this is the eula, the end User license agreement.

4:05So be sure to read this and then continue when you're done

4:09and you accept and agree

4:14and then you can decide how you want

4:15to install this software.

4:17I'm not going to continue,

4:19but basically you don't need to install Pie Charm, IDE.

4:22That's another integrated development environment.

4:24It's pretty cool. But we don't need that in this case

4:27because we're gonna be using Jupyter Notebook.

4:30So once you're in, so I'll, I'll say install for me

4:32and then you can change the location

4:34and then you can say install, which I'm not gonna do.

4:37So I'm just gonna go, go ahead and close out of here.

4:40Okay, so once you have it installed

4:42and you open your terminal, well,

4:43before we do that, let's go ahead

4:45and download the course files.

4:48So these course files are really important

4:50for multiple reasons.

4:51Here's the first reason. There's gonna be a set

4:54of completed files

4:56and that will show you all of the correct code that works.

4:59So let's say you're working on something

5:00and there you're just having a problem,

5:02or you can look at the correct code, you can download

5:04that notebook or just open that notebook and try it out and

5:07and make a comparison there.

5:08The other reason is that there's starter files,

5:11so we're gonna start with the same documents

5:13and then you fill it in as you progress,

5:15and then you'll have all of your Jupyter Notebooks with all

5:19of the code that you learned in this course.

5:21So it's really a great resource.

5:23So let's take a look at the URL so

5:24that you can download this as well.

5:28So the UL is up here. Let me make this a little bit bigger.

5:39This is really big. Lemme see right here.

5:42You should be able to see that.

5:43Basically that is github.com,

5:47cbt n dash data, dash science dash ml.

5:51And it's also linked above this video.

5:53So just click on that and then we'll be on the same page.

5:57So what we're gonna do here is we're gonna go to, let me,

6:00this is a little bit too big, so let me get down to 150.

6:03Okay, so we're gonna go to Python pandas

6:06for data manipulation,

6:08and it's a pin for repository, so it's super easy to find.

6:12And what you wanna do here is one of two things.

6:14First, you can go to download zip

6:17and then manually add these to your working directory.

6:20If you don't have GitHub, if you do have GitHub,

6:23then I would recommend Forking this,

6:24and that will create a repository in your account

6:27and then go ahead and start.

6:28So you can get updates as I make any changes as needed.

6:33And then what I'm gonna do is just copy this

6:37and then head over to the terminal.

6:42Okay, so when you first install Anaconda,

6:46you're gonna see that you're in a base

6:48environment for Windows users.

6:50You want to go to the start menu and type Anaconda,

6:53and you're gonna probably see something like

6:55Anaconda Prompt.

6:57Go ahead and click on that.

6:58And that's gonna be your version of the terminal on a Mac,

7:01I'm using the terminal.

7:02This is what you're looking at for you on a Windows machine.

7:05You want to use the Anaconda prompt,

7:07and then the syntax is all gonna be the same.

7:10So it should work pretty much identically.

7:12So I just wanted to point that out

7:15so we have a base environment,

7:16but in the next video we're gonna create our

7:18environment using conduct.

7:20So we're just setting things up right now,

7:22so don't worry about this base for now.

7:26Okay, so the first thing we want

7:27to do is create our working environment.

7:29So I'm gonna go to my documents folder,

7:32and you can go wherever you would like

7:34and create your folder there.

7:36So I'm going to go to Documents and say Get Clone

7:40and then paste that folder there

7:42or that link to get it to clone from GitHub.

7:45But you can also do this manually, right?

7:47So you download that zip file, open that zip file,

7:51and put it in whichever directory

7:52that you wanna have those files in.

7:54And then it'll be the same.

7:56There's just whatever's more comfortable for you.

8:00Okay, so I'm gonna hit enter, and now I'm cloning

8:02and I've cloned all of those files.

8:04And I'm gonna jump into that folder by typing cd.

8:08And then let me spell that correctly.

8:13cd, Python, pandas for Data Manipulation.

8:17Cool, I'm in that folder now.

