Python Development Environments

0:00(no audio)

0:07<v ->Hello, everyone.</v>

0:08My name is Jonathan Barrios,

0:09and in these set of videos we're gonna talk about

0:12Python development environments.

0:14So you might be asking yourself,

0:16well, why don't we just install a code editor,

0:18like Visual Studio Code, and be done with it?

0:21That's a great question.

0:22However, there are so many tools available to us,

0:25especially when we're working with teams,

0:27and in the context of data analysis and data science

0:30that it's worth going over the landscape of tools

0:33available to us.

0:34Let's take a look.

0:36This is Visual Studio Code.

0:38This is the Code Editor part,

0:40and Visual Studio Code, it is also an IDE,

0:43which means that it's a code editor with some added tools.

0:46For example, this section down here

0:49allows you to do some debugging, connect to the terminal,

0:52and even connect to a GitHub repo.

0:55All of these are extensions that really, again,

0:58extend the functionality of the code editor.

1:00So this is more of an integrated development environment,

1:03which is really great, and I really enjoy using this.

1:07However, I recommend Google CoLab,

1:09which is an interactive notebook,

1:11when working with data science and data analysis.

1:15This is Google CoLab, a very straightforward tool

1:20where you interact with text cells, code cells,

1:25and when you run your script or your code,

1:29you get the output just below.

1:30One of the greatest things about this

1:32is that there's little to no setup.

1:34You just have to connect your Google account,

1:37and then you're ready to go.

1:38If you wanna move on to Jupyter Notebook,

1:40it's a best practice to install Anaconda.

1:43Anaconda is a Python distribution that contains

1:46all of the data science and data analysis packages

1:49that you're gonna need for your projects,

1:52and it's a great tool to use to install

1:55Jupyter Notebook locally.

1:57Jupyter Notebook kind of looks like an article

2:01where you would have a little bit of text and some examples,

2:04a little bit of code that outputs something,

2:06so you really don't need to know Python

2:09to read this interactive notebook, right?

2:12And compared to Visual Studio Code,

2:15you show that to somebody that doesn't know Python,

2:17it's gonna be intimidating,

2:18and it's not nearly as accessible

2:20as an interactive notebook.

2:22The difference between CoLab and Jupyter Notebook

2:25is that Jupyter Notebook is installed

2:27locally on your machine alongside with Anaconda,

2:31so that means there's more setup involved.

2:34Google Cloud, on the other hand, has very little setup,

2:37almost no setup at all,

2:39and Conda environments is a very important aspect,

2:42again, requiring a little bit more setup

2:45for Jupyter Notebooks,

2:46but this allows you to keep your environment

2:48separated from all of your other projects.

2:50So you might be asking yourself,

2:52well, why is that important?

2:53Well, this environment.yama file allows you

2:57to save or freeze all of the dependencies

3:00that you have in your project,

3:01and easily share that with the team,

3:04and they can use this file

3:05to install that same project on their machine,

3:08so it's a great way to collaborate.

3:11This is important because different packages

3:13might have different versions, which may create conflicts.

3:16So this is a great way to deal with that.

3:19Finally, you have Docker, which is super popular,

3:22and it's similar to how Conda works,

3:25except Conda is more at the Python level,

3:27so it's dealing with libraries and packages

3:30where Docker is dealing at the OS level.

3:33You can install Anaconda with Docker,

3:36and have Docker in a self-contained environment

3:39using the same OS.

3:40This is important when you're working

3:42in a production environment

3:43where there's sometimes conflicts

3:45because of the different OS that you have installed,

3:48so this is a great way to deal with that.

3:51So in this set of videos,

3:52you're gonna learn what Python development environments are,

3:56why they're important,

3:58and best of all, how to select the best data science

4:01and data analysis environment for your projects.

4:04Well, I hope you're excited.

4:06I know that I am,

4:07and we're right now at Basecamp,

4:09so once you understand

4:10all about the development environments,

4:12you're gonna be in a great way to progress

4:14on your Python journey.

4:15See you in the next video soon.

4:18(no audio)

0:07<v ->In this video we're gonna be talking</v>

0:09about the differences between code editors and IDEs

0:13versus interactive notebooks.

0:15Normally, if you're learning Python

0:17for Python programming,

0:19you would probably use a code editor or an IDE,

0:22such as Visual Studio Code.

0:24But in the scope of data analysis, data science,

0:27and even machine learning,

0:29it's advantageous to talk about interactive notebooks.

0:32In this case, we're gonna be using Google Colab

0:35for the interactive notebook

0:36and Visual Studio Code for the code editor/IDE.

0:40Let's take a look.

0:42Here we're looking at Kaggle's State of Data Science

0:44and Machine Learning 2020 Survey.

0:47It's worth noting that data science

0:49is a larger field that includes data analysis.

0:53And machine learning is very, very closely tied

0:56to data science as well.

0:58So looking at the two numbers,

1:00clearly you can see that interactive notebooks

1:03take the lion's share of the results.

1:05However, there is some overlap

1:07and I will say that myself,

1:09I use both interactive notebooks and code editors

1:13because there's advantages to using either one.

1:15However, when you're learning data science

1:18and machine learning and data analysis,

1:21interactive notebooks is a great way to start.

1:24Let's take a look at some of the differences.

1:26When you're working with a code editor,

1:28you're working with one single document,

1:31let's call this app.py.

1:37This .py file extension is really important

1:40when you're actually working with a code editor

1:43because it lets the code editor know

1:45what kind of programming language you're working on.

1:48Additionally, when you write a line of code,

1:51nothing happens. (chuckles)

1:53If you write a bunch of lines of codes,

1:55you have some functions and some function calls,

1:57still nothing happens.

1:59You have to open a terminal

2:02and then type in Python,

2:05and the name of the file,

2:06which in this case is app.py.

2:09Once you do that, then you'll get the results

2:12from the app.py file.

2:15So this is not an interactive way of programming.

