Python Training , study and exam guide
1 Introduction to Python
1.1 What is Python?
1.2 History of Python
1.3 Features of Python
1.4 Python Applications
1.5 Setting up the Python Environment
1.6 Running Your First Python Program
2 Python Basics
2.1 Python Syntax and Indentation
2.2 Variables and Data Types
2.2 1 Numbers
2.2 2 Strings
2.2 3 Lists
2.2 4 Tuples
2.2 5 Sets
2.2 6 Dictionaries
2.3 Operators
2.3 1 Arithmetic Operators
2.3 2 Comparison Operators
2.3 3 Logical Operators
2.3 4 Assignment Operators
2.3 5 Membership Operators
2.3 6 Identity Operators
2.4 Input and Output
2.4 1 Input Function
2.4 2 Output Function
2.5 Comments
2.5 1 Single-line Comments
2.5 2 Multi-line Comments
3 Control Flow
3.1 Conditional Statements
3.1 1 If Statement
3.1 2 If-Else Statement
3.1 3 Elif Statement
3.1 4 Nested If Statements
3.2 Loops
3.2 1 For Loop
3.2 2 While Loop
3.2 3 Nested Loops
3.3 Loop Control Statements
3.3 1 Break Statement
3.3 2 Continue Statement
3.3 3 Pass Statement
4 Functions
4.1 Defining Functions
4.2 Function Arguments
4.2 1 Positional Arguments
4.2 2 Keyword Arguments
4.2 3 Default Arguments
4.2 4 Variable-length Arguments
4.3 Return Statement
4.4 Lambda Functions
4.5 Scope of Variables
4.5 1 Local Variables
4.5 2 Global Variables
4.6 Recursion
5 Data Structures
5.1 Lists
5.1 1 List Operations
5.1 2 List Methods
5.1 3 List Comprehensions
5.2 Tuples
5.2 1 Tuple Operations
5.2 2 Tuple Methods
5.3 Sets
5.3 1 Set Operations
5.3 2 Set Methods
5.4 Dictionaries
5.4 1 Dictionary Operations
5.4 2 Dictionary Methods
5.5 Advanced Data Structures
5.5 1 Stacks
5.5 2 Queues
5.5 3 Linked Lists
6 Modules and Packages
6.1 Importing Modules
6.2 Creating Modules
6.3 Standard Library Modules
6.3 1 Math Module
6.3 2 Random Module
6.3 3 DateTime Module
6.4 Creating Packages
6.5 Installing External Packages
7 File Handling
7.1 Opening and Closing Files
7.2 Reading from Files
7.2 1 read()
7.2 2 readline()
7.2 3 readlines()
7.3 Writing to Files
7.3 1 write()
7.3 2 writelines()
7.4 File Modes
7.5 Working with CSV Files
7.6 Working with JSON Files
8 Exception Handling
8.1 Try and Except Blocks
8.2 Handling Multiple Exceptions
8.3 Finally Block
8.4 Raising Exceptions
8.5 Custom Exceptions
9 Object-Oriented Programming (OOP)
9.1 Classes and Objects
9.2 Attributes and Methods
9.3 Constructors and Destructors
9.4 Inheritance
9.4 1 Single Inheritance
9.4 2 Multiple Inheritance
9.4 3 Multilevel Inheritance
9.5 Polymorphism
9.6 Encapsulation
9.7 Abstraction
10 Working with Libraries
10.1 NumPy
10.1 1 Introduction to NumPy
10.1 2 Creating NumPy Arrays
10.1 3 Array Operations
10.2 Pandas
10.2 1 Introduction to Pandas
10.2 2 DataFrames and Series
10.2 3 Data Manipulation
10.3 Matplotlib
10.3 1 Introduction to Matplotlib
10.3 2 Plotting Graphs
10.3 3 Customizing Plots
10.4 Scikit-learn
10.4 1 Introduction to Scikit-learn
10.4 2 Machine Learning Basics
10.4 3 Model Training and Evaluation
11 Web Development with Python
11.1 Introduction to Web Development
11.2 Flask Framework
11.2 1 Setting Up Flask
11.2 2 Routing
11.2 3 Templates
11.2 4 Forms and Validation
11.3 Django Framework
11.3 1 Setting Up Django
11.3 2 Models and Databases
11.3 3 Views and Templates
11.3 4 Forms and Authentication
12 Final Exam Preparation
12.1 Review of Key Concepts
12.2 Practice Questions
12.3 Mock Exams
12.4 Exam Tips and Strategies
5 4 1 Dictionary Operations Explained

5 4 1 Dictionary Operations Explained

Key Concepts

Dictionary operations in Python allow you to manipulate dictionaries, which are collections of key-value pairs. The key concepts include:

1. Adding and Updating Items

You can add new key-value pairs to a dictionary or update the value of an existing key. This is done using the assignment operator =.

Example:

student = {"name": "Alice", "age": 25}

# Adding a new key-value pair
student["major"] = "Computer Science"
print(student)  # Output: {'name': 'Alice', 'age': 25, 'major': 'Computer Science'}

# Updating an existing key's value
student["age"] = 26
print(student)  # Output: {'name': 'Alice', 'age': 26, 'major': 'Computer Science'}
    

Analogy: Think of a dictionary as a filing cabinet where each drawer (key) holds a specific document (value). You can add a new drawer or replace the document in an existing drawer.

2. Accessing Items

You can access the value associated with a specific key using the key inside square brackets [] or the get() method.

Example:

student = {"name": "Alice", "age": 25}

# Accessing using square brackets
print(student["name"])  # Output: Alice

# Accessing using the get() method
print(student.get("age"))  # Output: 25
    

Analogy: Think of a dictionary as a phone book where you look up a person's name (key) to find their phone number (value).

3. Removing Items

You can remove a key-value pair from a dictionary using the del statement or the pop() method.

Example:

student = {"name": "Alice", "age": 25, "major": "Computer Science"}

# Removing using del statement
del student["major"]
print(student)  # Output: {'name': 'Alice', 'age': 25}

# Removing using pop() method
age = student.pop("age")
print(age)  # Output: 25
print(student)  # Output: {'name': 'Alice'}
    

Analogy: Think of a dictionary as a pantry where you can remove a specific item (key-value pair) when it is no longer needed.

4. Dictionary Methods

Python provides several built-in methods to manipulate dictionaries. Some common methods include keys(), values(), and items().

Example:

student = {"name": "Alice", "age": 25, "major": "Computer Science"}

# Getting all keys
print(student.keys())  # Output: dict_keys(['name', 'age', 'major'])

# Getting all values
print(student.values())  # Output: dict_values(['Alice', 25, 'Computer Science'])

# Getting all key-value pairs
print(student.items())  # Output: dict_items([('name', 'Alice'), ('age', 25), ('major', 'Computer Science')])
    

Analogy: Think of a dictionary as a library where you can list all the books (keys), their authors (values), or both (items).

Putting It All Together

By understanding and using these dictionary operations effectively, you can manipulate key-value pairs in Python. Dictionaries are particularly useful for storing and retrieving data based on unique keys.

Example:

student = {"name": "Alice", "age": 25}
student["major"] = "Computer Science"
print(student["name"])  # Output: Alice
del student["age"]
print(student.keys())  # Output: dict_keys(['name', 'major'])