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- Welcome to Python
Fundamentals part three of five.

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My name is Paul Deitel and
I'll be your instructor

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for these LiveLessons videos.

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Part three is based on
chapters eight through 10

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of our textbook, Intro to Python

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for Computer Science and Data Science,

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learning to program with
AI, big data, and the cloud,

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and also chapters eight through
10 of our professional book,

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Python for Programmers.

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The prerequisite for part three are either

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Python Fundamentals
LiveLessons parts one and two,

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or equivalent Python
programming experience.

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Many people find that
as they work their way

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through the videos, it's
helpful to have a copy

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of the book at hand.

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However, this is not required.

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There is additional
information in the books

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that I don't cover as
part of these videos.

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If you're interested in getting
a copy of one of the books,

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you can find them in various
print and electronic formats

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at the sites that are listed for you.

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If you're a college instructor,

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you'll probably want to take a look

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at Intro to Python for Computer
Science and Data Science,

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which is our college
textbook version of the book.

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This one is in full color, it's 880 pages,

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240 pages more than
Python for Programmers,

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and in particular, it
contains a ton of exercises,

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557 self-check exercises,
as well as 471 additional

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end-of-chapter exercises and projects.

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Now many professionals find that

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they like to work with
the textbook version,

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specifically because of the exercises.

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If you're interested in learning more

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about Intro to Python for
Computer Science and Data Science,

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please take a look at the
links that I've provided below.

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The first one is for an
architectural diagram

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in which we show you the four-part

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modular structure of this book,

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and that really applies
both to the college textbook

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and our professional book,
Python for Programmers, as well.

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The for programmers book
has one fewer chapter,

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but the overall architecture
of the two books is the same.

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And also, in the for programmers book,

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we have less of the lower-end
pedagogical material

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that's really geared to people

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who are novices in programming.

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The second link is for the
full table of contents,

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where you can see everything
that we're going to cover

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throughout the book.

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And the third one is the book's preface,

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where you can learn
more about our approach

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to teaching Python, data science,

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artificial intelligence,
and various big data topics.

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I'd also like to recommend
that you take a look

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at these two additional links.

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The first one is for the full book cover.

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And in particular, if you
look at the back cover copy,

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you'll see a nice summary of everything

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that we're going to do throughout
these LiveLessons videos.

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And I'd also recommend
that you take a moment

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to read through the technical

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and academic reviewer testimonials

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to really get a sense of all the things

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that they liked about
how we presented Python

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and the various data science,
AI, and big data topics.

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To begin this lesson, we'll
take a look at lesson eight,

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strings, a deeper look.

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Now because Python is
a scripting language,

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it is frequently used in all sorts of

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string processing capabilities.

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So as we go into lesson eight here,

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we'll be looking at a lot
of little individual details

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of how to work with strings in Python.

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We'll take a look at
various string methods,

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look at how to do various

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string formatting capabilities as well.

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And as part of that, by the way,

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we're going to be not only talking about

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the F-string concept that you saw

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in some of the earlier lessons

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for creating formatted string content,

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but also looking at an older
style of string formatting

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that you will frequently encounter

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in other people's Python programming code.

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And of course, Python has been around now

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for an extremely long time.

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So it is important for you to have a sense

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of other ways of doing things,

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even if they're not the
most current, as well.

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One of the key aspects
that we'll be looking at

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for string processing in this lesson

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is Python's extensive set

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of regular expression
processing capabilities.

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And we'll introduce a variety of those

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as part of this lesson,

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and then start to use
those a little bit more

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as we move into the intro
to data science section,

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where we will continue our discussion

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of the pandas library from
lesson seven in part two.

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But we're also going to use pandas now

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to perform some data manipulation
and data preparation.

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And we'll combine the
regular expression concepts

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that we learn with pandas to do that.

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In lesson nine, files and exceptions,

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we're going to take a look at
several different file formats

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and introduce Python's
version of exception handling.

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With respect to file processing,

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we'll look at basic text files
for both input and output.

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We'll also take a look at the very popular

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Java Script Object
Notation, or JSON for short.

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JSON is one of the most popular
data interchange formats,

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and we'll be using that
extensively in subsequent lessons.

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So we want to introduce it to you here

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in the context of writing
Java Script Object Notation,

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out to a file on disc, and
also reading data back in

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from such a file as well,

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and turning it back into a Python object.

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Now we're going to get in depth

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into the Python exception
handling mechanism.

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And if you're coming into this videos

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from a language like C++, or Java, or C#,

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you're going to see a
lot of familiar concepts.