2:19On the other hand, interactive notebooks

2:21use something called code cells and text cells,

2:24but we're just gonna talk about code cells.

2:27So in this example,

2:28let's say we had a print statement,

2:33and we're just gonna say "Hello."

2:36So, print("hello").

2:37Once you hit Shift + Enter,

2:39you immediately get an output, "Hello."

2:42And that is true for any code that you write

2:45inside of code cells.

2:46It's a way of interactively programming.

2:49So you write some code here, then you run it,

2:52you write some more code here, then you run it,

2:54and you get your output, and so on and so forth.

2:57So it's an interactive way of programming.

2:59So let's take a look

3:00at each of these applications one at a time.

3:03I went ahead and created this empty folder called vs_code,

3:07and to open it with Visual Studio Code,

3:09you can just drag it into the icon,

3:11and that opens up Visual Studio Code.

3:14And you can see that we're in the directory

3:16of that empty folder, there's no files in here.

3:20So before we get started,

3:21let's look at each one of these sections.

3:24I'm gonna close this Start section

3:25because it just allows you to open a file,

3:29create a new file, and then clone a Git repository.

3:33Okay, so let's take a look.

3:37Down here we have the Terminal.

3:39And so this is what makes Visual Studio Code an IDE

3:43because it has additional functionality

3:45on top of the code editor,

3:48which is this section up here,

3:49this is where you would actually write your code.

3:54The section on the bottom

3:55where the terminal's located also has a Debug console,

3:58an Output, and then you can see any errors that you have,

4:02and with helpful information as to which file

4:05and which line that error is being generated from.

4:09You also have the file explorer

4:12which is what we're looking at now,

4:14and there's no files inside of "vs_code."

4:16Also, you have a search capacity

4:18so that you can search for files and extensions.

4:22You can connect to a Git repository,

4:24so that's a source control functionality.

4:28And some debugging, right?

4:31So you can actually, which makes sense,

4:33is very common for IDEs to have

4:35some sort of a debugging tool.

4:37And finally have these extensions.

4:39These extensions allow you to extend the functionality

4:42of the code editor.

4:44So let's take a look at these extensions.

4:49If I were to type Python,

4:51which seems to be already generated for us,

4:56this is an interesting...

4:59Lemme go ahead and close this.

5:01Well, I'll just minimize this or make it smaller.

5:03So this code extension's very interesting

5:05because it offers things

5:07like auto completion and intellisense,

5:11but it also has something really interesting.

5:13Lemme scroll here to the bottom, show this to you.

5:16You can also use Jupyter Notebook,

5:18which is an interactive notebook.

5:21Personally, I've actually found

5:23that this is a little buggy, so I don't use it,

5:25I prefer to use an interactive notebook,

5:27and that'll be either Google Colab or Jupyter Notebook.

5:31But you do have that option,

5:32and there's so many different things

5:34that you can do inside of Visual Studio Code

5:38to extend your functionality

5:40that it's a little mind boggling.

5:41And it's one of the things that I really like

5:43about Visual Studio Code.

5:46Right, so let's close this

5:47and let's go back to the code editor.

5:50To create a file, you can either right click

5:53and select New File, or you can use this icon.

5:57Let's use the name app.file to create a new file.

6:02The .py file extension is really important

6:06because it lets the code editor know

6:08what language you'll be using.

6:09Once I type .py, you're gonna see

6:11just to the left of app,

6:13a Python app or a Python icon up here.

6:17Now you can see that you're using a Python file extension.

6:21So let's go ahead and close this here,

6:22this is just telling us about a recommended extension.

6:26Let's go ahead and try a print statement.

6:28(keyboard keys clicking)

6:33So if I save this, nothing happens, right?

6:35This is where you would need to go to the terminal

6:38and then type in: python, (keyboard keys clicking)

6:44and then the name of the file,

6:45which in this case is app.py.

6:47And then you would get your output down here.

6:50So this is definitely not an interactive way of working.

6:54What is cool about Visual Studio Code,

6:56is that it has boilerplate, auto completion,

6:59intellisense, and different functionality,

7:01like for example, if I wanted three lines

7:04or four print statements,

7:06I could just use that Shift option + Down arrow key,

7:10and then immediately get all of these print statements.

7:13So it saves a lot of time.

7:14You can also use the Option key

7:16to select multiple lines at the same time,

7:19which is very helpful.

7:21Let me save that.

7:22And if I want to run this again,

7:24because I'm not seeing any output,

7:26I can type in python app.py.

7:30And if you noticed, I just hit Up arrow

7:32and that'll recall the last command that I've used.

7:36So I'll hit Enter, and then I get

7:38four "Hello World" statements.

7:42This is also very useful for web development,

7:44not just Python.

7:45For example, if I created an index.html file

7:50and I use the pound sign and then I hit Tab,

7:54it auto completes the boilerplate

7:56to to start building a website.

7:58You can even use a live server

8:01to see the website right next to the code editor

8:04so that it really speeds up development,

8:07not just for Python development,

8:08but all kinds of different languages, super useful.

8:13So let's go ahead and save this file,

8:15go back to app.py.

8:16And this is an introduction to Visual Studio Code

8:19and how it's not an interactive development environment,

8:22it's a code editor with wonderful functionality.

8:25However, if you show this to somebody

8:27who doesn't know Python,

8:29then it's not as accessible, right?

8:31They may be a little bit overwhelmed

8:32and if there's a lot of code,

8:35even if you have comments,

8:36it's not gonna be as accessible.

8:38So let's take a look at an interactive notebook.

8:44This is Google Colab,

8:45and the main difference that you see here,

8:47let's go ahead and take a tour of this notebook.

8:51So first of all, you can see that we have a code cell here.

8:55If you wanted a text cell,

8:57you would just simply go to that button

9:00or you could hover and select either a code or text.

9:06So this is a text file and here's another, it's a code.

9:11Text cell and a code cell.

9:14So let's type in the same "Hello World."

9:17So I'm gonna type in a print statement.

