Py Python Complete Tutorial

Install Python

Step 1: Check if Python is Already Installed

Step 2: Download Python

Go to 👉 https://www.python.org/downloads/ and download the latest version (3.12+).

Step 3: Verify Installation

Step 4: Run Python

Best IDEs for Python

python --version
python -m pip --version
python -m venv .venv
# Windows: .venv\Scripts\activate
# macOS/Linux: source .venv/bin/activate
python -m pip install requests

Python Syntax

1️⃣ Execute Python Syntax

2️⃣ Indentation (VERY IMPORTANT)

Python uses indentation (spaces at start of line) to indicate a block of code.

3️⃣ Python Variables

4️⃣ Case Sensitive

score = 85

if score >= 80:
    print("Excellent")
    print("Certificate eligible")
else:
    print("Keep practicing")

print("Done")

Python Comments

Comments explain code, make it readable, and prevent execution while testing.

1️⃣ Single-Line Comment

2️⃣ Multi-Line Comment

3️⃣ Multi-Line Strings as Comments

def calculate_discount(price, percentage):
    """Return price after applying discount percentage."""
    # Business rule: negative discount is treated as zero.
    if percentage < 0:
        percentage = 0
    return price - (price * percentage / 100)

print(calculate_discount(1000, 10))

Python Variables

Variables are containers for storing data values. Python has no command for declaring a variable — it's created the moment you assign a value.

1️⃣ Creating Variables

2️⃣ Variables Can Change Type

3️⃣ Casting (Specify the type)

4️⃣ Get the Type

5️⃣ Variable Naming Rules

• ✅ Must start with a letter or underscore (_)
• ❌ Cannot start with a number
• ✅ Can contain letters, numbers, underscores
• ⚠️ Case-sensitive (age, Age, AGE are different)
• ❌ Cannot use Python keywords (if, for, class, etc.)

6️⃣ Multi-word Variable Naming Styles

7️⃣ Assign Multiple Values

8️⃣ Output Variables

9️⃣ Global Variables

🚀 Real-Time Example: User Profile

user_name = "Anu"
age = 22
skills = ["Python", "AWS"]

same_skills = skills
same_skills.append("SQL")

print(user_name)
print(skills)

Python Data Types

Python has several built-in data types categorized into groups:

Get Data Type

Setting Data Types — All Examples

values = ["text", 10, 10.5, True, None, [1, 2], {"id": 1}, {1, 2}, (1, 2)]

for value in values:
    print(type(value).__name__, "=>", value)

Python Numbers

There are 3 numeric types in Python: int, float, complex.

1️⃣ int — Integer (whole number)

2️⃣ float — Decimal number

3️⃣ complex — Complex number

4️⃣ Type Conversion

5️⃣ Random Numbers

🚀 Real-Time: Calculator

from decimal import Decimal

print(0.1 + 0.2)
print(Decimal("0.1") + Decimal("0.2"))

price = Decimal("99.99")
qty = 3
total = price * qty
print(total)

Python Casting

Casting = converting a variable from one type to another.

1️⃣ int() — Convert to Integer

2️⃣ float() — Convert to Float

3️⃣ str() — Convert to String

🚀 Real-Time: User Input Calculator

def safe_int(value, default=0):
    try:
        return int(value)
    except (TypeError, ValueError):
        return default

print(safe_int("100"))
print(safe_int("abc"))
print(safe_int(None))

Python Strings

1️⃣ Create String

2️⃣ Multiline String

3️⃣ Strings as Arrays

4️⃣ String Slicing

5️⃣ String Length

6️⃣ Check String — in / not in

7️⃣ Concatenate Strings

8️⃣ Format Strings — f-string (Best Way)

9️⃣ Escape Characters

🔟 ALL String Methods (Quick List)

String Methods in Action

🚀 Real-Time: Username Validator

email = "  ANU@Example.COM  "
clean = email.strip().lower()

print(clean)
print(clean.endswith(".com"))
print(clean.split("@"))
print(f"Email domain: {clean.split('@')[1]}")

Python Booleans

Booleans represent True or False.

