Basic Python

Values, Types, and Symbols

Expressions and Statements

(Follow along in the iPython interpreter…)

Values

All of programming is really about manipulating values.

Values are pieces of unnamed data: 42, 'Hello, world'
In Python, all values are objects.
- Try dir(42) - lots going on behind the curtain!
Every value has a type
- Try type(42) - the type of a value determines what it can do.

Literals for the Basic Value types:

A “literal” is something you can put in your code to directly get a value. Python has literals for the key built in types.

Numbers:

floating point: 3.4
integers: 456

Text:

"a bit of text"
'a bit of text'
(either single or double quotes work – why? If you don’t know, try looking it up in one of the referenced sources!)

Boolean values:

True
False

The nothing object:

None

(There are intricacies to all of these that we’ll get into later.)

Code structure

Each line is a unit of code. Each line is made up of these components:

Comments:

In [3]: # everything after a '#' is a comment

Expressions:

An expression is a unit of code that evaluates to a value:

In [4]: # evaluating an expression results in a value

In [5]: 3 + 4
Out[5]: 7

Statements:

statements carry out an action, but do not evaluate to a value, that is, you can’t assign to them (or put them in a lamda, or…)

In [6]: # statements carry out an action, do not evaluate a value, may contain an expression

In [7]: line_count = 42

In [8]: return something

Statements include function (and class) definitions (def), loop constructs (for, while), code forking constructs (if), exception handling (try, except), and a handful of other more advanced constructs.

The print() function does what you’d expect, and is very handy when playing with code:

In [1]: print("something")
something

You can print multiple things:

In [2]: print("the value is", 5)
the value is 5

Any Python object can be printed (though it might not be pretty…)

In [1]: class bar(object):
   ...:     pass
   ...:

In [2]: print(bar)
<class '__main__.bar'>

Code Blocks

Separate blocks of code are delimited by a colon and indentation. Everything indented after a colon is “inside” that block. It can be a function definition, or a loop construct, or a handful of other more advanced constructs.

def a_function():
    a_new_code_block
# end_of_the_block on previous line

for i in range(100):
    print(i**2)

try:
    do_something_bad()
except:
    fix_the_problem()

Python uses indentation to delineate structure. This means that in Python, whitespace is significant (but ONLY for newlines and indentation).

The standard is to indent with 4 spaces.

SPACES ARE NOT TABS

TABS ARE NOT SPACES

Python requires spaces for indents. You can probably set your editor to replace tabs with spaces. This is a good idea as it is easier to type one tab than 4 spaces.

These two blocks look the same:

for i in range(100):
    print(i**2)

for i in range(100):
    print(i**2)

But they are not:

for i in range(100):
\s\s\s\sprint i**2

for i in range(100):
\tprint i**2

ALWAYS INDENT WITH 4 SPACES

Make sure your editor is set to use spaces only –

Even when you hit the <tab> key

[Python itself allows any number of spaces (and tabs), but you are just going to confuse yourself and others if you do anything else]

Expressions

An expression is made up of values and operators.

An expression is evaluated to produce a new value: 2 + 2
- The Python interpreter can be used as a calculator to evaluate expressions.
Integer vs. float arithmetic
- (Python 3 smooths this out).
- Always use / when you want division with float results, // when you want floored (integer) results (no remainder):

In [1]: 3 / 4
Out[1]: 0.75

In [2]: 3 // 4
Out[2]: 0

Type conversions: You usually need to convert types explicitly:

In [4]: 3 * "4"
Out[4]: '444'

In [5]: 3 * int("4")
Out[5]: 12

Type errors - checked at run time only:

In [10]: '3' * '4'
---------------------------------------------------------------
TypeError                     Traceback (most recent call last)
<ipython-input-10-1e6fdc328f08> in <module>
----> 1 '3' * '4'

TypeError: can't multiply sequence by non-int of type 'str'

Symbols

Symbols are how we give names to values (objects).

