MITx 6.00.1x Introduction to Computer Science and Programming Using Python - 1. Introduction to Python

Welcome to
6.00.1x
16.00.1X LECTURE

OVERVIEW OF COURSE
 learn computational modes of
thinking
 master the art of computational
problem solving
 make computers do what you want
them to do
6.00.1X LECTURE 2
https://ohthehumanityblog.files.wordpress.com/2014/09/computerthink.gif

TOPICS
 represent knowledge with data structures
 iteration and recursion as computational metaphors
 abstraction of procedures and data types
 organize and modularize systems using object classes
and methods
 different classes of algorithms, searching and sorting
 complexity of algorithms
6.00.1X LECTURE 3

WHAT DOES A COMPUTER DO
 Fundamentally:
◦ performs calculations
a billion calculations per second!
two operations in same time light travels 1 foot
◦ remembers results
100s of gigabytes of storage!
typical machine could hold 1.5M books of standard size
 What kinds of calculations?
◦ built-in to the language
◦ ones that you define as the programmer
6.00.1X LECTURE 4

SIMPLE CALCULATIONS
ENOUGH?
 Searching the World Wide Web
◦ 45B pages; 1000 words/page; 10 operations/word to find
◦ Need 5.2 days to find something using simple operations
 Playing chess
◦ Average of 35 moves/setting; look ahead 6 moves; 1.8B
boards to check; 100 operations/choice
◦ 30 minutes to decide each move
 Good algorithm design also needed to accomplish a
task!
6.00.1X LECTURE 5

ENOUGH STORAGE?
 What if we could just pre-compute information and
then look up the answer
◦ Playing chess as an example
◦ Experts suggest 10^123 different possible games
◦ Only 10^80 atoms in the observable universe
6.00.1X LECTURE 6

ARE THERE LIMITS?
 Despite its speed and size, a computer does have
limitations
◦ Some problems still too complex
◦ Accurate weather prediction at a local scale
◦ Cracking encryption schemes
◦ Some problems are fundamentally impossible to compute
◦ Predicting whether a piece of code will always halt with an answer
for any input
6.00.1X LECTURE 7

TYPES OF KNOWLEDGE
 computers know what you tell them
 declarative knowledge is statements of fact.
◦ there is candy taped to the underside of one chair
 imperative knowledge is a recipe or “how-to”
knowledge
1) face the students at the front of the room
2) count up 3 rows
3) start from the middle section’s left side
4) count to the right 1 chair
5) reach under chair and find it
6.00.1X LECTURE 9

A NUMERICAL EXAMPLE
 square root of a number x is y such that y*y = x
 recipe for deducing square root of number x (e.g. 16)
1) Start with a guess, g
2) If g*g is close enough to x, stop and say g is the
answer
3) Otherwise make a new guess by averaging g and x/g
4) Using the new guess, repeat process until close enough
6.00.1X LECTURE 10
g g*g x/g (g+x/g)/2
3 9 5.333 4.1667
4.1667 17.36 3.837 4.0035
4.0035 16.0277 3.997 4.000002

WHAT IS A RECIPE
1) sequence of simple steps
2) flow of control process
that specifies when each
step is executed
3) a means of determining
when to stop
Steps 1+2+3 = an algorithm!
6.00.1X LECTURE 11

COMPUTERS ARE MACHINES
 how to capture a
recipe in a mechanical
process
 fixed program
computer
◦ calculator
◦ Alan Turing’s Bombe
 stored program
computer
◦ machine stores and
executes instructions
6.00.1X LECTURE 13
http://www.upgradenrepair.com/computerparts/computerparts.htm
CC-BY SA 2.0 dIaper

BASIC MACHINE ARCHITECTURE
6.00.1X LECTURE 14
MEMORY
CONTROL
UNIT
ARITHMETIC
LOGIC UNIT
INPUT OUTPUT
program counter do primitive ops

STORED PROGRAM COMPUTER
 sequence of instructions stored inside computer
◦ built from predefined set of primitive instructions
1) arithmetic and logic
2) simple tests
3) moving data
 special program (interpreter) executes each
instruction in order
◦ use tests to change flow of control through sequence
◦ stop when done
6.00.1X LECTURE 15