1️⃣ Boolean Values

2️⃣ bool() Function

3️⃣ Falsy Values

• False, None, 0, 0.0
• "" (empty string)
• [] (empty list)
• {} (empty dict)
• () (empty tuple)

🚀 Real-Time: Login Check

values = ["hello", "", 0, 1, [], [1], None]
for value in values:
    print(repr(value), bool(value))

Python Operators

1️⃣ Arithmetic Operators

2️⃣ Assignment Operators

3️⃣ Comparison Operators

4️⃣ Logical Operators

5️⃣ Identity Operators (is, is not)

6️⃣ Membership Operators (in, not in)

7️⃣ Bitwise Operators

🚀 Real-Time: BMI Calculator

price = 1000
is_premium = True
coupon_valid = False

discount_allowed = is_premium or coupon_valid
final_price = price * 0.9 if discount_allowed else price

print(discount_allowed)
print(final_price)

Python Lists

Lists are ordered, changeable, and allow duplicates. Created with [ ].

1️⃣ Create a List

2️⃣ Mixed Types

3️⃣ Access Items

4️⃣ Check if Exists

5️⃣ Change Items

6️⃣ Add Items

7️⃣ Remove Items

8️⃣ Loop Through List

9️⃣ List Comprehension

🔟 Sort List

1️⃣1️⃣ Copy List

1️⃣2️⃣ Join Lists

1️⃣3️⃣ All List Methods

🚀 Real-Time: Shopping Cart

tasks = []
tasks.append("learn Python")
tasks.append("build API")
tasks.insert(1, "practice lists")

for i, task in enumerate(tasks, start=1):
    print(i, task)

completed = tasks.pop(0)
print("Completed:", completed)
print("Pending:", tasks)

Python Tuples

Tuples are ordered, unchangeable, allow duplicates. Created with ( ).

1️⃣ Create Tuple

2️⃣ One Item Tuple (need comma!)

3️⃣ Access Tuple Items

4️⃣ Update Tuple (Trick: convert to list)

5️⃣ Unpacking Tuples

6️⃣ Loop Tuple

7️⃣ Join Tuples

8️⃣ Tuple Methods

🚀 Real-Time: GPS Coordinates

def min_max_avg(numbers):
    return min(numbers), max(numbers), sum(numbers) / len(numbers)

low, high, avg = min_max_avg([10, 20, 30])
print(low, high, avg)

single = ("apple",)
print(type(single).__name__)

Python Sets

Sets are unordered, unchangeable*, no duplicates. Created with { }.

1️⃣ Create Set

2️⃣ Duplicates Removed Automatically

3️⃣ Access (No Index — Use Loop)

4️⃣ Add Items

5️⃣ Remove Items

6️⃣ Set Operations (Union, Intersection, Difference)

7️⃣ Subset / Superset

8️⃣ All Set Methods

🚀 Real-Time: Remove Duplicates from List

expected = {"S101", "S102", "S103"}
submitted = {"S101", "S103", "S104"}

print("Completed:", expected & submitted)
print("Missing:", expected - submitted)
print("Extra:", submitted - expected)

Python Dictionaries

Dictionaries store data as key:value pairs. Ordered, changeable, no duplicate keys. Created with { }.

1️⃣ Create Dictionary

2️⃣ Access Values

3️⃣ Check if Key Exists

4️⃣ Change/Update Values

5️⃣ Add Items

6️⃣ Remove Items

7️⃣ Loop Through Dictionary

8️⃣ Copy Dictionary

9️⃣ Nested Dictionary

🔟 Dictionary Comprehension

1️⃣1️⃣ All Dictionary Methods

🚀 Real-Time: Student Database

students = {
    "S101": {"name": "Anu", "marks": 88},
    "S102": {"name": "Ravi", "marks": 76},
}

sid = "S101"
student = students.get(sid)
if student:
    print(student["name"], student["marks"])

for sid, info in students.items():
    print(sid, info["name"])

Python If...Else

Python supports usual logical conditions: ==, !=, <, >, <=, >=

1️⃣ if Statement

2️⃣ elif

3️⃣ else

4️⃣ Short Hand If

5️⃣ and / or / not

6️⃣ Nested If

7️⃣ pass Statement

🚀 Real-Time: Grade Calculator

marks = 83
attendance = 76

if marks >= 80 and attendance >= 75:
    result = "Distinction"
elif marks >= 40 and attendance >= 60:
    result = "Pass"
elif attendance < 60:
    result = "Attendance shortage"
else:
    result = "Fail"

print(result)

Python While Loop

Execute a set of statements as long as a condition is true.