9:19(keyboard keys clicking)

9:23And all I have to do is Shift + Enter,

9:26and then I immediately get that output.

9:29That output is right here,

9:31and that's the same case for every code cells.

9:34Let's go ahead and do some more.

9:36I will do some math operations,

9:38so let's say 10*3.

9:42Shift + Enter, and then you get 30.

9:45Text cells allow you to...

9:48and you can also navigate by moving these up.

9:50Let's say we wanted to add a title to this.

9:52I double click into that text cell,

9:54and let's say I wanted a heading.

9:56This is the heading toggle.

9:58I have new section,

9:59and immediately you see a preview

10:01of what it's gonna look like.

10:02So let's just say:

10:06Welcome to Google's Colab. (keyboard keys clicking)

10:13Shift + Enter, and then we have this nice formatting.

10:16If I click on this caret,

10:18it'll absorb those two cells.

10:20So it's a really great way to organize your files

10:24and allow people to see section by section

10:26so they don't get overwhelmed.

10:28Again, it's more accessible.

10:29So let's open this again,

10:32and there we have our code cells.

10:35What's important to remember

10:36is that there's a lot of other functionality

10:39that we're not talking about.

10:40For example, if you wanted to connect to Google Drive,

10:43you could do that by clicking on this folder,

10:45and then you would have your Google Drive files here,

10:48which is super helpful.

10:50So that's pretty much the difference

10:52between Google Colab,

10:54which is an interactive notebook,

10:55and Visual Studio Code,

10:58which is a code editor and IDE.

11:00Now that you have a better understanding

11:02of code editors and IDEs

11:04compared to interactive notebooks,

11:06let's go ahead and deep dive into Google Colab

11:08and get that set up in the next video,

11:10see you there.

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

0:09In this video we're actually gonna install

0:11our development environment,

0:12and for that environment

0:14we're gonna be using Google's Colab.

0:16All we really need to do is search for Google Colab

0:19and then we can actually associate it

0:21with our Google account.

0:23So the only thing that you really need to keep in mind

0:25is that you wanna be logged in.

0:27So this is, you can see my icon,

0:29I'm logged in and ready to go.

0:31So make sure you're logged in before you get started.

0:35So now that I'm logged in,

0:37I'm gonna just type in Google Colab,

0:42Google Colab, and it's the first result.

0:45Let's go ahead and check that out.

0:48So we're gonna research,

0:50I think either one will be the same.

0:51So let's try this first one.

0:55So I don't have Google Colab actually installed

0:57in this account on purpose so that you can see

1:00exactly what it's like

1:01if you've never installed Google Colab.

1:04So I'm just gonna head down to new notebook.

1:07Well actually, let's click on this notebook.

1:10This is the introductory notebook

1:12and it actually gives you a lot of really useful stuff.

1:15So the first thing you want to do

1:17is just click copy to Drive.

1:21Okay, so now this is a copy of that document.

1:25So if you head over to your Drive,

1:27let me just go ahead and type Drive.

1:34Now, there we go.

1:35So now you can see you actually have

1:37that copy that you made.

1:39And by doing that, you actually have created

1:41or connected Google Colab to your account.

1:44So if I click in here, you're gonna see that document.

1:47Right?

1:48Now if I wanted to create a new Colab notebook,

1:51all I have to do is right click, go to more,

1:54and then click on Google Colaboratory.

1:58I do that and here we go.

2:00I have a brand new Google Colab interactive notebook.

2:04This is pretty cool.

2:05One of the great things about Colab

2:07is that you can actually import an IPy notebook.

2:11What does that mean?

2:12A dot

2:14I-P-Y-N-B, right?

2:19Is the file extension of a Jupyter Notebook file.

2:22And the reason it has that extension is

2:24that Jupyter Notebook used to be called IPython Notebook.

2:29So they just kept the file extension,

2:31however it's now called Jupyter Notebook.

2:34So let's go ahead and upload a Jupyter Notebook file

2:38and take a look.

2:40To do that,

2:41head to file, upload notebook.

2:45I'm going to grab one from my desktop,

2:49right here,

2:51and you can see there's that file extension ipynb.

2:54So IPython Notebook, which is now Jupyter Notebook.

2:58I click open.

3:01So here's our Google Colab notebook,

3:03which is actually a Jupyter Notebook file,

3:06which is really awesome.

3:07So they're both the same.

3:08One is a cloud version of a Jupyter notebook,

3:11and the other one is actually a local Jupyter notebook.

3:14The files are the same.

3:16The only difference that you'll notice

3:18is in the user interface,

3:20but there's nothing that's gonna be

3:21so different that'll hang you up.

3:23They're both sort of interchangeable.

3:25So let's dive into this document.

3:29The first thing we can do is print "Hello, world!"

3:31So to, we're gonna go over

3:33all of these things when we learn Python,

3:35but for now use, so quotation marks,

3:38and then, hello.

3:41Helloo!

3:42Alright, "Hello, world" exclamation mark.

3:46Wow, I can't type today. Alright, cool.

3:50So once we run that,

3:51the first time you're gonna notice

3:52it says connecting over here, then initializing,

3:55and then you get your output.

3:56So if I were to run this again, it's immediate, right?

3:58So the very first time, you're gonna have a little bit

4:01of an initialization waiting period.

4:04Okay, so that's our first code cell.

4:06This is a text cell up here.

4:08If you wanted to change anything here

4:10or if you wanted to see

4:11what the Markdown is, double click here.

4:15And then you can see that raw code.

4:17This is a preview over here on the right,

4:18which is kind of nice.

4:20And I'm using two pound signs

4:24and this means h2.

4:25So I can toggle between the different sizes,

4:28you can notice how it's now pretty small.

4:31If I do it again, it'll go to nothing,

4:33then to an h1, but I prefer an h2.

4:38And when you have the minimum of an h2

4:40and you run that cell,

4:41you can actually collapse that section.

4:44So we're done with this print "Hello world!"