BASIC PRIMITIVES
 Turing showed you can compute anything
using 6 primitives
 modern programming languages have
more convenient set of primitives
 can abstract methods to create new
primitives
 anything computable in one language is
computable in any other programming
language
6.00.1X LECTURE 16
By GabrielF (Own work) [CC BY-
SA 3.0
(http://creativecommons.org/lic
enses/by-sa/3.0)], via
Wikimedia Commons

CREATING RECIPES
 a programming language provides a set of primitive
operations
 expressions are complex but legal combinations of
primitives in a programming language
 expressions and computations have values and
meanings in a programming language
6.00.1X LECTURE 18

ASPECTS OF LANGUAGES
 primitive constructs
◦ English: words
◦ programming language: numbers, strings, simple
operators
6.00.1X LECTURE 19

ASPECTS OF LANGUAGE
 syntax
◦ English: "cat dog boy"  not syntactically valid
"cat hugs boy"  syntactically valid
◦ programming language: "hi"5  not syntactically valid
3.2*5  syntactically valid
6.00.1X LECTURE 20

 static semantics is which syntactically valid strings have
meaning
◦ English: "I are hungry"  syntactically valid
but static semantic error
◦ programming language: 3.2*5  syntactically valid
3+"hi"  static semantic error
6.00.1X LECTURE 21

 semantics is the meaning associated with a
syntactically correct string of symbols with no static
semantic errors
◦ English: can have many meanings –
◦ “Flying planes can be dangerous”
◦ “This reading lamp hasn’t uttered a word since
I bought it?”
◦ programming languages: have only one meaning but may
not be what programmer intended
6.00.1X LECTURE 22

WHERE THINGS GO WRONG
 syntactic errors
◦ common and easily caught
 static semantic errors
◦ some languages check for these before running program
◦ can cause unpredictable behavior
 no semantic errors but different meaning than what
programmer intended
◦ program crashes, stops running
◦ program runs forever
◦ program gives an answer but different than expected
6.00.1X LECTURE 23

OUR GOAL
 Learn the syntax and semantics of a programming
language
 Learn how to use those elements to translate
“recipes” for solving a problem into a form that the
computer can use to do the work for us
 Learn computational modes of thought to enable us
to leverage a suite of methods to solve complex
problems
6.00.1X LECTURE 24

PYTHON PROGRAMS
 a program is a sequence of definitions and commands
◦ definitions evaluated
◦ commands executed by Python interpreter in a shell
 commands (statements) instruct interpreter to do
something
 can be typed directly in a shell or stored in a file that
is read into the shell and evaluated
6.00.1X LECTURE 26

OBJECTS
 programs manipulate data objects
 objects have a type that defines the kinds of things
programs can do to them
 objects are
◦ scalar (cannot be subdivided)
◦ non-scalar (have internal structure that can be accessed)
6.00.1X LECTURE 27

SCALAR OBJECTS
 int – represent integers, ex. 5
 float – represent real numbers, ex. 3.27
 bool – represent Boolean values True and False
 NoneType – special and has one value, None
 can use type() to see the type of an object
In [1]: type(5)
Out[1]: int
In [2]: type(3.0)
Out[2]: float
6.00.1X LECTURE 28

TYPE CONVERSIONS (CAST)
 can convert object of one type to another
 float(3) converts integer 3 to float 3.0
 int(3.9) truncates float 3.9 to integer 3
6.00.1X LECTURE 29

PRINTING TO CONSOLE
 To show output from code to a user, use print
command
In [11]: 3+2
Out[11]: 5
In [12]: print(3+2)
5
6.00.1X LECTURE 30

EXPRESSIONS
 combine objects and operators to form expressions
 an expression has a value, which has a type
 syntax for a simple expression
<object> <operator> <object>
6.00.1X LECTURE 31

OPERATORS ON ints and floats
 i+j  the sum
 i-j  the difference
 i*j  the product
 i/j  division
 i//j  int division
 i%j  the remainder when i is divided by j
 i**j  i to the power of j
6.00.1X LECTURE 32
- if both are ints, result is int
- if either or both are floats, result is float
- result is int, quotient without remainder
- result is float

SIMPLE OPERATIONS
 parentheses used to tell Python to do these
operations first
◦ 3*5+1 evaluates to 16
◦ 3*(5+1) evaluates to 18
 operator precedence without parentheses
◦ **
◦ *
◦ /
◦ + and – executed left to right, as appear in expression
6.00.1X LECTURE 33

