Files, Paths & JSON

Always Use with

The with statement guarantees the file closes, even if an exception is thrown halfway through:

# read an entire file
with open("notes.txt", encoding="utf-8") as f:
    content = f.read()

# read line by line (memory-friendly for big files)
with open("notes.txt", encoding="utf-8") as f:
    for line in f:
        print(line.rstrip())

Writing

with open("log.txt", "w", encoding="utf-8") as f:   # w = overwrite
    f.write("first line\n")

with open("log.txt", "a", encoding="utf-8") as f:   # a = append
    f.write("another line\n")

Modes: "r" read (default), "w" write/truncate, "a" append, "rb"/"wb" binary. Always pass encoding="utf-8" — don’t depend on the OS default.

pathlib — Modern Path Handling

Stop concatenating path strings. Path objects handle separators, names, and globbing across operating systems:

from pathlib import Path

base = Path("projects") / "coding-india"     # joins with the right separator
base.mkdir(parents=True, exist_ok=True)

p = base / "data.json"
p.exists()           # False
p.suffix             # '.json'
p.stem               # 'data'

# read/write tiny files in one call
p.write_text('{"ok": true}', encoding="utf-8")
print(p.read_text(encoding="utf-8"))

# find files
for f in base.glob("**/*.py"):               # recursive glob
    print(f)

JSON — the Data Format of the Web

import json

user = {"name": "Ravi", "score": 82, "tags": ["python", "django"]}

# Python → JSON string
text = json.dumps(user, indent=2)

# JSON string → Python
data = json.loads(text)
data["score"]                  # 82

# straight to/from files
with open("user.json", "w", encoding="utf-8") as f:
    json.dump(user, f, indent=2)

with open("user.json", encoding="utf-8") as f:
    user = json.load(f)

Mapping: JSON object ⇄ dict, array ⇄ list, null ⇄ None, true/false ⇄ True/False. Remember: dumps/loads work with strings, dump/load with files.

CSV

import csv

with open("scores.csv", encoding="utf-8", newline="") as f:
    for row in csv.DictReader(f):
        print(row["name"], row["score"])     # each row is a dict

For serious tabular work, this is where pandas takes over — covered in the Data Science series.