Podcast
Questions and Answers
Which of the following is the most accurate definition of an 'effective' algorithm?
Which of the following is the most accurate definition of an 'effective' algorithm?
- An algorithm that is feasible and always produces the correct result. (correct)
- An algorithm that is easy to understand but may not produce the correct result.
- An algorithm that uses natural language for its representation.
- An algorithm that is written in pseudocode.
When would using pseudocode be most advantageous over using natural language to represent an algorithm?
When would using pseudocode be most advantageous over using natural language to represent an algorithm?
- When the algorithm needs to be executed directly by a computer.
- When the algorithm needs to be easily understood by non-programmers.
- When the algorithm needs a precise and code-like representation. (correct)
- When the algorithm is very simple and straightforward.
What characteristic distinguishes Python lists from other data structures?
What characteristic distinguishes Python lists from other data structures?
- Lists are immutable, meaning they cannot be changed once created.
- Lists are ordered collections, allowing duplicate values and modification. (correct)
- Lists can only contain elements of the same data type.
- Lists use curly braces `{}` to define their elements.
If you need to store a collection of items where the order matters and you anticipate needing to add or remove items, which data structure would be most suitable?
If you need to store a collection of items where the order matters and you anticipate needing to add or remove items, which data structure would be most suitable?
Consider the following Python code:
my_list = [10, "apple", 3.14, True]
print(len(my_list))
What will be the output of this?
Consider the following Python code:
my_list = [10, "apple", 3.14, True]
print(len(my_list))
What will be the output of this?
Which of the following statements is correct regarding the data types of items within a single Python list?
Which of the following statements is correct regarding the data types of items within a single Python list?
Given the list my_list = ['apple', 'banana', 'cherry']
, how can you access the second item ('banana')?
Given the list my_list = ['apple', 'banana', 'cherry']
, how can you access the second item ('banana')?
What will be the output of the following Python code?
my_list = [10, 20, 30, 40, 50]
print(my_list[1:4])
What will be the output of the following Python code?
my_list = [10, 20, 30, 40, 50]
print(my_list[1:4])
Which of the following scenarios best illustrates the Last In, First Out (LIFO) principle of a stack data structure?
Which of the following scenarios best illustrates the Last In, First Out (LIFO) principle of a stack data structure?
When deciding on the most suitable data structure for a program, why is it important to consider access patterns?
When deciding on the most suitable data structure for a program, why is it important to consider access patterns?
Which data structure is most suitable for implementing a 'call history' feature, where the most recent calls need to be accessed quickly?
Which data structure is most suitable for implementing a 'call history' feature, where the most recent calls need to be accessed quickly?
An algorithm is designed to sort a list of numbers. Which of the following is NOT a required characteristic of this algorithm?
An algorithm is designed to sort a list of numbers. Which of the following is NOT a required characteristic of this algorithm?
A program uses a queue to manage print jobs. What is the most likely outcome if the program adds print jobs faster than the printer can process them?
A program uses a queue to manage print jobs. What is the most likely outcome if the program adds print jobs faster than the printer can process them?
In the context of algorithm design, what does 'definiteness' refer to?
In the context of algorithm design, what does 'definiteness' refer to?
Which of the following best describes a scenario where a tree data structure would be more appropriate than a linked list or an array?
Which of the following best describes a scenario where a tree data structure would be more appropriate than a linked list or an array?
Why is the finiteness
property crucial for an algorithm to be considered effective?
Why is the finiteness
property crucial for an algorithm to be considered effective?
What will be the output of the following Python code snippet?
thislist = ["apple", "banana", "cherry"]
thislist.pop(1)
print(thislist)
What will be the output of the following Python code snippet?
thislist = ["apple", "banana", "cherry"]
thislist.pop(1)
print(thislist)
What happens when you try to execute the following Python code?
thislist = ["apple", "banana", "cherry"]
del thislist
print(thislist)
What happens when you try to execute the following Python code?
thislist = ["apple", "banana", "cherry"]
del thislist
print(thislist)
Which of the following statements accurately describes a tuple in Python?
Which of the following statements accurately describes a tuple in Python?
Which of the following is a primary reason for using tuples in Python?
Which of the following is a primary reason for using tuples in Python?