4:46You can see how the code cell works.

4:48Now let's look at pandas.

4:50So we're just gonna take a preview of what it looks like

4:53to look at a data frame,

4:56which is basically a spreadsheet for Google Colab.

5:01Don't worry about any of this, we're just kind of previewing

5:04what it will look like

5:06once you get up and running with pandas.

5:10So we have import pandas as pd

5:12and then we're gonna read this CSV,

5:14this is a CSV file

5:18comma separated values dataset

5:21that is on my GitHub.

5:23And all I have to do to see that is run this cell.

5:28And there you can see that we have this pandas data frame,

5:30which is basically like an Excel spreadsheet.

5:33The main difference here,

5:35if you notice that there's a zero,

5:36when you're working with Excel,

5:38it would start with one, go to two, three, four,

5:41and so on.

5:42But it's basically a spreadsheet.

5:44And if you wanted to see a slightly different version,

5:46you can click on this magic wand

5:48and you get something that looks a lot more like Excel.

5:51So it's basically a spreadsheet.

5:53And here you have, again, it's the zero based index

5:57that has to do with Python.

5:58And you'll learn more about that when you learn Python

6:01or if you already know Python, you already know that

6:04it uses a zero based index.

6:06What's cool about this format, this table,

6:08is that you can actually separate it by val,

6:10you can preview it in as many

6:13sort of list items as you want.

6:17So we can go to 50 list items and so on and so forth.

6:20All right, so this, that's pretty much it.

6:22To close this, I'm gonna click this

6:24and it gets rid of that.

6:26Just organize this by closing that pandas section.

6:29And then let's talk a little bit about markup.

6:32So markup is very similar to HTML.

6:35So if you already know HTML,

6:36the good thing is that you can actually use HTML

6:39inside of these text cells.

6:41So let's take a look at how they work.

6:45If I double click here,

6:46you're gonna see everything that's inside of this.

6:48So h2 heading,

6:50if I wanted to actually just type in h2,

6:56and then you need to write a space, add a space,

6:59and then you have the same equivalent

7:01as if we were using two pound signs, right?

7:04So you can see that Markdown is a superset of HTML

7:08and they're sort of interchangeable.

7:10Markdown is a markup language that is a superset of HTML.

7:14It's baked into Colab

7:15and you can toggle not just the headings,

7:17but bold, italics, links,

7:19indents, ordered and unordered lists and much more.

7:23So what do I mean by that? Well, here's a toggle.

7:26Here's bold, italics. This is a code example.

7:30So format as code.

7:31And if you wanted to add a link, pictures,

7:34indent, here are the lists.

7:37You can actually add a horizontal rule,

7:40and this is LaTex, which we're gonna talk about next.

7:42And nobody needs introduction to emojis.

7:44You can add some emojis, and reposition Markdown preview.

7:49So this will change this preview here

7:51and put it to the bottom,

7:53but I prefer actually having it on the right

7:56so that I can actually see what's going on above and below.

8:00So to run this cell,

8:01to get back to where we were, shift enter.

8:04So here's some headings, and we can actually add list items.

8:09If I hit enter, it just takes me to the next line.

8:11It takes some getting used to.

8:12If you hit shift enter, then that's, oh, okay,

8:15this is something that you might see.

8:18"Colab is for interactive use.

8:20So please confirm that you're present

8:22and ready to continue."

8:23"I'm not a robot."

8:24Okay, so you'll see that from time to time.

8:27If you're working in a notebook

8:28and you leave it idle, Google will check in

8:30because you're using Google's resources.

8:33So that means that you're using their computer

8:36and somewhere in their data center.

8:39So they like to check in with us.

8:41Okay, so here's the lists.

8:42So if you wanted to create a list item,

8:44you would just click, let's do an ordered list.

8:49That's as easy as that.

8:50If you wanted to do an unordered list, same thing.

8:53And it just shows you the how to create them, right?

8:57So if you wanted to create a third one,

8:58you would just follow the same instructions.

9:04Right?

9:05And then it shows up in the preview

9:06and then again, shift enter,

9:08and then you're inside, or you exited that code cell.

9:12Alright, so let's move down to LaTex. So let's take a look.

9:16LaTex is also baked into Colab

9:19and to display Colab, all you do is click on this pitchfork.

9:21So this is just an image. So lemme show you what I mean.

9:24If I double click on this cell,

9:28you can see that this is the image.

9:30So this is one way to actually add an image.

9:32You can also use this icon,

9:35and we'll talk about the differences a little bit later.

9:37But first, let's say I wanted to create

9:44a pi symbol, right?

9:45Or theta or something like that.

9:48All you need to do is click on this pitchfork

9:50and then type pi.

9:52And then you can see pi from the dropdown menu

9:54and all of the, I mean this is so convenient.

9:59Yeah, so here's pi, and you can see it pop up over here.

10:04Now if that's too small for you,

10:06you can also increase the size,

10:10but before we do that, let me show you a few ways

10:12of notating LaTex.

10:14So one way is definitely with this pitchfork.

10:17The other method is actually to use these dollar signs.

10:20So let me show you again with the example of pi.

10:23So you create a dollar sign or two dollar signs,

10:27and then inside of that you would use

10:29the forward slash and then pi.

10:33And then again it shows you that notation,

10:35and then you get pi.

10:36However, you can also add different things.

10:40So you can change the size, for example,

10:43forward slash large makes it bigger, right?

10:47Large. And you can do small and I think medium.

10:51Let's try medium. No.

10:54So we can do small.

10:59Right. And I think by default it already is small.

11:02I think it's the same size. Yeah.

11:03So if you have multiple lines,

11:05you can actually just have a dollar sign and some math here.

11:12Let's say times nine.

11:15And then you add some space, maybe a division.

11:20Let's see, no.

11:24So to actually add a division sign,

11:27we can forward slash that.

11:33Let's try that one more time.

11:39There we go.

11:40So division. So it's pretty straightforward.

