1
00:00:08,028 --> 00:00:10,240
- Welcome to Python Fundamentals
live lessons part two.

2
00:00:10,240 --> 00:00:11,630
My name is Paul Deitel,

3
00:00:11,630 --> 00:00:15,330
and I'll be your instructor
for these live lessons videos.

4
00:00:15,330 --> 00:00:18,350
Part two is based on
chapters five through seven

5
00:00:18,350 --> 00:00:19,620
of our textbook,

6
00:00:19,620 --> 00:00:22,820
Intro to Python for Computer
Science and Data Science.

7
00:00:22,820 --> 00:00:25,790
Learning to program with
AI, Big Data and the Cloud.

8
00:00:25,790 --> 00:00:29,030
And chapters five through seven
of Python for Programmers,

9
00:00:29,030 --> 00:00:30,623
which is our professional book.

10
00:00:32,180 --> 00:00:35,220
The prerequisite for part two
of these live lessons videos

11
00:00:35,220 --> 00:00:38,050
is Python Fundamentals, part one,

12
00:00:38,050 --> 00:00:40,963
or equivalent Python
programming experience.

13
00:00:42,470 --> 00:00:44,430
Many people find that
as they work their way

14
00:00:44,430 --> 00:00:45,410
through the videos,

15
00:00:45,410 --> 00:00:48,550
it's helpful to have a
copy of the book at hand.

16
00:00:48,550 --> 00:00:51,070
However, this is not required.

17
00:00:51,070 --> 00:00:53,370
There is additional
information in the books

18
00:00:53,370 --> 00:00:55,368
that I don't cover as
part of these videos.

19
00:00:55,368 --> 00:00:59,010
If you're interested in getting
a copy of one of the books,

20
00:00:59,010 --> 00:01:00,900
you can find them in various print

21
00:01:00,900 --> 00:01:04,533
and electronic formats at the
sites that are listed for you.

22
00:01:06,120 --> 00:01:07,480
If you're a college instructor,

23
00:01:07,480 --> 00:01:09,160
you'll probably want to take a look

24
00:01:09,160 --> 00:01:12,400
at Intro to Python for Computer
Science and Data science,

25
00:01:12,400 --> 00:01:16,030
which is our college
textbook version of the book.

26
00:01:16,030 --> 00:01:17,740
This one is in full color.

27
00:01:17,740 --> 00:01:22,610
It's 880 pages, 240 pages more
than Python for Programmers.

28
00:01:22,610 --> 00:01:26,310
And in particular, it
contains a ton of exercises.

29
00:01:26,310 --> 00:01:29,210
557 self check exercises,

30
00:01:29,210 --> 00:01:33,670
as well as 471 additional
End-of-Chapter exercises

31
00:01:33,670 --> 00:01:35,170
and projects.

32
00:01:35,170 --> 00:01:36,970
Now many professionals find that they like

33
00:01:36,970 --> 00:01:38,750
to work with the textbook version,

34
00:01:38,750 --> 00:01:41,773
specifically because of the exercises.

35
00:01:42,887 --> 00:01:45,060
If you're interested in learning more

36
00:01:45,060 --> 00:01:48,480
about Intro to Python for
Computer Science and Data Science,

37
00:01:48,480 --> 00:01:51,500
please take a look at the
links that I've provided below.

38
00:01:51,500 --> 00:01:54,140
The first one is for an
architectural diagram

39
00:01:54,140 --> 00:01:57,590
in which we show you the
four part modular structure

40
00:01:57,590 --> 00:01:58,423
of this book,

41
00:01:58,423 --> 00:02:01,410
and that really applies
both the college textbook

42
00:02:01,410 --> 00:02:05,250
and our professional book,
Python for Programmers as well.

43
00:02:05,250 --> 00:02:08,960
The For programmers book
has one fewer chapter,

44
00:02:08,960 --> 00:02:12,020
but the overall architecture
of the two books is the same.