What is the output of the following Python code?
coordinates = (3, 4)
new_coordinates = coordinates + (5,)
print(len(coordinates))
print(3 in coordinates)
What is the output of the following Python code?
coordinates = (3, 4)
new_coordinates = coordinates + (5,)
print(len(coordinates))
print(3 in coordinates)
Given the tuple my_tuple = (1, 2, 3, 4, 5)
, what will the following code print?
my_tuple = (1, 2, 3, 4, 5)
if 6 in my_tuple:
print("6 is present in the tuple")
else:
print("6 is not present in the tuple")
Given the tuple my_tuple = (1, 2, 3, 4, 5)
, what will the following code print?
my_tuple = (1, 2, 3, 4, 5)
if 6 in my_tuple:
print("6 is present in the tuple")
else:
print("6 is not present in the tuple")
What will be the output of the following Python Code:
name = ("ana", "boy", "cherry")
print(name[-2:-1])
What will be the output of the following Python Code:
name = ("ana", "boy", "cherry")
print(name[-2:-1])
How can you modify a tuple in Python, given that tuples are immutable?
How can you modify a tuple in Python, given that tuples are immutable?
What will be printed when the following Python code is executed?
def index():
thislist = ["apple", "banana", "cherry", "orange", "kiwi", "melon", "mango"]
print(thislist[2:5])
index()
What will be printed when the following Python code is executed?
def index():
thislist = ["apple", "banana", "cherry", "orange", "kiwi", "melon", "mango"]
print(thislist[2:5])
index()
What will be printed when the following Python code is executed?
def index_remove():
thislist = ["apple", "banana", "cherry", "orange", "kiwi", "melon", "mango"]
print(thislist[:4])
index_remove()
What will be printed when the following Python code is executed?
def index_remove():
thislist = ["apple", "banana", "cherry", "orange", "kiwi", "melon", "mango"]
print(thislist[:4])
index_remove()
What will be printed when the following Python code is executed?
def index_negative():
thislist = ["apple", "banana", "cherry", "orange", "kiwi", "melon", "mango"]
print(thislist[-4:-1])
index_negative()
What will be printed when the following Python code is executed?
def index_negative():
thislist = ["apple", "banana", "cherry", "orange", "kiwi", "melon", "mango"]
print(thislist[-4:-1])
index_negative()
What will be printed when the following Python code is executed?
def index_list1():
fruits = ["apple", "banana", "cherry"]
fruits = "melon"
print(fruits)
index_list1()
What will be printed when the following Python code is executed?
def index_list1():
fruits = ["apple", "banana", "cherry"]
fruits = "melon"
print(fruits)
index_list1()
What will be the output of the following Python code?
def index_change():
fruits = ["apple", "banana", "cherry", "orange", "kiwi", "mango"]
fruits[1:2] = ["pineapple", "watermelon"]
print(fruits)
index_change()
What will be the output of the following Python code?
def index_change():
fruits = ["apple", "banana", "cherry", "orange", "kiwi", "mango"]
fruits[1:2] = ["pineapple", "watermelon"]
print(fruits)
index_change()
What will be printed when the following Python code is executed?
def add_list():
fruits = ["apple", "banana", "cherry"]
fruits.insert(2, "watermelon")
print(fruits)
add_list()
What will be printed when the following Python code is executed?
def add_list():
fruits = ["apple", "banana", "cherry"]
fruits.insert(2, "watermelon")
print(fruits)
add_list()
What will be printed when the following Python code is executed?
def insert():
thislist = ["apple", "banana", "cherry"]
tropical = ["mango", "pineapple", "papaya"]
thislist.extend(tropical)
print(thislist)
insert()
What will be printed when the following Python code is executed?
def insert():
thislist = ["apple", "banana", "cherry"]
tropical = ["mango", "pineapple", "papaya"]
thislist.extend(tropical)
print(thislist)
insert()
What will be printed when the following Python code is executed?
def list_remove():
thislist = ["apple", "banana", "cherry"]
thislist.remove("banana")
print(thislist)
list_remove()
What will be printed when the following Python code is executed?
def list_remove():
thislist = ["apple", "banana", "cherry"]
thislist.remove("banana")
print(thislist)
list_remove()
What type of object is returned when using the .keys()
method on a dictionary in Python?
What type of object is returned when using the .keys()
method on a dictionary in Python?
Given the dictionary student = {"name": "Alice", "age": 20, "city": "New York"}
, what will len(student)
return?
Given the dictionary student = {"name": "Alice", "age": 20, "city": "New York"}
, what will len(student)
return?
What is the primary difference between using my_dict = {}
and del my_dict
when working with dictionaries?
What is the primary difference between using my_dict = {}
and del my_dict
when working with dictionaries?
Which method returns a view object that displays a list of a dictionary's key-value pairs?
Which method returns a view object that displays a list of a dictionary's key-value pairs?
If student = {"name": "Alice", "age": 20, "city": "New York"}
, what data type will be the elements in the view object returned by student.values()
?
If student = {"name": "Alice", "age": 20, "city": "New York"}
, what data type will be the elements in the view object returned by student.values()
?
