Python Programming Language
1. Data Structures
- Lists, Tuples, Sets, and Dictionaries: Understand the properties and use cases of each.
- Comprehensions: Efficient ways to create lists, sets, and dictionaries.
- Immutable vs. Mutable Types: Differences and their implications on program behavior.
2. Control Flow
- Conditional Statements:
if, elif, and else.
- Loops:
for, while, and the use of break, continue, and else with loops.
- Error Handling:
try, except, finally, and custom exceptions.
3. Functions
- Defining Functions:
def, parameters, default arguments, and return statements.
- Lambda Functions: Inline, anonymous functions.
- Decorators: Modifying the behavior of functions or methods.
- Closures and Higher-Order Functions: Functions that return functions and pass functions as arguments.
4. Object-Oriented Programming (OOP)
- Classes and Objects: Creating and using classes and objects.
- Inheritance and Polymorphism: Reusing code and designing flexible systems.
- Encapsulation: Managing access to data with public and private attributes.
- Dunder Methods: Special methods like
__init__, __str__, and __repr__.
5. Modules and Packages
- Importing Modules: Standard libraries and custom modules.
- Creating Packages: Organizing code into modules and packages.
- Virtual Environments: Managing dependencies with
venv or virtualenv.
6. File Handling
- Reading and Writing Files:
open(), read(), write(), and file context management with with.
- Handling Different File Types: Text files, CSV, JSON, etc.
7. Iterators and Generators
- Iterators: Understanding
iter() and next().
- Generators: Using
yield to produce a sequence of results lazily.
- Generator Expressions: Similar to list comprehensions but generate values on the fly.
8. Pythonic Practices
- PEP 8: Python's style guide for writing readable code.
- Writing Idiomatic Python: Using list comprehensions, unpacking, and context managers effectively.
- Zen of Python: Understand principles like simplicity, readability, and explicitness.
9. Libraries and Frameworks
- Standard Library: Key modules like
os, sys, math, itertools, datetime, random.
- Popular Libraries:
NumPy, Pandas, Requests, Matplotlib, Scikit-learn, etc.
- Web Frameworks: Flask, Django for web development.
10. Performance and Optimization
- Time Complexity: Analyzing the performance of algorithms.
- Memory Management: Understanding how Python manages memory, reference counting, and garbage collection.
- Profiling and Debugging: Using
cProfile, timeit, and debugging tools like pdb.
11. Concurrency
- Multithreading vs. Multiprocessing: Differences, GIL, and when to use each.
- Asyncio: Writing asynchronous code with
async and await.
12. Testing
- Unit Testing: Using
unittest, pytest for writing test cases.
- Mocking: Creating mock objects to simulate and test code behavior.