Python File Handling | Read, Write, CSV, JSON, and pathlib

Introduction

“Files are everyday work”

Reading and writing files underpins data persistence, logging, and configuration in real projects.

1. Text files

Reading

# Read entire file
with open('data.txt', 'r', encoding='utf-8') as f:
    content = f.read()
    print(content)
# Read all lines into a list
with open('data.txt', 'r', encoding='utf-8') as f:
    lines = f.readlines()
    for line in lines:
        print(line.strip())
# Iterate line by line (memory-friendly)
with open('data.txt', 'r', encoding='utf-8') as f:
    for line in f:
        print(line.strip())

Writing

# Overwrite (w)
with open('output.txt', 'w', encoding='utf-8') as f:
    f.write("First line\n")
    f.write("Second line\n")
# Append (a)
with open('output.txt', 'a', encoding='utf-8') as f:
    f.write("Third line\n")
# Write multiple lines
lines = ["line 1\n", "line 2\n", "line 3\n"]
with open('output.txt', 'w', encoding='utf-8') as f:
    f.writelines(lines)

---

2. CSV files

Reading CSV

import csv
# As rows (lists)
with open('data.csv', 'r', encoding='utf-8') as f:
    reader = csv.reader(f)
    header = next(reader)  # first row (header)
    
    for row in reader:
        print(row)
# As dict rows (recommended)
with open('data.csv', 'r', encoding='utf-8') as f:
    reader = csv.DictReader(f)
    
    for row in reader:
        print(row['name'], row['age'])

Writing CSV

import csv
# Write rows as lists
data = [
    ['Name', 'Age', 'City'],
    ['Alice', 25, 'Seoul'],
    ['Bob', 30, 'Busan']
]
with open('output.csv', 'w', newline=', encoding='utf-8') as f:
    writer = csv.writer(f)
    writer.writerows(data)
# Write dict rows
data = [
    {'name': 'Alice', 'age': 25, 'city': 'Seoul'},
    {'name': 'Bob', 'age': 30, 'city': 'Busan'}
]
with open('output.csv', 'w', newline=', encoding='utf-8') as f:
    fieldnames = ['name', 'age', 'city']
    writer = csv.DictWriter(f, fieldnames=fieldnames)
    
    writer.writeheader()
    writer.writerows(data)

---

3. JSON files

Reading JSON

import json
# From file
with open('data.json', 'r', encoding='utf-8') as f:
    data = json.load(f)
    print(data)
# From string
json_str = '{"name": "Alice", "age": 25}'
data = json.loads(json_str)
print(data['name'])  # Alice

Writing JSON

import json
data = {
    "name": "Alice",
    "age": 25,
    "hobbies": ["reading", "movies", "sports"],
    "address": {
        "city": "Seoul",
        "district": "Gangnam"
    }
}
# Write to file
with open('output.json', 'w', encoding='utf-8') as f:
    json.dump(data, f, ensure_ascii=False, indent=2)
# Serialize to string
json_str = json.dumps(data, ensure_ascii=False, indent=2)
print(json_str)

---

4. Paths with pathlib

Using pathlib

from pathlib import Path
# Current directory
current = Path.cwd()
print(current)
# Join paths
data_dir = Path('data')
file_path = data_dir / 'users.json'
print(file_path)  # data/users.json
# Exists?
if file_path.exists():
    print("file exists")
# Create directory
data_dir.mkdir(exist_ok=True)
# Read/write text
file_path.write_text("Hello, World!", encoding='utf-8')
content = file_path.read_text(encoding='utf-8')
print(content)
# Path parts
print(file_path.name)    # users.json
print(file_path.stem)    # users
print(file_path.suffix)  # .json
print(file_path.parent)  # data

---

5. Practical examples

Log analysis

from collections import Counter
from pathlib import Path
def analyze_log(log_file):
    """Count error types from a log file."""
    error_counts = Counter()
    
    with open(log_file, 'r', encoding='utf-8') as f:
        for line in f:
            if 'ERROR' in line:
                error_type = line.split(':')[1].strip()
                error_counts[error_type] += 1
    
    return error_counts
# Usage
errors = analyze_log('app.log')
for error, count in errors.most_common(5):
    print(f"{error}: {count} occurrences")

Simple config helper

import json
from pathlib import Path
class Config:
    def __init__(self, config_file='config.json'):
        self.config_file = Path(config_file)
        self.data = self.load()
    
    def load(self):
        if self.config_file.exists():
            with open(self.config_file, 'r', encoding='utf-8') as f:
                return json.load(f)
        return {}
    
    def save(self):
        with open(self.config_file, 'w', encoding='utf-8') as f:
            json.dump(self.data, f, ensure_ascii=False, indent=2)
    
    def get(self, key, default=None):
        return self.data.get(key, default)
    
    def set(self, key, value):
        self.data[key] = value
        self.save()
# Usage
config = Config()
config.set('database_url', 'localhost:5432')
print(config.get('database_url'))

---

Practical tips

File handling tips

# ✅ Use with (automatic close)
with open('file.txt', 'r') as f:
    content = f.read()
# ❌ Manual close (can leak on exceptions)
f = open('file.txt', 'r')
content = f.read()
f.close()
# ✅ Always specify encoding for text
with open('file.txt', 'r', encoding='utf-8') as f:
    pass
# ✅ Check existence first
from pathlib import Path
if Path('file.txt').exists():
    pass

---

Summary

Key takeaways

1. Text files: with open(path, mode, encoding='utf-8') for safe I/O.
2. CSV: use csv.reader / csv.writer or DictReader / DictWriter; set newline=' on write.
3. JSON: json.load / json.dump for files; mind ensure_ascii and indent for readability.
4. Paths: pathlib.Path for joining paths and reading small text files.
5. Context managers: prefer with so files close even when errors occur.

Next steps

• [Exception handling](/en/blog/python-series-08-exception-handling/
• File automation (later in the series)

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Related posts

• Browse the Python series
• [Python environment setup | Install Python on Windows and Mac](/en/blog/python-series-01-environment-setup/
• [Python basics | Variables, operators, control flow](/en/blog/python-series-02-basic-syntax/
• [Python data types | Lists, dicts, tuples, sets](/en/blog/python-series-03-data-types/

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자주 묻는 질문 (FAQ)

Q. 이 내용을 실무에서 언제 쓰나요?

A. Learn Python file I/O: text files, CSV with csv module, JSON with json, pathlib paths, encoding, and the with statement—… 실무에서는 위 본문의 예제와 선택 가이드를 참고해 적용하면 됩니다.

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A. 각 글 하단의 이전 글 또는 관련 글 링크를 따라가면 순서대로 배울 수 있습니다. Python 시리즈 목차에서 전체 흐름을 확인할 수 있습니다.

Q. 더 깊이 공부하려면?

A. cppreference와 해당 라이브러리 공식 문서를 참고하세요. 글 말미의 참고 자료 링크도 활용하면 좋습니다.

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같이 보면 좋은 글 (내부 링크)

이 주제와 연결되는 다른 글입니다.

• [Python Exception Handling | try-except· else](/en/blog/python-series-08-exception-handling/
• [Node.js File System: Complete Guide to the fs Module](/en/blog/nodejs-series-05-filesystem/
• [C++ std::filesystem Complete Guide | Cross-Platform Path](/en/blog/cpp-series-37-1-filesystem-basics/

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