11:44That dollar sign is, it's kind of unusual.

11:48So let's see if we can get rid of that.

11:50I think they might need to be on the same line.

11:59There we go.

12:00(chuckles)

12:02Okay, so it takes some getting used to.

12:05This is a good example of

12:07one using the dollar signs

12:09and then you can add multiple lines

12:11or you can have them on the same line.

12:14Whenever I'm creating formulas, I like to use this,

12:16have them all in one line like this

12:19and I'm using large, if I took this away,

12:22you would see that it would be much smaller, right?

12:25So I like to have the size a little bit larger than normal

12:29sometimes because it's easier to read.

12:32So that's pretty much LaTex,

12:34and there's some resources down here.

12:36This is a really great cheat sheet.

12:38Let's take a look at this.

12:42So anything that you need to see,

12:44any sort of symbols that you're not aware of,

12:48if here's some set and logic,

12:52and let's see, what else?

12:54I mean this is all kinds of stuff.

12:57What's even better is that if you don't know

12:59what the name of one of these symbols is,

13:02if you don't have a math background,

13:04what you can do is just go back to Google

13:06and then just type it in.

13:07So it's really useful to Google for LaTex formulas,

13:11and symbols if you forget what they are.

13:14Okay, before we wrap things up,

13:16let me show you how to create images.

13:20You already know that you can use a link.

13:22So this one is a link.

13:23So if I double click in here, you can see that I have a link

13:26to a file in Google Drive.

13:30So there's another way to add images.

13:31So let's go ahead and do that.

13:33So here's our new text cell to add an image

13:35by using this insert image icon.

13:37What I'm gonna do is just

13:39take a screenshot of this image.

13:42So this is my website

13:43and this is an article about what is data analytics.

13:46So now I have a screenshot of this image.

13:49So let's say I took a screenshot

13:50and I wanted to add it to the Google Colab.

13:52All I need to do is click on this image,

13:55here's that screenshot, and I'll click on open.

14:00When you do that, you get all of this stuff.

14:03So that's why I like to use links.

14:05However, when you run this cell,

14:09you just get that image.

14:10So this is a pretty large sized image,

14:13so it takes a lot more vector graphics code.

14:16While you can use this insert image,

14:18just keep in mind that it will behind the scenes,

14:21give you a lot of code.

14:23So, there's other ways of doing that.

14:25For example, you can actually maybe even,

14:29let's see if we can grab this image address

14:34and create a link.

14:36So the way you would do that is pound bracket,

14:39and then the name of the file, which is,

14:42just say data, just for a brevity's sake.

14:45And inside of the parentheses is where you would add that,

14:50And that's a much cleaner way to do it.

14:53Otherwise you get a lot of vector code.

14:55So the best way to do that is just to right click

14:57and then grab that image address.

15:00Okay?

15:01As you can see,

15:02Google Colab is really easy to use.

15:05There's almost no setup

15:06and it's easy to

15:08sort of navigate it from the very beginning.

15:10There's a lot of helpful links

15:11and tools inside of that initial

15:14Google Colab document that you have saved.

15:16So definitely check that out.

15:18Compare that to Jupyter Notebook

15:20where you have to install Anaconda,

15:21then install Jupyter Notebook

15:23using Conda virtual environments, right?

15:26So all of that takes a little bit of getting used to,

15:29but the main difference is

15:31that Google Colab uses Google resources in the cloud.

15:35So computers and CPU somewhere else.

15:39And also you're leveraging the Google account

15:41and Google Drive,

15:42while Jupyter Notebook is something

15:44that you install locally on your machine.

15:46Well, I hope you found this informative

15:48and I will see you in the next video.

0:07<v ->In this video, we're gonna install</v>

0:09Jupyter Notebook with Anaconda.

0:11But before we do, I do wanna mention

0:14that this is an optional video and here's why.

0:17If you're just getting started with Python

0:19and data analysis, Google Colab is enough to get started.

0:24However, if you don't have a Google account

0:27and you don't want a Google account,

0:28or maybe your internet connection is not so great,

0:31this is another alternative.

0:33And I wanted to give as many resources as I can

0:36to accommodate as many learners as possible.

0:39So don't feel like you have to watch this video.

0:42Feel free to skip.

0:44Also, you can come back

0:45when you feel more comfortable with Python.

0:47Okay, so without further ado, let's get started.

0:51This is the Anaconda website at anaconda.com.

0:54And to install, we're just gonna click Get Started,

0:58and then Download Anaconda installers.

1:01I'm on a Mac and I could click this to install it,

1:04but I wanna talk about these additional installers first.

1:08So we can install this on Windows, Mac, and Linux.

1:12And I'm gonna talk about Windows and Mac for a little bit.

1:16For example, 32 bit and 64 bit.

1:20Well, 64 bit is a much more common installation than 32 bit,

1:26but make sure that you're using the correct bit

1:28so that you can avoid getting errors

1:30when you're installing Anaconda.

1:33When you're on a Mac,

1:34I would recommend the 64 bit Graphical Installer

1:38over the command line.

1:39They're both the same thing.

1:41However, the Graphic Installer

1:43is much more straightforward

1:44and you can avoid some errors

1:45if you don't have a lot of experience with the command line.

1:49So I'm gonna click on the Graphic Installer to get started.

1:55Okay, so let's click Continue,

1:58and let's take a look at this.

2:00It's important to read this,

2:01and we're just gonna kind of go over

2:04some of the key points together.

2:06So by default, there's gonna be a modification

2:09to your bash profile

2:11to activate the base environment of conda.

2:14Furthermore, this is the default location

2:17where Anaconda's installed,

2:18in case you wanna delete it.

2:21For Windows, keep in mind that you don't need to check

2:24the Add Anaconda to my path.

2:27And if you're new to Python and pandas,

2:29you can leave Register Anaconda 3

2:31as my default Python checked.

2:34That's the only difference between the Mac

2:36and the Windows installer.

2:40This is a standard EULA agreement,

2:43so end user license agreement.