45
00:02:12,020 --> 00:02:13,800
And also in the For programmers book,

46
00:02:13,800 --> 00:02:17,210
we have less of the lower
end pedagogical material,

47
00:02:17,210 --> 00:02:18,920
that's really geared to people

48
00:02:18,920 --> 00:02:21,320
who are novices in programming.

49
00:02:21,320 --> 00:02:23,640
The second link is for
the full table of contents

50
00:02:23,640 --> 00:02:24,730
where you can see everything

51
00:02:24,730 --> 00:02:26,530
that we're going to cover
throughout the book.

52
00:02:26,530 --> 00:02:28,660
And the third one is the books preface

53
00:02:28,660 --> 00:02:30,240
where you can learn more about

54
00:02:30,240 --> 00:02:33,230
our approach to teaching
Python, Data Science,

55
00:02:33,230 --> 00:02:36,473
Artificial Intelligence,
and various Big Data topics.

56
00:02:37,740 --> 00:02:38,900
I'd also like to recommend

57
00:02:38,900 --> 00:02:41,710
that you take a look at
these two additional links.

58
00:02:41,710 --> 00:02:44,190
The first one is for the full book cover.

59
00:02:44,190 --> 00:02:47,860
And in particular, if you
look at the back cover copy,

60
00:02:47,860 --> 00:02:50,400
you'll see a nice summary of everything

61
00:02:50,400 --> 00:02:53,220
that we're going to do throughout
these live lessons videos.

62
00:02:53,220 --> 00:02:55,300
And I'd also recommend
that you take a moment

63
00:02:55,300 --> 00:02:57,610
to read through the technical

64
00:02:57,610 --> 00:03:00,380
and academic reviewer testimonials,

65
00:03:00,380 --> 00:03:02,240
to really get a sense of all the things

66
00:03:02,240 --> 00:03:03,440
that they liked about

67
00:03:03,440 --> 00:03:07,000
how we presented Python, and
the various Data Science,

68
00:03:07,000 --> 00:03:09,053
AI and big data topics.

69
00:03:10,900 --> 00:03:12,800
Back in lessons three and four

70
00:03:12,800 --> 00:03:15,850
of Python Fundamentals
live lessons part one,

71
00:03:15,850 --> 00:03:19,440
we started to introduce a couple
of the key data structures

72
00:03:19,440 --> 00:03:23,010
in Python, specifically lists and tuples.

73
00:03:23,010 --> 00:03:26,080
And here in lesson five of part two,

74
00:03:26,080 --> 00:03:29,530
we're going to take a look at
those sequence data structures

75
00:03:29,530 --> 00:03:31,350
in much more detail.

76
00:03:31,350 --> 00:03:33,550
We'll talk about the
various ways of creating

77
00:03:33,550 --> 00:03:36,750
and manipulating both lists and tuples.

78
00:03:36,750 --> 00:03:37,860
With respect to list,

79
00:03:37,860 --> 00:03:39,500
we're going to show you some sorting

80
00:03:39,500 --> 00:03:42,460
and searching capabilities
that are built in.

81
00:03:42,460 --> 00:03:45,660
We're also going to go
in-depth into a variety

82
00:03:45,660 --> 00:03:48,700
of functional style
programming capabilities,

83
00:03:48,700 --> 00:03:52,050
both built into the language syntactically

84
00:03:52,050 --> 00:03:54,380
via what are known as comprehensions,

85
00:03:54,380 --> 00:03:58,100
and also via library
functions called Filter,

86
00:03:58,100 --> 00:04:00,620
Map and Reduce respectively.

87
00:04:00,620 --> 00:04:01,453
For the first time,

88
00:04:01,453 --> 00:04:02,286
we're going to introduce

89
00:04:02,286 --> 00:04:05,060
a two dimensional list data structure.

90
00:04:05,060 --> 00:04:07,390
And we're only going to
show one example of that.

91
00:04:07,390 --> 00:04:08,470
Because in reality

92
00:04:08,470 --> 00:04:11,730
when you get into
multi-dimensional data in Python,

93
00:04:11,730 --> 00:04:14,020
it's almost always going to be implemented

94
00:04:14,020 --> 00:04:17,930
in the context of the
NumPy open source library,

95
00:04:17,930 --> 00:04:22,430
which is the topic of lesson
seven in part two here.