Symbols must begin with an underscore or letter.
Symbols can contain any number of underscores, letters and numbers.
- this_is_a_symbol
- this_is_2
- _AsIsThis
- 1butThisIsNot
- nor-is-this
Symbols (names) don’t have a type; values do.
- This is why Python is “Dynamic”.

Symbols and Type

Evaluating the type of a symbol will return the type of the value to which it is bound.

In [19]: type(42)
Out[19]: int

In [20]: type(3.14)
Out[20]: float

In [21]: a = 42

In [22]: b = 3.14

In [23]: type(a)
Out[23]: int

In [25]: a = b

In [26]: type(a)
Out[26]: float

wait! a has a different type?!? – yes, because it’s the type of the value: 3.1, names don’t actually have a type, the same name can refer to any type.

Assignment

A symbol is bound to a value with the assignment operator: =

This attaches a name to a value.
A value can have many names (or none!)
Assignment is a statement, it returns no value.

Evaluating the name will return the value to which it is bound

In [26]: name = "value"

In [27]: name
Out[27]: 'value'

In [28]: an_integer = 42

In [29]: an_integer
Out[29]: 42

In [30]: a_float = 3.14

In [31]: a_float
Out[31]: 3.14

Variables?

In most languages, what Python calls symbols or names are called “variables”.
In fact, we will probably call them variables in this class.
That’s because they are used, for the most part, for the same purposes.
But often a “variable” is defined as something like: “a place in memory that can store values”.
That is NOT the same thing as a symbol or name in Python!
A name can be bound to a value – but that has nothing to do with a location in memory.

In-Place Assignment

You can also do “in-place” assignment with +=.

In [32]: a = 1

In [33]: a
Out[33]: 1

In [34]: a = a + 1

In [35]: a
Out[35]: 2

In [36]: a += 1

In [37]: a
Out[37]: 3

also: -=, *=, /=, **=, \%=

Note: This is a bit tricky – if the value is mutable, it is in-place assignment – that is the object itself is changed. But if the value is immutable (can’t be changed), then it is replaced with a new object.

Example with an immutable type:

In [11]: a = 5  # a is an integer -- an immutable type.

In [12]: b = a  # a and b are names for the SAME integer

In [13]: a += 5

In [14]: a
Out[14]: 10  # a is changed

In [15]: b
Out[15]: 5  # b is not.

Example with a mutable type:

In [16]: a = [1, 2, 3] # a is a mutable list

In [17]: b = a  # b is now another name for the same list

In [18]: a += [4, 5, 6] # in-place add more to a

In [19]: b
Out[19]: [1, 2, 3, 4, 5, 6]

In [20]: # b is changed --it's the SAME list.

Multiple Assignment

You can assign multiple names from multiple expressions in one statement:

In [48]: x = 2

In [49]: y = 5

In [50]: i, j = 2 * x, 3 ** y

In [51]: i
Out[51]: 4

In [52]: j
Out[52]: 243

Python evaluates all the expressions on the right before doing any assignments.

Nifty Python Trick

Using this feature, we can swap values between two names in one statement:

In [51]: i
Out[51]: 4

In [52]: j
Out[52]: 243

In [53]: i, j = j, i

In [54]: i
Out[54]: 243

In [55]: j
Out[55]: 4

Multiple assignment and symbol swapping can be very useful in certain contexts.

Deleting

You can’t actually directly delete values in Python…

del only deletes a name (or “unbinds” the name…)

In [56]: a = 5

In [57]: b = a

In [58]: del a

In [59]: a
---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-59-60b725f10c9c> in <module>()
----> 1 a

NameError: name 'a' is not defined

The object is still there…Python will only delete it if there are no references to it.

In [15]: a = 5

In [16]: b = a

In [17]: del a

In [18]: a
---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-18-60b725f10c9c> in <module>()
----> 1 a

NameError: name 'a' is not defined

In [19]: b
Out[19]: 5

Identity

Every value in Python is an object.