What is the primary difference between using square bracket access (e.g., student['age']
) and the get()
method (e.g., student.get('age')
) when accessing dictionary values?
What is the primary difference between using square bracket access (e.g., student['age']
) and the get()
method (e.g., student.get('age')
) when accessing dictionary values?
What is the result of running this code?
name = ["ana", "boy", "chery"]
name1 = ("den", "bing")
name += name1
print(name)
What is the result of running this code?
name = ["ana", "boy", "chery"]
name1 = ("den", "bing")
name += name1
print(name)
Given the dictionary student = {"name": "Alice", "age": 20, "city": "New York"}
, what happens when you execute del student['age']
?
Given the dictionary student = {"name": "Alice", "age": 20, "city": "New York"}
, what happens when you execute del student['age']
?
What is the purpose of the in
keyword when used with a dictionary in Python?
What is the purpose of the in
keyword when used with a dictionary in Python?
What is the output of the following code?
student = {"name": "Alice", "age": 20, "city": "New York"}
student.pop("age")
print(student)
What is the output of the following code?
student = {"name": "Alice", "age": 20, "city": "New York"}
student.pop("age")
print(student)
What is the result of the following code?
names = ["ana", "boy", "chery"]
names1 = ("den", "bing")
concatenated_names = [*names, *names1]
print(concatenated_names)
What is the result of the following code?
names = ["ana", "boy", "chery"]
names1 = ("den", "bing")
concatenated_names = [*names, *names1]
print(concatenated_names)
Which of the following statements best describes a Python dictionary?
Which of the following statements best describes a Python dictionary?
Given student = {"name": "Alice", "age": 20, "city": "New York"}
, what will student.keys()
return?
Given student = {"name": "Alice", "age": 20, "city": "New York"}
, what will student.keys()
return?
Flashcards
Data Structures
Data Structures
Ways data is organized, stored, and manipulated in a program.
Arrays
Arrays
A collection of elements identified by an index number.
Linked Lists
Linked Lists
Linear data where each element points to the next.
Stacks
Stacks
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Queues
Queues
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Trees
Trees
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Algorithm
Algorithm
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Algorithm input
Algorithm input
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Algorithm Effectiveness
Algorithm Effectiveness
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Natural Language (Algorithms)
Natural Language (Algorithms)
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Flowcharts
Flowcharts
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Pseudocode
Pseudocode
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Python Lists
Python Lists
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List Item Data Types
List Item Data Types
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Ordered Lists
Ordered Lists
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Changeable Lists
Changeable Lists
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dictionary.keys()
dictionary.keys()
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dictionary.values()
dictionary.values()
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dictionary.items()
dictionary.items()
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len(dictionary)
len(dictionary)
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dictionary = {} or del dictionary
dictionary = {} or del dictionary
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List Index Range
List Index Range
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List Start Index Range
List Start Index Range
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Negative List Index Range
Negative List Index Range
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Change List Item
Change List Item
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Change List Item Range
Change List Item Range
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Insert List Item
Insert List Item
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Append List Item
Append List Item
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Extend List
Extend List
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List pop(index)
List pop(index)
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del
keyword in lists
del
keyword in lists
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Tuples
Tuples
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Why use Tuples?
Why use Tuples?
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Tuple Operations
Tuple Operations
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Membership Testing (in/not in)
Membership Testing (in/not in)
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Access Tuple Items
Access Tuple Items
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Modify a tuple
Modify a tuple
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List +=
operator
List +=
operator
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*
operator for lists
*
operator for lists
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Dictionary (Python)
Dictionary (Python)
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Dictionary Characteristics
Dictionary Characteristics
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Dictionary .get()
method
Dictionary .get()
method
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Dictionary del
keyword
Dictionary del
keyword
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Dictionary .pop()
method
Dictionary .pop()
method
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Dictionary in
operator
Dictionary in
operator
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Study Notes
- Data structures are fundamental building blocks in computer programming.
- Data structures define how data is organized, stored, and manipulated in a program.
- Common data structures: arrays, linked lists, stacks, queues and trees.
Common Data Structures
Arrays
- Arrays are a collection of elements, each identified by an index.
- An example is a shopping list where each item is stored in a numbered position.
Linked Lists
- Linked Lists are a linear collection of data elements where each element points to the next.
- A playlist where each song points to the next one is an example.
Stacks
- Stacks are a LIFO (Last In, First Out) structure.
- New elements are added and removed from the top.
- A stack of plates where the last plate placed is the first one taken out is an example
Queues
- Queues a FIFO (First In, First Out) structure.
- Elements are added at the rear and removed from the front.
- A waiting line at a ticket counter is an example.