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Python uses some different key words

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and also has some additional concepts

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that you won't find in those
other programming languages.

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So I'll be sure to point
all of that out to you.

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Now when we get to the intro
to data science section

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in this lesson, we're going to focus on

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comma separated value files.

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And one of the key reasons for that

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is part of our focus
on processing data sets

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in a lot of the higher-end lessons.

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Many of the data sets
that you will work with

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are provided in comma
separated value format,

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so it is important to know

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how to get such data into an application,

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and sometimes how to write
out such data as well.

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So in the context of both the CSV module

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that's built into Python,
and the pandas library,

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we will be showing you
how to load and store

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comma separated value data.

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To finish up part three, we'll
take a look at lesson 10,

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object-oriented programming.

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Now over the course of a year and a half

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of research for this project,

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as I mentioned in an earlier lesson,

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we rarely encountered the need

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to create custom class definitions.

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In fact, I think for the project,

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we wound up creating only four
or five or six of them total,

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outside of lesson 10, where
we show you the mechanisms

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for creating your own
new data types in Python.

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So the vast majority of
Python programming is done,

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as what we like to refer to,
as object-based programming,

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where you create an object
of an existing class,

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probably imported from one of the many

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open-source libraries that are out there,

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and then you interact with that object.

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In lesson 10, we focus
on how you can create

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your own new custom data types.

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So for instance, if
you're creating a library

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that other people are going to use,

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a lot of what I present
here in this lesson

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is going to be relevant to you.

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If you are going to need to
create any custom classes,

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which we do a few times in
some of the subsequent lessons,

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you'll probably find that you can look at

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the classes we defined and
figure out what they do,

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if you are already an
object-oriented developer.

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If you are not, however, I will cover

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object-oriented programming
in detail here in lesson 10.

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Some of the things that
we'll be looking at

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are how to create custom classes,

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how to use a concept called properties

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for accessing data inside of an object.

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And by the way, one of the things

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that you're going to find in Python

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is that its approach to
object-oriented programming

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is a lot different from
languages like C++, Java, and C#,

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which all have concepts
of what we call public,

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protected, and private members of a class.

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In Python, everything is
public no matter what you do.

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So one of the things
that we're going to do

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is look at Python's conventions

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that Python programmers follow

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to indicate aspects of a class definition

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that should not be used by somebody

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who is a client of the class.

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So you create an object, you
interact with that object,

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but there's still specific
attributes and behaviors

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that you should be using to
interact with those objects.

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And there are others that are
meant for internal use only.

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And in Python, internal use only is done

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with naming conventions, not key words,

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like public, protected, and private.

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So we're going to talk about those issues

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and show you how you can
simulate the concept of private.

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But even those things are
still publicly accessible,

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as I will demonstrate to
you throughout this lesson.

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Some of the other things
we'll take a look at here

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are how to create new classes
from existing classes.

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So we'll talk about inheritance

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and polymorphism capabilities,

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and you'll see that those
are very similar to some of

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the C-based object-oriented
programming languages.

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We're also going to introduce
a really key concept in Python

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known as duck typing.

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So basically the idea is
if it looks like a duck,

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and it walks like a duck,

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and it makes the same sounds
as a duck, it must be a duck.

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So basically in Python,
when you pass an object,

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for example, into a function,

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as long as that object
has the capabilities

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the function expects it to have,

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the function will be able to

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interact with that object and use it.

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So for instance, a lot of
the visualization libraries

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expect you to provide objects
of the numpy array data type.

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But because Python lists have a lot of

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the same capabilities as numpy arrays,

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you can often pass in a list
where an array is expected.

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Some of the other things we'll look at

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are additional syntax features
like operator overloading,

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where you can create
your own new operators

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to work with your custom data types.

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So for instance, if you wanted to create

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some new mathematical type,

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you could overload the plus
operator and the minus operator,

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and the multiplication
operator, et cetera,

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so that you can use objects of your type

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as conveniently as using
integers, for instance.

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We'll talk about a concept
called named tuples,

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which are tuples where you
can access the elements

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by their names instead
of by index numbers.

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We'll also introduce a
relatively new feature to Python

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that was introduced in Python
3.7 called data classes.

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And we'll show you some
unit testing capabilities

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that are built into the Python
standard library as well.

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In the intro to data science section,

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to finish up part three,
we're going to start to

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take a look at some more
data sciencey stuff.

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In particular, we're going
to look at time series

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and a concept called
simple linear regression.

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And this will be our first introduction

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into some of the topics that
we're going to start to see

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in more detail in parts three and four.