Every object is unique and has a unique identity, which you can inspect with the id builtin:

In [68]: id(i)
Out[68]: 140553647890984

In [69]: id(j)
Out[69]: 140553647884864

In [70]: new_i = i

In [71]: id(new_i)
Out[71]: 140553647890984

Testing Identity

You can find out if the values bound to two different symbols are the same object using the is operator:

In [72]: count = 23

In [73]: other_count = count

In [74]: count is other_count
Out[74]: True

In [75]: count = 42

In [76]: other_count is count
Out[76]: False

NOTE: Checking the id of an object, or using “is” to check if two objects are the same is rarely used except for debugging and understanding what’s going on under the hood. They are not used regularly in production code.

Equality

You can test for the equality of certain values with the == operator

In [77]: val1 = 20 + 30

In [78]: val2 = 5 * 10

In [79]: val1 == val2
Out[79]: True

In [80]: val3 = '50'

In [81]: val1 == val3
Out[84]: False

A string is never equal to a number!

Singletons

Python has three “singletons” – a value for which there is only one instance:

True, False, and None

To check if a name is bound to one of these, you use is:

a is True

b is False

x is None

Note that in contrast to English – “is” is asking a question, not making an assertion – a is True means “is a set to the True object?”

Operator Precedence

Operator Precedence determines what evaluates first:

4 + 3 * 5 != (4 + 3) * 5

To force statements to be evaluated out of order, use parentheses – expressions in parentheses are always evaluated first:

(4 + 3) * 5 != 4 + (3 * 5)

Python follows the “usual” rules of algebra.

Python Operator Precedence

Parentheses and Literals:

(), [], {}

"", b'', ''

Function Calls:

f(args)

Slicing and Subscription:

a[x:y]

b[0], c['key']

Attribute Reference:

obj.attribute

Exponentiation:

**

Bitwise NOT, Unary Signing:

~x

+x, -x

Multiplication, Division, Modulus:

*, /, %

Addition, Subtraction:

+, -

Bitwise operations:

<<, >>,

&, ^, |

Comparisons:

<, <=, >, >=, !=, ==

Membership and Identity:

in, not in, is, is not

Boolean operations:

or, and, not

Anonymous Functions:

lambda

String Literals

A “string” is a chunk of text.

You define a string value by writing a string literal:

In [1]: 'a string'
Out[1]: 'a string'

In [2]: "also a string"
Out[2]: 'also a string'

In [3]: "a string with an apostrophe: isn't it cool?"
Out[3]: "a string with an apostrophe: isn't it cool?"

In [4]: 'a string with an embedded "quote"'
Out[4]: 'a string with an embedded "quote"'

In [5]: """a multi-line
   ...: string
   ...: all in one
   ...: """
Out[5]: 'a multi-line\nstring\nall in one\n'

In [6]: "a string with an \n escaped character"
Out[6]: 'a string with an \n escaped character'

In [7]: r'a "raw" string, the \n comes through as a \n'
Out[7]: 'a "raw" string, the \\n comes through as a \\n'

Python3 strings fully support Unicode, which means they can support literally all the languages in the world (and then some – Klingon, anyone? – well sort of.)

Because Unicode is native to Python strings, you can get very far without even thinking about it. Anything you can type in your editor will work fine.

Keywords

Python defines a number of keywords

These are language constructs.

You cannot use these words as symbols.