1️⃣ Basic While

2️⃣ break Statement

3️⃣ continue Statement

4️⃣ else with While

🚀 Real-Time: Number Guessing

attempt = 0
max_attempts = 3
success = False

while attempt < max_attempts and not success:
    attempt += 1
    print("Trying", attempt)
    if attempt == 2:
        success = True

print("Success:", success)

Python For Loop

Iterate over a sequence (list, tuple, dict, set, string).

1️⃣ Loop Through List

2️⃣ Loop Through String

3️⃣ break / continue

4️⃣ range() Function

5️⃣ else with For

6️⃣ Nested For

7️⃣ enumerate()

8️⃣ zip()

🚀 Real-Time: Multiplication Table

names = ["Anu", "Ravi", "Priya"]
marks = [88, 76, 91]

for index, (name, mark) in enumerate(zip(names, marks), start=1):
    grade = "A" if mark >= 80 else "B"
    print(index, name, mark, grade)

Python Match Case (Python 3.10+)

Like switch-case in other languages.

1️⃣ Basic Match

2️⃣ Default Case

3️⃣ Combine Cases (|)

4️⃣ Match with Guards (if)

🚀 Real-Time: HTTP Status

command = "delete"

match command:
    case "create":
        print("Create resource")
    case "update":
        print("Update resource")
    case "delete":
        print("Delete resource")
    case _:
        print("Unknown command")

Python Functions

A function is a reusable block of code. Defined using def keyword.

1️⃣ Create & Call Function

2️⃣ Arguments / Parameters

3️⃣ Multiple Arguments

4️⃣ Default Parameter

5️⃣ Keyword Arguments

6️⃣ *args (Arbitrary Args)

7️⃣ **kwargs (Arbitrary Keyword Args)

8️⃣ Return Values

9️⃣ Type Hints

🚀 Real-Time: Tax Calculator

def calculate_grade(marks):
    if marks >= 90:
        return "A+"
    if marks >= 80:
        return "A"
    if marks >= 70:
        return "B"
    return "C"

print(calculate_grade(85))

Python Lambda

A small anonymous function — one-line, no name.

Syntax

lambda arguments : expression

1️⃣ Basic Lambda

2️⃣ Lambda Inside Function

3️⃣ Lambda with map()

4️⃣ Lambda with filter()

5️⃣ Lambda with sorted()

6️⃣ Lambda with reduce()

students = [("Anu", 88), ("Ravi", 76), ("Priya", 91)]

by_marks = sorted(students, key=lambda item: item[1], reverse=True)
print(by_marks)

even = list(filter(lambda n: n % 2 == 0, [1, 2, 3, 4]))
print(even)

Python Decorators

A decorator is a function that modifies another function.

1️⃣ Basic Decorator

2️⃣ Decorator with Arguments

3️⃣ Real-Time: Timer Decorator

from functools import wraps

def require_admin(func):
    @wraps(func)
    def wrapper(user, *args, **kwargs):
        if not user.get("is_admin"):
            return "Access denied"
        return func(user, *args, **kwargs)
    return wrapper

@require_admin
def delete_user(user):
    return "User deleted"

print(delete_user({"is_admin": False}))
print(delete_user({"is_admin": True}))

Python Generators

Generators yield values one at a time using yield — memory efficient.

1️⃣ Basic Generator

2️⃣ Range with Generator

3️⃣ Generator Expression

4️⃣ next() with Generator

🚀 Real-Time: Fibonacci Generator

def batches(items, size):
    for i in range(0, len(items), size):
        yield items[i:i+size]

for batch in batches([1, 2, 3, 4, 5], 2):
    print(batch)

Python Recursion

A function that calls itself to solve a problem.

1️⃣ Factorial

2️⃣ Fibonacci

3️⃣ Sum of List

4️⃣ Power Function

def sum_nested(items):
    total = 0
    for item in items:
        if isinstance(item, list):
            total += sum_nested(item)
        else:
            total += item
    return total

print(sum_nested([1, [2, 3], [4, [5]]]))

Python Classes & Objects

A class is a blueprint, an object is an instance of that class.