8:18And you can see that

8:20that change from documents to this folder.

8:22And if I use the LS command,

8:24I can see what's inside of that.

8:26So I want to, I don't want

8:28to jump into these and I'll show you why.

8:30What we're gonna do now is just type Jupyter Notebook.

8:36All right, so let me show you what happened.

8:38So right now, there's no command line.

8:40I can't type anything because it's running server.

8:43This is Jupyter Notebook running a server,

8:46and it's in a browser, so it actually runs in a browser.

8:50And that's how Jupyter Notebook works.

8:52So let's take a look at the completed and the starter files.

8:56So here are the starter files, click on that.

8:59If you go to data sets, these are all the data sets

9:01that come with that, but they're also inside of each

9:06sort of skill.

9:07So this is Skill three, Panda series intro,

9:10and you can see that these are already there.

9:13And that'll make more sense when we start

9:14to use these files.

9:15So if you go to the first one, there's nothing there

9:18because there's no, we are just installing things

9:21and there's no Jupyter Notebook yet.

9:24The second one, which is gonna be the next set of videos,

9:27that's the first one that has an actual Jupyter Notebook.

9:30So let's go ahead and click on this one. Cool.

9:35So this is our Jupiter notebook.

9:37Let's make sure that we have Python installed.

9:40You can see that it says Python Kernel three,

9:43and I'm just gonna say print.

9:48We'll just do the hello world, the famous hello world

9:50to make sure the Python is installed.

9:54Okay. And I do shift enter.

9:57That's how you execute these cells.

9:59You can also just click on run,

10:03but I think using shift enter is actually

10:06a better way to do that.

10:07Okay, so we have our development environment set up

10:10with Anaconda,

10:12and in the next video we're gonna talk about

10:14Conda environments.

10:15And that's the last thing we need to do

10:17before we get started,

10:19and it's really important to get into that.

10:20But once you know how all this works,

10:22it's pretty straightforward

10:23and it's a best practice to use Anaconda

10:26and Conda environments.

10:27And once you have Jupyter Notebook up

10:29and running with Pandas, it's gonna be a lot of fun.

10:32So I hope this has been informative,

10:34and I'd like to thank you for viewing.

0:07<v ->Welcome back.</v>

0:08This is the last video of setup

0:10for a development environment,

0:11and we're gonna dive into conda environments.

0:14Alright, so let's take a look at where we left off

0:17and we're gonna jump into the Jupyter notebook

0:19and take it from there.

0:22So this is the Jupyter Notebook,

0:24and what I'd like to do is show you this other tab,

0:27and you can see that it's, these are our notebooks

0:29and this one is green.

0:31So what we wanna do is click on this and click shut down.

0:34So this notebook is now shut down.

0:36So we can just close this tab, say leave,

0:40and then you want to quit, click on quit here.

0:44And when you do that, it shuts down the server.

0:47So this server was running, and now it's shut down.

0:51We'll get into this a lot more in the next set

0:53of videos when we get into the Jupyter Notebook

0:56set of videos.

0:57But for now, I just wanted to show you that,

0:59well there was a server and now it's not running anymore.

1:02I think this is kind of important to mention

1:04because look at all this stuff, it looks kind of wild.

1:07So I just want to kind of, sort of explain that.

1:09Let's go ahead and use the clear command to clear this up.

1:13So if I type clear,

1:15we'll get a a little bit nicer looking sort of terminal.

1:18So how do we want to install conda environments?

1:22Well, the easiest way is to make sure

1:25that we have conda installed first.

1:26So let's just say conda --version.

1:30Hit that, you get conda 4.1, 4.11.

1:35Okay, cool, so we have that set up.

1:37Now we want to create our conda environment.

1:42So make sure that you're still in your working directory.

1:44So this is my working directory

1:46that I downloaded from GitHub.

1:48And when you're working in the future,

1:49you can create any folder and start from there.

1:51But this is where we're working from for now.

1:54And what we're gonna do is say conda create --name,

2:00and that's gonna be the name of the environment,

2:01and I'm just gonna say something like pandas.