False     class       finally      is          return
None      continue    for          lambda      try
True      def         from         nonlocal    while
and       del         global       not         with
as        elif        if           or          yield
assert    else        import       pass
break     except      in           raise

If you try to use any of the keywords as symbols, you will cause a SyntaxError:

In [13]: del = "this will raise an error"
  File "<ipython-input-13-c816927c2fb8>", line 1
    del = "this will raise an error"
        ^
SyntaxError: invalid syntax

In [14]: def a_function(else='something'):
   ....:     print(else)
   ....:
  File "<ipython-input-14-1dbbea504a9e>", line 1
    def a_function(else='something'):
                      ^
SyntaxError: invalid syntax

builtins

Python also has a number of pre-bound symbols, called builtins

Try this:

In [6]: dir(__builtins__)
Out[6]:
['ArithmeticError',
 'AssertionError',
 'AttributeError',
 'BaseException',
 'BufferError',
 ...
 'vars',
 'xrange',
 'zip']

You are free to rebind these symbols:

In [15]: type('a new and exciting string')
Out[15]: str

In [16]: type = 'a slightly different string'

In [17]: type('type is no longer what it was')
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-17-907616e55e2a> in <module>()
----> 1 type('type is no longer what it was')

TypeError: 'str' object is not callable

In general, this is a BAD IDEA – hopefully your editor will warn you.

Exceptions

Notice that the first batch of __builtins__ are all Exceptions

Exceptions are how Python tells you that something has gone wrong.

There are several exceptions that you are likely to see a lot of:

NameError: indicates that you have tried to use a symbol that is not bound to a value.
TypeError: indicates that you have tried to use the wrong kind of object for an operation.
SyntaxError: indicates that you have mis-typed something.
AttributeError: indicates that you have tried to access an attribute or method that an object does not have (this often means you have a different type of object than you expect)

Functions

What is a function?

A function is a self-contained chunk of code.

You use them when you need the same code to run multiple times, or in multiple parts of the program.

Functions allow you to take code that would otherwise be duplicated potentially many times, and put it in one place. Then all you do is call that code to use it.

This is often referred to as “DRY” – “Don’t Repeat Yourself”.

It also helps to keep the code clean and maintainable, as there is only one place to make a change. This in turn helps reduce defects.

Functions can take and return information.

The minimal function has at least one statement.

def a_name():
    a_statement

Pass Statement does nothing (Note the indentation!)

def minimal():
    pass

This, of course, has limited use – you will generally have multiple statements in a function – and they will do something.

However, the pass statement can help you by allowing you to create placeholder functions that you will come back to later to develop and embelish.

Functions: `def`

def is a statement:

it is executed

it creates a local name

it does not return a value

Function defs must be executed before the functions can be called:

In [23]: unbound()
---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-23-3132459951e4> in <module>()
----> 1 unbound()

NameError: name 'unbound' is not defined

In [18]: def simple():
   ....:     print("I am a simple function")
   ....:

In [19]: simple()
I am a simple function

Calling Functions

You call a function using the function call operator (parentheses):

In [2]: type(simple)
Out[2]: function

In [3]: simple
Out[3]: <function __main__.simple>

In [4]: simple()
I am a simple function

Calling a function is how you run the code in that function.

Functions: Call Stack

Functions can call functions – this makes what is called an execution stack. That is what a “trace back”, often referred to in exceptions, is – the function call stack.

In [5]: def exceptional():
   ...:     print("I am exceptional!")
   ...:     print 1/0
   ...:
In [6]: def passive():
   ...:     pass
   ...:
In [7]: def doer():
   ...:     passive()
   ...:     exceptional()
   ...:

You’ve defined three functions, one of which will call the other two.

When an error occurs, you are presented with a “traceback” of the call stack:

Functions: Tracebacks

In [8]: doer()
I am exceptional!
---------------------------------------------------------------------------
ZeroDivisionError                         Traceback (most recent call last)
<ipython-input-8-685a01a77340> in <module>()
----> 1 doer()

<ipython-input-7-aaadfbdd293e> in doer()
      1 def doer():
      2     passive()
----> 3     exceptional()
      4

<ipython-input-5-d8100c70edef> in exceptional()
      1 def exceptional():
      2     print("I am exceptional!")
----> 3     print(1/0)
      4

ZeroDivisionError: integer division or modulo by zero

The error occurred in the doer function – but the traceback shows you where that was called from.

Note that this listed in reverse order – reverse of the order in which the functions are called.

In a more complex system, this can be VERY useful – learn to read tracebacks!