1️⃣ Create a Class

2️⃣ __init__() Constructor

3️⃣ __str__() Method

4️⃣ Object Methods

5️⃣ Modify / Delete Properties

6️⃣ pass Statement

🚀 Real-Time: Bank Account

class Product:
    def __init__(self, name, price, stock):
        self.name = name
        self.price = price
        self.stock = stock

    def is_available(self):
        return self.stock > 0

p = Product("Laptop", 55000, 3)
print(p.name)
print(p.is_available())

Python Inheritance

Inheritance lets a class inherit attributes/methods from another class.

1️⃣ Parent Class

2️⃣ Child Class

3️⃣ Child __init__() with super()

4️⃣ Multi-Level Inheritance

5️⃣ Multiple Inheritance

🚀 Real-Time: Vehicle System

class Notification:
    def send(self, message):
        raise NotImplementedError

class EmailNotification(Notification):
    def send(self, message):
        return f"Email sent: {message}"

n = EmailNotification()
print(n.send("Welcome"))

Python Polymorphism

"Many forms" — same method name, different behavior.

1️⃣ Function Polymorphism

2️⃣ Class Method Polymorphism

3️⃣ Method Overriding

🚀 Real-Time: Shape Area

class PdfExporter:
    def export(self):
        return "PDF exported"

class CsvExporter:
    def export(self):
        return "CSV exported"

for exporter in [PdfExporter(), CsvExporter()]:
    print(exporter.export())

Python Encapsulation

Hiding data inside class — using private attributes.

1️⃣ Public, Protected, Private

2️⃣ Getter / Setter

3️⃣ @property Decorator

class Product:
    def __init__(self, price):
        self.price = price

    @property
    def price(self):
        return self._price

    @price.setter
    def price(self, value):
        if value < 0:
            raise ValueError("Price cannot be negative")
        self._price = value

p = Product(100)
print(p.price)

Magic / Dunder Methods

Methods with double underscores like __init__, __str__, etc.

Common Magic Methods

Operator Overloading Example

__call__ Example

class Cart:
    def __init__(self):
        self.items = []

    def add(self, item):
        self.items.append(item)

    def __len__(self):
        return len(self.items)

    def __str__(self):
        return f"Cart({self.items})"

cart = Cart()
cart.add("Book")
print(len(cart))
print(cart)

Python File Handling

Use open() to work with files. Modes: r=read, w=write, a=append, x=create.

1️⃣ Read a File

2️⃣ Read with 'with' (Recommended)

3️⃣ Read Line by Line

4️⃣ Write to File

5️⃣ Append to File

6️⃣ Delete File

🚀 Real-Time: Log Writer

from datetime import datetime

def write_log(message):
    with open("app.log", "a", encoding="utf-8") as f:
        f.write(f"{datetime.now()} - {message}\n")

write_log("Application started")
print("Logged")

Python JSON

1️⃣ Parse JSON (string → dict)

2️⃣ Convert dict → JSON

3️⃣ Pretty Print

4️⃣ Read/Write JSON File

5️⃣ Conversion Table

import json

user = {"name": "Anu", "skills": ["Python", "AWS"], "active": True}
text = json.dumps(user, indent=2)
print(text)

parsed = json.loads(text)
print(parsed["skills"][0])

Python Try...Except

1️⃣ Basic Try-Except

2️⃣ Multiple Except Blocks

3️⃣ else Block

4️⃣ finally Block

5️⃣ Raise an Exception

6️⃣ Common Exception Types

🚀 Real-Time: Safe Division

def divide(a, b):
    try:
        result = a / b
    except ZeroDivisionError:
        return "Cannot divide by zero"
    except TypeError:
        return "Numbers required"
    else:
        return result
    finally:
        print("divide attempted")

print(divide(10, 2))
print(divide(10, 0))

Python Dates

1️⃣ Current Date & Time

2️⃣ Create Date

3️⃣ Format Dates

4️⃣ Format Codes

5️⃣ Date Arithmetic

🚀 Real-Time: Age Calculator

from datetime import datetime, timedelta, timezone

created_at = datetime.now(timezone.utc)
expires_at = created_at + timedelta(hours=24)

print(created_at.strftime("%Y-%m-%d"))
print(expires_at > created_at)

Python RegEx

Module: re — for pattern matching in strings.

