1. Lists

  • What are Lists?
  • Lists Vs Arrays
  • Characterstics of a List
  • How to create a list
  • Access items from a List
  • Editing items in a List
  • Deleting items from a List
  • Operations on Lists
  • Functions on Lists

What are Lists

List is a data type where you can store multiple items under 1 name. More technically, lists act like dynamic arrays which means you can add more items on the fly.

In Python, a list is a built-in data type used to store a collection of items. It is a mutable, ordered sequence, meaning you can change its elements, and the order of elements is preserved. Lists are defined by enclosing a comma-separated sequence of elements in square brackets ([]).

  • Why Lists are required in programming?

Array Vs Lists

  • Fixed Vs Dynamic Size
  • Convenience -> Hetrogeneous
  • Speed of Execution
  • Memory
Creating a list named L with three elements: 1, 2, and 3

L = [1, 2, 3]

Printing the unique identifier (memory address) of the entire list L

print(“ID of L (the whole list):”, id(L))

Printing the unique identifier of the first element (1) in the list

print(“ID of L[0] (first element):”, id(L[0]))

Printing the unique identifier of the second element (2) in the list

print(“ID of L[1] (second element):”, id(L[1]))

Printing the unique identifier of the third element (3) in the list

print(“ID of L[2] (third element):”, id(L[2]))

Printing the unique identifier of the integer 1

print(“ID of 1 (integer 1):”, id(1))

Printing the unique identifier of the integer 2

print(“ID of 2 (integer 2):”, id(2))

Printing the unique identifier of the integer 3

print(“ID of 3 (integer 3):”, id(3))

How lists are stored in memory

Contiguous Memory Allocation:

  • Dynamic arrays allocate memory in a contiguous block, meaning all the elements of the list are stored in adjacent memory locations.

Characterstics of a List

  • Ordered
  • Changeble/Mutable
  • Hetrogeneous
  • Can have duplicates
  • are dynamic
  • can be nested
  • items can be accessed
  • can contain any kind of objects in python

L = [1,2,3,1]
L1 = [3,2,1]

L == L1(False because, although the lists have some common elements, they are not in the same order.)

Creating a List

Empty

print([ ])

1D -> Homo

print([1,2,3,4,5])

2D

print([1,2,3,[4,5]]) #prints a 2D list with one sublist.

3D

print([[[1,2],[3,4]],[[5,6],[7,8]]]) #prints a 3D list with nested sublists.

Hetrogenous

print([1,True,5.6,5+6j,’Hello’]) #prints a heterogeneous list with different data types.

Using Type conversion

print(list(‘hello’)) #uses type conversion to create a list from the characters of the string ‘hello’.

Accessing Items from a List

Indexing

L = [[[1,2],[3,4]],[[5,6],[7,8]]]

positive

print(L[0][0][1])

#For the indexing example, the variable L is a 3D list. The line print(L[0][0][1]) uses positive indexing to access the element at the first level (index 0), second level (index 0), and third level (index 1). The output is 2 because it retrieves the element at the specified position.

Slicing

L = [1,2,3,4,5,6]

print(L[::-1])

#For the slicing example, the variable L is a 1D list. The line print(L[::-1]) uses slicing with [::-1] to reverse the list. The output is [6, 5, 4, 3, 2, 1]. The [::-1] syntax is a common way to reverse a list in Python.

Adding Items to a List

append

L = [1,2,3,4,5]
L.append(True)
print(L)

Explanation:

  • append is used to add a single element to the end of the list.
  • In this example, True is appended to the list L, so the list becomes [1, 2, 3, 4, 5, True].
extend

L = [1,2,3,4,5]
L.extend([6,7,8])
print(L)

Explanation:

  • extend is used to add elements from an iterable (like a list) to the end of the list.
  • In this example, the elements [6, 7, 8] are added to the end of the list L, resulting in [1, 2, 3, 4, 5, 6, 7, 8].

insert

L = [1,2,3,4,5]

L.insert(1,100)
print(L)

Explanation:

  • insert is used to add an element at a specific index in the list.
  • In this example, the value 100 is inserted at index 1 in the list L, resulting in [1, 100, 2, 3, 4, 5].