2:44So go ahead and read that and and accept and continue.

2:48Finally, here's where you can change

2:49your installation location.

2:51If you wanted to make some customizations

2:53as far as your path, you could do that here,

2:56but I always just go ahead and click Install

2:58and use the default location.

3:01Once you've installed Anaconda,

3:03go ahead and open the terminal.

3:05For Windows users, go ahead and click on Start menu

3:08and search for Anaconda.

3:10Then click on the Anaconda prompt,

3:12which will launch a command prompt or terminal

3:15where you can actually type the commands,

3:17just like I'm about to do now.

3:18The first thing that I'm gonna do

3:20is I'm going to type cd to change directories

3:23and navigate to my desktop.

3:28And you wanna create a folder.

3:31I've created a folder,

3:33and I use the makedir command and python_da.

3:39I'm not gonna hit enter

3:40because I've already created this folder.

3:42So what I'm going to do, I'm gonna change directories

3:45and navigate into the python_da folder.

3:49Once I do that, I moved from desktop to python_da.

3:54Let's see what's inside of here. There should be nothing.

3:57So the LS command to list the contents shows nothing.

4:01So this is great.

4:02Now what we wanna do is launch Jupyter Notebook

4:05within this folder.

4:07So all you have to do is type jupyter notebook

4:13and hit enter.

4:16Okay, so this is Jupyter Notebook.

4:19This is not an actual notebook.

4:21We're gonna create that now by going to New

4:25and then selecting Python 3.

4:27If you have Python 2 installed,

4:29you may see more than one kernel here.

4:31You may see a Python 2 kernel, but let's go ahead

4:34and use the Python 3 kernel.

4:39Okay, so this should look familiar

4:41and very similar to Google Colab,

4:43and we can do some cool stuff.

4:45Like for example, if you wanna access the terminal

4:49and type in, you know,

4:51what is the version of Python?

4:56Shift + enter, and there's the version of Python.

5:00So this pound sign allows you to communicate

5:04with the terminal or the command line,

5:05and it's super useful.

5:09You can also type in the print statement,

5:11which we did before, and the colab,

5:18and you get that immediate output.

5:21One thing I would like to say

5:22is that I'm not using dark mode in Jupyter Notebook,

5:25but I was doing that in Google Colab.

5:28So that's the main difference.

5:29Now that we have Jupyter Notebook up and running,

5:32let's talk about how to close it.

5:34So instead of just closing this, you wanna make sure

5:37that you save your work.

5:39Then you can close the tab.

5:42Then you want to go to, you wanna check this.

5:46So what's important to note is that this icon is green.

5:50Once you shut it down, it'll turn gray,

5:53and then you wanna quit this instance of the server.

6:00Let me explain what that means.

6:01So if you go over here, all of these commands just happened

6:05because this server was actually running

6:08and hosting Jupyter Notebook over here in the browser.

6:12So it's really important that we follow those steps

6:15when you start and you close a Jupyter Notebook session.

6:18So let's try it one more time.

6:20So I'm gonna open Jupyter Notebook,

6:22which is gonna run the server,

6:25and then it opens up this page.

6:28So let me go back here

6:29to show you what's happening behind the scenes.

6:32So here you can see that there's no command line,

6:35there's no cursor because the server is running.

6:39So once that server is running,

6:42this is the homepage that we stopped earlier.

6:44So I'm gonna close that.

6:45So once the server is running

6:47and we have a notebook that we created,

6:49all you have to do is click on it to open that notebook.

6:53And there we are. We're back at the Jupyter Notebook.

6:56When you wanna close it, you follow the same steps.

6:59I haven't done anything here,

7:00so I really don't need to save anything.

7:03So I can just close this

7:05and then click on this and then shut down.

7:09You can also delete, edit, and view the selected notebook.

7:13So like let's click View,

7:16and that'll just, it's another way of double clicking it.

7:20So let's go ahead and shut this down.

7:21You can also duplicate it,

7:23and if you have a bunch of notebooks running,

7:24you can check them out here.

7:27Let's go ahead and shut down.

7:31And if we go back to the terminal, you'll see that,

7:34oh, it didn't shut down, so I'm glad we did that.

7:37All we did is stopped this instance of this notebook,

7:41so it's no longer running.

7:43However, the server's still running.

7:45So if you wanna turn that off, you have to hit Quit.

7:49Okay, so you shut down Jupyter, basically the server,

7:52and you'll need to relaunch it.

7:55All right, so here we are.

7:57Let's go ahead and type conda info

8:03or conda list.

8:08These are all of the packets

8:09that are in the base installation,

8:12so this is why we're gonna be using conda

8:15so that we can have our own project directory.

8:18When you do that, the base will change to that name

8:21of that project directory.

8:23So that's it, we've installed Jupyter Notebook

8:25with Anaconda using the base installation,

8:28and we covered the startup and shutdown process

8:31for opening a Jupyter Notebook and closing the server.

8:35I hope this has been informative, and thank you for viewing.

0:07<v ->Now that you have Jupyter Notebook installed</v>

0:09using Anaconda, it's time to move away

0:12from the base environment.

0:14We looked at the base environment a little bit last time,

0:17but let's dive into it and take a look at this together.

0:21If I list everything inside

0:22of this environment using the conda command,

0:24(keyboard clacking) conda list,

0:26(keyboard clacking)

0:27I can see everything that's inside of this environment.

0:31And as you can see, it's a lot.

0:35Right, so jupyter notebook is there.

0:36I mean, everything is in here.

0:37So everything that you need will be available.

0:40So we can just type jupyter notebook,

0:44(keyboard clacking)

0:45and it'll work right outta the box.

0:46It'll just start working.

0:48And that's not what we want,

0:50we don't want everything inside of the base environment

0:52to be available for every project.

0:55Furthermore, the more that you install

0:57into this environment,

0:58the list gets longer and longer and longer.

1:01So what we wanna do

1:03is create something called a conda environment.