1️⃣ Basic Match

2️⃣ Common Functions

3️⃣ Special Characters

🚀 Real-Time: Email Validator

🚀 Phone Validator

import re

email = "anu@example.com"
pattern = r"^[\w.-]+@[\w.-]+\.\w+$"
print(bool(re.fullmatch(pattern, email)))

text = "Order INV-2026-001 is paid"
print(re.findall(r"INV-\d{4}-\d{3}", text))

Python Math

1️⃣ Built-in Math Functions

2️⃣ math Module

3️⃣ random Module

# Practice example for Python Math
print("Learning: Python Math")

Python Modules

A module = a Python file (.py) with reusable code.

1️⃣ Create a Module

2️⃣ Use Module

3️⃣ Rename / Alias

4️⃣ from..import

5️⃣ List Module Functions

6️⃣ Built-in Modules

# math_utils.py
def add(a, b):
    return a + b

# app.py
from math_utils import add
print(add(2, 3))

Python PIP

PIP is a package manager for Python — install/manage external libraries.

1️⃣ Check PIP Version

2️⃣ Install a Package

3️⃣ Use Installed Package

4️⃣ Uninstall / List / Upgrade

5️⃣ Virtual Environment (venv)

📦 Popular Packages

python -m pip install requests
python -m pip show requests
python -m pip freeze > requirements.txt
python -m pip install -r requirements.txt

NumPy Basics

NumPy = Numerical Python. Used for fast array operations. Install: pip install numpy

1️⃣ Create Arrays

2️⃣ 2D Array (Matrix)

3️⃣ Special Arrays

4️⃣ Indexing & Slicing

5️⃣ Math Operations

6️⃣ Aggregations

7️⃣ Reshape

🚀 Real-Time: Student Marks

import numpy as np
marks = np.array([[80, 90, 70], [75, 85, 95]])
print(marks.mean(axis=1))
print(marks[:, 1])

Pandas Basics

Pandas = Data analysis library. Install: pip install pandas

1️⃣ Series (1D)

2️⃣ DataFrame (2D)

3️⃣ Read CSV

4️⃣ Access Data

5️⃣ Filter Data

6️⃣ Add / Modify Columns

7️⃣ Sort Data

8️⃣ Group By

9️⃣ Handle Missing Data

🔟 Save to CSV

🚀 Real-Time: Sales Analysis

import pandas as pd
sales = pd.DataFrame({"product": ["A","B","A"], "qty": [2,3,4], "price": [100,200,100]})
sales["total"] = sales["qty"] * sales["price"]
print(sales.groupby("product")["total"].sum())

Matplotlib Basics

Matplotlib = Plotting library. Install: pip install matplotlib

1️⃣ Line Plot

2️⃣ Bar Chart

3️⃣ Pie Chart

4️⃣ Scatter Plot

5️⃣ Histogram

6️⃣ Multiple Lines

7️⃣ Subplots

🚀 Real-Time: Sales Dashboard

import matplotlib.pyplot as plt
months = ["Jan", "Feb", "Mar"]
sales = [100, 150, 130]
plt.plot(months, sales, marker="o")
plt.title("Sales")
plt.savefig("sales.png")
print("saved")

CSV File Handling

CSV (Comma Separated Values) is the most common format for importing/exporting data from spreadsheets and databases.

1️⃣ Reading CSV

2️⃣ Reading as Dictionary

3️⃣ Writing CSV

4️⃣ Writing with DictWriter

5️⃣ CSV with Pandas (Easier)

import csv

rows = [{"name": "Anu", "marks": 88}, {"name": "Ravi", "marks": 76}]
with open("students.csv", "w", newline="", encoding="utf-8") as f:
    writer = csv.DictWriter(f, fieldnames=["name", "marks"])
    writer.writeheader()
    writer.writerows(rows)

print("CSV written")

Virtual Environments

Virtual environments isolate project dependencies so different projects can use different package versions without conflicts.

