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Ch-8.pdf
1.
Python Programming ing Problem
Solving Approach Reema Thareja 1 © OXFORD UNIVERSITY PRESS 2017. ALL RIGHTS RESERVED. 1
2.
2 CHAPTER 8 Data Structures ©
OXFORD UNIVERSITY PRESS 2017. ALL RIGHTS RESERVED.
3.
Data Structure: Sequence 3 A
data structure is a group of data elements that are put together under one name. Data structure defines a particular way of storing and organizing data in a computer so that it can be used efficiently. Sequence is the most basic data structure in Python. In sequence, each element has a specific index. This index value starts from zero and is automatically incremented for the next element. In Python, sequence is the generic term for an ordered set. For example, we have already studied strings which are a sequence of characters. Python has some basic built-in functions that help programmers to manipulate elements that form a part of a sequence. These functions include finding the length of a sequence, finding the largest and smallest elements in a sequence, etc. Other operations that can be performed on a sequence © OXFORD UNIVERSITY PRESS 2017. ALL RIGHTS RESERVED.
4.
Lists 4 List is a
versatile data type available in Python. It is a sequence in which elements are written as a list of comma-separated values (items) between square brackets. The key feature of a list is that it can have elements that belong to different data types. The syntax of defining a list can be given as, List_variable = [val1, val2,...] © OXFORD UNIVERSITY PRESS 2017. ALL RIGHTS RESERVED. Exampl es:
5.
Access Values in
Lists 5 Similar to strings, lists can also be sliced and concatenated. To access values in lists, square brackets are used to slice along with the index or indices to get value stored at that index. The syntax for the slice operation is given as, seq = List[start:stop:step] © OXFORD UNIVERSITY PRESS 2017. ALL RIGHTS RESERVED. Exampl e:
6.
Updating Values in
Lists 6 Once created, one or more elements of a list can be easily updated by giving the slice on the left-hand side of the assignment operator. You can also append new values in the list and remove existing value(s) from the list using the append() method and del statement respectively. © OXFORD UNIVERSITY PRESS 2017. ALL RIGHTS RESERVED. Exampl e:
7.
Nested Lists 7 Nested list
means a list within another list. We have already said that a list has elements of different data types which can include even a list. © OXFORD UNIVERSITY PRESS 2017. ALL RIGHTS RESERVED. Exampl e:
8.
Cloning Lists 8 If you
want to modify a list and also keep a copy of the original list, then you should create a separate copy of the list (not just the reference). This process is called cloning. The slice operation is used to clone a list. © OXFORD UNIVERSITY PRESS 2017. ALL RIGHTS RESERVED. Exampl e:
9.
Basic List Operations 9 ©
OXFORD UNIVERSITY PRESS 2017. ALL RIGHTS RESERVED.
10.
List Methods 10 © OXFORD
UNIVERSITY PRESS 2017. ALL RIGHTS RESERVED.
11.
Using Lists as
Stack 11 Stack is an important data structure which stores its elements in an ordered manner. Stack is a linear data structure which uses the same principle, i.e., the elements in a stack are added and removed only from one end. Hence, a stack is called a LIFO (Last-In- First-Out) data structure, as the element that was inserted last is the first one to be taken out. © OXFORD UNIVERSITY PRESS 2017. ALL RIGHTS RESERVED.
12.
Using Lists as
Stack 12 © OXFORD UNIVERSITY PRESS 2017. ALL RIGHTS RESERVED. Exampl e:
13.
Using Lists as
Queues 13 Queue is an important data structure which stores its elements in an ordered manner. In computer systems, the operating system makes full use of queues for the following tasks. • To maintain waiting lists for a single shared resource like printer, disk, CPU, etc. • To transfer data asynchronously (data not necessarily received at same rate as sent) between two processes (IO buffers), e.g., pipes, file IO, and sockets. • As buffers on MP3 players and portable CD players, iPod playlist, etc. • Handling interrupts. • Queues are also used in the playlist of jukebox to add songs to the end and play from the front of the list. Queue supports three basic operations—insert, delete, and peep (or peek). In Python, you can easily implement a queue by using the append() method to insert an element at the end of the queue, pop() method with an index 0 to delete the first element from the © OXFORD UNIVERSITY PRESS 2017. ALL RIGHTS RESERVED.
