Python File I/O: Reading, Writing, and Context Managers

Opening files with open()

The built-in open() function takes a filename and mode. Always use a context manager.

# Basic (manual close — risky)
f = open("data.txt", "r")
content = f.read()
f.close()

# Better: context manager
with open("data.txt", "r") as f:
    content = f.read()
# File is automatically closed here

File modes

The second argument to open() is the mode.

open("file.txt", "r")   # read (default)
open("file.txt", "w")   # write (creates/overwrites)
open("file.txt", "a")   # append (adds to end)
open("file.txt", "x")   # exclusive create (fails if exists)
open("file.txt", "rb")  # read binary
open("file.txt", "r+")  # read and write

Reading from files

There are three ways to read. Choose based on whether you want everything at once or line by line.

with open("data.txt", "r") as f:
    content = f.read()           # entire file as string

with open("data.txt", "r") as f:
    lines = f.readlines()        # list of lines (with \n)

# Most Pythonic — memory-efficient:
with open("data.txt", "r") as f:
    for line in f:
        print(line.strip())      # strip removes trailing \n

Writing to files

write() writes a string. writelines() writes a list of strings. Neither adds newlines automatically.

with open("output.txt", "w") as f:
    f.write("First line\n")
    f.write("Second line\n")

lines = ["line 1\n", "line 2\n"]
with open("output.txt", "w") as f:
    f.writelines(lines)

with open("log.txt", "a") as f:
    f.write("New entry\n")

Working with JSON files

JSON is the most common file format in Python exams and real-world code.

import json

data = {"name": "Alice", "score": 95}
with open("data.json", "w") as f:
    json.dump(data, f, indent=2)

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

# String conversion (no file):
json_str = json.dumps(data)   # dict → string
back = json.loads(json_str)   # string → dict

Exam tip

The most common file I/O exam question involves the "w" mode overwriting an existing file (vs "a" for append). Also know that iterating over a file object directly (`for line in f`) is more memory-efficient than f.readlines().