2:07Pandas CBTN, right?

2:11For CBTnuggets.

2:13Alright, so if I click enter,

2:16oh well, that's a good mistake.

2:19I didn't add the a at the end of conda.

2:22And so that's what happened. So let's try that again.

2:24Alright, so it's running

2:26and it's asking me, "Hey, do you want to proceed?"

2:28I'm gonna say, "Yes, I do."

2:29And what's cool about this is it's gonna say this is

2:33what you need to enter to activate that.

2:35So I'm gonna copy that and I'm gonna paste that here.

2:40And when I run this, you're gonna see

2:43that we went from the base environment to now, to CBTN.

2:47Okay? So that's really important. So let me clear this up.

2:52And this is how you know that you're in

2:53that new conda environment, right?

2:57This is where you want to install things.

2:59However, if we start to type Jupyter Notebook,

3:02it's not gonna work because we have to install everything

3:05that we're gonna use in this project.

3:07So it's a clean slate,

3:08let's go ahead and add the packages in the

3:10libraries that we're going to use.

3:13Okay, so first what we wanna do is type, we're gonna type

3:19conda install, and we're gonna install pandas,

3:25jupyter notebook,

3:28jupyter notebook.

3:31And we're gonna install numpy and matplotlib.

3:37Okay, let's enter that and see if that'll work.

3:41You wanna proceed? Yes, I do.

3:44And then this will take a couple minutes to install.

3:47Once it's done installing,

3:48then we can actually type in Jupyter Notebook again,

3:51and that'll open up.

3:52And this is your conda environment.

3:54When this is done, I'm also gonna show you how

3:56to deactivate it and how to look at other,

4:00or let's say like you just forget the

4:02name of the conda environment,

4:03I'm gonna show you how to look that up.

4:07Okay, so here we are. Let me clear all of this up.

4:10So I'm gonna say enter clear

4:12and I'm going to deactivate this.

4:15So I'm gonna say conda deactivate.

4:21And then I'm now back at the base environment.

4:24But let's say I want to activate it again

4:25and I type in conda activate,

4:28maybe this has been like I did it, you know,

4:31maybe tomorrow, right, or next week,

4:33and I just forgot what it was called.

4:34I'm like, what did I name it, panda something, right?

4:38Well what you can do here is conda list evns,

4:44evns, for environments.

4:47Once I enter that, it's gonna show me what I have.

4:52Did I do that correctly? No, I didn't.

4:56So conda list

4:58or is it conda env list?

5:10Yeah, so I just mixed up the syntax,

5:13but basically it shows you every project that you have.

5:17So I have multiple projects.

5:19So this is the one that I usually use,

5:21or this is the one that I created and activated.

5:24So I'm like, okay, cool, I'll just copy that

5:26and I'm gonna say, conda activate.

5:31And then you say panda cbtn, and boom, I'm in there.

5:34Now, I'm gonna say jupyter notebook

5:38and let's see what happens.

5:41Drum roll please.

5:44Boom, it works.

5:45So what's happening in the background, we have

5:47that server running again

5:49and Jupyter Notebook is working inside of the browser.

5:53And you can go to your starter files

5:55and in the next set of videos, this is where gonna start,

5:58right here on 2_3 where we're gonna take a tour

6:01of the user interface.

6:04Okay, so that was a lot of information.

6:06The first time that you might hear this,

6:08it might be a little overwhelming,

6:11or just a lot of, you know, it might be a little bit,

6:12the concepts might be a little bit fuzzy,

6:13but every time that you work with Python and Pandas

6:17and Jupyter Notebooks and conda, you're gonna do this,

6:21there's gonna be a startup and a shutdown process,

6:23and I'm gonna show you that the next set of videos.

6:26And we're gonna do that every single time.

6:27So it's gonna be something that's gonna become very,

6:29very natural.

6:30The first time you do this,

6:31it may, if you have a programing background, no sweat.

6:35But if this is something that new to you

6:36and you're coming from Excel,

6:38this is gonna be something that'll become very familiar

6:40and you can always review this video

6:42and it's pretty much straightforward.

