Introduction to Python - Part Two

Introduction to Python II CSE 391: Artificial Intelligence adapted from slides by Matt Huenerfauth for CSE 391, 1/05

Dictionaries: A Mapping type Dictionaries store a mapping between a set of keys and a set of values. Keys can be any immutable type. Values can be any type A single dictionary can have store different types of values You can define, modify, view, lookup, and delete the key-value pairs in the dictionary.

Creating and accessing dictionaries >>> d = { ‘user’ : ‘bozo’ , ‘pswd’ :1234} >>> d[ ‘user’ ] ‘ bozo’ >>> d[ ‘pswd’ ] 1234 >>> d[ ‘bozo’ ] Traceback (innermost last): File ‘<interactive input>’ line 1, in ? KeyError: bozo

Updating Dictionaries >>> d = { ‘user’ : ‘bozo’ , ‘pswd’ :1234} >>> d[ ‘user’ ] = ‘clown’ >>> d {‘user’:‘clown’, ‘pswd’:1234} Keys must be unique. Assigning to an existing key replaces its value. >>> d[ ‘id’ ] = 45 >>> d {‘user’:‘clown’, ‘id’:45, ‘pswd’:1234} Dictionaries are unordered New entry might appear anywhere in the output. (Dictionaries work by hashing )

Removing dictionary entries >>> d = { ‘user’ : ‘bozo’ , ‘p’ :1234, ‘i’ :34} >>> del d[ ‘user’ ] # Remove one. >>> d {‘p’:1234, ‘i’:34} >>> d.clear() # Remove all. >>> d {}y

Useful Accessor Methods >>> d = { ‘user’ : ‘bozo’ , ‘p’ :1234, ‘i’ :34} >>> d.keys() # List of keys. [‘user’, ‘p’, ‘i’] >>> d.values() # List of values. [‘bozo’, 1234, 34] >>> d.items() # List of item tuples. [(‘user’,‘bozo’), (‘p’,1234), (‘i’,34)]

Defining Functions No header file or declaration of types of function or arguments. def get_final_answer (filename): “ Documentation String” line1 line2 return total_counter The indentation matters… First line with different indentation is considered to be outside of the function definition. Function definition begins with “def.” Function name and its arguments. The keyword ‘return’ indicates the value to be sent back to the caller. Colon.

Python and Types Python determines the data types in a program automatically. “Dynamic Typing” But Python’s not casual about types, it enforces them after it figures them out. “Strong Typing” So, for example, you can’t just append an integer to a string. You must first convert the integer to a string itself. x = “the answer is ” # Decides x is string. y = 23 # Decides y is integer. print x + y # Python will complain about this.

Calling a Function The syntax for a function call is: >>> def myfun (x, y): return x * y >>> myfun(3, 4) 12 Parameters in Python are “Call by Assignment.” Sometimes acts like “call by reference” and sometimes like “call by value” in C++. Mutable datatypes: Call by reference. Immutable datatypes: Call by value.

Functions without returns All functions in Python have a return value even if no return line inside the code. Functions without a return return the special value None . None is a special constant in the language. None is used like NULL , void , or nil in other languages. None is also logically equivalent to False. The interpreter doesn’t print None

Function overloading? No. There is no function overloading in Python. Unlike C++, a Python function is specified by its name alone The number, order, names, or types of its arguments cannot be used to distinguish between two functions with the same name. Two different functions can’t have the same name, even if they have different arguments. But: see operator overloading in later slides (Note: van Rossum playing with function overloading for the future)

Functions are first-class objects in Python Functions can be used as any other data type They can be Arguments to function Return values of functions Assigned to variables Parts of tuples, lists, etc … >>> def myfun (x): return x*3 >>> def applier (q, x): return q(x) >>> applier(myfun, 7) 21

True and False True and False are constants in Python. Other values equivalent to True and False : False : zero, None , empty container or object True : non-zero numbers, non-empty objects Comparison operators: ==, !=, <, <=, etc. X and Y have same value: X == Y Compare with X is Y : X and Y are two names that refer to the identical same object.

Boolean Logic Expressions You can also combine Boolean expressions. true if a is true and b is true: a and b true if a is true or b is true: a or b true if a is false: not a Use parentheses as needed to disambiguate complex Boolean expressions.