96
00:04:22,430 --> 00:04:26,090
We'll also revisit simulation
a little bit in this lesson.

97
00:04:26,090 --> 00:04:28,470
And in particular, we're going to show you

98
00:04:28,470 --> 00:04:32,450
how to visualize the
results of a simulation,

99
00:04:32,450 --> 00:04:36,140
in this case using the
libraries called Matplotlib

100
00:04:36,140 --> 00:04:37,290
and Seabourn.

101
00:04:37,290 --> 00:04:40,690
Seaborne is actually
built on top of Matplotlib

102
00:04:40,690 --> 00:04:43,483
and simplifies many of its capabilities.

103
00:04:45,020 --> 00:04:47,590
In lesson six, we'll
continue our discussion

104
00:04:47,590 --> 00:04:49,870
of Python's built in data structures

105
00:04:49,870 --> 00:04:52,100
with dictionaries and sets.

106
00:04:52,100 --> 00:04:54,520
And just like we do for lists and tuples,

107
00:04:54,520 --> 00:04:57,350
we'll talk about the different
ways of creating objects

108
00:04:57,350 --> 00:04:59,700
of these types and manipulating them.

109
00:04:59,700 --> 00:05:03,210
And that will include some
functional style programming

110
00:05:03,210 --> 00:05:06,570
in the context of this
thing called comprehensions,

111
00:05:06,570 --> 00:05:09,550
that we will first
introduce in lesson five.

112
00:05:09,550 --> 00:05:12,800
As part of lesson six in the
intro to data science section,

113
00:05:12,800 --> 00:05:14,360
we'll be talking once again

114
00:05:14,360 --> 00:05:16,410
about visualizations implemented

115
00:05:16,410 --> 00:05:18,840
with Matplotlib and Seabourn.

116
00:05:18,840 --> 00:05:22,890
But in this case, we're going
to do a dynamic animated,

117
00:05:22,890 --> 00:05:25,007
die rolling simulation.

118
00:05:25,007 --> 00:05:27,850
And one of the cool things
about this particular example

119
00:05:27,850 --> 00:05:29,710
is that it gives you a way

120
00:05:29,710 --> 00:05:33,833
to really visualize the law
of large numbers in action.

121
00:05:36,070 --> 00:05:38,540
To finish off part two of
these live lessons videos,

122
00:05:38,540 --> 00:05:40,580
we'll take a look at lesson seven

123
00:05:40,580 --> 00:05:43,670
on array oriented programming with NumPy.

124
00:05:43,670 --> 00:05:46,420
Now NumPy is one of the most popular

125
00:05:46,420 --> 00:05:48,670
Python open source libraries.

126
00:05:48,670 --> 00:05:51,340
It's used extensively by other libraries.

127
00:05:51,340 --> 00:05:55,730
Something like 450 of them
are built on top of NumPy.

128
00:05:55,730 --> 00:05:57,070
So a lot of the techniques

129
00:05:57,070 --> 00:05:59,313
that I'm going to show you in this lesson,

130
00:05:59,313 --> 00:06:02,940
will also apply to
other libraries as well.

131
00:06:02,940 --> 00:06:03,773
So for instance,

132
00:06:03,773 --> 00:06:05,920
when we get to the intro
to data science section

133
00:06:05,920 --> 00:06:07,120
at the end of this lesson,

134
00:06:07,120 --> 00:06:09,920
we will introduce the Pandas library,

135
00:06:09,920 --> 00:06:12,910
which also is built on top of NumPy.

136
00:06:12,910 --> 00:06:14,973
So a lot of what you see
earlier in this lesson,

137
00:06:14,973 --> 00:06:18,320
will also apply to Pandas as well.

138
00:06:18,320 --> 00:06:19,230
Throughout this lesson

139
00:06:19,230 --> 00:06:21,220
we'll do some of the same types of things

140
00:06:21,220 --> 00:06:24,270
that we did with Python's
built in data structures.