6:45So I hope this has been informative

6:46and I'd like to thank you for viewing.

0:07<v ->Congratulations on making it</v>

0:08to the end of this set of videos.

0:11In this section, this video is about a challenge

0:15and so at some of these,

0:17at the end of some of the sections of videos,

0:20there may be a challenge.

0:21And so I wanna talk to you a little bit about the challenge

0:24and kind of go over each one of these.

0:25And the reason for that is how about if you get it wrong?

0:30I could add a hint inside of the questions,

0:32but it's not as effective as me talking about it

0:34and showing you explicitly, right, through a video.

0:38And I find that that's a lot more effective.

0:40So that's what we're gonna do.

0:41Not every end of section will actually have a challenge,

0:45but if there is a challenge, there will be a video as well.

0:49So it's really important that you go ahead

0:52and try to do this on your own.

0:54So answer all of the questions.

0:56And if you get them wrong, no worries, just keep going.

0:59Watch this video and then we'll clear it up

1:02and then go back and then revisit the answers

1:05that you got wrong.

1:06So let's get started.

1:07Let's check this out.

1:09Okay, so here is the terminal.

1:12So what is the first question?

1:15So first of all, we're setting this up.

1:17We're saying navigate to your project directory of choice

1:20for this course.

1:21Well, I'm already there.

1:22This is what mine is, right?

1:24So that's where I'm gonna be working out of.

1:26So that's good.

1:27Next it says, open the terminal or add a conda prompt

1:31and activate the conda environment for this course.

1:34So what do I mean by that?

1:36Well, open the terminal.

1:38Well, it's already open.

1:39So you have to open the terminal

1:41in order to navigate to the directory anyway.

1:43So a conda prompt is if you're on Windows.

1:46And how do you activate this environment?

1:51Well, first of all, I'm going to deactivate

1:55'cause I'm not, let me go ahead and do that first.

2:01Okay, so I'm gonna hit clear and we'll start over.

2:04Okay, so how do we activate a conda environment?

2:08Well, first of all, I think I forgot the name of it, right?

2:11So what's that command?

2:12Conda, conda env list.

2:16And okay, so mine is Pandas, CBTN, right here.

2:20So that's what I'm gonna use.

2:22So I'm just gonna copy that and say conda activate.

2:26So that's the answer, right?

2:28Because I don't know, well activate

2:31because I don't know what the name of your command is.

2:33So, and there's some also some, you know,

2:37some clearly wrong answers in the questions as well.

2:40So this is how you activate it.

2:41Let's go ahead and do that.

2:42Okay, cool. That is the first question.

2:45Second question, how do you launch Jupyter Notebook?

2:49Okay, so there's one that's kind of funny,

2:54it's not this, right?

2:58It's, and it's not even conda.

3:00this is a correct answer, right?

3:05And if I hit that,

3:06then that would actually open Jupyter Notebook

3:08and we're not gonna do that.

3:09But that is the correct answer.

3:11And the reason I added this

3:13is because that's the name of the package, right?

3:16So this is what,

3:18if you're installing it, that's what it would be.

3:20So let's say conda list.

3:23And this should show us all of our packages.

3:26And if you look here, that's Jupyter Notebook.

3:29So that's why I kind of put that there to throw you off.

3:31But it's also good, I think a good learning opportunity

3:35because that's the name of the package, right?

3:36So it's kind of good to know,

3:38but the actual command is Jupyter Notebook.

3:42Alright, let's go to the next question.

3:45So how do you deactivate a conda environment?

3:48Well, first of all, let me clear this up.

3:49This is a lot of information, okay?

3:51So I can tell that I'm in my, whoops,

3:54I'm in my activated conda environment,

3:58simply gonna write conda, deactivate.

4:05If I hit that, then I'm back into my base environment.

4:09And so that's the correct answer.

4:11And so what is the next one?

4:12Does pandas have documentation?

4:15The answer is true because yes, it does have documentation.

4:19It is right here at pandas.pidata.org.