Editing items in a List

L = [1,2,3,4,5]

editing with indexing

L[-1] = 500

editing with slicing

L[1:4] = [200,300,400]

print(L)

Explanation:

  • Editing with Indexing (L[-1] = 500):
    • L[-1] refers to the last element in the list.
    • Using indexing, we change the value of the last element from 5 to 500.
  • Editing with Slicing (L[1:4] = [200, 300, 400]):
    • L[1:4] represents a slice of the list from index 1 to (4-1) which is index 3.
    • Using slicing, we replace the elements at indices 1 to 3 with the values [200, 300, 400].
    • The list is modified to [1, 200, 300, 400, 500].
  • Printing the Modified List (print(L)):
    • Finally, we print the modified list after both editing operations. The output will be [1, 200, 300, 400, 500].

Deleting items from a List

del

L = [1,2,3,4,5]

indexing

del L[-1]

slicing

del L[1:3]
print(L)

remove

L = [1,2,3,4,5]

L.remove(5)

print(L)

pop

L = [1,2,3,4,5]

L.pop()

print(L)

clear

L = [1,2,3,4,5]

L.clear()

print(L)

Explanation:

  • Deleting with Indexing (del L[-1]):
    • del L[-1] deletes the last element of the list using indexing.
  • Deleting with Slicing (del L[1:3]):
    • del L[1:3] deletes elements from index 1 to (3-1) which is index 2 using slicing.
  • Removing (L.remove(5)):
    • L.remove(5) removes the first occurrence of the value 5 from the list.
  • Popping (L.pop()):
    • L.pop() removes and returns the last element of the list.
  • Clearing (L.clear()):
    • L.clear() removes all elements from the list, leaving it empty.

Operations on Lists

  • Arithmetic
  • Membership
  • Loop
Lists

L1 = [1, 2, 3, 4]
L2 = [5, 6, 7, 8]

Addition (+): Concatenating two lists

result_addition = L1 + L2

Multiplication (*): Repeating a list

result_multiplication = L1 * 3

Printing the results

print(“Result of Addition:”, result_addition)
print(“Result of Multiplication:”, result_multiplication)

Explanation:

  • Addition (L1 + L2):
    • The + operator is used for list concatenation. It combines the elements of L1 and L2 to create a new list.
    • The result is [1, 2, 3, 4, 5, 6, 7, 8].
  • Multiplication (L1 * 3):
    • The * operator is used for list repetition. It repeats the elements of L1 three times to create a new list.
    • The result is [1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4].

Concatenation/Merge

# Lists
L1 = [1, 2, 3, 4]
L2 = [5, 6, 7, 8]

# List Concatenation (+): Combining elements of L1 and L2
result_addition = L1 + L2

# List Repetition (*): Repeating elements of L1 three times
result_multiplication = L1 * 3

# Printing the results
print("Result of Addition:", result_addition)
print("Result of Multiplication:", result_multiplication)

# New lists
L1 = [1, 2, 3, 4, 5]
L2 = [1, 2, 3, 4, [5, 6]]

# Checking membership with 'not in' and 'in'
result_not_in_L1 = 5 not in L1  # Checks if 5 is not in L1
result_in_L2 = [5, 6] in L2    # Checks if [5, 6] is in L2

# Printing the membership results
print("Is 5 not in L1?", result_not_in_L1)
print("Is [5, 6] in L2?", result_in_L2)

Explanation:

  • List Concatenation (L1 + L2):
  • The + operator is used for list concatenation, combining the elements of L1 and L2 to create a new list.
  • Result: [1, 2, 3, 4, 5, 6, 7, 8]
  • List Repetition (L1 * 3):
  • The * operator is used for list repetition, repeating the elements of L1 three times to create a new list.
  • Result: [1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4]
  • Membership Tests (5 not in L1 and [5, 6] in L2):
  • The not in and in operators are used to check if an element is not or is present in a list, respectively.
  • Result: True for “Is 5 not in L1?” and True for “Is [5, 6] in L2?”