BINDING VARIABLES AND
VALUES
 equal sign is an assignment of a value to a variable
name
pi = 3.14159
pi_approx = 22/7
 value stored in computer memory
 an assignment binds name to value
 retrieve value associated with name or variable by
invoking the name, by typing pi
6.00.1X LECTURE 35

ABSTRACTING EXPRESSIONS
 why give names to values of expressions?
 reuse names instead of values
 easier to change code later
pi = 3.14159
radius = 2.2
area = pi*(radius**2)
6.00.1X LECTURE 36

PROGRAMMING vs MATH
 in programming, you do not “solve for x”
pi = 3.14159
radius = 2.2
# area of circle
radius = radius+1
6.00.1X LECTURE 37

CHANGING BINDINGS
 can re-bind variable names using new assignment
statements
 previous value may still stored in memory but lost the
handle for it
 value for area does not change until you tell the
computer to do the calculation again
6.00.1X LECTURE 38
pi
radius
area
3.14
2.2
15.1976
3.2
pi = 3.14
radius = 2.2
radius = radius+1

COMPARISON OPERATORS ON
int and float
 i and j are any variable names
i>j
i>=j
i<j
i<=j
i==j  equality test, True if i equals j
i!=j  inequality test, True if i not equal to j
6.00.1X LECTURE 40

LOGIC OPERATORS ON bools
 a and b are any variable names
not a  True if a is False
False if a is True
a and b  True if both are True
a or b  True if either or both are True
6.00.1X LECTURE 41

If right clear,
go right
If right blocked,
go forward
If right and
front blocked,
go left
If right , front,
left blocked,
go back
6.00.1X LECTURE 42

BRANCHING PROGRAMS
The simplest branching statement
is a conditional
◦ A test (expression that evaluates to
True or False)
◦ A block of code to execute if the
test is True
◦ An optional block of code to
execute if the test is False
6.00.1X LECTURE 43

A SIMPLE EXAMPLE
x = int(input('Enter an integer: '))
if x%2 == 0:
print(‘’)
print('Even')
else:
print(‘’)
print('Odd')
print(’Done with conditional')
6.00.1X LECTURE 44

SOME OBSERVATIONS
The expression x%2 == 0 evaluates to True when
the remainder of x divided by 2 is 0
Note that == is used for comparison, since = is
reserved for assignment
The indentation is important – each indented set of
expressions denotes a block of instructions
◦ For example, if the last statement were indented, it would
be executed as part of the else block of code
Note how this indentation provides a visual structure
that reflects the semantic structure of the program
6.00.1X LECTURE 45

NESTED CONDITIONALS
if x%2 == 0:
if x%3 == 0:
print('Divisible by 2 and 3’)
else:
print('Divisible by 2 and not by 3’)
elif x%3 == 0:
print('Divisible by 3 and not by 2’)
6.00.1X LECTURE 46

COMPOUND BOOLEANS
if x < y and x < z:
print('x is least’)
elif y < z:
print('y is least’)
else:
print('z is least’)
6.00.1X LECTURE 47

CONTROL FLOW - BRANCHING
if <condition>:
<expression>
<expression>
...
if <condition>:
<expression>
<expression>
...
else:
<expression>
<expression>
...
if <condition>:
<expression>
<expression>
...
elif <condition>:
<expression>
<expression>
...
else:
<expression>
<expression>
...
 <condition> has a value True or False
 evaluate expressions in that block if <condition> is True
6.00.1X LECTURE 48

INDENTATION
 matters in Python
 how you denote blocks of code
x = float(input("Enter a number for x: "))
y = float(input("Enter a number for y: "))
if x == y:
print("x and y are equal”)
if y != 0:
print("therefore, x / y is", x/y)
elif x < y:
print("x is smaller”)
else:
print("y is smaller”)
print("thanks!”)
6.00.1X LECTURE 49

= vs ==
x = float(input("Enter a number for x: "))
y = float(input("Enter a number for y: "))
if x == y:
print("x and y are equal”)
if y != 0:
print("therefore, x / y is", x/y)
elif x < y:
print("x is smaller”)
else:
print("y is smaller”)
print("thanks!”)
6.00.1X LECTURE 50

WHAT HAVE WE ADDED?
 Branching programs allow us to make choices and do
different things
 But still the case that at most, each statement gets
executed once.
 So maximum time to run the program depends only
on the length of the program
 These programs run in constant time
6.00.1X LECTURE 51

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