14.
Using Lists as
Queues 14 © OXFORD UNIVERSITY PRESS 2017. ALL RIGHTS RESERVED. Exampl e:
15.
List Comprehensions 15 Python also
supports computed lists called list comprehensions having the following syntax. List = [expression for variable in sequence] Where, the expression is evaluated once, for every item in the sequence. List comprehensions help programmers to create lists in a concise way. This is mainly beneficial to make new lists where each element is the obtained by applying some operations to each member of another sequence or iterable. List comprehension is also used to create a subsequence of those elements that satisfy a certain condition. © OXFORD UNIVERSITY PRESS 2017. ALL RIGHTS RESERVED. Exampl e:
16.
Looping in Lists 16 Python's
for and in constructs are extremely useful especially when working with lists. The for var in list statement is an easy way to access each element in a list (or any other sequence). For example, in the following code, the for loop is used to access each item in the list. for i in list: print(i) © OXFORD UNIVERSITY PRESS 2017. ALL RIGHTS RESERVED. Exampl e:
17.
Using the enumerate()
and range() Functions 17 enumerate() function is used when you want to print both index as well as an item in the list. The function returns an enumerate object which contains the index and value of all the items of the list as a tuple. The range() function is used when you need to print index. © OXFORD UNIVERSITY PRESS 2017. ALL RIGHTS RESERVED. Exampl es:
18.
Using an Iterator 18 You
can create an iterator using the built-in iter() function. The iterator is used to loop over the elements of the list. For this, the iterator fetches the value and then automatically points to the next element in the list when it is used with the next() method. © OXFORD UNIVERSITY PRESS 2017. ALL RIGHTS RESERVED. Exampl e:
19.
filter() Function 19 The filter()
function constructs a list from those elements of the list for which a function returns True. The syntax of the filter() function is given as, filter(function, sequence) As per the syntax, the filter() function returns a sequence that contains items from the sequence for which the function is True. If sequence is a string, Unicode, or a tuple, then the result will be of the same type; otherwise, it is always a list. © OXFORD UNIVERSITY PRESS 2017. ALL RIGHTS RESERVED. Exampl e:
20.
map() Function 20 © OXFORD
UNIVERSITY PRESS 2017. ALL RIGHTS RESERVED. The map() function applies a particular function to every element of a list. Its syntax is same as the filter function After applying the specified function on the sequence, the map() function returns the modified list. The map() function calls function(item) for each item in the sequence and returns a list of the return values. Example: Program that adds 2 to every value in the list
21.
reduce() Function 21 © OXFORD
UNIVERSITY PRESS 2017. ALL RIGHTS RESERVED. The reduce() function with syntax as given below returns a single value generated by calling the function on the first two items of the sequence, then on the result and the next item, and so on. Example: Program to calculate the sum of values in a list using the reduce() function
22.
Tuple 22 Like lists, tuple
is another data structure supported by Python. It is very similar to lists but differs in two things. • First, a tuple is a sequence of immutable objects. This means that while you can change the value of one or more items in a list, you cannot change the values in a tuple. • Second, tuples use parentheses to define its elements whereas lists use square brackets. Creating Tuple Creating a tuple is very simple and almost similar to creating a list. For creating a tuple, generally you need to just put the different comma-separated values within a parentheses as shown below. Tup1 = (val 1, val 2,...) where val (or values) can be an integer, a floating number, a character, or a string. © OXFORD UNIVERSITY PRESS 2017. ALL RIGHTS RESERVED.
23.
Utility of Tuples 23 In
real-world applications, tuples are extremely useful for representing records or structures as we call in other programming languages. These structures store related information about a subject together. The information belongs to different data types. For example, a tuple that stores information about a student can have elements like roll_no, name, course, total marks, avg, etc. Some built-in functions return a tuple. For example, the divmod() function returns two values—quotient as well as the remainder after performing the divide operation. © OXFORD UNIVERSITY PRESS 2017. ALL RIGHTS RESERVED. Exampl es:
24.