Special Properties of and and or Actually and and or don’t return True or False . They return the value of one of their sub-expressions (which may be a non-Boolean value). X and Y and Z If all are true, returns value of Z. Otherwise, returns value of first false sub-expression. X or Y or Z If all are false, returns value of Z. Otherwise, returns value of first true sub-expression.

The “and-or” Trick A trick to implement a simple conditional result = test and expr1 or expr2 When test is True , result is assigned expr1. When test is False , result is assigned expr2. Works almost like (test ? expr1 : expr2) expression of C++. But if the value of expr1 is ever False, the trick doesn’t work. Avoid (hard to debug), but you may see it in the code.

Control of Flow There are several Python expressions that control the flow of a program. All of them make use of Boolean conditional tests. if Statements while Loops assert Statements

if Statements if x == 3: print “X equals 3.” elif x == 2: print “X equals 2.” else : print “X equals something else.” print “This is outside the ‘if’.” Be careful! The keyword if is also used in the syntax of filtered list comprehensions . Note: Use of indentation for blocks Colon ( : ) after boolean expression

while Loops x = 3 while x < 10: x = x + 1 print “Still in the loop.” print “Outside of the loop.”

break and continue You can use the keyword break inside a loop to leave the while loop entirely. You can use the keyword continue inside a loop to stop processing the current iteration of the loop and to immediately go on to the next one.

assert An assert statement will check to make sure that something is true during the course of a program. If the condition if false, the program stops. assert (number_of_players < 5)

Generating Lists using “List Comprehensions”

List Comprehensions A powerful feature of the Python language. Generate a new list by applying a function to every member of an original list. Python programmers use list comprehensions extensively. You’ll see many of them in real code. The syntax of a list comprehension is somewhat tricky. Syntax suggests that of a for -loop, an in operation, or an if statement all three of these keywords (‘ for ’, ‘ in ’, and ‘ if ’) are also used in the syntax of forms of list comprehensions.

Using List Comprehensions 1 >>> li = [3, 6, 2, 7] >>> [ elem*2 for elem in li ] [6, 12, 4, 14] [ expression for name in list ] Where expression is some calculation or operation acting upon the variable name . For each member of the list , the list comprehension sets name equal to that member, calculates a new value using expression , It then collects these new values into a list which is the return value of the list comprehension. Note: Non-standard colors on next several slides to help clarify the list comprehension syntax.

Using List Comprehensions 2 [ expression for name in list ] If list contains elements of different types, then expression must operate correctly on the types of all of list members. If the elements of list are other containers, then the name can consist of a container of names that match the type and “shape” of the list members. >>> li = [(‘a’, 1), (‘b’, 2), (‘c’, 7)] >>> [ n * 3 for (x, n) in li ] [3, 6, 21]

[ expression for name in list ] expression can also contain user-defined functions. >>> def subtract(a, b): return a – b >>> oplist = [(6, 3), (1, 7), (5, 5)] >>> [ subtract(y, x) for (x, y) in oplist ] [-3, 6, 0] Using List Comprehensions 3

Filtered List Comprehension 1 [ expression for name in list if filter ] Filter determines whether expression is performed on each member of the list . For each element of list , checks if it satisfies the filter condition . If it returns False for the filter condition , it is omitted from the list before the list comprehension is evaluated.

Filtered List Comprehension 2 [ expression for name in list if filter ] >>> li = [3, 6, 2, 7, 1, 9] >>> [ elem * 2 for elem in li if elem > 4 ] [12, 14, 18] Only 6, 7, and 9 satisfy the filter condition. So, only 12, 14, and 18 are produced.

Nested List Comprehensions Since list comprehensions take a list as input and produce a list as output, they are easily nested: >>> li = [3, 2, 4, 1] >>> [ elem*2 for elem in [ item+1 for item in li ] ] [8, 6, 10, 4] The inner comprehension produces: [4, 3, 5, 2]. So, the outer one produces: [8, 6, 10, 4].

For Loops / List Comprehensions Python’s list comprehensions and split/join operations provide natural idioms that usually require a for-loop in other programming languages. As a result, Python code uses many fewer for-loops Nevertheless, it’s important to learn about for-loops. Caveat ! The keywords for and in are also used in the syntax of list comprehensions, but this is a totally different construction.

