Python Basics it will teach you about data types

Overview
• Names & Assignment
• Data types
• Sequences types: Lists, Tuples, and
Strings
• Mutability
• Understanding Reference Semantics in
Python

A Code Sample (in IDLE)
x = 34 - 23 # A comment.
y = “Hello” # Another one.
z = 3.45
if z == 3.45 or y == “Hello”:
x = x + 1
y = y + “ World” # String concat.
print x
print y

Enough to Understand the Code
• Indentation matters to meaning the code
• Block structure indicated by indentation
• The first assignment to a variable creates it
• Dynamic typing: no declarations, names don’t have
types, objects do
• Assignment uses = and comparison uses ==
• For numbers + - * / % are as expected.
• Use of + for string concatenation.
• Use of % for string formatting (like printf in C)
• Logical operators are words (and,or,not)
not symbols
• The basic printing command is print

Basic Datatypes
• Integers (default for numbers)
z = 5 / 2 # Answer 2, integer division
• Floats
x = 3.456
• Strings
• Can use ”…" or ’…’ to specify, "foo" == 'foo’
• Unmatched can occur within the string
“John’s” or ‘John said “foo!”.’
• Use triple double-quotes for multi-line strings or
strings than contain both ‘ and “ inside of them:
“““a‘b“c”””

Whitespace
Whitespace is meaningful in Python, especially
indentation and placement of newlines
•Use a newline to end a line of code
Use when must go to next line prematurely
•No braces {} to mark blocks of code, use
consistent indentation instead
• First line with less indentation is outside of the block
• First line with more indentation starts a nested block
•Colons start of a new block in many constructs,
e.g. function definitions, then clauses

Comments
• Start comments with #, rest of line is ignored
• Can include a “documentation string” as the
first line of a new function or class you define
• Development environments, debugger, and
other tools use it: it’s good style to include one
def fact(n):
“““fact(n) assumes n is a positive
integer and returns facorial of n.”””
assert(n>0)
return 1 if n==1 else n*fact(n-1)

Assignment
• Binding a variable in Python means setting a
name to hold a reference to some object
• Assignment creates references, not copies
• Names in Python don’t have an intrinsic type,
objects have types
• Python determines type of the reference auto-
matically based on what data is assigned to it
• You create a name the first time it appears on the
left side of an assignment expression: x = 3
• A reference is deleted via garbage collection
after any names bound to it have passed out of
scope
• Python uses reference semantics (more later)

Naming Rules
• Names are case sensitive and cannot start
with a number. They can contain letters,
numbers, and underscores.
bob Bob _bob _2_bob_ bob_2 BoB
• There are some reserved words:
and, assert, break, class, continue,
def, del, elif, else, except, exec,
finally, for, from, global, if,
import, in, is, lambda, not, or,
pass, print, raise, return, try,
while

Naming conventions
The Python community has these
recommended naming conventions
• joined_lower for functions, methods and,
attributes
• joined_lower or ALL_CAPS for constants
• StudlyCaps for classes
• camelCase only to conform to pre-existing
conventions
• Attributes: interface, _internal, __private

Everything is an object
• Python data is represented by objects or by
relations between objects
• Every object has an identity, a type and a value
• Identity never changes once created Location
or address in memory
• Type (e.g., integer, list) is unchangeable and
determines the possible values it could have
and operations that can be applied
• Value of some objects is fixed (e.g., an integer)
and can change for others (e.g., list)

Python’s built-in type hierarchy

Sequence types:
Tuples, Lists, and
Strings

Sequence Types
• Sequences are containers that hold objects
• Finite, ordered, indexed by integers
• Tuple: (1, “a”, [100], “foo”)
• An immutable ordered sequence of items
• Items can be of mixed types, including collection types
• Strings: “foo bar”
• An immutable ordered sequence of chars
• Conceptually very much like a tuple
• List: [“one”, “two”, 3]
• A Mutable ordered sequence of items of mixed types

Similar Syntax
• All three sequence types (tuples,
strings, and lists) share much of the
same syntax and functionality.
• Key difference:
• Tuples and strings are immutable
• Lists are mutable
• The operations shown in this section
can be applied to all sequence types
• most examples will just show the
operation performed on one

Sequence Types 1
• Define tuples using parentheses and commas
>>> tu = (23, ‘abc’, 4.56, (2,3), ‘def’)
• Define lists are using square brackets and
commas
>>> li = [“abc”, 34, 4.34, 23]
• Define strings using quotes (“, ‘, or “””).
>>> st = “Hello World”
>>> st = ‘Hello World’
>>> st = “””This is a multi-line
string that uses triple quotes.”””