1:06A conda environment is just a way to isolate your projects,

1:09so that each project has only the libraries

1:12and dependencies that you need for that one project.

1:15And this is the best practice,

1:17and anyone that you send your frozen file to

1:20so that they can spin up your environment

1:22will be very grateful that you did.

1:24Let's check it out.

1:26To create a conda environment,

1:28we're gonna be using a Conda Cheat Sheet,

1:31which is available for download below this video.

1:34This is a great Cheat Sheet

1:35that has all the basic commands that you're gonna need.

1:38You could also check out their documentation,

1:41but I find that this is a very handy way to move forward,

1:43especially when you're first learning

1:45how to use Conda environments.

1:48So what do we wanna do first?

1:49Okay, we already used conda list here,

1:55but what we wanna do is create a new environment.

1:58We're gonna use the conda create command right here,

2:01and name refers to the name of the environment.

2:04The reason that this is named 3.5

2:08is because 3.5 is the version of Python.

2:11So that makes sense in this case.

2:13What we're gonna do is we're just gonna name it python_da.

2:18That said, it's really useful to have the option

2:21to specify the version of Python that you want.

2:24We won't need to do that in this case,

2:26but it's worth mentioning.

2:28First, I'm going to clear my terminal,

2:31and we're gonna enter conda create,

2:34(keyboard clacking)

2:36dash, dash, name,

2:37and we're going to use python_da

2:41for our environment name.

2:43Again, if we wanted to use Python version,

2:46(keyboard clacking) we would just add that here.

2:49So you could say,

2:502.7.

2:53Hopefully, you don't have to use Python two,

2:55but if you did for any reason,

2:56this is how you would do that,

2:58and maybe you would change the name

3:00of the Conda environment.

3:03Okay, so let's go ahead and create this.

3:07Yes, I wanna proceed.

3:10All right, so it's been created.

3:11To activate, here's the command,

3:13kind of nice,

3:14and to deactivate,

3:16here's the command.

3:16So conda's kind of thoughtful.

3:18Thank you, conda.

3:20All right, let's try this out.

3:21So what does it mean to activate an environment?

3:24Well, first of all,

3:25notice that we are in the base environment.

3:28Lemme go ahead and clear this again.

3:30(keyboard clacking)

3:32The base environment

3:33is what you will see when you first install Anaconda,

3:38and that's where all of the dependencies

3:40and packages are listed in that base environment.

3:43We've already created our environment,

3:45so let's go ahead and list that,

3:46(keyboard clacking) conda env list.

3:49(keyboard clacking)

3:51Whoops, there we go.

3:54All right, so now you can see we have base and python_da.

3:59Let's go ahead and activate that.

4:01This is what I copied

4:02when conda so graciously told us

4:05what we needed to do to activate this.

4:06So let's hit Enter.

4:08And now you can see that we've moved from base

4:12and now we're in the python_da environment.

4:16So let's list everything that's inside of this environment,

4:20python_da.

4:22Nothing, and that's exactly what we want.

4:25Add some packages.

4:26(keyboard clacking)

4:27So conda install,

4:30we're gonna ask for pandas,

4:32(keyboard clacking)

4:34jupyter, (keyboard clacking)

4:36and let's ask for seaborn

4:40to visualize some data.

4:42(keyboard clacking)

4:45Do I wanna proceed?

4:46Yes, I do.

4:48This doesn't take a very long time to download,

4:51but I'm gonna go ahead and skip this part.

4:53Okay, we've got everything installed.

4:55Let me clear everything and get back to the top.

4:58Okay, so let's list what we have

5:00in this environment using the conda list command.

5:05Just from those three packages,

5:07this is everything that we have,

5:10and these includes dependencies.

5:12So I'm sure you can see from just three libraries

5:15that we've imported that you wanna be very selective

5:18when you're isolating your packages.

5:20Otherwise, when you share your projects,

5:22you're gonna be sending a large list of dependencies

5:24and packages for no reason at all.

5:27Great, so we're in python_da,

5:30and we can use pandas,

5:33jupyter notebook, and so on and so forth.

5:36If we wanted to type in jupyter notebook,

5:38(keyboard clacking)

5:41it should work.

5:42(keyboard clacking)

5:46Well, that's a typo. (chuckling)

5:47(keyboard clacking)

5:49I'll say, "Wait a minute, what did we do wrong?"

5:52And I keep on forgetting the y.

5:54(keyboard clacking) All right, let's try that.

6:01Okay, here we go.

6:04Great, so we've managed to run the jupyter notebook

6:09from our root directory,

6:11and that's not what we want.

6:13We wanna be inside of the folder.

6:15In my case, my example project is in the Desktop,

6:20so let's check that out.

6:21And there it is, python_da.

6:23So you can navigate to that here,

6:25and then you would see this Untitled jupyter notebook

6:28that we created before.

6:30However, it's a best practice to launch jupyter notebook

6:35from the directory,

6:36your project directory.

6:37So from python_da.

6:40So let's go ahead and do that.

6:41First, I'm gonna quit the server.

6:45All right, and that's fine.

6:47So we've quit that.

6:49I'll come back here

6:50and you can see that we've shut down our server.

6:52So now I'm in my route directory,

6:54but I would like to navigate to my project directory.

6:58Usually, I would use documents,

7:00but I'm gonna be using Desktop,

7:01just for ease of navigation.

7:04So I'm gonna change directories into the Desktop,

7:07and now you can see that I'm in the Desktop here.

7:10I'm gonna list the packages

7:12or list the contents of that directory.

7:14And we have python_da.

7:16So I'm gonna change directories again.

7:18I'm gonna use tab to auto complete

7:21and enter that directory.

7:22And I can see that I'm there.

7:23Again, I'm gonna list the contents of that directory.

7:27And we see the Untitled ipython notebook,

7:30which is a jupyter notebook.

7:32Let's go ahead and launch jupyter notebook again.

