Python _dataStructures_ List, Tuples, its functions

Python Programming – Unit 3
Part – 2
Datastructures
1
https://www.slideshare.net/slideshow/python_funct
ions_modules_-user-define-functions/275958501
Dr.VIDHYA B
ASSISTANT PROFESSOR & HEAD
Department of Computer Technology
Sri Ramakrishna College of Arts and Science
Coimbatore - 641 006
Tamil Nadu, India

Python Data Structures
- Is a group of data elements that are put together
under one name
- Defines a particular way of storing and organizing
data in a computer so that it can be used efficiently

1. LIST
- It is a sequence in which elements are written as a
list of comma separated values.
- Elements can be of different data types
- It takes the form
list_variable = [val1, val2, val3,…,valn]

List (Examples)
list1=[1,2,3,4,5] Output:
print(list1)  [1,2,3,4,5]
list2 =[‘A’, ‘B’, ‘C’, ‘d’, ‘e’] Output:
print(list2)  [‘A’, ‘B’, ‘C’, ‘d’,
‘e’]
list3=[“Apple”, “Banana”] Output:
print(list3)  [‘Apple’, ‘Banana’]
list4 = [1, ‘a’, “Dog”] Output:
print(list4)  [1, ‘a’, ‘Dog’]

Accessing Values in List
- Similar to strings, lists can also be sliced and
concatenated
- square brackets are used to slice along with the
index/indices to get values stored at that index.
-For example:
seq = list[start:stop:step]
seq = list[::2] #get every other element, starting with index 0
seq = list[1::2] #get every other element, starting with index 1

Accessing Values in List (Example)
l1 = [1,2,3,4,5,6,7,8,9,10]
print(“List is:”, l1)
print(“First element is:”, l1[0])
print(“Index 2 – 5th
element:”, l1[2:5])
print(“From 0th
index, skip one element:”, l1[ : : 2])
print(“From 1st
index, skip two elements:”, l1[1::3])
Output:
List is: [1,2,3,4,5,6,7,8,9,10]
First element is: 1
Index 2 – 5th
element: [3,4,5]
From 0th
index, skip one element: [1,3,5,7,9]
From 1st
index, skip two elements: [2,5,8]

Updating Values in List
- A list can be updated by giving the slice on the left-hand
side of the assignment operator
- New values can be appended in the list with the method
append( ). The input to this method will be appended at
the end of the list
- Existing values can be removed by using the del statement.
Value at the specified index will be deleted.

Updating Values in List (Example)
l1 = [1,2,3,4,5,6,7,8,9,10]
print(“List is:”, l1)
l1[5] = 100
print(“After update:”, l1)
l1.append(200)
print(“After append”, l1)
del l1[3]
print(“After delete:”, l1)
del l1[2:4]
del l1[:]
Output:
List is: [1,2,3,4,5,6,7,8,9,10]
After update: [1,2,3,4,5,100,7,8,9,10]
After append: [1,2,3,4,5,100,7,8,9,10, 200]
After delete: [1,2,3,5,100,7,8,9,10, 200]
After delete: [1,2,100,7,8,9,10, 200]
After delete: [ ]

Slice Operation on List
#insert a list in another list using slice operation
li=[1,9,11,13,15]
print(“Original List:”, li)
li[2]=[3,5,7]
print(“After inserting another list, the updated list is:”, li)
Output:
Original List: [1,9,11,13,15]
After inserting another list, the updated list is: [1,9,[3,5,7],13,15]

Nested List
-List within another list
Example:
l1 = [1, ‘a’, “abc”, [2,3,4,5], 8.9]
i = 0
while i<(len[l1]):
print(“l1[“ , i , “] =“, l[i])
i+ = 1
Output:
l1[0] = 1
l1[1] = a
l1[2] = abc
l1[3] = [2,3,4,5]
l1[4] = 8.9

