🔷 Introduction

In real-world applications, programs rarely work only with variables in memory. Instead, they interact with files to store, read, and process data such as logs, configurations, CSV datasets, and JSON APIs.

In this lesson, you will move beyond basic file handling and learn how to work with files efficiently, safely, and professionally.

You will learn:

✔ Reading large files efficiently
✔ Working with CSV files
✔ Working with JSON data
✔ Using context managers (with)
✔ Creating custom context managers

🟩 1. Reading Files Efficiently

❌ Problem: Loading large files

with open("data.txt", "r") as f:
    content = f.read()   # loads everything into memory

👉 Not efficient for large files.

✅ Solution: Read line by line

with open("data.txt", "r") as f:
    for line in f:
        print(line.strip())

✔ Uses less memory
✔ Faster for large datasets

✅ Read using readline()

with open("data.txt") as f:
    line = f.readline()
    print(line)

🟩 2. Working with CSV Files

Python provides a built-in module: csv

🔹 Reading CSV

import csv

with open("data.csv", newline="") as f:
    reader = csv.reader(f)
    for row in reader:
        print(row)

🔹 Writing CSV

import csv

with open("output.csv", "w", newline="") as f:
    writer = csv.writer(f)
    writer.writerow(["Name", "Age"])
    writer.writerow(["Ali", 22])

🔹 Using DictReader

import csv

with open("data.csv") as f:
    reader = csv.DictReader(f)
    for row in reader:
        print(row["Name"])

🟩 3. Working with JSON Files

JSON is widely used in APIs and data exchange.

🔹 Writing JSON

import json

data = {"name": "Ali", "age": 22}

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

🔹 Reading JSON

import json

with open("data.json") as f:
    data = json.load(f)

print(data["name"])

🔹 Pretty print JSON

json.dump(data, f, indent=4)

🟩 4. Context Managers (with)

Context managers automatically handle file closing.

❌ Without with

f = open("file.txt")
data = f.read()
f.close()

✅ With with

with open("file.txt") as f:
    data = f.read()

✔ Cleaner
✔ Safer
✔ No risk of forgetting .close()

🟩 5. Custom Context Managers

You can create your own context manager using:

• __enter__
• __exit__

Example:

class MyFile:
    def __enter__(self):
        print("File opened")
        return self

    def __exit__(self, exc_type, exc_val, exc_tb):
        print("File closed")

with MyFile():
    print("Working...")

🟧 6. Exercises (Hidden Solutions)

Exercise 1 — Read a file line by line

with open("file.txt") as f:
    for line in f:
        print(line.strip())

Exercise 2 — Write data to a CSV file

import csv

with open("test.csv", "w", newline="") as f:
    writer = csv.writer(f)
    writer.writerow(["Name", "Age"])
    writer.writerow(["Sara", 25])

Exercise 3 — Read JSON and print a value

import json

with open("data.json") as f:
    data = json.load(f)

print(data["name"])

Exercise 4 — Use DictReader

import csv

with open("data.csv") as f:
    reader = csv.DictReader(f)
    for row in reader:
        print(row)

Exercise 5 — Create a custom context manager

class Test:
    def __enter__(self):
        print("Start")
        return self

    def __exit__(self, *args):
        print("End")

with Test():
    print("Inside")

🟦 Conclusion

In this lesson, you learned how to:

✔ Efficiently read large files
✔ Work with CSV and JSON data
✔ Use context managers for safe file handling
✔ Build your own context managers

These skills are essential for:

• Data analysis
• Backend development
• Automation scripts
• API integration