Sequence Types 2
• Access individual members of a tuple, list, or
string using square bracket “array” notation
• Note that all are 0 based…
>>> tu = (23, ‘abc’, 4.56, (2,3), ‘def’)
>>> tu[1] # Second item in the tuple.
‘abc’
>>> li = [“abc”, 34, 4.34, 23]
>>> li[1] # Second item in the list.
34
>>> st = “Hello World”
>>> st[1] # Second character in string.
‘e’

Positive and negative indices
>>> t = (23, ‘abc’, 4.56, (2,3), ‘def’)
Positive index: count from the left, starting with 0
>>> t[1]
‘abc’
Negative index: count from right, starting with –1
>>> t[-3]
4.56

Slicing: Return Copy of a Subset
>>> t = (23, ‘abc’, 4.56, (2,3), ‘def’)
Returns copy of container with subset of original
members. Start copying at first index, and stop
copying before the second index
>>> t[1:4]
(‘abc’, 4.56, (2,3))
You can also use negative indices
>>> t[1:-1]
(‘abc’, 4.56, (2,3))

Slicing: Return Copy of a Subset
>>> t = (23, ‘abc’, 4.56, (2,3), ‘def’)
Omit first index to make a copy starting from the
beginning of container
>>> t[:2]
(23, ‘abc’)
Omit second index to make a copy starting at
1st index and going to end of the container
>>> t[2:]
(4.56, (2,3), ‘def’)

Copying the Whole Sequence
• [ : ] makes a copy of an entire sequence
>>> t[:]
(23, ‘abc’, 4.56, (2,3), ‘def’)
• Note the difference between these two lines
for mutable sequences
>>> l2 = l1 # Both refer to same ref,
# changing one affects both
>>> l2 = l1[:] # Independent copies, two
refs

The ‘in’ Operator
• Boolean test whether a value is inside a container:
>>> t = [1, 2, 4, 5]
>>> 3 in t
False
>>> 4 in t
True
>>> 4 not in t
False
• For strings, tests for substrings
>>> a = 'abcde'
>>> 'c' in a
True
>>> 'cd' in a
True
>>> 'ac' in a
False
• Careful: the in keyword is also used in the syntax of
for loops and list comprehensions

+ Operator is Concatenation
• The + operator produces a new tuple, list, or
string whose value is the concatenation of its
arguments.
>>> (1, 2, 3) + (4, 5, 6)
(1, 2, 3, 4, 5, 6)
>>> [1, 2, 3] + [4, 5, 6]
[1, 2, 3, 4, 5, 6]
>>> “Hello” + “ “ + “World”
‘Hello World’

Lists are mutable
>>> li = [‘abc’, 23, 4.34, 23]
>>> li[1] = 45
>>> li
[‘abc’, 45, 4.34, 23]
• We can change lists in place.
• Name li still points to the same
memory reference when we’re done.

Tuples are immutable
>>> t = (23, ‘abc’, 4.56, (2,3), ‘def’)
>>> t[2] = 3.14
Traceback (most recent call last):
File "<pyshell#75>", line 1, in -toplevel-
tu[2] = 3.14
TypeError: object doesn't support item assignment
•You can’t change a tuple.
•You can make a fresh tuple and assign its
reference to a previously used name.
>>> t = (23, ‘abc’, 3.14, (2,3), ‘def’)
•The immutability of tuples means they are
faster than lists

Functions vs. methods
• Some operations are functions and others methods
• Remember that (almost) everything is an object
• You just have to learn (and remember or lookup) which
operations are functions and which are methods
len() is a function on collec-
tions that returns the num-
ber of things they contain
>>> len(['a', 'b', 'c'])
3
>>> len(('a','b','c'))
3
>>> len("abc")
3
index() is a method on col-
lections that returns the
index of the 1st occurrence
of its arg
>>> ['a’,'b’,'c'].index('a')
0
>>> ('a','b','c').index('b')
1
>>> "abc".index('c')
2

Lists methods
• Lists have many methods, including index,
count, append, remove, reverse, sort, etc.
• Many of these modify the list
>>> l = [1,3,4]
>>> l.append(0) # adds a new element to the end of the list
>>> l
[1, 3, 4, 0]
>>> l.insert(1,200) # insert 200 just before index position 1
>>> l
[1, 200, 3, 4, 0]
>>> l.reverse() # reverse the list in place
>>> l
[0, 4, 3, 200, 1]
>>> l.sort() # sort the elements. Optional arguments can give
>>> l # the sorting function and direction
[0, 1, 3, 4, 200]
>>> l.remove(3) # remove first occurence of element from list
>>> l
[0, 1, 4, 200]

Tuple details
• The comma is the tuple creation operator, not parens
>>> 1,
(1,)
• Python shows parens for clarity (best practice)
>>> (1,)
(1,)
• Don't forget the comma!
>>> (1)
1
• Trailing comma only required for singletons others
• Empty tuples have a special syntactic form
>>> ()
()
>>> tuple()
()

Tuples vs. Lists
• Lists slower but more powerful than tuples
• Lists can be modified and they have many handy
operations and methods
• Tuples are immutable & have fewer features
• Sometimes an immutable collection is required (e.g.,
as a hash key)
• Tuples used for multiple return values and parallel
assignments
x,y,z = 100,200,300
old,new = new,old
• Convert tuples and lists using list() and tuple():
mylst = list(mytup); mytup = tuple(mylst)

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