1️⃣ Create a Virtual Environment

2️⃣ Using the Virtual Environment

3️⃣ Deactivate

4️⃣ Recreate from requirements.txt

5️⃣ Why Use Virtual Environments?

python -m venv .venv
# Windows
.venv\Scripts\activate
# macOS/Linux
source .venv/bin/activate
pip install flask

Flask — Web Framework

Flask is a lightweight Python web framework. Install: pip install flask

1️⃣ Hello World App

2️⃣ Routes & Dynamic URLs

3️⃣ Templates (Jinja2)

my_app/
├── app.py
├── templates/
│   └── index.html
└── static/
    └── style.css

4️⃣ Form Handling

5️⃣ Redirects & Flash Messages

from flask import Flask, jsonify

app = Flask(__name__)

@app.get("/health")
def health():
    return jsonify({"status": "ok"})

if __name__ == "__main__":
    app.run(debug=True)

Flask REST API

Build RESTful APIs with Flask using JSON request/response patterns.

1️⃣ Basic REST API

2️⃣ POST — Create Resource

3️⃣ PUT — Update Resource

4️⃣ DELETE — Remove Resource

5️⃣ Testing with curl

from flask import Flask, request, jsonify
app = Flask(__name__)
students = []

@app.post("/students")
def create_student():
    data = request.get_json()
    if not data.get("name"):
        return jsonify({"error": "name required"}), 400
    student = {"id": len(students)+1, "name": data["name"]}
    students.append(student)
    return jsonify(student), 201

Django — Full-Stack Framework

Django is a high-level Python web framework with batteries included. Install: pip install django

1️⃣ Create a Django Project

2️⃣ Project Structure

mysite/
├── manage.py
├── mysite/
│   ├── __init__.py
│   ├── settings.py
│   ├── urls.py
│   └── wsgi.py
└── blog/
    ├── models.py
    ├── views.py
    ├── urls.py
    └── templates/

3️⃣ Models (Database)

4️⃣ Views

5️⃣ URLs

6️⃣ Migrations

# models.py
from django.db import models

class Student(models.Model):
    name = models.CharField(max_length=100)
    marks = models.IntegerField()

    def __str__(self):
        return self.name

FastAPI — Modern API Framework

FastAPI is a modern, high-performance framework for building APIs. Install: pip install fastapi uvicorn

1️⃣ Hello World

2️⃣ Path & Query Parameters

3️⃣ Request Body with Pydantic

4️⃣ CRUD API Example

5️⃣ Auto-Generated Docs

FastAPI automatically generates interactive API docs:

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

class Student(BaseModel):
    name: str
    marks: int

@app.post("/students")
def create_student(student: Student):
    return {"name": student.name, "passed": student.marks >= 40}

JWT Authentication

JWT (JSON Web Tokens) enables stateless authentication for APIs. Install: pip install PyJWT

1️⃣ How JWT Works

2️⃣ Create a JWT Token

3️⃣ Verify a JWT Token

4️⃣ Flask + JWT (Protected Route)

import jwt
from datetime import datetime, timedelta, timezone

secret = "change-me"
payload = {"user_id": 1, "exp": datetime.now(timezone.utc) + timedelta(minutes=30)}
token = jwt.encode(payload, secret, algorithm="HS256")
print(jwt.decode(token, secret, algorithms=["HS256"])["user_id"])

SQLite — Built-in Database

SQLite comes with Python — no installation needed. Perfect for small apps and prototyping.

1️⃣ Create Database & Table

2️⃣ Insert Data

3️⃣ Query Data

4️⃣ Update & Delete

5️⃣ Using Context Manager

import sqlite3

with sqlite3.connect("app.db") as conn:
    cur = conn.cursor()
    cur.execute("CREATE TABLE IF NOT EXISTS users (id INTEGER PRIMARY KEY, name TEXT)")
    cur.execute("INSERT INTO users (name) VALUES (?)", ("Anu",))
    cur.execute("SELECT * FROM users")
    print(cur.fetchall())

MySQL with Python

Connect Python to MySQL databases. Install: pip install mysql-connector-python

1️⃣ Connect to MySQL

2️⃣ Create Table

3️⃣ CRUD Operations

4️⃣ Close Connection

# Example pattern
sql = "SELECT * FROM users WHERE email = %s"
params = ("anu@example.com",)
# cursor.execute(sql, params)