Trees
- Trees are a hierarchical structure with a root node and child nodes.
- A file system structure with folders and files is an example.
- The choice of data structure depends on the specific problem being solved. Key factors to consider include access patterns, insertion and deletion, memory usage and performance.
Classification of Data Structures
- Data structures can be classified as linear or non-linear.
- Linear data structures can be further classified as static or dynamic.
- Static Data Structures: Array
- Dynamic Data Structure: Queue, Stack, Linked list
- Non-Linear Data Structures: Tree, Graph
Algorithms
- An algorithm is a step-by-step recipe for solving a problem, composed of a set of instructions a computer can follow to accomplish a task.
- A good algorithm should be correct and produce the right answer, and efficient, using minimal resources such as time and memory.
- The steps for finding the largest number in a list provides an example, composed of a set of instructions that a computer can follow to accomplish a task.
Criteria for Algorithms
- Input: Algorithms must clearly specify what data they take as input.
- Output: Algorithms must clearly define their result.
- Definiteness: Algorithm steps must be clear.
- Finiteness: Algorithms must terminate after a finite number of steps.
- Effectiveness: Algorithms must be feasible and produce the correct result.
Representing Algorithms
- Algorithms can be represented in Natural Language using everyday language to describe steps
- Or they can be represented in Flowcharts using diagrams for the flow of control,
- Or in Pseudocode, a mixture of natural language and code-like syntax.
- Natural language can be use to find the factorial of a number n by starting with a variable results and set it to 1, then looping through every number i from 1 to n and multiplying result by i.
- After completing all multiplications, the result will be the factorial of n.
- Pseudocode example for calculating the factorial of a number:
- Function factorial(n):
- result = 1
- For i = 1 to n:
- Result = result * i
- Return result
- Function factorial(n):
Python's Built-In Collections
Lists
- Lists are ordered collections of items, are mutable and can be changed.
- Use square brackets [] to create lists
- Ex: numbers = [1,2,3,4,5]
Characteristics of Python Lists
- List items are ordered, changeable, and allow duplicate values.
- List items are indexed, the first item has index [0], the second item has index [1] etc.
- The items have a defined order, and that order will not change. If new items are added to a list, the new items will be placed at the end of the list.
- Lists are changeable, allowing items to be changed, added, and removed in a list after it has been created.
Lists Length
- Use the len() to determine how many items a list has.
Lists Items Data Types
- List items can be of any data type such as String, int and boolean data types.
Access List Items
- List items are indexed and can be accessed them by referring to the index number.
- Range of Indexes can be specified by specifying where to start and where to end the range.
- Range of negative indexes can be useful to start the search from the end of the list.
Change List Items.
- Change the value of a specific item, by referring to the index number..
- A range of list item values can be changed.
- New list items can be inserted to the list without replacing any of the existing values using the insert) method.
- Items can be appended to the end of the list using the append() method.
- To append elements from another list to the current list, use the extend() method.
- The remove() method removes the specified item in a list.
- Also, the pop() method removes the specified index in a list, and the del keyword also removes the specified index.
Python's Built-In Collections
Tuples
- Tuples are ordered collections of items.
- Tuples contain elements of different data types.
- Tuples are immutable (cannot be changed); parentheses () create tuples.
- Example: coordinate = (3,4)
- Tuples promote efficiency and data integrity
Tuple operations
- Indexing and slicing: Access specific elements or tuples.
- Concatenation: Combine tuples using the + operator.
- Length: Find the number of elements using len().
- Membership testing: Check if an element is present using in and not in.
- Tuples can be accessed directly by specifying the index, while negative numbers are useful by referring to the values from the end.
Tuple Modification
- Python does not allow modification of tuples after they are created.
- Tuples can be maniouplated by converting to a list, and back.
- The + operator or * operator can concatenate values and return the values inside a new list.
Python's Built-In Collections Cont
Dictionaries
- Dictionaries are unordered collections of key-value pairs and they are mutable using curly braces {} to create.
- Example: person = {"name": "Alice", "age": 30, "city": "New York"}
Key Characteristics of Python Dictionary
- Unordered
- Mutable
- Uses Key Value Pairs surrounded by Curly Braces
Dictionary Operations
- The get() method provides a safer method to access values in a dictionary and returns a default value if the key is not found, preventing a KeyError.
- Using del deletes a key-value pair.
- The pop() method removes and return an element from a dictionary.
- Using in checks if a specific key exists within a dictionary.
- keys() extracts all the keys from a dictionary.
- values() returns a view of the dictionary's values.
- items() returns a view of the dictionary's key-value pairs.
- len(my_dict) returns the number of key-value pairs.
- my_dict = {} / del my_dict empties or deleted the dictionary
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