Accessing Values in
a Tuple 24 Like other sequences (strings and lists) covered so far, indices in a tuple also starts at 0. You can even perform operations like slice, concatenate, etc. on a tuple. For example, to access values in tuple, slice operation is used along with the index or indices to obtain value stored at that index © OXFORD UNIVERSITY PRESS 2017. ALL RIGHTS RESERVED. Exampl e:
25.
Deleting Elements in
Tuple 25 Since tuple is an immutable data structure, you cannot delete value(s) from it. Of course, you can create a new tuple that has all elements in your tuple except the ones you don't want (those you wanted to be deleted). © OXFORD UNIVERSITY PRESS 2017. ALL RIGHTS RESERVED. Exampl es:
26.
Basic Tuple Operations 26 ©
OXFORD UNIVERSITY PRESS 2017. ALL RIGHTS RESERVED.
27.
Tuple Assignment 27 Tuple assignment
is a very powerful feature in Python. It allows a tuple of variables on the left side of the assignment operator to be assigned values from a tuple given on the right side of the assignment operator. Each value is assigned to its respective variable. In case, an expression is specified on the right side of the assignment operator, first that expression is evaluated and then assignment is done. © OXFORD UNIVERSITY PRESS 2017. ALL RIGHTS RESERVED. Exampl e:
28.
Tuples for Returning
Multiple Values and Nested Tuples 28 © OXFORD UNIVERSITY PRESS 2017. ALL RIGHTS RESERVED. Exampl es:
29.
Checking the Index:
index() method 29 The index of an element in the tuple can be obtained by using the index() method. If the element being searched is not present in the list, then error is generated. The syntax of index() is given as, list.index(obj) where, obj is the object to be found out. © OXFORD UNIVERSITY PRESS 2017. ALL RIGHTS RESERVED. Exampl es:
30.
count()Method and List
Comprehension and Tuples 30 © OXFORD UNIVERSITY PRESS 2017. ALL RIGHTS RESERVED. Exampl es:
31.
Variable-length Argument Tuples 31 Many
built-in functions like max(), min(), sum(), etc. use variable-length arguments since these functions themselves do not know how many arguments will be passed to them. It allows a function to accept a variable (different) number of arguments. This is especially useful in defining functions that are applicable to a large variety of arguments. For example, if you have a function that displays all the parameters passed to it, then even the function does not know how many values it will be passed. In such cases, we use a variable-length argument that begins with a '*' symbol. Any argument that starts with a '*' symbol is known as gather and specifies a variable-length argument. © OXFORD UNIVERSITY PRESS 2017. ALL RIGHTS RESERVED. Exampl e:
32.
The zip() Function 32 ©
OXFORD UNIVERSITY PRESS 2017. ALL RIGHTS RESERVED. The zip() is a built-in function that takes two or more sequences and "zips" them into a list of tuples. The tuple thus, formed has one element from each sequence. Example: Program to show the use of zip() function
33.
Advantages of Tuple
over List 33 • Tuples are used to store values of different data types. Lists can however, store data of similar data types. • Since tuples are immutable, iterating through tuples is faster than iterating over a list. This means that a tuple performs better than a list. • Tuples can be used as key for a dictionary but lists cannot be used as keys. • Tuples are best suited for storing data that is write-protected. • Tuples can be used in place of lists where the number of values is known and small. • If you are passing a tuple as an argument to a function, then the potential for unexpected behavior due to aliasing gets reduced. • Multiple values from a function can be returned using a tuple. © OXFORD UNIVERSITY PRESS 2017. ALL RIGHTS RESERVED.
34.
Sets 34 Sets is another
data structure supported by Python. Basically, sets are same as lists but with a difference that sets are lists with no duplicate entries. Technically, a set is a mutable and an unordered collection of items. This means that we can easily add or remove items from it. A set is created by placing all the elements inside curly brackets {}, separated by comma or by using the built-in function set(). The syntax of creating a set can be given as, © OXFORD UNIVERSITY PRESS 2017. ALL RIGHTS RESERVED. Example: To create a set, you can write,
35.
