PROBLEM SOLVING AND PYTHON PROGRAMMING PPT

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Python Programming
Using Problem Solving Approach
Reema Thareja
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© OXFORD UNIVERSITY PRESS 2017.ALL RIGHTS RESERVED.

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CHAPTER 8
Data Structures

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Data Structure: Sequence
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 include indexing, slicing,
adding, multiplying, and checking for membership.

Lists
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,...]

AccessValues in Lists
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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
Exam
ple:

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UpdatingValues in Lists
In Python, lists are mutable, which means you can
directly update their elements without creating a new
list. You can update values in a list by accessing
elements using their index and assigning new values.

1. Using Indexing to Update an Element
You can directly update an element in a list by referring to
its index.
my_list = [10, 20, 30, 40, 50]
my_list[2] = 100
print(my_list)
Output:
Copy code
[10, 20, 100, 40, 50]

2. Using append() to Add a New Element
If you want to add a new element to the end of the list, you can use the
append() method.
Example: Adding a new item to the list
my_list = [10, 20, 30, 40]
my_list.append(50) # Adding 50 to the end of the list
print(my_list)
Output:
[10, 20, 30, 40, 50]

3. Using insert() to Insert an Element at a Specific Index
The insert() method allows you to insert an element at any specific index
in the list.
Example:
my_list = [10, 20, 30, 40]
my_list.insert(2, 25) # Insert 25 at index 2
print(my_list)
Output:
[10, 20, 25, 30, 40]

4. Using remove() to Remove an Element by Value
You can remove an element from the list by specifying its value
using the remove() method.
my_list = [10, 20, 30, 40, 50]
my_list.remove(30) # Remove the element with the value 30
print(my_list)
Output:
[10, 20, 40, 50]

5. Using pop() to Remove an Element by Index
The pop() method allows you to remove and return an element at a specific index.
Example:
my_list = [10, 20, 30, 40, 50]
popped_value = my_list.pop(2) # Remove and return the element at index 2 (which
is 30)
print(my_list)
print("Popped value:", popped_value)
Output:
[10, 20, 40, 50]
Popped value: 30

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Nested Lists
A nested list is simply a list that contains other lists as its
elements.These inner lists can also contain other lists, creating a
multi-level structure. Nested lists are useful when you need to
represent more complex data structures, like matrices (2D lists) or
other hierarchical data.
.
Example:
nested_list = [ [1, 2, 3], [4, 5, 6],[7, 8, 9] ]

Accessing Elements in a Nested List:
To access elements in a nested list, you use multiple indices: the first index
accesses the outer list, and the second index accesses the element within the inner
list.
nested_list = [
[1, 2, 3],
[4, 5, 6],
[7, 8, 9]
]
print(nested_list[0]) # Output: [1, 2, 3]
print(nested_list[0][1]) # Output: 2
print(nested_list[2][2])

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Nested List Example (Matrix Representation):
A common use of nested lists is to represent a matrix (2D array), where each
sublist represents a row in the matrix.
Example:
matrix = [
[1, 2, 3],
[4, 5, 6],
[7, 8, 9]
]
print(matrix[1][2]) # Output: 6
matrix[2][0] = 100
print(matrix) # Output: [[1, 2, 3], [4, 5, 6], [100, 8, 9]]

WHILE LOOP

USING FOR LOOP
my_list = ["a", "b", "c", "d"]
for i in range(len(my_list)):
print(f"Index {i}: {my_list[i]}")

OUTPUT
Index 0: a
Index 1: b
Index 2: c
Index 3: d

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Cloning Lists
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.
Example:

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Basic List Operations

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List Methods

Using Lists as Stack
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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.

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Using Lists as Stack
Example:

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Using Lists as Queues
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 queue, and slice operation to print the value of
the last the element in the queue. © OXFORD UNIVERSITY PRESS 2017.ALL RIGHTS RESERVED.

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Using Lists as Queues
Example:

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List Comprehensions
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.
Example:

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Looping in Lists
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)
Example:

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Using the enumerate() and range() Functions
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.
Examples:

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Using an Iterator
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.
Example:

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filter() Function
The filter() function constructs a list from those elements of the list for which a function returnsTrue.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 isTrue. If sequence is a string, Unicode, or a tuple, then the result will be of the same type;
otherwise, it is always a list.
Example:

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map() Function
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

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reduce() Function
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

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Tuple
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.

CreatingTuple
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.

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AccessingValues in aTuple
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
Example:

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Deleting Elements inTuple
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).
Examples:

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BasicTuple Operations

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Tuple Assignment
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.

NESTED TUPLES

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The zip() Function
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

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Advantages ofTuple over List
• 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.
• Tuples are used to format strings.

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Sets
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,
Example:To create a set, you can write,

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Set Operations

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Set Operations

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Set Operations

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Set Operations

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Dictionaries
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}
Example:

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AccessingValues
Example:

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Adding an Item in a Dictionary
Example:

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Modifying an Entry
Example:

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Deleting Items
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]
Example:

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Nested Dictionaries
Example:

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Sorting Items and Looping over Items in a Dictinonary
Examples:

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Built-in Dictionary Functions and Methods

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Difference between a List and a Dictionary
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
dictionary.This is because Python uses complex algorithms (called hashing) to provide fast access to the items
stored in the dictionary.This also makes dictionary preferable to use over a list of tuples.

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String Formatting with Dictionaries
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

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When to use which Data Structure?
• 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.

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