Data types usually used in python for coding

Python I Some material adapted from Upenn cmpe391 slides and other sources

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

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

Python PEPs  Where do such conventions come from? • The community of users • Codified in PEPs  Python's development is done via the Python Enhancement Proposal (PEP) process  PEP: a standardized design document, e.g. proposals, descriptions, design rationales, and explanations for language features • Similar to IETF RFCs • See the PEP index  PEP 8: Style Guide for Python Code

Accessing Non-Existent Name Accessing a name before it’s been properly created (by placing it on the left side of an assignment), raises an error >>> y Traceback (most recent call last): File "<pyshell#16>", line 1, in -toplevel- y NameError: name ‘y' is not defined >>> y = 3 >>> y 3

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 number 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)

Data types usually used in python for coding

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