Loops

# Lists
L1 = [1, 2, 3, 4, 5]
L2 = [1, 2, 3, 4, [5, 6]]
L3 = [[[1, 2], [3, 4]], [[5, 6], [7, 8]]]

# Looping through elements in L3
for i in L3:
    print(i)

Explanation:

  • Looping through L3:
  • The for loop is used to iterate through each element (i) in the list L3.
  • The indentation (whitespace before print(i)) is crucial in Python, indicating that print(i) is part of the loop.
  • This loop prints each element of L3 on a new line.

The output will be:

[[1, 2], [3, 4]]
[[5, 6], [7, 8]]

Each line represents one element from the nested list L3.

List Functions

Certainly! Here’s the code with comments explaining each operation:

# List
L = [2, 1, 5, 7, 0]

# Length of the list
print("Length of L:", len(L))

# Minimum value in the list
print("Minimum value in L:", min(L))

# Maximum value in the list
print("Maximum value in L:", max(L))

# Sorted list in reverse order
print("Sorted in reverse order:", sorted(L, reverse=True))

# Count occurrences of 5 in the list
L_count = [1, 2, 1, 3, 4, 1, 5]
count_of_5 = L_count.count(5)
print("Count of 5 in L_count:", count_of_5)

# Index of the first occurrence of 1 in the list
L_index = [1, 2, 1, 3, 4, 1, 5]
index_of_1 = L_index.index(1)
print("Index of the first occurrence of 1 in L_index:", index_of_1)

# Permanently reverse the list
L_reverse = [2, 1, 5, 7, 0]
L_reverse.reverse()
print("Reversed list (permanently):", L_reverse)

# Sort vs Sorted
L_sort = [2, 1, 5, 7, 0]
print("Original list:", L_sort)
print("Sorted list (using sorted):", sorted(L_sort))
print("Original list after using sorted:", L_sort)
L_sort.sort()
print("Original list after using sort:", L_sort)

# Shallow copy of the list
L_copy = [2, 1, 5, 7, 0]
print("Original list:", L_copy)
print("ID of original list:", id(L_copy))
L1_copy = L_copy.copy()
print("Shallow copy (L1_copy):", L1_copy)
print("ID of L1_copy:", id(L1_copy))

Explanation:

  • len(L): Returns the length (number of elements) in the list.
  • min(L): Returns the minimum value in the list.
  • max(L): Returns the maximum value in the list.
  • sorted(L, reverse=True): Returns a new list with elements sorted in reverse order.
  • L.count(5): Counts the occurrences of the value 5 in the list.
  • L.index(1): Returns the index of the first occurrence of the value 1 in the list.
  • L.reverse(): Permanently reverses the order of elements in the list.
  • sorted(L_sort): Returns a new sorted list without modifying the original list.
  • L_sort.sort(): Sorts the original list in-place.

Remember that sorted() returns a new list, while sort() modifies the original list in place.

List Comprehension

List Comprehension provides a concise way of creating lists.

image.png

Advantages of List Comprehension

  • More time-efficient and space-efficient than loops.
  • Require fewer lines of code.
  • Transforms iterative statement into a formula.
Add 1 to 10 numbers to a list
# Initializing an empty list
L = []

# Using a for loop to append numbers from 1 to 10 to the list
for i in range(1, 11):
    L.append(i)

# Printing the list
print("List after using a for loop:", L)


# Using list comprehension to create a list with numbers from 1 to 10
L = [i for i in range(1, 11)]

# Printing the list
print("List using list comprehension:", L)

Explanation:

  • Using a for loop:
  • An empty list L is initialized.
  • A for loop is used to iterate over the range from 1 to 10 (inclusive).
  • The append() method is used to add each number to the list.
  • Using list comprehension:
  • The list L is created using a list comprehension.
  • The expression [i for i in range(1, 11)] generates a list containing numbers from 1 to 10.
  • This is a more concise and Pythonic way of achieving the same result as the for loop.