For Loops 1 A for-loop steps through each of the items in a list, tuple, string, or any other type of object which the language considers an “iterator.” for <item> in <collection> : <statements> If <collection> is a list or a tuple, then the loop steps through each element of the sequence. If <collection> is a string, then the loop steps through each character of the string. for someChar in “Hello World” : print someChar Note: Non-standard colors on these slides.

For Loops 2 for <item> in <collection> : <statements> <item> can be more complex than a single variable name. When the elements of <collection> are themselves sequences, then <item> can match the structure of the elements. This multiple assignment can make it easier to access the individual parts of each element. for (x, y) in [(a,1), (b,2), (c,3), (d,4)] : print x

For loops and the range() function Since a variable often ranges over some sequence of numbers, the range() function returns a list of numbers from 0 up to but not including the number we pass to it. range(5) returns [0,1,2,3,4] So we could say: for x in range(5) : print x (There are more complex forms of range() that provide richer functionality…)

Lambda Notation Functions can be defined without giving them names. This is most useful when passing a short function as an argument to another function. >>> applier( lambda z: z * 4, 7) 28 The first argument to applier() is an unnamed function that takes one input and returns the input multiplied by four. Note: only single-expression functions can be defined using this lambda notation. Lambda notation has a rich history in program language research, AI, and the design of the LISP language.

Default Values for Arguments You can provide default values for a function’s arguments These arguments are optional when the function is called >>> def myfun (b, c=3, d= “hello” ): return b + c >>> myfun(5,3, ”hello” ) >>> myfun(5,3) >>> myfun(5) All of the above function calls return 8.

The Order of Arguments You can call a function with some or all of its arguments out of order as long as you specify them (these are called keyword arguments). You can also just use keywords for a final subset of the arguments. >>> def myfun (a, b, c): return a-b >>> myfun(2, 1, 43) 1 >>> myfun(c=43, b=1, a=2) 1 >>> myfun(2, c=43, b=1) 1

Multiple Assignment with Sequences We’ve seen multiple assignment before: >>> x, y = 2, 3 But you can also do it with sequences. The type and “shape” just has to match. >>> (x, y, (w, z)) = (2, 3, (4, 5)) >>> [x, y] = [4, 5]

Assignment creates a name, if it didn’t exist already. x = 3 Creates name x of type integer. Assignment is also what creates named references to containers. >>> d = { ‘a’ :3, ‘b’ :4} We can also create empty containers: >>> li = [] >>> tu = () >>> di = {} Empty Containers 1 Note: an empty container is logically equivalent to False. (Just like None.)

Empty Containers 2 Why create a named reference to empty container? To initialize an empty list, for example, before using append. This would cause an unknown name error a named reference to the right data type wasn’t created first >>> g.append(3) Python complains here about the unknown name ‘g’! >>> g = [] >>> g.append(3) >>> g [3]

String Operations A number of methods for the string class perform useful formatting operations: >>> “hello” .upper() ‘ HELLO’ Check the Python documentation for many other handy string operations.

String Formatting Operator: % The operator % allows strings to be built out of many data items in a “fill in the blanks” fashion. Allows control of how the final string output will appear. For example, we could force a number to display with a specific number of digits after the decimal point. Very similar to the sprintf command of C. >>> x = “abc” >>> y = 34 >>> “%s xyz %d” % (x, y) ‘ abc xyz 34’ The tuple following the % operator is used to fill in the blanks in the original string marked with %s or %d. Check Python documentation for whether to use %s, %d, or some other formatting code inside the string.

Printing with Python You can print a string to the screen using “print.” Using the % string operator in combination with the print command, we can format our output text. >>> print “%s xyz %d” % ( “abc” , 34) abc xyz 34 “ Print” automatically adds a newline to the end of the string. If you include a list of strings, it will concatenate them with a space between them. >>> print “abc” >>> print “abc” , “def” abc abc def

String to List to String Join turns a list of strings into one string. <separator_string>. join( <some_list> ) >>> “;” .join( [“abc”, “def”, “ghi”] ) “ abc;def;ghi” Split turns one string into a list of strings. <some_string> .split( <separator_string> ) >>> “abc;def;ghi” .split( “;” ) [“abc”, “def”, “ghi”] Note: Non-standard colors on this slide to help clarify the string syntax.

Convert Anything to a String The built-in str() function can convert an instance of any data type into a string. You can define how this function behaves for user-created data types. You can also redefine the behavior of this function for many types. >>> “Hello ” + str(2) “ Hello 2”

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