Functions: `return`

Every function ends by returning a value.

This is actually the simplest possible function:

def fun():
    return None

If you don’t explicitly put return there, Python will return None:

In [9]: def fun():
   ...:     pass
   ...:
In [10]: fun()
In [11]: result = fun()
In [12]: print(result)
None

Note that the interpreter eats None – you need to call print() to see it.

More on return

Only one return statement in a function will ever be executed.

Ever.

Anything after an executed return statement will never get run.

This is useful when debugging!

In [14]: def no_error():
   ....:     return 'done'
   ....:     # no more will happen
   ....:     print(1/0)
   ....:
In [15]: no_error()
Out[15]: 'done'

However, functions can return multiple results:

In [16]: def fun():
   ....:     return 1, 2, 3
   ....:
In [17]: fun()
Out[17]: (1, 2, 3)

Remember multiple assignment?

In [18]: x, y, z = fun()
In [19]: x
Out[19]: 1
In [20]: y
Out[20]: 2
In [21]: z
Out[21]: 3

Functions: parameters

In a def statement, the values written inside the parens are parameters

In [22]: def fun(x, y, z):
   ....:     q = x + y + z
   ....:     print(x, y, z, q)
   ....:

x, y, z are local names – so is q

Functions: arguments

When you call a function, you pass values to the function parameters as arguments

In [23]: fun(3, 4, 5)
3 4 5 12

The values you pass in are bound to the names inside the function and used.

The name used outside the object is separate from the name used inside the function.

Making a Decision

“Conditionals”

In order to do anything interesting at all, you need to be able to write code to make a decision.

if and elif (else if) allow you to make decisions:

In [12]: def test(a):
   ....:     if a == 5:
   ....:         print("that's the value I'm looking for!")
   ....:     elif a == 7:
   ....:         print("that's an OK number")
   ....:     else:
   ....:         print("that number won't do!")

In [13]: test(5)
that's the value I'm looking for!

In [14]: test(7)
that's an OK number

In [15]: test(14)
that number won't do!

There is more to it than that, but this will get you started.

What’s the difference between these two?

if a:
    print('a')
elif b:
    print('b')

## versus...
if a:
    print('a')
if b:
    print('b')

Lists

A way to store a bunch of stuff in order.

Pretty much like an “array” or “vector” in other languages.

To make a list literal you use square brackets and commas between the items:

a_list = [2,3,5,9]
a_list_of_strings = ['this', 'that', 'the', 'other']

You can put any type of object in a list…

Lists are a key Python data type with lots of functionality that we will get into later.

`for` loops

Sometimes called a ‘determinate’ loop.

When you need to do something to all the objects in a sequence:

In [10]: a_list = [2,3,4,5]

In [11]: for item in a_list:
   ....:     print(item)
   ....:
2
3
4
5

`range()` and for

range builds sequences of numbers automatically

Use it when you need to do something a set number of times:

num_stars = 4
In [31]: for i in range(num_stars):
    print('*', end=' ')
   ....:
* * * *

NOTE: range(n) creates an “iterable” – something you can loop over. We will cover iterables in greater depth in a later lesson.

`assert`

Writing tests that demonstrate that your program works is an important part of learning to program.

The Python assert statement is useful in writing simple tests: for your code.

In [1]: def add(n1, n2):
   ...:     return n1 + n2
   ...:

In [2]: assert add(3, 4) == 7

In [3]: assert add(3, 4) == 10

---------------------------------------------------------------------
AssertionError                     Traceback (most recent call last)
<ipython-input-3-6731d4ac4476> in <module>()
----> 1 assert add(3, 4) == 10

AssertionError:

Intricacies

This is enough to get you started.

Each of the feature we have covered has intricacies special to Python.

We’ll get to those over the next couple of lessons – or really, the rest of the program!

Enough For Now

That’s it for our basic intro to Python.

You now know enough Python to do some basic exercises in Python programming.