Basics
Python is a high-level, interpreted language known for its readability and versatility from web development to AI and data science.
Hello World & Variables
print("Hello, World!")
# Variables (no type declaration needed)
name = "Aditya"
age = 17
is_student = True
gpa = 9.5
# f-strings
print(f"I'm {name}, age {age}")
Data Structures
Lists, Dicts, Tuples, Sets
# List ordered, mutable
fruits = ["apple", "banana", "cherry"]
fruits.append("mango")
# Dictionary key-value pairs
student = {"name": "Aditya", "grade": 11, "school": "Aspee Nutan"}
print(student["name"])
# Tuple ordered, immutable
coords = (19.076, 72.877)
# Set unordered, unique values
skills = {"Python", "AI", "Web Dev"}
# List comprehension
squares = [x**2 for x in range(10)]
# Dict comprehension
word_len = {w: len(w) for w in ["hello", "world"]}
Functions & Lambdas
Defining Functions
def greet(name, greeting="Hello"):
return f"{greeting}, {name}!"
# *args and **kwargs
def flex(*args, **kwargs):
print(args) # tuple of positional args
print(kwargs) # dict of keyword args
# Lambda
double = lambda x: x * 2
nums = [1, 2, 3, 4]
doubled = list(map(lambda x: x * 2, nums))
Object-Oriented Programming
Classes & Inheritance
class Developer:
def __init__(self, name, lang):
self.name = name
self.lang = lang
def introduce(self):
return f"I'm {self.name}, I code in {self.lang}"
class AIStudent(Developer):
def __init__(self, name, lang, school):
super().__init__(name, lang)
self.school = school
def introduce(self):
return f"{super().introduce()} at {self.school}"
me = AIStudent("Aditya", "Python", "Aspee Nutan")
print(me.introduce())
File Handling
Reading & Writing Files
# Reading
with open("data.txt", "r") as f:
content = f.read()
# Writing
with open("output.txt", "w") as f:
f.write("Hello from Python!")
# CSV
import csv
with open("data.csv", "r") as f:
reader = csv.DictReader(f)
for row in reader:
print(row)
# JSON
import json
with open("config.json", "r") as f:
data = json.load(f)
Popular Libraries
pandasData manipulation and analysisnumpyNumerical computingmatplotlib/plotlyData visualisationstreamlitBuild data apps fastrequestsHTTP requestsflask/fastapiWeb frameworksgoogle-generativeaiGemini API SDKbeautifulsoup4Web scrapingscikit-learnMachine learning
Best Practices
- Follow PEP 8 style guide
- Use virtual environments (
venv) for every project - Write docstrings for functions and classes
- Use type hints for clarity:
def greet(name: str) -> str: - Handle exceptions with specific
exceptclauses - Prefer list comprehensions over manual loops
- Use
withstatements for file/resource handling - Keep functions short and focused
React
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