4:26Okay, and the next question is, does pandas have a GUI,

4:30a graphical user interface like Excel and Google Sheets?

4:34And the answer there is no.

4:37And the reason for that is that is

4:42well, I mean that should be pretty straightforward.

4:44But if it's not, let's go ahead

4:46and check out an example of pandas.

4:51So let's say we can go to this,

4:56I don't know, let's go to the documentation.

4:58So I'm gonna click this

4:59and I'm just gonna enter, I think,

5:01let's say data frame.

5:08Alright, so here's a pandas data frame

5:10and we'll scroll down and we'll see an example.

5:12So Pandas kind of looks like this.

5:14So that's not a GUI, graphical user interface.

5:17That's basically a code cell where you type code.

5:20So the answer is false.

5:23Okay, next question.

5:25Is Pandas open source?

5:27If you don't know the answer to that,

5:29then you can just come here

5:31and you can see that it is open source clearly.

5:35So the answer for that one is true.

5:39But what happens if you're working with pandas

5:42and you don't know the answer to something,

5:44and maybe I'm not explicitly addressing that in the videos,

5:49that could very well be the case

5:50because I can't cover everything.

5:52So I'm gonna share something very important with you.

5:55And if you're a programmer,

5:56have a programming background,

5:58you probably probably already know about Stack Overflow,

6:02but if not, this Stack Overflow is your friend.

6:07Let me show you what I mean.

6:09To get there, all you have

6:10to do is come to the pandas documentation,

6:13so pandas.piata.org, and you just go to ask a question

6:19and it automatically takes you to Stack Overflow.

6:22So I would definitely suggest getting,

6:24creating an account here, going to sign up

6:27and just having an account because you can ask questions.

6:30So let's go ahead and do that together.

6:33Maybe we'll do a conda question.

6:34Let's see.

6:36So this says Pandas here, right?

6:38So maybe I can ask a question.

6:41I'll just leave it there and I'll say,

6:46activate conda environment.

6:53I'm not a robot.

6:55Okay, so what happens, let's see,

6:58other conda environments do not activate,

7:01couldn't import missing dependencies.

7:07To be quite honest, this is too easy.

7:10(laughing)

7:13So what do I mean by that?

7:13Well, I think that there's gonna be some things

7:17that you want to look for

7:19and you'll know where to go for like a cheat sheet

7:22or the documentation versus Stack Overflow.

7:24So let me dive into that

7:25and kind of give you like a quick rundown of how to do that.

7:28So first the answer is right here, right?

7:31If you click on this, it's not a great question,

7:34but if you click on this you, it was kind of highlighted

7:37that you could use that.

7:38So if I manually type conda activate pandas, it'll work.

7:45Well that kind of sort of works.

7:47But I wanna share this with you.

7:48This is the conda cheat sheet.

7:50And if, how do you find this?

7:53Well, it's here in the conda or the docs.conda.io.

7:58And I think if you just type in cheat sheet,

8:03we should be able to get that.

8:06♪ Do do do ♪

8:08Is that it? Let's try it.

8:17No, but this is pretty cool

8:18because it has, I mean you could actually just

8:23look through the documentation.

8:25Oh there it is. Cheat sheet.

8:26I kind of like the cheat sheet

8:27because it has everything there

8:28and if you click on it, it just downloaded it for me.

8:32So let me just go back to where I had it sort of.

8:37I think it was here.

8:38One more, there we go.

8:39So I already had it opened up, so let's go ahead

8:41and zoom into this.

8:45There.

8:46Alright, so conda info is pretty cool,

8:48so let's check that out.

8:49So what is conda info?

8:55Wow, so a lot of information version, numbers, build,

9:00directories, base environment,

9:04directories.

9:06Cool, so a lot of information,

9:10I think this might be useful,

9:11maybe a little bit, not useful right now.

9:13So let's check something else out conda update.

9:16So you could update that way.

9:18You can conda install and then package name.

9:20That's what we did when we installed Jupyter Notebook,

9:23Pandas, NumPy and Matplotlib.

9:25So that's cool.

9:28You can update a package as well.