MongoDB with Python

MongoDB is a NoSQL document database. Install: pip install pymongo

1️⃣ Connect to MongoDB

2️⃣ Insert Documents

3️⃣ Query Documents

4️⃣ Update & Delete

from pymongo import MongoClient
client = MongoClient("mongodb://localhost:27017")
col = client.school.students
col.insert_one({"name": "Anu", "skills": ["Python"]})
print(col.find_one({"name": "Anu"})["skills"])

SQLAlchemy — Python ORM

SQLAlchemy maps Python classes to database tables. Install: pip install sqlalchemy

1️⃣ Setup & Create Engine

2️⃣ Define Models

3️⃣ CRUD Operations

4️⃣ Querying with Filters

from sqlalchemy import create_engine, Column, Integer, String
from sqlalchemy.orm import declarative_base

Base = declarative_base()
class User(Base):
    __tablename__ = "users"
    id = Column(Integer, primary_key=True)
    name = Column(String)

Scikit-learn — Machine Learning

Scikit-learn provides simple ML algorithms. Install: pip install scikit-learn

1️⃣ ML Workflow

2️⃣ Linear Regression

3️⃣ Classification (Decision Tree)

4️⃣ Common Algorithms

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

X, y = load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
model = RandomForestClassifier(random_state=42)
model.fit(X_train, y_train)
print(accuracy_score(y_test, model.predict(X_test)))

Requests — HTTP Library

The requests library makes HTTP calls simple. Install: pip install requests

1️⃣ GET Request

2️⃣ POST Request

3️⃣ Headers & Authentication

4️⃣ Query Parameters

5️⃣ Error Handling

6️⃣ Download a File

import requests

try:
    r = requests.get("https://jsonplaceholder.typicode.com/posts/1", timeout=5)
    r.raise_for_status()
    print(r.json()["id"])
except requests.exceptions.RequestException as e:
    print("API error", e)

Async / Await

Asynchronous programming lets you run multiple tasks concurrently without blocking — ideal for I/O operations like API calls and file reading.

1️⃣ Basic Async Function

2️⃣ Running Multiple Tasks

3️⃣ Async HTTP Requests (aiohttp)

4️⃣ Sync vs Async Comparison

import asyncio

async def task(name, delay):
    await asyncio.sleep(delay)
    return name

async def main():
    result = await asyncio.gather(task("A", 2), task("B", 1))
    print(result)

asyncio.run(main())

Testing in Python

Testing ensures your code works correctly. Python has built-in unittest and the popular pytest library.

1️⃣ Using unittest

2️⃣ Using pytest (Recommended)

3️⃣ Testing Exceptions

4️⃣ Fixtures (Setup/Teardown)

import pytest

def divide(a, b):
    if b == 0:
        raise ValueError("zero")
    return a / b

def test_divide():
    assert divide(10, 2) == 5

def test_zero():
    with pytest.raises(ValueError):
        divide(1, 0)

Docker for Python

Docker packages your Python app with all dependencies into a portable container.

1️⃣ Dockerfile for Python App

2️⃣ Build & Run

3️⃣ Docker Compose (Multi-Service)

4️⃣ Common Docker Commands

5️⃣ .dockerignore

FROM python:3.12-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY . .
CMD ["python", "app.py"]

Final Project — Student Management API

Build a complete REST API combining Flask, SQLite, JWT authentication, and testing.

1️⃣ Project Structure

student_api/
├── app.py          (main application)
├── models.py       (database models)
├── auth.py         (JWT authentication)
├── tests/
│   └── test_api.py
├── requirements.txt
├── Dockerfile
└── .env

2️⃣ Main Application (app.py)

3️⃣ Testing (test_api.py)

4️⃣ Requirements & Run

🎯 Project Checklist

def calculate_grade(marks: int) -> str:
    if marks >= 90:
        return "A+"
    if marks >= 80:
        return "A"
    if marks >= 70:
        return "B"
    return "C"

print(calculate_grade(88))

Input and Output

Input means taking data from the user. Output means showing data to the user. In beginner Python, we use input() and print().

Important Point

input() always returns a string. If the user types 25, Python receives it as "25", not as number 25.