Set Operations 35 © OXFORD
UNIVERSITY PRESS 2017. ALL RIGHTS RESERVED.
36.
Set Operations 36 © OXFORD
UNIVERSITY PRESS 2017. ALL RIGHTS RESERVED.
37.
Set Operations 37 © OXFORD
UNIVERSITY PRESS 2017. ALL RIGHTS RESERVED.
38.
Set Operations 38 © OXFORD
UNIVERSITY PRESS 2017. ALL RIGHTS RESERVED.
39.
Dictionaries 39 Dictionary is a
data structure in which we store values as a pair of key and value. Each key is separated from its value by a colon (:), and consecutive items are separated by commas. The entire items in a dictionary are enclosed in curly brackets({}). The syntax for defining a dictionary is dictionary_name = {key_1: value_1, key_2: value_2, key_3: value_3} If there are many keys and values in dictionaries, then we can also write just one key- value pair on a line to make the code easier to read and understand. This is shown below. dictionary_name = {key_1: value_1, key_2: value_2, key_3: value_3, ….} © OXFORD UNIVERSITY PRESS 2017. ALL RIGHTS RESERVED. Exampl e:
40.
Accessing Values 40 © OXFORD
UNIVERSITY PRESS 2017. ALL RIGHTS RESERVED. Exampl e:
41.
Adding and Modifying
an Item in a Dictionary 41 © OXFORD UNIVERSITY PRESS 2017. ALL RIGHTS RESERVED. Exampl e:
42.
Modifying an Entry 42 ©
OXFORD UNIVERSITY PRESS 2017. ALL RIGHTS RESERVED. Exampl e:
43.
Deleting Items 43 You can
delete one or more items using the del keyword. To delete or remove all the items in just one statement, use the clear() function. Finally, to remove an entire dictionary from the memory, we can gain use the del statement as del Dict_name. The syntax to use the del statement can be given as, del dictionary_variable[key] © OXFORD UNIVERSITY PRESS 2017. ALL RIGHTS RESERVED. Exampl e:
44.
Sorting Items and
Looping over Items in a Dictinonary 44 © OXFORD UNIVERSITY PRESS 2017. ALL RIGHTS RESERVED. Exampl es:
45.
Nested Dictionaries 45 © OXFORD
UNIVERSITY PRESS 2017. ALL RIGHTS RESERVED. Exampl e:
46.
Built-in Dictionary Functions
and Methods 46 © OXFORD UNIVERSITY PRESS 2017. ALL RIGHTS RESERVED.
47.
Built-in Dictionary Functions
and Methods 47 © OXFORD UNIVERSITY PRESS 2017. ALL RIGHTS RESERVED.
48.
Built-in Dictionary Functions
and Methods 48 © OXFORD UNIVERSITY PRESS 2017. ALL RIGHTS RESERVED.
49.
Difference between a
List and a Dictionary 49 First, a list is an ordered set of items. But, a dictionary is a data structure that is used for matching one item (key) with another (value). • Second, in lists, you can use indexing to access a particular item. But, these indexes should be a number. In dictionaries, you can use any type (immutable) of value as an index. For example, when we write Dict['Name'], Name acts as an index but it is not a number but a string. • Third, lists are used to look up a value whereas a dictionary is used to take one value and look up another value. For this reason, dictionary is also known as a lookup table. Fourth, the key-value pair may not be displayed in the order in which it was specified while defining the © OXFORD UNIVERSITY PRESS 2017. ALL RIGHTS RESERVED.
50.
String Formatting with
Dictionaries 50 © OXFORD UNIVERSITY PRESS 2017. ALL RIGHTS RESERVED. Python also allows you to use string formatting feature with dictionaries. So you can use %s, %d, %f, etc. to represent string, integer, floating point number, or any other data. Example: Program that uses string formatting feature to print the key-value pairs stored in the dictionary
51.
When to use
which Data Structure? 51 • Use lists to store a collection of data that does not need random access. • Use lists if the data has to be modified frequently. • Use a set if you want to ensure that every element in the data structure must be unique. • Use tuples when you want that your data should not be altered. © OXFORD UNIVERSITY PRESS 2017. ALL RIGHTS RESERVED.
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