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- Welcome to lesson five,

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sequences, lists, and tuples.

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Back in part one in
lessons three and four,

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we began to introduce these
two key data structures

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that you will use extensively

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in your Python application development.

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Here we're going to talk about

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all of the additional details

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of these two key data structures.

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We'll start out by creating
a variety of objects

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of these types,
initializing those objects,

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and showing you how to access
their individual elements,

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and also ranges of elements as well.

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We'll also talk, for lists,

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about both sorting and
searching the lists,

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and for tuples, about
searching tuples as well.

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We'll show you how to pass
objects of these types

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to functions and methods,

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and we'll begin talking
about various methods

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built into the list type itself.

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We'll continue lesson
five with our discussion

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of functional-style programming

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in the context of Python.

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Python itself is not a
functional programming language,

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or a pure functional programming language.

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It is a multi-paradigm language

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that has some capabilities

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of functional-style programming,

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and that's what we're going
to be presenting to you

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in this part of the lesson.

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We'll introduce the concept

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of lambda expressions in Python,

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which are basically anonymous
function definitions.

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We'll take a look at various filter,

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mapping, and reduction capabilities.

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Initially, we'll do that in the context

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of what's known as a list comprehension,

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which is a syntax element
built into the language

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for conveniently creating new lists

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from other sequences of values.

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Then we'll also take a
look at filter, map, reduce

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in the context of functions

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that are built into the
Python standard library.

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We'll also introduce
as part of this lesson

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two-dimensional lists very briefly.

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The only reason we're going to do that

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is to show you the syntax,

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because once we move over to lesson seven,

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we're going to introduce

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array-oriented programming with NumPy.

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That's the way that you're
normally going to build

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a multi-dimensional data
structure in Python.

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We'll finish up this particular lesson

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with a visualization introduction

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in the context of our intro
to data science section.

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In this particular section,

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we're going to create a
die-rolling simulation

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that produces a static visualization.

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Basically we're going
to roll a six-sided die

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some number of times

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and then produce a static
bar plot at the end

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that shows how many ones we got,

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how many twos we got, all
the way up through sixes.

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If we have a good random number generator,

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we should get approximately 1/6

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of whatever total number
of rolls should be ones,

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twos, threes, fours, fives,
and sixes respectively.

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In the next lesson,

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we'll do that same visualization again,

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but using dynamic visualization

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in the context of animation techniques

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provided by the Matplotlib library.