141
00:06:24,270 --> 00:06:27,640
We'll show you various ways
of creating NumPy arrays.

142
00:06:27,640 --> 00:06:30,230
We'll manipulate them
lots of different ways.

143
00:06:30,230 --> 00:06:32,210
And one of the things
that you're going to see

144
00:06:32,210 --> 00:06:36,340
with NumPy arrays is that
you do a tremendous amount

145
00:06:36,340 --> 00:06:38,780
of functional style programming.

146
00:06:38,780 --> 00:06:40,330
So there's a concept

147
00:06:40,330 --> 00:06:43,460
that's built into NumPy
called broadcasting

148
00:06:43,460 --> 00:06:45,050
that allows you for instance,

149
00:06:45,050 --> 00:06:48,170
to take a NumPy array
which could be millions

150
00:06:48,170 --> 00:06:53,050
or billions of elements and
multiply every element by two.

151
00:06:53,050 --> 00:06:55,640
And the expression that
you use to write that,

152
00:06:55,640 --> 00:06:58,640
looks just like a regular
arithmetic expression,

153
00:06:58,640 --> 00:07:01,400
but somehow NumPy behind the scenes,

154
00:07:01,400 --> 00:07:03,640
can parallelize that operation

155
00:07:03,640 --> 00:07:06,170
and do it extremely efficiently.

156
00:07:06,170 --> 00:07:10,100
So one of the key things with
NumPy is its performance.

157
00:07:10,100 --> 00:07:12,950
And we will actually do
performance comparisons

158
00:07:12,950 --> 00:07:17,870
to show you that NumPy can
be up to 100 times faster

159
00:07:17,870 --> 00:07:20,150
than doing similar operations

160
00:07:20,150 --> 00:07:24,180
on Python's built in list data structure.

161
00:07:24,180 --> 00:07:25,700
We're going to show you by the way,

162
00:07:25,700 --> 00:07:27,870
both one dimensional NumPy arrays

163
00:07:27,870 --> 00:07:30,010
and multi dimensional NumPy arrays.

164
00:07:30,010 --> 00:07:33,220
And as we move into the
intro to data science section

165
00:07:33,220 --> 00:07:34,210
of this lesson,

166
00:07:34,210 --> 00:07:36,530
we'll be focusing on the Pandas library,

167
00:07:36,530 --> 00:07:38,590
which is built on top of NumPy.

168
00:07:38,590 --> 00:07:41,373
So a lot of what I showed
you earlier in the lesson,

169
00:07:41,373 --> 00:07:44,610
will also apply in the context of Pandas.

170
00:07:44,610 --> 00:07:45,930
What Pandas does though,

171
00:07:45,930 --> 00:07:49,080
is it gives you even more capabilities.

172
00:07:49,080 --> 00:07:50,120
And in particular,

173
00:07:50,120 --> 00:07:53,070
you'll frequently use
Pandas in subsequent lessons

174
00:07:53,070 --> 00:07:54,260
to do things like,

175
00:07:54,260 --> 00:07:57,250
load data into your system for analysis,

176
00:07:57,250 --> 00:07:59,490
prepare the data for analysis,

177
00:07:59,490 --> 00:08:01,000
get to know the data

178
00:08:01,000 --> 00:08:04,550
by doing things like calculating
descriptive statistics,

179
00:08:04,550 --> 00:08:07,660
or doing basic graphing
of the data as well.

180
00:08:07,660 --> 00:08:09,070
So here we'll just start

181
00:08:09,070 --> 00:08:12,060
to introduce the functionality
of the Pandas library

182
00:08:12,060 --> 00:08:15,140
and its two key data structures series

183
00:08:15,140 --> 00:08:16,610
which are one dimensional,

184
00:08:16,610 --> 00:08:18,990
and data frames, which
are two dimensional.

185
00:08:18,990 --> 00:08:21,300
And then we'll continue that discussion

186
00:08:21,300 --> 00:08:24,950
in subsequent intro to data
science sections in part three,

187
00:08:24,950 --> 00:08:29,333
and we'll use Pandas a lot
in the later lessons as well.