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Questions and Answers
Explain the difference between a list and a tuple in Python. Why might you choose one over the other?
Explain the difference between a list and a tuple in Python. Why might you choose one over the other?
Lists are mutable, ordered collections defined with square brackets []
, while tuples are immutable, ordered collections defined with parentheses ()
. Tuples are more memory efficient and can be used as keys in dictionaries due to their immutability, whereas lists are preferred when the collection needs to be modified.
Describe the purpose of virtual environments in Python development and how they can be created and activated.
Describe the purpose of virtual environments in Python development and how they can be created and activated.
Virtual environments isolate project dependencies to avoid conflicts. They are created using venv
via python3 -m venv myenv
(for example) and activated using a script within the environment's directory, such as source myenv/bin/activate
on Unix-like systems.
How does Python's dynamic typing differ from static typing, and what are the advantages and disadvantages of dynamic typing?
How does Python's dynamic typing differ from static typing, and what are the advantages and disadvantages of dynamic typing?
In dynamic typing, variable types are checked during runtime, whereas in static typing, types are checked before runtime. Dynamic typing allows for faster development and more flexible code but can lead to runtime errors that might be caught earlier with static typing.
Explain the use of try
and except
blocks in Python. Provide a scenario where using these blocks would be crucial.
Explain the use of try
and except
blocks in Python. Provide a scenario where using these blocks would be crucial.
Describe briefly how to define a class in Python that inherits from a parent class and overrides one of the parent's methods.
Describe briefly how to define a class in Python that inherits from a parent class and overrides one of the parent's methods.
Explain the purpose and usage of list comprehensions in Python. Provide a short example.
Explain the purpose and usage of list comprehensions in Python. Provide a short example.
What are lambda functions in Python, and when might you choose to use them instead of regular functions?
What are lambda functions in Python, and when might you choose to use them instead of regular functions?
Explain the concept of decorators in Python and provide a simple example showing how to define and use one.
Explain the concept of decorators in Python and provide a simple example showing how to define and use one.
What are Python generators, and how do they differ from regular functions in terms of memory usage and execution?
What are Python generators, and how do they differ from regular functions in terms of memory usage and execution?
Briefly describe the purpose of the Pandas library in Python, and give an example of a common operation performed using Pandas.
Briefly describe the purpose of the Pandas library in Python, and give an example of a common operation performed using Pandas.
Flashcards
What is Python?
What is Python?
A high-level, general-purpose language that emphasizes code readability through significant indentation.
Variable Declaration
Variable Declaration
Variables in Python are declared by simply assigning a value to them.
Lists
Lists
Ordered, mutable collections that are defined using square brackets.
Tuples
Tuples
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Dictionaries
Dictionaries
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Encapsulation
Encapsulation
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Error Handling
Error Handling
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Virtual Environments
Virtual Environments
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List Comprehensions
List Comprehensions
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Decorators
Decorators
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Study Notes
- Python is a high-level, general-purpose programming language
- Python emphasizes code readability through the use of significant indentation
- Python is dynamically typed and garbage-collected
- Python supports multiple programming paradigms including structured, object-oriented, and functional programming
Key Features
- Interpreted: Python code is executed line by line, which makes debugging easier
- Dynamically Typed: Variable types are checked during runtime, reducing development time
- High-Level: Python abstracts low-level details, allowing developers to focus on problem-solving
- Cross-Platform: Python runs on various operating systems such as Windows, macOS, and Linux
- Large Standard Library: Python includes a vast collection of modules and functions for various tasks
- Open Source: Python is free to use and distribute, fostering community-driven development
Basic Syntax
- Variables: Declared by simply assigning a value; for example,
x = 5
- Data Types: Includes integers, floats, strings, Booleans, lists, tuples, and dictionaries
- Operators: Supports arithmetic, comparison, logical, and bitwise operators
- Control Structures:
if
,else
,elif
are used for conditional execution;for
andwhile
loops are used for iteration - Functions: Defined using the
def
keyword; for example,def my_function(x): return x * 2
Data Structures
- Lists: Ordered, mutable collections of items, defined using square brackets
[]
- Tuples: Ordered, immutable collections of items, defined using parentheses
()
- Dictionaries: Unordered collections of key-value pairs, defined using curly braces
{}
- Sets: Unordered collections of unique elements
Object-Oriented Programming (OOP)
- Classes: Blueprints for creating objects, defined using the
class
keyword - Objects: Instances of classes
- Inheritance: Allows a class to inherit attributes and methods from another class
- Polymorphism: Enables objects of different classes to be treated as objects of a common type
- Encapsulation: Bundling of data and methods that operate on that data within a class
Common Libraries
- NumPy: Used for numerical computations and array operations
- Pandas: Used for data manipulation and analysis
- Matplotlib: Used for creating visualizations and plots
- Scikit-learn: Used for machine learning tasks
- TensorFlow/Keras: Used for deep learning
- Requests: Used for making HTTP requests
- Beautiful Soup: Used for web scraping
File Handling
- Opening Files: Use the
open()
function to open files in read, write, or append mode - Reading Files: Use methods like
read()
,readline()
, andreadlines()
to read file content - Writing Files: Use the
write()
method to write data to a file - Closing Files: Use the
close()
method or thewith
statement to ensure files are properly closed
Error Handling
- Try-Except Blocks: Use
try
andexcept
blocks to handle exceptions and prevent program crashes - Raising Exceptions: Use the
raise
keyword to raise custom exceptions - Finally Block: The
finally
block executes regardless of whether an exception was raised
Modules and Packages
- Modules: Python files containing functions, classes, or variables that can be imported into other programs
- Packages: Collections of modules organized in directories
- Importing Modules: Use the
import
statement to import modules to access their contents
Virtual Environments
- Purpose: Isolating project dependencies to avoid conflicts between different projects
- Creation: Use the
venv
module to create virtual environments - Activation: Activate the virtual environment to use its specific set of packages
- Pip: A package installer for installing, upgrading, and managing packages within the virtual environment
List Comprehensions
- Concise way to create lists based on existing iterables
- Example:
squares = [x**2 for x in range(10)]
creates a list of squares from 0 to 9
Lambda Functions
- Anonymous functions defined using the
lambda
keyword - Typically used for short, simple operations
- Example:
add = lambda x, y: x + y
Decorators
- Functions that modify the behavior of other functions
- Use the
@
syntax to apply decorators to functions - Example:
@my_decorator def my_function(): ...
Generators
- Functions that produce a sequence of values using the
yield
keyword - Generate values on demand, saving memory compared to lists
Common Use Cases
- Web Development: Building web applications using frameworks like Django and Flask
- Data Science: Analyzing and visualizing data using libraries like NumPy, Pandas, and Matplotlib
- Machine Learning: Developing machine learning models using libraries like Scikit-learn and TensorFlow
- Scripting and Automation: Automating tasks and writing scripts for system administration
- Game Development: Creating games using libraries like Pygame
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