Databases and Data Storage in Python

Python offers powerful tools for working with databases and data storage. Whether you're working with relational databases, NoSQL databases, or simple file-based storage, Python provides easy-to-use libraries and modules.

📦 1. File-Based Data Storage

a. Text Files (CSV, TXT)

Modules: csv, open()

Use Case: Lightweight storage, data exchange between systems

python

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import csv

# Writing to CSV

with open('data.csv', 'w', newline='') as file:

    writer = csv.writer(file)

    writer.writerow(["Name", "Age"])

    writer.writerow(["Alice", 25])

# Reading from CSV

with open('data.csv', 'r') as file:

    reader = csv.reader(file)

    for row in reader:

        print(row)

b. JSON Files

Modules: json

Use Case: Store structured data, APIs, configuration files

python

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import json

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

# Write to JSON file

with open("data.json", "w") as f:

    json.dump(data, f)

# Read from JSON file

with open("data.json", "r") as f:

    result = json.load(f)

    print(result)

🗃️ 2. Relational Databases (SQL)

a. SQLite

Module: sqlite3 (built-in)

Use Case: Lightweight embedded database

python

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import sqlite3

conn = sqlite3.connect('example.db')

cursor = conn.cursor()

cursor.execute('''CREATE TABLE IF NOT EXISTS users (name TEXT, age INTEGER)''')

cursor.execute('''INSERT INTO users (name, age) VALUES (?, ?)''', ("Alice", 25))

conn.commit()

cursor.execute('SELECT * FROM users')

print(cursor.fetchall())

conn.close()

b. MySQL / PostgreSQL

Libraries: mysql-connector-python, psycopg2, or ORM like SQLAlchemy

bash

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pip install mysql-connector-python psycopg2-binary SQLAlchemy

🌐 3. NoSQL Databases

a. MongoDB

Library: pymongo

Use Case: Document-based storage (flexible schema)

bash

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pip install pymongo

python

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from pymongo import MongoClient

client = MongoClient("mongodb://localhost:27017/")

db = client["mydatabase"]

collection = db["users"]

collection.insert_one({"name": "Alice", "age": 25})

for user in collection.find():

    print(user)

⚙️ 4. Object-Relational Mapping (ORM)

a. SQLAlchemy

Allows you to interact with databases using Python classes and objects.

python

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from sqlalchemy import create_engine, Column, Integer, String

from sqlalchemy.orm import declarative_base, sessionmaker

engine = create_engine("sqlite:///example.db")

Base = declarative_base()

class User(Base):

    __tablename__ = 'users'

    id = Column(Integer, primary_key=True)

    name = Column(String)

    age = Column(Integer)

Base.metadata.create_all(engine)

Session = sessionmaker(bind=engine)

session = Session()

user = User(name="Bob", age=30)

session.add(user)

session.commit()

Summary Table

Storage Type Tools / Libraries Best For

Text/CSV/JSON open, csv, json Simple data formats, configs

SQLite sqlite3 Lightweight local database

MySQL/PostgreSQL mysql-connector, psycopg2, SQLAlchemy Full-scale relational databases

MongoDB pymongo Schema-less, flexible data

ORM SQLAlchemy, Django ORM Pythonic database interaction

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