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)

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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)

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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

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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'))

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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

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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
• 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
• Python basics | Variables, operators, control flow
• Python data types | Lists, dicts, tuples, sets

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Frequently Asked Questions (FAQ)

Q. When would I use this in practice?

A. Learn Python file I/O: text files, CSV with csv module, JSON with json, pathlib paths, encoding, and the with statement—…

Q. What should I read before this?

A. Follow the previous article or related articles links at the bottom of each post to learn in sequence. See the Python series index for the full picture.

Q. Where can I study this more deeply?

A. Check cppreference and the relevant library’s official documentation. The reference links at the end of the article are also worth using.

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Related Articles (Internal Links)

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Keywords Covered in This Article (Related Search Terms)

This article covers Python, File I/O, CSV, JSON, pathlib, with Statement.