Cloning List
Example:
def Cloning(li1):
li_copy = li1[:]
return li_copy
li1 = [4, 8, 2, 10, 15, 18]
li2 = Cloning(li1)
print("Original List:", li1)
print("After Cloning:", li2)
Output:
Original List: [4,8,2,10,15,18]
After Cloning : [4,8,2,10,15,18]
Cloning
If there is a need
to modify a list
and also to keep a
copy of the
original list, then
a separate copy
of the list must be
created

Basic List Operations
1 2
Operation len Concatenation
Description Returns length of the list Joins two list
Example len([1,2,3,4,5,6,7,8,9,10]) [1,2,3,4,5] + [6,7,8,9,10]
Output 10 [1,2,3,4,5,6,7,8,9,10]
3 4
Operation Repetition in
Description Repeats elements in the list Checks if the values is present
in the list
Example “Hello”, “World” *2 ‘a’ in [‘a’, ‘e’, ‘i’, ‘o’, ‘u’]
Output [‘Hello’, ‘World’, ‘Hello’,
‘World’]
True

Basic List Operations (Continued…)
5 6
Operation not in max
Description Checks if the value is not available
in the list
Returns maximum value in the
list
Example 3 not in [0,2,4,6,8] n=[2,3,4]
print(max(n))
Output True 4
7 8
Operation min sum
Description Returns minimum value in the list Adds the values in the list that
has number
Example n=[2,3,4]
print(min(n))
n=[2,3,4]
print(“Sum:”, sum(n))
Output 2 9

9 10
Operation all any
Description Returns true if all elements of the
list are true (or if the list is
empty)
Returns true if any elements of
the list are true . If empty False
Example n=[0,1,2,3]
print(all(n))
n=[0,1,2,3]
print(any(n))
Output False True
11 12
Operation list sorted
Description Converts an iterable (tuple,
string, set, dictionary) to a list
Returns a new sorted list. The
original list is not sorted
Example list1=list(“HELLO”)
print(list1)
list1=[1,5,3,2]
list2 = sorted(list1)
print(list2)
Output [‘H’, ‘E’, ‘L’, ‘L’, ‘O’] [1,2,3,5]

Few more examples on Indexing, Slicing and other
operations
list_a =[“Hello”, “World”, “Good”, “Morning”]
print(list_a[2]) #index starts at 0
print(list_a[-3]) #3rd
element from the end
print(list_a[1:]) # Prints all elements starting from index 1
Output:
Good
World
[ “World”, “Good”, “Morning”]

List Methods
1 2
Operation append( ) count()
Description Appends an element to the list Counts the number of times an
element appears in the list
Example n=[6,3,7,0,1,2,4,9]
append(10)
print(n)
n=[6,3,7,0,1,2,4,9]
print(n.count(4))
Output [6,3,7,0,1,2,4,9,10] 1
3 4
Operation index() insert( )
Description Returns the lowest index of obj in
the list
Inserts obj at the specified index
in the list
Example n = [6,3,7,0,1,2,4,9]
print(n.index(7))
n = [6,3,7,0,1,2,4,9]
n.insert(3,100)
print(n)
Output 2 n = [6,3,7,100,1,2,4,9]

List Methods (Continued…)
5 6
Operation pop( ) remove( )
Description Removes the element in the specified
index. If no index is specified, last
element will be removed
Removes the specified element
from the list.
Example n = [6,3,7,0,1,2,4,9]
print(n.pop( ))
print(n)
n = [6,3,7,0,1,2,4,9]
print(n.remove(0))
print(n)
Output 9 [6,3,7,0,1,2,4] [6,3,7,1,2,4,9]
7 8
Operation reverse( ) sort( )
Description Reverses the elements in the list Sorts the elements in the list
Example n = [6,3,7,0,1,2,4,9]
n.reverse( )
print(n)
n = [6,3,7,0,1,2,4,9]
n.sort( )
print(n)
Output [9,4,2,1,0,7,3,6,] [0, 1, 2, 3, 4, 6, 7, 9]