Both approaches result in the same list [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]. The second method using list comprehension is often preferred for its brevity and readability.

scalar multiplication on a vector

v = [2,3,4]
s = -3

[-6,-9,-12]

[s*i for i in v]

Add squares

L = [1,2,3,4,5]

[i**2 for i in L]

Print all numbers divisible by 5 in the range of 1 to 50

[i for i in range(1,51) if i%5 == 0]

find languages which start with letter p

languages = [‘java’,’python’,’php’,’c’,’javascript’]

[language for language in languages if language.startswith(‘p’)]

Nested if with List Comprehension

basket = [‘apple’,’guava’,’cherry’,’banana’]
my_fruits = [‘apple’,’kiwi’,’grapes’,’banana’]

add new list from my_fruits and items if the fruit exists in basket and also starts with ‘a’

[fruit for fruit in my_fruits if fruit in basket if fruit.startswith(‘a’)]

Print a (3,3) matrix using list comprehension -> Nested List comprehension

[[i*j for i in range(1,4)] for j in range(1,4)]

cartesian products -> List comprehension on 2 lists together

L1 = [1,2,3,4]
L2 = [5,6,7,8]

[i*j for i in L1 for j in L2]

2 ways to traverse a list

  • itemwise
  • indexwise

Itemwise:

# List of numbers
L = [1, 2, 3, 4]

# Iterating itemwise using a for loop
for i in L:
    print(i)

Explanation:

  • This loop iterates over each element (i) in the list L.
  • It prints each element on a new line.
  • The output will be:
  1
  2
  3
  4

Indexwise:

# List of numbers
L = [1, 2, 3, 4]

# Iterating indexwise using a for loop and range
for i in range(0, len(L)):
    print(L[i])

Explanation:

  • This loop uses range(0, len(L)) to generate indices from 0 to len(L)-1.
  • It iterates over these indices and prints the elements of the list L at those indices.
  • The output will be the same as the itemwise loop:
  1
  2
  3
  4

Note: While both approaches achieve the same result for this specific example, iterating itemwise (for i in L) is often more readable and Pythonic when you only need the values, not the indices. The indexwise approach using range is useful when you need both the value and the index in the loop.

Zip

The zip() function returns a zip object, which is an iterator of tuples where the first item in each passed iterator is paired together, and then the second item in each passed iterator are paired together.

If the passed iterators have different lengths, the iterator with the least items decides the length of the new iterator.

Write a program to add items of 2 lists indexwise

L1 = [1,2,3,4]
L2 = [-1,-2,-3,-4]

list(zip(L1,L2))

[i+j for i,j in zip(L1,L2)]

L = [1,2,print,type,input]

print(L)

Disadvantages of Python Lists

  • Slow
  • Risky usage
  • eats up more memory
  • lists are mutable

In Python, lists are mutable, which means that you can modify their contents. Mutable objects are those whose state or value can be changed after they are created. Let’s explore how mutability works with lists:

# Creating a list
my_list = [1, 2, 3, 4, 5]

# Modifying the list
my_list[0] = 10
my_list.append(6)
del my_list[1]

# Printing the modified list
print("Modified list:", my_list)

Explanation:

  • my_list[0] = 10: Changes the first element of the list from 1 to 10.
  • my_list.append(6): Appends the value 6 to the end of the list.
  • del my_list[1]: Deletes the element at index 1 in the list.

The output will be:

Modified list: [10, 3, 4, 5, 6]

These operations demonstrate the mutability of lists. Once a list is created, you can change, add, or remove elements from it, making lists a versatile and powerful data structure in Python. Keep in mind that mutability has implications when working with multiple references to the same list or when passing lists as arguments to functions.

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