9:30And then you can do help.

9:32So this is kind of awesome.

9:34So conda, let's see what we could do.

9:37Conda update or command name and then help.

9:40Let's try that.

9:41So let's say you're trying to install,

9:43so like conda install --help

9:51Scroll all the way up to the top,

9:54it's a lot of information.

9:55So here's where it begins.

9:58So conda install

10:00and then here are all of the different commands

10:02that you can use.

10:03So path, channel, I mean it's a lot of information.

10:10This is sort of like the documentation built

10:12into the terminal or the conda prompt.

10:16So that's kind of cool.

10:17It's a lot of information,

10:18but I thought I'd take this opportunity

10:19to kinda show you this.

10:20And here's conda create,

10:24conda env list.

10:25Remember we used that one already and we can do conda list.

10:29We did that too.

10:30And what does conda list do?

10:32So let's go ahead and clear this.

10:33So we know that conda env list

10:36will list the the environments.

10:38And what does conda list do?

10:39Well it's packages, right?

10:41I was looking to see what Jupyter Notebook is called

10:44and that's what I used.

10:45There's also seaborne

10:49and well there's a lot of 'em here.

10:50Why is that?

10:53Okay, because I am in my base environment.

10:56So this is, I'm glad I made that mistake.

10:59If you say conda list, it's gonna show all the packages

11:02that you have in your project,

11:04but the base environment has all of them.

11:06So this is not gonna work.

11:08So I'm gonna activate it and then say conda list.

11:11So let's try that out.

11:14Okay, so up arrow allows me

11:16to go through all the previous commands that I entered.

11:20So that's conda, activate pandas_cbtn.

11:24Now I'm in my conda environment,

11:27so I'm gonna say conda list.

11:31Okay, much shorter list, still a lot,

11:34but you can see that notebook is here

11:36and that's what I did last time.

11:37So conda list, really great command,

11:41very useful for where we're at right now

11:43in our journey with Pandas.

11:46And so what else?

11:50Conda env create, we already did that,

11:52conda search PACKAGENAME, you can do that.

11:55See a list of all packages,

11:57well that's a URL.

11:59Conda install jupyter.

12:01Cool, we already did that.

12:06And you might notice

12:07that there's slight differences in syntax.

12:09I'm able to get it to work without using a dash

12:12like jupyter-notebook.

12:14I'm able to just say Jupyter Notebook

12:15with a space and that works.

12:16So this important to sort of mention that

12:20there may be slight variations in syntax.

12:23For example, right here it says conda create name.

12:28And then once you create that, you say activate

12:31in that you don't have to say conda activate.

12:33That's interesting.

12:33Let's see if that works.

12:35So I'm gonna deactivate

12:40and I'm gonna just say activate pandas_cbtn.

12:48I gotta spell pandas correctly

12:52and that didn't work for me.

12:54So let's look at that.

12:56So what did, oh that's for Windows.

13:01For Linux it says source activate.

13:05Okay, well for me it's conda.

13:07So let's do source activate.

13:13Okay, you could do that.

13:14But look, if I, just to make my point,

13:17you can actually do this.

13:21Conda activate and it works.

13:24So I was able to use source activate pandas

13:28and then the name or conda activate pandas, you know CBTN.

13:32So either one works.

13:33So there's a little bit of slight differences in the syntax,

13:36which I think is super interesting.

13:40Alright, so that's a lot of great stuff.

13:42So I hope this was useful.

13:44I hope I answered the questions and a little bit more.

13:47Again, the documentation is awesome, it's really helpful.

13:50And the conda cheat sheet

13:52when you're first getting started, it's a great resource.

13:54So you can download that and it's free

13:57and it's on docs.conda.io.

14:02So let me share that.

14:03Let me just show you that real quick.

14:09And I think if you just type cheat sheet here

14:12and that's how you would get there.

14:15Let's see.

14:21Getting started and I think it was right here.

14:23Yeah, there it is.

14:24And if you click on that, that's gonna download it.

14:27I hope this has been informative

14:28and I'd like to thank you for viewing.