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Getting Setup
5:26Let's get our Jupyter Notebook and miniconda environment all setup.
There are a couple of ways to get
things installed here, but by far
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the best way to experiment with a package
in my opinion is by using Anaconda.
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If you haven't heard of Anaconda,
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it's a distribution of all
the popular Python data libraries.
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I'm actually going to use a smaller
version of it called Miniconda, but
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either will do.
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We offer content on how to get it
installed if you don't have it already,
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it's in the teacher's notes.
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If you don't have a Minconda or Anaconda
installed, why don't you pause me and
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get that installed and
then come follow along.
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Let's get things cooking.
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So here I am at my command prompt, and
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I'm gonna make sure that
I have Conda installed.
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So I'll type conda --version,
awesome, I do.
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Okay, now I'll create a directory
called intro-to-numpy,
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and I'm gonna change to that directory,
so cd intro-to-numpy.
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And now, I'll make sure that
I create a new environment.
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So I'm gonna say conda create,
and I'm gonna name it 100 days.
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And then what we want to
install is numpy and jupyter,
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you just kinda list them off here,
the things that you want.
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So we definitely want numpy and jupyter.
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And this will kick off and
set everything up for us.
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And Conda saves environments to make
sure that things are always at the right
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version, by providing
a virtual environment.
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So, I'm gonna let this go, it's gonna take
a little bit, see you when it's done.
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And there we go,
everything is all installed, awesome.
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So, I'll activate our new environment.
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So, let's say conda activate 100 days.
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And I've got a big error message here.
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Let's go ahead and
I'm gonna grab this here, this echo,
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we're gonna echo into our
profile.d file here on Mac.
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Let's paste that here.
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So now that I've pasted that into
the profile D I need to restart my bash.
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So, I can say bash profile and
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now, I should be able to say conda
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activate 100days, awesome.
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And now, I can open up our Jupyter server.
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So I'll say jupyter notebook.
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And that popped open a brand-new window,
which I'll bring over here to us.
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And I'm going to choose, over here,
this New., we'll do new Python 3 notebook.
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And I'm gonna come up here and
I'm going to
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rename this to introduction to NumPy,
okay?
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And I want this first cell here,
I'm gonna make this first cell Markdown.
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And I'm gonna write heading one
of introduction to Numpy, and
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I will just say learning Numpy.
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So I'd like to repeat that I'd love for
you to follow along and
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make your own notebook.
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I'll give you mine when the course is
over so you can have all of my notes, but
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please feel free to make your own comment.
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I find that when using these notebooks,
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it helps to capture your
thoughts to review later.
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I really liked using these for
cheat sheets.
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So we can actually run this cell and
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enter the next one by choosing
option enter on a Mac.
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So option enter.
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Just a quick heads up,
I love keyboard shortcuts,
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you can pick up some great
productivity by learning them.
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So, open the list here, and kinda type
what it was that you were looking to do.
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So we were looking to run that cell.
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So if I type run, and we'll see here
we have run cell and insert below.
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That's what I did, so
this is option enter,
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this'll look different if
you're running this on Windows.
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Pretty cool, right?
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Okay, so the new cell that was created
here is Python by default, all right?
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So it's code.
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I wanna make sure that we got
things installed correctly, so
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what I'm gonna do is I'm
going to import NumPy.
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Now, as it turns out, data scientists
are just as lazy as programmers and
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don't like typing more than we need to.
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So, a very common way to use
NumPy is to import it as NP.
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So, you say Import numpy as np.
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And that will allow you to skip
some unnecessary characters,
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we don't wanna type more than we need to.
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Well, of course, that stands for NumPy,
but I like to think of it as no problem.
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And that's because when we're done here,
NumPy will be no problem.
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Like most great libraries, the NumPy
module exposes its dunder version.
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So we'll just write that here,
let's put that in the same cell here.
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We'll say, np.__verson__.
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And remember in Jupiter notebooks,
when you run a cell, the last line,
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it will show its output.
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So here we go, and
you'll see that I'm running 1.14.5.
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Now, you are most likely using
a more current version than I am.
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And there shouldn't be
any breaking changes for
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this course between your version and mine.
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With that said,
I can't predict the future yet.
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So, therefore,
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if there are any problems, I'll makes sure
to list those in the teacher's notes.
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So please, take a moment
right now to check those out,
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I don't want you to have any problems.
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I want you to have no problem,
np, with NumPy, np.
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Alright, now that we have our
environment all set up correctly,
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let's dive into learning the most critical
data structure of NumPy, the array.
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