List Methods (Continued…)
9 Insert()
Remove()
Sort()
The above methods only
modify the list and do not
return any value
Operation extend
Description Adds the element in a list to the end
of the another list
Example n1 = [1,2,3,4,5]
n2 = [6,7,8,9,10]
n1.extend(n2)
print(n1)
Output [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

February 23, 2025 20
Using Lists as Stacks
- Stack is a DS which stores
its elements in an ordered
manner.
- Eg: Pile of Plates
- Stack is a linear DS uses the
same principle (ie) elements
are added and removed only
from one end.
- Hence Stack follows LIFO
(Last in First Out)

- Stacks in computer science is used in function
calls.

- Stack supports 3 operations:
PUSH: Adds an element at the end of the stack.
POP: Removes the element from the stack.
PEEP: Returns the value of the last element from the
stack(without deleting it).
In python list methods are used to perform the above
operation
- PUSH- append () method
- POP- pop() method
- PEEP- slicing operation is used

Using Lists as Queues
- Queue is a DS which stores its elements in an ordered manner.
- Eg:
*People moving in escalator
*People waiting for bus
*Luggage kept at conveyor belt.
*Cars lined at toll bridge.
Stack is a linear DS uses the same principle (ie) elements are
added at one end and removed from other end.
- Hence Queue follows FIFO (First in First Out)

Using Lists as Queues

List Comprehensions
Python supports computed lists called List Comprehensions
Syntax:
List= [expression for variable in sequence]
- Beneficial to make new list where each element is
obtained by applying some operations to each
member of another sequence or iterable.
- Also used to create a subsequence of those
elements that satisfy certain conditions.
An iterable is an object that can be used repeatedly
in a subsequent loop statements. Eg: For loop

List Comprehensions

Looping in Lists
Python’s for and
in construct useful
when working
with lists, easy to
access each
element in a list.

Looping in Lists
Multiple ways to
access a List:
Iterator Function:
Loop over the
elements when
used with next()
method.
Uses built-in
iter()function

What is Functional Programming ?
Functional Programming
decomposes a problem into set of
functions
Map( ), filter( ), reduce( )

Filter Function
- Filter function constructs a list from those
elements of the list for which a function returns
True.
- filter(function, sequence)
- If sequence is a string, Unicode or a tuple, then the
result will be of same type otherwise it is always a
list.

Filter Function (Example)
def check(x):
if (x % 2 == 0 or x % 4 = =0):
return 1
events = list(filter(check, range(2, 22)))
print(events)
Output:
[ 2, 4, 6, 8, 10, 12, 14, 16, 18, 20]
Explanation:
The filter function returns True or False. Functions that returns a
Boolean value called as Predicates. Only those values divisible
by 2 or 4 is included in newly created list.

Map Function
- Map() function applies a particular function to
every element of a list. Its syntax is same as the
filter function.
- map(function, sequence)
- After applying the specified function on the
sequence the map() function returns the modified
list.
- It calls function(item)for each item in a sequence
a returns a list of return values.

Map Function (Example)
Explanation: Map() calls add_2() which adds 2 to every
element in the list

Map Function (Example)
def add(x,y):
return x+y
List1 = [1,2,3,4,5]
List2 = [6,7,8,9,10]
List3 = list(map(add, list1, list2))
Print(“sum of” , list1, “ and”, list2, “ =“, list3)
Output:
sum of [1,2,3,4,5] and [6,7,8,9,10] = [7,9,11,13,15]
Explanation:
Here more than one sequence is passed in map().
- Function must have as many as arguments as there are sequences.
- Each argument is called with corresponding item from each
sequence

Reduce Function
- Reduce() function returns a single value
generated by calling the function.
- reduce(function, sequence)

Reduce Function (Example)

Python _dataStructures_ List, Tuples, its functions

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