Example: Safe Input

name = input("Enter name: ")
age_text = input("Enter age: ")

try:
    age = int(age_text)
    print(f"Hello {name}, next year you will be {age + 1}")
except ValueError:
    print("Age must be a number")

Production Scope

Common Mistakes

None and Missing Values

None means no value, missing value, or nothing returned. It is not the same as 0, False, or empty string.

Correct Checks

user = None

if user is None:
    print("No user found")

age = 0
if age is not None:
    print("Age is available")

Production Scope

Common Mistake

Mutability and Copying

Mutable objects can change after creation. Lists, dictionaries, and sets are mutable. Strings, numbers, and tuples are immutable.

Reference vs Copy

a = [1, 2, 3]
b = a
b.append(4)

print(a)
print(b)

Deep Copy for Nested Data

import copy

original = [[1, 2], [3, 4]]
deep = copy.deepcopy(original)
deep[0].append(99)

print(original)
print(deep)

Production Scope

All Comprehensions

Comprehensions are short syntax for creating lists, dictionaries, sets, and generators from existing iterables.

Examples

numbers = [1, 2, 3, 4, 5]

squares = [n * n for n in numbers]
even_squares = [n * n for n in numbers if n % 2 == 0]
lookup = {n: n * n for n in numbers}
unique_remainders = {n % 2 for n in numbers}
total = sum(n * n for n in numbers)

print(squares)
print(even_squares)
print(lookup)
print(unique_remainders)
print(total)

Common Mistake

pathlib Paths

pathlib is the modern way to work with file and folder paths. It is cleaner than joining strings manually.

from pathlib import Path

folder = Path("reports")
folder.mkdir(exist_ok=True)

file_path = folder / "summary.txt"
file_path.write_text("Report ready", encoding="utf-8")

print(file_path.exists())
print(file_path.read_text(encoding="utf-8"))

Production Scope

Logging

Logging is the production replacement for print debugging. It records application events with levels.

import logging

logging.basicConfig(
    level=logging.INFO,
    format="%(asctime)s %(levelname)s %(message)s"
)

try:
    result = 10 / 0
except ZeroDivisionError:
    logging.exception("Division failed")

Production Scope

Type Hints

Type hints show what type a variable, parameter, or return value should have. Python does not enforce them automatically, but tools and IDEs use them.

from typing import TypedDict

class User(TypedDict):
    id: int
    name: str
    is_admin: bool

def greet(user: User) -> str:
    return f"Hello {user['name']}"

print(greet({"id": 1, "name": "Anu", "is_admin": True}))

Production Scope

argparse CLI Tools

argparse helps create command-line tools with arguments, flags, validation, and automatic help text.

import argparse

parser = argparse.ArgumentParser(description="Simple greeting CLI")
parser.add_argument("name")
parser.add_argument("--times", type=int, default=1)

args = parser.parse_args()

for _ in range(args.times):
    print(f"Hello {args.name}")

Production Scope

Environment Variables

Environment variables store configuration outside code: database URLs, API keys, environment names, debug flags, and secrets.

import os

APP_ENV = os.environ.get("APP_ENV", "development")
DEBUG = os.environ.get("DEBUG", "false").lower() == "true"

print(APP_ENV)
print(DEBUG)

Production Scope

subprocess

subprocess lets Python run external commands safely and capture their output.

import subprocess

result = subprocess.run(
    ["python", "--version"],
    capture_output=True,
    text=True
)

print(result.returncode)
print(result.stdout or result.stderr)

Production Scope

Threading

Threading runs multiple tasks in the same process. It is most useful for I/O-bound tasks such as API calls, downloads, and file operations.

from concurrent.futures import ThreadPoolExecutor
import time

def work(n):
    time.sleep(1)
    return n * n

with ThreadPoolExecutor(max_workers=3) as executor:
    results = list(executor.map(work, [1, 2, 3]))

print(results)

Production Scope

Multiprocessing

Multiprocessing runs code in separate processes, allowing CPU-bound Python work to use multiple CPU cores.

from concurrent.futures import ProcessPoolExecutor

def cpu_task(n):
    return sum(i * i for i in range(n))

if __name__ == "__main__":
    with ProcessPoolExecutor() as executor:
        results = list(executor.map(cpu_task, [10000, 20000, 30000]))

    print(results)

Production Scope