7:34(keyboard clacking)

7:39There we go. (keyboard clacking)

7:40Okay, so here we are in our project directory,

7:44and we know that, because we launched from python_da

7:49and I know that this is the file that's there.

7:52Now, if I wanna launch this,

7:54I can double click it or you can select it

7:56and click View.

8:00Great, so this should look familiar.

8:02We can also,

8:03as we did in the last video,

8:05access the terminal by using the pound sign.

8:09Excuse me, by using the exclamation mark

8:11or the bank's symbol.

8:13And you can type things like conda env list.

8:18(keyboard clacking)

8:20And this will return,

8:21oops, Shift + Enter.

8:23And this will return everything,

8:25all of our environments, right?

8:27So we have python_da and base.

8:31You can also use the python,

8:33or a conda list command

8:36to see everything that's part of this environment.

8:40And here we are.

8:42And scroll through that,

8:44and you can see all of your packages

8:46and dependencies for the python_da environment.

8:52Okay, so this is a great introduction

8:54to using conda environments.

8:57Let's go ahead and close this notebook.

8:59I'm gonna save the changes,

9:02and I'm gonna close the tab.

9:04And if you remember, this is the sequence

9:05for shutting things down.

9:06We can see that it's still active,

9:08because the notebook is green.

9:10And it's already checked.

9:11So if it's not,

9:13just be sure to check it

9:15so that you can actually click Shutdown.

9:17Great, it's grayed out,

9:19we know that it's shut down.

9:20And what we wanna do is move to the Quit.

9:24And what is that gonna do?

9:25It's gonna shut down the server.

9:27Perfect, we've shut down the server.

9:29And if we go back to the terminal,

9:31you can see that we've shut down the server.

9:34Okay, great.

9:35So what do we do now?

9:36Well, if you wanna deactivate the environment,

9:40you can say conda deactivate.

9:42(keyboard clacking)

9:44You don't need to list the name,

9:46because it's the active directory.

9:48So let's hit enter,

9:50and there we go, we're back at the base environment.

9:54So this is pretty much everything that we need to do.

9:58As you can see,

9:59using conda is pretty straightforward

10:01once you know the commands and you have the cheat sheet.

10:04So be sure to download that cheat sheet,

10:06it's a PDF, and keep that for your reference.

10:09Again, I hope you found this informative,

10:11and I'd like to thank you for watching.

0:07<v ->So far, you've managed to install</v>

0:09Aconda Jupyter Notebook

0:12and use Conda environments to keep your projects isolated.

0:17The last concept that I want to talk to you about is Docker.

0:21So we're not gonna really get into Docker

0:23because it has a notable learning curve,

0:26and you should definitely learn Docker.

0:28But what I do wanna talk about

0:29is the differences between Conda and Docker.

0:33Conda works at the Python level,

0:36keeping your packages organized and isolated,

0:39while Docker actually has a tech stack

0:42and an application inside of a container.

0:46Before we get into the differences between a Docker image

0:49and a Docker container,

0:50let's go ahead and take a look at how to install Docker.

0:54Again, we're not gonna install Docker and learn Docker.

0:57I'm just gonna show you a brief, high level overview

1:00of how to get started.

1:02So the first thing we're gonna do

1:03is search for Docker Desktop,

1:05and we're just gonna click on this first link.

1:08It's also available for Windows and Linux.

1:11In my case, I would be using the Apple chip, the M1 chip,

1:17but we're not gonna click on that and download that.

1:19But this is how you would get started

1:20downloading Docker Desktop.

1:24Now we wanna search for an image.

1:26So let's talk about the differences

1:28between an image and a container.

1:31An image contains the tech stack that you're gonna need

1:35to run the application.

1:37So that's gonna be basically, here's your tech stack,

1:41whatever that you're gonna be using.

1:43And then we're gonna be running Anaconda for example.

1:51A container, on the other hand, is an instance.

1:56So once you have this image

1:58and you use the command docker run, again,

2:01we're not gonna get into the specifics of how to use Docker,

2:04but then you would get an instance,

2:06and so you would have a running instance

2:08of that Docker container,

2:09and then you can do things like run a Jupyter Notebook

2:13or Matplotlib,

2:17pandas, whatever you want it to run,

2:19so that that Anaconda instance would be available

2:22inside of this container.

2:24You could also decide to delete that container

2:28and then run a new container using the same

2:31docker run command.

2:32And then you would have this new container.

2:35They would be identical if you didn't

2:37use anything different.

2:39However, it's important to note that you can start,

2:44stop, and delete a container.

2:48So you can delete this and then start a new instance.

2:50So that's the difference between a Docker image

2:54and a Docker container.

2:57So let's search for a Docker image from Google,

3:02and we're gonna search for Anaconda3

3:08Docker image.

3:11There we go.

3:13And we're gonna click on this

3:14first one right here.

3:15The difference between Anaconda3

3:18and regular Anaconda is

3:20basically version 2.7, and that's not what we want.

3:24We want to be working with Python three,

3:27so this is why we're gonna click on this command.

3:32So this is where you can actually pull Docker into

3:35Docker Desktop,

3:36and this would be the image that you would execute,

3:39and then you would have available an instance,

3:42so you can run an instance,

3:43and then you could use that instance to

3:46run Jupyter Notebook.

3:47It's much like a virtual machine,

3:49if you know what a virtual machine is,

3:51which is a complete operating system,

3:53Docker just uses the tech stack to be able

3:55to run the application.

3:57So in comparison, it's much lighter.

4:01But it's important to mention when you're using conda

4:04and you share your project

4:06with somebody in a production environment,

4:08there may be some conflicts due to the operating system.

4:11So using a Docker image is a great way

4:15to work in a production environment

4:17so that you can minimize conflicts.

4:19If you wanna learn more about Docker,

4:21you can go to the Docker website

4:23or check out one of our courses to learn more about Docker.

4:28So that's everything

4:29for the Python development environment skill.

4:33I hope you found this informative,

4:34and thank you for watching.